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2.000000 15.856460\n", + "75% 53.736423 3.000000 25.392592\n", + "max 81.665638 9.000000 218.246601\n" ] }, { "data": { - "image/png": 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CnXPOObr22mv10ksvhb32W2+9pUGDBum4444Le++eddZZYW3Vgq3aLrroIr300kv6+eefW3z+AIDoY/7UdedPDUlLS9MZZ5zR5PHNmc80ZNmyZUpJSanzs2jKPOu4445TXFycrrvuOi1cuLDeZWMa4/V6NXv2bB199NGKi4uTzWZTXFycNm/eXO986lDvlxUrVqisrEw33HBDnZakQbHwvkfXRuIPiBEXXnihnE5naJLwzjvvaPfu3WGLLO/evVtvvvmm7HZ72OOYY46RpDrtfnr06NHk1w+2W5w5c2ad4wdL/htrJ9QWMjIywr52OBySFEo6BdtMZmdnh42z2Wx1ntuQq6++WitXrtS3334rKdDT3uFwhCXH9u3bV+/3Ljc3NyyOpsbdkIceeki//e1vNXz4cL3yyitatWqV1qxZo7PPPrvOc5sa98HfG6nu96sp/v73v+uPf/yjXnvtNZ1++ulKT0/XeeedF1ofJqi+731OTk4oHinw3jJNU9nZ2XXeW6tWrQq9r4LvwQsvvLDOuPvuu0+maaqoqEjFxcXy+/2h16nvtQEAnRPzpa43X2rq8ysqKnTqqafq888/11/+8hd9/PHHWrNmjf7973/X+zqJiYlKTU0N27Zv375G5zYt0dB8xe12q6KiImx7fd/PKVOmaP78+br22mv13nvvafXq1VqzZo26d+9e7/duyZIlSkhI0LXXXlvnQtTu3bv19ddf13nvpqSkyDTN0Hv3tNNO02uvvSav16vf/OY36tmzpwYNGhS21jMAoONg/tR1508Nac7PT2refKYhDV2zaso8q1+/fvrggw+UlZWlG2+8Uf369VO/fv30yCOPNOm1b7nlFt15550677zz9Oabb+rzzz/XmjVrdOyxx9b7vTzU972wsFCS1LNnzwZfMxbe9+jaDn3LAoB2kZCQoEsvvVQLFizQrl279PTTTyslJSW0IK8kZWZmasiQIfrrX/9a7zGCE4Wghu46qU9mZqYk6fbbb9f5559f75gBAwY0+XhS4A+jy+Wqs/3giUxTBf/w7t69W4cddlhou9frbfIxL730Ut1yyy169tln9de//lXPP/+8zjvvPKWlpYW9zq5du+o8N7jwc/B71VqLFi3SmDFj9Nhjj4VtLy8vb3HcB66XF1TfQsmHkpSUpHvuuUf33HOPdu/eHar+O/fcc0OTWGn/9/7ASVHw9YLbMjMzZRiGPv3009Bk6UDBbcHv67x58zRixIh648rOzpbH45FhGPWeV0vOFQDQcTBfOrTONl9qqo8++kg7d+7Uxx9/HKryk9Tgujz1/dwzMjIandu0REPzlbi4OCUnJzcaU2lpqd566y3dfffduu2220LbXS5XaK3ogy1evFh33nmnRo8erffff1/HHXdcaF9mZqYSEhLqrPN04P6gX/7yl/rlL38pl8ulVatWac6cOZoyZYqOOOIIjRw58pDnDQCIHcyfDq2rzZ+a8/OTmjefaUhGRoZWr17dpGPX59RTT9Wpp54qn8+nL774QvPmzdP06dOVnZ2tSy65pNHnLlq0SL/5zW9Cax8G7d27V926dWvS6x+oe/fukhS2nt/BIvG+B5qDij8ghlxzzTXy+Xx64IEH9M477+iSSy5RYmJiaP+kSZO0fv169evXTyeccEKdx8ETsfo0dHfQgAED1L9/f3311Vf1HvuEE05QSkpKs87niCOO0Ndffx227aOPPmry3UAHO+200yRJ//rXv8K2/+///q+8Xm+TjpGWlqbzzjtPzz33nN566y0VFBSEtV2QpLFjx+qbb77Rl19+Gbb9ueeek2EYOv3001sU/8EMw6iTCPv666/rXRy5KXGPHj1a69ev1zfffBO2fcmSJa2KMzs7W1deeaUuvfRSbdq0SVVVVWH7Fy9eHPb1Cy+8IEkaM2aMpMD71jRN/fzzz/W+rwYPHixJOvnkk9WtWzd98803Db4H4+LilJSUpJNOOkn//ve/VVNTE3rd8vJyvfnmm606VwBA7GO+1LjONl9qquAFrIPnVk888USTjxGMuaG5TUs0NF859dRTw1p31ccwDJmmWeec/vnPfzbYVis9PV0ffPCBBg4cqNNPP12rVq0K7Zs0aZK2bNmijIyMet+7RxxxRJ3jORwOjR49Wvfdd58k6T//+U9TTx0AEEOYPzWus82f2qoyMKg185mg008/XeXl5aGWs0HNnWdZrVYNHz5c//jHPyQp9L1s7Jzru/729ttvt7id+ahRo+R0OvX444/LNM16x0TifQ80BxV/QAw54YQTNGTIEM2dO1emaYa1XZCke++9V0uXLtWoUaN08803a8CAAaqpqdGPP/6od955R48//nijZeZSoDw+ISFBixcv1sCBA5WcnKzc3Fzl5ubqiSee0IQJE3TWWWfpyiuv1GGHHaaioiJt3LhRX375pV5++eVmnc/UqVN155136q677tLo0aP1zTffaP78+XI6nc3+3kjSMccco0svvVQPPvigrFarzjjjDG3YsEEPPvignE6nLJam3ctw9dVX61//+pd+97vfqWfPnho3blzY/j/84Q967rnnNHHiRN17773q3bu33n77bT366KP67W9/q6OOOqpF8R9s0qRJ+vOf/6y7775bo0eP1qZNm3TvvfeqT58+9U4sDxX39OnT9fTTT2vChAm69957lZ2drRdeeCFUodfU748kDR8+XJMmTdKQIUOUlpamjRs36vnnn9fIkSPDPhzExcXpwQcfVEVFhU488UStWLFCf/nLXzRhwgSdcsopkgIJveuuu05XXXWVvvjiC5122mlKSkrSrl279Nlnn2nw4MH67W9/q+TkZM2bN09XXHGFioqKdOGFFyorK0uFhYX66quvVFhYGKqO/POf/6yzzz5bZ555pmbMmCGfz6f77rtPSUlJDd4BDwDoHJgvNa6zzZeaatSoUUpLS9P/+3//T3fffbfsdrsWL16sr776qsnHGD9+vE477TTdeuutqqys1AknnKD/+7//0/PPP9/iuKxWq84880zdcsst8vv9uu+++1RWVqZ77rnnkM9NTU3VaaedpgceeECZmZk64ogjtHz5cj311FON3p2ekpKivLw8nX/++TrzzDP1xhtv6PTTT9f06dP1yiuv6LTTTtMf/vAHDRkyRH6/Xz/99JPef/99zZgxQ8OHD9ddd92lHTt2aOzYserZs6dKSkr0yCOPhK2ZCADoWJg/Na6zzZ8GDRokSXryySeVkpKi+Ph49enTp8ltSw/WmvlM0G9+8xs9/PDD+s1vfqO//vWv6t+/v9555x299957h3zu448/ro8++kgTJ07U4YcfrpqamlAHg+D3OCUlRb1799brr7+usWPHKj09PTR/mjRpkp599ln94he/0JAhQ7R27Vo98MADh3xPNyQ5OVkPPvigrr32Wo0bN07Tpk1Tdna2vv/+e3311VeaP3++JLX5+x5oFhNATHnkkUdMSebRRx9d7/7CwkLz5ptvNvv06WPa7XYzPT3dHDZsmHnHHXeYFRUVpmma5tatW01J5gMPPFDvMV588UXzF7/4hWm3201J5t133x3a99VXX5kXXXSRmZWVZdrtdjMnJ8c844wzzMcffzw0ZtmyZaYkc9myZaFtd999t3nwrxSXy2XeeuutZq9evcyEhARz9OjRZn5+vtm7d2/ziiuuCI175plnTEnmmjVrwp5f3+vU1NSYt9xyi5mVlWXGx8ebI0aMMFeuXGk6nU7zD3/4Q2Pf2hCfz2f26tXLlGTecccd9Y7Ztm2bOWXKFDMjI8O02+3mgAEDzAceeMD0+XyhMY19nw/+vtbH5XKZM2fONA877DAzPj7ePP74483XXnvNvOKKK8zevXu3KO7169eb48aNM+Pj48309HTzmmuuMRcuXGhKMr/66qtG4znQbbfdZp5wwglmWlqa6XA4zL59+5p/+MMfzL1794bGXHHFFWZSUpL59ddfm2PGjDETEhLM9PR087e//W3ovXigp59+2hw+fLiZlJRkJiQkmP369TN/85vfmF988UXYuOXLl5sTJ04009PTTbvdbh522GHmxIkTzZdffjls3BtvvGEOGTLEjIuLMw8//HDzb3/7W73vQwBA58N8ab/OPl9qznmvWLHCHDlypJmYmGh2797dvPbaa80vv/zSlGQ+88wzoXHBOUx9SkpKzKuvvtrs1q2bmZiYaJ555pnmt99+26RYDxQ87/vuu8+85557zJ49e5pxcXHm0KFDzffeey9sbPB9UVhYWOc4O3bsMC+44AIzLS3NTElJMc8++2xz/fr1TXp/uFwu84ILLjDj4+PNt99+2zRN06yoqDD/+7//2xwwYIAZFxdnOp1Oc/DgweYf/vAHs6CgwDRN03zrrbfMCRMmmIcddpgZFxdnZmVlmeecc4756aefNvn8AQCxh/nTfp19/mSapjl37lyzT58+ptVqDZsLjR492jzmmGPqfc7o0aPN0aNH14mjKfOZpgjOa5KTk82UlBTzggsuMFesWFFnrnbwz3zlypXmr371K7N3796mw+EwMzIyzNGjR5tvvPFG2PE/+OADc+jQoabD4TAlhd4LxcXF5jXXXGNmZWWZiYmJ5imnnGJ++umndc43+L44+PpT8PtwYIymaZrvvPOOOXr0aDMpKclMTEw0jz76aPO+++4LG9OU9z0QCYZpNlCPCgAdxIoVK3TyySdr8eLFmjJlSrTDiTnXXXedXnzxRe3bt09xcXFtdtwrr7xS//u//9viVhoAAKD9MF8CAABonq4+f/rxxx/Vp08fPfDAA5o5c2a0wwHQDLT6BNChLF26VCtXrtSwYcOUkJCgr776Sn/729/Uv3//BhfL7Uruvfde5ebmqm/fvqqoqNBbb72lf/7zn/rv//7vNk36AQCA2MV8CQAAoHmYPwHoTEj8AehQUlNT9f7772vu3LkqLy9XZmamJkyYoDlz5ig+Pj7a4UWd3W7XAw88oB07dsjr9ap///566KGH9Pvf/16SZJqmfD5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7e6t58+Z6+eWXZbPZJBVPuVS/fn1J0uTJk+1TS5Q3JcHhw4fl5eVV5ieXfv31V1ksFv373/+2t23dulWDBw9W3bp15ePjo0suuUQfffSRw3ol0xHMmTNHTz/9tKKiohQUFKSrrrpKO3fuPO0+/vbbb7r99tt18cUXy8/PTw0aNNCgQYO0ZcuWs4577dq16tKli3x8fNSgQQNNmDBB77//viwWi/bu3XvamE7e7j333KOYmBh5e3urfv366tatm5YvX24f06tXL7Vq1UrfffedLrvsMvn6+tq3WfI6KFFQUKB//OMfatasmf35br/9dh0+fLjUtufNm6cuXbrI399fAQEB6tevn37++edS4z788EM1bdrU/vr4+OOPK71/AACcDvmSI2flSyUOHTqkoUOHKjg4WOHh4brjjjscjq8kvfHGG+rRo4fCwsLk7++v1q1ba9q0aSosLHQYV5LDfPvtt+ratav8/Px0xx13SCr+tPhNN92kwMBABQcH6+abb1Zqamql4zxVXl6exo0bp4iICPn6+qpnz56l8pqS18GWLVvUt29fBQYGqnfv3pKkZcuWafDgwYqOjpaPj48uuugijR49ulJTev76669q1KiRLr30UqWlpUkqfh2OHj1a0dHR8vLyUsOGDTV58mQVFRU5rPvWW2+pbdu2CggIUGBgoJo1a6annnrqrI8DAKB2IH9y5Iz8adKkSXrsscckSQ0bNrTHX3L3Ynx8vAYOHKj58+erXbt28vHx0eTJk+19J+9nSRyzZs06bT5TGb/++quuvvpq+fn5KTQ0VPfee6+ys7NLjStrqs/PPvtMl156qYKDg+Xn56dGjRrZ87fVq1erU6dOkqTbb7/dvs+TJk2SJG3YsEG33HKL4uPj5evrq/j4eA0dOlRJSUkO2yiZwnbVqlW67777FBoaqpCQEA0ZMkQHDhwoFWdCQoK6dOmigIAABQQE6JJLLtEHH3zgMGb58uXq3bu3goKC5Ofnp27dumnFihVnfOyAC8oAqFZmzpxpJJnvv//eFBYWmoKCArNv3z4zZswY4+bmZr755hv72BMnTpg2bdoYf39/869//cssXbrUTJgwwXh4eJhrrrnGPm7Xrl0mMDDQDBkyxBhjjNVqNVdeeaUJCwszBw4csI8bOXKk8fLyMrGxseaFF14wS5cuNZMmTTIeHh5m4MCBDnHGxcWZkSNH2h+npaWZBg0amPr165u3337bfPPNN+bBBx80ksx9991njDEmLy/PfPPNN0aSufPOO8369evN+vXrzW+//Vbu8bj++utNTEyMsVqtDu3jx483Xl5e5siRI8YYY3799VcTGBhoGjdubD7++GPz5ZdfmqFDhxpJ5sUXX7Svt2rVKiPJxMfHm+HDh5svv/zSzJkzx8TGxpqLL77YFBUVVXh+1qxZYx599FHz+eefmzVr1pgFCxaY6667zvj6+ppff/31jOPevHmz8fHxMW3atDFz5841X3zxhbnmmmtMfHy8kWT27NlTYTwn69evn6lfv7559913zerVq83ChQvNs88+a+bOnWsf07NnTxMSEmKioqLMv//9b7NkyRIzZswYI8k88MAD9nFWq9VcffXVxt/f30yePNksW7bMvP/++6ZBgwamRYsWJjc31z72hRdeMBaLxdxxxx1m8eLFZv78+aZLly7G39/fbNu2zT6u5LU9ePBgs2jRIjNr1ixz0UUXmZiYGBMXF1fp/QQAgHzJkavlSxMnTjSSTNOmTc2zzz5rli1bZqZPn268vb3N7bff7jD2kUceMW+99Zb55ptvzMqVK80rr7xiQkNDS43r2bOnqVevnomJiTGvvfaaWbVqlVmzZo3Jzc01zZs3N8HBwea1116z5zaxsbFGkpk5c2aFsZ6sZL9jYmJK5StBQUHm999/t48dOXKk8fT0NPHx8Wbq1KlmxYoVZsmSJcYYY9566y0zdepU88UXX5g1a9aYjz76yLRt29Y0bdrUFBQUlDpOhw8fNsYYs3r1alO3bl0zePBgc/z4cWOMMQcPHrTnSu+8845Zvny5ef755423t7cZNWqU/bnmzJljJJmHHnrILF261Cxfvty8/fbbZsyYMZXefwBAzUb+5MiV8qd9+/aZhx56yEgy8+fPt8efmZlpPyaRkZGmUaNG5j//+Y9ZtWqV+fHHH8s8XmeSz5xOamqqCQsLMw0aNDAzZ840X331lRk+fLg9z1q1apV97MiRIx2u7axbt85YLBZzyy23mK+++sqsXLnSzJw509x2223GGGMyMzPtr8lnnnnGvs/79u0zxhjz2WefmWeffdYsWLDArFmzxsydO9f07NnT1K9f3547GfPX67pRo0bmoYceMkuWLDHvv/++qVu3rrniiisc9mfChAlGkhkyZIj57LPPzNKlS8306dPNhAkT7GM++eQTY7FYzHXXXWfmz59vFi1aZAYOHGjc3d3N8uXLK33sgAuNwh9QzZT8wTp18fb2Nm+++abD2LfffttIMp9++qlD+4svvmgkmaVLl9rb5s2bZySZV1991Tz77LPGzc3Nod+Y4j/SksyMGTMc2l944QUjyaxdu9bedmpi8cQTTxhJ5ocffnBY97777jMWi8Xs3LnTGGPM4cOHjSQzceLESh2PL774otS+FBUVmaioKHPDDTfY22655Rbj7e1tkpOTHdbv37+/8fPzMxkZGcaYvxKgkxNVY4z59NNPjSSzfv36SsV1ciwFBQXm4osvNo888sgZx33jjTcaf39/h6TFarWaFi1anHHhLyAgwIwdO7bCMT179jSSzP/+9z+H9rvvvtu4ubmZpKQkY8xfF4/++9//OoxLTEw0kuyvxeTkZOPh4WEeeughh3HZ2dkmIiLC3HTTTfZ9ioqKMu3btzc2m80+bu/evcbT05PCHwDgjJAvOXK1fKmkoDVt2jSH9vvvv9/4+Pg45AIns1qtprCw0Hz88cfG3d3dHD161N5XksOsWLHCYZ233nqr3NzmbAt/5eUrd911l72t5HXwn//8p8LntNlsprCw0CQlJZWK8+TC3yeffGK8vLzMmDFjHC5Ajh492gQEBNhztBL/+te/jCT7h6wefPBBU6dOnUrvKwCg9iF/cuRq+dNLL71U7nWguLg44+7ubt/XU/vKKvxVJp85nccff9xYLBazadMmh/Y+ffqctvBXkquUHJ+ylFxjqky+VlRUZHJycoy/v7/D66jkdX3//fc7jJ82bZqRZA4ePGiMMeaPP/4w7u7uZvjw4eVu4/jx46ZevXpm0KBBDu1Wq9W0bdvWdO7c+bRxAlWFqT6Baurjjz9WYmKiEhMT9fXXX2vkyJF64IEH9Prrr9vHrFy5Uv7+/vrb3/7msG7JLf4n34Z+00036b777tNjjz2mf/zjH3rqqafUp0+fMrc9fPhwh8fDhg2TJK1atarceFeuXKkWLVqoc+fOpWIxxmjlypWn3+ky9O/fXxEREZo5c6a9bcmSJTpw4IB9eoCS7ffu3VsxMTGltp+bm6v169c7tF977bUOj9u0aSNJpaYMOFVRUZGmTJmiFi1ayMvLSx4eHvLy8tLu3bu1Y8eOM457zZo1uvLKKxUaGmpvc3Nz00033VRhHGXp3LmzPvzwQ/3jH//Q999/X2qKrBKBgYGl9n/YsGGy2Wz69ttvJUmLFy9WnTp1NGjQIBUVFdmXSy65RBEREfbpJpYsWaKioiKNGDHCYZyPj4969uxpH7dz504dOHBAw4YNk8VisW83Li5OXbt2PeN9BQBAIl8q4Wr5UkXr5+Xl2aewlKSff/5Z1157rUJCQuTu7i5PT0+NGDFCVqtVu3btcli/bt26uvLKKx3aVq1aVW5uc7bKy1fKOrc33HBDqba0tDTde++9iomJkYeHhzw9PRUXFydJDvliiRdeeEGjRo3SP//5T82YMUNubn+9jV+8eLGuuOIKRUVFOeRa/fv3l1ScS0rFeWBGRoaGDh2q//3vf5WaVhQAUDuRPxVz1fypPG3atFGTJk0qPf5M8pnyrFq1Si1btlTbtm1LPffplEzjedNNN+nTTz/V/v37K71dScrJydHjjz+uiy66SB4eHvLw8FBAQICOHz9eZj51uuO+bNkyWa1WPfDAA+Vuc926dTp69KhGjhzpkHfZbDZdffXVSkxM1PHjx89oP4ALhcIfUE01b95cHTt2VMeOHXX11VfrnXfeUd++fTV+/HhlZGRIktLT0xUREeHwh1ySwsLC5OHhofT0dIf2O+64Q4WFhfLw8NCYMWPK3K6Hh4dCQkIc2iIiIuzbK096eroiIyNLtUdFRZ123Yp4eHjotttu04IFC+z7/eGHHyoyMlL9+vU76+2fuo/e3t6Sir8cuSLjxo3ThAkTdN1112nRokX64YcflJiYqLZt2zqseyZxh4eHl9pOWW2nM2/ePI0cOVLvv/++unTponr16mnEiBGlvuOmrOc+9RwfOnRIGRkZ9nn+T15SU1PtF5MOHTokqTihO3XcvHnz7ONKnrdkO2VtGwCAM0W+9Fc8rpQvVXb95ORkXX755dq/f79mzJih7777TomJiXrjjTfK3E5ZsZeXS51LflFevnLq8fHz81NQUJBDm81mU9++fTV//nyNHz9eK1as0I8//qjvv/9eUtnHbtasWWrQoIFuueWWUn2HDh3SokWLSuVZLVu2lCR7rnXbbbfpP//5j5KSknTDDTcoLCxMl156qZYtW3Z2BwEAUGORP/0VjyvmT+UpK4aKVDafqUjJ66Ayz32qHj16aOHChfYPi0dHR6tVq1aaM2dOpbY9bNgwvf7667rrrru0ZMkS/fjjj0pMTFT9+vXLPJanO+6HDx+WJEVHR5e7zZJrXH/7299K5V4vvviijDE6evRopeIHLjQKf0AN0qZNG504ccL+6eeQkBAdOnRIxhiHcWlpaSoqKnK4i+z48eO67bbb1KRJE/n6+uquu+4qcxtFRUWlkoCSwtGpf0RPFhISooMHD5ZqL/ki3ZNjOVO333678vLyNHf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" ] @@ -1477,13 +1486,13 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 59, "id": "8a320b4c", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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uHebNm/fZ28xWnHaRV5cuXTB//ny8fPkSffv2LbGYiIhIcXFggYiISsS0adOgpKSEP/74Q3x3/3nz5sHFxQWtW7fGxIkTUb58eaxevRr37t2Dn5/fZ31LPG7cOOzduxctW7bE+PHjUbduXWRkZCAiIgLHjx/HL7/8IvOPt+zH+P3444/o06cPnj9/jtmzZ8PY2BiPHz+WKGtnZ4ezZ8/i4MGDMDY2RsWKFWFlZZVvm0pKSli4cCEGDBiALl26YOTIkUhJScFff/2FpKQkzJ8/v9jHOG3aNLx48QJt27aFiYkJkpKSsGzZMqioqMDZ2bnY25PGxcUF5cuXR79+/TBp0iR8/PgR//vf//JdtgFk5mLfvn343//+h3r16kFJSQn169cv9j7Xr1+PrVu3YuPGjahTpw7q1KmD0aNHY/LkyWjWrBkaNmwoc92pU6fiwIEDaNOmDaZNmwZ1dXWsWrUq3yNGLSwsMGvWLPz+++94+vQpOnToAB0dHURHR+Off/6BhoaGzMc3yqMo9fv9999j27Zt6NSpE37++Wc0bNgQKioqePHiBc6cOYPu3bujZ8+exdrvzz//DE1NTXh6euLt27dYvnz5Z/WrbMVpF3k1a9YMnp6eGDp0KG7cuIGWLVtCQ0MDkZGR4kfS/vDDD58dIxERKQ4OLBARUYmZOnUqlJSUxNOzd+zYgdOnT2P69Olwd3dHRkYG7O3tceDAgXw3lSsuDQ0NXLhwAfPnz8fatWsRFhYGNTU1mJmZoV27drCwsJC57tChQxETE4O///4bPj4+qF69On777Te8ePEi3x+by5Ytw08//YTvv/9e/IhLWTef69+/PzQ0NDBv3jy4ublBWVkZjRs3xpkzZ9C0adNiH2OjRo1w48YNTJ48GbGxsdDW1kb9+vVx+vRp1KlTp9jbk8ba2hp79+7F1KlT0atXL+jp6aF///6YMGECOnbsKFH2559/xv379zFlyhQkJydDEAQIglCs/d29exdjx47FkCFD4O7uLl6+aNEi8eM1b926BW1tbanr29ra4uTJk/jll18wZMgQ6OjoYNCgQejduzc8PT0lynp5ecHGxgbLli0TP/7TyMgIDRo0wKhRo4oVd0lQVlbGgQMHsGzZMmzZsgXz5s1DuXLlYGJiAmdnZ9jZ2cm1XQ8PD2hoaGDQoEF49+4d1q9f/9mxFqddSLNmzRo0btwYa9aswerVq5GRkYEqVaoUOnBERERfJ5FQ3N8IiIiIiIiIiIiy8KkQRERERERERCQ3DiwQERERERERkdw4sEBEREREREREcuPAAhERERERERHJjQMLRERERERERCQ3DiwQERERERERkdw4sEBEREREREREcuPAAhERERERERHJjQMLRERERERERCQ3DiwQERERERERkdw4sEBEREREREREcuPAAhERERERERHJjQMLRERERERERCQ3DiwQERERERERkdw4sEBEREREREREcuPAAhERERERERHJjQMLRERERERERCQ3DiwQERERERERkdw4sEBEREREREREcuPAAhERERERERHJjQMLRERERERERCQ3DixQiWvVqhXGjRtX1mEQERERERHRF8CBBSIiIiIiIiKSGwcWvlEZGRlYsGABLC0toaqqCjMzM8yZMwcAcPfuXbRp0wZqamrQ09ODp6cn3r59K17X3d0dPXr0wKJFi2BsbAw9PT389NNPSE1NFZdZvXo1atasiQoVKqBy5cro06ePeN1z585h2bJlEIlEEIlEePbs2Rc9diIiIiIiIvpyypV1AFQ6vLy8sG7dOixZsgTNmzdHZGQkHj58iPfv36NDhw5o3Lgxrl+/jpiYGAwfPhyjR4+Gr6+veP0zZ87A2NgYZ86cwZMnT+Dm5gYHBweMGDECN27cwNixY7FlyxY0bdoUCQkJuHDhAgBg2bJlePToEWxtbTFr1iwAgIGBQVmkgIiIiIiIiL4AkSAIQlkHQSXrzZs3MDAwwMqVKzF8+HCJz9atW4fJkyfj+fPn0NDQAAAcPnwYXbt2xatXr1C5cmW4u7vj7NmzCA0NhbKyMgCgb9++UFJSwo4dO7Bv3z4MHToUL168QMWKFfPtv1WrVnBwcMDSpUtlxpiSkoKUlBSJZaqqqlBVVf3MoyciIiIiIqIviZdCfIOCg4ORkpKCtm3bSv3M3t5ePKgAAM2aNUNGRgZCQkLEy+rUqSMeVAAAY2NjxMTEAABcXFxgbm6O6tWrY9CgQdi2bRvev39frBjnzZsHLS0tide8efOKe6hERERERERUxjiw8A1SU1OT+ZkgCBCJRFI/y71cRUUl32cZGRkAgIoVK+Lff/+Fn58fjI2NMW3aNNjb2yMpKanIMXp5eSE5OVni5eXlVeT1iYiIiIiISDFwYOEbVLNmTaipqeHUqVP5PrOxsUFQUBDevXsnXnbp0iUoKSmhVq1aRd5HuXLl0K5dOyxcuBB37tzBs2fPcPr0aQBA+fLlkZ6eXuD6qqqqqFSpksSLl0EQERERERF9fXjzxm9QhQoVMHnyZEyaNAnly5dHs2bNEBsbi/v372PAgAGYPn06hgwZghkzZiA2NhZjxozBoEGDULly5SJtPzAwEE+fPkXLli2ho6ODw4cPIyMjA1ZWVgAACwsLXLt2Dc+ePYOmpiZ0dXWhpMQxLCIiIiIiom8R/9r7Rv3xxx/45ZdfMG3aNNSuXRtubm6IiYmBuro6jh07hoSEBDRo0AB9+vRB27ZtsXLlyiJvW1tbG/v27UObNm1Qu3Zt/P333/Dz80OdOnUAABMnToSysjJsbGxgYGCAiIiI0jpMIiIiIiIiKmN8KgQRERERERERyY0zFoiIiIiIiIhIbhxYICIiIiIiIiK5cWCBiIiIiIiIiOTGgQUiIiIiIiIikhsHFoiIiIiIiIhIbhxYICIiIiIiIiK5cWCBiIiIiIiIiOTGgQUiIiIiIiIikhsHFoiIiIiIiIhIbhxYICIiIiIiIiK5cWCBiIiIiIiIiOTGgQUiIiIiIiIikhsHFoiIiIiIiIhIbhxYICIiIiIiIiK5lSvrAIi+FodUrMo6BJnMg8+VdQgyKSGjrEP4KmUo8LivAFFZhyBTedGnsg6hQKmCSlmHIFOqoLi/EpQXpZZ1CDIpcn8QQSjrEGRS5NgAnoO/RcpIL+sQZFLkOq1jaVzWIdBXQnHPmkRERERERESk8DiwQERERERERERy48ACEREREREREcmNAwtEREREREREJDcOLBARERERERGR3DiwQERERERERERy48ACEREREREREcmNAwtEREREREREJDcOLBARERERERGR3DiwQERERERERERy48ACEREREREREcmNAwtEREREREREJDcOLFCpEYlE8Pf3L+swiIiIiIiIqBSVK+sAiL5mus3ro/ovHtByskWFKoa40ftHRB84VfA6LRrAZtFv0LSpiZRXMQj1Xo+ItTskyhj1bI9aM36Geg0zvA+NQMi0JYgOOClXjIIgYNd2X5w4ehDv3r5BTSsbDP9hHMzMqxW43pVL57BjywZERb6CkXEV9B88HI2atpQoczRwPwL27UBiQgJMzSww1HM0bGztixzbkUB/+O/bicSEeJiaWcDDczRsbOvKLH/vbhA2rluN5xHPoKurjx59vkeHTt3En0eEh8Fv60aEPnmE2JhoDBvxE7r26FPkeL6W2BS5TnPi24iTWfFZWtlgxA/jYVpIfFcvnZWIr9/gERLxPbgXhIC9O/D0SQgSE+IxaeocNGzSoshxHQ4MwL69u5GYEA8zcwsM9/wRdWztZJa/d/c2Nqz7GxHhz6Crp4devd3QsXNX8eeXL13Anp1+iIx8ibS0dFSpWhU9evZB67YuRY4pN0WuV0EQsGe7D04fC8Dbt29gWasOhv0wAabm1Qtc79qlM9i1dT2iI1+isnFVuA3yRMOmzuLPjx/ej5OH9yM2OhIAYGJWDb36DYVj/SZFju1IoH/WscXD1KwahhXSV++L+2qYuK+6duouUebKpXPw2+IjkdPGTYve1rIpcp0qct4U+fwLKHa9Kur5V9FjU+Q2V9J9NSI8DDu2bkTokxDExkRj6Iif0LXHd3LFRpQbZywQfQZlDXW8vhOC+z/PKlJ5NQsTNDi4FgkXb+Jigx54suBv1FnyO4x6theX0W7sAMftS/ByWwAu1OuOl9sC4OS3FNoNZf8QKYj/Hj8c3L8Lw0eNw4Ila6Cto4tZU3/Bh/fvZa4TEnwPi+fPhHOb9vBeuSHz3/kz8OjhA3GZS+dPY+O6lejtNgiLlq9Dbdu6mDN9MmJjoosU18Xzp+GzbhX6uA2E9/J1sLGti9kFrB8dFYk/p3vBxrYuvJevQ2+3AdiwZgWuXDonLpOSkoLKRlUwyN0TOjq6RczQ1xUboLh1mhPfdgTu3wWPUeMwf8narPgmFCm+lm1c4b3SBy3buGLx/OkS8X38+BEW1WrAY9S4YsUDABfOncH6tf9DX7f+WLrib9jUscPMaV4yjy0qKhIzp/0Omzp2WLrib3zXtz/WrVmFyxfPi8tUrFgR333fHwu9l2P56rVo284Vy5b8hX9vXi92fIBi1+uBvdtw2H8Hho6agLmLN0BbRxdz/xiHD+/fyVznUfA9LFswHS1au2LBik1o0doVyxb8gcch98Vl9PQM0G/IKMxZugFzlm5AHft6WPTnb3ge/rRIcV0UH9tAeC9fj9q2dvhz+qRC+upvqG1rB+/l69FLSl8NCb4P76ycLl65XmpOi0pR61SR86bo519Aces1MzbFO/8qemyK3OZKo69mxmaMQe6e0C6B/kCUjQMLJeDo0aNo3rw5tLW1oaenhy5duiA0NBQA0KRJE/z2228S5WNjY6GiooIzZ84AACIjI9G5c2eoqamhWrVq2L59OywsLLB06dIi7X/GjBkwMzODqqoqqlSpgrFjx4o/s7CwwOzZs9G/f39oamqiSpUqWLFihcT6ycnJ8PT0hKGhISpVqoQ2bdrg9u3bEmUOHjyIevXqoUKFCqhevTpmzpyJtLQ08eePHz9Gy5YtUaFCBdjY2ODEiRNFzt/XLPbYeTyavhRR/kU7XnPP7/ExIhIPfpmLtw+f4rnPHjz33YfqE4aJy1QbMwRxJy8jdOFavAt5itCFaxF3+iosxgwpdnyCICAwYDd6uw1C42YtYWZRHWMmeCElJQUXzsmeAREYsAf2jvXQq+9AmJiao1ffgbCzr4fAgN3iMgf370Kb9p3QzrULTMwsMMxzDPT0DXDscECRYjuwfzfatu8EF9fOMDUzh4fnaOjpG+Lo4QNSyx87fAD6Bobw8BwNUzNzuLh2RhuXjvDft0tcpmYta7h7jEIL5zYop6JSxCx9XbEpcp1mx3coYDd6uQ1C42bOWfFNyYpPdj85FLAbdR3ro1ffgaiaK75DueJzqt8Y/QaPQONmzjK3I0vA/r1o174D2nfoBFMzc4wY+SP0DQxx+NBBqeWPHg6EgaEhRoz8EaZm5mjfoRPauXTA/n058djVdUCTps1hamYOY+Mq6NajFyyqVceD+/eKHZ8i16sgCDgSsAs93IagYdNWMLWojh8nTEVKSgouFVCnhw/shJ1jA/ToOxhVTc3Ro+9g2NrXx5GAnH5Rr1FzODZoiipVzVClqhm+HzwSFSqoSQw+FOSguK92gYmZOTw8x0BP31DmseX01TEwMTOHi2sXtHHpiIB9O3O2GbAH9o710bvvAJiYmqN33wGws3dCYMCeIsWUTZHrVJHzpsjnX0Cx61VRz7+KHpsit7nS6Ks1a1ljiMcPaO7cFiqf2R+IcuPAQgl49+4dJkyYgOvXr+PUqVNQUlJCz549kZGRgQEDBsDPzw+CIIjL79y5E5UrV4azc+YJcPDgwXj16hXOnj2LvXv3Yu3atYiJiSnSvvfs2YMlS5ZgzZo1ePz4Mfz9/WFnJzm196+//kLdunXx77//wsvLC+PHjxf/4S8IAjp37oyoqCgcPnwYN2/ehJOTE9q2bYuEhAQAwLFjxzBw4ECMHTsWDx48wJo1a+Dr64s5c+YAADIyMtCrVy8oKyvj6tWr+PvvvzF58uTPzuu3SLuxA2JPXpJYFnv8ArTq2UJULvPKJJ3GDog7eVGiTNyJC9Bp4ljs/UVHRSIpMQH2TvXFy1RUyqOOrT1CgmX/8fPo4X3YOzaQWObg1AAhwZm/7KempiL0ySM45Clj79SgwO1my1m/vsRyB6f6eChj/ZCHD+DgJFne0akBQh+HSAxyfS5Fjg1Q3DrNFiOOL2c7KirlYSNHfPZODYu1b1lSU1Px5MkjOOatI8d6eBgs/dvUh8EP4OhYT7J8vfp48viR1DoVBAG3g/7FyxcvUKeAKaqyKHK9xkS/QlJiPOo6NpSIrbatAx4F35W53uOH91E3z37rOjWUuU5GejounzuJlI8fUcvattC4Mo8tROrxPwyWPjDx6OF9ODjlLd9Qoq8+eng/X74cnRrK3KYsilqnipw3RT//Aopbr4Binn8VPTZFbnOl1VeJSgvvsVACevfuLfF+w4YNMDQ0xIMHD+Dm5obx48fj4sWLaNEi83qv7du3o3///lBSUsLDhw9x8uRJXL9+HfXrZ56k1q9fj5o1axZp3xERETAyMkK7du2goqICMzMzNGzYUKJMs2bNxLMmatWqhUuXLmHJkiVwcXHBmTNncPfuXcTExEBVVRUAsGjRIvj7+2PPnj3w9PTEnDlz8Ntvv2HIkMxvzKtXr47Zs2dj0qRJmD59Ok6ePIng4GA8e/YMJiYmAIC5c+eiY8eOMuNOSUlBSkqKxDJVVVVxDN8q1cr6SImOk1j2KSYeSioqKK+vg5SoWKga6SMlOl6iTEp0PFSNDIq9v6TEzMEhbW3JqW5a2jqIjZU9tTIpMQHaOjoSy7R1dMTbe/M6GRkZ6dDKs11t7ZwyBclcPwPa2nn2oa2DpMREqeskJibAUUr59PR0vH6dDF1dvUL3WxSKHBuguHWaLTExXmp82tq6iI2Nkju+z/FaRp1qFbD9pMQEaOWNR0qdvnv3FkMHfY/U1FQoKSlh1E9j4ehUT9omC6TI9ZpdTitv/rR1ERdTUJ3G59uvlrZuvv1GPAvFHxNHIvXTJ1RQU8Mvv8+FiVnB11wDsvuqVgHHlpiYAIdC+qq0ui+orciiqHWqyHlT9PMvoLj1Cijm+VfRY1PkNldafZWotHDGQgkIDQ1F//79Ub16dVSqVAnVqmX+QhQREQEDAwO4uLhg27ZtAICwsDBcuXIFAwYMAACEhISgXLlycHJyEm/P0tISOnlOorJ89913+PDhA6pXr44RI0Zg//79+UYkmzRpku99cHAwAODmzZt4+/Yt9PT0oKmpKX6FhYWJL+e4efMmZs2aJfH5iBEjEBkZiffv3yM4OBhmZmbiQQVp+8xr3rx50NLSknjNmzevSMf81cs1ewUAIBLlXy6tTN5lUpw/cwIDencQv9LT07JWF+UNAiLkXZaX5OeCkH87eTcrCFIWFrgLafsoqHie8hCyIi3GPotKQWJT9Do9f+Y4BvZ2Fb/S09Olb6cI8eX9XBAEKccpv3zbKqRS82VYyF+namrqWLpyDbyXrsLAIcPgs+5v3L0TVGgsilyvF88cw5A+7cSv9DQZsRWhfoqyTpWqZliw3BezvdfApWMPrF4yBy8iwgrcbuH7KHr5nL6aq4zUtlhwHIpcp1L3oCB5k7GzvKGV2c8GRa5XRT7/KnJs0neiOG2usH2VRF8lKg2csVACunbtClNTU6xbtw5VqlRBRkYGbG1t8enTJwDAgAED8PPPP2PFihXYvn076tSpA3v7zDv8CjL+WJS1PC9TU1OEhITgxIkTOHnyJH788Uf89ddfOHfuXIHXTWWfdDIyMmBsbIyzZ8/mK6OtrS0uM3PmTPTq1StfmQoVKkiNtbATvpeXFyZMmCCx7FufrQAAKdFx+WYelDfQRUZqKj7FJ2WWiYqDqpG+RBlVQ918Mx2kadCoGWpa1Ra/T01NBZD5TYFOrlHq5KSkfN8A5Katk/9bxeSkRPE3lhUraUFJSTl/meTEfCPr0mSuryR1/bzfimbT0dFFYr6YkqCsrIyKlSoVus+iUrTYFL1OGzRqjppWNuL3aeL4EqCjm9OOk5MS832bmTc+aTmUlfPiqJRVp9K2L+vYMvMl+W1VcnL+OlVSUkKVKlUBANVrWOJFRAT27PKDXV2HAmNS5Hqt16g5LK3q5Iot82dZUt46LaBPZMamh6REydlX0tYpp6ICoyqZA9M1atZG6OOHOHJgN0aMniRz2znHJqVek5PyfeubTUdqvrLrVSsr7vxlXifJ3mY2Ra7T3BQtb9JiU5TzL6DY9arI519Fji03RWxzeWMr6b5KVFo4Y+EzxcfHIzg4GFOnTkXbtm1Ru3ZtJOb5ZbRHjx74+PEjjh49iu3bt2PgwIHiz6ytrZGWloZbt26Jlz158gRJSUlFjkFNTQ3dunXD8uXLcfbsWVy5cgV37+Zcw3r16lWJ8levXoW1tTUAwMnJCVFRUShXrhwsLS0lXvr6+uIyISEh+T63tLSEkpISbGxsEBERgVevXon3ceXKlQJjVlVVRaVKlSRe/4WBhaSrQdBv21RimYFLcyTfvAch61vBxKtB0G/bTKKMfrvmSLxyC4VRU1eHcRUT8cvUzALaOrq4c+uGuExqairu37sNq9qyr2GuZV0Ht4NuSCy7fes6rGpn/rGhoqKCGpa1cPuWZJk7t24UuN1ssta/fesmrGWsb2Vtg9u3bkosC7p1AzVqWqFcuZIbI1W02BS9TvPGZyIjvgdFiO9OkOTTFDLjK7w9FUZFRQWWlrUQlK+ObsK6to3Udaxr2+Qrf+vfG7CsWavAOhUgiP/wKIgi16uaugaMqpiIXyZm1aCto4e7t3LqJy01FcH3glCrtuzHdda0riOxTuZ+rxe4DpA5sJ49mFGQzGOzktJXb8C6dh2p69SyriOl/HWJviotp0G3rsvcZjZFrtPcFC1v+WNTnPMvoNj1qsjnX0WOLTdFbHOSsZV8XyXFc/78eXTt2hVVqlSBSCSCv79/oeucO3dO4sb6f//9d74ye/fuhY2NDVRVVWFjY4P9+/eXQvQ5OLDwmXR0dKCnp4e1a9fiyZMnOH36dL5v4jU0NNC9e3f88ccfCA4ORv/+/cWfWVtbo127dvD09MQ///yDW7duwdPTE2pqakWa5uXr64sNGzbg3r17ePr0KbZs2QI1NTWYm5uLy1y6dAkLFy7Eo0ePsGrVKuzevRs///wzAKBdu3Zo0qQJevTogWPHjuHZs2e4fPkypk6dihs3Mk9M06ZNw+bNmzFjxgzcv38fwcHB2LlzJ6ZOnSrehpWVFQYPHozbt2/jwoUL+P333z87t18DZQ11VLK3RiX7zIEa9WomqGRvjQqmxgAAqz8nwH7jAnH58LU7oGZeBbX/+g2a1tVh4t4bpkN74+liH3GZZys3Q9+lGapPHAENq+qoPnEE9Ns2wbMVm4odn0gkQpfu32Hvrm24dvk8Ip49xcol86CqqooWzu3E5ZZ7z8FW37Xi95279cHtf29g/+7tePE8HPt3b8edoJvo0j3nOcdde/bFqeOHcOr4IbyIeIaNa1ciLjYG7XM9x7kg3Xp+h5PHD+Pk8cN4HhEOn7WrEBcbDddOXQEAW3zXYZn3XHF5107dEBsTDZ91q/A8Ihwnjx/GqeOH0aNXX3GZ1NRUhIU+QVjoE6SlpSE+Pg5hoU8Q+eplsfKmyLEpcp1mx9e5+3fYt2urOL5V4vhcJOLb5rtG/L6TOL5tePk8HPt3b8PdoBvonCu+Dx/eIyz0McJCHwPIvIlaWOjjIj2KrXvP3jhx7AhOHD+C5xHhWL92NWJjY9Axq043bVyPJYvmi8t36NQFMTEx2LD2f3geEY4Tx4/g5PGj6NkrJ57dO7fj1r83ERX5Ci+eR8B/3x6cOXUCrVq3y7f/ouRNUetVJBKhY/e+8N+9Gf9cPofnz55i9dI5UFVVRbNcdbrKezb8fP8nft+xW1/cuXUdAXu24uXzcATs2Yp7QdfRsXtOv/Db9DeC7wUhJjoSEc9CsWPzGjy4dwvNW7VHUXTt+V3WsR3Gi4hw+KxdibjYaPGxbfVdK7Wvbly3Ci8iwnEqq6927+UmLtOlW28E/Xsd+7Jyuk+c0+I9g16R61SR86bI519AsetVUc+/ih6bIre50uirmbFl5istLQ0J8XEIC32MyFcvihUblZx3797B3t4eK1euLFL5sLAwdOrUCS1atMCtW7cwZcoUjB07Fnv37hWXuXLlCtzc3DBo0CDcvn0bgwYNQt++fXHt2rXSOgyIhKLOuSeZTp48ibFjx+Lp06ewsrLC8uXL0apVK+zfvx89evQAABw+fBidO3dGy5Ytce7cOYn1IyMj4eHhgdOnT8PIyAjz5s3DuHHjMGvWLIwcObLAffv7+2P+/PkIDg5Geno67Ozs8Oeff6Jt27YAMh83OWzYMNy/fx+BgYGoWLEivLy8xAMLAPDmzRv8/vvv2Lt3L2JjY2FkZISWLVti3rx5MDU1BZD5ZIhZs2bh1q1bUFFRgbW1NYYPH44RI0YAAB49egQPDw/8888/sLCwwPLly9GhQweJHHztDqlY5Vum27Ihmpzakm/58837cMfDC3U3zIO6eVVcbTc4Z50WDWDj7QVNm5pIeRWD0EXrELF2h8T6Rr1cYTVzHNSrm+B96HOETFtS4CMtzYPPyfxMEATs2u6L40cO4N3bt6hpVRsjfhgHM4vq4jLTfvsZBoZGGDPBS7zsysWz2L5lA2KiXqGyURX0HzwCjZu1lNj20cD98N+7A4kJ8TAzrwZ3z9GoY2svUUYJGTJjOxLoj/17dyAxIQFm5hYY5vmTeP3li+cjJiYKf85fKi5/724QNq5bjYjwZ9DV00PPPv3QIdcvXDHRURg5rF++/dSxs5fYTlGUdWwZBYz7lnWdCoVcqZkZ30acyBXf8B/G54lvLAwNjTB6whSJ+Py2rBfHl/fxYffu3MIMr5+RV6u2HcTbKS+S/U334cAA7NuzCwkJCTC3sIDHiB9ga5f5BIelixciJjoKcxcsztnf3dtYv/Z/iAgPh66eHnr3cUPHzl3Fn2/d5IMLF84hPi4W5curwsTUFF279UQL59YyY0gVZF+iVtb1mioUMBNDELBnuw9OHQ3Au7dvYGllg2GjfoFprthm/jYaBpWN8OP4qeJlVy+ewa6taxEd9QqVjari+8GeaNi0lfjzv5fNw73bN5CUEA91DQ2YWViiW58BEk+gAIDyItmzQI4E+sN/r19WX62Gobn66orF8xATE4XZ85eJy9+/G5T5B0GuvuraqbvENi9fPAu/LRsQHRWJykZVMGDw8Hw5FeemgP5Q1nUqguxf8co6b4XFVtY/G77Wc3BZnn8LU9axKSNdZmxl3eYKqtOS7qsx0ZEYJSO23NsRL7c0lhkblTyRSFTo30+TJ0/GgQMHxPfMA4BRo0bh9u3b4lnjbm5ueP36NY4cOSIu06FDB+jo6MDPz690YufAguJ58eIFTE1NcfLkSfEAgbwsLCwwbtw4jBs3rmSC+w+TNrCgKAoaWChrBQ0skGwF/VJb1gobWChLBQ0sKIKCBhbKWkEDC2WtoIGFsqbI/aGgP97LmiLHBvAc/C0qaGChrClynXJgQT7yPgGvKAMLLVu2hKOjI5YtyxkI2r9/P/r27Yv379+LnxQ4fvx4jB8/XlxmyZIlWLp0KcLDw+U7qEIo7m8R/yGnT5/G27dvYWdnh8jISEyaNAkWFhZo2VL6KD8RERERERHJVpZfCl7/vR9mzpwpsWz69OmYMWPGZ287KioKlStXllhWuXJlpKWlIS4uDsbGxjLLREXJfrzr5+LAggJITU3FlClT8PTpU1SsWBFNmzbFtm3boKKigm3btsm8HMLc3Bz379//wtESERERERGRLKX9BLx8jxXNfiR2ruXSypT4o1pz4cCCAnB1dYWrq6vUz7p164ZGjRpJ/aygx0lme/bs2eeERkRERERE9NURqZTdJSZFuexBXkZGRvlmHsTExKBcuXLQ09MrsEzeWQwliQMLCq5ixYqoWLFiWYdBREREREREZaxJkyY4ePCgxLLjx4+jfv364i+emzRpghMnTkjcY+H48eNo2lTysfcliQMLRERERERE9E1RKqe4N8XM7e3bt3jy5In4fVhYGIKCgqCrqwszMzN4eXnh5cuX2Lx5M4DMJ0CsXLkSEyZMwIgRI3DlyhVs2LBB4mkPP//8M1q2bIkFCxage/fuCAgIwMmTJ3Hx4sVSOw7FveUtERERERER0Tfsxo0bcHR0hKOjIwBgwoQJcHR0xLRp0wAAkZGRiIiIEJevVq0aDh8+jLNnz8LBwQGzZ8/G8uXL0bt3b3GZpk2bYseOHdi4cSPq1q0LX19f7Ny5U+Yl9iWBj5skKiI+blI+fNykfPioM/nwcZPy4+Mm5aPI/UGRH+moyLEBPAd/i/i4Sfl8zY+bPFqpdpntu8Pr4DLbd1lR3N8iiIiIiIiIiOQgUlHcAcJvEbNNRERERERERHLjjAUiIiIiIiL6pnwtN2/8VnDGAhERERERERHJjQMLRERERERERCQ3XgpBRERERERE3xSRCi+F+JI4Y4GIiIiIiIiI5MYZC0RFZB58rqxDkCm8tnNZhyCTWfD5sg5BJkV+pjXJJ03gjzV5qYjSyjoEmRT5Ge/0bVLkNqeEjLIOQSZFzhtj++/hzRu/LM5YICIiIiIiIiK58asdIiIiIiIi+qbwHgtfFmcsEBEREREREZHcOLBARERERERERHLjpRBERERERET0TeHNG78szlggIiIiIiIiIrlxxgIRERERERF9U0TKnLHwJXHGAhERERERERHJjQMLRERERERERCQ3XgpBRERERERE3xQlXgrxRXHGAhERERERERHJjTMWiIiIiIiI6JsiUuKMhS+JMxZkaNWqFcaNG/dF9mVhYYGlS5cWqaxIJIK/v3+pxPElj5mIiIiIiIi+DZyxQFQCBEHAru2+OHH0IN69fYOaVjYY/sM4mJlXK3C9K5fOYceWDYiKfAUj4yroP3g4GjVtKVHmaOB+BOzbgcSEBJiaWWCo52jY2NoXGpNu8/qo/osHtJxsUaGKIW70/hHRB04VvE6LBrBZ9Bs0bWoi5VUMQr3XI2LtDokyRj3bo9aMn6FewwzvQyMQMm0JogNOFhqPNJl524iTWXmztLLBiB/Gw7SQvF29dFYib/0Gj5DI24N7QQjYuwNPn4QgMSEek6bOQcMmLYoV25FAf/jv24nEhHiYmlnAw3M0bGzryix/724QNq5bjecRz6Crq48efb5Hh07dxJ9HhIfBb+tGhD55hNiYaAwb8RO69uhTrJiyKWJ7yx+f4tXrkUD/rGOLh6lZNQwrpE7vi+s0TFynrp26iz+PCA/Djq0bEfokBLEx0Rg64id07fFdkePJS5HrtbRiu3/vNgL2+uHpk0dZdfonGhWzrxb32O7fDYLvulV4HvEMOrp66NGnn0S9FiXuolLkOi3p/pAdt98WH4m4GzctXn1mx6ao519Acc9xgGLX63+pP5TkzwdFzhtRbpyx8IWkp6cjIyOjTPadmppaJvv9L/Hf44eD+3dh+KhxWLBkDbR1dDFr6i/48P69zHVCgu9h8fyZcG7THt4rN2T+O38GHj18IC5z6fxpbFy3Er3dBmHR8nWobVsXc6ZPRmxMdKExKWuo4/WdENz/eVaRjkHNwgQNDq5FwsWbuNigB54s+Bt1lvwOo57txWW0GzvAcfsSvNwWgAv1uuPltgA4+S2FdkPZP3wL4r9nOwL374LHqHGYv2RtVt4mFClvLdu4wnulD1q2ccXi+dMl8vbx40dYVKsBj1Hj5Irr4vnT8Fm3Cn3cBsJ7+TrY2NbF7ALyHh0ViT+ne8HGti68l69Db7cB2LBmBa5cOicuk5KSgspGVTDI3RM6OrpyxZVNEdubZHyKV68Xxcc2EN7L16O2rR3+nD6pkDr9DbVt7eC9fD16yaxTYwxy94T2Z9YpoNj1WlqxpXz8AItqlhguZ18t7rFFR0VizvTJqG1bF4uWr0Nvt4HwWbNcol6LEndRKWqdlkZ/CAm+D++suBevXC933hT9/Aso5jkOUOx6Bf5b/aEkfz4oat6+BiJlpTJ7/Rf9N4+6iDIyMjBp0iTo6urCyMgIM2bMEH+2ePFi2NnZQUNDA6ampvjxxx/x9u1b8ee+vr7Q1tZGYGAgbGxsoKqqivDwcMTExKBr165QU1NDtWrVsG3btmLHFRkZiY4dO4q3sXv3bvFnz549g0gkwq5du9CqVStUqFABW7duRXx8PPr16wcTExOoq6vDzs4Ofn5+Be7n6NGj0NLSwubNmwEAL1++hJubG3R0dKCnp4fu3bvj2bNn4vJnz55Fw4YNoaGhAW1tbTRr1gzh4eHFPr6vjSAICAzYjd5ug9C4WUuYWVTHmAleSElJwYVzsr/JDwzYA3vHeujVdyBMTM3Rq+9A2NnXQ2BATn0e3L8Lbdp3QjvXLjAxs8AwzzHQ0zfAscMBhcYVe+w8Hk1fiij/E0U6DnPP7/ExIhIPfpmLtw+f4rnPHjz33YfqE4aJy1QbMwRxJy8jdOFavAt5itCFaxF3+iosxgwp0j5yEwQBhwJ2o5fbIDRu5pyVtylZeZMd86GA3ajrWB+9+g5E1Vx5O5Qrb071G6Pf4BFo3My52HEBwIH9u9G2fSe4uHaGqZk5PDxHQ0/fEEcPH5Ba/tjhA9A3MISH52iYmpnDxbUz2rh0hP++XeIyNWtZw91jFFo4t0E5FRW54gIUt73ljk8R6/WguE67wMTMHB6eY6Cnbyjz2HLqdAxMzMzh4toFbVw6ImDfTnGZmrWsMcTjBzR3bguVz6hTQLHrtTRjc6rfOPPbz2bFnw0gz7EdPxwAfQNDDPMcAxMzC7Rz7YI2Lp1wYF/OzKyixF0UilynpdEfDgbsgb1jffTuOwAmpubo3XcA7OydEBiwp4gZy6TI519Acc9xgGLX63+tP5TUzwdFzhtRXhxYKMCmTZugoaGBa9euYeHChZg1axZOnMj8oaGkpITly5fj3r172LRpE06fPo1JkyZJrP/+/XvMmzcP69evx/3792FoaAh3d3c8e/YMp0+fxp49e7B69WrExMQUK64//vgDvXv3xu3btzFw4ED069cPwcHBEmUmT56MsWPHIjg4GK6urvj48SPq1auHwMBA3Lt3D56enhg0aBCuXbsmdR87duxA3759sXnzZgwePBjv379H69atoampifPnz+PixYvQ1NREhw4d8OnTJ6SlpaFHjx5wdnbGnTt3cOXKFXh6ekIk+vZvmhIdFYmkxATYO9UXL1NRKY86tvYICb4nc71HD+/D3rGBxDIHpwYICb4PIHOmSeiTR3DIU8beqUGB25WXdmMHxJ68JLEs9vgFaNWzhahc5lVTOo0dEHfyokSZuBMXoNPEsdj7ixHnLef4VFTKw0aOvNk7NSyxnOTkvb7Ecgen+ngoYx8hDx/AwUmyvKNTA4Q+DkFaWlqJxJVN0dubItZr5rGFSD3+h1nHLy0eB6e85RuWSp0Cil2vpRXb55Ln2EIe3pdom9kx5a7XkopbUeu0tPrDo4f388Xk6NRQ5jZlx6a4519AMc9xgGLXK/Df6w8lRVHz9rVQUhaV2eu/iPdYKEDdunUxffp0AEDNmjWxcuVKnDp1Ci4uLhI3OaxWrRpmz56NH374AatXrxYvT01NxerVq2Fvn3mt0qNHj3DkyBFcvXoVjRo1AgBs2LABtWvXLlZc3333HYYPHw4AmD17Nk6cOIEVK1ZI7HvcuHHo1auXxHoTJ04U/3/MmDE4evQodu/eLY4l2+rVqzFlyhQEBASgdevWADIHGpSUlLB+/XrxYMHGjRuhra2Ns2fPon79+khOTkaXLl1Qo0YNACj2cX2tkhITAADa2pJT3bS0dRAbK3s6WVJiArR1dCSWaevoiLf35nUyMjLSoZVnu9raOWVKkmplfaREx0ks+xQTDyUVFZTX10FKVCxUjfSREh0vUSYlOh6qRgbF3l9iYuZ28uZNW1sXsbFRMtcrLG+fKzPvGdDWzrMPbR0kJSZKXScxMQGOUsqnp6fj9etk6OrqlUhsgOK3N0WsV1l1qlXAsSUmJsDhC9UpoNj1WlqxfS55ji0pMUFKO9BFeno63rxOho6uXonFrah1Wlr9ISkxAVp54tYqZt4U/fybuT/FO8cBil2vwH+vP5QURc0bkTQcWChA3bqS140bGxuLZxecOXMGc+fOxYMHD/D69WukpaXh48ePePfuHTQ0NAAA5cuXl9hGcHAwypUrh/r1c0Ydra2toa2tXay4mjRpku99UFCQxLLc+wAy7/Ewf/587Ny5Ey9fvkRKSgpSUlLEsWbbu3cvoqOjcfHiRTRs2FC8/ObNm3jy5AkqVqwoUf7jx48IDQ1F+/bt4e7uDldXV7i4uKBdu3bo27cvjI2NpR5D9v5zU1VVhaqqapFyUJbOnzmBNSu9xe+nzJgPAFJmZwgQobARS8nPBSH/dvJuVhCkLCwpgiD5Pns/uZdLK5N3mRTnzxzH2lx585qxQGIX4s0XIW95PxcEoeRnx4ik1U1BxfOUR2ZOCm8DBVP09vY11Wu+bQmCnHX6+RS5Xr90bJ+ruG1W2nFIKZVvm4XFrch1KnUPpdAfpPfhIoeUe2d5QyuT8y/wdZ3jMuNSjHplf5DP15Y3RcfHTX5ZHFgoQN5rokQiETIyMhAeHo5OnTph1KhRmD17NnR1dXHx4kV4eHhI3ChRTU1NogMLWX98lcblAXm3mXfAwNvbG0uWLMHSpUvF94YYN24cPn36JFHOwcEB//77LzZu3IgGDRqIt5uRkYF69epJvSeEgUHmt9UbN27E2LFjcfToUezcuRNTp07FiRMn0Lhx43zrzJs3DzNnzpRYNn36dIn7WCiqBo2aoaZVzmyM7DpPTIyHTq5R6uSkpHyjxblp6+jmGxVOTkqEVtYoeMVKWlBSUs5fJjkx38h6SUiJjss386C8gS4yUlPxKT4ps0xUHFSN9CXKqBrq5pvpIE2DRs1R08pG/D5NnLcE6OjmbDM5KTHftyO5aevoIjFf3pLEeftcmXlXkpp3WfvQkRGTsrIyKlaq9FnxKHp7+xrqNbtO820/OSnftzXZdKTmK7tOtT47JkWu1y8V2+eS59ikt7NEiXqVN25FrtPcSqs/SIv7dZLsbRYUm6Kcf4Gv4xwHKF69/tf7g7y+lrwRScN7LMjhxo0bSEtLg7e3Nxo3boxatWrh1atXha5Xu3ZtpKWl4caNG+JlISEhSEpKKtb+r169mu+9tbV1getcuHAB3bt3x8CBA2Fvb4/q1avj8ePH+crVqFEDZ86cQUBAAMaMGSNe7uTkhMePH8PQ0BCWlpYSLy2tnJOoo6MjvLy8cPnyZdja2mL79u1S4/Hy8kJycrLEy8vLqzhpKDNq6uowrmIifpmaWUBbRxd3buXUa2pqKu7fuw2r2rYyt1PLug5uB92QWHb71nVY1a4DIHNgq4ZlLdy+JVnmzq0bBW5XXklXg6DftqnEMgOX5ki+eQ9C1jWDiVeDoN+2mUQZ/XbNkXjlVqHbz5s3Exl5e1CEvN0Jui6xLDNvJZMTWXm/fesmrGXsw8raBrdv3ZRYFnTrBmrUtEK5cp83fqvo7e1rqNfMY7OSUqc3YJ11/NLiyV/+eonUKaDY9fqlYvtc8hyblXUdieMAgKA89Spv3Ipcp7mVVn+QFnfQresytyk7NsU5/wJfxzkOULx6/a/3B3l9LXkjkoYDC3KoUaMG0tLSsGLFCjx9+hRbtmzB33//Xeh6VlZW6NChA0aMGIFr167h5s2bGD58ONTU1Iq1/927d8PHxwePHj3C9OnT8c8//2D06NEFrmNpaYkTJ07g8uXLCA4OxsiRIxEVJf1awFq1auHMmTPYu3ev+F4SAwYMgL6+Prp3744LFy4gLCwM586dw88//4wXL14gLCwMXl5euHLlCsLDw3H8+HE8evRI5n0WVFVVUalSJYnX13AZhDQikQhdun+Hvbu24drl84h49hQrl8yDqqoqWji3E5db7j0HW33Xit937tYHt/+9gf27t+PF83Ds370dd4Juokv3nOccd+3ZF6eOH8Kp44fwIuIZNq5dibjYGLTP9XxuWZQ11FHJ3hqV7DMHndSrmaCSvTUqmGZenmL15wTYb1wgLh++dgfUzKug9l+/QdO6Okzce8N0aG88XewjLvNs5WbouzRD9YkjoGFVHdUnjoB+2yZ4tmKTXHnr3P077Nu1VZy3VeK8uUjkbZvvGvH7TuK8bcPL5+HYv3sb7gbdQOdcefvw4T3CQh8jLDRz8Cw6KhJhoY+L/Ailbj2/w8njh3Hy+GE8jwiHz9pViIuNhmunrgCALb7rsMx7rri8a6duiI2Jhs+6VXgeEY6Txw/j1PHD6NGrr7hMamoqwkKfICz0CdLS0hAfH4ew0CeIfPWy2HlTxPaWOz5FrNeuPb/LOrbDeBERDp+1KxEXGy0+tq2+a6XW6cZ1q/AiIhynsuq0ey83cZnMOs2MJy0tDQnxcQgLfYzIVy+KnK/ceVPUei3N2PLWaUwx+2phx7bVdy2We88Rl2/fqXtWva7Ei4hnOHX8EE4fP4xuvb4vVtxlnbfPrdPS6A9duvVG0L/XsS8r7n3iuPsUK2+KfP4FFPccByh2vf7X+kNJ/XxQ5Lx9DXjzxi+Ll0LIwcHBAYsXL8aCBQvg5eWFli1bYt68eRg8eHCh627cuBHDhw+Hs7MzKleujD///BN//PFHsfY/c+ZM7NixAz/++COMjIywbds22NjYFLjOH3/8gbCwMLi6ukJdXR2enp7o0aMHkpOTpZa3srLC6dOn0apVKygrK8Pb2xvnz5/H5MmT0atXL7x58wZVq1ZF27ZtUalSJXz48AEPHz7Epk2bEB8fD2NjY4wePRojR44s1rF9rXr06YdPn1KwdvUSvHv7FjWtamPa7EVQU1cXl4mLjYFIlDOWZ21jiwmTp2H7lg3YsXUDKhtVwYTJM1DLOqcum7Vsgzevk7HbbzMSE+JhZl4NU2YugKGhUaExadWzRZNTW8TvbRZNAQA837wPdzy8oGpsADXTnHtgfHj2Ate7esLG2wvmPwxAyqsY3B8/B1H7j4vLJF65hVsDJsBq5jhYzRyL96HPcav/eCT9c0fOvPXHp08pWLd6sThvf8z2zpO3aCjlutTH2sYO4ydPh9+W9diZlbfxefIW+jgEM7x+Fr/ftH4lAKBV2w4YPWFKoXE1b9kGb16/xi6/zUhMSICZuQWmzpwvzntiQjxiY3Oe5lLZyBhTZ87DxnWrcSQwALp6evAYOQZNcj0yLDEhHhPGjhC/D9i3EwH7dqKOnT3+nL+0GFlTzPYmGZ/i1WtOnW7KqtNq+D3XsSUmxCMu142wMut0PnzWrcKRQH8ZdRqHX2TU6ez5y4qVM0Cx67W0Ygt9HILpXuPE733XrwKQWadjJhQ+i62wY8usV8m++vvMBdi4biWOZtXrsJFjJeq1KHGXdd4+t05Loz9kx+23ZQN2bPVBZaMq+GXy9GLnTdHPv4BinuMAxa7XzLz9d/pDSf58UNS8EeUlEoQi3HWNiHDviey7PZe18NryPfP6SzALPl/WIcikjPSyDkGmDAWeUCaUyO0LS4cSMso6hAIpcu7o2yOSemNKxaDIsQFAOpTLOgSZFPk8p8jnOEVuc4qcN1vLr3eg4YZzk8ILlZL6566U2b7LiuL+5kpERERERERECo8DCwpk27Zt0NTUlPqqU6dkbnZFREREREREVJJ4jwUF0q1bNzRq1EjqZ3kffUlERERERETSiZT4HfqXxIEFBVKxYkVUrFixrMMgIiIiIiIiKjIOLBAREREREdE3RaSkuDfF/BZxfggRERERERERyY0zFoiIiIiIiOiboqTMGQtfEmcsEBEREREREZHcOLBARERERERERHLjpRBERERERET0TeHNG78szlggIiIiIiIiIrlxxgIRERERERF9U0RK/A79S2K2iYiIiIiIiEhuHFggIiIiIiIiIrnxUgiiIlJCRlmHIJNZ8PmyDkGmiNotyzoEmRQ5b8pIL+sQZEqHclmHIFMGx8vlVk6UVtYhyJQuKG6bE0Eo6xBkEqC4Ny5T5NgAxa5X+vawvZUO3rzxy+JvYEREREREREQkN85YICIiIiIiom+KkjJnLHxJnLFARERERERERHLjjAUiIiIiIiL6pvAeC18WZywQERERERERkdw4sEBEREREREREcuOlEERERERERPRNESnxO/QvidkmIiIiIiIiIrlxxgIRERERERF9U3jzxi+LMxaIiIiIiIiISG4cWCAiIiIiIiIiufFSCCIiIiIiIvqm8FKIL4szFophxowZcHBwKNV9tGrVCuPGjRO/t7CwwNKlS0t1n0RERERERETy4owFZP4x7+DgUOgf8BMnTsSYMWO+TFBZrl+/Dg0NjSKVtbCwwLhx4yQGJqj0HQn0h/++nUhMiIepmQU8PEfDxrauzPL37gZh47rVeB7xDLq6+ujR53t06NRN/HlEeBj8tm5E6JNHiI2JxrARP6Frjz5yxycIAnZt34iTRw/i3ds3sLSywYgfxsPUvFqB6129dBY7tmxAVOQrGBlXQb/BI9CoaUvx5w/uBSFg7w48fRKCxIR4TJo6Bw2btChSTLrN66P6Lx7QcrJFhSqGuNH7R0QfOFXwOi0awGbRb9C0qYmUVzEI9V6PiLU7JMoY9WyPWjN+hnoNM7wPjUDItCWIDjhZpJjyUsS8ZWObkz93jE2+2A4HBsB/b1abM7eAh+dPqFNgm7sNn3Wr8Tz8GXT19NGztxs6dM5pc8ePBuLMqROICA8DANSwrIWBQzxQy6p2seICsvPmixNZeatpZYPhP4yDWSF5u3LpnETe+g8eLpG3+/duI2CvH54+eZSVtz/RSI6+GrBvR1ZfrYZhhfTV++K+Gibuq66duos/jwgPw46tGxH6JASxMdEYOuIndO3xXbFiyk2Rc1dasQHA0cD9WfWSAFMzCwz1HA0bW/tvIraSbnPZcftt8ZGIu3HT4tUnwLzJmzdFjk3RccbCl8UZC0UgCALS0tKgqakJPT29L7pvAwMDqKurf9F9UtFdPH8aPutWoY/bQHgvXwcb27qYPX0yYmOipZaPjorEn9O9YGNbF97L16G32wBsWLMCVy6dE5dJSUlBZaMqGOTuCR0d3c+O0X/PdgTu3wWPUeMwf8laaOvoYtbUCfjw/r3MdUKC72Hx/Jlo2cYV3it90LKNKxbPn45HDx+Iy3z8+BEW1WrAY9S4YsekrKGO13dCcP/nWUUqr2ZhggYH1yLh4k1cbNADTxb8jTpLfodRz/biMtqNHeC4fQlebgvAhXrd8XJbAJz8lkK7oewfvgVRxLwBbHOfkzvGJl9sF8+dgc/aVfjObQAWr1gLmzp2mD3ttwLb3OxpXrCpY4fFK9aiT9/+WL9mJS5fPC8uc+/ObbRwboPZ8xZjgfdKGBgYYsbUSYiPiy12fP57/HBw/y4MHzUOC5asycrbL0XKm3Ob9vBeuSHz3/kzJPKW8vEDLKpZYvhn9NWN61ait9tAeC9fj9q2dvhz+qRC+upvqG1rB+/l69FLZl81xiB3T2iXSF9VzNyVZmyXxPUyCIuWr0Nt27qYU8A59GuKrTTaXEjwfXhnxb145XqpcRcV81b8vClybER5/ecHFtzd3XHu3DksW7YMIpEIIpEIvr6+EIlEOHbsGOrXrw9VVVVcuHAh36UQ7u7u6NGjB2bOnAlDQ0NUqlQJI0eOxKdPn4q073fv3mHw4MHQ1NSEsbExvL2985XJeynEjBkzYGZmBlVVVVSpUgVjx44FkDnrIjw8HOPHjxcfBwDEx8ejX79+MDExgbq6Ouzs7ODn5yexj1atWmHs2LGYNGkSdHV1YWRkhBkzZkiUSUpKgqenJypXrowKFSrA1tYWgYGB4s8vX76Mli1bQk1NDaamphg7dizevXtXpDx8zQ7s34227TvBxbUzTM3M4eE5Gnr6hjh6+IDU8scOH4C+gSE8PEfD1MwcLq6d0calI/z37RKXqVnLGu4eo9DCuQ3Kqah8VnyCIOBQwG70chuExs2cYWZRHWMmTEFKSgounDshc71DAbtR17E+evUdiKqm5ujVdyDs7OvhUMBucRmn+o3Rb/AING7mXOy4Yo+dx6PpSxHlLzuG3Mw9v8fHiEg8+GUu3j58iuc+e/Dcdx+qTxgmLlNtzBDEnbyM0IVr8S7kKUIXrkXc6auwGDOk2PEpat4AtrnPyR1jky+2gP270a59R7h0yGxzw0eOhr6BIY4ekt7mjh4+CANDQwwfmdXmOnRGW5eOCMjV5iZM+h2dunRH9RqWMDE1w49jf4GQIeDO7VvFik0QBAQG7EZvt0Fo3KxlVt68svIme7ZSYMAe2DvWQ6++A2GSK2+BefLWf/BwNG7WUuZ2CnJQ3Fe7wMTMHB6eY6Cnb4hjhwOkls/pq2NgYmYOF9cuaOPSEQH7dorL1KxljSEeP6C5c1uolEBfVdTclWZsB/fvQpv2ndDOtQtMzCwwzHMM9PQNZNbL1xRbabS5gwF7YO9YH737DoCJqTl69x0AO3snBAbsKVJM2Zg3+fKmyLF9DURKSmX2+i/6bx51LsuWLUOTJk0wYsQIREZGIjIyEqampgCASZMmYd68eQgODkbdutK/9Tx16hSCg4Nx5swZ+Pn5Yf/+/Zg5c2aR9v3rr7/izJkz2L9/P44fP46zZ8/i5s2bMsvv2bMHS5YswZo1a/D48WP4+/vDzs4OALBv3z6YmJhg1qxZ4uMAMr+pqlevHgIDA3Hv3j14enpi0KBBuHbtmsS2N23aBA0NDVy7dg0LFy7ErFmzcOJE5i+zGRkZ6NixIy5fvoytW7fiwYMHmD9/PpSVlQEAd+/ehaurK3r16oU7d+5g586duHjxIkaPHl2kPHytUlNTEfrkERwc60ssd3Cqj4fB96SuE/LwARycJMs7OjVA6OMQpKWllXiMMVGRSEpMgL1TA/EyFZXysLG1R4iMGAHg0cP7sHdsILHM3qlhgeuUJu3GDog9eUliWezxC9CqZwtRucwrunQaOyDu5EWJMnEnLkCniWOx96eoeWObY2xfOjZxm8vThhwc6+Nh8H2p64QE38/XRh3r1ceTAtrcp5QUpKenQVOzYrHiixbnLWd/KirlUUeOvDk4NUCIjGMqrsy8hUjdh6y8PXp4Hw5Oecs3LLW+qqi5K83Ycs6heftMgyL3GUWNrbTa3KOH9/PF5OjUUOY2ZWHeip83RY6NSJr//D0WtLS0UL58eairq8PIyAgA8PDhQwDArFmz4OLiUuD65cuXh4+PD9TV1VGnTh3MmjULv/76K2bPng2lAkar3r59iw0bNmDz5s3ifWzatAkmJiYy14mIiICRkRHatWsHFRUVmJmZoWHDhgAAXV1dKCsro2LFiuLjAICqVati4sSJ4vdjxozB0aNHsXv3bjRq1Ei8vG7dupg+fToAoGbNmli5ciVOnToFFxcXnDx5Ev/88w+Cg4NRq1YtAED16tXF6/7111/o37+/+N4ONWvWxPLly+Hs7Iz//e9/qFChQoE5/Fq9eZ2MjIwMaGvrSCzX1tZBUmKi1HUSExPgKKV8eno6Xr9Ohq5uyV5qk5gYn7UPySmz2tq6iI2NkrleUmICtHXyxKmjg6TEhBKNr6hUK+sjJTpOYtmnmHgoqaigvL4OUqJioWqkj5ToeIkyKdHxUDUyKPb+FDVvbHOM7UvHJqvNaenoIFHGPpISE6GVN6ZC2tzmjeugq6cPe8d6xYov+zjz5k1LWwexsbKnQZdZ3rRl7yMxMQEOX7CvKmruSjO2zHpJh1a+flb0+BU1ttJqc0mJCfn6s5Yc9c28FT9vihwbkTT/+YGFgtSvX7/QMvb29hL3QGjSpAnevn2L58+fw9zcXOZ6oaGh+PTpE5o0aSJepqurCysrK5nrfPfdd1i6dCmqV6+ODh06oFOnTujatSvKlZNdjenp6Zg/fz527tyJly9fIiUlBSkpKfluCJl3RoaxsTFiYmIAAEFBQTAxMREPKuR18+ZNPHnyBNu2bRMvEwQBGRkZCAsLQ+3a+W/GlR1HbqqqqlBVVZV5LApLJHljGEHItyhP8TzlIWQux+ffYOb8meNYuzLnkhqvGQukhQgBQqH7y/u5IAj5Yv+iBEHyfXYsuZdLK5N3mRRfXd7Y5hhbKcYmfSd5Ayt4H/ljkr4cAPbt3oEL507jzwWLUb58+QLDOH/mBNbkytuUGfOzwsu73cLzBikxlnTe8m1PEOTsq59PkXP3pWOTUi0yT6KKHJvUPZRCm5N+jik4DuZNvrx9bbEpOiXlb/CgFBgHFgpQ1KcxSFPYD1ihCH/s5GVqaoqQkBCcOHECJ0+exI8//oi//voL586dk3mtpbe3N5YsWYKlS5fCzs4OGhoaGDduXL77QORdXyQSISMjAwCgpqZWYFwZGRkYOXKk+H4PuZmZmUldZ968efkuGZk+fXq+ezsosoqVtKCkpJRvhDc5ORFaeUaLs+no6Ob7li85KSlztkmlSp8dU4NGzVHTykb8Pi01FUDmCLaOrn6ufeb/RjE3bRlxyjqu0pYSHZdv5kF5A11kpKbiU3xSZpmoOKga6UuUUTXUzTfTQZqvJW9sc4ztS8SWm8w2l5SU71u0nJjyf/OVnJwotc35792JPbu2YdacRbCoVqPQeBo0aoaauZ4ckSrOWzx0cn2jn5yUlO9bTskYdaUck+x+VFzZectXN8lJ+b5ZzaYjNabsvqr12TEpcu6+VGyZ9aIstX3Kas+KHFtupdXmpMX9Okn2NrMxb/Ll7WuJjUia//w9FoDMyxnS09PlWvf27dv48OGD+P3Vq1ehqalZ4CUNAGBpaQkVFRVcvXpVvCwxMRGPHj0qcD01NTV069YNy5cvx9mzZ3HlyhXcvXtX5nFcuHAB3bt3x8CBA2Fvb4/q1avj8ePHxTrGunXr4sWLFzJjc3Jywv3792FpaZnvJevbJy8vLyQnJ0u8vLy8ihVXWVNRUUENy1q4feuGxPLbt27Curat1HWsrG1w+5bkfTSCbt1AjZpWBc48KSo1dXUYVzERv0zMLKCto4s7uWJMTU3Fg3u3YSUjRgCoZV0Hd4KuSyy7fet6geuUpqSrQdBv21RimYFLcyTfvAch65rBxKtB0G/bTKKMfrvmSLxS+M3gvpa8sc0xti8RW27ZbS4oXxu6CevadaSuY1W7Tv7y/96AZZ42t3/PDuzy24rpsxfAspbs2Xq55c2bqYy83S9C3m4H5e1H12El45iKKzNvVlL66g2ZeatlXUdK+eul1lcVKXdfKjZZ59A7t27I3K4ix5ZbabU5aXEH3bouc5vZmDf58va1xPa1ECmJyuz1X8SBBWQ+eeHatWt49uwZ4uLixN/UF8WnT5/g4eGBBw8e4MiRI5g+fTpGjx5d4P0VAEBTUxMeHh749ddfcerUKdy7dw/u7u4Frufr64sNGzbg3r17ePr0KbZs2QI1NTXxJRcWFhY4f/48Xr58ibi4zG9pLS0tceLECVy+fBnBwcEYOXIkoqJkX68rjbOzM1q2bInevXvjxIkTCAsLw5EjR3D06FEAwOTJk3HlyhX89NNPCAoKwuPHj3HgwAGMGTNG5jZVVVVRqVIlidfXeBlEt57f4eTxwzh5/DCeR4TDZ+0qxMVGw7VTVwDAFt91WOY9V1zetVM3xMZEw2fdKjyPCMfJ44dx6vhh9OjVV1wmNTUVYaFPEBb6BGlpaYiPj0NY6BNEvnpZ7PhEIhE6d/8O+3ZtxbXL5xHx7ClWLZkHVVVVtHDOuX/Icu852Oa7Rvy+U7c+uP3vDezfvQ0vn4dj/+5tuBt0A5275zwv/cOH9wgLfYyw0MyBquioSISFPi7S452UNdRRyd4aleytAQDq1UxQyd4aFUyNAQBWf06A/cYF4vLha3dAzbwKav/1GzStq8PEvTdMh/bG08U+4jLPVm6GvkszVJ84AhpW1VF94gjot22CZys2fTN5A9jmPid3jE2+2Lr3/A4njx3GyeNH8DwiHBvytrmN67B00Txx+Q6duma2ubWrs9rcEZw8fgTdc7W5fbt3YNvmjRg97lcYGhohMSEBiQkJEgP1Rc1bl+7fYe+ubeK8rRTnrZ1E3rb6rhW/7yzO23a8eB6O/bu3407QTXQpIG8xxcxb157f4dTxQzh1/DBeRITDZ+1KxMVGo32nbgCArb5rpfbVjetW4UVEOE5l9dXuvdzEZTL7amZMaWlpSIiPQ1joY0S+elGsvCl67koztq49+2bVyyG8iHiGjWtXIi42RlwvX3NspdHmunTrjaB/r2NfVtz7xHH3KVJMzNvn5U2RYyPKi5dCAJg4cSKGDBkCGxsbfPjwARs3bizyum3btkXNmjXRsmVLpKSk4Pvvvy/ydP6//voLb9++Rbdu3VCxYkX88ssvSE5OllleW1sb8+fPx4QJE5Ceng47OzscPHgQenqZU8pmzZqFkSNHokaNGkhJSYEgCPjjjz8QFhYGV1dXqKurw9PTEz169ChwP9Ls3bsXEydORL9+/fDu3TtYWlpi/vzM6+Xq1q2Lc+fO4ffff0eLFi0gCAJq1KgBNze3Qrb69Wvesg3evH6NXX6bkZiQADNzC0ydOR+Ghpk30ExMiEdsbIy4fGUjY0ydOQ8b163GkcAA6OrpwWPkGDTJ9Ri4xIR4TBg7Qvw+YN9OBOzbiTp29vhz/tJix9ijT398+pSCdasX493bt6hpVRt/zPaGWq57g8TFRkMp1+U71jZ2GD95Ovy2rMfOrRtQ2agKxk+egVrWOVO3Qx+HYIbXz+L3m9avBAC0atsBoydMKTAmrXq2aHJqi/i9zaLM8s8378MdDy+oGhtALWuQAQA+PHuB6109YePtBfMfBiDlVQzuj5+DqP3HxWUSr9zCrQETYDVzHKxmjsX70Oe41X88kv65U9yUAVDMvAFsc5+TO8YmX2zNnVvj9ZvX2Lk9q81ZWOCPmfNgWDmzzSUkJuRrc3/MmgeftatwOKvNDR85Gk2b5zx68MihAKSlpWLh3BkS+3LrPxj9BroXKV/ZevTph0+fUrB29RJx3qbNXpQnbzEQiXIG7q1tbDFh8jRs37IBO7LyNkFK3qZ7jRO/912/CkBm3sZMKHyGXU5f3ZTVV6vh95kLJPpqXK6b1mX21fnwWbcKRwL9ZfTVOPwio6/Onr+sGFnLpKi5K83YmrVsgzevk7HbbzMSE+JhZl4NU3LVy9ccW2m0uey4/bZswI6tPqhsVAW/TJ4uETfzVnp5U+TYiPISCfJc7E8AAHd3dyQlJcHf37+sQ6Ev4MGTV2UdgkzpUC7rEGSKqC3fc8y/BLPg82UdgkzKkO/yrC9Bkdsbya+cqOQfaVhS0gXFbXMiKO6vUUKJ3PaRFA3bnHwUOW+KrI6lceGFFFTYsKLNWikN1XwOlNm+ywovhSAiIiIiIiIqI6tXr0a1atVQoUIF1KtXDxcuXJBZ1t3dHSKRKN+rTp2c+2T4+vpKLfPx48dSOwYOLJSSiIgIaGpqynxFRESUdYhERERERETfpK/l5o07d+7EuHHj8Pvvv+PWrVto0aIFOnbsKPPvxWXLliEyMlL8ev78OXR1dfHdd99JlKtUqZJEucjISFSoUEHufBaG91j4DL6+vjI/q1KlCoKCggr8nIiIiIiIiP67Fi9eDA8PDwwfPhwAsHTpUhw7dgz/+9//MG/evHzltbS0oKWV8xhif39/JCYmYujQoRLlRCIRjIyKfv+Yz8WBhVJSrlw5WFpalnUYRERERERE/zll+djHlJQUpKSkSCxTVVXN9xS8T58+4ebNm/jtt98klrdv3x6XL18u0r42bNiAdu3aiZ8UmO3t27cwNzdHeno6HBwcMHv2bDg6OspxNEXDSyGIiIiIiIiISsi8efPEMwuyX9JmH8TFxSE9PR2VK1eWWF65cmVERUUVup/IyEgcOXJEPNshm7W1NXx9fXHgwAH4+fmhQoUKaNasGR4/fvx5B1YAzlggIiIiIiIiKiFeXl6YMGGCxLK8sxVyE4kkZ1cIgpBvmTS+vr7Q1tZGjx49JJY3btwYjRs3Fr9v1qwZnJycsGLFCixfvrwIR1B8HFggIiIiIiKib4pIqewm50u77EEafX19KCsr55udEBMTk28WQ16CIMDHxweDBg1C+fLlCyyrpKSEBg0alOqMBV4KQURERERERPSFlS9fHvXq1cOJEycklp84cQJNmzYtcN1z587hyZMn8PDwKHQ/giAgKCgIxsbGnxVvQThjgYiIiIiIiL4pZXnzxuKYMGECBg0ahPr166NJkyZYu3YtIiIiMGrUKACZl1W8fPkSmzdvllhvw4YNaNSoEWxtbfNtc+bMmWjcuDFq1qyJ169fY/ny5QgKCsKqVatK7Tg4sEBERERERERUBtzc3BAfH49Zs2YhMjIStra2OHz4sPgpD5GRkYiIiJBYJzk5GXv37sWyZcukbjMpKQmenp6IioqClpYWHB0dcf78eTRs2LDUjkMkCIJQalsn+oY8ePKqrEOQKR3KZR2CTBG1W5Z1CDKZBZ8v6xBkUkZ6WYcgkyK3N5JfOVFaWYcgU7qguG1OBMX9NUrA1/FtHRUP25x8FDlviqyOZelNnS9tz3/sXWb7Nl29t8z2XVY4Y4GIiIiIiIi+KWV588b/ImabiIiIiIiIiOTGGQtERERERET0bREp7qU53yIOLBB9AxT5enxFvo+BIt//wW/SmbIOQSbHBoZlHYJMrrbRZR1CgZSQUdYhyKTI9zFQ5Ps/fMoo+NnhZUlJpLjtTdHxenz5KHLeeI4jKl0cWCAiIiIiIqJvytfyuMlvBe+xQERERERERERy48ACEREREREREcmNl0IQERERERHRN4WPm/yymG0iIiIiIiIikhtnLBAREREREdE3hTdv/LI4Y4GIiIiIiIiI5MaBBSIiIiIiIiKSGy+FICIiIiIiom8Kb974ZTHbRERERERERCQ3zlggIiIiIiKibwpv3vhlccYCEREREREREcmNAwtEREREREREJDcOLJQyd3d39OjRo8AyFhYWWLp0qfh9VFQUXFxcoKGhAW1tbbn2KwgCPD09oaurC5FIhKCgILm2U1StWrXCuHHjSnUfRERERERERSFSEpXZ67+I91hQANevX4eGhob4/ZIlSxAZGYmgoCBoaWnh7NmzaN26NRITE4s80HD06FH4+vri7NmzqF69OvT19UspejoS6A//fTuRmBAPUzMLeHiOho1tXZnl790NwsZ1q/E84hl0dfXRo8/36NCpm/jziPAw+G3diNAnjxAbE41hI35C1x59vsn4BEHAru0bcfLoQbx7+waWVjYY8cN4mJpXK3C9q5fOYseWDYiKfAUj4yroN3gEGjVtKf78wb0gBOzdgadPQpCYEI9JU+egYZMWRY5Lt3l9VP/FA1pOtqhQxRA3ev+I6AOnCl6nRQPYLPoNmjY1kfIqBqHe6xGxdodEGaOe7VFrxs9Qr2GG96ERCJm2BNEBJ4scV2692lRE6wbq0FBTQujzT/A9mIyXMWkFrqNeQYTvXCqhQZ0KUK+ghNjENGw/8hq3H6UAAJZMNISBTv4fCyeuvsOmg8lFjk0QBNw8uRIPr+1CyofXMDSri2bdp0HXqGaR1n8SdAin/X6BuU1buA5ZJfHZ/SvbcefcBrx/EwudypZo0nUKjKvVL1Zsu7b74kRWm6tpZYPhP4yDWSFt7sqlcxJtrv/g4RJt7v692wjY64enTx5ltbk/0agYbQ74GvpqyecNAI4G7kfAvh1ITEiAqZkFhnqOho2tfZFjOxwYgP17dyExIR5m5hbw8PwRdQrM2234rPsfIsKfQVdPHz17u6Fj567iz48fPYQzp44jPPwZAKCGZS0MGuKBWlbWRY4pmyAI2L19I04eO4C3b9+gZi0bDP9hQtHOcVvXIzryFSobV0G/QZ4Sedu/awuuXTmPly/CUb68Kqxq22KA+w+oamJW7PgUtV4Vva9mHls8TM2qYVghffW+uK+Gifuqa6fu+eL22+IjEXfjpsWLqzRiiwgPw46tGxH6JASxMdEYOuIndO3xXbHjKo3YgJLLmyL31ZL+2XD8aCDOnj6OiGdhADLPcQOGDEctq9pFjolIGs5YUAAGBgZQV1cXvw8NDUW9evVQs2ZNGBoayrXN0NBQGBsbo2nTpjAyMkK5cvn/WPj06ZPcMVOmi+dPw2fdKvRxGwjv5etgY1sXs6dPRmxMtNTy0VGR+HO6F2xs68J7+Tr0dhuADWtW4Mqlc+IyKSkpqGxUBYPcPaGjo/tNx+e/ZzsC9++Cx6hxmL9kLbR1dDFr6gR8eP9e5johwfeweP5MtGzjCu+VPmjZxhWL50/Ho4cPxGU+fvwIi2o14DFqnFxxKWuo4/WdENz/eVaRyqtZmKDBwbVIuHgTFxv0wJMFf6POkt9h1LO9uIx2Ywc4bl+Cl9sCcKFed7zcFgAnv6XQbij7lwNZurTQRMdmGth0MBnTVsci6W0GfhuqhwrlZY+QKysDvw3Vg4GOMpZtT8SvS2OwwT8Zia/TxWWmrY7DT/OixK95PnEAgH/ufShWfLfPrcfdC75o1uMP9ByzG2qaBji8fhg+pbwtdN03iS9x7dBCGEkZLAi9fRhXDs6DY5tR6DV2P4ws6uOIjyfeJr4qcmz+e/xwcP8uDB81DguWrMlqc78Uqc05t2kP75UbMv+dP0OizaV8/ACLapYYLmebU/y+Wjp5u3T+NDauW4neboOwaPk61LatizkFHHdeF86dwYa1q/GdW38sWbEGNnXsMGuaV4F5mzVtCmzq2GHJijXo07cf1q9ZicsXz4vL3L1zGy2c2+DPed5Y6L0CBgaGmDF1EuLjYouYrRwBe7cj0H8nPEaNx/zF66Cto4vZf4wvNG9LFsyAc2tXLFqxEc6tXbFkwTQ8DrkvLnP/XhBcO/fE3EVr8MfsJUhPT8eff0zAx4/F66uKWq+lGVtJ9NXMYxsI7+XrUdvWDn9On1RIX/0NtW3t4L18PXpJ6ashwffhnRX34pXrpcZdVrFlnkeMMcjdE9qfcR5R5LwBittXS+Nnw/27QWjRsg1mz1uC+d6roG9oiJl//CrXOU7hKSmV3es/6L951KVgz549sLOzg5qaGvT09NCuXTu8e/dO/PmiRYtgbGwMPT09/PTTT0hNTRV/lvtSCAsLC+zduxebN2+GSCSCu7s7WrduDQDQ0dERLyuIu7s7xowZg4iICIhEIlhYWADIvFxh9OjRmDBhAvT19eHi4oJnz57lu1QiKSkJIpEIZ8+eFS978OABOnXqBE1NTVSuXBmDBg1CXFycxH4zMjIwadIk6OrqwsjICDNmzCh2Hr82B/bvRtv2neDi2hmmZubw8BwNPX1DHD18QGr5Y4cPQN/AEB6eo2FqZg4X185o49IR/vt2icvUrGUNd49RaOHcBuVUVL7Z+ARBwKGA3ejlNgiNmznDzKI6xkyYgpSUFFw4d0LmeocCdqOuY3306jsQVU3N0avvQNjZ18OhgN3iMk71G6Pf4BFo3MxZrthij53Ho+lLEeUvO47czD2/x8eISDz4ZS7ePnyK5z578Nx3H6pPGCYuU23MEMSdvIzQhWvxLuQpQheuRdzpq7AYM6TY8XVopoGAs29x48FHvIhJw5o9iSivIkJTezWZ6zjXy5zdsGRrAh5HfEJ8UjoehX9CRFTOLIc37zOQ/Dbn5WhVAdHxaQgOK/ogpCAIuHtxMxzbjEI12/bQNaqF1m7zkZb6EU9uBRa4bkZGOk7v+BX1XMagkq5Jvs/vXPCFVYPesG74HXQq10DTblOgqWWEB1f9ihxbYMBu9HYbhMbNWma1Oa+sNid75khgwB7YO9ZDr74DYZKrzQXmaXP9Bw9H42YtZW6nIIreV0srbwf370Kb9p3QzrULTMwsMMxzDPT0DXDscECRYgvYvwft2ndE+w6ZeRs+8ifoGxjiyKGDUssfPXwQBoaGGD7yJ5iamaN9h85o69JBIm+/TJqCTl26o3oNS5iYmuGnsROQkSHg9u1bRcxYpsxz3C70chuMRk0zz3GjJ/yOlJQUXCzoHHcg8xzXs+8gVDU1R8++g2Cb5xw3dZY3WrfrBFPzarCobokfx3khLjYaT5+EFCs+Ra1XRe6rB8V9tQtMzMzh4TkGevqGMo8tp6+OgYmZOVxcu6CNS0cE7NuZs82APbB3rI/efQfAxNQcvfsOgJ29EwID9pR5bDVrWWOIxw9o7twWKp9xHlHkvClyXy2Nnw3jf52Kjl16oFrWOe7HMRMhZAi4c/vfImaMSDoOLJSAyMhI9OvXD8OGDUNwcDDOnj2LXr16QRAEAMCZM2cQGhqKM2fOYNOmTfD19YWvr6/UbV2/fh0dOnRA3759ERkZiWXLlmHv3r0AgJCQEPGygixbtgyzZs2CiYkJIiMjcf36dfFnmzZtQrly5XDp0iWsWbOmyMfn7OwMBwcH3LhxA0ePHkV0dDT69u0rUW7Tpk3Q0NDAtWvXsHDhQsyaNQsnThTtD7OvUWpqKkKfPIKDo+Q3qw5O9fEw+J7UdUIePoCDk2R5R6cGCH0cgrS0gqexf2vxxURFIikxAfZODcTLVFTKw8bWHiEy4gOARw/vw96xgcQye6eGBa5T2rQbOyD25CWJZbHHL0Crni1EWbOFdBo7IO7kRYkycScuQKeJY7H2ZaCjDO2Kyrj75KN4WVo68PBZCmqalZe5npN1BTx5/glDumlhlVdlzBtrgG7OmhDJmOSgrAw0c1DDuZuyv62R5k3CC3x4EwuTms1ytlWuPIyrN0B0eMF/mP17chXUNHRh3TD/dP30tE+Ie3lfYrsAYFKrWaHbzRYtbnM5bVxFpTzqyNHmHJwaICT4vow1ikfR+2pp5S3nuPP25wZF6s/i9fPkwcGxHh7KqJuHwQ/g4FhPYpljvQZ48viRzLylpKQgPT0NFTUrFhpTbjHRWXlzzHuOcygkb/ek5K3gc9z7rC8yNDUrFTk+Ra3X0oztc2UeW4jUfchqc48e3oeDU/76zN1XHz28ny9fjk4NZW7zS8ZWEhQ5b4Di9tUv9bPhU9Y5TrNi0c8fXwuRSFRmr/8i3mOhBERGRiItLQ29evWCubk5AMDOzk78uY6ODlauXAllZWVYW1ujc+fOOHXqFEaMGJFvWwYGBlBVVYWamhqMjIwAALq6mVPPDA0Ni3SPBS0tLVSsWBHKysribWSztLTEwoULxe+fPXtW6Pb+97//wcnJCXPnzhUv8/HxgampKR49eoRatWoBAOrWrYvp06cDAGrWrImVK1fi1KlTcHFxybfNlJQUpKSkSCxTVVWFqqpqofEoijevk5GRkQFtbR2J5draOkhKTJS6TmJiAhyllE9PT8fr18nQ1dX7z8SXmBiftX3JqZXa2rqIjY2SuV5SYgK0dfLEqKODpMSEEoutuFQr6yMlWnIGz6eYeCipqKC8vg5SomKhaqSPlOh4iTIp0fFQNTIo1r60K2aOBye/zZBYnvw2A/rayjLXM9RVhr62Ki7ffo+/NiXASE8ZQ7ppQ0kJ8D+T/xKF+rUz78Nw/t/iDSy8f5M5lVKtomRbUdPUK/CShahn/yLk+l70Hucv9fOP7xMhZKRDTTP/dt+/iZO6Tl7ZbSRvm9PS1kFsrOwp2qXd5hS9r5ZW3jKPOx1a+c4BRcvta1l509FBooz1pcZUSN42b1wHXT192OcZkChMUtY5Lu/xaWnrIC6m4HNc/nV0ZeZEEARsWr8S1jZ1YWZRvRjxKWa9lmZsn0tWX9Uq4NgSExPgUEhfTUpMgFaeuLWKGXdpxVYSFDlvgOL21S/1s2Gz79rMc5xD8c5xRHlxYKEE2Nvbo23btrCzs4Orqyvat2+PPn36QCfrZFenTh0oK+f8wm9sbIy7d++WSaz16xf9JmfZbt68iTNnzkBTUzPfZ6GhoRIDC7kZGxsjJiZG6jbnzZuHmTNnSiybPn3613n5RJ5RSUHItyhP8TzlkTmzRYRSGt1UkPjOnzmOtSu9xe+9ZiyQFh4ECIXuK+/ngiCU/ehw1gwlsex4ci+XVibvsjya2qthWHct8ftFm6X/wiICgAI2JRKJ8PpdOjb4J0MQgGevUqFT6Q06t9CUOrDgXF8dtx+nIOlNhpSt5Xh86yAu7Jsuft9h6N9Z8eSrWJkN71PKW5zZ8Sta9J6NCho6UsvkPo68m5W13fNnTmBNrjY3ZcZ8qdtAEdoc8rU5adv5TArTV79s3vKdAwpoK1L3IGX9guomf9uUnbd9u3fgwrkzmLPAG+XLy54RBAAXzhzHmlWLxO+9pks/xxVasZASfwHnuA1/L0HEs1DMXrhK6ufZFLlev7a+Kr1+il4+p6/mKiP155pixFZSFCVvit5XpeykWGEV52fD/j1+uHjuNGbPX1LoOY6oMBxYKAHKyso4ceIELl++jOPHj2PFihX4/fffce3aNQDId02aSCRCRkbBv6yXltxPnwAApaybiwi5/rjJff8HIPPeCV27dsWCBQvybc/Y2Fj8/+Icp5eXFyZMmCCx7GuarQAAFStpQUlJKd/IdHJyIrTyjBZn09HRzfdNWnJSEpSVlVGxUslOQVO0+Bo0ao6aVjbi92lZ7SwxMQE6ujlPLUlOSsz3DURu2jJilHVMX0JKdFy+mQflDXSRkZqKT/FJmWWi4qBqJPl0FlVD3XwzHfL6N/gjQp/n3OOgXLnMXwy0NJUk/uivpKmUbxZDbklv0pGeLjmO8TI2DdoVlaGsDKTn3MMRetrKsK2hiqXbpX8jkpu5TWsYmuYMKqanZcb6/k0c1Cvl3Hz2w7v4fLMNsr2Of443iS9xbNMP4mWCkHks67zqwG3iEWhoGUGkpJxvdsLHt/FQl7HdBo2aoWauu1ynittcPHRyfWuTnJSU71vO3LR18n8DlZwkux8Vl+L11S+Tt8zjVpZ63Hm/oZOmUlbeEvN8c5ecJHt9aeePpGTpedu/dxf27NqOmXP+gkW1GoXGU79Rc1hKOccl5T3HJSfl+yY+b4zZ36DmrCO9LWz4ewluXLuEmfNXQE+/4Js9K3K9fm19NV/fS07K9811Nh2pMWW3OS2Zcb9Okr3NLxlbSVC0vCl6X81W2j8b/PfuxJ5d2zBzjneRznFfI9F/9CaKZYXZLiEikQjNmjXDzJkzcevWLZQvXx779+8vkW1njyCm5/7Nv4QYGGT+MRQZGSlelvtGjgDg5OSE+/fvw8LCApaWlhKvvAMVRaWqqopKlSpJvL62gQUVFRXUsKyF27duSCy/fesmrGvbSl3HytoGt2/dlFgWdOsGatS0kvrkjm8pPjV1dRhXMRG/TMwsoK2jizu54ktNTcWDe7dhJSM+AKhlXQd3gq5LLLt963qB65S2pKtB0G/bVGKZgUtzJN+8ByHrmsbEq0HQbyt5fwD9ds2ReKXg+wN8/CQgOiFd/HoZk4akN+mwtawgLqOsDFhbqOJxhOybLD4O/4TKesoS33IY65VD4ut05D21ODup4/W7DASFfERhyqtqQkvfXPzSqWwJtYoGePH4srhMetonRD69jsrm0u8noW1QHX3GH0Dvn/eLX+a126BK9Ubo/fN+aGgZQblceehXrYOXubYLAC8eX5a53bxtzlRGm7tfhDZ3OyhvP7oOq9p1Cs1PUSh6Xy2tvMk67ju3bhSpP+esnzcPN2Eto26sa9sgKG/5f2/AsmYtibzt27MTu/y2Yvrs+ahZy6rQWICCznE556vMc1xQIXmzlcg1kP8cJwgC1v9vCa5dPo/pc5aislGVYsenSPX6dfVVKyl99YbMNlfLuo6U8tcl+qq0uINuXZe5zS8ZW0lQtLwpel/NVpo/G/bv3YHdO7Zg2qyFsKxZtHMcUWE4sFACrl27hrlz5+LGjRuIiIjAvn37EBsbi9q1S+Z5sObm5hCJRAgMDERsbCzevi38kW1FpaamhsaNG2P+/Pl48OABzp8/j6lTp0qU+emnn5CQkIB+/frhn3/+wdOnT3H8+HEMGzasVAY7vibden6Hk8cP4+Txw3geEQ6ftasQFxsN106Zz0Tf4rsOy7xz7k3h2qkbYmOi4bNuFZ5HhOPk8cM4dfwwevTKuRFmamoqwkKfICz0CdLS0hAfH4ew0CeIfPXym4pPJBKhc/fvsG/XVly7fB4Rz55i1ZJ5UFVVRQvnnPtyLPeeg22+OTca7dStD27/ewP7d2/Dy+fh2L97G+4G3UDn7jnP1f7w4T3CQh8jLPQxgMwbgYWFPi7yo86UNdRRyd4alewzn1uvXs0EleytUcE0c4aO1Z8TYL8xZwZP+NodUDOvgtp//QZN6+owce8N06G98XSxj7jMs5Wboe/SDNUnjoCGVXVUnzgC+m2b4NmKTcXKGwAcvfQO3Zw1Ud+mAkwMy2Fkb218ShVw+XbO46tG9tFG3/Y5N5s7+c87aKorYVDnSjDSU4aDlSq6tdLEiWvvJLYtEgEtndRw4d/3kGdilUgkgl3zwQg6swZh904gIeoRzu72QjmVCrB07CIud2bnZPxzJHPqczkVVega1ZJ4qapVhIqqBnSNakG5XObgat0W7nh4fQ8eXt+LxOhQXD44D2+TIlG78fdFjq1L9++wd9c2cZtbKW5z7cTllnvPwVbfteL3ncVtbjtePA/H/t3bcSfoJroU0OZiitnmFL2vllbeuvbsi1PHD+HU8UN4EfEMG9euRFxsDNrneuZ6Qbr37IMTxw7j5PEjeB4RjvVrVyMuNgYdsvK2eeN6LFk0X1y+Q6euiI2JwYa1q7PydgQnjx+RyNu+3TuwbfNGjBk3EYaGRkhMSEBiQgI+fCjeoxwzz3F9sW93rnPc0rlQVVVF81znuBXef2Kb79+Sebt1Hf57Ms9x/nvyn+PW/28xLpw9jp9/nYYK6upITIxHYmJ8vnsXFRafotarIvfVrj2/yzq2w3gREQ6ftSsRFxstPratvmul9tWN61bhRUQ4TmX11e693MRlunTrjaB/r2NfVtz7xHHnv5Htl44t8zySma+0tDQkxMchLPQxIl+9KPPYSipvitxXS+Nnw/49fti+2Qejx/36Wee4r4FISVRmr/8iXgpRAipVqoTz589j6dKleP36NczNzeHt7Y2OHTti586dhW+gEFWrVsXMmTPx22+/YejQoRg8eLDMp0rIw8fHB8OGDUP9+vVhZWWFhQsXon379uLPq1SpgkuXLmHy5MlwdXVFSkoKzM3N0aFDB/GlFP9VzVu2wZvXr7HLbzMSExJgZm6BqTPnw9Aw86aZiQnxiI3Nuc9EZSNjTJ05DxvXrcaRwADo6unBY+QYNMn1WMTEhHhMGJtzY8+AfTsRsG8n6tjZ48/5S7+p+Hr06Y9Pn1KwbvVivHv7FjWtauOP2d5QU1cXl4mLjYZSrq/ZrW3sMH7ydPhtWY+dWzegslEVjJ88A7Wsc6Y1hj4OwQyvn8XvN61fCQBo1bYDRk+YUmhcWvVs0eTUFvF7m0WZ6zzfvA93PLygamwANdOcy4A+PHuB6109YePtBfMfBiDlVQzuj5+DqP3HxWUSr9zCrQETYDVzHKxmjsX70Oe41X88kv65U5yUAQACL7xFeRUR3LtpQb2CEkJffMKCjfH4+CnnOgd9LWWJyx4SkjOwYGM8BnbSwtwxGkh8nY5jl9/h4HnJgco6NVShr1Ou2E+DyM3eeTjSUj/iov8sfPqQDEPTuug0fAPKq+bcp+Vt0qtiX/dcw74TPr5Pwr+nVuH961joGtVEx6FrUFGnapG30aNPP3z6lIK1q5eI29y02YvytLkYiEQ55zZrG1tMmDwN27dswI6sNjdBSpub7jVO/N53feY1tK3adsCYCV6FxqX4fbV08tasZRu8eZ2M3X6bkZgQDzPzapgyc4H4uAvTwrk13rx5jZ3btyAhIQHmFhaYNnMeDCtXzsxBYjzi8uRt2qy52LB2NQ4HHoCunh6GjxyNps1zHj145NABpKWlYsFcyfsAfd9/MPoNLN7jYbv37o9PKSlY/z9vvHv7FpZWtTF11uJ857jcv4Ra1bbDuEnTsWPreuzYuh5GRlUxfvJM1LTK+Rb2+GF/AMAMr7ES+/txnBdat+tU5PgUtV5LM7aS66ubsvpqNfye69gSE+IRl+sGk5l9dT581q3CkUB/qX01O26/LRuwY6sPKhtVwS+Tp0vEXRSlEVtiQhx+kXEemT1/WZnGVlJ5AxS3r5bGz4YjhwKQlpaKhXNnSOzLrf8QfD/AvdCYiGQRCUIhdw4jIgDAgyey72hPsqVD9pMKylpEbfmeY/4l+E06U9YhyOTYwLjwQmXE1bZo3zqWFSWUzf11iiJDgScxlhOV7CM0S9KnDMW94ZmSSHHbm6ITFXQ3XPoqpQuK+/uIIp/jbCyLfvmGoomfMbzM9q03Y32Z7buscMYCERERERERfVv+4zOrvzRm+ysUEREBTU1Nma+IiIiyDpGIiIiIiIj+Izhj4StUpUqVfE9uyPs5ERERERHRf9V/9SaKZYUDC1+hcuXKwdLSsqzDICIiIiIiIuLAAhEREREREX1bcj81hkofs01EREREREREcuPAAhERERERERHJjZdCEBERERER0beFN2/8ojhjgYiIiIiIiIjkxhkLRERERERE9E0RKfE79C+J2SYiIiIiIiIiuXFggYiIiIiIiIjkxkshiIiIiIiI6Jsi4s0bvygOLBAVUQYn+MhFGellHYJMfpPOlHUIMvVb2LqsQ5ApeGdwWYfw1VLk84gIQlmHINPHDNWyDkGmCkopZR2CTOmCclmHIJMAxf6FX5HjU+S+qsh5U1FKLesQZErNUCnrEIg+GwcWiIiIiIiI6NsiUtzB/G8Rs01EREREREREcuOMBSIiIiIiIvqm8B4LXxZnLBARERERERGR3DiwQERERERERERy46UQRERERERE9G1R4nfoXxKzTURERERERERy44wFIiIiIiIi+qaIRLx545fEGQtEREREREREJDcOLBARERERERGR3HgpBBEREREREX1bePPGL4rZJiIiIiIiIiK5ccYCERERERERfVNESrx545fEGQvF5O7ujh49ehRYxsLCAkuXLhW/j4qKgouLCzQ0NKCtrV2q8SmSvHkgIiIiIiKibw9nLJSC69evQ0NDQ/x+yZIliIyMRFBQELS0tHD27Fm0bt0aiYmJ/6mBhm+ZIAjYtd0XJ44exLu3b1DTygbDfxgHM/NqBa535dI57NiyAVGRr2BkXAX9Bw9Ho6YtJcocDdyPgH07kJiQAFMzCwz1HA0bW/tvIrYjgf7w37cTiQnxMDWzgIfnaNjY1pVZ/t7dIGxctxrPI55BV1cfPfp8jw6duok/jwgPg9/WjQh98gixMdEYNuIndO3Rp8jx5NWrTUW0bqAODTUlhD7/BN+DyXgZk1bgOuoVRPjOpRIa1KkA9QpKiE1Mw/Yjr3H7UQoAYMlEQxjo5D/1nrj6DpsOJhcak27z+qj+iwe0nGxRoYohbvT+EdEHThW8TosGsFn0GzRtaiLlVQxCvdcjYu0OiTJGPduj1oyfoV7DDO9DIxAybQmiA04WGo80giDg31Or8PCfXUj58BqGpnXRtPsf0K1cs0jrh94+hNM7JsLcpi3aD1opXh4Zdh13zvsg7uV9vH8TC5eBK2BRp12xY1PU/qDIsR0J9M9aPx6mZtUwrJC+el/cV8PEfdW1U/d8cftt8ZGIu3HTFkWOKZsgCNiz3Qenjh3A27dvULOWDYb9MAGm5tULXO/apbPYuXU9oiNforJxVXw/aAQaNnUWf3788H6cOOyP2OhIAICJWTX07ucOx/pNihzb4cAA7N+7C4kJ8TAzt4CH54+oU+A57jZ81v0PEeHPoKunj5693dCxc9ecmI4ewplTxxEe/gwAUMOyFgYN8UAtK+six5RNkesUUOz+oMixKXK9KnLeDgcGwH9v1u8j5hbw8PypCH11NZ7n6qsdOuf8PnL8aCDOnDqBiPAwAJl9deAQD9Syql3kmLIJgoDd2zfiZK5z3PAfJsC0kLxdvXQWO7auR3TkK1Q2roJ+gzwl8vbgXhAO7PXD09AQJCbE49ff56Bhk5YFbPErJOJ36F8Ss10KDAwMoK6uLn4fGhqKevXqoWbNmjA0NCzDyCSlpqaWdQjfDP89fji4fxeGjxqHBUvWQFtHF7Om/oIP79/LXCck+B4Wz58J5zbt4b1yQ+a/82fg0cMH4jKXzp/GxnUr0dttEBYtX4fatnUxZ/pkxMZEf/WxXTx/Gj7rVqGP20B4L18HG9u6mF3A+tFRkfhzuhdsbOvCe/k69HYbgA1rVuDKpXPiMikpKahsVAWD3D2ho6NbxAxJ16WFJjo208Cmg8mYtjoWSW8z8NtQPVQoL3tanbIy8NtQPRjoKGPZ9kT8ujQGG/yTkfg6XVxm2uo4/DQvSvya5xMHAPjn3ocixaWsoY7Xd0Jw/+dZRSqvZmGCBgfXIuHiTVxs0ANPFvyNOkt+h1HP9uIy2o0d4Lh9CV5uC8CFet3xclsAnPyWQruh7F+qCnL7/HrcveiLpt2mosdPu6BWUR9HNnjgU8q7Qtd9k/gS1w7/BSOLevk+S/v0AbrGVmjabapccQGK2x8UObaL4vUHwnv5etS2tcOf0ycV0ld/Q21bO3gvX49eUvpqSPB9eGfFvXjleqlxF9WBvdtwyH8nho6agLmL10NLRw9z/hhfYN4eBd/D0gXT0aK1Kxau8EWL1q5YumAaHofcF5fR0zNA/yGjMHfpesxduh629k74608vPA9/WqS4Lpw7gw1rV+M7t/5YsmINbOrYYdY0rwLzNmvaFNjUscOSFWvQp28/rF+zEpcvnheXuXvnNlo4t8Gf87yx0HsFDAwMMWPqJMTHxRYxW5kUvU4Bxe0Pihyboterwubt3Bn4rF2F79wGYPGKtbCpY4fZ034rMG+zp3nBpo4dFq9Yiz59++frq/ey+urseYuxwHul3H0VAAL2bkeg/054jBqP+YvXQVtHF7MLOceFBN/DkgUz4NzaFYtWbIRza1csyXOOS/n4EebVLeExanyxYyKShgMLMuzZswd2dnZQU1ODnp4e2rVrh3fvcn4pXrRoEYyNjaGnp4effvpJ4o/03JcAWFhYYO/evdi8eTNEIhHc3d3RunVrAICOjo54WWFatWqFsWPHYtKkSdDV1YWRkRFmzJghUSYiIgLdu3eHpqYmKlWqhL59+yI6OuekOGPGDDg4OMDHxwfVq1eHqqoqBEGASCTCmjVr0KVLF6irq6N27dq4cuUKnjx5glatWkFDQwNNmjRBaGioeFuhoaHo3r07KleuDE1NTTRo0AAnT8r37ebXThAEBAbsRm+3QWjcrCXMLKpjzAQvpKSk4MI52TkJDNgDe8d66NV3IExMzdGr70DY2ddDYMBucZmD+3ehTftOaOfaBSZmFhjmOQZ6+gY4djjgq4/twP7daNu+E1xcO8PUzBwenqOhp2+Io4cPSC1/7PAB6BsYwsNzNEzNzOHi2hltXDrCf98ucZmatazh7jEKLZzboJyKSpHikKVDMw0EnH2LGw8+4kVMGtbsSUR5FRGa2qvJXMe5XubshiVbE/A44hPik9LxKPwTIqJyZjm8eZ+B5Lc5L0erCoiOT0Nw2KcixRV77DweTV+KKP8TRSpv7vk9PkZE4sEvc/H24VM899mD5777UH3CMHGZamOGIO7kZYQuXIt3IU8RunAt4k5fhcWYIUXaR26CIODepc1waD0S1WzbQ9eoFlp9Nx9pqR8RGhRY4LoZGek4s3MSnNqNRkVd03yfm1q1RIP241DNtr2UtYsWm6L2B0WO7aC4r3aBiZk5PDzHQE/fUOb6OX11DEzMzOHi2gVtXDoiYN/OnG0G7IG9Y3307jsAJqbm6N13AOzsnRAYsKdIMWUTBAGHA3ajp9tgNGrqDDOL6vhpwu9ISUnBxXPHZa53+MAu1HWsj559B6GqqTl69h0EW/t6OByQcz6p16g5HBs0QZWqZqhS1QzfDx6JChXU8DikaH9QBezfg3btO6J9h8xz3PCRP0HfwBBHDh2UWv7o4YMwMDTE8JE/wdTMHO07dEZblw4S57hfJk1Bpy7dUb2GJUxMzfDT2AnIyBBw+/atImYskyLXKaDY/UGRY1PkelXkvAXs34127TvCRdxXR0PfwBBHD0n/fSSnr2b9PtKhM9q6dERArr46YdLvEn31x7G/QMgQcKeYfVUQBBwK2IVeuc5xo8XnONm/Bxw6sFvqOe5Qrrw51m+MfoNGoFGumVpEn4MDC1L8n737Do+ieAM4/r1UAukJkAAplJCQBAKhSBMUpEtHEAsiCFhAEVRE/AkBKZYI0pRelV6CFKVIRxAwAYHQQkmAkF4I5dLu98cll1xyl0bJJb6f59kHbm929t2Znb3L3OxsZGQkAwcOZMiQIYSGhnLgwAH69OmDSqUCYP/+/YSFhbF//35WrFjB8uXLWb58uc68Tp48SefOnenfvz+RkZH8+OOPbNq0CYBLly5p1hXFihUrqFSpEidOnODbb79l8uTJ7NmjvqioVCp69epFfHw8Bw8eZM+ePYSFhTFgwACtPK5evcr69evZtGkTISEhmvVTpkxh0KBBhISE4OXlxWuvvcaIESMYP348p06dAmDkyJGa9CkpKXTt2pW9e/cSHBxMp06d6N69O+Hh4UU6lvIk6m4kiQnx+Pk30awzNTXDx9ePS6Hn9G53+eJ5/Bo11VrX0L8pl0LVvclpaWmEXb1Mwzxp/PybFphvWYgtZ/smWusb+jfhop7tL128QEN/7fSN/JsSduUS6ekF355QXJXtjLG1Mubfq48069Iz4OINJR6uZnq38/eqwNWIVN7qYcO88VWZ/mFlerS1RKFnkIOxMbRqaMHB0/p/dXhcts0bErP3qNa6mN2HsWnsi8JEfUuGXfOGxO49opUmds9h7Fo0Kvb+7iXc4uG9WGp4tNKsMzYxw7lmU6JuFvyFKnjffCpUssOraclvXymIobYHQ45Nvf0lnfu4GHpe5zaXL56noX/e9M202urli+fzxdTIv5nePPWJjrpDYkIcDRo106wzNTXD27chlwsst3Na2wD4+T+nd5vMjAyOHtyL8tEj6nr5FBqXptzzXLMaNmqs9xgvhl6gYSPtkTqNGjfl6pXLeq9xSqWSjIx0rCytCo1JOzbDrVMw3PZgyLEZer0adrnpaqtN9B7jpdDz+b6/NGrchKsFfB9JzWqrlsVoqwDRUVnlluv4sq9xBZfbOR3l1qzI53m5YaQoveU/SOZY0CEyMpL09HT69OmDm5sbAPXr19e8b2dnx9y5czE2NsbLy4tu3bqxb98+hg0bli+vypUrY25ujoWFBU5OTgDY26uHaFepUqVYcyw0aNCAiRMnAuDh4cHcuXPZt28fHTp0YO/evZw9e5br16/j4qL+pW/VqlX4+Phw8uRJmjZVX1xSU1NZtWoVlStX1sr77bffpn///gCMGzeOFi1a8L///Y9OnToB8NFHH/H2229r0vv5+eHnl3Pv2tdff82WLVvYtm2bVgeEPkqlEqVSqbXO3Nwcc3PzIpeHoUhMiAfA1lZ76L2NrR0xMfqH4SUmxGNrZ6e1ztbOTpPfveQkMjMzsMmTr61tTpqyGpt6+0xsbfPsw9aOxIQEndskJMTTSEf6jIwMkpOTsLd3KHS/RWVrpe5zTUrJ1FqflJKJo62x3u2q2BvjaGvOsTMP+G5FPE4OxrzVwxYjI9i6PyVf+ib11PMwHPrn6XUsmFd1RBkVq7UuNToOI1NTzBztUN6NwdzJEWVUnFYaZVQc5k7a14mieHhPvS8LS0et9RaWDtxLvKN3u7s3/uHSqU30+XBLsfdZVIbaHgw5Nn1t1aaA7RMS4mlYSFtNTIjHJk/cNnZFL69s2enzHp+NrV2Bw6ATE+KxKcIxhd8I48tP3iUtNZUKFhZ8MmEaNVwLvq8ZIFnfNc7OjgQ9x6izLgu5xq1ctgh7B0f8GuW/dUgfQ69TMNz2YMixGXq9lrlyK7CtJuQrk6fRVtX7Un8267rGxUbfLWC7eB3b2JeoPQpRVNKxoIOfnx/t27enfv36dOrUiY4dO9KvXz/ssi4iPj4+GBvn/HHh7OzMv//++9TjatBA+35nZ2dnoqOjAQgNDcXFxUXTqQDg7e2Nra0toaGhmo4FNze3fJ0KefOuWrUqoN2ZUrVqVR49ekRycjLW1tbcv3+fgIAAtm/fzp07d0hPT+fhw4dFHrEwffp0AgICtNZNnDgx3+0dhujQ/j0smBuoef3FpBkAKPL9LK1CQWE9ltrvq1T588mbrUqlY2UZiE33LnTto6DkedKjyor08XqGW/pZMKSnjeb19yt1f/Aq1DstML7k+xks2ZqESgU37qRhZ32Pbs9b6uxYaNukImeuKEm8l6kjtydIlSfo7HLMvV5XmrzrdLga/BuHt07SvO781k/qzfOGUMA5l6q8z/71n/F8n8lUqGSnM01JGHJ7MOTYdO5BRwYla6u50uSLu+A8AQ7v382ied9pXn8+8dus/eULr9C88sWoo9yqVXfl29nLuH8/hRNHDzBv5lQmzZhTpM4F/XHpDyxfXav0X+M2b1jL4YP7mfpNIGZm+kdS6Y/NMOoUDLs9GHJsOvdgIPVa1spNVwbFaavZH5f62+qffP3ND4W21cP7d7Ng3vea1+MnfqMzvKJc5HSfC/+tX9IVMnnjMyUdCzoYGxuzZ88ejh07xu7du5kzZw4TJkzgxIkTAJjmuXdboVCQmfmU/zAoZL8qPReLvOtzP61CX97Z6XWty97fp59+yh9//MH3339PnTp1sLCwoF+/fqSmFu0+8fHjxzNmzBitdWVltELT51rhkWtW3+z5NRIS4rDL1UudlJiYr5c9N1u7/D3HSYkJml/RrKxtMDIyzp8mKSFfz3pZiC039fZGOrfP+ytiNjs7+3y/HiQlJmJsbIyVtXWh+yzIP6GPCIvIOXdNTNTnu42lkdYf/daWRvlGMeSWeC+DjAztv8dvx6Rja2WMsTFk5MzhiIOtMb61zZn1q+4RGk+KMio238gDs8r2ZKalkRqXqE5zNxZzJ+0RBuZV7PONdNDF1bsdfVxyOiYzMtTl+CAllorWOZPVPkqJx8JS96iSe3HhpCTc5o+V72vWqVTqcl48wZf+Y3Zi7eBaaCx5GXJ7MOTYcstuq/naXlJivl/DstnpjCm7rdrojTs5UX+e2Zo81xoPT2/N67Q09fmWmBCPnX3OOZyclFBgXjr3r+P6Y2JqilO1GgDU9vAi7EooO7dtYPjIzwqM01pTbtrtOylRf7nb6rjGJSbpvsZt2bSejet/JWDqd7jXrF1gLHkZWp2CYbcHQ44tN0Or17JWbrrKQX9bzT8aIikpQWdb3bppHRvX/8Lkqd8Xqa02ea41dXJd49Kzyi3vNS4pKTHf6A/tGO01ox1yx6jvO5YQT4J04+ihUCho1aoVAQEBBAcHY2ZmxpYtT2aIbnZvZUbuvzIek7e3N+Hh4URERGjWXbhwgaSkJOrVK/6jbQpz+PBhBg8eTO/evalfvz5OTk7cuHGjyNubm5tjbW2ttZSVjgWLihVxrlZDs7i4umNrZ8/Z4FOaNGlpaZw/dwbPer5686nr5cOZkFNa684En8Sznvr+XVNTU2rXqcuZYO00Z4NP6c3XkGPLTd/2Z4JP46Vne08vb84En9ZaFxJ8itoenpiYPF4f6aNUFVHxGZrldnQ6ifcy8K1TQZPG2Bi83M25Eq6/8+zKzVSqOhhr/Yjg7GBCQnIGeZt7W/+KJN/PJOTSI56mxOMhOLZvqbWucofWJJ0+hyrrXtCE4yE4tm+llcbxpdYk/FX4JFNm5pWwcXTTLHZV6mBh5cjtK8c0aTLSU4m8fpKqbo105mFTuRZ9Pwqiz6jNmsWtXjuq1XqOPqM2U8nGqbiHDRh2ezDk2HJTb++po62ewque7rkG6nr56Eh/Uqut6oo7JPik3jyzWVSsiFO1GpqlhmtNbO0cOBt8UpMmPS2NC+dCqFtguflqbQNwNvjvArcBQJXzRb8gOeWe95p1Wu8xetXzJiRv+n9OUcejrtY1bvPGdaxfs5qJU2bgUdez0Fh0x2Y4dQqG3R4MObbcDK1ey1a51c3f9gpoq571fPS0Ve3vI1s2rs1qq99Qp4htNW+51dCUW871Ki3rGldwuflqlTVkl1vhZSJESUnHgg4nTpxg2rRpnDp1ivDwcDZv3kxMTMwT+wPdzc0NhULB9u3biYmJISUl/xDp4nrppZdo0KABr7/+Ov/88w9///03gwYNom3btjRp0qTwDIqpTp06bN68mZCQEM6cOcNrr732TEZtGCKFQsHLPV9h0/pfOHHsEOE3rjF35nTMzc15vu1LmnSzA6eyevlCzetuPfpx5p9TbNnwK7cibrJlw6+cDTnNyz1f0aTp3rs/+3bvYN/uHdwKv8GyhXOJjYmmY9ceFIUhx9aj9yvs3b2Tvbt3EhF+k6UL5xEbE0Wnrupntq9avogfA6dp0nfq2oOY6CiWLppHRPhN9u7eyb7dO+nVp78mTVpaGtfDrnI97Crp6enExcVyPewqkXduFymm3H4/ep8ebS1p4l2BGlVMGNHXltQ0FcfO5DwWckQ/W/p3zJmIae/f97GsaMSb3axxcjCmoac5PV6wZM8J7ccsKhTQxt+Cw/88oLjNxrhSRaz9vLD2Uz+3vmLNGlj7eVHBxRkAz6/H4LfsG036mwvXYuFWjXrffY6lVy1qDO6Ly9t9ufbDUk2aG3NX4tihFbU+GUYlz1rU+mQYju1bcGPOiuIFh/qc8201iJADC7l+fg/xdy9zcOMXmJhWoHbDlzXp9q8fx9+//wCAiak59k51tRazClaYmlfC3qkuxibqztg05X3i7oQSdycUUE8UGXcnlJQC5m7IG5uhtgdDjq1771eytt/JrfCbLF04l9iYKM32q5cv1NlWly2ax63wm+zLaqs9++RMJvxyj76E/HOSzVlxb9bEXbyJOxUKBV17vsLWDav4+9hBwm9cY/6sqZibm9O6bc7TQ+YGTuHX5T9rXnfp8Qpng08StHE1tyNuErRxNf+GnKJrz5zryZoVCwg9d4boqEjCb4SxduUCzp8LpvULRXsqSc/e/djzx0727t5FRPhNFi+cT2xMNJ2zrnErly1m5vczNOk7d+1OTHQ0SxbOz7rG7WLv7l1a17jNG9byy8pljBr9CVWqOJEQH09CfDwPHxbtcbXZDLlOwbDbgyHHZsj1asjl1rP3K+zN1VaX5P0+smwRs76frkmvbqtRLM3TVnvqaKsjR3/6WG1VoVDQrWd/Nm9YrSm3ebOmZV3jOmjSzQn8ml9yXeO69ejHmeCTbN34C7cjbrJ14y/8G3KKbrnK7eHDB1y/doXr164A6okir1+7UqxHrxo8mbzxmZJbIXSwtrbm0KFDzJo1i+TkZNzc3AgMDKRLly6sW7eu8AwKUb16dQICAvj88895++23GTRokN6nShSVQqFg69atjBo1ijZt2mBkZETnzp2ZM2fOY8ery8yZMxkyZAgtW7bE0dGRcePGkZyc/FT2VRb06jeQ1FQlC+fP5H5KCh6e9fhqyvdYVKyoSRMbE611r5eXty9jxn3Fr6uWsHb1Eqo6VWPMuEnU9coZAteqTTvuJSexYc1KEuLjcHWryRcB31ClStF/wTXU2Fq3ace95GTWr1lJQnw8rm7ufBkwQ7N9QnwcMTHRmvRVnZz5MmA6yxbNZ9f2IOwdHBg6YhQtWuU8JikhPo4xH+ZMohq0eR1Bm9fhU9+Pr2fMKnKZAWw/nIKZqYLBPWyoWMGIsFupfLMsjkepOfc5ONoYa932EJ+UyTfL4nijqw3TRlUiITmDP47d57dD2p2HPrXNcbQzKdHTIGwa+9Ji3yrNa+/vvwAgYuVmzg4dj7lzZSyyOhkAHt64xcnuw/EOHI/be6+jvBPN+Y+ncndLzqP4Ev4KJvj1MXgGjMYz4EMehEUQ/NrHJP59ttjxAfi1eYeMNCVHgyaT+jCZyi4N6DJkMWbmObdi3U+MLPa9jzG3z7NjUc4jMI/vUHegePj34oVXpuvbTIuhtgdDji2nra7Iaqs1mZBr+4T4OGJzTb6mbqszWLpoHru2b9XZVrPjXrNqCWtXL6WqUzXGjpuoFXdR9ej7OqlKJUt++oH7Kfeo4+nNF5NnapVbXEwURkY55eZZrz4ffTaJdasXsW71Yqo6VeejcZPx8Mz5hTIpMZ55P0whIT6OipUq4epemy8CAmmQZ6Z1fZ5v+yL37iWz7tdVxMfH4+buzlcB06mSNY9RQkIcsXmucV9NnsaShfPZuX0b9g4OvDNiJC1bt9Gk2bVjG+npaXwzLUBrX6++NoiBbxT98bCGXqdguO3BkGMz9Ho12HJr+yLJ95JZ92vW9xF3d/4XMJ0qVdXbxyfE5/s+8r/J01m6cB47s76P5G+rQaSnp/HttEla+xrw2iAGvjG4OMVGz76vkapUsvinQO6npFDHsx5fTv4hT7lFocj1x6xnvfqM/mwia1cvZu3qxTg5VefjcQFa17hrVy4x6YsPNa9XLJ4LQNv2nRn58YRixSgEgEKlKsLsXEIIzl3VP/uu0M8Iwx3JMm1ZaUeg38BvXyztEPQKXRda2iHo1blBdOGJhE6KgmYmLWVpKsP9HaSCkbLwRKUkQ6X/KTalTfWYk+7+lxlyWzXkejVWPLlbkJ+0tEzTwhOVkgYeVQpPZKAeLPmq1PZdcejkUtt3aZFbIYQQQgghhBBCCFFihvsTwH9IeHg43t76h5RduHABV9fiz4guhBBCCCGEEP9J/7HHa5Y26VgwANWqVSMkJKTA94UQQgghhBBCCEMkHQsGwMTEhDp16pR2GEIIIYQQQgghRLHJHAtCCCGEEEIIIcoXI6PSW4pp/vz51KxZkwoVKtC4cWMOHz6sN+2BAwdQKBT5losXL2ql27RpE97e3pibm+Pt7c2WLVuKHVdxSMeCEEIIIYQQQghRCtatW8fo0aOZMGECwcHBPP/883Tp0oXw8PACt7t06RKRkZGaxcPDQ/PeX3/9xYABA3jzzTc5c+YMb775Jv379+fEiRNP7TikY0EIIYQQQgghRPmiUJTeUgw//PADQ4cO5Z133qFevXrMmjULFxcXfvrppwK3q1KlCk5OTprF2DjnEcOzZs2iQ4cOjB8/Hi8vL8aPH0/79u2ZNWtWSUqySKRjQQghhBBCCCGEeEKUSiXJyclai1KpzJcuNTWV06dP07FjR631HTt25NixYwXuo1GjRjg7O9O+fXv279+v9d5ff/2VL89OnToVmufjkI4FIYQQQgghhBDiCZk+fTo2NjZay/Tp0/Oli42NJSMjg6pVq2qtr1q1Knfv3tWZt7OzMwsXLmTTpk1s3rwZT09P2rdvz6FDhzRp7t69W6w8nwR5KoQQQgghhBBCiHJFUYJJFJ+U8ePHMWbMGK115ubmetMr8tw+oVKp8q3L5unpiaenp+Z1ixYtiIiI4Pvvv6dNmzYlyvNJkI4FIYQQQgghhBDiCTE3Ny+wIyGbo6MjxsbG+UYSREdH5xtxUJDmzZuzevVqzWsnJ6fHzrO45FYIIYQQQgghhBDli8Ko9JYiMjMzo3HjxuzZs0dr/Z49e2jZsmWR8wkODsbZ2VnzukWLFvny3L17d7HyLC4ZsSCEEEIIIYQQQpSCMWPG8Oabb9KkSRNatGjBwoULCQ8P59133wVg/Pjx3L59m5UrVwLqJz64u7vj4+NDamoqq1evZtOmTWzatEmT50cffUSbNm345ptv6NmzJ0FBQezdu5cjR448teOQjgUhhBBCCCGEEOWL0dObT+BJGjBgAHFxcUyePJnIyEh8fX3ZuXMnbm5uAERGRhIeHq5Jn5qayieffMLt27exsLDAx8eHHTt20LVrV02ali1bsnbtWr788kv+97//Ubt2bdatW8dzzz331I5DoVKpVE8tdyHKkX+vRpV2COIJ232uSmmHoJcq03AvzfUG1CvtEPRyDz1Q2iEUKNOA70A0IrO0QyiTVBjuF1cFhnsdMeTYwLDbqpxzJWPIsRlynfrWcSrtEErs4a/5n8LwrFi8Nr7U9l1aDPeqKYQQQgghhBBCCIMnt0IIIYQQQgghhChXFMWYRFE8PiltIYQQQgghhBBClJiMWBBCCCGEEEIIUb6UkckbywsZsSCEEEIIIYQQQogSk44FIYQQQgghhBBClJjcCiGEEEIIIYQQonyRyRufKSltIYQQQgghhBBClJiMWBBCCCGEEEIIUb4oZPLGZ0lGLAghhBBCCCGEEKLEpGNBCCGEEEIIIYQQJSa3QgghhBBCCCGEKF+M5Df0Z0lK+ykZPHgwvXr1KjCNu7s7s2bN0ry+e/cuHTp0oFKlStja2j7V+IQQQgghhBBCiCdBRiyUopMnT1KpUiXN65kzZxIZGUlISAg2NjYcOHCAF198kYSEhDLV0TB48GASExPZunVraYfyzKhUKtb/uoy9v//G/ZR71PH0Zth7H+PiVrPA7Y4fPcDaVUu4G3kHJ+dqDBw0jOdattG8f+FcCEGb1nLt6iUS4uP47MupNGvxvMT2jGI7vXcuF0+sR/kwmSquDWjV8yvsnTyKtP3VkB38uWYsbt7t6fTWPK33zv/1K2cPLuHBvRjsqtahRfcvcK7ZpFix/bNvHhf/zorNpQEte/4P+6pFiy3szA7+XPsJbt7t6fjmXM36yOsnOXtoKbG3z/PgXgwd3piDu89LRY7LvnUTao0dio2/LxWqVeFU3/eJ2rav4G2eb4r3959j6e2B8k40YYGLCV+4ViuNU++O1J30ERVru/IgLJxLX80kKmhvkePKtmv7VoI2ryUhPg4X15oMGT4Sb98GetOf/zeEZYvmExF+HXt7R3r1e5VOXXtq3g+/eZ21q5cRdvUSMdFRvD3sA7r3eqXYcWUz5PbwpMsO4K+jB1mzaqkm7tcGvUPzlsWL62nE9iTrVV2ny9mTVacent68895oXAup07+OHtSq09cGvaNVp+fPnSFo0xquXb2cVadf81w5q9Otm9dlxebO0EJiO6eJ7YYmts5de2jeD795nTWrlxF29TIx0VEMGfYB3Xv1K3Zc2Z5WvQL8vn1LVr3E4+LqztvDR+Lt61fM2AzzOmLI5WbI7cGQy83gyeMmnykp7VJUuXJlKlasqHkdFhZG48aN8fDwoEqVKqUYmSiurRt/ZfuW9Qx9dzQzZi7E1s6eyV+O4eGDB3q3uRR6jh9mBNCmXScC5y6lTbtO/DBjIpcvXtCkefToEe41azP03dES2zOO7czBxfx7eDmtev2P3qM2YGFZmZ2Lh5CqTCl023sJtzmx41ucdHQWhJ3ZyV+/TadRu3fp8+EWnNybsGvpcFIS7hQ9tkOL+ffIclr2+JJeH6zHwsqRXUuGkqq8X7TYdn6Hk3vjfO+lpz7E3tmTlj2+LHIsuRlXqkjy2Uuc/2hykdJbuNeg6W8LiT9ymiNNe3H1m5/xmTkBp94dNWlsmzek0a8zuf1LEIcb9+T2L0H4r5mFbTP9X/h0OXLoT5YtmkvfAW8QOHsx9Xzr8/XEz4iJjtKZPupuJF9P/Jx6vvUJnL2YPgNeZ8mCOfx19KAmjVKppKqTM28OHo6tnX2x4tHFUNvD0yi7S6HnCZwRQNt2Hflh7mLatutI4IxJWnGXVmxPsl63blzDb1vW8867o/lm5oKsOh1bpDpt264jgXOX6Cwb5aOHuNeswzvltE6XLppHvwFvEDh7Ed6+DZgycVwhsY3H27cBgbMX0VdvnVbjzcHDsXsibfXp1OtRTb28yfezF1HPtwFTCzh23bEZ5nVEHZthlpshtwcw3HITIi/pWHhMGzdupH79+lhYWODg4MBLL73E/fs5X+6///57nJ2dcXBw4IMPPiAtLU3zXu5bIdzd3dm0aRMrV65EoVAwePBgXnzxRQDs7Ow06x4nnuzbMwICAqhSpQrW1taMGDGC1NRUzfa///47rVu3xtbWFgcHB15++WXCwsK09nH79m0GDBiAnZ0dDg4O9OzZkxs3bgAwadIkVqxYQVBQEAqFAoVCwYEDB0pQsmWHSqViR9AG+gx4k+at2uLqXotRY75AqVRy+OAevdvtCNpAg0ZN6NP/Daq7uNGn/xvU92vMjqANmjT+TZozcNAwmrdqK7E949j+PbKSRu3epaZvR+yd6vLigBmkpz3iavD2ArfNzMzgz7Wf0rjDKKzta+R7/+zh5Xg27YtXs1ewq1qblj2+wNLGiQvH1xQ5tnNHV9LwxRGa2F54RR1bWEjhse1f9xn+L43Eyt4l3/sunm1o2nE0NX076ti6cDF/HOLyxFnc3aq//nJzG/4qj8IjuTB2GikXrxGxdCMRyzdTa8wQTZqao94idu8xwr5dyP1L1wj7diGxfx7HfdRbxYrtty0baN+xKx06vUwNVzeGDh+Fg2MV/tgZpDP9Hzu34Vi5CkOHj6KGqxsdOr1Muw5dCNq8TpPGo64Xbw19j9Zt22NqalqsePIy5PbwNMrut6CN+DVqQt/+r1PDxY2+/V+nvp8/24M2lnpsT6peVSoV24M20HfAmzRv1SarTsdn1an+ETfbgzbi16gxffq/QY1cdbo9T52+NugdmrdqozefghhynW7TxNYNF1c3hg4fiYNjFX7fua2Q2Ebi4upGh07daNehC1s3r9ek8ajrxeCh7/J823aYPIG2+rTq9bct62nXsSsvdXqZGq7uDBk+CgfHynrrRVdshnodMeRyM+T2YMjlViYYKUpv+Q+SjoXHEBkZycCBAxkyZAihoaEcOHCAPn36oFKpANi/fz9hYWHs37+fFStWsHz5cpYvX64zr5MnT9K5c2f69+9PZGQkP/74I5s2bQLg0qVLmnWPEw/Avn37CA0NZf/+/axZs4YtW7YQEBCgef/+/fuMGTOGkydPsm/fPoyMjOjduzeZmZkAPHjwgBdffBFLS0sOHTrEkSNHsLS0pHPnzqSmpvLJJ5/Qv39/OnfuTGRkJJGRkbRs2fJxitngRd+NJDEhHj//ppp1pqZmePv6cSn0nN7tLl88j1+jplrr/PybFbiNxPZsYrsXf4uH92Ko4dFKs87YxAznWk2Juhlc4Lb/7J2HRSV7vJrlH2abkZ5K7O3zWvkC1KjbqtB8NbEl3OLhvdj8sdUsPLbgffOpUMkOr6YlHwL8JNk2b0jM3qNa62J2H8amsS8KE/WdenbNGxK794hWmtg9h7Fr0ajI+0lLSyPs6qV8501D/6ZcDD2vc5vLF8/T0D9v+maEXblEenp6kfddVIbaHp5W2V2+eJ6GefJs5N9Mb57PMrYnJUpTpzkjl0xNzfApQZ029G/KpWKUTUEMv04v07CR9mivhv5NuKinzC5dvEBDf+30jfybPrW2+rTqNefY87bnpkVuz4Z6HQHDLTdDbg9guOUmhC4yx8JjiIyMJD09nT59+uDm5gZA/fr1Ne/b2dkxd+5cjI2N8fLyolu3buzbt49hw4bly6ty5cqYm5tjYWGBk5MTAPb26uF6VapUKdIcC4XFA2BmZsbSpUupWLEiPj4+TJ48mU8//ZQpU6ZgZGRE3759tdIvWbKEKlWqcOHCBXx9fVm7di1GRkYsXrwYhULdG7ds2TJsbW05cOAAHTt2xMLCAqVSqTkOXZRKJUqlUmudubk55ubmhR6noUlIiAPA1lZ7eKWtrT0xMXf1bpeYEI+tnZ32NnZ2JCbES2ylHNuDezEAWFg5aK23sHQo8JaFuzf+4dLJTfQdvVXn+48eJKDKzMDCMn++D+7FFim2h1npLCwd8+VxL7GQ2E5tos+HW4q0n2fBvKojyijt406NjsPI1BQzRzuUd2Mwd3JEGRWnlUYZFYe5U+Ui7+dechKZmZnY2mqfNza2+s+bhIR4GuZJb2trR0ZGBsnJSdjbO+jcrqQMtT08rbJLTIjHJk/cNsWM29DrNTuGvHVqY2tHTIz+ocZSp/ljs7W1IzEhQW9sjZ5hW31a9ao+9gxs8l0Dil6Ghnodyd6HrthKu9wMuT2A4ZabELpIx8Jj8PPzo3379tSvX59OnTrRsWNH+vXrh11WQ/bx8cHY2FiT3tnZmX///bfU4slOk3tehxYtWpCSkkJERARubm6EhYXxv//9j+PHjxMbG6sZqRAeHo6vry+nT5/m6tWrWFlZae370aNH+W6ZKMj06dO1RkoATJw4kUmTJpXgyJ+tQ/t3s3BuoOb1+EnfAKDIM+pJhQoFBQ+Fyvu+SqXSdNhIbM8utivBv3F480TN685v/6xzP6h0BJwlVZnC/rWf8nzfKVSoZKczTba8sarQn+/V4N84vHVSTmxv/ZQVW97Q9JdbqvI++9d/xvN9Jhca2zOXa0QVkFMOudfrSpN3XRHkO0dUKn3FrjO9Sl1ThZydRWPI7UHnPp5C2emO2zBiK4lD+/ewIFedfjFphu74ilCneaNRqXTl83gMuU7zbqQ+/pLE9vhl9qzrVUe16D14Q76OGHK56dyDgbSHslZuBk8mb3ympGPhMRgbG7Nnzx6OHTvG7t27mTNnDhMmTODEiRMA+e7NVCgUmj/Un3U8NWvWLHDb7AtN9+7dcXFxYdGiRVSrVo3MzEx8fX018zBkZmbSuHFjfvnll3x5VK5c9F8Qx48fz5gxY7TWlZXRCk2fa42Hp7fmdXrWvBkJCfHY2ef8ipyUmJCvtzo3Wzt7EvL0CiclJmJjq38bie3pxObm/SJVXHImA8xIV5/vD+7FUtE6ZyLVh/fj8o02yJYcF8G9hNv8seI9zTqVSt3eF433YcAnu6hk44TCyDjf6IRHKXFU1JOvq3c7+uSOLSMrthTt2B6lxOuN7V5cOCkJt/lj5fv5Yls8wZf+Y3Zi7eCqc9unSRkVm2/kgVllezLT0kiNS1SnuRuLuZP26AzzKvb5RjoUxMraBiMjo/znTVJivl9rstnZ2ef71SYpMRFjY2OsrG2KvG99DLk95Pa0ys5WR5rkRP15PsvYSqrpc63w8KyneZ2mqdM47HL9ap6UmJjvl8TcdJVNUmLCf6pO8+0rSf/x2+lpA+rYrIu8b32eVb2qj91Y57Hn/TU9JzbDvY4YcrnlZmjtoayUmxC6SDfOY1IoFLRq1YqAgACCg4MxMzNjy5YnM9TYzMwMgIyMjCcWz5kzZ3j48KHm9fHjx7G0tKRGjRrExcURGhrKl19+Sfv27alXrx4JeYYe+vv7c+XKFapUqUKdOnW0FhsbG03chcVsbm6OtbW11lJWOhYsKlbEuVoNzVLD1R1bO3vOBp/SpElLS+PCuTN41vPVm09dLx/OhpzUWncm+GSB20hsTyc2M3NLbBzdNItd1TpYWFXm1pVjmjQZ6alEXjtJVbdGOvOwrVyLfh9vo+9HWzSLW712VKv1HH0/2kIlGyeMTcxwrO7D7Vz5Aty6ckxvvmbmlbRjq1IHCytHrTwy0lOJvK4/NpvKtej7URB9Rm3WLNmx9Rm1mUo2+m9bepoSj4fg2F57DpbKHVqTdPocqqz7VBOOh+DYXntOCseXWpPwV9HmpAB1J2/tOp6cyXWuAZwJPoVXPR+d29T18tGR/iS1PTwxMXn8PnlDbg+5Pa2yq+vlw5kQ7TQhwSf15vksYyupvHXqoqdOzxehTvOWjbpOi142BTH8Oq2rY1+n8dJTZp5e3pwJPp1nv6eeWlt9WvWq79jPBp/Sm68hX0cMudxyM7T2UFbKrcxQKEpv+Q+SjoXHcOLECaZNm8apU6cIDw9n8+bNxMTEUK9evcI3LgI3NzcUCgXbt28nJiaGlJSCH3NXlHhSU1MZOnQoFy5cYNeuXUycOJGRI0diZGSkecrDwoULuXr1Kn/++We+UQWvv/46jo6O9OzZk8OHD3P9+nUOHjzIRx99xK1btwD1Ey7Onj3LpUuXiI2N1XoSRnmkUCjo1vMVNq9fzYljhwi/cY15M6djbm7O8207aNLNDpzKL8sXaF537dGPM/+cYsuGX7gdcZMtG37h35BTdOuZ87z0hw8fcD3sCtfDrgDqSXyuh10p8qOAJLaSx1a/9SBC9i/g+rk9xN+9zIEN4zExrUCdRi9r0u1fN46/d6mHLJqYmmPvVFdrMbewwtS8EvZOdTE2UXcUNnh+MBdPbuTiyU0kRIVx7LfppCRGUq/5q0WOzbfVIEIOLOT6eXVsBzd+gYlpBWo3zBXb+nH8/fsPemMzq5A/tjTlfeLuhBJ3JxRQTxQZdyeUlALmbsjNuFJFrP28sPbzAqBizRpY+3lRwcUZAM+vx+C37BtN+psL12LhVo16332OpVctagzui8vbfbn2w1JNmhtzV+LYoRW1PhlGJc9a1PpkGI7tW3BjzooixZSte+9X2Ld7B/t27+RW+E2WLpxLbEwUHbOedb96+UJ+DJymSd+paw9ioqNYtmget8Jvsm/3Tvbt3knPPgM0adLS0jTnWXp6OvFxsVwPu0LknVvFig0Muz08jbJ7uUdfQv45yeYNv3Ir4iabN/zK2ZDTvNyzeBOLGnK9KhQKXu75CpvW/6Kp07maOn1Jk2524FRWL1+oed1NU6fqstmiKRv9dRpdjuq0R+9X2Lt7J3t37yQi/CZLF84jNiaKTl27A7Bq+SKdsS1dNI+I8JvszYqtV5/+mjTqOr3K9bCrpKenExcXy/Wwq0TeuV2s2ODp1mv33v2z6mUHt8JvsGzhXGJjojX1UpTYDPU6YsjlZsjtwZDLTYi85FaIx2Btbc2hQ4eYNWsWycnJuLm5ERgYSJcuXVi3bl3hGRSievXqBAQE8Pnnn/P2228zaNAgvU+VKCyebO3bt8fDw4M2bdqgVCp59dVXNfMaGBkZsXbtWj788EN8fX3x9PRk9uzZvPDCC5rtK1asyKFDhxg3bhx9+vTh3r17VK9enfbt22OdNeRw2LBhHDhwgCZNmpCSksL+/fu18iiPevV7jdRUJYvm/8D9lBQ8POvxvymBWOSazyI2JgqjXD2YXt71+XjcRNasWsy61Uuo6lSNj8dNoq5XzrDGsCuXmDT+I83rFYvnAvBC+86MHPOFxPYUY/Nr+w7paY84snUyqQ+TqOLSgK7vLMHM3FKTJiXxTrHvQa3t15VHDxL5Z988HiTHYO/kQZe3F2BlV73Iefi1eYeMNCVHgyaT+jCZyi4N6DJkMWbmlTRp7idGoijmvYUxt8+zY1HOYxyP71B3Anj49+KFV6YXur1NY19a7Fulee39vbqsI1Zu5uzQ8Zg7V8Yiq5MB4OGNW5zsPhzvwPG4vfc6yjvRnP94Kne37NakSfgrmODXx+AZMBrPgA95EBZB8Gsfk/j32WIdW+s27biXnMz6NStIiI/H1a0mEwK+oUoV9WiNhPg4YnNNhFXVyZkvA2awdNE8dm3fir2DA0NHjKJFrketJcTHMvbDnMl4gzavI2jzOnzq+zFlxo/Fig8Mtz08jbLz8vZlzLivWLNqCWtXL6WqUzXGjpuoFXdRGHq99uo3kNRUJQvnz9TU6VdTvs9Tp9FabTW7bH5dtYS1WXU6RkedThw/WvN6+eJ5gLpOR40ZX2hcZaNOV2bF5s6XATO0YouJic4T23SWLZrPru1Beuo0jjF66vTrGbOKFR88vXpt1aYd95KT2LBmJQnxcbi61eSLXPVStNgM8zpiyOVmyO3BkMtNiLwUKlUJZsASZdLgwYNJTExk69atpR1KmfTv1aL1youyY/e5KoUnKiWqTMO9NNcb8GRGZT0N7qEHSjuEAmUa8EBBI57eHEDlmeqJTOf5dCgw3OuIIccGht1W5ZwrGUOOzZDr1LdO2e1oeLT9p1Lbd4WX3ys8UTljuFdNIYQQQgghhBBCGDy5FaIMCQ8Px9tb/xCqCxcu4Or67Gd2F0IIIYQQQgiD8h+dRLG0SMdCGVKtWjVCQkIKfL8gBc3PIIQQQgghhBBClIR0LJQhJiYm1KlTp7TDEEIIIYQQQgjDVsxJrMXjkdIWQgghhBBCCCFEiUnHghBCCCGEEEIIIUpMboUQQgghhBBCCFG+GMlv6M+SlLYQQgghhBBCCCFKTEYsCCGEEEIIIYQoX+Rxk8+UjFgQQgghhBBCCCFEiUnHghBCCCGEEEIIIUpMboUQQgghhBBCCFG+KOQ39GdJSlsIIYQQQgghhBAlJiMWhBBCCCGEEEKULzJ54zMlHQtCFJGZIrW0Q9ArXWW4TTnTgAdGdfKNKu0QyiRF6IHSDkGvG/VeKO0QCnRwTkhph6CXianhttXX2iaUdgh6KVCVdghlkiF/NoBh16shx6ZC/pArCUOuUyGKynD/GhFCCCGEEEIIIUrCyLA7MMsbKW0hhBBCCCGEEEKUmHQsCCGEEEIIIYQQosTkVgghhBBCCCGEEOWKSiZvfKZkxIIQQgghhBBCCCFKTEYsCCGEEEIIIYQoXxTyG/qzJKUthBBCCCGEEEKIEpOOBSGEEEIIIYQQQpSY3AohhBBCCCGEEKJ8kVshnikpbSGEEEIIIYQQQpSYjFgQQgghhBBCCFGuyOMmny0ZsSCEEEIIIYQQQogSk44FPV544QVGjx6tee3u7s6sWbOe2f6XL1+Ora3tM9tfQVQqFcOHD8fe3h6FQkFISEi+8hFCCCGEEEIIg6EwKr3lP0huhSiikydPUqlSpdIOo1T8/vvvLF++nAMHDlCrVi0cHR3ZvHkzpqampR2aQdi5PYjNmzaQEB+Hq5s77wx/Hx/f+nrTn/v3DEsW/Uz4zRvYOzjQp+8AunTrrnn/2NHDbFy3hsjI26SnZ1CtenV69e7Hi+07lCi+Xdu3ErR5LQnxcbi41mTI8JF4+zbQm/78vyEsWzSfiPDr2Ns70qvfq3Tq2lPzfvjN66xdvYywq5eIiY7i7WEf0L3XKyWKTaVSsf7XZez9/Tfup9yjjqc3w977GBe3mgVud/zoAdauWsLdyDs4OVdj4KBhPNeyjeb9C+dCCNq0lmtXL5EQH8dnX06lWYvnSxDbcvZkxebh6c07743GtZDY/jp6UCu21wa9oxXb+XNnCNq0hmtXL2fF9jXPGUhsAL9v35J1vsTj4urO28NH4u3rV+TYDPV8s2/dhFpjh2Lj70uFalU41fd9orbtK3ib55vi/f3nWHp7oLwTTVjgYsIXrtVK49S7I3UnfUTF2q48CAvn0lcziQraW+z4snVoYsJz9UywMIfw6Ey2Hk4jKkGlN31jT2MGvGiWb/0Xix6SnqH+f3NvY1r4mGBnpR4SGhWvYu/pNC5FZBYrtvaNjGnqaYyFOUTEqNh2LJ3oRP2x+XsY0a9N/s+Jr5YrNbG5Oyl4vr4x1R2MsK6kYNXeNEJvFi+u/1J7yI57zaqlWnE3b1m8a4ihxwZSr+WxXqVOy1+dCpHbf7M7pQQqV65MxYoVSzuMUhEWFoazszMtW7bEyckJExMT7O3tsbKyKu3QSt3hg/tZvPAn+g94jVlzfsbbpz4BX40nJjpKZ/q7dyMJ+GoC3j71mTXnZ17p/xqLFszj2JFDmjRWVla88uprfBs4m9nzF9L+pU78OPM7/jl9stjxHTn0J8sWzaXvgDcInL2Yer71+XriZ3rji7obydcTP6eeb30CZy+mz4DXWbJgDn8dPahJo1QqqerkzJuDh2NrZ1/smHLbuvFXtm9Zz9B3RzNj5kJs7eyZ/OUYHj54oHebS6Hn+GFGAG3adSJw7lLatOvEDzMmcvniBU2aR48e4V6zNkPfHf0Ysa3hty3reefd0Xwzc0FWbGOLFFvbdh0JnLtE/e+MSVqxKR89xL1mHd4xwNiOas6XN/l+9iLq+TZg6sRxes+XvAz5fDOuVJHks5c4/9HkIqW3cK9B098WEn/kNEea9uLqNz/jM3MCTr07atLYNm9Io19ncvuXIA437sntX4LwXzML22b6v/AV5IWGJjzfwIStR1KZvUnJvQcqhr1sjnkhfbgPlSomr3iotWT/4Q6QdF/FrhNpzN6kZPYmJVfvZPBWZzOq2hX93tM2DYxp5WvMb3+lM39bGikPVQzpbIpZIbE9SlUx7Vel1pI7NjMTBXfjVfz2V3qRY8nrv9QeLoWeJzAr7h/mLtYZd1mPLZvUa/mrV6nT8lenQuRmEB0LGzdupH79+lhYWODg4MBLL73E/fv3GTx4ML169SIgIIAqVapgbW3NiBEjSE1N1WyrUqn49ttvqVWrFhYWFvj5+bFx40at/C9cuEDXrl2xtLSkatWqvPnmm8TGxmrev3//PoMGDcLS0hJnZ2cCAwPzxZj3VgiFQsHixYvp3bs3FStWxMPDg23btmlts23bNjw8PLCwsODFF19kxYoVKBQKEhMTi1w2f/zxB/Xq1cPS0pLOnTsTGRmpee/kyZN06NABR0dHbGxsaNu2Lf/884/W9klJSQwfPlxTfu3atePMmTOa97PLOLfRo0fzwgsvaN4fNWoU4eHhKBQK3N3dgfy3ikRGRtKtWzcsLCyoWbMmv/766zO/faQ0BG3ZxEsdO9Oxc1dcXN0YNuJ9HCtXYeeO33Sm/33ndipXqcKwEe/j4upGx85dealDZ7Zs3qBJU79BQ1q0bI2LqxvOztXo0asP7jVrceH8uWLH99uWDbTv2JUOnV6mhqsbQ4ePwsGxCn/sDNKZ/o+d23CsXIWhw0dRw9WNDp1epl2HLgRtXqdJ41HXi7eGvkfrtu0fa9SKSqViR9AG+gx4k+at2uLqXotRY75AqVRy+OAevdvtCNpAg0ZN6NP/Daq7uNGn/xvU92vMjqCcMvRv0pyBg4bRvFXbEse2PWgDfQe8SfNWbbJiG58Vm/5fo7cHbcSvUWP69H+DGrli254nttcGvUPzVm305lNasf22ZT3tOnblpU4vU8PVnSHDR+HgWFnv+ZKXIZ9vMX8c4vLEWdzdqv/cys1t+Ks8Co/kwthppFy8RsTSjUQs30ytMUM0aWqOeovYvccI+3Yh9y9dI+zbhcT+eRz3UW+VKMbW9U348590zl3PJCpBxbo/0zA1gYZ1jAvdNuWh9pJb6M1MLoZnEpukIjZJxR9/p5OaBq5Vi/4VoKWPMQfOZHD+pjq2DQfT1bHVKjgPlarg2C7fymTPaXW+JfFfaw+/BW3Er1ET+vZ/nRoubvTt/zr1/fzZHrRRZ55lMTaQei2P9Sp1Wv7qtExQKEpv+Q8q9Y6FyMhIBg4cyJAhQwgNDeXAgQP06dMHlUo9vHLfvn2Ehoayf/9+1qxZw5YtWwgICNBs/+WXX7Js2TJ++uknzp8/z8cff8wbb7zBwYMHNfm3bduWhg0bcurUKX7//XeioqLo37+/Jo9PP/2U/fv3s2XLFnbv3s2BAwc4ffp0obEHBATQv39/zp49S9euXXn99deJj48H4MaNG/Tr149evXoREhLCiBEjmDBhQrHK5sGDB3z//fesWrWKQ4cOER4ezieffKJ5/969e7z11lscPnyY48eP4+HhQdeuXbl37x6gvoh369aNu3fvsnPnTk6fPo2/vz/t27fXxFmYH3/8kcmTJ1OjRg0iIyM5eVL3r+aDBg3izp07HDhwgE2bNrFw4UKio6OLdbxlTVpaGlevXqaRfxOt9Y0aNeZiqO5e34uhF2jUqLF2+sZNuHrlMunp+X+xU6lUnAn5h9u3buFTwLA3ffGFXb2EX6OmWusb+jflYuh5ndtcvniehv550zcj7MolnfE9jui7kSQmxOOXa3+mpmZ4+/pxKVR/J8rli+fzHZOff7MCtymuKE1sOXVramqGTwlia+jflEt6ytuQYlOfL5dpmK9smxapbA39fCsu2+YNidl7VGtdzO7D2DT2RWGivovQrnlDYvce0UoTu+cwdi0aFXt/9lYKrCspuByR83N+RiZcu5OJm1PBH9VmpjD+dXO+eKMCb3cxo5qD/i80CgX41TbGzBRuRhXtj3k7K7CuqODK7Zz0GZlw/W5moZ0TZqbw6QAzxr1qxqAOJjgXEFtJ/Nfaw+WL5/PF1Mi/md48y1ps2aRey1+9Sp2WvzoVIq9Sn2MhMjKS9PR0+vTpg5ubGwD16+fcn25mZsbSpUupWLEiPj4+TJ48mU8//ZQpU6bw8OFDfvjhB/78809atGgBQK1atThy5AgLFiygbdu2/PTTT/j7+zNt2jRNnkuXLsXFxYXLly9TrVo1lixZwsqVK+nQQX0P+4oVK6hRo0ahsQ8ePJiBAwcCMG3aNObMmcPff/9N586d+fnnn/H09OS7774DwNPTk3PnzjF16tQil01aWho///wztWvXBmDkyJFMnpwzjLddu3Za6RcsWICdnR0HDx7k5ZdfZv/+/fz7779ER0djbm4OwPfff8/WrVvZuHEjw4cPLzQGGxsbrKysMDY2xsnJSWeaixcvsnfvXk6ePEmTJuoPjMWLF+Ph4aE3X6VSiVKp1Fpnbm6uibMsSE5OIjMzE1tbO631NnZ2JCbo7rhJTIjHxk47va2tHRkZGSQnJ2Fv7wDA/fspvP3mq6SlpWFkZMS7H3xII//GurLU656++Gz1x5eQEE9D28LjexISEuKy8tce3m5ra09MzF292yUmxGObtwwLKPOSyM4rb2w2tnbExOgfWlmWY1OfLxnY5KuPosVv6OdbcZlXdUQZFau1LjU6DiNTU8wc7VDejcHcyRFlVJxWGmVUHOZOlYu9P6uK6j+4Ux5qz1mQ8lCFrZX+P8ZjEjJZvz+Nu/GZmJsqaF3fhPd7mTNro5LYpJy8nOwVfNDbHBNjSE2DlX+kEl3A3A1asVnoiw1sLQuILVHFpkPp3E1QUcFUPephxMumzNmSRlxy0fZdmP9ae9D1GVLQZ05Ziy2b1Gv5q1ep0/JXp2WCUan/hv6fUuodC35+frRv35769evTqVMnOnbsSL9+/bDLOuH9/Py05jZo0aIFKSkpREREEB0dzaNHjzQdAtlSU1Np1Ej9i9Hp06fZv38/lpaW+fYdFhbGw4cPSU1N1XRMANjb2+Pp6Vlo7A0a5PyCXKlSJaysrDS/0l+6dImmTbV7A5s1a1ZonrlVrFhR06kA4OzsrDUKIDo6mq+++oo///yTqKgoMjIyePDgAeHh4YD62FNSUnBw0P5y/vDhQ8LCwooVS0EuXbqEiYkJ/v7+mnV16tTR1KEu06dP1xp5AjBx4kQmTZr0xOJ6VhR5hzupVAUOgcr7TvboHEWudywsKjJr7gIePXzImTPBLF30M05OztRv0PCJxFfQCK286VVkx/d4Du3fzcK5ObcZjZ/0Tdb+8oSHSqssdMaY532VSpX/OIsV2x4W5Irti0kzsmLLV1uFxpa3pNSnQ9mJTdfpXJwhfYZyvj0Rqjx//GbHmnu9rjR51+nQyMOYPrkmNly2U32LX74tFbpW5giPVhEenT3KQcXNu6l81M+clr4mbDuapkkXk6hi1gYlFubgW9OY/i+a8fM2pc7OBb/aRvRqlfP1YOXutOzstUMrpJIiYlRExORsdDMqnQ96mdLC24jtxzMK2FI/aQ/6rn9FDskgY5N6LX/1KnVa/upUiMKUeseCsbExe/bs4dixY+zevZs5c+YwYcIETpw4UeB2CoWCzEz1sMwdO3ZQvXp1rfezf/nOzMyke/fufPPNN/nycHZ25sqVKyWOPe/9vrlj0vWHjqoIXzYLyz93HoMHDyYmJoZZs2bh5uaGubk5LVq00MxBkZmZibOzMwcOHMiXd/ajLI2MjPLFlZaWli99QfQdV0HHO378eMaMGaO1riyNVgCwtrbByMiIhDw9vEmJifl6l7PZ2tmTmJCgnT4pEWNjY6ysrTXrjIyMqFZNfU7Xql2HW+HhbFy/plgdC1b64ktKzNezn83Ozj5fj3VSYnZ8NkXety5Nn2uNh6e35nV61nmWkBCPnb1jrv0l5OtJz83Wzl5nmdvoKfOixdYKD896mtdpmtjisMv1q3lSYmK+X07yxpa//BLKRGzq88U4f5qkBL3nc26Gdr49LmVUbL6RB2aV7clMSyM1LlGd5m4s5k6OWmnMq9jnG+mgy4UbGYTnuhXBJGsaBSsLBfce5Fw7LSsouPew6J8dKiAiJhNHG+3Pn4xMNKMEbsWk41LFiNb1Tdh8KP/1PjQ8k4jonLmMTIzVeVlW1I6lUoX8oxgKi+12rAoHayOgZB0L//X2oCvu5ET9eZaV2KRey1+9Sp2WvzoVojAGMT5EoVDQqlUrAgICCA4OxszMjC1btgBw5swZHj7Mme3p+PHjWFpaUqNGDby9vTE3Nyc8PJw6depoLS4uLgD4+/tz/vx53N3d86WpVKkSderUwdTUlOPHj2v2kZCQwOXLlx/rmLy8vPLNR3Dq1KnHyjOvw4cP8+GHH9K1a1d8fHwwNzfXmpTS39+fu3fvYmJiku/YHR3VX4YrV66sNSEkQEhISLHi8PLyIj09neDgYM26q1evFjhJpbm5OdbW1lpLWetYMDU1pU6duoQEa8/HERJ8Gq963jq38arnnS998D+nqONRFxMT/f18KlTF7vAxNTWldh1PzgRrn3dngk/hVc9H5zZ1vXx0pD9JbQ/PAuMrCouKFXGuVkOz1HB1x9bOnrO59peWlsaFc2fwrOerN5+6Xj6cDdFuW2eCTxa4TXFjc9ET2/kixHYmJH/5eeopb0OKTX2+1M1X/2eDTxWpbA3tfHtcicdDcGzfUmtd5Q6tSTp9DlXWfaoJx0NwbN9KK43jS61J+CuYwijT1H/oZy9RCSqS76vwcMmZqNHYCGpVM+Lm3eJNbFjNwUirc0IfEz1zQqamQfy9nCU6UUXyAxV1quV8ZTA2gppORlqdI0XhbF+8jpK8/uvtQVfcIcEn9eZZVmKTei1/9Sp1Wv7qtCxSKRSltvwXlXrHwokTJ5g2bRqnTp0iPDyczZs3ExMTQ7166l7O1NRUhg4dyoULF9i1axcTJ05k5MiRGBkZYWVlxSeffMLHH3/MihUrCAsLIzg4mHnz5rFixQoAPvjgA+Lj4xk4cCB///03165dY/fu3QwZMoSMjAwsLS0ZOnQon376Kfv27ePcuXMMHjwYo8e8J2fEiBFcvHiRcePGcfnyZdavX8/y5cuBxxsWnVudOnVYtWoVoaGhnDhxgtdffx0LCwvN+y+99BItWrSgV69e/PHHH9y4cYNjx47x5Zdfajo52rVrx6lTp1i5ciVXrlxh4sSJnDtXvEnwvLy8eOmllxg+fDh///03wcHBDB8+HAsLiyd2rIaqZ+++7PljF3t27yIi/CaLF84nJiaaLl27A7Bi2WJmfj9Dk75z15eJjo5mycKfiAi/yZ7du9i7+3d693lFk2bDul8J/uc0dyPvcCsinK2bN7J/3x5eePGlYsfXvfcr7Nu9g327d3Ir/CZLF84lNiaKjl17ALB6+UJ+DMyZf6RT1x7EREexbNE8boXfZN/unezbvZOefQZo0qSlpXE97ArXw66Qnp5OfFws18OuEHnnVrFiUygUdOv5CpvXr+bEsUOE37jGvJnTMTc35/m2Obc3zQ6cyi/LF2hed+3RjzP/nGLLhl+4HXGTLRt+4d+QU3TrmVOGDx8+0MQI6kmjroddKfKjpxQKBS/3fIVN63/RxDZXE1tOPcwOnMrq5Qs1r7tpYvuVWxE32bLhV86GnOblAmKLNqDYuvfun3W+7OBW+A2WLZxLbEy05nwpjCGfb8aVKmLt54W1nxcAFWvWwNrPiwouzgB4fj0Gv2U5I9tuLlyLhVs16n33OZZetagxuC8ub/fl2g9LNWluzF2JY4dW1PpkGJU8a1Hrk2E4tm/BjTkrihVbtiP/ptOukQk+7kZUtVPQ/0VT0tIh5GrOr/sDXjSlc7OcTpeXGptQt4YR9lYKnB0UvPKCKdUcFBy/kDP5ZedmJrg7GWFnpcDJXkGnZibUrmZE8JWijxo4dj6DF/yM8XZTx9avjYk6tms5HQv92pjQsUlOb0W7RsZ4VFdgZ6XuUOjzvHryxr9Dc/ZrZqJ+z9le/Vlhb6n+v02losX1X2sPL/foS8g/J9mcFfdmTdz9ilZgZSA2kHotj/UqdVr+6lSIvEr9Vghra2sOHTrErFmzSE5Oxs3NjcDAQLp06cK6deto3749Hh4etGnTBqVSyauvvqp1H/6UKVOoUqUK06dP59q1a9ja2uLv788XX3wBQLVq1Th69Cjjxo2jU6dOKJVK3Nzc6Ny5s6bz4LvvviMlJYUePXpgZWXF2LFjSUpKeqzjqlmzJhs3bmTs2LH8+OOPtGjRggkTJvDee+89sV/mly5dyvDhw2nUqBGurq5MmzZN66kRCoWCnTt3MmHCBIYMGUJMTAxOTk60adOGqlWrAtCpUyf+97//8dlnn/Ho0SOGDBnCoEGD+Pfff4sVy8qVKxk6dCht2rTBycmJ6dOnc/78eSpUqPBEjtVQPd/2Re7dS2bdr6uJj4/Hzd2drwKmUSWrfBMS4omJyZkXw8nJmYmTp7J44U/s2L4NewcHho34gJatcx49qHz0iJ/nzyYuNgYzM3NquLgw5pPPeb7ti8WOr3WbdtxLTmb9mhUkxMfj6laTCQHfUKWKeiLOhPg4YnNNmlTVyZkvA2awdNE8dm3fir2DA0NHjKJFrsc2JsTHMvbDYZrXQZvXEbR5HT71/Zgy48dixder32ukpipZNP8H7qek4OFZj/9NCcQi17wqsTFRGOXqoPLyrs/H4yayZtVi1q1eQlWnanw8bhJ1vXJGiYRducSk8R9pXq9YPBeAF9p3ZuSYL4oY20BSU5UsnD9TE9tXU77PE1s0CkVOJ6SXty9jxn3Fr6uWsDYrtjE6Yps4frTm9fLF8zSxjRozvlRja9WmHfeSk9iwZiUJ8XG4utXki1znS2EM+XyzaexLi32rNK+9v1efBxErN3N26HjMnStjkdXJAPDwxi1Odh+Od+B43N57HeWdaM5/PJW7W3bnxPZXMMGvj8EzYDSeAR/yICyC4Nc+JvHvs0WOK7cDIepHOPZ+3gwLc4iIzmTRdiXKXIOVbK0UWlMdWJgr6NvWFKuKCh6lwu3YTH7alkpEdK7bKSwUvNreFOusNJFxmSzZmcqVW0UfbXDobAamxtCjpQkWZnArRsWyP9JIzR2bpUJreokKZtCrtSlWFvAoFe7EqVi4I41bsTmJqjsqGNbNTPO6W3P115LTlzPYdLhoTwb5L7WH7LjXrFrC2tVLqepUjbHjJmrFXdZjyyb1Wv7qVeq0/NWpwVOU+m/o/ykKVXFv/H+GBg8eTGJiIlu3bi3tUJ6IqVOn8vPPPxMREVHaoTx1t27dwsXFhb1799K+ffvSDueJuBRmuPWWrir1PkK9Mkt/YJReioJmxRN6GXK53aj3QmmHUKCDc0JKOwS9TEwNt62+1jah8ESlxJDbgyFTGcYUrXpJvZaMIder1GnJ+NRxLjyRgbr/19ZS23elFr1Kbd+lxXD/GikH5s+fT9OmTXFwcODo0aN89913jBw5srTDeir+/PNPUlJSqF+/PpGRkXz22We4u7vTpk2bwjcWQgghhBBCiCdIJSMWnikp7afoypUr9OzZE29vb6ZMmcLYsWM1t3F06dIFS0tLncu0adMKztgApaWl8cUXX+Dj40Pv3r2pXLkyBw4cyPdkCyGEEEIIIYQQ5YtB3wpRnt2+fVvraRe52dvbY28vj3wxNHIrRMnIrRDljyGXm9wKUXJyK0TJGHJ7MGSGPGQepF5LypDrVeq0ZMryrRApx7eV2r4tmxdt8tDyxHD/GinnqlevXtohCCGEEEIIIUT5VM6fTmdoDPfnCSGEEEIIIYQQQhg8GbEghBBCCCGEEKJckckbny0pbSGEEEIIIYQQQpSYdCwIIYQQQgghhBCixKRjQQghhBBCCCFE+aJQlN5STPPnz6dmzZpUqFCBxo0bc/jwYb1pN2/eTIcOHahcuTLW1ta0aNGCP/74QyvN8uXLUSgU+ZZHjx4VO7aiko4FIYQQQgghhBCiFKxbt47Ro0czYcIEgoODef755+nSpQvh4eE60x86dIgOHTqwc+dOTp8+zYsvvkj37t0JDg7WSmdtbU1kZKTWUqFChad2HDJ5oxBCCCGEEEKI8qWMTN74ww8/MHToUN555x0AZs2axR9//MFPP/3E9OnT86WfNWuW1utp06YRFBTEb7/9RqNGjTTrFQoFTk5OTzX23MpGaQshhBBCCCGEEGWAUqkkOTlZa1EqlfnSpaamcvr0aTp27Ki1vmPHjhw7dqxI+8rMzOTevXvY29trrU9JScHNzY0aNWrw8ssv5xvR8KRJx4IQQgghhBBCiHJFpVCU2jJ9+nRsbGy0Fl2jD2JjY8nIyKBq1apa66tWrcrdu3eLdJyBgYHcv3+f/v37a9Z5eXmxfPlytm3bxpo1a6hQoQKtWrXiypUrj1eoBZBbIYQQQgghhBBCiCdk/PjxjBkzRmudubm53vSKPBM+qlSqfOt0WbNmDZMmTSIoKIgqVapo1jdv3pzmzZtrXrdq1Qp/f3/mzJnD7Nmzi3oYxSIdC0IUUZrKtLRDEE+YEZmlHYJemQY8oMyQYzs4J6S0QyhQ21ENSzsEvU4vP1/aIeilQFXaIehlyO3BmIzSDkEvQ65TgAyMSzsEvQz5s8vQ69VQGfJ1RJSMubl5gR0J2RwdHTE2Ns43OiE6OjrfKIa81q1bx9ChQ9mwYQMvvfRSgWmNjIxo2rTpUx2xIGexEEIIIYQQQojyRWFUeksRmZmZ0bhxY/bs2aO1fs+ePbRs2VLvdmvWrGHw4MH8+uuvdOvWrdD9qFQqQkJCcHZ2LnJsxSUjFoQQQgghhBBCiFIwZswY3nzzTZo0aUKLFi1YuHAh4eHhvPvuu4D6torbt2+zcuVKQN2pMGjQIH788UeaN2+uGe1gYWGBjY0NAAEBATRv3hwPDw+Sk5OZPXs2ISEhzJs376kdh3QsCCGEEEIIIYQoV1QUPkeBIRgwYABxcXFMnjyZyMhIfH192blzJ25ubgBERkYSHh6uSb9gwQLS09P54IMP+OCDDzTr33rrLZYvXw5AYmIiw4cP5+7du9jY2NCoUSMOHTpEs2bNntpxKFQqldwMJUQRnLtatJlZhTZDvqgb8v3Hhny/pSHX6aq91qUdQoFkjoWS6ftcTGmHoJcht1VDvsYZOpljQTxLhnwdqV+n4Pv8DVnSP3tLbd82/gXPeVAeGe5ZLIQQQgghhBBCCIMnt0IIIYQQQgghhChXVMWYRFE8PiltIYQQQgghhBBClJiMWBBCCCGEEEIIUb7IiIVnSkpbCCGEEEIIIYQQJSYdC0IIIYQQQgghhCgxuRVCCCGEEEIIIUS5olIY7uOxyyMZsSCEEEIIIYQQQogSKxcdCy+88AKjR4/WvHZ3d2fWrFmlFo8hKc2yUCgUbN26tVT2LYQQQgghhPjvUimMSm35LyqXt0KcPHmSSpUqlXYY4j9EpVKx/tfl7Pn9N+6n3MPD05t33huNq1vNArf76+hB1q5awt3IOzg5V+O1Qe/wXMs2Wml+376FoM1rSYiPx8XVnbeHj8Tb168cxbaMvVmx1fH0Zth7H+NSSGzHjx7Qim3goGFasV04F0LQprVcu3qJhPg4PvtyKs1aPF/kuAB2bd/K1s3rSIiPw8XVnaHDR+Lt20Bv+nP/hrBs0Xwiwm9gb+9Ir36v0rlrD8374Tevs2b1MsKuXiYmOoohwz6ge69+xYopmyHXaU58hlmvAB2amPBcPRMszCE8OpOth9OISlDpTd/Y05gBL5rlW//FooekZ6j/39zbmBY+JthZqYddRsWr2Hs6jUsRmYXGY9+6CbXGDsXG35cK1apwqu/7RG3bV/A2zzfF+/vPsfT2QHknmrDAxYQvXKuVxql3R+pO+oiKtV15EBbOpa9mEhW0t9B49Glb3wj/OgoqmMHtONh1MoOYpKJt6+OmoG9rYy5GZLL+kHaZWFlA+0ZG1KmmwNQY4pLhtxMZRMYXLe9d27dmnbNxuLjWZEghbfW8pq1e17TVTl17at4Pv3mdtauXEXb1EjHRUbw97AO693qlaMHkYchtwZCvcYYcGxh+vT7J9gDqz441q5ZqfXY0b1n8a68ht1VDLjdDPt+EyK1cdqdUrlyZihUrlnYY4j9k68Y1/LZlPe+8O5pvZi7A1s6eyV+O5eGDB3q3uRR6jh9mBNC2XUcC5y5R/ztjEpcvXtCkOXroT5YtmkvfAW/y/exF1PNtwNSJ44iJjionsf3K9i3rGfruaGbMXJgV25gixdamXScC5y6lTbtO/DBjolZsjx49wr1mbYa+O7rIseR25NCfLF00j34D3iBw9iK8fRswpYBji7obydcTx+Pt24DA2YvoO+B1liyYw19HD2rSKJVKqjpV483Bw7Gzsy9RXNkMuU7V8RlmvQK80NCE5xuYsPVIKrM3Kbn3QMWwl80xNy14u4dKFZNXPNRasjsVAJLuq9h1Io3Zm5TM3qTk6p0M3upsRlW7wu/vNK5UkeSzlzj/0eQiHYOFew2a/raQ+COnOdK0F1e/+RmfmRNw6t1Rk8a2eUMa/TqT278EcbhxT27/EoT/mlnYNtP/RbkgLb0VNK+nYNepTBb/nkHKQxVvtDPGrAg/T9hUgg7+RtyMzt95U8EM3u5oTGYm/Lo/g/nbM9jzTyaPUosW1xHNOfsGgbMXU8+3Pl9P/KyQtvo59XzrEzh7MX30tlVn3hw8HNvHbquG2RYM+RpnyLFlM+R6fdLt4VLoeQKzPjt+mLtY52dHacX2pNqqIZcbGO75ViYoFKW3/AcVu2Nh48aN1K9fHwsLCxwcHHjppZe4f/8+gwcPplevXgQEBFClShWsra0ZMWIEqak53w5UKhXffvsttWrVwsLCAj8/PzZu3KiV/4ULF+jatSuWlpZUrVqVN998k9jYWM379+/fZ9CgQVhaWuLs7ExgYGC+GPMO/1coFCxevJjevXtTsWJFPDw82LZtm9Y227Ztw8PDAwsLC1588UVWrFiBQqEgMTGx0DJZvnw5tra2bN++HU9PTypWrEi/fv24f/8+K1aswN3dHTs7O0aNGkVGRs430dWrV9OkSROsrKxwcnLitddeIzo6WvP+gQMHUCgU7Nu3jyZNmlCxYkVatmzJpUuX8sXepEkTKlSogKOjI3369NF6/8GDBwwZMgQrKytcXV1ZuHCh5r0bN26gUChYv349zz//PBYWFjRt2pTLly9z8uRJmjRpgqWlJZ07dyYmJkaz3cmTJ+nQoQOOjo7Y2NjQtm1b/vnnn0LLqjxSqVRsD9pA3wFv0rxVG1zdazFqzHiUSiWHD+r/dXB70Eb8GjWmT/83qOHiRp/+b1DfrzHbgzZo0vy2ZT3tOnblpU4vU8PVnSHDR+HgWJk/dgaVi9h2BG2gz4A3ad6qbVZsX2TFtkfvdjuCNtCgURP69H+D6rli25ErNv8mzRk4aBjNW7UtUix5bduygfYdu9KhUzdcXN0YOnwkDo5V+H3nNp3p/9i5DcfKVRg6fCQurm506NSNdh26sHXzek0aj7peDB76Ls+3bYeJaSF/xRbAkOs0Oz5DrVeA1vVN+POfdM5dzyQqQcW6P9MwNYGGdYwL3TblofaSW+jNTC6GZxKbpCI2ScUff6eTmgauVQv/mI354xCXJ87i7lb95ZOb2/BXeRQeyYWx00i5eI2IpRuJWL6ZWmOGaNLUHPUWsXuPEfbtQu5fukbYtwuJ/fM47qPeKtI+8nrOy4jD5zK5GKEiJgmC/srE1AR83Qv+8qRQQO+Wxhw4m0nCvfwdC628jUh+ANuOZ3InDpLuw/UoFQkpRYvrN01bfZkarm4MHT4KB8cqes/ZnLY6ihqubnTo9DLtOnQhaPM6TRqPul68NfQ9Wrdtj+ljtlVDbQuGfI0z5NjAsOv1abSH34I24teoCX37v04NFzf69n+d+n7+bA/aqDPPZxnbk2qrhlxuhny+CZFXsToWIiMjGThwIEOGDCE0NJQDBw7Qp08fVCr1l4V9+/YRGhrK/v37WbNmDVu2bCEgIECz/ZdffsmyZcv46aefOH/+PB9//DFvvPEGBw8e1OTftm1bGjZsyKlTp/j999+Jioqif//+mjw+/fRT9u/fz5YtW9i9ezcHDhzg9OnThcYeEBBA//79OXv2LF27duX1118nPl49zvLGjRv069ePXr16ERISwogRI5gwYUJxioYHDx4we/Zs1q5dy++//64pm507d7Jz505WrVrFwoULtTpSUlNTmTJlCmfOnGHr1q1cv36dwYMH58t7woQJBAYGcurUKUxMTBgyJOfL444dO+jTpw/dunUjODhY0wmRW2BgIE2aNCE4OJj333+f9957j4sXL2qlmThxIl9++SX//PMPJiYmDBw4kM8++4wff/yRw4cPExYWxldffaVJf+/ePd566y0OHz7M8ePH8fDwoGvXrty7d69Y5VYeRN2NJDEhHj//nHI3NTXDx9ePS6Hn9G53+eJ5/Bo11VrX0L8pl0LPA5CWlkbY1cs0zJPGz79pgfmWldiiNbHl5GFqaoZ3CWLz829W5P0WJufYtNtRQ/8mXNSzj0sXL9DQXzt9I/+mhF25RHp6+hOJK5sh1ykYbr0C2FspsK6k4HJETgdvRiZcu5OJm1PBH4dmpjD+dXO+eKMCb3cxo5qD/j+oFQrwq22MmSncjCr8Vojism3ekJi9R7XWxew+jE1jXxQm6iEEds0bErv3iFaa2D2HsWvRqPj7swQrCwXXInM6BjIy4WaUCpfKBXcstPE14oFSRUiY7ltN6tZQcCdORb/WRozta8ywLsY0ql20X3rU5+wlnef1xazzOq/LF8/T0D9v+mZPpa0aalsw5GucIceWzbDr9cm3h8sXz+f7XGjk30xvns8ytifBkMsNDPd8E0KXYs2xEBkZSXp6On369MHNzQ2A+vXra943MzNj6dKlVKxYER8fHyZPnsynn37KlClTePjwIT/88AN//vknLVq0AKBWrVocOXKEBQsW0LZtW3766Sf8/f2ZNm2aJs+lS5fi4uLC5cuXqVatGkuWLGHlypV06NABgBUrVlCjRo1CYx88eDADBw4EYNq0acyZM4e///6bzp078/PPP+Pp6cl3330HgKenJ+fOnWPq1KlFLpu0tDR++uknateuDUC/fv1YtWoVUVFRWFpa4u3tzYsvvsj+/fsZMGAAgFYHQa1atZg9ezbNmjUjJSUFS0tLzXtTp06lbVt1b+Lnn39Ot27dePToERUqVGDq1Km8+uqrWh04fn7a90N37dqV999/H4Bx48Yxc+ZMDhw4gJeXlybNJ598QqdOnQD46KOPGDhwIPv27aNVq1YADB06lOXLl2vSt2vXTmsfCxYswM7OjoMHD/Lyyy8XWl5KpRKlUqm1ztzcHHNz80K3NTSJCeoOKltb7WF4NrZ2xMToH0KemBCPrZ2d1jpbOztNfveSk8jMzMAmT762tjlpynJsCQlxOmOztbUnJuZuiWN7XOpjy8TWNs8+bO1ITEjQuU1CQjyNdKTPyMggOTkJe3uHJxIbGHadguHWK4BVRfUfrCkPtf/ITXmowtZK/x+zMQmZrN+fxt34TMxNFbSub8L7vcyZtVFJbFJOXk72Cj7obY6JMaSmwco/UokuYO6GkjKv6ogyKlZrXWp0HEamppg52qG8G4O5kyPKqDitNMqoOMydKhd7f5YV1P+mPNJen/IIbAuYzsilMjSqo2DBzgy9aewsoUldBcdDVRw5n0E1BwWdmxiRkZnJ2esFl52+tmpTwDmbkBBPw2fUVg21LRjyNc6QY8vZX9mq18dtD4kJ8djkidummHEbcls15HJT78swz7ey4r86iWJpKVbHgp+fH+3bt6d+/fp06tSJjh070q9fP+yyTlw/Pz+tuQ1atGhBSkoKERERREdH8+jRI02HQLbU1FQaNVL/gnL69Gn279+v9Ud1trCwMB4+fEhqaqqmYwLA3t4eT0/PQmNv0CDnvtJKlSphZWWlue3g0qVLNG2q3avXrFmzQvPMrWLFippOBYCqVavi7u6udSxVq1bVutUhODiYSZMmERISQnx8PJmZ6l+2wsPD8fb21hm7s7MzANHR0bi6uhISEsKwYcMKjC339gqFAicnJ6048qapWrUqoN1plDf26OhovvrqK/7880+ioqLIyMjgwYMHhIeHFxhLtunTp2t1hoB61MSkSZOKtH1pOrR/Dwvm5tyC88WkGYC6bLWpUFDYL2/a76tU+fPJm61KpWNlmYhtNwtzxTZ+0je68yhCbHnfV6lUOo7xMSl0HX9ByfOkR/1HUeHlXDBDrlN1fIZbr408jOnTJmd47LKdqVmx5NuxjpU5wqNVhEdn/3Gs4ubdVD7qZ05LXxO2HU3TpItJVDFrgxILc/CtaUz/F834eZvyqXQuoMqTZ3Y55V6vK03edTr4uit4uVnOl7E1B7KOXVd2evIwM4FeLY3ZfiKTh0o9iVAX/Z14+POM+vPvboKKyjYqmngYcfa6/g4J7Tjyn7Qla6uPx5Dbgu6dGMY1Ts/OtPdVirGVtXp9Gu1Bd9yGEduTYijlVtbONyFyK1bHgrGxMXv27OHYsWPs3r2bOXPmMGHCBE6cOFHgdgqFQvNH844dO6hevbrW+9m/UmdmZtK9e3e++eabfHk4Oztz5cqV4oSrJe+9V7lj0tXQVEX48lVY/gXt8/79+3Ts2JGOHTuyevVqKleuTHh4OJ06ddKalyJv3tlxZudjYWFRotiyty9oH3nX5d5m8ODBxMTEMGvWLNzc3DA3N6dFixb5Ytdn/PjxjBkzRmtdWRmt0PS5Vnh41tO8TktT/2GRkBCHXa4e9KTExHy9xbnZ2tnn6zlOSkzAJqsX3MraBiMj4/xpkhLy9ayXjdha4+GZ02GWroktHjt7R+39FBJbQr7YEjWxPS71sRnpPDZ9+7DTE5OxsTFW1taPFY8h16k6PsOt1ws3MgjPdSuCSdY0ClYWCu49yLnGW1ZQcO9h0a/5KiAiJhNHG+3PjYxMiEtW53MrJh2XKka0rm/C5kNpOnIpOWVUbL6RB2aV7clMSyM1LlGd5m4s5k6OWmnMq9jnG+mgy+VbKhbE5vxRn11ulhbaoxYqmcP9PKMYstlZgZ2lglfb5nRQZH/MfjnQmHm/ZZCQAvceQUySdtnHJquo51r4l9/stprvvElKzDfyRhOXznaQ3VZtCt1nQQy5LeRmaNc4Q4+trNXrk24Puj47khP15/ksY3sSDK3cysr5Vlaonko3lNCn2ONDFAoFrVq1IiAggODgYMzMzNiyZQsAZ86c4eHDnBmtjh8/jqWlJTVq1MDb2xtzc3PCw8OpU6eO1uLi4gKAv78/58+fx93dPV+aSpUqUadOHUxNTTl+/LhmHwkJCVy+fPmxCsHLy4uTJ09qrTt16tRj5VmYixcvEhsby4wZM3j++efx8vLKN4qgKBo0aMC+fQU/luxpOHz4MB9++CFdu3bFx8cHc3NzrUk2C2Nubo61tbXWUlY6FiwqVsS5Wg3N4uLqjq2dPWeDc86ZtLQ0zp87g2c9X7351PXy4UyI9nl2JvgknvV8AHXHTu06dTkTrJ3mbPApvfmWpdhq6IntQhFiOxui3V7Vsenfpjj0HduZ4NN46dmHp5c3Z4K153oJCT5FbQ9PTEwe76m+hlynuuIzpHpVpqn/0M9eohJUJN9X4eGSM1GjsRHUqmbEzbvFmwuhmoORVueEPiaFzwlZbInHQ3Bs31JrXeUOrUk6fQ5V1v29CcdDcGzfSiuN40utSfgruND8U9MhISVniUmCew9V1HLO+YJmZARuVRVExOgug9gk+Gl7Ogt2ZmiWS7dU3IhSsWBnBklZk5lHxKhwtNb+4udgpSDpfqFhZp2znjra6im8ss7rvOp6+ehIf/KptFVDagu5Gdo1ztBjK1v1+uTbg67PjpDgk3rzfJaxPQmGVm5l5XwTQpdidSycOHGCadOmcerUKcLDw9m8eTMxMTHUq6f+NS01NZWhQ4dy4cIFdu3axcSJExk5ciRGRkZYWVnxySef8PHHH7NixQrCwsIIDg5m3rx5rFixAoAPPviA+Ph4Bg4cyN9//821a9fYvXs3Q4YMISMjA0tLS4YOHcqnn37Kvn37OHfuHIMHD8bI6PHunxkxYgQXL15k3LhxXL58mfXr12vmE3haQ4ZcXV0xMzNjzpw5XLt2jW3btjFlypRi5zNx4kTWrFnDxIkTCQ0N5d9//+Xbb799ChFrq1OnDqtWrSI0NJQTJ07w+uuvF2n0RHmkUCh4uecrbFr/CyeOHSL8xjXmzpyOubk5z7d9SZNuduBUVi/PeSJHtx79OPPPKbZs+JVbETfZsuFXzoac5uWeOc9g7t67P/t272Df7h3cCr/BsoVziY2JpmOu53OX5di69XyFzetXa2Kbp4kt55ap2YFT+WX5As3rrprYfuF2xE22bPiFf0NO0S1XbA8fPuB62BWuh6lHOUXdjeR62JUiPzaxR+9X2Lt7J3t37yQi/CZLF84jNiaKTl27A7Bq+SJ+DMyZC6ZT1x7EREexdNE8IsJvsnf3Tvbt3kmvPjkTz6alpXE97CrXw66Snp5OXFws18OuEnnndpFiyl1uhlqn2fEZar0CHPk3nXaNTPBxN6KqnYL+L5qSlg4hV3N+oR/woimdm+V8cX2psQl1axhhb6XA2UHBKy+YUs1BwfELOROIdW5mgruTEXZWCpzsFXRqZkLtakYEXyl8OL9xpYpY+3lh7aee96ZizRpY+3lRwUV965vn12PwW5Yzku/mwrVYuFWj3nefY+lVixqD++Lydl+u/bBUk+bG3JU4dmhFrU+GUcmzFrU+GYZj+xbcmLOiyGWV24mLmbT2McKzhoLKNtCzhRFp6XDuRk7HQs8WRrRrqP48zshUd0jkXh6lqjt7YpIgewDcidBMqjtCax8Fdpbq2zD8PRScvFy0jp7uvV/JOmd3civ8JksXziU2Jkpzzq5evlBnW122aB63wm+yL6ut9uwzQJNG3VbV51l6ejrxcbFcD7tC5J1bxSozQ24LhnyNM+TYwLDr9Wm0h5d79CXkn5Nszvrs2Kz57OhXrHIz5LZqyOVmyOebEHkVq8vP2tqaQ4cOMWvWLJKTk3FzcyMwMJAuXbqwbt062rdvj4eHB23atEGpVPLqq69q3TM/ZcoUqlSpwvTp07l27Rq2trb4+/vzxRdfAFCtWjWOHj3KuHHj6NSpE0qlEjc3Nzp37qzpPPjuu+9ISUmhR48eWFlZMXbsWJKSkh6rEGrWrMnGjRsZO3YsP/74Iy1atGDChAm89957T+1X9MqVK7N8+XK++OILZs+ejb+/P99//z09ehT9CzzACy+8wIYNG5gyZQozZszA2tqaNm3aPJWYc1u6dCnDhw+nUaNGuLq6Mm3aND755JOnvl9D1avfQFJTlSycP5P7KSl4eNbjqynfY5FrzpHYmGgUuSaR8fL2Zcy4r/h11RLWrl5CVadqjBk3ibpeOUPgWrVpx73kJDasWUlCfByubjX5IuAbqlRxKiexvUZqqpJF83/QxPa/KYF5YovCKFcHn5d3fT4eN5E1qxazLiu2j/PEFnblEpPGf6R5vWLxXABeaN+ZkWO+KDSu1m3acS85mfVrVpIQH4+rmztfBszQHFtCfBwxMTkjjKo6OfNlwHSWLZrPru1B2Ds4MHTEKFrkeoRTQnwcYz7MmQ8laPM6gjavw6e+H1/PmFXkMgPDrlN1fIZZrwAHQtIxNYHez5thYQ4R0Zks2q5EmetuBVsrhdbcARbmCvq2NcWqooJHqXA7NpOftqUSEZ3rdgoLBa+2N8U6K01kXCZLdqZy5VbhfyDbNPalxb5Vmtfe36uPJWLlZs4OHY+5c2UssjoZAB7euMXJ7sPxDhyP23uvo7wTzfmPp3J3y25NmoS/ggl+fQyeAaPxDPiQB2ERBL/2MYl/ny1SOeV17IIKU2MVXZsZYWEGt2Nh9Z8ZpOaanN2mkqLYtxHeiYf1hzJp19CINvXVIyT+OJWp1WFRkJy2uiKrrdZkQq5zNiE+jthck5qq2+oMli6ax67tW/W01VjG6mmrU2b8WKzjM9S2YMjXOEOOLZvh1+uTaw/Znx1rVi1h7eqlVHWqxthxE7XiLgpDbquGXG5guOdbWSCTNz5bClVxvwXoMXjwYBITE9m6deuTyK7UTZ06lZ9//pmIiIjSDkUYiHNX9c++K/Qz5PvbjCna5HClIbP4d6o9M4Zcp6v2Prn7vZ+GtqMalnYIep1eXrzHoD1LfZ+LKe0Q9DLktmrI1zhDl8FTuI/pCTHiyT/GVpQuQ76O1K9TtbRDKLGY8wXPA/g0VfZ5rtT2XVqe3E1KZdz8+fNp2rQpDg4OHD16lO+++46RI0eWdlhCCCGEEEIIIYpLnoLxTBlu99gzduXKFXr27Im3tzdTpkxh7Nixmts4unTpgqWlpc5l2rRpBWcshBBCCCGEEEKUY09sxEL2ZIdl1cyZM5k5c6bO9xYvXqz1tIvc7O2L/rgdIYQQQgghhBBPn0p+Q3+m5FaIIqhevXpphyCEEEIIIYQQQhgk6cYRQgghhBBCCCFEicmIBSGEEEIIIYQQ5YpKJm98pmTEghBCCCGEEEIIIUpMRiwIIYQQQgghhChXVAr5Df1ZktIWQgghhBBCCCFEiUnHghBCCCGEEEIIIUpMboUQQgghhBBCCFGuqJDJG58lGbEghBBCCCGEEEKIEpMRC0IIIYQQQgghyhWZvPHZktIWQgghhBBCCCFEicmIBSGKKE1luM3FVJFe2iHoZWLAsWWojEs7BL0UqEo7BL0MOTYTU8PuLz+9/Hxph6BX48E+pR2CXqrQg6Udgl7GZJR2CHplYLjXOENnyPWaKb8LljtGZJZ2COWSSiFzLDxLcmUSQgghhBBCCCFEiUnHghBCCCGEEEIIIUrMcMd2CyGEEEIIIYQQJSCPm3y2ZMSCEEIIIYQQQgghSkxGLAghhBBCCCGEKFfkcZPPlpS2EEIIIYQQQgghSkw6FoQQQgghhBBCCFFiciuEEEIIIYQQQohyRSZvfLZkxIIQQgghhBBCCCFKTEYsCCGEEEIIIYQoV2TyxmdLSlsIIYQQQgghhBAlJiMWhBBCCCGEEEKUKzLHwrMlIxYe0wsvvMDo0aM1r93d3Zk1a1apxVNceeMXQgghhBBCCCGKQ0YsPGEnT56kUqVKpR1Gibm7uzN69GjpbCgmlUrFxl+X8ucfQaSk3KNOXR+GvDcGF7daBW534uh+1q9eTFTkbao6V2fAm8Np1rKt5v3dO7ewd+cWYqIiAajhWpM+A9+mUZMWxYpt/a/L2fP7b9xPuYeHpzfvvDcaV7eaBW7319GDrF21hLuRd3ByrsZrg97huZZtNO+fP3eGoE1ruHb1MgnxcXz25dc81+L5IscFsHN7EFs3rSMhPg4XN3eGDv8AH98GetOf+/cMSxfNJ+LmDewdHOnddwCdu/XQvL/79+3s37eH8JvXAahdpy5vvDWUup71ihUXPL1yA/h9+xaCNq8lIT4eF1d33h4+Em9fvyLHtmv71qzt43BxrcmQ4SPxLqDczv8bwrJF84kIv469vSO9+r1Kp64988W9ZtVSrbibtyxefRp6bNnaNzKmqacxFuYQEaNi27F0ohNVetP7exjRr41pvvVfLVeSnqH+v7uTgufrG1PdwQjrSgpW7U0j9GZmsWNrW98I/zoKKpjB7TjYdTKDmKSibevjpqBva2MuRmSy/pD2vq0soH0jI+pUU2BqDHHJ8NuJDCLjC87TvnUTao0dio2/LxWqVeFU3/eJ2rav4G2eb4r3959j6e2B8k40YYGLCV+4ViuNU++O1J30ERVru/IgLJxLX80kKmhv0Q40D0Nvq1s3Z13jXN0ZWkh7OKdpDzc07aFz15xrXPjN66xZvYywq5eJiY5iyLAP6N6rX5HjyUtddsvYm1V2dTy9Gfbex7gUUnbHjx7QKruBg4Zpld2FcyEEbVrLtauXsj4fptKsmJ8PhhybIderIbeH/2JsT+K70pP+XA2/eZ21q5cRdvUSMdFRvD3sA7r3eqVYMQmhi4xYeMIqV65MxYoVSzsM8Yxt2/QLO7eu5e13xzDthyXY2tkz7X+jefjgvt5tLoee48dvJvL8i534Zs4Knn+xEz9+8z+uXDqvSePgUJmBb73L1FlLmDprCT5+jfn+68+JuHmtyLFt3biG37as5513R/PNzAXY2tkz+cuxPHzwQO82l0LP8cOMANq260jg3CXqf2dM4vLFC5o0ykcPca9Zh3feHV3kWHI7cnA/SxfO45UBr/PDnIV4+9RnylefExMdpTN91N1Ipnw1Hm+f+vwwZyH9+r/G4gVzOXbkkCbNubNneL5tO6ZM/4FvAudSuXIVJn35GXGxMcWO72mV29FDf7Js0Vz6DniT72cvop5vA6ZOHKf3uPM6otn+DQJnL6aeb32+nvhZgeX29cTPqedbn8DZi+kz4HWWLJjDX0cP5or7PIFZcf8wd7HOuMt6bNnaNDCmla8xv/2VzvxtaaQ8VDGksylm+fsNtDxKVTHtV6XWkt2pAGBmouBuvIrf/kovUVwALb0VNK+nYNepTBb/nkHKQxVvtDPGrAg/AdhUgg7+RtyMzt9BUsEM3u5oTGYm/Lo/g/nbM9jzTyaPUgvP17hSRZLPXuL8R5OLdAwW7jVo+ttC4o+c5kjTXlz95md8Zk7AqXdHTRrb5g1p9OtMbv8SxOHGPbn9SxD+a2Zh20z/F+WCGHJbXbpoHv0GvEHg7EV4+zZgSgHbq9vDeLx9GxA4exF9dbQHpVJJVadqvDl4OHZ29kUsIf22bvyV7VvWM/Td0cyYuTCr7MYUqezatOtE4NyltGnXiR9mTNQqu0ePHuFeszZDS/j5YMixGXq9Gmp7+K/G9tjflZ7C56r6fHPmzcHDsX0C1xFDplIYldryX1Smjnrjxo3Ur18fCwsLHBwceOmll7h//z6DBw+mV69eBAQEUKVKFaytrRkxYgSpqTnfmlQqFd9++y21atXCwsICPz8/Nm7cqJX/hQsX6Nq1K5aWllStWpU333yT2NhYzfv3799n0KBBWFpa4uzsTGBgYL4Y894KoVAoWLx4Mb1796ZixYp4eHiwbds2rW22bduGh4cHFhYWvPjii6xYsQKFQkFiYmKhZXLz5k26d++OnZ0dlSpVwsfHh507dxb5mHJ74YUXuHnzJh9//DEKhQKFIue+pGPHjtGmTRssLCxwcXHhww8/5P79nD+a3d3dmTZtGkOGDMHKygpXV1cWLlxYaPzlgUqlYlfQenoNeItmLV/Axb0W74/5EqVSydGDe/Rut3PbOuo3akqv/oOo7uJGr/6D8PVrwq6g9Zo0jZ9rTaOmLalW3ZVq1V15ddAIKlSw0Op8KCy27UEb6DvgTZq3aoOrey1GjRmPUqnk8EH9vw5uD9qIX6PG9On/BjVc3OjT/w3q+zVme9AGTRr/Js3Vvxy3aqM3n4IEbdnASx270KFzN1xc3XhnxEgcK1fh9x3bdKb/fedvVK5ShXdGjMTF1Y0OnbvRvkMXgjbnlNeYzybQ9eWe1Kpdhxourrz/4VhUmSrOngkuVmxPs9x+27Kedh278lKnl6nh6s6Q4aNwcKzMHzuDihTbb1s20L5jVzp0epkarm4MHT4KB8cqerf/Y+c2HCtXYejwUdRwdaNDp5dp16ELQZvX5eQZtBG/Rk3o2/91ari40bf/69T382d70EadeZbF2LK19DHmwJkMzt/MJCpBxYaD6ZiaQMNaBX8cqlSQ8lB7ye3yrUz2nFbnW1LPeRlx+FwmFyNUxCRB0F+ZmJqAr3vB94gqFNC7pTEHzmaScC9/x0IrbyOSH8C245nciYOk+3A9SkVCSuExxfxxiMsTZ3F3q/5rWW5uw1/lUXgkF8ZOI+XiNSKWbiRi+WZqjRmiSVNz1FvE7j1G2LcLuX/pGmHfLiT2z+O4j3qrSPvIzZDb6jZNe1Bf44YOH4mDYxV+36n7GpfTHrKucZ260a5DF7bmusZ51PVi8NB3eb5tO0xMC+kNK4RKpWJH0Ab6DHiT5q3aZpXdF1llp7++dwRtoEGjJvTp/wbVc5XdjjyfDwMHDaN5q7Z68ymrsRlyvRpye/ivxva435WexueqR10v3hr6Hq3btsf0Ma8jQuRWZjoWIiMjGThwIEOGDCE0NJQDBw7Qp08fVCr1l6h9+/YRGhrK/v37WbNmDVu2bCEgIECz/ZdffsmyZcv46aefOH/+PB9//DFvvPEGBw8e1OTftm1bGjZsyKlTp/j999+Jioqif//+mjw+/fRT9u/fz5YtW9i9ezcHDhzg9OnThcYeEBBA//79OXv2LF27duX1118nPl49/vTGjRv069ePXr16ERISwogRI5gwYUKRy+WDDz5AqVRy6NAh/v33X7755hssLS2LfEy5bd68mRo1ajB58mQiIyOJjFQPv//333/p1KkTffr04ezZs6xbt44jR44wcuRIre0DAwNp0qQJwcHBvP/++7z33ntcvHixyMdSVkVH3SExIY4GjZpp1pmamlHPtyGXQ//Vu92Vi+dp0Kip1roG/s30bpOZkcGxg3tRPnpEXS/fIsUWdTeSxIR4/PybaMXm4+vHpdBzere7fPE8fnlia+jflEuhRevQKExaWhphVy/TMFdcAA0bNeGisapLpgABAABJREFUnn1cCj1Pw0ba6Rs1bsLVK5dIT9f9K3GqUklGRjqWllbFiu9plZvmuPOk8fNvWmC+2dTbX9K5D33ldvnieRr6503fjLBc5Xb54vl8MTXyb6Y3z7IWWzY7K7CuqODK7Zw//jMy4frdTFyrFvxxaGYKnw4wY9yrZgzqYIKzw5OdEMrWEqwsFFyLzOkYyMiEm1EqXCoXvK82vkY8UKoICdN9O0fdGgruxKno19qIsX2NGdbFmEa1n86EVrbNGxKz96jWupjdh7Fp7IvCRD30wq55Q2L3HtFKE7vnMHYtGhV7f4bdVi/nu2Y19G/CRT3bX7p4Id81sZF/U6328CRFa8ou5xhNTc3wLkHZ+fk3K1K5lPXYDL1eDbU9/Fdje1xP63P1v0SFotSW/6IyM8dCZGQk6enp9OnTBzc3NwDq16+ved/MzIylS5dSsWJFfHx8mDx5Mp9++ilTpkzh4cOH/PDDD/z555+0aKG+N71WrVocOXKEBQsW0LZtW3766Sf8/f2ZNm2aJs+lS5fi4uLC5cuXqVatGkuWLGHlypV06NABgBUrVlCjRo1CYx88eDADBw4EYNq0acyZM4e///6bzp078/PPP+Pp6cl3330HgKenJ+fOnWPq1KlFKpfw8HD69u2rKYtatXLu6S/smOrWrauVl729PcbGxlhZWeHk5KRZ/9133/Haa69p5l3w8PBg9uzZmnKrUKECAF27duX9998HYNy4ccycOZMDBw7g5eVVpGMpqxIT1J1ENrZ2WuttbO2Jjb5bwHZx2NhqD0GzsbXX5Jct/EYY//tkBGmpqVSwsGDshGnUcC34nr+8sdnm248dMTH6hwgmJsRja6d9PLZ2dvliK6l7yUlkZmZim7fM7OxI0LOPxIQEbPLGZGtHRkYGyclJ2Ns75Ntm5bJF2Ds44teocbHie1rlpj7ujHz1bmtbtLLVW24FbJ+QEE9D24LLLTEhPl/Z2hSzvg05tmxWFuoP+pSH2n+ApzwEW0v9XwJiElVsOpTO3QQVFUzVox5GvGzKnC1pxCXrn5uhOCwrZMXySHt9yiOwLWDaHpfK0KiOggU7M/SmsbOEJnUVHA9VceR8BtUcFHRuYkRGZiZnrz+Z+LOZV3VEGaU9Ki41Og4jU1PMHO1Q3o3B3MkRZVScVhplVBzmTpWLvb+y1lbV2yfo3CYhIZ5GhbSHJykhIS5rH3mP0Z6YmII+u57u54Mhx2bo9Wqo7eG/Gtvjelqfq0I8LWWmY8HPz4/27dtTv359OnXqRMeOHenXrx92WQ3az89Pa26DFi1akJKSQkREBNHR0Tx69EjTIZAtNTWVRo3Uv5CcPn2a/fv3a37tzy0sLIyHDx+Smpqq6ZgA9R/inp6ehcbeoEHOfaOVKlXCysqK6OhoAC5dukTTpto9i82aNaOoPvzwQ9577z12797NSy+9RN++fTX7K+yY8nYs6HP69GmuXr3KL7/8olmnUqnIzMzk+vXr1KtXL99xKhQKnJycNMeZl1KpRKlUaq0zNzfH3Ny8SDGVpiP7/2DRvO80r8dNVP8/960jAKhU+dflUZRtqlV35ZvZy7l//x5/Hz3A/JlTmThjrs7OhUP797Bgbs4tOl9MmqF7P6hQFNqbqv2+SqUrn8dUzDLLG3PWgCWdx7J5w1oOH/yTr7/5ATMzswLDeNblpuOw868saA86y63o6VWo8kWav2wLzrMsxOZX24herXI+5lbuTsveSZ4YCs4nIkZFREzORjej0vmglyktvI3Yflz/H/QF8XVX8HKznFESaw5k6I1N35/+ZibQq6Ux209k8lCpJxHqsrwTD3+eUY/UuJugorKNiiYeRpy9XrL4C6TSU8C51+tKk3edDmWtreZNq95HQcn1tYfHv/Ye2r+bhbnKbvykb3SFiKoIZae7TZY8RkOOTfdODKNeDbk9SGxPztP4XBXiaSgzHQvGxsbs2bOHY8eOsXv3bubMmcOECRM4ceJEgdspFAoyM9Vfpnbs2EH16tW13s/+QzYzM5Pu3bvzzTff5MvD2dmZK1eulDj2vPcv5Y5J1weeqghfrrK98847dOrUiR07drB7926mT59OYGAgo0aNKvSYiiozM5MRI0bw4Ycf5nvP1dVV8/+CjjOv6dOna92qAjBx4kQmTZpU5LhKS+PnWlPH00fzOi1NPZdHYkI8dvaOmvVJSQn5RjHkZmvnQGKC9q92urYxMTXFqZp6ZExtj3qEXbnIrm0bGDbys3x5Nn2uFR65noCQlqb+QyohIQ67XL3USYmJ+XrZtWPLP3IiKbHg4ykOK2sbjIyMdOwjMV/PfE5M+Xvok5IS1KNsrK211m/dtI6N639h8tTvca9Zu9B4nlW5qY/bWOdx6Dvu3LLLLe+ojqSkxHy/1mSz0xlTYla52eiNOzlRf55lJbbQ8EwionPm2jExVl9rLSsquJdr1EKlCvlHMRREBdyOVeFgbQSU7A/zy7dULIjN2dbEWP2vpYX2qIVK5nA/zyiGbHZWYGep4NW2OR0U2R8nXw40Zt5vGSSkwL1HEJOkfXyxySrquT75r5nKqNh8Iw/MKtuTmZZGalyiOs3dWMydHLXSmFexzzfSQZey1lZ1ba/vOmpnZ5+//Wjag7XObYqj6XOt8fD01rxO15Rdns+uxPyjw3Kz1RPn43w+GHJsuRlavRpye5DYHt/T+lz9L1E96U5FUaAyM8cCqP9QbdWqFQEBAQQHB2NmZsaWLVsAOHPmDA8f5symdfz4cSwtLalRowbe3t6Ym5sTHh5OnTp1tBYXFxcA/P39OX/+PO7u7vnSVKpUiTp16mBqasrx48c1+0hISODy5cuPdUxeXl6cPHlSa92pU6eKlYeLiwvvvvsumzdvZuzYsSxatKhIx6SLmZkZGRnaX5Sz88mbR506dQr9JVif8ePHk5SUpLWMHz++RHk9axYVK+FUrYZmqeFaE1s7B/4NzqnH9LQ0Qs+FULdefb35eHj5aG0DcDb4ZIHbgLrjKbszI39sFXGuVkOzuLi6Y2tnz9ngnHMqLS2N8+fO4FlP/zwNdb18OBOifR6eCT6JZz0fPVsUj6mpKbXr1CUkWHuOkpDg03jp2YdnPZ/86f85RR0PT0xMcvpIt2xcy/o1q5k45Rvq1C18RBE8u3LLPu4zwdppzgafKjDfbOrtPfNtfyb4lN5yq+vloyP9SWrnKjddcYcEn9SbZ1mJLTUN4u/lLNGJKpIfqKhTLeejz9gIajoZER5VvEkXne21OyeKKzUdElJylpgkuPdQRS3nnC9BRkbgVlWhNVoit9gk+Gl7Ogt2ZmiWS7dU3IhSsWBnBklZk5lHxKhwtNb+cuVgpSBJ/0NrSizxeAiO7VtqravcoTVJp8+hyrq/N+F4CI7tW2mlcXypNQl/FT7Jatlqq/m3PxN8Gi8923t6eXMm3zXxlFZ7eBx5y66GnrK7UISyOxui/dmlLruizf1T1mLLzdDq1ZDbg8T2+J7W56oQT0uZ6Vg4ceIE06ZN49SpU4SHh7N582ZiYmI0w/BTU1MZOnQoFy5cYNeuXUycOJGRI0diZGSElZUVn3zyCR9//DErVqwgLCyM4OBg5s2bx4oVKwD1JIjx8fEMHDiQv//+m2vXrrF7926GDBlCRkYGlpaWDB06lE8//ZR9+/Zx7tw5Bg8ejJHR4xXhiBEjuHjxIuPGjePy5cusX7+e5cuXA0UbSjV69Gj++OMPrl+/zj///MOff/6pKZPCjkkXd3d3Dh06xO3btzVPjxg3bhx//fUXH3zwASEhIVy5coVt27YxatSoEh+3ubk51tbWWktZuA1CF4VCQZee/dm6YSV/HztIxI1rzJ81FXNzc1q1zbn9Zl7gFNYs/0nzukuP/pwNPknQxtXcjrhJ0MbVnAs5SZeeOZNrrlnxM6HnQoiOiiT8RhhrVy7gwrlgWr/QkaJQKBS83PMVNq3/hRPHDhF+4xpzZ07H3Nyc59u+pEk3O3Aqq5fnPMWjW49+nPnnFFs2/MqtiJts2fArZ0NO83LPnOccP3z4gOthV7geph7NE303kuthV4r8eKeevV9h7x872bt7FxHhN1mycB6xMVF06todgFXLFjHr++ma9J27dicmOoqlC+cTEX6Tvbt3sXf3Lnr2ySmvzRvW8svKZYwc/SlVqjiREB9PQny8VqdjaZdb99792bd7B/t27+BW+A2WLZxLbEw0HXM907wg3Xu/krX9Tm6F32TpwrnExkRptl+9fCE/BubMq9Kpaw9ioqNYtmget8Jvsm/3Tvbt3knPPgM0aV7u0ZeQf06yOSvuzZq4i/ccdUOOLdux8xm84GeMt5sRVe0U9GtjQlo6hFzL6Vjo18aEjk2MNa/bNTLGo7oCOyt1h0Kf59WTN/4dmnMdNTNRv+dsr75u21uq/29TwPwIeZ24mElrHyM8ayiobAM9WxiRlg7nbuR0LPRsYUS7hurPnYxMdYdE7uVRKijT1P/PHjB2IjST6o7Q2keBnaX6Ngx/DwUnLxfemWJcqSLWfl5Y+6nnyqlYswbWfl5UcFGPevP8egx+y3JGxd1cuBYLt2rU++5zLL1qUWNwX1ze7su1H5Zq0tyYuxLHDq2o9ckwKnnWotYnw3Bs34Ibc1YUvbCyGHJb7dH7Ffbu3sne3TuJCL/J0rzXuOWLdLaHpYvmZV3j1O2hV65rXFpaGtfDrnI97Crp6enExcVyPewqkXdul6jsuvV8hc3rV2vKbp6m7HI+u2YHTuWX5Qs0r7tqyu4XbkfcZMuGX/g35BTdCvh8iCrm54Mhx2bI9WrI7eG/Gtvjfld6Gp+r6vNNHVN6ejrxcbFcD7tC5J1bRYqpLFGpFKW2/BeVma4ra2trDh06xKxZs0hOTsbNzY3AwEC6dOnCunXraN++PR4eHrRp0walUsmrr76qNax+ypQpVKlShenTp3Pt2jVsbW3x9/fniy++AKBatWocPXqUcePG0alTJ5RKJW5ubnTu3FnTefDdd9+RkpJCjx49sLKyYuzYsSQlJT3WcdWsWZONGzcyduxYfvzxR1q0aMGECRN47733ivSHdkZGBh988AG3bt3C2tqazp07M3PmzCIfU16TJ09mxIgR1K5dG6VSiUqlokGDBhw8eJAJEybw/PPPo1KpqF27NgMGDNCZx39Rj76vk6pUsvSnQO6n3KOOpzdfTJ6FRcWcvypiY6JQGOVcaDzr1efDzwJYv3oh61cvoqpTdT4aNxmPXLdZJCUmMO+HKSTGx1GxUiVc3eswPiBQ6wkUhenVbyCpqUoWzp/J/ZQUPDzr8dWU77HINSdJbEw0ilzP3PXy9mXMuK/4ddUS1q5eQlWnaowZN4m6XjlDVcOuXGLi+NGa18sXzwPghfadGTWm8NEnrdu+SPK9ZNb9upKE+Hhc3d35X8B0qlRVTxwanxBPTEzOHB1VnZz53+TpLF04j53bg7B3cOCdESNp2TrnEU67dgSRnp7Gt9Mmae1rwGuDGPjG4CKVV7anVW6t2rTjXnISG9asJCE+Dle3mnwR8A1VqjhRFK3btONecjLr16xQl5tbTSbk2j4hPo7YXJNNVXVy5suAGSxdNI9d27di7+DA0BGjaJHrUWvZca9ZtYS1q5dS1akaY8dN1Iq7rMeW7dDZDEyNoUdLEyzM4FaMimV/pJGalpPG1lKhdbt/BTPo1doUKwv1H+534lQs3JHGrdicRNUdFQzrljOCq1tz9cfr6csZbDpctJm4j11QYWqsomszIyzM4HYsrP4zg9Rcm9tUUhTrdjlQz6+w/lAm7Roa0aa+eoTEH6cytTos9LFp7EuLfas0r72/V39mRqzczNmh4zF3royFS86tdQ9v3OJk9+F4B47H7b3XUd6J5vzHU7m7ZbcmTcJfwQS/PgbPgNF4BnzIg7AIgl/7mMS/zxbruLIZflvNusa5ufNlwAyt9pD3GvdlwHSWLZrPrqxrXN72kBAfx5gPh2leB21eR9DmdfjU9+PrGbNKUHavkZqqZNH8HzRl978pgXnKLgqjXD90eHnX5+NxE1mzajHrssruYx2fD5PGf6R5vWLxXED9+TByzBdlOjZDr1dDbQ//1dge+7vSU/hcTYiPZaye823KjB+LXGZC5KVQFfcbigEaPHgwiYmJbN26tbRDeSKmTp3Kzz//TERERGmHInIJvlL4/b+lxVRhuI8QMlY8hcnhnpAMlXHhiUqJQu+UfaIgvxws+rwQpaFCBcM95xoPfjLDd58Gt9CDpR2CXkYU7zaaZykDwz3fDJ1xCedPeRYyy86AY1FEhvyZ71On6POyGZorYTdLbd8etd1Kbd+lpcyMWCjP5s+fT9OmTXFwcODo0aN89913jBw5srTDEkIIIYQQQgghCiVdngbgypUr9OzZE29vb6ZMmcLYsWM1t3F06dIFS0tLncu0adMKzlgIIYQQQgghhHjKysWtEOXZ7du39U48Z29vj729YQ/7LU/kVoiSkVshSsaQh0UaMrkVouTkVoiSkVshyie5FUI8S4b8mV+Wb4W4HBZeavuuW9u11PZdWuRWCANXvXr10g5BCCGEEEIIIYTQSzoWhBBCCCGEEEKUKyr+m499LC0ylkoIIYQQQgghhBAlJh0LQgghhBBCCCGEKDG5FUIIIYQQQgghRLkit0I8WzJiQQghhBBCCCGEECUmIxaEEEIIIYQQQpQrMmLh2ZIRC0IIIYQQQgghhCgxGbEghBBCCCGEEKJcUalkxMKzJCMWhBBCCCGEEEIIUWLSsSCEEEIIIYQQQpSS+fPnU7NmTSpUqEDjxo05fPhwgekPHjxI48aNqVChArVq1eLnn3/Ol2bTpk14e3tjbm6Ot7c3W7ZseVrhA3IrhBBFZqZIK+0Q9DLkyWkyVMalHYJeJor00g5Br0eZ5qUdgl6mBlxur7VNKO0QCqRAVdoh6KUKPVjaIeh1s17b0g5Br+mdF5Z2CHpNm9GktEPQy848pbRDEE+BEZmlHYJeGRju9xHxdBjy9+Pc1q1bx+jRo5k/fz6tWrViwYIFdOnShQsXLuDq6pov/fXr1+natSvDhg1j9erVHD16lPfff5/KlSvTt29fAP766y8GDBjAlClT6N27N1u2bKF///4cOXKE55577qkch0KlUhnutxwhDMj5q5GlHYJeZeXCaWikY6FkDLljwdDbgkF3LBhw2UnHQslIx0LJGZNR2iHolWnAA46lY6FkDPmzwbeOU2mHUGKl+d3dp45zkdM+99xz+Pv789NPP2nW1atXj169ejF9+vR86ceNG8e2bdsIDQ3VrHv33Xc5c+YMf/31FwADBgwgOTmZXbt2adJ07twZOzs71qxZU5JDKpThXpmEEEIIIYQQQogSUKEotUWpVJKcnKy1KJXKfDGmpqZy+vRpOnbsqLW+Y8eOHDt2TOdx/fXXX/nSd+rUiVOnTpGWllZgGn15PgnSsSCEEEIIIYQQQjwh06dPx8bGRmvRNfogNjaWjIwMqlatqrW+atWq3L17V2fed+/e1Zk+PT2d2NjYAtPoy/NJkDkWhBBCCCGEEEKIJ2T8+PGMGTNGa525uf7bXBUK7dsRVSpVvnWFpc+7vrh5Pi7pWBBCCCGEEEIIUa6U5txB5ubmBXYkZHN0dPw/e/cdFcX1NnD8uyBgp4pYKBaKiIqIxo7RKIm9G2uMRtPUKCnGxMSWRM2r0ViSX+zG3sVCjN3Yo0Swo2LBgtQFrEjZ94+FhYVdmiIrPp9z5ujO3pl55s69M+ydO3cwNjbO1pMgMjIyW4+DdHZ2djrTlyhRAmtr6xzT6FvniyCPQgghhBBCCCGEEC+ZqakpDRo0YM+ePVrz9+zZQ9OmTXUu06RJk2zpd+/ejbe3NyYmJjmm0bfOF0F6LAghhBBCCCGEKFZUKsN921Fmfn5+DBw4EG9vb5o0acKCBQsICwvjo48+AtSPVdy9e5c///wTUL8BYt68efj5+TFs2DCOHz/O4sWLtd728Nlnn9GyZUumT59Oly5d8Pf3Z+/evRw5cqTQ9kMaFoQQQgghhBBCiCLQp08fYmJimDx5MuHh4Xh4eBAQEICjoyMA4eHhhIWFadJXq1aNgIAAxowZw/z586lcuTJz5syhR48emjRNmzZl7dq1jB8/nu+++44aNWqwbt063njjjULbD4UqfaQHIUSOivJduLkx5PfPG7ISiuSiDkGvp6m5P5dXVEwMON8MvS4Y8rvKDTnvbtXyKeoQ9Jr69oKiDkGvn6Z5F3UIelmaPSzqEHJkTEpRh6BXqgE/yWxEalGHoFcKxkUdgl6GfG3wqGlX1CEU2NmrkUW27brOtkW27aIiPRaEEEIIIYQQQhQrqQbcYF4cGW6TpxBCCCGEEEIIIQye9FgQQgghhBBCCFGsGPIjfsWR9FgQQgghhBBCCCFEgUmPhSLQqlUrPD09mT17dlGHIl6Av3ZsxX/zWpSxMdg7VGPI8BG4e9TVm/7CuSCWLvyN22E3sLKyoWvPd/Ft30UrzfGjh1izYgn3w+9hV6ky/QZ9QOOmLQoU364dW9Lii8XewYn3h4/A3aNejvEtWzif22E3sbSypmvPvjrjW7tisVZ8bzRtme/YVCoV61cvY8+u7Tx6+ABnV3c++Hg0Do7Vclwut+1fOB+M/6Y1XL92BWVsDF+N/4E3muQv/wJ2+LNl03qUsTE4ODoxdPgn1M7huJ4/F8yShb8TdusmVtY2dOvRh3c6dNJ8v3vXTg7s282tWzcBqFHThYHvDcXF1S1fcYE63zauXsK+v7fx8OEDnF3cGfKxH/aO1XNc7uTRg6xbuYiI8LtUrFSFdwcOo1HTjEHxdgdsYU/AVqIi1AOVVnWoRo++g6nv3STPsb3o+hB26wZrVy4l9FoIUZERvD/sUzp17ZXneLIqrDIH+a9rWRly3hlqvlk196b650Mx9/KgZGVbTvf4hIht+3JepkVD3Gd8TVl3ZxLvRRI6cxFhC9ZqpbHr1g6XiZ9RuoYDj0PDCPl+FhH+e/MUU1ZD+jrS2bcS5cqW4OKVB/zyv6vcCHuc4zK9Oleh2zuVqVjBjLiEJA4ei+aP5dd5lqQe4G3DojeoVLFktuU277zLL/+7lufYVCoV29f9wT97NvP40QOqOXvQb9jXVHGooXeZu2GhbFv7O7dCLxETFU6f9z/nrU79tdJcuRDI3/5/civ0EvHKaD4ZO5P6b7yZ57jSY1u/eil708pcTVd3hn08BvtcytyJowe1ylzfQcO0ytzF80H4b1rL9WshadeHH2mUz+vDXzu2snXzurS66sTQXOrqeU1dvampq2+376z5PuzWDdasXErotStERUYwZNindOraM18xpTPUugqvQr4ZZnkz5GNq6F6V100WF9JjoRAdPHgQhUJBXFxcUYciCsmRf/azdOE8evQZwMw5i6jlUYcfJnxFVGSEzvQR98P5YcLX1PKow8w5i+jepz+L/5jL8aOHNGlCLl1g5rRJ+LRuxy/zFuHTuh0zp03kyuWL+Y7vqCa+gcyYs5BaHnX5ccLYHOP7ccJYannUZcachfToM4Alf8zJEt95fkmLb+a8xc8V39aNa9i+ZT0ffDSa6bP+wMLSisnjP+fJY/1/dOdl+4lPn+BUrSYffDQ63zEBHD50gMULfqNXn37MmvsH7rXrMPn7cTnm2+Tvv8G9dh1mzf2Dnr37suiPeRw78o8mzbmzwbTwac0PU2fy88y5VKhgy8TxXxETHZXv+LZtWsXOret4/yM/fvplEeaW1vz43Zgc8+3KpfPMnj6BFm/68vPcZbR405fZ07/nasgFTRpr6wr0e+8jfpq9iJ9mL8Kjnhf/98M4bt+6nqe4CqM+JCYmUtGuEgMHD8fC0iqPOaRfYZW5/Na1rAw97ww134zLlCbhbAgXPpucp/SlnKrScPsCYo8EcqRhV65N/x+1Z32LXbd2mjQWjT2pv3oWd1f5c7hBF+6u8sdrzWwsGun/AaRP/x729OlalV/+uMYHfv8Ro3zGrMl1KVVK/+j0bX1s+ei96ixde4v+n5xi2twrtGlegQ/fy2g4HOb3H50HHtNMo8cHA3DgSP7OJ7u2LGfP9lX0GzaWb6evwNzCmlmTPubpk0d6l3mW+BSbilXoPnAU5hY2OtMkJj6lqpML/YaNzVc8mW3duJodW9Yz9KPRTJu1IK3M+eWpzLVs7cvMeUto2dqXX6ZN0CpzT58+xalaDYYW8Ppw5J/9LFk4n559BjBzzkLcPeoyJZfr6g8TxuHuUZeZcxbSQ29drczAwcOxLKZ11fDzzTDLmzo2wzymQmQlDQvFRFJSUlGH8FravmUDbdq1p61vR6o6ODJ0+EisbWz5O8BfZ/q/A7ZhU8GWocNHUtXBkba+HWnd9h38N6/LWKf/RurV96ZH7/5UtXekR+/+1KnnxQ7/jQWIbz2t27XnLd+OVHVwYsjwkVjbVNAb3+4Af2wq2DJk+EiqOjjxlm9HWrdtz7bNGXfzdvhvpF79BnTvPYCq9o507z2AOvUasMN/Q75iU6lU7PDfQI8+A2ncrCUOTtUZ6TeOxMREDh/Sf2cwL9v38m6s7uXRLP+9KAD8t2zkrXbv0O7tDtg7OPLBh59iU8GWv3Zu15l+V8B2Ktja8sGHn2Lv4Ei7tzvQpu3bbN28XpPm86++oX3HLlSvUZOq9g58OsqP1FQVwcFn8hWbSqUiwH8D3foM4o2mPjg4VedTv29JTEzkyKHdepcL2LaeuvW96dZ7IFXsHenWeyAe9RoQ4J8RY4M3mlO/YRMqV3GgchUH3h30ISVLluJqSN4ajQqjPji7uPHe0I9p7tMGExOTPOaSboVZ5vJb17Iy5Lwz5HyL+vsfrkyYzf2te/KU3nH4uzwNC+fi5z/x8PJ1bi/ZyO1lm6nuN0STptrI94jee4zQnxfwKOQ6oT8vIHr/CZxGvpenbWTWq3MV/lwfxj/Ho7kR9pgfZ13GzMyYdj76X0Pm4Vaec5fi2XMokvuRiZw6o2TvP5G41SynSROXkERsXMbUtKE1d+494cz5+DzHplKp2LdjNe17DMWrcRuqONbk/VGTeZb4lJP//KV3uWrOten13hgaNfelhJ5yVcerGd36fYpX4zZ5jidrbDv9N9C9z0AaN/NJK3PfpJU5/cd6p/8G6tb3pnvvAVTJVOZ2Zrk+9B00jMbNCvYK022auqq+PgwdPgJrG1t2BWzTmT6jro7A3sGRtr4daN32Ha3rg7OLG4OHfkQLn9Z68zQvDLmuGnq+GWp5M+RjKkRWr1XDwvbt27GwsCA1Vf2O3aCgIBQKBV9++aUmzYcffkjfvn0BOHbsGC1btqRUqVLY29szatQoHj3KaMVfuXIl3t7elCtXDjs7O/r160dkpPp9qTdv3uTNN9Xd/iwtLVEoFAwePFizbGpqKl999RVWVlbY2dkxceJErVjj4+MZPnw4tra2lC9fntatWxMcHKz5fuLEiXh6erJkyRKqV6+OmZkZKlXO78Bt1aoVo0aNynG7v/zyC3Xq1KFMmTLY29vzySef8PBhxrumb926RadOnbC0tKRMmTLUrl2bgIAAAJRKJf3796dChQqUKlUKZ2dnli5dmmNMr7KkpCRCr4VQr35DrfmeXg25fOmCzmWuXL6Ap1fW9I0IvRpCcnJyRpos66zv1UjvOnOO70q2ddXzakjIpfM6lwm5fIF62eJrmC0+Xfscks/4Iu6HE6eMpZ5XxnvWTUxMqe1RT298L3L7+mjyzUv7/e+e9RvoPQaXL13Es34DrXn1GzTk2tUrmnzLKjExkZSUZMqVLafze30iI+4Rp4yhbv1GmnkmJqa4e3hyJcd8O6+1DEA9rzf0LpOaksLRQ3tJfPoUF7faucZVWPXhRSqsMleQupaZoeedoeZbQVg09iRq71GteVG7D2PewANFCfXToZaNPYnee0QrTfSew1g2qZ+vbVWuWBIbKzP+PaPUzEtKVhF0Pg4Pt/J6lzt7MR7XGuWo5VxOs57G3lYcPx2jM32JEgravVmRnXvv5yu+6Ii7xMdFU9uzsWaeiYkpLrUbEBpyNl/retEiNWUuo2yoz3P5L3P1vBq9sPKUUWazXB+8vLms97p6Mdv1pH6W6+qLYqh11dDzzVDLGxjuMX1VqFAU2fQ6eq0aFlq2bMmDBw84c0Z9h/DQoUPY2Nhw6FBGt6qDBw/i4+PDuXPn8PX1pXv37pw9e5Z169Zx5MgRRowYoUn77NkzpkyZQnBwMFu3buXGjRuaxgN7e3s2bdoEQEhICOHh4fz666+aZZcvX06ZMmU4efIkP//8M5MnT2bPHnWrqEqlokOHDty/f5+AgAACAwPx8vKiTZs2xMbGatZx7do11q9fz6ZNmwgKCspTHuS0XQAjIyPmzJnD+fPnWb58Ofv37+err77SfP/pp5+SmJjIP//8w7lz55g+fTply5YF4LvvvuPixYv89ddfXLp0id9//x0bG93dJIuDBwnxpKamYmFhqTXf3MKSOGWszmWUyljMs6S3sLAkJSWFhAT1naY4ZSzmllnWaal/nTnHl4K5hXb3QIsc4otTxurYHytSUlJ4kCk+iyzxWRQgvvT0FlniM7ewRJnDul7U9vVJ0HNcLSz1x6UzpizHNas/ly7EytqGelkaJHKTvp9Zj2tO5S59uaxlT9cyYTdDGdSzLf27tWbRbzP44tufqOqQ83OcUHj14UUqrDJXkLqWmaHnnaHmW0GYVbQhMSJaa96zyBiMTEwwtVHHamZnQ2KE9o/4xIgYzOwq5GtbVpamAMTGPdOar4x7pvlOl32Ho1i06ga/Tffk4JYWrF/0BmfOxbFy422d6Vs2tqFsmRIE7Mtfw0J8nHofy1tYa80vb2FFfFy0rkVeGqVSHVvWMmdhYZXrea4wrw/66qq6zCp1LqPUcV193eqqoeeboZa39G3oiq2oj6kQurxWgzeam5vj6enJwYMHadCgAQcPHmTMmDFMmjSJBw8e8OjRI65cuUKrVq346aef6NevH6NHjwbA2dmZOXPm4OPjw++//07JkiUZMiSj62T16tWZM2cOjRo14uHDh5QtWxYrK3VltbW1xcLCQiuWunXrMmHCBM26582bx759+2jbti0HDhzg3LlzREZGYmZmBsCMGTPYunUrGzduZPjw4YC6YWPFihVUqJD3P3Zy2i6g2V+AatWqMWXKFD7++GN+++03AMLCwujRowd16tTR7He6sLAw6tevj7e3ulXVyclJbxyJiYkkJiZqzTMzM9Ps76tEocjSKqlSkXVWTulVqHuaZJ6ryNLSqcplnTnHly287DNziA909YTJGp+u5bT9c2APf8ybqfn8zcRpereXdf9fxPbzS1e+5bSNbDGrVLrnA5s3rOXwoQP8OH0mpqb6f2AAHD6wm4Xz/0/z+esJP+cQX46ryl72dOxT5SoO/DxnKY8ePeTk0YPMn/UjE6fNzVPjgq5tvIj6UFAvu8zlt65l24KB5N2rlm/5lrV3X/q2Ms/XlSaXXoFtfWz58lMXzeevJp9LW1eWhAqF7tNqmvoe5gzq7cjM/13lYsgDqlYqyWfDaxId+4zl68Kype/Q1o6TgbHExD7TsbYMJw4FsPKPHzWfR347R3dCle7zVmH658BuFmQqc+MmTgd0lI08lDnd188XvD86z6U5JddXV58vrleurhpMvhlueXvljqmBk8EbX67XqmEB1I8DHDx4ED8/Pw4fPswPP/zApk2bOHLkCHFxcVSsWBE3NzcCAwO5du0aq1at0iyrUqlITU3lxo0b1KpVizNnzjBx4kSCgoKIjY3VPGIRFhaGu7t7jnHUras9CFSlSpU0j1EEBgby8OFDrK217yI8efKE0NBQzWdHR8d8NSrktl2AAwcO8NNPP3Hx4kUSEhJITk7m6dOnPHr0iDJlyjBq1Cg+/vhjdu/ezVtvvUWPHj006/z444/p0aMH//33H+3ataNr1640bdpUZxxTp05l0qRJWvMmTJiQ7dEMQ1auvDlGRkbZWozj4+OytQCns7TM3vodHxeHsbEx5cqbA2ChI01CnP515hyfcfbtxSuz3QVIZ2FplX1/4pS5xhcfp8x29zSrhm80w9m1luZz+rggSmUMllYZZT0+Li5bK3vWGAuy/bwqrzmu2ndR4uPyl29x8enHVbvL85ZN69m4fjWTfvw/nKrpH3k9nfcbzXF2zTifJCWpfzzEKWOxtMroEZQQr8yxjOgsV/HZ862EiQl2lasCUMPZjdCrlwjYtoHhI74iJ4VVH57HyypzBalrmRla3r0q+VYQiRHR2XoemFawIjUpiWcxceo096Mxs9PubWdma5Wtp0NWR/6N4eKV0xnrNVF3CrWyNCVGmfGj39LcJFsvhsw+GFCNvw9EsGO3ugfC9VuPKFnSmK9GuPDn+jCt9o2KFczwrmfJt1NzfxTMs5EP1V08NJ/Tj2tCXAwWVhl5khAfm60XQ2FrmOU8l6wpc9rnufg4ZbYefZnpvobFvbDrQ3pd1VVm9W3DUk9Muq4P+fWq1FXDyzfDLW+vyjEVQpfX6lEIUDcsHD58mODgYIyMjHB3d8fHx4dDhw5pHoMA9RgIH374IUFBQZopODiYq1evUqNGDR49ekS7du0oW7YsK1eu5NSpU2zZsgVQ9yTITdaBtBQKhaZhIjU1lUqVKmltOygoiJCQEK3xIMqUKZPv/c9pu7du3aJ9+/Z4eHiwadMmAgMDmT9/PpBxYvvggw+4fv06AwcO5Ny5c3h7ezN37lwA3nnnHW7dusXo0aO5d+8ebdq04YsvvtAZx7hx44iPj9eaxo0bl+/9KUomJibUqOlK8JnTWvODz5zGrZbuZ9Jd3GrrSH+KGs6ulEh7vtfFrTbBQdppgs6c0rvOnONzyba9s2dO41rLQ+cyrm61OXsm+7Zziy/4zClcc4mvVOnSVKpcVTPZOzhhYWmltb2kpCQunA/WG9/zbD+vMvItUGt+0JlAvcfArZY7QVnT/3eams4umnwD2LxxHevXrGTClGk4u7jmKZ5SpUtjV7mqZqrqUA0LS2vOnjmlSZOclMTF80G45JhvHlrLAJw982+OywCgyvijKyeFVR+ex8sqcwWpa5kZWt69KvlWEHEngrBpo93gXaFtc+IDz6NKe25beSIImzbNtNLYvNUc5fGcB1p98iSFu+FPNdONsMdExybS0DPjj/QSJRR4elhw/nKC3vWUNDNClardpSE1Vf3EbtYbiR3eskMZ/4zjp3SPv6C13lJlsK3koJkq21fH3MKGi8EnNGmSk5K4ciGQGq75fwPG88ha5qrqKXMX81DmzgZpn+fUZe7FlCd9ZTb4TCBueq+r7jquJ6dfq7pq6PlmSOXtVTmmQujy2jUspI+zMHv2bHx8fFAoFPj4+HDw4EGthgUvLy8uXLhAzZo1s02mpqZcvnyZ6Ohopk2bRosWLXBzc9O68w9oujinpKTkK0YvLy/u379PiRIlsm27MMcsOH36NMnJycycOZPGjRvj4uLCvXv3sqWzt7fno48+YvPmzXz++ecsXLhQ812FChUYPHgwK1euZPbs2SxYsEDntszMzChfvrzW9Co+BtGpWy/27d7Jvt0B3Am7xZIF84iOiqBd2nuWVy5bwK8zf9Kk923fmajICJYunM+dsFvs2x3Avt0BdOneR5OmY+ceBP13is0bVnPn9i02b1jN2aBAOnbJ/7uZO3XrnRbfTu6E3WTpgnlER0VqxTdnZkbX2Hbtu6TFN487YTfZt3sn+3cH0Ln7u5o0HTr3JPi/02xJi2+LJr5e+YpNoVDQsUsvNq1fxclj/xB28zrzZk3FzMyMFj5vadLNmfkjK5dllKO8bP/Jk8fcCL3KjdCrgHpgphuhV/P8CqUu3Xqy5+8A9u7+i9tht1i04DeioyJ5u30nAP5cuohZM6Zp0r/dvhNRkZEsXvAbt8NusXf3X+zd/Rddu/fWpNm8YS2r/lzKyNFfYGtrhzI2FmVsLE+ePMl3vrXv0outG1bw77FDhN28zm+zf8TMzIzmPhmvzJs3cwqrl/1P8/mdzr04e+YU/htXcvf2Lfw3ruRc0Gnad8mIcc3yP7h0PpjIiHDCboay9s8/uHD+DM1btSMvCqM+JCUlaY5lcnIysTHR3Ai9Svi9O/nKt/S8K6wyl1tde5XzzpDzzbhMacrXc6N8PTcASlerSvl6bpS0rwSA6w9+1Fs6XZP+1oK1lHKsTK3/+5qybtWpOrgH9u/34PovSzRpbs77E5u2zaj+xTDKuFan+hfDsGnThJtzl+cr3wA2bLvLwF4OtGxsTTWH0nw72pXExBR2H8r4e2H8GFc+HJTxqNHRf2Po2r4ybVpUoFLFknh7WvJB/2oc+TeGtPsAgLqRof1bduzaH0FKKvmmUCho07EfAZuW8N+J/dy9dY2l8yZgalaSN1q+o0m3+Nfv2LxyruZzclISYTdCCLsRQnJyEsrYSMJuhBAZnvGYxtMnjzVpAKIj7xJ2I4SYqPA8x9ahSy82r1+pKXPzNWWurSbdnJk/smrZH5rP7TVlbhV3b99iy4ZVnAs6TYccrg8R+bw+dO7Wi727A9i7O4DbYbdYsmA+0VER+KZdH1YsW6izri5ZOD/t+qCuq5mvD+q6eo0boddITk4mJiaaG6HXCL93N08xZc43Q62rhp5vhlreDPmYvgpk8MaX67V7FCJ9nIWVK1dqBlNs2bIlvXr1IikpiVatWgEwduxYGjduzKeffsqwYcMoU6YMly5dYs+ePcydOxcHBwdMTU2ZO3cuH330EefPn2fKlCla23J0dEShULBjxw7at29PqVKlNAMd5uStt96iSZMmdO3alenTp+Pq6sq9e/cICAiga9eumjEMXrQaNWqQnJzM3Llz6dSpE0ePHuV///ufVprRo0fzzjvv4OLiglKpZP/+/dSqpe6y9f3339OgQQNq165NYmIiO3bs0HxXXDVv2ZoHCQmsX7McZWwsDo7V+HbSdGxt7QBQxsYQHZVx8ahoV4nxk6axZOF8/tqxFStra4Z+OJImmV5D5Obugd/Y71mzYjFrVy6hol1lPh87ARe3nB+v0aVZy9Y8SIhnw5o/UcbG4OBYjW+yxZfxB25Fu0p8O2k6SxfOY1dafEM+HKUzvtUrFrN25WIq2lXGb+zEAsXXtWdfnj1LZMFvs3j08CHOrrX4fsoMSpUurUkTHRWJQpHRBpqX7YdeDWHCuNGaz8sWqXvetGrzNiP9cu8Z08LnTR48SGDd6hXExsbi6OTE95OmYluxojrflNnz7fvJP7F4wW8E7NiGlbU1H3w4gqbNM153+dfObSQnJzH9J+1HgN7tN4i+A/L3GrvOPfrzLDGRxb//wqOHD6jp6s43k2dp5VtMVARGRhn55lqrDp99NZF1KxeybuUiKtpV4bOxk3F2zbgjHh8Xy/xfpqCMjaF0mTI4ONXgm0kzqZtl1Gh9CqM+KGOj+XzUMM1n/83r8N+8jtp16jFl2q/5yjcovDKXW1171fPOUPPNvIEHTfat0Hx2n/ENALf/3MzZoeMwq1SBUmmNDABPbt7hVKfhuM8ch+PH/Um8F8mFMT9yf0vGq1qVx89wpr8frpNG4zppFI9Db3Om3xji/s3/mxJWbbqNmakRfh87U66sCRevJDDm+7M8eZJxw6FihZJk7qCwfN0tVCoYNqAaFaxNiUtI4ui/MSxYcUNr3d6eltjZlmTnnvwN2pjZ293eI+nZU1YvmMajRwlUd/ZgzPe/UbJURo/I2Oj7KDKdS+KUUUz5vK/m827/Fez2X4FL7QZ8OUV9o+FW6EVmfD9ck2b90l8AaPJmJ4aM1D4H6tO1Zz+ePUtk4W+/aMrcd1NmZilzERhl6sbh5l6HMWMnsGbFItallbkxOq4PE8d9pvm8fNE8QH19GOH3Ta5xZdTVP9PqqhPjJ03TqqtRWa4P4ydNZenC3/hrh7+euhqDn566+sO02XnKr3SGWlcNP98Ms7ypYzPMYypEVgpVbu8oLIa++OILZs6cyfnz56ldW/1HtaenJ/fu3SMiIkIzsMmpU6f49ttvOX78OCqViho1atCnTx+++UZ9IlizZg3ffPMN4eHheHl5MW7cODp37syZM2fw9PQEYMqUKfz2229EREQwaNAgli1bRqtWrfD09GT27NmamLp27YqFhQXLli0D4MGDB3z77bds2rSJqKgo7OzsaNmyJVOnTsXe3p6JEyeydevWPL8NAsjTdmfNmsX//d//ERcXR8uWLenfvz+DBg1CqVRiYWHByJEj+euvv7hz5w7ly5fn7bffZtasWVhbW/PDDz+wevVqbt68SalSpWjRogWzZs2iWrW8Dfpm6C5cy9udlqLwuraMPq8Sihf/SsMX5Wmq4fbgMTHgfDP0uqDIadS+ImbIeXerVsHeAf8yTH1bd888Q/DTtMK5EfEiWJo9zD1RETImf71NX6ZUA+5wbEQBus+8JCkYF3UIehnytcGj5qvb0PDv5fgi23Yjt+cfK+pV81o2LAhRENKwUPxIw0LBSMNCwRnyH4+GnHfSsFAw0rBQcNKwUDDSsFAwhnxtkIaFgnkdGxZeu0chhBBCCCGEEEIUb4bbzFU8GW6Tp8iXsLAwypYtq3cKC8v+7mshhBBCCCGEEOJ5SY+FYqJy5co5jrdQuXLllxeMEEIIIYQQQojXhjQsFBPpr6YUQgghhBBCiNedSmW4YwcVR/IohBBCCCGEEEIIIQpMeiwIIYQQQgghhChWDPltR8WR9FgQQgghhBBCCCFEgUnDghBCCCGEEEIIIQpMHoUQQgghhBBCCFGsyOCNL5f0WBBCCCGEEEIIIUSBSY8FIYQQQgghhBDFigze+HJJjwUhhBBCCCGEEEIUmPRYEEIIIYQQQghRrKSqijqC14s0LAiRR9KdqmAUGO5Z/VmqaVGHoFdJo8SiDkGvZJXhXjoMubwBpBpwR0FjUoo6BL2mvr2gqEPQa9yu4UUdgl6nP7hc1CHo5Vs7oahDyJEh11VDloJxUYeglyGf4+RvTFEcyFlTCCGEEEIIIYQQBWa4t52EEEIIIYQQQogCkJ4gL5f0WBBCCCGEEEIIIUSBSY8FIYQQQgghhBDFikolPRZeJumxIIQQQgghhBBCiAKThgUhhBBCCCGEEEIUmDwKIYQQQgghhBCiWFEZ9huoix3psSCEEEIIIYQQQogCkx4LQgghhBBCCCGKlVR53eRLJT0WhBBCCCGEEEIIUWDSY0EIIYQQQgghRLEir5t8uaTHghBCCCGEEEIIIQpMGhaEEEIIIYQQQghRYNKwkItWrVoxevToog6jUA0ePJiuXbsWdRhCCCGEEEII8UKoVEU3vY5kjIU0Bw8e5M0330SpVGJhYVHU4dCqVSs8PT2ZPXt2UYci8kClUrF+9TL27NrOo4cPcHZ154OPR+PgWC3H5Y4fPcTaFYu5H34Pu0qV6TfoA95o2lIrza4dW/DfvBZlbCz2Dk68P3wE7h71ikVsf+3YmrZ8DPYO1RgyfATuHnX1pr9wLoilC3/jdtgNrKxs6NrzXXzbd9F8H3brBmtXLiX0WghRkRG8P+xTOnXtled4MlOpVGxYvZS9f2/j4cMHOLu488HHftjnkm8njh5k7cpFRITfo2KlyvQdOFwr37asX8HJ4/9w984tTE3NcK3lQf/BH1OlqkOeYwvY4c+WTetRxsbg4OjE0OGfUDuHfDt/LpglC38n7NZNrKxt6NajD+906KT5fveunRzYt5tbt24CUKOmCwPfG4qLq1ueY8qssMrchfPB+G9aw/VrV1DGxvDV+B94o0mLPMf1ostbesxrVizRirlx07zHlJk635ayNy3farq6M+zjMXkrc5nyre+gYVr5dvF8EP6b1nL9Wkhavv1Io3zkG6jzbuvmdWl558TQXPLuvCbvbmry7u32nTXfh926wZqVSwm9doWoyAiGDPuUTl175iumzIb0daSzbyXKlS3BxSsP+OV/V7kR9jjHZXp1rkK3dypTsYIZcQlJHDwWzR/Lr/MsSf0X4YZFb1CpYslsy23eeZdf/nct15ismntT/fOhmHt5ULKyLad7fELEtn05L9OiIe4zvqasuzOJ9yIJnbmIsAVrtdLYdWuHy8TPKF3DgcehYYR8P4sI/725xqOLSqUicM88Lp1cT+KTBGwd6tK86/dY2TnnaflrQTvZt/pznGq3wfe9+VrfXTi2muBDi3n8IArLijVp2vkbKlXzztN6DfnaAIZ9XTX82OQcl1+Gfu0SIp30WCgCSUlJL2Q9KpWK5OTkF7Iu8Xy2blzD9i3r+eCj0Uyf9QcWllZMHv85Tx7r/8M25NJ5fpk2CZ/W7Zg5b7H632kTuXL5oibN0X/2s3ThPHr0GciMOQup5VGXHyeMJSoy4pWP7Yhm+QHMnLOIWh51+GHCV3qXj7gfzg8TvqaWRx1mzllE9z79WfzHXI4fPaRJk5iYSEW7SgwcPBwLS6s85pBu/ptWs2PrOoZ+NIZpvyzEwtKKKd+NyTXfZk2fiM+bvsyYuxSfN32ZNf17roZc0KS5cD4I3w7d+GnGH3w3ZRYpKSn88J0fT58+yVNchw8dYPGC3+jVpx+z5v6Be+06TP5+XI75Nvn7b3CvXYdZc/+gZ+++LPpjHseO/KNJc+5sMC18WvPD1Jn8PHMuFSrYMnH8V8RER+Uxt7QVVplLfPoEp2o1+eCj0fmOqTDKW8ilC8xMi/mXeYt0xpwfWzeuZseW9Qz9aDTTZi1Iyze/POVby9a+zJy3hJatffll2gStGJ4+fYpTtRoMLUC+gTrvliycT88+A5g5ZyHuHnWZkkNdV+fdONw96jJzzkJ66K2rlRk4eDiWz1lX+/ewp0/XqvzyxzU+8PuPGOUzZk2uS6lSxnqXaetjy0fvVWfp2lv0/+QU0+ZeoU3zCnz4XnVNmmF+/9F54DHNNHp8MAAHjuStXhiXKU3C2RAufDY5T+lLOVWl4fYFxB4J5EjDrlyb/j9qz/oWu27tNGksGntSf/Us7q7y53CDLtxd5Y/XmtlYNNL/IyMnwQcXcfbwMpp1/Y7uozZQulwFdi4cwrOnD3Nd9oHyLid2/oydjsaCa0EBHNs+lfqtP6LHZ1uwq+ZNwOLhPFDey3W9hn5tAMO9rhp+bHKOK0hshn7tMmQqFEU2vY4MtmFh+/btWFhYkJqaCkBQUBAKhYIvv/xSk+bDDz+kb9++ABw7doyWLVtSqlQp7O3tGTVqFI8ePdKkXblyJd7e3pQrVw47Ozv69etHZGQkADdv3uTNN98EwNLSEoVCweDBgzXLpqam8tVXX2FlZYWdnR0TJ07UijU+Pp7hw4dja2tL+fLlad26NcHBwZrvJ06ciKenJ0uWLKF69eqYmZmhyqGPzODBgzl06BC//vorCoUChULBzZs3OXjwIAqFgr///htvb2/MzMw4fPgwiYmJjBo1CltbW0qWLEnz5s05deqU1jovXLhAhw4dKF++POXKlaNFixaEhobq3H5gYCC2trb8+OOP+dq/FStW4OTkhLm5Oe+++y4PHjzQu4/FiUqlYof/Bnr0GUjjZi1xcKrOSL9xJCYmcviQ/rtIO/w3Uq9+A7r3HkBVe0e69x5AnXoN2OG/QZNm+5b1tG7Xnrd8O1LVwYkhw0dibVOBvwP8X/nYtm/ZQJt27Wnr25GqDo4MHT4Saxtbvcv/HbANmwq2DB0+kqoOjrT17Ujrtu/gv3mdJo2zixvvDf2Y5j5tMDExyVMcuqhUKnb6r6d7n0G80dQHB6fqjPD7lsTERI4c2qN3uZ3bNlC3vjfdeg+kir0j3XoPxKNeA3Zmyrfxk2fy5lvtsXeshlP1mnwyehzRURFcvxaSp9j8t2zkrXbv0O7tDtg7OPLBh59iU8GWv3Zu15l+V8B2Ktja8sGHn2Lv4Ei7tzvQpu3bbN28XpPm86++oX3HLlSvUZOq9g58OsqP1FQVwcFn8phjGQqzzHl5N1bfVWnWUu969CmM8rbdfyP16nvTo3d/qto70qN3f+rU82KH/8Z8x6cucxvo3mcgjZv5pOXbN2n5lkOZ81eXue69B1AlU77tzJJvfQcNo3Ezn3zHBbBNk3fqMjd0+AisbWzZFbBNZ/qMvBuBvYMjbX070LrtO1plztnFjcFDP6KFT2tKPEddBXXPgz/Xh/HP8WhuhD3mx1mXMTMzpp2Prd5lPNzKc+5SPHsORXI/MpFTZ5Ts/ScSt5rlNGniEpKIjcuYmja05s69J5w5H5+nuKL+/ocrE2Zzf6v+45eZ4/B3eRoWzsXPf+Lh5evcXrKR28s2U91viCZNtZHvEb33GKE/L+BRyHVCf15A9P4TOI18L0/byEylUnHuyJ94tf6I6nXaYWXnwpt9ppGc9JRrQTtyXDY1NYX9a77Eu+1IyltVzfb9ucPLcGvYg1pv9MKyYg2adf6GshZ2XDyxJte4DPnaAIZ9XTX02OQcl3+Gfu0SIjODbVho2bIlDx484MwZ9R+2hw4dwsbGhkOHMlrcDh48iI+PD+fOncPX15fu3btz9uxZ1q1bx5EjRxgxYoQm7bNnz5gyZQrBwcFs3bqVGzduaBoP7O3t2bRpEwAhISGEh4fz66+/apZdvnw5ZcqU4eTJk/z8889MnjyZPXvUJ0GVSkWHDh24f/8+AQEBBAYG4uXlRZs2bYiNjdWs49q1a6xfv55NmzYRFBSU477/+uuvNGnShGHDhhEeHk54eDj29vaa77/66iumTp3KpUuXqFu3Ll999RWbNm1i+fLl/Pfff9SsWRNfX1/N9u/evUvLli0pWbIk+/fvJzAwkCFDhujs7XDw4EHatGnDpEmT+Pbbb/O8f6GhoWzdupUdO3awY8cODh06xLRp03Lcz+Ii4n44ccpY6nll3LUxMTGltkc9Qi6d17vclcsXqFe/odY8T6+GhFxS391OSkoi9NoVPLOkqefVMMf1vgqxqZcP0bmNy5cu6FzmyuULeHplTd+I0KshL7znTmREWr5lis/ExBR3D89c8u28jn1qlOMyj9MaQMuWLZ9rXJp899K+Q+hZv4HefLt86SKe9RtozavfoCHXrl7Rm2+JiYmkpCRTrmw5nd/npLDK3PMorPJ25fKFbHWgvlcjvevMSaQm37KWufznW71cylx+ZNT1LGXOy5vLerYRcvlitjJa36thodTVyhVLYmNlxr9nlBkxJ6sIOh+Hh5v+OnX2YjyuNcpRy7mcZj2Nva04fjpGZ/oSJRS0e7MiO/fef6HxZ2bR2JOovUe15kXtPox5Aw8UJdRPrlo29iR67xGtNNF7DmPZpH6+t/cg9g6PH0RR1aWZZp5xCVMqVW9IxK2cGxUD986nZBkr3Bpl79qdkvyMqLsXtNYLUNW5GRE3c16voV8bwHCvq4Yem5zjChqbYV+7hMjMYMdYMDc3x9PTk4MHD9KgQQMOHjzImDFjmDRpEg8ePODRo0dcuXKFVq1a8dNPP9GvXz/NIIvOzs7MmTMHHx8ffv/9d0qWLMmQIRkt/tWrV2fOnDk0atSIhw8fUrZsWays1N2UbG1ts42xULduXSZMmKBZ97x589i3bx9t27blwIEDnDt3jsjISMzMzACYMWMGW7duZePGjQwfPhxQN2ysWLGCChUq5GnfTU1NKV26NHZ2dtm+nzx5Mm3btgXg0aNH/P777yxbtox33nkHgIULF7Jnzx4WL17Ml19+yfz58zE3N2ft2rWalnoXF5ds6/X392fgwIH88ccfmp4ged2/1NRUli1bRrly6j/SBg4cyL59+zS9HrJKTEwkMTFRa56ZmZlmG6+SOKW6gcXCQrurm7mFJVFR+rsIxiljsbC01JpnYWmpWd+DhHhSU1Mwz7JeC4uMNK9qbOrlU7Gw0N6GeQ7LK5WxeGZJb2FhSUpKCgkJ8VhZWee63byKU8akxZM936Ij9f+wiFPG6ljGSu8+qVQqli+ah5t7XRycqutMk1mCnnyzsLREqWcbOo9lLvn259KFWFnbUC9Lg0ReFFaZex6FVd7ilLGYZ4nZvIAxK9PKXNZ8s7CwIioq5zJXWPkG+vNOXdeVOpdRKmOp/5LqqpWlKQCxcc+0Y4h7RkXb7OMjpNt3OAoLcxN+m+6JQgElShixJeAuKzfe1pm+ZWMbypYpQcC+wmtYMKtoQ2JEtNa8Z5ExGJmYYGpjSeL9KMzsbEiM0G78SIyIwcwu978tsnr8QP1IR6my2sejVFlrHsbpf2Th/s3/CDm1iR6jt+r8/ukjJarUlOzrLWfN4wfROpdJZ+jXBjDc66qhxybnuBcXmyFduwxd6ms6iGJRMdiGBVAPYHjw4EH8/Pw4fPgwP/zwA5s2beLIkSPExcVRsWJF3NzcCAwM5Nq1a6xatUqzrEqlIjU1lRs3blCrVi3OnDnDxIkTCQoKIjY2VvOIRVhYGO7u7jnGUbeu9rOLlSpV0jxGERgYyMOHD7G21j6JPHnyROtRA0dHxzw1KuSFt3dGK2loaChJSUk0a5ZxZ8DExIRGjRpx6dIlQP0YSYsWLXLs/nfy5El27NjBhg0b6Natm2Z+XvfPyclJ06gA2nmky9SpU5k0aZLWvAkTJmR7zMQQ/XNgD3/Mm6n5/M1Edc8MhSLr81QqFLk+Y6X9vUqVfT1ZV6tS6Zj5CsSmcws6VpDT4lnTq1DpiDT/Dh/YzR/zZ2g+j5swPW17WRKqMyHHdeneJ93LLP7fLMJuhjLl5/k6v9e/DV1h6Y8r27FWqXTPBzZvWMvhQwf4cfpMTE1Nc43lZZe551EY5S3rPqlyWWe6fw7sZkGmfBs3UXeZU+Uh33TH8IKf78yaF7lUBf1593xxtfWx5ctPMxrGv5p8Ln0DWQPIPi+T+h7mDOrtyMz/XeViyAOqVirJZ8NrEh37jOXrwrKl79DWjpOBscTEPtOxthco62OS6fmYeb6uNHkYgvzqf9v5Z/MEzed33v+f9ja0V6pzHc+ePmT/mi9p2WMKpcpY6kyjFVdm+ajPhnJtAMO+rhp2bHKOe3GhGc61S4icGHzDwuLFiwkODsbIyAh3d3d8fHw4dOgQSqUSHx/1s1Spqal8+OGHjBo1Kts6HBwcePToEe3ataNdu3asXLmSChUqEBYWhq+vL8+e5f5HQtYf5AqFQtMwkZqaSqVKlTh48GC25TL3fChTpkw+9jxnmdeVPlZDtpNIppNuqVKlcl1njRo1sLa2ZsmSJXTo0EHzgyKv+5dTHukybtw4/Pz8tOa9Kr0VGr7RDGfXWprP6YNxKpUxWGZqpY6Pi8vWyp6ZhWX2O9nxcUrM01qay5U3x8jIOHuaeGW21utXIbbM1MsbZbvLHh8fl+2OSDpLnTHFYWxsTLny5rluMyfebzSnpmtGA2NyWr7FKWOxtLLRii/r3ZbM1PmmfTcxPj4j3zJb/L9ZnD55lEnT5mJto/9Z8MzKa/JN+y5KfJz+fLewtMqWz3Hx6fmm3VV8y6b1bFy/mkk//h9O1WrkKaaXVeaeR2GVN10xJ8TpX2dmDd9ojrOOMqfMWubilNnuLGWm6/jGx8W9kHyDjLzTVdf1bcNST0y6ylx+Hfk3hotXTms+m5qon+i0sjQlRplxPbc0N8nWiyGzDwZU4+8DEezYrb5Tev3WI0qWNOarES78uT5M63d6xQpmeNez5NuphdtNODEiOlvPA9MKVqQmJfEsJk6d5n40ZnY2WmnMbK2y9XTQxdH9TXo6ZNwoSUlW58+TB9GUKZ9xDnryMIbS5XTfcU2Ivc0D5V12LftYM0+lUl/rF3xdmz5f/kVZczsURsY8ydI74cnDmGy9GLIytGsDGPZ11bBjk3Pci4rNkK5drxqVSlpLXiaDHWMBMsZZmD17Nj4+PigUCnx8fDh48KBmfAUALy8vLly4QM2aNbNNpqamXL58mejoaKZNm0aLFi1wc3PLdjc9/Yd0SkpKvmL08vLi/v37lChRItu2bWxscl+BHqampnmKJX0fjxzJeOYyKSmJ06dPU6uW+mJTt25dDh8+nOPbKGxsbNi/fz+hoaH06dNHk7aw9s/MzIzy5ctrTa9Kw0Kp0qWpVLmqZrJ3cMLC0oqzZzL+2E1KSuLC+WBca3noXY+LW22Cg05rzQs+cwrXWrUBdWNNjZouBJ/RTnP2zGm96zXk2DJTL++abfngM6dxS9uGzpiypT9FDWdXSpR4vjbSrPlWVZNvGYOgJiUlcfF8UC755qGV1+kxZl5GpVKx6PdZnDz2DxN+nE1Fu8p5jjMj3wO15gedCdSbb2613AnKmv6/09R0dtHKt80b17F+zUomTJmGs4trnmN6WWXueRRWedMVc9CZU3rXmZn+MqedbxfzkG9ng7QH681a5p6HvroefCYQNz3bcHVz11FGT7+QuvrkSQp3w59qphthj4mOTaShZ8YPgBIlFHh6WHD+coLe9ZQ0M0KVpY9saqp6HO+sd+06vGWHMv4Zx0/pHn/hRYk7EYRNm6Za8yq0bU584HlUac9GK08EYdNGe+wCm7eaozye+0CrpiXLYm7jqJksK9akdLkK3Ll6TJMmJfkZ4ddPUdGxvs51WFSoTi+/bfQcvUUzObm3pnKNN+g5egtlze0wLmFKhSq1tdYLcOfqMSo66V5vOkO7NoBhX1dfpdjkHFfQ2Azr2iVETgy6YSF9nIWVK1fSqlUrQN3Y8N9//2nGVwAYO3Ysx48f59NPPyUoKIirV6+ybds2Ro4cCah7LZiamjJ37lyuX7/Otm3bmDJlita2HB0dUSgU7Nixg6ioKB4+zP1VSwBvvfUWTZo0oWvXrvz999/cvHmTY8eOMX78eE6fPp37CvRwcnLi5MmT3Lx5k+joaL13/8uUKcPHH3/Ml19+ya5du7h48SLDhg3j8ePHDB06FIARI0aQkJDAu+++y+nTp7l69SorVqwgJER7FHpbW1v279/P5cuX6du3L8nJyYW2f8WJQqGgY5debFq/ipPH/iHs5nXmzZqKmZkZLXze0qSbM/NHVi5boPncoXNPgv87zZYNq7lz+xZbNqzmbFAgHbtkvFu7U7fe7Nu9k327d3In7CZLF8wjOiqSdpnelfyqxtapW6+05QO4E3aLJQvmER0VoVl+5bIF/DrzJ0163/adiYqMYOnC+dwJu8W+3QHs2x1Al+59NGmSkpK4EXqVG6FXSU5OJjYmmhuhVwm/dydPMWXOtw5derN5w0pNvs2f/RNmZmY092mrSTd35g+sWvY/7Xw7c4qtG1dx9/Yttm5cxbmg03TIlG+Lfv+Fwwd389mX31OydGmUyhiUyphsY47o06VbT/b8HcDe3X9xO+wWixb8RnRUJG+37wTAn0sXMWtGxsCpb7fvRFRkJIsX/MbtsFvs3f0Xe3f/RdfuvTVpNm9Yy6o/lzJy9BfY2tqhjI1FGRvLkyd5ewVm1rwrrDL35MljzfEF9WBgN0Kv5ul1Z4VR3jp27kHQf6fYnBbzZk3M+X9XubrM9WLz+kxlTpNvGWVuzswfWbXsD83n9pp8U5e5LRuyl7ms+RaRj3wD6NytF3t3B7B3dwC3w26xZMF8oqMi8E0rcyuWLdSZd0sWzk8rc+q8y1zm1HX1GjdCr5GcnExMTDQ3Qq8Rfu9uvvNuw7a7DOzlQMvG1lRzKM23o11JTExh96GMGwjjx7jy4aBqms9H/42ha/vKtGlRgUoVS+LtackH/atx5N8YMl9uFQpo/5Ydu/ZHkKK/E55OxmVKU76eG+XruQFQulpVytdzo6R9JQBcf/Cj3tLpmvS3FqyllGNlav3f15R1q07VwT2wf78H139Zoklzc96f2LRtRvUvhlHGtTrVvxiGTZsm3Jy7PH/BoS5zdZoP4sz+P7hxfg+x969wcP04SpiUpKZnR026/WvHcvIvdZf2EiZmWNm5aE2mJcthalYGKzsXjEuob9LUaTGYy/9u5PKpTSgjQjm2bSoP48Jxb/xurnEZ8rUhPd8M9bpq6LHJOS7/5zhDv3YZOpWq6KbXkUE/CgHw5ptv8t9//2kaESwtLXF3d+fevXtad+QPHTrEt99+S4sWLVCpVNSoUYM+fdSVqEKFCixbtoxvvvmGOXPm4OXlxYwZM+jcOeNkWKVKFSZNmsTXX3/N+++/z6BBg1i2bFmu8SkUCgICAvj2228ZMmQIUVFR2NnZ0bJlSypWrFjg/f7iiy947733cHd358mTJ9y4cUNv2mnTppGamsrAgQN58OAB3t7e/P3331imdS2ztrZm//79fPnll/j4+GBsbIynp6fWuAzp7Ozs2L9/P61ataJ///6sXr26UPavuOnasy/PniWy4LdZPHr4EGfXWnw/ZQalSpfWpImOikShyGjLc3P3wG/s96xesZi1KxdT0a4yfmMn4uKW0XWwWcvWPEiIZ8OaP1HGxuDgWI1vJk3H1jb7oJ6vWmzNW7bmQUIC69csRxkbi4NjNb7NtLwyNoboTINNVbSrxPhJ01iycD5/7diKlbU1Qz8cSZNMr5dSxkbz+ahhms/+m9fhv3kdtevUY8q0X/OcZwBdevTjWWIii36fyaOHD6npWovxk3/Jkm8RKIwybm+61qrD6K8msHblItauXISdXRXGjJ2Es2vGXYDdAVsBmDhO+9GtT0aP48232ucaVwufN3nwIIF1q1cQGxuLo5MT30+aim1afVQqY4iOyvhBVdGuEt9P/onFC34jYMc2rKyt+eDDETRtnvHKxr92biM5OYnpP2mPe/Juv0H0HZD/19gVVpkLvRrChHGjNZ+XLVKPTdGqzduM9BuXY0yFUd7SY16zYjFrVy6hol1lPh87QSvm/OVbP549S2Thb79o8u27KTOzlTmjTLfU3dzrMGbsBNasWMS6tHwboyPfJo77TPN5+aJ5mnwb4fdNrnFl5N2faXnnxPhJ07TyLipLmRs/aSpLF/7GXzv89dTVGPz01NUfps3OR67Bqk23MTM1wu9jZ8qVNeHilQTGfH+WJ08yev1VrFBSaxCv5etuoVLBsAHVqGBtSlxCEkf/jWHBCu1rrbenJXa2Jdm5J/+DNpo38KDJvhWaz+4z1Hl9+8/NnB06DrNKFSiV1sgA8OTmHU51Go77zHE4ftyfxHuRXBjzI/e37NakUR4/w5n+frhOGo3rpFE8Dr3NmX5jiPv3bL7jA6jX6gOSk55yZMtkEp/EY2tflw7DFmNasqwmzcO4e/l+nr2mZ3sSH8cRuHc+jxOisLJz5p0hf1DOskquyxr6tQEM97pq+LHJOS6/57hX4dolRDqFSvW6tqkIkT/nrxXeaODFmSKnEdSKWIrKuKhD0MvUqJAHiXsOySrDbZM25PIGkGrAHQWNyd+jgC/T8DFXizoEvcbtGl7UIegVsvFyUYegl29tw76mqgphEL7XgSHnmyGf4ww532rXrJR7IgMV8J/+x8ALW3sv/YPmF1eG+9ehEEIIIYQQQghRAKkG3GBTHBnurZNiLCwsjLJly+qdwsKyv+pKCCGEEEIIIYQwRNJjoQhUrlyZoKCgHL8XQgghhBBCCFEw8sD/yyUNC0Ug/dWNQgghhBBCCCHEq04ehRBCCCGEEEIIIUSBSY8FIYQQQgghhBDFikolgze+TNJjQQghhBBCCCGEEAUmPRaEEEIIIYQQQhQrqTJ440slPRaEEEIIIYQQQghRYNJjQQghhBBCCCFEsSKvm3y5pMeCEEIIIYQQQgghCkwaFoQQQgghhBBCCFFg8iiEEEIIIYQQQohiRYW8bvJlkh4LQgghhBBCCCGEKDDpsSBEHimQEWAKwpBbi40UqUUdgl4pKuOiDkEvqQsFZ0xKUYegVwqGW+Z+muZd1CHodfqDy0Udgl6uPd2KOgS9VJcOFXUIOTLka5chn0cMmSGf44ww3L9HXmXyusmXS3osCCGEEEIIIYQQosCkYUEIIYQQQgghhDBwSqWSgQMHYm5ujrm5OQMHDiQuLk5v+qSkJMaOHUudOnUoU6YMlStXZtCgQdy7d08rXatWrVAoFFrTu+++m6/YpGFBCCGEEEIIIUSxolIV3VRY+vXrR1BQELt27WLXrl0EBQUxcOBAvekfP37Mf//9x3fffcd///3H5s2buXLlCp07d86WdtiwYYSHh2umP/74I1+xyRgLQgghhBBCCCGEAbt06RK7du3ixIkTvPHGGwAsXLiQJk2aEBISgqura7ZlzM3N2bNnj9a8uXPn0qhRI8LCwnBwcNDML126NHZ2dgWOT3osCCGEEEIIIYQoVoqyx0JiYiIJCQlaU2Ji4nPtz/HjxzE3N9c0KgA0btwYc3Nzjh07luf1xMfHo1AosLCw0Jq/atUqbGxsqF27Nl988QUPHjzIV3zSsCCEEEIIIYQQQrwgU6dO1YyDkD5NnTr1udZ5//59bG1ts823tbXl/v37eVrH06dP+frrr+nXrx/ly5fXzO/fvz9r1qzh4MGDfPfdd2zatInu3bvnKz55FEIIIYQQQgghhHhBxo0bh5+fn9Y8MzMznWknTpzIpEmTclzfqVOnAFAosr8KV6VS6ZyfVVJSEu+++y6pqan89ttvWt8NGzZM838PDw+cnZ3x9vbmv//+w8vLK9d1gzQsCCGEEEIIIYQoZlJVuf/YLixmZmZ6GxKyGjFiRK5vYHBycuLs2bNERERk+y4qKoqKFSvmuHxSUhK9e/fmxo0b7N+/X6u3gi5eXl6YmJhw9epVaVgQQgghhBBCCCEMmY2NDTY2Nrmma9KkCfHx8fz77780atQIgJMnTxIfH0/Tpk31LpfeqHD16lUOHDiAtbV1rtu6cOECSUlJVKpUKc/7IWMsCCGEEEIIIYQoVorb6yZr1arF22+/zbBhwzhx4gQnTpxg2LBhdOzYUeuNEG5ubmzZsgWA5ORkevbsyenTp1m1ahUpKSncv3+f+/fv8+zZMwBCQ0OZPHkyp0+f5ubNmwQEBNCrVy/q169Ps2bN8hyfNCwIIYQQQgghhBAGbtWqVdSpU4d27drRrl076taty4oVK7TShISEEB8fD8CdO3fYtm0bd+7cwdPTk0qVKmmm9DdJmJqasm/fPnx9fXF1dWXUqFG0a9eOvXv3YmxsnOfY5FEIIYQQQgghhBDFSmH1HChKVlZWrFy5Msc0qkw77uTkpPVZF3t7ew4dOvTcsRX7HgutWrVi9OjRRR1GjpYtW5btPaIvm0KhYOvWrQDcvHkThUJBUFBQnpYdPHgwXbt2LbTYhBBCCCGEEEIYrmLTY+HgwYO8+eabKJXKIv+Rnl99+vShffv2+VqmVatWeHp6Mnv27Bcej729PeHh4XkaRETAXzu24r95LcrYGOwdqjFk+AjcPerqTX/hXBBLF/7G7bAbWFnZ0LXnu/i276KV5vjRQ6xZsYT74fewq1SZfoM+oHHTFsUuPpVKxfrVy9izazuPHj7A2dWdDz4ejYNjtRyXO370EGtXLNba/htNW2bsw/lg/Det4fq1KyhjY/hq/A+80SR/8RVWbAC7dmxJOyax2Ds48f7wEbh71MtzbIZ8TCW256urWzevS4vPiaG5xHdeE99NTXxvt++s+T7s1g3WrFxK6LUrREVGMGTYp3Tq2rNAsanrw1L2ptWHmq7uDPt4DPa51IcTRw9q1Ye+g4Zp1YeL54Pw37SW69dC0urqjzQqQF3dvu4P/tmzmcePHlDN2YN+w76mikMNvcvcDQtl29rfuRV6iZiocPq8/zlvdeqvlebKhUD+9v+TW6GXiFdG88nYmdR/4818xxa4Zx6XTq4n8UkCtg51ad71e6zsnPO0/LWgnexb/TlOtdvg+958re8uHFtN8KHFPH4QhWXFmjTt/A2Vqnnnab1Wzb2p/vlQzL08KFnZltM9PiFi276cl2nREPcZX1PW3ZnEe5GEzlxE2IK1WmnsurXDZeJnlK7hwOPQMEK+n0WE/948xZSVIZ+DDbk+GPJ5xJBjM/RjasjXLiHSFfseC4UhKSnpha6vVKlS2NravtB15lX6oB2ZGRsbY2dnR4kSxabdqdAc+Wc/SxfOo0efAcycs4haHnX4YcJXREVmfxUMQMT9cH6Y8DW1POowc84iuvfpz+I/5nL8aEb3o5BLF5g5bRI+rdvxy7xF+LRux8xpE7ly+WKxi2/rxjVs37KeDz4azfRZf2BhacXk8Z/z5PFjvcuEXDrPL2nbnzlvsc7tJz59glO1mnzw0eh8x1TYsR3VHJOBzJizkFoedflxwli9xyQrQz6mEtvz1dUlC+fTs88AZs5ZiLtHXabkUC7U8Y3D3aMuM+cspIeO+BITE6loV5mBg4djaWmV75gy27pxNTu2rGfoR6OZNmtBWn3wy1N9aNnal5nzltCytS+/TJuglT9Pnz7FqVoNhj5HXd21ZTl7tq+i37CxfDt9BeYW1sya9DFPnzzSu8yzxKfYVKxC94GjMLfQ3YiemPiUqk4u9Bs2tsCxBR9cxNnDy2jW9Tu6j9pA6XIV2LlwCM+ePsx12QfKu5zY+TN2OhoLrgUFcGz7VOq3/ogen23Brpo3AYuH80B5L09xGZcpTcLZEC58NjlP6Us5VaXh9gXEHgnkSMOuXJv+P2rP+ha7bu00aSwae1J/9SzurvLncIMu3F3lj9ea2Vg00v8DKCeGeg5Wx2aY9cGQzyOGHBsY9jE15GuXoUtVFd30Oiq0hoXt27djYWFBamoqAEFBQSgUCr788ktNmg8//JC+ffsCcOzYMVq2bEmpUqWwt7dn1KhRPHqU8UfBypUr8fb2ply5ctjZ2dGvXz8iIyMBddf9N99U30WwtLREoVAwePBgzbKpqal89dVXWFlZYWdnx8SJE7VijY+PZ/jw4dja2lK+fHlat25NcHCw5vuJEyfi6enJkiVLqF69OmZmZjk+q5Lffc/6KET69lasWIGTkxPm5ua8++67PHjwAFA/enDo0CF+/fVXFAoFCoWCmzdvAnDx4kXat29P2bJlqVixIgMHDiQ6Olqz7latWjFixAj8/PywsbGhbdu22eLX9SjEhQsX6NChA+XLl6dcuXK0aNGC0NBQreVmzJhBpUqVsLa25tNPP33hDTCGaPuWDbRp1562vh2p6uDI0OEjsbax5e8Af53p/w7Yhk0FW4YOH0lVB0fa+nakddt38N+8LmOd/hupV9+bHr37U9XekR69+1Onnhc7/DcWq/hUKhU7/DfQo89AGjdriYNTdUb6jSMxMZHDh/Tf4drhv5F69RvQvfcAqto70r33AOrUa8AO/w2aNF7ejdWt781a6l1PUcW2fct6Wrdrz1u+Hanq4MSQ4SOxtqmg95hkZcjHVGIreF3dpomvA/YOjgwdPgJrG1t2BWzLJb4R2Ds40ta3A63bvsPWzes1aZxd3Bg89CNa+LSmhIlJvmNKp1Kp2Om/ge59BtK4mU9affgmrT7s0bvcTv8N1K3vTffeA6iSqT7szFJX+w4aRuNmPgWObd+O1bTvMRSvxm2o4liT90dN5lniU07+85fe5ao516bXe2No1NxXb97U8WpGt36f4tW4TYFjO3fkT7xaf0T1Ou2wsnPhzT7TSE56yrWgHTkum5qawv41X+LddiTlrapm+/7c4WW4NexBrTd6YVmxBs06f0NZCzsunliTp9ii/v6HKxNmc3+r/uOXmePwd3kaFs7Fz3/i4eXr3F6ykdvLNlPdb4gmTbWR7xG99xihPy/gUch1Qn9eQPT+EziNfC9P28jMkM/BhlwfDPk8YsixGfIxNfRrlxCZFVrDQsuWLXnw4AFnzpwB4NChQ9jY2GgNDHHw4EF8fHw4d+4cvr6+dO/enbNnz7Ju3TqOHDnCiBEjNGmfPXvGlClTCA4OZuvWrdy4cUPTeGBvb8+mTZsA9SiY4eHh/Prrr5plly9fTpkyZTh58iQ///wzkydPZs8e9YlCpVLRoUMH7t+/T0BAAIGBgXh5edGmTRtiY2M167h27Rrr169n06ZNuY49kJ991yc0NJStW7eyY8cOduzYwaFDh5g2bRoAv/76K02aNGHYsGGEh4cTHh6ueXzBx8cHT09PTp8+za5du4iIiKB3795a616+fDklSpTg6NGj/PHHHznuC8Ddu3dp2bIlJUuWZP/+/QQGBjJkyBCSk5M1aQ4cOEBoaCgHDhxg+fLlLFu2jGXLluW67ldZUlISoddCqFe/odZ8T6+GXL50QecyVy5fwNMra/pGhF4N0eTnlcsX8MyyzvpejfSu81WNL+J+OHHKWOp5ZdyNMzExpbZHPUIunde73JXLF3TuU0g+t18UsamPyZVs+VfPq2GO601nyMdUYitYbBnxXcGzvvadaU8vby7rKRchly/i6aWdvr5XQ634XpRITX3I2FcTE1PcC1Af6nk1ylNZz6voiLvEx0VT27OxVmwutRsQGnL2hW2nIB7E3uHxgyiqumS8qsu4hCmVqjck4taZHJcN3DufkmWscGuUvVt3SvIzou5e0FovQFXnZkTczHm9BWXR2JOovUe15kXtPox5Aw8Uab0bLRt7Er33iFaa6D2HsWxSP9/bM9RzMBhufTDk84ghxwaGfkwN99r1KlCpFEU2vY4KrWHB3NwcT09PDh48CKh/SI8ZM4bg4GAePHjA/fv3uXLlCq1ateL//u//6NevH6NHj8bZ2ZmmTZsyZ84c/vzzT54+fQrAkCFDeOedd6hevTqNGzdmzpw5/PXXXzx8+BBjY2OsrNRdoGxtbbGzs8Pc3FwTS926dZkwYQLOzs4MGjQIb29v9u1TP0t44MABzp07x4YNG/D29sbZ2ZkZM2ZgYWHBxo0ZLXfPnj1jxYoV1K9fn7p166JQ6C8w+dl3fVJTU1m2bBkeHh60aNGCgQMHamI2NzfH1NSU0qVLY2dnh52dHcbGxvz+++94eXnx008/4ebmRv369VmyZAkHDhzgypUrmnXXrFmTn3/+GVdXV9zc3HI9lvPnz8fc3Jy1a9fi7e2Ni4sL77//vtb7Ui0tLZk3bx5ubm507NiRDh06aOLVJTExkYSEBK0pMTEx11gMyYOEeFJTU7GwsNSab25hSZwyVucySmUs5lnSW1hYkpKSQkKC+rUwccpYzC2zrNNS/zpf1fjS01tYaHdfNLewRJnDuuKUsVhk2b5FAbZfFLGpj0kK5lnWa5HDMcnMkI+pxFaw2HKKT10ulHrj05U+c3wvilIZk7b+rOXWKsd9fRl1NT5OHVt5C2ut+eUtrIiPi9a1yEvz+EEUAKXKasdWqqw1jx/oj+3+zf8IObWJlj2n6Pz+6SMlqtSU7Ostl/N6n4dZRRsSI7TX/SwyBiMTE0xt1MfYzM6GxIgYrTSJETGY2VXI9/YM9RwMhlsfDPk8Ysixqbf1ah1TQ7l2CZFVoY6x0KpVKw4ePIhKpeLw4cN06dIFDw8Pjhw5woEDB6hYsSJubm4EBgaybNkyypYtq5l8fX1JTU3lxo0bAJw5c4YuXbrg6OhIuXLlND/Kw8LCco2jbl3t5/sqVaqkeYwiMDCQhw8fYm1trbX9GzduaHX1d3R0pEKFvF8c87rv+jg5OVGuXDmdMesTGBjIgQMHtPYjfRuZ98XbO2+DO6ULCgqiRYsWmOTQzax27dpa7znNLd6pU6dibm6uNU2dOjVfcRmKbI1MKhU5tDtlS69C/VhN5rkKsqTJZZ2vQnz/HNhD/x5va6aUlGTd8aHKtn4dUWbZvq715N3Ljk3HIck+M6ctGMgxldhebF3NuqC67BQkvue7U/LPgd0M6OGrmVJSUnSFhyoP9UF3/hQ8vhOHAhjRr5lmSq+r2aiePx/y6+p/21k83kszpabHpnN/dcf27OlD9q/5kpY9plCqjKXONBmryHZAnitvc5X1EdD0bWWerytNHt73ZsjnYEOuD7o3YhjnET0b096WnOPyxOCvXUKkKdTR+Vq1asXixYsJDg7GyMgId3d3fHx8OHToEEqlUvMoQGpqKh9++CGjRo3Ktg4HBwcePXpEu3btaNeuHStXrqRChQqEhYXh6+urc/DBrLL+IFYoFJrxD1JTU6lUqZKmd0Fmmcc9KFOmTD72PO/7XpCY9UlNTaVTp05Mnz4923eVKlXS/D+/+1KqVKlc0+Q33nHjxuHn56c1z8zMLF9xFbVy5c0xMjLKdvckPj4u292QdJaW2Vu/4+PiMDY2plx5dS8bCx1pEuL0r/NVia/hG81wdq2l+Zw+BodSGYOlVcadt/i4uGx3ADLTtf34OGW2Fvr8eFmxqY+JcfY08cpsdyR0MbRjKrG92Lqqq1zoK9eWllbZ90cTX/l8bT+rhm80x9nVXfM5WVMfYrG0yhjoMD5Ome2uV2YWemJ8nrrq2ciH6i4ems/pdTUhLgYLq4zG/4T42Gy9GAqbo/ub9HTIuJGRkqz+++TJg2jKlM8YoPnJwxhKl9MdW0LsbR4o77Jr2ceaeSqV+lq64Ova9PnyL8qa26EwMuZJlt4JTx7GZOvF8KIkRkRn63lgWsGK1KQknsXEqdPcj8bMTnsgTDNbq2w9HXQx5HOwIdeHzAztPGLIsb1qx9RQr12vgjy0a4oXqFB7LKSPNTB79mx8fHxQKBT4+Phw8OBBrTEGvLy8uHDhAjVr1sw2mZqacvnyZaKjo5k2bRotWrTAzc0t291wU1NTAE2rY155eXlx//59SpQokW3bz/O6xbzue0GZmppm29f0fHRycsq2L/ltTMisbt26HD58+IUOxmhmZkb58uW1pletYcHExIQaNV0JPnNaa37wmdO41aqtcxkXt9o60p+ihrOr5i0cLm61CQ7SThN05pTedb4q8ZUqXZpKlatqJnsHJywsrTibaXtJSUlcOB+May0PvevRtf3gM6dwzWf+FEVs6mPiki2Pz545neN60xnaMZXYXmRdzV4ugs8E4qanXLi6uRN8JjDLtk9rxVdQWetDVT314WIe6sPZoFNa89T1Ifeyrk/JUmWwreSgmSrbV8fcwoaLwSc0aZKTkrhyIZAargV7G0FBmZYsi7mNo2ayrFiT0uUqcOfqMU2alORnhF8/RUXH+jrXYVGhOr38ttFz9BbN5OTemso13qDn6C2UNbfDuIQpFarU1lovwJ2rx6jopHu9zyvuRBA2bZpqzavQtjnxgedRpT23rTwRhE0b7XEfbN5qjvJ47uM+GPI52JDrQ2aGdh4x5NherWNquNcuIbIq1IaF9LEGVq5cqXl0oWXLlvz3339aYwyMHTuW48eP8+mnnxIUFMTVq1fZtm0bI0eOBNS9FkxNTZk7dy7Xr19n27ZtTJmi/eyho6MjCoWCHTt2EBUVxcOHub/KCeCtt96iSZMmdO3alb///pubN29y7Ngxxo8fz+nTp3NfwXPue0E5OTlx8uRJbt68SXR0NKmpqXz66afExsbSt29f/v33X65fv87u3bsZMmRIvhtcMhsxYgQJCQm8++67nD59mqtXr7JixQpCQkKeax+Kg07derFv90727Q7gTtgtliyYR3RUBO3S3rO8ctkCfp35kya9b/vOREVGsHThfO6E3WLf7gD27Q6gS/c+mjQdO/cg6L9TbN6wmju3b7F5w2rOBgXSsUv+381syPEpFAo6dunFpvWrOHnsH8JuXmferKmYmZnRwuctTbo5M39k5bIFms8dOvck+L/TbEnb/hbN9ntp0jx58pgboVe5EXoVUA/MdCP0ap5fJ1aYsXXq1jvtmOzkTthNli6YR3RUpOaY5MaQj6nEVvC62rlbL/buDmDv7gBuh91iyYL5REdF4Nu+EwArli3UGd+ShfO5HXaLvWnxde2eMVhvUlISN0KvcSP0GsnJycTERHMj9Brh9+7mKzaFQkGHLr3YvH6lpj7M19SHjDcLzZn5I6uWZQwI3F5TH1Zx9/YttmxYxbmg03TIoa5GFKCutunYj4BNS/jvxH7u3rrG0nkTMDUryRst39GkW/zrd2xeOVfzOTkpibAbIYTdCCE5OQllbCRhN0KIDM94vPLpk8eaNADRkXcJuxFCTFR4nmOr03wQZ/b/wY3ze4i9f4WD68dRwqQkNT07atLtXzuWk3/NBKCEiRlWdi5ak2nJcpialcHKzgXjEuqbKHVaDObyvxu5fGoTyohQjm2bysO4cNwbv5un2IzLlKZ8PTfK11M/Llm6WlXK13OjpL26d6PrD37UW5rR+/HWgrWUcqxMrf/7mrJu1ak6uAf27/fg+i9LNGluzvsTm7bNqP7FMMq4Vqf6F8OwadOEm3OX5ymmrHlnqOdgQ64PhnweMeTYDPmYGvq1y9DJ6yZfrkJ9FALgzTff5L///tP8kLa0tMTd3Z179+5Rq5a621vdunU5dOgQ3377LS1atEClUlGjRg369FFXggoVKrBs2TK++eYb5syZg5eXFzNmzKBz54yLQJUqVZg0aRJff/0177//PoMGDcrTWwkUCgUBAQF8++23DBkyhKioKOzs7GjZsiUVK1Ys9H0vqC+++IL33nsPd3d3njx5wo0bN3BycuLo0aOMHTsWX19fEhMTcXR05O2338bIqOBtSNbW1uzfv58vv/wSHx8fjI2N8fT0pFmzZrkvXMw1b9maBwkJrF+zHGVsLA6O1fh20nRsbe0AUMbGEB2VcfGoaFeJ8ZOmsWThfP7asRUra2uGfjiSJpleQ+Tm7oHf2O9Zs2Ixa1cuoaJdZT4fOwEXN/ds23/V4+vasy/PniWy4LdZPHr4EGfXWnw/ZQalSpfWpImOikShyCi/6dtfvWIxa1cupqJdZfzGTtTafujVECaMG635vGzRfABatXmbkX7jijS2Zi1b8yAhng1r/kQZG4ODYzW+yXRMcmPIx1RiexF19c+0+JwYP2maVnxRURk99dTxTWXpwt/4a4e/zviUsTH4jRqm+ey/eR3+m9dRu049fpg2O1/xde3Zj2fPEln42y+a+vDdlJlZ6kMERpke0nVzr8OYsRNYs2IR69LqwxgddXXiuM80n5cvmgeo6+oIv2/yFNvb3d4j6dlTVi+YxqNHCVR39mDM979RslRGT73Y6PsoMl0H45RRTPm8r+bzbv8V7PZfgUvtBnw5ZSEAt0IvMuP74Zo065f+AkCTNzsxZOSkPMVWr9UHJCc95ciWySQ+icfWvi4dhi3GtGRZTZqHcffy/Ux2Tc/2JD6OI3DvfB4nRGFl58w7Q/6gnGWVPC1v3sCDJvtWaD67z1Dn9e0/N3N26DjMKlWglH3GI5RPbt7hVKfhuM8ch+PH/Um8F8mFMT9yf8tuTRrl8TOc6e+H66TRuE4axePQ25zpN4a4fwv2dg5DPQerYzPM+mDI5xFDjg1ehWNqmNcuITJTqFTy9IkQeXHhWt7uUgltqpc8gFpxoUBOzcWRIR/XFIxzT1RElIllc09URE5fK517oiLi2jP3Nz8VFcdLh4o6hBwZ8rXLmIL3Qn2dGfI5zoicx1ErSrVrVso9kYFaeqDotv3+m0W37aJSqI9CCCGEEEIIIYQQoniThoUCCAsL03qlY9YpL6/AFEIIIYQQQgghioNCH2OhOKpcuTJBQUE5fi+EEEIIIYQQomjIA/8vlzQsFED6qymFEEIIIYQQQojXnTQsCCGEEEIIIYQoVl7X1z4WFRljQQghhBBCCCGEEAUmDQtCCCGEEEIIIYQoMHkUQgghhBBCCCFEsSKDN75c0mNBCCGEEEIIIYQQBSY9FoQQQgghhBBCFCupqUUdwetFeiwIIYQQQgghhBCiwKTHghBCCCGEEEKIYkXGWHi5pMeCEEIIIYQQQgghCkwaFoQQQgghhBBCCFFg8iiEEHmkQPpTFYQKRVGH8Eoy5HwzwnBHQ0o18PZyOY8UjKXZw6IOQS/f2glFHYJeqkuHijoEvW7V8inqEHJ09H9nizoEvRSGe3mgX8vYog5BCA15FOLlMuy/wIQQQgghhBBCCGHQpMeCEEIIIYQQQohiJVV6LLxU0mNBCCGEEEIIIYQQBSYNC0IIIYQQQgghhCgweRRCCCGEEEIIIUSxoirS0RsNeJTVQiI9FoQQQgghhBBCCFFg0mNBCCGEEEIIIUSxIq+bfLmkx4IQQgghhBBCCCEKTHosCCGEEEIIIYQoVlJTizqC14v0WBBCCCGEEEIIIUSBScOCEEIIIYQQQgghCkwehRBCCCGEEEIIUazI4I0vl/RYyIebN2+iUCgICgoq6lDyRKFQsHXr1udax8SJE/H09Hwh8QghhBBCCCGEKH6kx8JL0K5dO/bt28fRo0dp3LhxvpdXKBRs2bKFrl27vvjgxHP7a8dWtm5ehzI2BnsHJ4YOH4G7R1296c+fC2Lpwt+4HXYTKysbuvZ8l7fbd9Z8H3brBmtWLiX02hWiIiMYMuxTOnXtWSzjU6lUrF+9jD27tvPo4QOcXd354OPRODhWy3G540cPsXbFYu6H38OuUmX6DfqAN5q21Eqza8cW/DevRRkbi72DE+8PH4G7R70ij+3C+WD8N63h+rUrKGNj+Gr8D7zRpEWe4yrM2OD5882QyxtI3j1fXV3K3rR8q+nqzrCPx2CfS76dOHpQK9/6DhqmlW8Xzwfhv2kt16+FpNWHH2lUoPpgmLH9tWNrWpmIwd6hGkNyOaYXNMf0huaY+rbvovk+7NYN1q5cSui1EKIiI3h/2Kd06torXzFlZoj1waq5N9U/H4q5lwclK9tyuscnRGzbl/MyLRriPuNryro7k3gvktCZiwhbsFYrjV23drhM/IzSNRx4HBpGyPeziPDfm2s8+rSpb0xDV2NKmcHtKBXbjiUTGaf/9qiXsxE9W5pkm//9skSSU9T/d7JT0KKOMVWsjShfRsGKvUlcupX/0edap8dmqo5t+/GcY6tfU3dsE5ZnxAbwhpsRzeuUoFwpiIxTsfNkMrci8n5L+EXXB1CXxTUrlmiVxcZN81dPQc4jz3MeMWSp0mPhpSqWPRaePXtW1CFohIWFcfz4cUaMGMHixYuLOhzxgh35Zz9LFs6nZ58BzJyzEHePukyZMJaoyAid6SPuh/PDhHG4e9Rl5pyF9OjTn8V/zOX40UOaNImJiVS0q8zAwcOxtLQq1vFt3biG7VvW88FHo5k+6w8sLK2YPP5znjx+rHeZkEvn+WXaJHxat2PmvMXqf6dN5Mrli5o0R//Zz9KF8+jRZyAz5iyklkddfsxhv19mbIlPn+BUrSYffDQ6z7G8rNieN98MvbyB5F1Bbd24mh1b1jP0o9FMm7UgLd/88pRvLVv7MnPeElq29uWXaRO08u3p06c4VavB0OeqD4YZ2xFNmRjAzDmLqOVRhx8mfJXLMf2aWh51mDlnEd31HtNKDBw8HItiWh+My5Qm4WwIFz6bnKd9KOVUlYbbFxB7JJAjDbtybfr/qD3rW+y6tdOksWjsSf3Vs7i7yp/DDbpwd5U/XmtmY9FI/4+znLSsa0wzD2O2H0/mt21JPHyiYsjbJphm/22u5ekzFT+tTtSaMv9wNy2h4H6suiGgoFrUMaZZbe3Y3n/bBNNcbiU+faZi6ppErSlzbHWqGdH+jRIcCk5mvn8SNyNSea+dCeZl8hZXYdSHkEsXmJlWFn+Zt0hnWcwrOY8I8fyKRcNCq1atGDFiBH5+ftjY2NC2bVsOHTpEo0aNMDMzo1KlSnz99dckJ2ecqHft2kXz5s2xsLDA2tqajh07EhoaqrXef//9l/r161OyZEm8vb05c+ZMvmNbunQpHTt25OOPP2bdunU8evRI63snJydmz56tNc/T05OJEydqvgfo1q0bCoVC8xng999/p0aNGpiamuLq6sqKFSuybT88PJx33nmHUqVKUa1aNTZs2KD1/dixY3FxcaF06dJUr16d7777jqSkpGzrWbFiBU5OTpibm/Puu+/y4MEDzXetWrVi1KhRfPXVV1hZWWFnZ6eJv7jbtmUDbdq1p61vB+wdHBk6fATWNrbsCtimM/3fAduwqWDL0OEjsHdwpK1vB1q3fYetm9dr0ji7uDF46Ee08GlNCZNc/kp5heNTqVTs8N9Ajz4DadysJQ5O1RnpN47ExEQOH9J/F2mH/0bq1W9A994DqGrvSPfeA6hTrwE7/DPK9vYt62ndrj1v+XakqoMTQ4aPxNqmAn8H+Bd5bF7ejdV3VJq11LueoortefPNkMsbSN4VlEqlYqf/Brr3GUjjZj5p+fZNWr7t0bvcTv8N1K3vTffeA6iSKd92ZqkPfQcNo3Ezn2IX23bNMe1IVQdHhg4fibWNrd4ykXFMR1LVwZG2vh1p3fYd/Dev06RxdnHjvaEf09ynDSbFtD5E/f0PVybM5v5W/ccvM8fh7/I0LJyLn//Ew8vXub1kI7eXbaa63xBNmmoj3yN67zFCf17Ao5DrhP68gOj9J3Aa+V6etpFV09rGHAxO4cKtVCKUKjYcSsakBHhWz/nPapUKHj7RnjK7cieVPYHq9RZUs7TYLt5KJTJOxcZ/kjExhno1ni+2Zh7GBF5J5fSVVKLiVQScTCH+kYo33IzzFFdh1Ift/hupV9+bHr37U9XekR69+1Onnhc7/DfmKaaMfZfziBAvQrFoWABYvnw5JUqU4OjRo/z000+0b9+ehg0bEhwczO+//87ixYv54YcfNOkfPXqEn58fp06dYt++fRgZGdGtWzdS0154+ujRIzp27IirqyuBgYFMnDiRL774Il8xqVQqli5dyoABA3Bzc8PFxYX169fnvmAmp06dAtQNFOHh4ZrPW7Zs4bPPPuPzzz/n/PnzfPjhh7z//vscOHBAa/nvvvuOHj16EBwczIABA+jbty+XLl3SfF+uXDmWLVvGxYsX+fXXX1m4cCGzZs3SWkdoaChbt25lx44d7Nixg0OHDjFt2jStNMuXL6dMmTKcPHmSn3/+mcmTJ7NnT97+KHhVJSUlEXrtCp71vbXme3p5c/nSeZ3LhFy+iKeXdvr6Xg0JvRqi1fD1OsQXcT+cOGUs9TJtz8TElNoe9QjREx/AlcsXqFe/odY8T6+GhFy6AGTeb+009bwa5rjelxHbi2Co+Wbo5Q0k7woqUpNvGftnYmKKewHyrZ5XozzXw1c5NvUxDdFZbi7rOR9cuXwBT6+s6Ru9dvUhvywaexK196jWvKjdhzFv4IGihPo2vWVjT6L3HtFKE73nMJZN6ud7e5bloHxpBVfvZvz4T0mFG/dTcaiY85/VpibwZR9Txr5ryqC2Jahkrcj39nOLrVxpBdeyxHbzfioOtrnH9kVvU77qY8rAt0pQySojNmMjqGyt4No97QaPa3dzXy8UXn24cvlCtnJW36uR3nXqI+eR4kulKrrpdVRsGhZq1qzJzz//jKurKwEBAdjb2zNv3jzc3Nzo2rUrkyZNYubMmZqGgx49etC9e3ecnZ3x9PRk8eLFnDt3josX1d2XVq1aRUpKCkuWLKF27dp07NiRL7/8Ml8x7d27l8ePH+Pr6wvAgAED8v04RIUKFQCwsLDAzs5O83nGjBkMHjyYTz75BBcXF/z8/OjevTszZszQWr5Xr1588MEHuLi4MGXKFLy9vZk7d67m+/Hjx9O0aVOcnJzo1KkTn3/+ebbGj9TUVJYtW4aHhwctWrRg4MCB7Nun/bxj3bp1mTBhAs7OzgwaNAhvb+9saTJLTEwkISFBa0pMTMxX3hS1BwnxpKamYmFhqTXfwsKSOKVS5zJKZazO9CkpKSQkxL9W8cUpY9PWr90Nz9zCEmXad/qWs7DMEqOlpWZ96v1OwTzLetX7rX+9LyO2F8FQ883QyxtI3hWUUhmTtv6s+2eV4/69jPpgqLHpO6bmOZQJpTIWc6kP+WZW0YbEiGitec8iYzAyMcHURh2nmZ0NiRExWmkSI2Iws6uQ7+2VK6X+wf3wifYvh4dPoGwp/Q0FUXEqNv2TzIo9Saw7kERyCnzY0QTr8i+ucUFvbE8zvtMlOl7FpsPJrNybxLqD6tiGZ4qttBkYGyl073Pp3OMqrPoQp4zFPEtZNC9APZbziBAvRrFpWPD2zmhxv3TpEk2aNEGhyDiJNmvWjIcPH3Lnzh1AfRe+X79+VK9enfLly1OtmnpwlrCwMM066tWrR+nSGWfMJk2a5CumxYsX06dPH0qktZj37duXkydPEhISUrCdzOTSpUs0a9ZMa16zZs20eiNA9pibNGmilWbjxo00b94cOzs7ypYty3fffafJg3ROTk6UK1dO87lSpUpERkZqpalbV/s5RV1pMps6dSrm5uZa09SpU3PYYwOm0L5Yq1TZZmVJniU96gu1ghd75yLTBrW3V0Tx/XNgD/17vK2ZUlKSdW4PVHnYlq590p6XdbUqlY6ZRRRbfhhyvunehGGUN5C8K6h/DuxmQA9fzZSSkqIrPFR5yLes36tUquesD4Ybm85t6CgUBTumz++Vqw/5kfX2YPp2Ms/XlSYPtxXr1TBiwiBTzWSc/peznk3qcztKRVBoKvdjVdyMULFmfzLR8SqauBf8T/F61Y34fqCpZjLKYVU57entKBXBabHdilCx9kAyMfEqGtfSXqHObM7HndnCqA+663HOcch55PWhSlUV2fQ6KjZvhShTJmP0GF2VWpV2Nkyf36lTJ+zt7Vm4cCGVK1cmNTUVDw8PzcCPqufswxIbG8vWrVtJSkri999/18xP7wUxffp0AIyMjLJtS9cYB7ro2se8nMzS05w4cYJ3332XSZMm4evri7m5OWvXrmXmzJla6bM+f6VQKDQ9P/KTJrNx48bh5+enNc/MzCzX2A1JufLmGBkZZWs1jo9XZmstTmdpaZXtTlB8XBzGxsaUK1++WMfX8I1mOLvW0nxOL+dKZQyWVtZa28t6ByAzC8vsdxDi4zL2Sb3fxjr3O2ur/8uOrSAMOd8yM7TyBpJ3BdXwjeY4u7prPidr8i0WSyubTNtTZrtbmJmFnhifrz4YbmyZpR/TbNuIj8t2Jz+dpc5yln5MzZ87plelPuRXYkR0tp4HphWsSE1K4llMnDrN/WjM7Gy00pjZWmXr6aDLpbBUbkdmDApewlj9N1TZ0goeZLqDX6Zk9p4COVEBd6NVWJc3AlJyS64/tigdsZXSjq1sAWK7E63Cxlwd2+NESElVUa60dkuCep9zX19h1QddZTEhTv8608l5RIjCUWx6LGTm7u7OsWPHtH6wHzt2jHLlylGlShViYmK4dOkS48ePp02bNtSqVQtllu6m7u7uBAcH8+RJxhnzxIkTeY5h1apVVK1aleDgYIKCgjTT7NmzWb58ueY5pwoVKhAeHq5ZLiEhgRs3bmity8TERNOamq5WrVocOaL9vOCxY8eoVauW1rysMZ84cQI3NzcAjh49iqOjI99++y3e3t44Oztz69atPO/j8zAzM6N8+fJa06vWsGBiYkKNmi4EnzmtNT/4TCButTx0LuPq5k7wmUCteUFnTlPD2VXTs6W4xleqdGkqVa6qmewdnLCwtOJspviSkpK4cD4YVz3xAbi41SY4KOs+ncK1Vm1A/36fPXNa73pfVmwFYcj5lpmhlTeQvCuorPlWVU++XcxDvp0NOqU1T51vuefJqxhbZupj6qrjmJ7GTc/5wMWtto70p167+pBfcSeCsGnTVGtehbbNiQ88jyrtby3liSBs2mj38rR5qznK47kPyv0sCWIfZEyRcSoSHquoWTnjT2hjI6hmZ0RYRP4GXaxkpd0AkF/PkrPH9uCxippVtGNzsjMiLLIAsT1Wx5aSCvditPcZoGblvK23sOqDrrIYdOaU3nWmk/PI6yNVVXTT66hYNix88skn3L59m5EjR3L58mX8/f2ZMGECfn5+GBkZYWlpibW1NQsWLODatWvs378/293zfv36YWRkxNChQ7l48SIBAQHZxi/IyeLFi+nZsyceHh5a05AhQ4iLi2Pnzp0AtG7dmhUrVnD48GHOnz/Pe++9h7Gx9gi7Tk5O7Nu3j/v372saQL788kuWLVvG//73P65evcovv/zC5s2bsw0wuWHDBpYsWcKVK1eYMGEC//77LyNGjADU41KEhYWxdu1aQkNDmTNnDlu2bMl3fr/OOnfrxd7dAezdHcDtsFssWTCf6KgIfNt3AmDFsoX8OvMnTXrf9p2JioxgycL53A67xd7dAezbHUDX7r01aZKSkrgReo0boddITk4mJiaaG6HXCL93t1jFp1Ao6NilF5vWr+LksX8Iu3mdebOmYmZmRguftzTp5sz8kZXLFmg+d+jck+D/TrNlw2ru3L7Flg2rORsUSMcuGe9g7tStN/t272Tf7p3cCbvJ0gXziI6KpF37zkUe25Mnj7kRepUboVcB9aBRN0Kv5vm1hIacb4Zc3iTvnq+udujSi83rV2rybb4m39pq5duqZX9oPrfX5Nsq7t6+xZYNqzgXdJoOOdSHiALUB0ONrVO3XmllIoA7YbdYsmAe0VERmjKxctkCncd06cL53Am7xb60Y9qlex9NGvUxVceUnJxMbEw0N0KvEn7vTp5iypp3hlgfjMuUpnw9N8rXU98EKV2tKuXruVHSvhIArj/4UW/pdE36WwvWUsqxMrX+72vKulWn6uAe2L/fg+u/LNGkuTnvT2zaNqP6F8Mo41qd6l8Mw6ZNE27OXZ7vfAM4diGFVvWMcXc0oqKlgp4tS5CUDEHXM35k92xZgnbeGX/Pta5vjHMVBZbl1D/au7dQD97476WMG0emJdTfpQ+caFVW/f+8vtIR4OiFFHzqqmOztVDQo0UJklIgODRLbA0yxeZpTM3MsTVPi+1yRmxHz6fQwMWIBs5GVDBX0L6RMeZltdPkpDDqQ8fOPQj67xSb08riZk1Z7Jn3DEPOI89zHhEis2LZdFWlShUCAgL48ssvqVevHlZWVgwdOpTx48cD6scP1q5dy6hRo/Dw8MDV1ZU5c+bQqlUrzTrKli3L9u3b+eijj6hfvz7u7u5Mnz6dHj165Lr9wMBAgoODWbhwYbbvypUrR7t27Vi8eDFdunRh3LhxXL9+nY4dO2Jubs6UKVOy9ViYOXMmfn5+LFy4kCpVqnDz5k26du3Kr7/+yv/93/8xatQoqlWrxtKlS7X2AWDSpEmsXbuWTz75BDs7O1atWoW7u7r7V5cuXRgzZgwjRowgMTGRDh068N133702r4p8EZq3bM2DhATWr/kTZWwsDo5OjJ80DVtbOwCUsTFERWWMNVHRrhLjJ01l6cLf+GuHP1bW1gz9cCRNMr2GSBkbg9+oYZrP/pvX4b95HbXr1OOHabOLVXxde/bl2bNEFvw2i0cPH+LsWovvp8ygVKaxTaKjIlEoMtpA3dw98Bv7PatXLGbtysVUtKuM39iJuLhldGts1rI1DxLi2bDmT5SxMTg4VuObSdM1+12UsYVeDWHCuNGaz8sWzQegVZu3Gek37pXON0Mvb5J3z1NX+/HsWSILf/tFk2/fTZmZJd8iMMr0OJ6bex3GjJ3AmhWLWJeWb2N01IeJ4z7TfF6+aB6grg8j/L55pWPLOKbL045pNb7NVCaUsTFER2X8uFAf02ksWTifv3Zs1XNMo/lczzGdMu3XPOVXZoZYH8wbeNBkX8brs91nqPP69p+bOTt0HGaVKlAqrZEB4MnNO5zqNBz3meNw/Lg/ifciuTDmR+5v2a1Jozx+hjP9/XCdNBrXSaN4HHqbM/3GEPfv2XznGcA/Z1MwMYbOTUtQyhTuRKlY+ncSzzI9yWpRVqE1JkFJU+ja3IRypeDpM3UPgAU7k7gTnZGoio2CYR1MNZ87NFb/mR54JYVNh/M2ov/hcymYlIDOTUpQMj22XUk8y7S4eRkdsTXLiC08RsXCLLGdu5FKabNk3vQsQbnSEKFU8efuJOK036KuV2HUh/SyuGbFYtauXEJFu8p8PnaCVlnMKzmPFPw8IkQ6hep5BxMQ4jVx8dq9og7hlZRaPDtGvdaMKPg71guboZc3Q867FPL2PnqhzZCPqcqAh2u7Vcsn90RF6Oj/Ctbo8DIU1hiYL0K/li/+zR8viiFfHwz5PFK7ZqXcExmo6RuLLl/H9jTc8lZYXr89FkIIIYQQQgghxAsjDQsF9NFHH1G2bFmd00cffVTU4QkhhBBCCCHEays1VVVk0+uoWI6x8DJMnjw520CJ6cq/4NcGCiGEEEIIIYQQhkoaFgrI1tYWW1vbog5DCCGEEEIIIYQoUtKwIIQQQgghhBCiWJFXFLxcMsaCEEIIIYQQQgghCkx6LAghhBBCCCGEKFakx8LLJT0WhBBCCCGEEEIIUWDSY0EIIYQQQgghRLGSKl0WXirpsSCEEEIIIYQQQogCk4YFIYQQQgghhBBCFJg8CiGEEEIIIYQQolhRpRZ1BK8X6bEghBBCCCGEEEKIApMeC0IIIYQQQgghihWVDN74UknDghB5lGrAHXxUKIo6BL0UGO5J3ZBjM+Rjash1wZCPKUAKxkUdgl7GpBR1CK8kQ64PhnweOfq/s0UdQo6afVS3qEPQ6/jCc0Udgl6GXOaMMNx+8Yacb0LkleFeDYUQQgghhBBCCGHwpMeCEEIIIYQQQohiJdVwO6kUS9JjQQghhBBCCCGEEAUmPRaEEEIIIYQQQhQrMnjjyyU9FoQQQgghhBBCCFFg0rAghBBCCCGEEEKIApOGBSGEEEIIIYQQxUqqquimwqJUKhk4cCDm5uaYm5szcOBA4uLiclxm8ODBKBQKralx48ZaaRITExk5ciQ2NjaUKVOGzp07c+fOnXzFJg0LQgghhBBCCCGEgevXrx9BQUHs2rWLXbt2ERQUxMCBA3Nd7u233yY8PFwzBQQEaH0/evRotmzZwtq1azly5AgPHz6kY8eOpKSk5Dk2GbxRCCGEEEIIIUSxoirMrgO5SExMJDExUWuemZkZZmZmBV7npUuX2LVrFydOnOCNN94AYOHChTRp0oSQkBBcXV31LmtmZoadnZ3O7+Lj41m8eDErVqzgrbfeAmDlypXY29uzd+9efH198xSf9FgQQgghhBBCCCFekKlTp2oeV0ifpk6d+lzrPH78OObm5ppGBYDGjRtjbm7OsWPHclz24MGD2Nra4uLiwrBhw4iMjNR8FxgYSFJSEu3atdPMq1y5Mh4eHrmuNzPpsSCEEEIIIYQQolgpyrdNjhs3Dj8/P615z9NbAeD+/fvY2tpmm29ra8v9+/f1LvfOO+/Qq1cvHB0duXHjBt999x2tW7cmMDAQMzMz7t+/j6mpKZaWllrLVaxYMcf1ZiUNC0IIIYQQQgghxAuSn8ceJk6cyKRJk3JMc+rUKQAUCkW271Qqlc756fr06aP5v4eHB97e3jg6OrJz5066d++ud7nc1puVPAqRRzdv3kShUBAUFPRStxsbG8vgwYOxs7PDysqKvn37Ehsb+1JjEEIIIYQQQgjx4o0YMYJLly7lOHl4eGBnZ0dERES25aOioqhYsWKet1epUiUcHR25evUqAHZ2djx79gylUqmVLjIyMl/rlR4Lhaxdu3bs27ePo0ePZnutR16MHDmSM2fOsHXrVszNzRk9ejRffPEFS5YsKYRo80ahULBlyxa6du1aZDEYGpVKxfrVy9izazuPHj7A2dWdDz4ejYNjtRyXO370EGtXLOZ++D3sKlWm36APeKNpS600u3ZswX/zWpSxsdg7OPH+8BG4e9TLZ2xL2ZsWW01Xd4Z9PAb7XGI7cfSgVmx9Bw3Tiu3i+SD8N63l+rUQlLExfDX+Rxo1aZHnuDJiM8x8+2vH1rTlY7B3qMaQ4SNw96irN/2Fc0EsXfgbt8NuYGVlQ9ee7+Lbvku2uNesWKIVd+Om+cszMOx8M+T4DPmYgmHX1b92bGXr5nVpeefE0Fzy7rwm725q8u7t9p0134fdusGalUsJvXaFqMgIhgz7lE5de+YrplchNkOtC9rxGWaZA2hT35iGrsaUMoPbUSq2HUsmMk5/32YvZyN6tjTJNv/7ZYkkpw1s7mSnoEUdY6pYG1G+jIIVe5O4dCs1T/FYNfem+udDMffyoGRlW073+ISIbftyXqZFQ9xnfE1Zd2cS70USOnMRYQvWaqWx69YOl4mfUbqGA49Dwwj5fhYR/nvzFJMurT2N8XYxopQp3IlWsf1ESo75llmdakb08SnBxbBUVu9P1sw3LQFveRnj7mBEmZIQHqti58kU7sbkva+5IdeHF319CLt1g7UrlxJ6LYSoyAjeH/Ypnbr2ynM8mRlyvhm61CIcvDE/bGxssLGxyTVdkyZNiI+P599//6VRo0YAnDx5kvj4eJo2bZrn7cXExHD79m0qVaoEQIMGDTAxMWHPnj307t0bgPDwcM6fP8/PP/+c5/UWux4Lz549K+oQNMLCwjh+/DgjRoxg8eLFBVrHrl27+Pzzz2ncuDHVq1enRYsW7Nq167niKmgeGVLeGpqtG9ewfct6PvhoNNNn/YGFpRWTx3/Ok8eP9S4Tcuk8v0ybhE/rdsyct1j977SJXLl8UZPm6D/7WbpwHj36DGTGnIXU8qjLjxPGEhWZvbVSf2yr2bFlPUM/Gs20WQvSYvPLU2wtW/syc94SWrb25ZdpE7Rie/r0KU7VajD0o9F5jiV7bIaZb0c0yw9g5pxF1PKoww8TvtK7fMT9cH6Y8DW1POowc84iuvfpz+I/5nL86KFMcV9gZlrcv8xbpDPuvDLUfDPk+Az9mILh1tUj/+xnycL59OwzgJlzFuLuUZcpOeS7Ou/G4e5Rl5lzFtJDR94lJiZS0a4yAwcPx9LSqkBxGXpsYJh1QTs+wyxzAC3rGtPMw5jtx5P5bVsSD5+oGPK2CabZ2w20PH2m4qfViVpTcqa3pZmWUHA/VsX248n6V6KHcZnSJJwN4cJnk/OUvpRTVRpuX0DskUCONOzKten/o/asb7HrljFAmkVjT+qvnsXdVf4cbtCFu6v88VozG4tG+n/U5qSFhxFN3Y3YcSKZ33ck8+CJisHtSmCah1uJFmXgbW9jbt7P3tDSrZkxNSop2Hg4mbn+SVy7p+J93xKUK5332Ay1PhTG9UF9HqnEwMHDsSjm5xHx8tSqVYu3336bYcOGceLECU6cOMGwYcPo2LGj1hsh3Nzc2LJlCwAPHz7kiy++4Pjx49y8eZODBw/SqVMnbGxs6NatGwDm5uYMHTqUzz//nH379nHmzBkGDBhAnTp1NG+JyItXvmGhVatWjBgxAj8/P2xsbGjbti2HDh2iUaNGmJmZUalSJb7++muSkzMuILt27aJ58+ZYWFhgbW1Nx44dCQ0N1Vrvv//+S/369SlZsiTe3t6cOXMm37EtXbqUjh078vHHH7Nu3ToePXqk9b2TkxOzZ8/Wmufp6cnEiRM1n2NjY7G2tubJkyc0bdqUCxcusGzZMq1lLl68SPv27SlbtiwVK1Zk4MCBREdH55hHQK75pGs5JycnALp164ZCodB8Dg4O5s0336RcuXKUL1+eBg0acPr06Xzn2atIpVKxw38DPfoMpHGzljg4VWek3zgSExM5fEj/HYcd/hupV78B3XsPoKq9I917D6BOvQbs8N+gSbN9y3pat2vPW74dqergxJDhI7G2qcDfAf55jm2n/wa69xlI42Y+abF9kxbbHr3L7fTfQN363nTvPYAqmWLbmSk2L+/G9B00jMbNfPIUi67YDDXftm/ZQJt27Wnr25GqDo4MHT4Saxtbvcv/HbANmwq2DB0+kqoOjrT17Ujrtu/gv3ldxjr9N1Kvvjc9evenqr0jPXr3p049L3b4b8xjjqkZcr4ZcnyGfEzBsOvqNk3edcDewZGhw0dgbWPLroBtOtNn5N0I7B0caevbgdZt32Hr5vWaNM4ubgwe+hEtfFpTwiSXX4qvaGyGWhcyx2eoZQ6gaW1jDgancOFWKhFKFRsOJWNSAjyr5/ynq0oFD59oT5lduZPKnkD1evMr6u9/uDJhNve36s+fzByHv8vTsHAufv4TDy9f5/aSjdxetpnqfkM0aaqNfI/ovccI/XkBj0KuE/rzAqL3n8Bp5Hv5jg+gqbsxh86mcDFMRWScik2HUzApAfVyyTeFAnq1LMH+oBRiH2rf5S1hDO6ORvwdmMLNCBWxD2B/UArKhyrecDXOU1yGXB8K4/rg7OLGe0M/prlPG0yK8XnE0KlUqiKbCsuqVauoU6cO7dq1o127dtStW5cVK1ZopQkJCSE+Ph4AY2Njzp07R5cuXXBxceG9997DxcWF48ePU65cOc0ys2bNomvXrvTu3ZtmzZpRunRptm/fjrFx3uo4FIOGBYDly5dTokQJjh49yk8//UT79u1p2LAhwcHB/P777yxevJgffvhBk/7Ro0f4+flx6tQp9u3bh5GREd26dSM1NVXzfXrLT2BgIBMnTuSLL77IV0wqlYqlS5cyYMAA3NzccHFxYf369bkvqMfu3bu5du0aq1ev1noVSHh4OD4+Pnh6enL69Gl27dpFRESEphuLrjz6448/uHv3bq75pGu59IFDli5dSnh4uOZz//79qVq1KqdOnSIwMJCvv/76uU6kr5KI++HEKWOp5+WtmWdiYkptj3qEXDqvd7krly9Qr35DrXmeXg0JuXQBgKSkJEKvXcEzS5p6Xg1zXG9mkZrYMtZhYmKKewFiq+fVKM/bzQtDzTf18iE6t3E5bRu6YvL0ypq+EaFXQzSNdVcuX8gWU32vRnrXqY+h5pshx2foxxQMt65m5Lu31nxPL28u69lGyOWLeHppp6/v1VAr74p7bGCYdSEzQy1zAJbloHxpBVfvZvz4T0mFG/dTcaiY85+upibwZR9Txr5ryqC2JahknfeBx140i8aeRO09qjUvavdhzBt4oCih7kJg2diT6L1HtNJE7zmMZZP6+d6eZVkoV1rBtXsZP2pSUuHmfRUOtjnnw5v1jHn0FAKvZm9wMVKAsZFCq+cHQFIyOFbMW/4aan0orOvDi2Ko+SaKjpWVFStXriQhIYGEhARWrlyJhYWFVhqVSsXgwYMBKFWqFH///TeRkZE8e/aMW7dusWzZMuzt7bWWKVmyJHPnziUmJobHjx+zffv2bGlyUyzGWKhZs6bm+Y8///wTe3t75s2bh0KhwM3NjXv37jF27Fi+//57jIyM6NGjh9byixcvxtbWlosXL+Lh4cGqVatISUlhyZIllC5dmtq1a3Pnzh0+/vjjPMe0d+9eHj9+jK+vLwADBgxg8eLFvP/++wXaRysrKxISEti4cSO9emU8o/X777/j5eXFTz/9pJm3ZMkS7O3tuXLlCi4uLtnyCODbb7/NNZ90LZfOwsICOzs7zeewsDC+/PJL3NzcAHB2ds5xfxITE0lMTNSal5/RUw1JnFI9mKaFhXZXN3MLS6Ki9Hcni1PGYpHltS4Wlpaa9T1IiCc1NQXzLOu1sMhIkxulMkZnbBYWVkRF6X99TG6xvQiGmm/q5VOxsNDehnkOyyuVsXhmSW9hYUlKSgoJCfFYWVkTp4zFPEvc5gXIU0PNN0OOz9CPqXp7hllX9eWdOt+VOpdRKmOpn0veFffYwDDrQmaGWuYAypVS/1h9+ET7rt/DJ2BRVv8P2ag4FZv+Sea+UkVJE3Wvhw87mjB3SxIxCS//WWuzijYkRkRrzXsWGYORiQmmNpYk3o/CzM6GxIgYrTSJETGY2VXI9/bK6s03VY755mCroIGzEfO3Jen8/lkyhEWm8mY9Y6Liknn4FOpWM6JqBQUxCXmLzVDrQ2FdH14UQ803IXQpFj0WvL0zWvEuXbpEkyZNtF6N0axZMx4+fMidO3cACA0NpV+/flSvXp3y5ctTrZp68JOwsDDNOurVq0fp0hkPjjVp0iRfMS1evJg+ffpQIq1Fum/fvpw8eZKQkJAC7WOLFi34/vvv6du3L5988glJSeqTf2BgIAcOHKBs2bKaKf3HfebHOzLnUfo+5pZPupbTx8/Pjw8++IC33nqLadOmZXu0JKupU6dibm6uNU2dOjVP2ypq/xzYQ/8eb2umlBR163T217GoUJBbS7729ypV9vVkXa1KpWOmJrbdDOjhq5lSUlJ0ryMPsWX9Pr+vnMkem+Hmm84t6FhBTotnTa9ClS1S3XmacxyGnm+GHp/2soZxTMGw66rujeg6Njkl15d3hXD32EBiM/S6YMhlrl4NIyYMMtVMxul/nWZpC8htE7ejVASFpnI/VsXNCBVr9icTHa+iiXsR/rmbtUt0+k5knq8rTR66UterbsR3/U00U3q+ZV1Socg+L51pCejVogRbjyXzOFFPImDjYXV5HtvHlIkDTWhSy4iz11P1hmno9SHbFgrh+lAQr1q+GTpVatFNr6Ni0WOhTJkymv/rurilP+eSPr9Tp07Y29uzcOFCKleuTGpqKh4eHprBCZ/3uZjY2Fi2bt1KUlISv//+u2Z+ei+I6dOnA2BkZJRtW+kNBrpMmjSJd955h+7du2NsbMzcuXNJTU2lU6dOmnVmlj7SJ2jnEeQtn3Qtp8/EiRPp168fO3fu5K+//mLChAmsXbtWMyhIVuPGjcPPz09r3qvSW6HhG81wdq2l+Zx+zJTKGCwztVLHx8Vlay3OzMLSKlurcHycEvO0VvBy5c0xMjLOniZema1lPSO25ji7ums+J2tii8XSKmO02fg4ZbY7rVljU2aLLU4TW0EYcr5lpl7eKPv+x8dla9lPZ6kzpjiMjY0pV95cb9wJcfrXmc7Q883Q48tY1nCOKRh2Xc0sPe905bu+bVjqiUmdd+VfSFyGGJuh1wVDLnOXwlK5HZkxQHQJY/XfIWVLK3iQ6e57mZLZ78bnRAXcjVZhXd4ISMkt+QuXGBGdreeBaQUrUpOSeBYTp05zPxozO+3R4M1srbL1dNDlUlgqt6MyfsGk51u5UgqtfCpTUsEjPflmVV6BZTkFA9pk/CRI/zNw0iATft2SROwDiH0Ai3epx7kwM1H3HunjY4zyge71Gnp9SFdY14eCelXyTQhdikWPhczc3d05duyY1g/2Y8eOUa5cOapUqUJMTAyXLl1i/PjxtGnThlq1amV7Z6e7uzvBwcE8eZIx4s+JEyfyHMOqVauoWrUqwcHBBAUFaabZs2ezfPlyzfNXFSpUIDw8XLNcQkICN27cyHHdjRs3ZtiwYezbp369kZeXFxcuXMDJyYmaNWtqTTk1CuSWTzkxMTHR3OnIzMXFhTFjxrB79266d+/O0qVL9a7DzMyM8uXLa02vSsNCqdKlqVS5qmayd3DCwtKKs2cyBqtMSkriwvlgXGt56F2Pi1ttgoO0B7gMPnMK11q1AXU+16jpQvAZ7TRnz5zWu96ssVXVE9vFPMR2NuiUjtj0L5MbQ863zNTLu2ZbPvjMadzStqEzpmzpT1HD2VXTa0lX3EFnTuldZzpDzzdDjy9jWcM5pmDYdTUzffkefCYQNz3bcHVzJ/hMoNa8oDOntfKuOMZm6HXBkMvcsyQ0P15jH0BknIqExypqVs74M9XYCKrZGREWkb9bgZWstBsnXqa4E0HYtNF+BVyFts2JDzyPKu1vQeWJIGzaNNNKY/NWc5THcx80/Fly9nx78FhFjcoZN4iMjdSv2AyL1J0H0fEq5mxNYv62ZM10+baKG+Eq5m9LJl573HGSktWNCiVNoWYVIy7d1n08DL0+pCus60NBvSr59qpIVamKbHodFbuGhU8++YTbt28zcuRILl++jL+/PxMmTMDPzw8jIyMsLS2xtrZmwYIFXLt2jf3792e7c96vXz+MjIwYOnQoFy9eJCAggBkzZuQ5hsWLF9OzZ088PDy0piFDhhAXF8fOnTsBaN26NStWrODw4cOcP3+e9957T+/Imxs3buTatWucO3eOXbt24enpCcCnn35KbGwsffv25d9//+X69evs3r2bIUOG6Pzxn9d8yomTkxP79u3j/v37KJVKnjx5wogRIzh48CC3bt3i6NGjnDp1ilq1auW4nuJCoVDQsUsvNq1fxclj//w/e/cdFcXVh3H8uyAioFJExAIooiAWFEs0FowNY40tRo2+9pKoUaKJJiZqNIkmosZoiqhg19hrjC3YNTbsFQvY6GCn7vvH6sLCLgKWHfX3OWePMntn5tk7s7O7d+7cIezaFWZO+xFzc3Pq+6TfomWG//csCpqt/btlm46cOHaENSuWcCP8OmtWLOFkyFFatU0fQ6N1uw/ZsXUTO7Zu4kbYNQJnzyQ6KpJmGe65/qxsLdt2YvVfi7TZZmmzNdXJtjjoT+3fLbTZFnMz/DprVizmVMgRWmbI9ujRQ66GXuJq6CVAM8DQ1dBLOb5NkZLrrXW7Tk/m38yNsOvMmz2T6KgI7fyLgmbzi3/6uCa+LdoQFRlBYMAsboRdZ8fWzezYupm27Ttry7Rq04GQY4dZ/ST3am3ujjnK9DrUm5LzKXmbPq03pb5X27TrxPatm9m+dTPhYdeZN3sW0VER+LZoDcDCoAC9dTcvYBbhYdfZ/qTuPmifPqhwcnIyV0MvczX0MikpKcTERHM19DK3b93MVb0pOZtS3wsZ8yl1nwPYfyaVhl6meLqYUMxWRccG+UhOgZAr6T9kOzbIR7Ma6d+bGlUzpVxJFbaFNA0K7etrBm/871z696H8+TTPFbfT/Pi2K6j5v3UOOmiaWllS2MuDwl6aS04ty5SisJcHBZw0PUTdJ/rhFZjeg/T67GVYuJSgws+jKOjhSqmeHXDq1YErU+dpy1ybuQD7pnVxHdEPK3dXXEf0w75xHa79Oj/HdaVTb2dT8aliSgVnFQ42KtrXMyU5BU5kqLcO9Uxp6q2pt5RUTYNExsfjJDWJKZrpqU9mcyuh0tRtQShbXEWf5vmITlBzTM9gj/oo+f3wMj4fNMcRzXsgJSWF2JhoroZe4vatG1nW/7rWmxCZvRGXQmRUsmRJNm/ezMiRI/Hy8sLOzo4+ffowZswYQHP5wbJlyxg6dCiVKlXC3d2dGTNm0LBhQ+0yChYsyIYNGxg4cCDVqlXD09OTyZMnZxn0UZ+jR49y4sQJAgICsjxXqFAhmjVrxty5c2nbti2jR4/mypUrtGrVCmtrayZMmGCwx8Iff/zB/v37sbCwoFGjRkybNg2AEiVKsG/fPr788kt8fX1JTEzExcWF5s2bZ9tA8Kx6yo6/vz9+fn4EBARQsmRJLl68SExMDD169CAiIgJ7e3vat2/P+PHjn7msN8UHHbuQlJTI7N+m8eD+fcq5V+DbCVOwyDBOR3RUJCpV+jbx8KyE35ffsmThXJYtmksxxxL4fTmO8h7pXVXrNmjEvbsJrFi6gLjYGJxdyvDV+Mk4ODiSUx907EpSUiIBv03VZvtmgn+mbBGYZLgExsOzMsO/HMvShXNY/iTb8EzZQi9dYNzoz7R/z58zE4CGjZsz2O+r17re6jVoxL27d/lr6XziYmNxdinD1xnmj4uNITrDoEnFHIszZvwk5gXM4u+Na7ErUoQ+A4ZQJ8Ot1p7mXrpwLssWzaOYYwk+/3KsTu6cUmq9KTmf0reppt6U+V5Nr7sFT+quNGPGT9Kpu6ioSG15Td39SGDAb/y9cZ3euouLjcFvaD/t3+tWL2fd6uVUrOzFxEnTc1xnSs4Gynwv6OZT5j4HsPtkKmam0ObdfFjkhxtRagL/SSYpwxWjNgVVOtf4F8gPH9Qzo5AFPE6CWzFqZm9K5kZ0eqGS9ir6tcyv/btlbc1X4aMXU1m1J/sR/a2rV6LOjvTbunlO0byW8AWrOdlnNObFi2LhlH4Z6qNrNzjcuj+e/qNxGdSNxFuRnBn+PXfWbNWWiTtwnOPd/HAfPwz38UN5GBrO8a7Dif/vZI7qKbM9p9Mwy6eiTe18FDDX1FvQ1hSSMrw0m4Iq7ZgAOVUgv4pm3qYUtoJHiXDmehrbjqWSlovFKPX98DI+H+Jio/ncwHFkwqRfcl5pCq6318HLvO2jyEqllhoXIkdOXzY8SraxqV/GgGgviCqXX15eJSVnU/I2VTIlb1OANAV3FDQ1wjXobwIlb1MlH0eW7rIxdoRs1R1YxdgRDDoQcMrYEQz6qJ5y7yig5M8HJb9XK7m9vg0Nn//24NmFXhL/T3I2Tt2bRLmfhkIIIYQQQgghhFA8aVjIg4EDB+rc3jHjY+DAgcaOJ4QQQgghhBBvtbQ0tdEeb6M3boyFV+G7775jxIgRep8r/AJvpSWEEEIIIYQQQiidNCzkgYODAw4ODsaOIYQQQgghhBBCDxlJ8NWSSyGEEEIIIYQQQgiRZ9KwIIQQQgghhBBCiDyTSyGEEEIIIYQQQrxR1G/pIIrGIj0WhBBCCCGEEEIIkWfSY0EIIYQQQgghxBslTUZvfKWkx4IQQgghhBBCCCHyTHosCCGEEEIIIYR4o8gYC6+W9FgQQgghhBBCCCFEnknDghBCCCGEEEIIIfJMLoUQQgghhBBCCPFGkUshXi1pWBAih9SojB3BIBPSjB1BvGAqlPthmKbgzm5KrjdQ9ntVydtV5I0pqcaOYJBKuR+pABwIOGXsCAbV6VfZ2BEMUp0LNnYEIYSRSMOCEEIIIYQQQog3inRYeLXk9IQQQgghhBBCCCHyTBoWhBBCCCGEEEIIkWdyKYQQQgghhBBCiDeKDN74akmPBSGEEEIIIYQQQuSZ9FgQQgghhBBCCPFGUaulx8KrJD0WhBBCCCGEEEIIkWfSY0EIIYQQQgghxBslTcZYeKWkx4IQQgghhBBCCCHyTBoWhBBCCCGEEEIIkWdyKYQQQgghhBBCiDeKDN74akmPBSGEEEIIIYQQQuSZNCw8p2vXrqFSqQgJCTF2FCGEEEIIIYQQgDpNbbTH20guhXiFrl27RpkyZTh+/DhVq1Y1dpw8GzduHGvXrpXGlAzUajV/LQlk+5YNPLh/Dzd3T/oNGo6TS5ls5zu4L5hlC+dy5/YtHIuXoEuPfrzzbgPt82dPh7Bu1TKuXL5AXGwMX4z5nlp16ucq298b17Ju9TLiYmNwci5D7/6D8axUxWD5M6dCCAz4jfCwq9jZ2fNBx4/wbdFWp8yBfbtYunCeNnfXHn2p/W7ucr1t2cKuX2XZokBCL18gKjKCXv0+pfUHnXKd62VkgxdXb6Dc94PS603J+TTbNIhtT7ZpOXdP+g4ahvMztumBfbt0tmnXHn11tinAlo1rnrzuWJycS9Or/2A8K3lJtpec7e+Na1m7evmT/a00fZ6xv53W7m/XtPtb8xZttM+HXb/K0kWBhF6+SFRkBL37fUrrDzrmOI8+jaqZUtPdFIv8EB6lZsOBFCLjDX9hr+ZmQscGZlmmj52fSEpq+t/veJhQr3I+CllAZLyaTYdSuB6Rux8CjaqaUqO8CRb54Ua0mg0HU7PNllHlMiZ09snH2bA0luxM0U7Pnw+aeJvi6WyCVQG4Hatm06FUbsY8e7l29Wrg+nkfrL0rUaCEA0c6fELE+h3Zz1O/Jp5TRlHQsxyJtyIJ9Z9D2OxlOmUc2zWj/LjPsCzrzMPQMC58O42Iddtz9DozU/IxTsnZlHwcESKjt6bHQlJSkrEjKIZarSYlJeXZBV/QfG+DtSuXsHHNX/QZOIxJ02ZjY2vHd2P8ePTwocF5Lpw7zdRJ42nQyBf/mfNo0MiXqZPGcvH8WW2Zx48fU7pMWfoMHJanXHt37yQwYCYdOn+M/4w5VKhUmYljvyAqMkJv+Yg7t5k4dhQVKlXGf8Yc2nfuxtw/f+XAvl0Zcp/Bf9J4fBo1Y+rMOfg0aob/pHE6uSVb1myJiYkUcyxO9579sbG1y1Wel53tRdXbU0p8Pyi93pSeb+3KpWxY8xd9Bw5j8rQ/n2zTz3O0TX0aNcN/5ly969+nfd3dmTIjgAqVqvD92C8Nvm7J9mKy7d29k3kBs+jY+WP8ZwTgWakKE7KZX7O/jcazUhX8ZwTQweAxrgTde/bH9jmOcU/Vr2xK3YqmbDiQwm/rk7n/SE2v5mbkf8YpscdJan5cmqjzyNioULmMCS3eyceuEynMWpfMtYg0/tfMDGurXGSrZMK7niZsPJjC7xtTuPdITc9m+Z6ZDcDGCprXMOXanbQsz7Wra0rZ4ipW7knh13XJXL6lppdvPgpZPnu5plaW3D15gTOffZej12BRuhQ1N8wmdu9R9tb8gMuT/6DitK9xbNcsPWvtqlRbMo2bi9exp3pbbi5eh/fS6djUMvyD2xAlH+OUnA2UexwRIrM3tmGhYcOGDB48GD8/P+zt7WnatCm7du2iVq1amJubU7x4cUaNGqXzQ3nLli3Uq1cPGxsbihQpQqtWrQgNDdVZ7n///Ue1atUoUKAANWrU4Pjx43nOGBwcjEqlYseOHdSoUQNLS0veffddLly4oC0zbtw4qlatysKFCyldujTW1tZ89NFH3Lt3T1tGrVbz008/4erqioWFBV5eXqxcuTLLev755x9q1KiBubk5e/bsITExkaFDh+Lg4ECBAgWoV68ehw8fzna+hQsXMn78eE6cOIFKpUKlUhEUFKTN6uzsjLm5OSVKlGDo0KF5rpvXiVqtZtO6FbTv3J3adX1wLu3KEL+vSExMZM+ubQbn27RuBVWq1aD9hx9T0smF9h9+TGWv6mxat0JbxrtGbbr06Eftuj55yrZhzQoaN2tBU99WlHJ2oU//IRSxd+Cfzev0lv9n83rsizrQp/8QSjm70NS3FY2avs+61cvTl7luJV7VatDhw26UcnKhw4fdqOzlzcZ1K/UuU7JplCvvwf/6DKKeT2PMzLKeUTNmthdVb6Dc94PS603J+dRqNRvXraBD5+7UrtvgyTYd/WSbGj5zuXHdSryqVaf9hx9TKsM23Zhhm25Y8xeNmrWgiW8rSjmXpnf/IRSxL2rwdUu2F5NtvXZ/a4mTswt9+g+miL0DWzav11s+fX8bjJOzC019W9Ko6fusXf2Xtky58h707DOQ+j6NyPccx7in6lY0JfhEKmevpxEZr2bl7hTMTMGrbPZfXdVquP9I96Gz3EqmHL2YxpGLaUQlqNl8KJWEB2re8TDNcbZ3PU3ZdTKVs2FqIuPVrNqTilk+8HLNPptKBZ0a5GNnSCqx93V7IeQzBU8XE/45msq1CDWx92BnSCpx99W84/7sbFH/7Obi2OncWWv4OJuRS/+PeBx2m7Of/8D981cIn7eS8KDVuPr11pYpM+R/RG/fT+hPs3lw4QqhP80meudBSg/5X47WkZGSj3FKzqbk48jrQC6FeLXe2IYFgPnz55MvXz727dvHDz/8QIsWLahZsyYnTpzg999/Z+7cuUycOFFb/sGDB/j5+XH48GF27NiBiYkJ7dq1Iy0tTft8q1atcHd35+jRo4wbN44RI0Y8d86vv/4af39/jhw5Qr58+ejdu7fO86Ghoaxdu5aNGzeyceNGdu3axaRJk7TPjxkzhsDAQH7//XfOnDnD8OHD+fjjj9m1a5fOcr744gt+/PFHzp07R5UqVfjiiy9YtWoV8+fP59ixY7i5ueHr60tsbKzB+Zo1a8bnn39OxYoVuX37Nrdv36Zz586sXLmSadOm8eeff3Lp0iXWrl1L5cqVn7tuXgeRd24THxeLl3dN7TQzs/x4VvLiwrnTBue7eP4MXtVq6kzz8q6V7Ty5kZycTOjlC1nWUdW7JufPnTGYqap35vK1CL10QdsId/H8GapmWmY171oGlynZXlwvHyXX21NKfD8ovd6Uni9Cu01raKeZmeWnYh62aVXvmlx4sn7N676YJaOXd80cb3fJlvts6fPX0Jle1bsG5w3Mf+H8Wap665av5l3zhR/jnrItBIUsVVy+mX5WPzUNrt1Jw9kh+6+u+c1gxIf5+aJzfro3yUdxO5X2OVMTKFFExeVbur0FLt989nK12Qo+yXYr/YeDJpsaZwdVNnPCe16mPHgMRy9l7a1gogJTE5VO7wqA5BRwKZb9cvPCpnZVorbv05kWtXUP1tUrocqn6XphW7sq0dv36pSJ3rYH2zrVcrUuJR/jlJwNlHscEUKfN3qMBTc3N3766ScAFixYgJOTEzNnzkSlUuHh4cGtW7f48ssv+fbbbzExMaFDhw4688+dOxcHBwfOnj1LpUqVWLx4MampqcybNw9LS0sqVqzIjRs3GDRo0HPl/P777/Hx0ZyBGzVqFC1btuTx48cUKFAAgLS0NIKCgihUqBAA3bt3Z8eOHXz//fc8ePCAqVOnsnPnTurUqQOAq6sre/fu5c8//9QuF+C7776jadOmgKaR5PfffycoKIj3338fgICAALZt28bcuXMZOXKk3vkAChYsSL58+XB0dNROCwsLw9HRkSZNmmBmZoazszO1atV6rnp5XcTFxQBgY6Pb9dPGxo6oqDsG54uPi8XG1lZ3Hltb4uNiDcyRO/fuJpCWloaNje46rG0MryMuLpaqmcrb2NiSmprK3bsJ2NkVIT4uFutMua1zmfttzPYiKLne0tenvPeD0utN6fmels+8Ta1tbImKMtxl9lnbVPO6U7HOsq/kPKNky302Q/ubZv44vfPExcVS7RUc454qZKH5IX3/ke5Zv/uPwcbK8I/s6AQ1q/akEBGnxtxM07OgfyszZq5NJuauGktzzY/3LMt9BAVzcLkBQEFD2R6psSloOJuzg4rq5UyYtT5Z7/NJKRAWmcZ7XqZExadw/zFUKWNCqaIqYu7mLFtumBezJzEiWjdDZAwmZmbkt7cl8U4U5o72JEbE6JRJjIjB3LFortal5GOckrOBco8jr4s0ud3kK/VGNyzUqJHeunfu3Dnq1KmDSpV+0K9bty7379/nxo0bODs7ExoayjfffMPBgweJjo7W9lQICwujUqVKnDt3Di8vLywt0z99nv6Yfx5VqqRfq1a8eHEAIiMjcXZ2BqB06dLaRoWnZSIjIwE4e/Ysjx8/1vnhD5oxJapV021RzlgfoaGhJCcnU7duXe00MzMzatWqxblz5wzOZ0inTp2YPn06rq6uNG/enBYtWtC6dWvy5dO/iyUmJpKYmKgzzdzcHHNz82euy9h2/7uV2TP9tX+PHjcZ0HRxzEiNGhXZn2XI/LxardbZR1+ELMtTq7Nkza68Gs1BOeNU/bkl27OyvShKqrfX6f2gpHpTcr7d/27jzwzb9Ktxk/Tny8E2zfwOUKuzLkfPy846UbI9Vzb9q9C3juyKG9rfnv896uVqQtu66d8XFmzT/+Nbs17DwqPUhEellwiLSOHTtmbUrmDCpkPpXQEy/9ZQqQwv2MvVhDZ10i9FWLg9RW8Olcpwtvz5oFP9fKzdn8LDRAOFgJV7UmhXNx9fds5Papqa2zFqTl5Jo0SRl9TBWG9FZJqur0wef6wp5Rin5Gyv3XFEiAze6IYFK6v0kXj0fUFVPzkwPp3eunVrnJycCAgIoESJEqSlpVGpUiXtwI/qPB5InyXj9dZPszxt1Mj8/NMyT59/+u+mTZsoWbKkTrnMP9Iz10fG9WWcnnlaxvkMcXJy4sKFC2zbto3t27fzySef8PPPP7Nr1y6915P/+OOPjB8/Xmfa2LFjGTdu3DPXZWw136lHOXdP7d8pyZovQHFxsdja2WunJ8THZWmtzsjG1o64TK3CCfHxWNsYnic3ChW2xsTEJOs6EuKztFA/ZWtrl6WlOiE+HlNTUwoVttbmzlzmbrzhZUo26xyv21jZnqfeXof3gxLrTcn5ar5Tl3LuFbR/J2u3aQy2Gc5MJ8THZzkjlpG+9SfEx2m3qeZ1m2YtkxCX5eyhZHu+bBk93d/0zW/o/WZr4P2p2d8KP3Odz3IuLI3wqPRBtvOZar6HFLRQcS9Dz4CCBbL2FMiOGs0dG+ytTYBUHiZCapqaQpa6LQlWBbKOxaCbLf072dNshSx0ez5YFVDxwEA2u8IqbAup+Lhx+tfup1+1xvcw45c1ycTeg9h7MHdLCmb5wNxMk6mzjylx917898/EiOgsPQ/yF7UjLTmZpJh4TZk70Zg72uuUMXewy9LT4VmUdoxTcrbX5Tjyunhbxzowljd6jIWMPD092b9/v07jwP79+ylUqBAlS5YkJiaGc+fOMWbMGBo3bkyFChWIy9Ql0NPTkxMnTvDoUfqnz8GDB1/Za9DH09MTc3NzwsLCcHNz03k4OTkZnM/NzY38+fOzd2/6tXPJyckcOXKEChUqGJwPIH/+/KSmpmaZbmFhQZs2bZgxYwbBwcEcOHCAU6dO6V3G6NGjSUhI0HmMHj06h6/auCwsLSleopT2Ucq5NDa2dpw8fkRbJjk5mbOnT+BeoZLB5ZT3qMjJkMM6004cP5ztPLlhZmZGWTd3TmTIpVnHETwqVDSYKWv5w5Qt567tfVLeoyInQnTLhBw/bHCZku3Ftd8qsd5eh/eDEutNyfkyb1MnA9v0TA62aeb1a7ZpxQyvu3yW13Hy+BGDy5VsecuWkaH5Txw/ioeB+d09PDlx/KjOtJDjR17YMS4pBe0P69h7mltA3nuoxq1k+tdUUxMo7WhCWGTW8QmyU9xOxb2Hmu9+qWlwK0aNWwndr79uJQwv11C2siXST8JosqkIi9T/IyY6Qc2MtcnMWp+ifZwPV3P1tppZ61NIeKBbPjlF06hQID+4lTThXHjuXnNOxB8Mwb7xuzrTijatR8LR06ifjBMQdzAE+8Z1dcrYN6lH3IHcDVyutGOckrO9LscRIfR5axoWPvnkE8LDwxkyZAjnz59n3bp1jB07Fj8/P0xMTLC1taVIkSLMnj2by5cvs3PnTvz8/HSW0bVrV0xMTOjTpw9nz55l8+bNTJkyxUivSKNQoUKMGDGC4cOHM3/+fEJDQzl+/DizZs1i/vz5BuezsrJi0KBBjBw5ki1btnD27Fn69evHw4cP6dOnT7brLF26NFevXiUkJITo6GgSExMJCgpi7ty5nD59mitXrrBw4UIsLCxwcXHRuwxzc3MKFy6s83gdLoPQR6VS0bJtJ1b/tYhD+3cTdu0Ks6b9iLm5OfV90i9RmeH/PYuD/tT+3aJNR04cO8KaFYu5GX6dNSsWcyrkCC3bdtKWefToIVdDL3E19BKgGcTnauilHN8KqHW7TuzYuokdWzdzI+w682bPJDoqgmZP7j2+KGg2v/j/oC3v26INUZERBAbM4kbYdXZs3cyOrZtp276ztkyrNh0IOXaY1SuWcCP8OqtXLOFkyFFatc3d/crftmzJycnabZmSkkJsTDRXQy9x+9YNo2d7UfUGyn0/KL3elJxPpVLRqm0nVv21WLtNZ2q3aRNtuRn+37MoaLb275babapZ/xrt+tO3aet2Hz553Zu4EXaNwNkziY6K1L5uyfZysrVp14ntWzezfetmwsOuM2/2LKKjIvBt0RqAhUEBeve3eQGzCA+7zvYn+9sH7T/UltEc4y5zNfQyKSkpxMREczX0Mrdv3cxRpsz2nUnFp4opni4mONio6FA/H8mpcCI0/Ud2xwb5aFY9/TKFRlVNcSupwraQpkGhfb18FC+i4r/z6SdD9p1OpXp5E6qXM6GotYoWtUyxLqhb5ln2n9Vkq+CswsFGRft6piSnwIkr6dk61DOlqbcmW0qqpkEi4+NxkprEFM301CezuZVQUa6kCtuCULa4ij7N8xGdoOaYnsEeMzO1sqSwlweFvTwAsCxTisJeHhRw0lxi6z7RD6/Aydry12cvw8KlBBV+HkVBD1dK9eyAU68OXJk6T1vm2swF2Deti+uIfli5u+I6oh/2jetw7VfD3y8NUfIxTsnZlHwcESKzN/pSiIxKlizJ5s2bGTlyJF5eXtjZ2dGnTx/GjBkDgImJCcuWLWPo0KFUqlQJd3d3ZsyYQcOGDbXLKFiwIBs2bGDgwIFUq1YNT09PJk+enGXQx1dtwoQJODg48OOPP3LlyhVsbGzw9vbmq6++yna+SZMmkZaWRvfu3bl37x41atTgn3/+wTabrlUAHTp0YPXq1bz33nvEx8cTGBiIjY0NkyZNws/Pj9TUVCpXrsyGDRsoUuTFDuikVB907EpSUiIBv03lwf37lHOvwDcT/LHIMB5HdFQEJhkuM/HwrMzwL8eydOEcli+aSzHHEgz/chzlPdK7lYdeusC40Z9p/54/ZyYADRs3Z7Bf9tsXoF6DRty7e5e/ls4nLjYWZ5cyfD1+Mg4OmoE342JjiM4w+E8xx+KMGT+JeQGz+HvjWuyKFKHPgCHUyXB7Pw/PSvh9+S1LF85l2aJ5FHMswedfjtXJnRNvW7a42Gg+H9pP+/e61ctZt3o5FSt7MWHSL29EvT2lxPeD0utN6fk+6NiFpKREZv82TbtNv50wJdM2jUSlSj9f8XT9SxbOZdmTbeqXaZvWbdCIe3cTWLF0AXGxMTi7lOGrDK9bsr2cbOn724In+1tpxoyfpLO/RUVFastr9rcfCQz4jb83rjNwjIvBz8AxbuKk6Tmus6f2nNLcwrFNnXwUyA83otQEbkkmKcNNKKytVDqX+xfIDx/UNaOQBTxOgtsxagI2JXMjOr3QqatpWJqn8F7VfBSyhIg4NQu2JhOfqddAttlOp2GWT0Wb2vkoYK7JFrQ1RSebTUGV9rr7nCqQX0Uzb1MKW8GjRDhzPY1tx1LJSW9u6+qVqLNjofZvzymaY2L4gtWc7DMa8+JFsXjSyADw6NoNDrfuj6f/aFwGdSPxViRnhn/PnTVbtWXiDhzneDc/3McPw338UB6GhnO863Di/zuZq9cFyj7GKTkbKPc48jp4WZexC/1UaqlxIXLk1OWc9RIwBhNefDdJIQxJU3BnN3kv5J36pQw1KoxJye+HxbuVfeJBZaLc90Odfsq9nXfpc8HGjvBaUvLxt5Lb69vQ0OOb20Zb94IJxZ9d6A3z1vRYEEIIIYQQQgjxdkiTwRtfKeWednoNDRw4kIIFC+p9DBw40NjxhBBCCCGEEEKIF056LLxA3333HSNGjND7XOEXcDsmIYQQQgghhBBCaaRh4QVycHDAwcHB2DGEEEIIIYQQ4q2mlkshXim5FEIIIYQQQgghhBB5Jj0WhBBCCCGEEEK8UeTmh6+W9FgQQgghhBBCCCFEnkmPBSGEEEIIIYQQbxR1WpqxI7xVpMeCEEIIIYQQQggh8kwaFoQQQgghhBBCCJFncimEEEIIIYQQQog3SprcbvKVkh4LQgghhBBCCCGEyDPpsSCEEEIIIYQQ4o0it5t8taTHghBCCCGEEEIIIfJMeiwI8QZQozJ2hNeSCuW2ZCt5m0q95Z2S607JTFDuLcNSMTV2hNdS1waxxo6QLSUfS1Tngo0dwaBrFRoaO4JBq8bsMXYEg9IUfGY9aJyxE4jXhTQsCCGEEEIIIYR4o6hl8MZXSi6FEEIIIYQQQgghRJ5JjwUhhBBCCCGEEG8U6bHwakmPBSGEEEIIIYQQQuSZNCwIIYQQQgghhBAiz+RSCCGEEEIIIYQQb5Q0tXLvKPQmkh4LQgghhBBCCCGEyDPpsSCEEEIIIYQQ4o0igze+WtJjQQghhBBCCCGEEHkmPRaEEEIIIYQQQrxRpMfCqyU9FoQQQgghhBBCCIWLi4uje/fuWFtbY21tTffu3YmPj892HpVKpffx888/a8s0bNgwy/MfffRRrrJJjwUhhBBCCCGEEELhunbtyo0bN9iyZQsA/fv3p3v37mzYsMHgPLdv39b5+++//6ZPnz506NBBZ3q/fv347rvvtH9bWFjkKps0LAghhBBCCCGEeKOo1W/WpRDnzp1jy5YtHDx4kHfeeQeAgIAA6tSpw4ULF3B3d9c7n6Ojo87f69at47333sPV1VVnuqWlZZayuSENC7kQFBTEsGHDntndJKPg4GDee+894uLisLGxeWnZhHGp1Wr+WhLI9i0beHD/Hm7unvQbNBwnlzLZzndwXzDLFs7lzu1bOBYvQZce/Xjn3Qba58+eDmHdqmVcuXyBuNgYvhjzPbXq1M9DtiC2PclWzt2TvoOG4fyMbAf27dLJ1rVHX51sAFs2rmHd6mXExcbi5FyaXv0H41nJ643I9vfGtU/mj8HJuQy9+w/Gs1IVg+XPnAohMOA3wsOuYmdnzwcdP8K3RdssuZcunKeTu/a7udueoOx6U3o+JWd70ftc2PWrLFsUSOjlC0RFRtCr36e0/qBTjvNkpPR6W7t6+ZN6K02fZ9TbaW29XdPWW/MWbbTPh12/ytJFgYRevkhUZAS9+31K6w865jhPRkr+bFByvSn5+AvKfz8ose7s6tXA9fM+WHtXokAJB450+ISI9Tuyn6d+TTynjKKgZzkSb0US6j+HsNnLdMo4tmtG+XGfYVnWmYehYVz4dhoR67bnKttTbX2s8KleAMsCJly5mcyizfe4FZWa7TwW5io6NLbC28McKwsTouJSWb71PqcuJ2Up26KeJR0bF2TbwYcs/ed+rrJ90NAKn+oWWD3JtmDT3WdmsyygokOjglSvkJ5t2dZ7nLyUpF3mBw0L6syTcD+Vz6ZE5yqb0C8xMZHExESdaebm5pibm+d5mQcOHMDa2lrbqABQu3ZtrK2t2b9/v8GGhYwiIiLYtGkT8+fPz/Lc4sWLWbRoEcWKFeP9999n7NixFCpUKMf5ZIyFN0xQUBAqlYoKFSpkee6vv/5CpVJRunTp516PvutwVCoVKSkpz73s19HalUvYuOYv+gwcxqRps7GxteO7MX48evjQ4DwXzp1m6qTxNGjki//MeTRo5MvUSWO5eP6stszjx48pXaYsfQYOe45sS9mw5i/6DhzG5Gl/Psn2eY6y+TRqhv/MuZp/J43TybZv904CA2bSoXN3pswIoEKlKnw/9kuiIiNe+2x7tfN/jP+MOVSoVJmJY78wOH/EndtMHDuKCpUq4z9jDu07d2Pun79yYN+uDLnP4P8k99SZc/Tmziml1tvrkE+p2V7GPpeYmEgxx+J079kfG1u7HNaQfkqut3kBs+jY+WP8ZwTgWakKE7KZX1Nvo/GsVAX/GQF0MFhvJejesz+2z11vyvxsUHK9Kf34C8p+Pyi17kytLLl78gJnPvvu2YUBi9KlqLlhNrF7j7K35gdcnvwHFad9jWO7ZtoyNrWrUm3JNG4uXsee6m25uXgd3kunY1PLcEOKIe/XtaRZHQsWbb7PhIBYEu6nMaK7DQXyqwy/JhMY0d2GItam/LbiLl/NjGH+hnvE30vLUrZ0iXz4eFsQfic519la1LXEt44lizbfY3xADAn30xjZwzb7bKYworst9jamzPwrgVG/RhO44S5xd3Wz3YhM4bMpUdrHmN9icp1PydLS0oz2+PHHH7XjIDx9/Pjjj8/1eu7cuYODg0OW6Q4ODty5cydHy5g/fz6FChWiffv2OtO7devG0qVLCQ4O5ptvvmHVqlVZyjyLNCy8gaysrIiMjOTAgQM60+fNm4ezs/MLW0+/fv24ffu2ziNfvqydYJKSsrbavknUajWb1q2gfefu1K7rg3NpV4b4fUViYiJ7dm0zON+mdSuoUq0G7T/8mJJOLrT/8GMqe1Vn07oV2jLeNWrTpUc/atf1yXO2jetW0KFzd2rXbfAk2+gn2Qy36G9ctxKvatVp/+HHlMqQbWOGbBvW/EWjZi1o4tuKUs6l6d1/CEXsi/LP5nWvfbYNa1bQuFkLmvq2opSzC336D6GIvYPB+f/ZvB77og706T+EUs4uNPVtRaOm77Nu9fL0Za5biVe1GnT4sBulnFzo8GE3Knt5s3HdyhxlekrJ9ab0fErO9jL2uXLlPfhfn0HU82mMmZlZjnLoo+R6W6+tt5Y4ObvQp/9gitg7sGXzer3l0+ttME7OLjT1bUmjpu+zdvVf2jLlynvQs89A6vs0It9z1ptSPxuUXG9KPv6Cst8PSq67qH92c3HsdO6sNbzvZ+TS/yMeh93m7Oc/cP/8FcLnrSQ8aDWufr21ZcoM+R/R2/cT+tNsHly4QuhPs4neeZDSQ/6Xq2wATd+xYOOehxw7n8jNqFTmrr1LfjMV71Q2fHa5frUCWFmYMHN5ApfDk4lJSONSeDLhEbon2czNVPRvX5j5G+7y4HHuu+Y3q23Jht0POHoukZuRqQSsScDcTEXtygUMztOgmgUFLVTMWBafni0sa7a0NDUJ99O0j3sP36xLB4xp9OjRJCQk6DxGjx6tt+y4ceMMDrD49HHkyBFAMxBjZmq1Wu90febNm0e3bt0oUEB3/+nXrx9NmjShUqVKfPTRR6xcuZLt27dz7NixHL/m17phYcOGDdjY2JCWpml9CwkJQaVSMXLkSG2ZAQMG0KVLFwD2799PgwYNsLCwwMnJiaFDh/LgwQNt2aSkJL744gtKliyJlZUV77zzDsHBwQbXHxMTQ61atWjTpg2PHz8GYPPmzZQvXx4LCwvee+89rl27lmWeLl26UKpUKSwtLalcuTJLly7VPr9gwQKKFCmSpetMhw4d6NGjR47qJV++fHTt2pV58+Zpp924cYPg4GC6du2apfzvv/9O2bJlyZ8/P+7u7ixcuDBH63l6HU7GB0Dp0qWZOHEiPXv2xNramn79+uVoea+ryDu3iY+Lxcu7pnaamVl+PCt5ceHcaYPzXTx/Bq9qNXWmeXnXynae3IrQZquhk61iHrJV9a7JhXNnAEhOTib08kWqZslfM8f5lZpNM/8Fves4/2Qd+jJV9c5cvhahly5oe/FcPH8mS6Zq3rUMLtMQpdbb65BPqdle1j73oii73i5StVoNnelVvWtw3sD8F86fpaq3bvlq3jVfSr0p9bNByfWm9OMvKP39oOy6yw2b2lWJ2r5PZ1rU1j1YV6+E6slJLNvaVYnevlenTPS2PdjWqZardRW1McGmkClnQtNPhKWkwoVrybiVMtxIVtXdnNAbyXzcohDTPrfnu0F2tKxnSebfdx+3KMjJS0mcvZr73gpFbU2xKWTK6UzZzl9Lws0p+2yXbyTTvWUhfhlhz8RPitCqftZsxezyMe1ze37+zJ5BHa0pamua64xCP3NzcwoXLqzzMHQZxODBgzl37ly2j0qVKuHo6EhERNYeSFFRURQrVuyZmfbs2cOFCxfo27fvM8t6e3tjZmbGpUuXnv1in3itGxYaNGjAvXv3OH78OAC7du3C3t6eXbvSu3AFBwfj4+PDqVOn8PX1pX379pw8eZLly5ezd+9eBg8erC3bq1cv9u3bx7Jlyzh58iSdOnWiefPmeiv0xo0b1K9fHw8PD1avXk2BAgUIDw+nffv2tGjRgpCQEPr27cuoUaN05nv8+DHVq1dn48aNnD59WjuS56FDhwDo1KkTqamprF+fftYgOjqajRs30qtXrxzXTZ8+fVi+fDkPn3TLCwoKonnz5ll2ujVr1vDZZ5/x+eefc/r0aQYMGECvXr34999/c7wufX7++WcqVarE0aNH+eabb55rWUoXF6fpNmZjo9v108bGjvi4WIPzxcfFYmNrqzuPrW228+TW02VlzmZtY0vcc2S7dzeBtLRUrLO85pznV2o2zfxp2NjorsM6m/nj4mKxzlTexsaW1NRU7t5N0Oa2zpTbOg/bW6n19jrkU2q2l7XPvSivW71p5o/TO09cXKze8i+j3pT62aDkelP68ffpsjTreD3eD0qqu9wwL2ZPYoTutf5JkTGYmJmR316Tx9zRnsQI3a77iRExmDsWzdW6ChfU/By6e1/3MoG7D9KwLmj4p1JRW1NqeJpjYgLTl8SzcfcDfOtY0qq+pbZMrYrmuBQ3Y+X23I2p8NTT9d99kLtsDram1PQsgIlKxdTF8WzYfZ/mdaxo3cBKWyb0RjIBaxLwXxhP4Ia7WBc0YUwfW6wscnbm+3WgTlMb7ZEb9vb2eHh4ZPsoUKAAderUISEhgf/++08776FDh0hISODdd9995nrmzp1L9erV8fJ69rgtZ86cITk5meLFi+f4dbzWDQvW1tZUrVpV26sgODiY4cOHc+LECe7du8edO3e4ePEiDRs25Oeff6Zr164MGzaMcuXK8e677zJjxgwWLFjA48ePCQ0NZenSpaxYsYL69etTtmxZRowYQb169QgMDNRZ78WLF6lbty5NmjRh/vz52u7/v//+O66urkybNg13d3e6detGz549deYtWbIkI0aMoGrVqri6ujJkyBB8fX1ZsULTFc7CwoKuXbvqrHPx4sWUKlWKhg0b5rhuqlatStmyZVm5ciVqtZqgoCB69+6dpdyUKVPo2bMnn3zyCeXLl8fPz4/27dszZcqUZ67jt99+o2DBgtrH559/rn2uUaNGjBgxAjc3N9zc3LLMm5iYyN27d3UemXtpKNXuf7fycQdf7SM1VTN4TuZWYDVqVGR/cM78fG66MunPto1uHZprH6mpKU+yZV7ms7ORJVvW5WR5zWo9E1+DbHrXoGcB2c2eubwadZak+rd39jmUXm9KzqfkbHrX8BL2ubx43eotc1nNOrIrbqjenq/mlPzZoH8lyqi3nKzLWMdfeP3eD0qqu+eWeUT/pyvNOF1fmWfcCaB2ZXN+G22vfZiaapabZS6VnmmZVnX3QRpBG+5x/XYK/51JZOOeB7xXQ3OLPtvCJnRpXoiANXdJyX6cRa06lQvwx1dFtQ9TEwMv8xnLeZotcMNdrt9O4dDpRDbseUCjGum3Dzx1OYkj5xK5EZnC2StJTF2saVisVzV3txgUr06FChVo3rw5/fr14+DBgxw8eJB+/frRqlUrnYEbPTw8WLNmjc68d+/eZcWKFXp7K4SGhvLdd99x5MgRrl27xubNm+nUqRPVqlWjbt26Oc732t8VomHDhgQHB+Pn58eePXuYOHEiq1atYu/evcTHx1OsWDE8PDw4evQoly9fZvHixdp51Wo1aWlpXL16ldOnT6NWqylfvrzO8hMTEylSpIj270ePHlGvXj26dOnCL7/8olP23Llz1K5dW+cgXadOHZ0yqampTJo0ieXLl3Pz5k3tiKFWVuktiP369aNmzZrcvHmTkiVLEhgYSM+ePXP9paJ3794EBgbi7OzM/fv3adGiBTNnzsySuX///jrT6tatq31tixcvZsCAAdrn/v77b+rX14wE3K1bN77++mvtcxnvelGjhm6Xycx+/PFHxo8frzNt7NixjBs3Lsevz1hqvlOPcu6e2r9TkjVd2+LiYrG1s9dOT4iPy9LKn5GNrV2WsxsJ8fFZzh7kLltdyrmnD9yZrM0Wg61d+n6cEB+f5cxJ5myZz0YkxMdpsxUqbI2JiWnWMglxWc6WvA7ZMtLMb5J12yTEZzmT9JSt3kzxmJqaUqiwtcHcd+MNL/MppdebkvMpOVtGL2ufy6vXrd70zW/oOGpr4LirqbfCz1xndpT82ZCR0upNXzalHH/h9Xs/KKnunkdiRHSWngf5i9qRlpxMUky8psydaMwd7XXKmDvYZenpkFnIhSSu3EjvnfN0eDDrgiYkZOi1UNjSJEsvhowS7qWRmqb7o/92dCo2hUwxNYHSxfNhXdCEb/unbz9TExXlXcxoVMuC/hOjsjQYHL+QSOjN9Esm8pnqz1bISvfvzOLvpZGaptZZ/q2oFE02U0jV09CRlAzhESkUs3tzLodQqw3X0etq8eLFDB06lGbNNAOZtmnTJsvvuwsXLpCQkKAzbdmyZajVau0QARnlz5+fHTt28Msvv3D//n2cnJxo2bIlY8eOxdQ05/vDa91jATQNC3v27OHEiROYmJjg6emJj48Pu3bt0l4GAZpRQQcMGEBISIj2ceLECS5dukTZsmVJS0vD1NSUo0eP6pQ5d+6cTgOCubk5TZo0YdOmTdy4cUMnS07ulerv78+0adP44osv2LlzJyEhIfj6+uoMcFitWjW8vLxYsGABx44d49SpU1l6PuREt27dOHjwIOPGjaNHjx56B1YEPa3VGc6MtGnTRqc+MjYYWFtba3skuLm5YW+ffnDP2FCiT24GNFEaC0tLipcopX2Uci6Nja0dJ48f0ZZJTk7m7OkTuFeoZHA55T0qcjLksM60E8cPZztPbrM5Gch2JgfZToQc0ZmmyVYRADMzM8q6lefEcd0yJ48fMbhcJWfLSDO/e5b5Txw/gseTdejNlKX8YcqWc9e+7/TlDjl+2OAyn1J6vSk5n5KzZfSy9rm8er3qLev8J44fxcPA/O4enpw4flRnWsjxIy+l3pT02ZCR0uotazblHH/hdXs/KKvunkf8wRDsG+t27S7atB4JR0+jfjL+Q9zBEOwb655NtW9Sj7gDx7Nd9uMkNZFxqdrHrahU4u+l4umaX1vG1ATcS5tx+YbhcREuhSfjYGeq03ugWBFT4u+lkpoG564m881vMYz7I1b7uHozmYMnExn3R6zejhWPk9RExqZqH0+zVSybIZspeJTOz+Xw7LMVs8un07PEsYgpcfdS9TYqgKYRo0TRfMRn02AhjM/Ozo5FixZpe3wvWrRI5+QuaH7LZf7t2L9/fx4+fIi1ddaTD05OTuzatYuYmBgSExO5fPkyv/zyC3Z2uWtAfO0bFp6OszB9+nR8fHxQqVT4+PgQHBys07Dg7e3NmTNndH4IP33kz5+fatWqkZqaSmRkZJbnnw5KCGBiYsLChQupXr06jRo14tatW9rnPD09OXjwoE6+zH/v2bOHtm3b8vHHH+Pl5YWrq6veMRz69u1LYGAg8+bNo0mTJjg5OeW6buzs7GjTpg27du3SexkEaLrU7N2rO/DN/v37tberLFSokE5dWFi8mO5RuRnQROlUKhUt23Zi9V+LOLR/N2HXrjBr2o+Ym5tT36epttwM/+9ZHPSn9u8WbTpy4tgR1qxYzM3w66xZsZhTIUdo2Tb9PvOPHj3kauglroZq9pGIO7e5Gnopx7eeUqlUtGrbiVV/LdZmm6nN1kQn26Kg2dq/W2qzLeFG+HXWrFjCyZCjtMqQrXW7D9mxdRM7tm7iRtg1AmfPJDoqkmYZ7mv+umZr3a7Tk/k3cyPsOvNmzyQ6KkI7/6Kg2fzi/4O2vG+LNkRFRhAYMIsbYdfZsXUzO7Zupm37ztoyrdp0IOTYYVY/yb1amzt393lXcr0pPZ+Ss72MfS45OVl7/EhJSSE2JpqroZe4fetGlvW/rvXWpl0ntm/dzPatmwkPu8682bOIjorAt0VrABYGBeitt3kBswgPu872J/X2QfsPM9XbZa6GXiYlJYWYmGiuhl7m9q2bua43pX42KLnelHz8BWW/H5Rcd6ZWlhT28qCwlwcAlmVKUdjLgwJOmuu33Sf64RU4WVv++uxlWLiUoMLPoyjo4Uqpnh1w6tWBK1PTBya/NnMB9k3r4jqiH1burriO6Id94zpc+3V+rrIBbDv0iFb1LfH2yE/Joqb0+aAwSclqDp1Kv0y37weF6NA4/cTZv0ceUdBCRZf3C1LMzpQq5fLTsp4VOw8/AjSNBDejUnUeiclqHjxK42ZUDq+NALYefEjr+lZ4e5hT0sGUvh8UJjFZzcFTj7Vl+rUrTMfGBdOzHX6IlYWKbs0LUayIKV7l8tOqvhU7/3ukLdO5WUHcXcywtzHBtWQ+Bn9og4W5in0hj3hTvC5jLLwpXvtLIZ6Os7Bo0SJtz4IGDRrQqVMnkpOTteMSfPnll9SuXZtPP/2Ufv36YWVlxblz59i2bRu//vor5cuXp1u3bvTo0QN/f3+qVatGdHQ0O3fupHLlyrRo0UK7TlNTUxYvXkyXLl1o1KgRwcHBODo6MnDgQPz9/fHz82PAgAEcPXqUoKAgnbxubm6sWrWK/fv3Y2try9SpU7lz5472h/xT3bp1Y8SIEQQEBLBgwYI8109QUBC//fabzuUcGY0cOZIPP/wQb29vGjduzIYNG1i9ejXbtxu+ZZLI6oOOXUlKSiTgt6k8uH+fcu4V+GaCPxaW6QP4REdFYJKh6djDszLDvxzL0oVzWL5oLsUcSzD8y3GU90jvSht66QLjRn+m/Xv+HE1Xp4aNmzPY76scZutCUlIis3+bps327YQpmbJFolKltzN6eFbC78tvWbJwLsueZPPLlK1ug0bcu5vAiqULiIuNwdmlDF+Nn4yDgyM5pdRs9Ro04t7du/y1dD5xsbE4u5Th6wzzx8XGEB2V/gW+mGNxxoyfxLyAWfy9cS12RYrQZ8AQ6mS4FdzT3EsXzmXZonkUcyzB51+O1cn9utfb65BPqdlexj4XFxvN50PT78qzbvVy1q1eTsXKXkyYpHsp3+tfbwue1FtpxoyfpFNvUVGRmertRwIDfuPvjesM1FsMfgbqbeKk6bmsN2V+Nii53pR+/IXX4f2gvLqzrl6JOjvS7zrmOUWzn4YvWM3JPqMxL14UC6f0QeIeXbvB4db98fQfjcugbiTeiuTM8O+5s2artkzcgeMc7+aH+/hhuI8fysPQcI53HU78fydzlQ3g730PyZ9PxcctCmFlYcKVG8n4L4zncVL6D0Q7a1My/l6Mu5uG/6J4PvItxHeDLIi7m8b2Qw/ZvO9hrtefnc37HpLfTEWPlppsoTeSmbIwTidbEWtTnR4QsXfTmLIwnq7NCzJxUBHi7qay7dBDNu1Nz2ZX2JSBHa0pZGnCvQdphN5IZsKcWGISpMeCyBuVOif99xVuxIgR+Pv7c/r0aSpW1HTNqlq1Krdu3SIiIkLbrf/w4cN8/fXXHDhwALVaTdmyZencuTNffaU5uCUnJzNx4kQWLFjAzZs3KVKkCHXq1GH8+PFUrlyZoKAghg0bRnx8PAApKSl07tyZc+fOERwcjIODAxs3bmT48OGEh4dTq1YtevXqRe/evYmLi8PGxobY2Fh69+7Njh07sLS0pH///oSFhZGQkMDatWt1XlePHj3YtGkTt27dyvHZ/MwZM5s+fTrTp0/XuQ3m77//zpQpUwgPD6dMmTKMGTOG7t27Z7uehg0bUrVqVaZPn57ludKlSzNs2DCGDRuWo8yvi1OXc3YmyBhU2Q4vJAxRcr2pX8KAaML4ZJ/LGxOU+0U3FeVej2xKzs+KvmpK3t9A2fmUfBy5VqGhsSMYtGrMHmNHMChNwT/HgsY9+zaGStWi9ymjrXvzvMpGW7exvBENC2+qpk2bUqFCBWbMmGHsKAJpWHgTKbnelPylVuSd7HN5Iw0LeSMNC3mn5HxKPo5Iw0LeSMPCy/F+z9z3XnlR/g6qYrR1G8trfynEmyg2NpatW7eyc+fOLKN8CiGEEEIIIYQQSiINCwrk7e1NXFwckydP1rknKUDFihW5fv263vn+/PNPunXr9ioiCiGEEEIIIYRipb2Bt5tUMmlYUKCM4x9ktnnzZu09lDMrVuz17aokhBBCCCGEEOL1JA0LrxkXFxdjRxBCCCGEEEIIIbSkYUEIIYQQQgghxBtFnabcQTHfRCbPLiKEEEIIIYQQQgihn/RYEEIIIYQQQgjxRlGnyeCNr5L0WBBCCCGEEEIIIUSeSY8FIYQQQgghhBBvFBlj4dWSHgtCCCGEEEIIIYTIM2lYEEIIIYQQQgghRJ7JpRBCCCGEEEIIId4oarUM3vgqSY8FIYQQQgghhBBC5Jn0WBAih0xJNXYEg9SojB3htZSqNjV2BIPMTJKNHcGgNLW0SYtXKxXlvleV/Nmg5HpTOhPkTGderBqzx9gRDOowsb6xIxhk8t9pY0d4I6XJ4I2vlHw7FEIIIYQQQgghRJ5Jw4IQQgghhBBCCCHyTC6FEEIIIYQQQgjxRlGnySVNr5L0WBBCCCGEEEIIIUSeSY8FIYQQQgghhBBvFLUM3vhKSY8FIYQQQgghhBBC5Jn0WBBCCCGEEEII8UZRq2WMhVdJeiwIIYQQQgghhBAiz6RhQQghhBBCCCGEEHkml0IIIYQQQgghhHijyOCNr5b0WBBCCCGEEEIIIUSeSY8FIYQQQgghhBBvFHWaDN74KkmPBSGEEEIIIYQQQuSZNCwYWVBQEDY2NrmaJzg4GJVKRXx8/EvJJIQQQgghhBBC5JRcCiGeKTU1lZ9++on58+dz/fp1LCwsKF++PAMGDKBXr17Gjmd0f29cy9rVy4mLjcHJuTR9+g/Gs1IVg+VPnwohMOA3wsOuYWdnzwcdP6J5izba58OuX2XpokBCL18kKjKC3v0+pfUHHZ8r37rVy57kK0PvZ+Q7o813VZvPt0VbnXzLFgUSevkCUZER9Or3Ka0/6KSIbAAH9u1i6cJ53Ll9C8fiJejaoy+1362f62xqtZoVSwLZ/s967t+/R7nynvQd5IeTS5ls5zu4L5hli+YQcfsWxYqXoEv3/rzzbgPt82v+WsihA7u5eeM6+fOb416hEt16DqJkKeccZ9u8cR1rVz3Z51xK06f/p1TMdp87wbyA3wi/fg27Iva069CZ5i3T97mtWzby745thF2/CkBZt/J8/L8+lHevkONMTyl5myo5m9LzqdVq/loSxLYtG3hw/x7l3D3pO2gYzs94PxzYt4tlC+fqrD/j++HM6ROsW7WUK5cvEhcbwxdjJvJOndzl02QLZPuTbG7unvQbNDxn79UM2br06KeT7ezpENatWsaVyxeeZPueWrnMpvTPByXXnZKzyedq3o9zbX2s8KleAMsCJly5mcyizfe4FZWa7TwW5io6NLbC28McKwsTouJSWb71PqcuJ2Up26KeJR0bF2TbwYcs/ed+jjLZ1auB6+d9sPauRIESDhzp8AkR63dkP0/9mnhOGUVBz3Ik3ook1H8OYbOX6ZRxbNeM8uM+w7KsMw9Dw7jw7TQi1m3PUaaM1Go1W1b+xoGdK3l0/y7ObpXp2HsMxZ3cDM5zO/wyf6+YSfiVs8RF3+KDHl/SsEV3nTKpqSlsWfkbR/du4l58NIVti1LTpy3N2g3AxOTNOPe8d4OPsSO8Vd6MvUa8VOPGjWP69OlMmDCBs2fP8u+//9KvXz/i4uKMHc3o9u7eybyAWXTs/DH+MwLwrFSFCWO/JCoyQm/5iDu3mTh2NJ6VquA/I4AOnbsx989fObBvl7ZMYmIixRxL0L1nf2xt7Z47X2DATDp0/hj/GXOoUKkyE8d+8Yx8o6hQqTL+M+bQ3mC+4nTv2R+b58j3MrJdOHcG/0nj8WnUjKkz5+DTqBn+k8Zx8fzZXOdbt2oJG9cup8/A4UyaGoCNrR0TvhnOo4cPDc5z4dxppk0eh897vkz5NRCf93yZNvlbLl04oy1z5nQIvi3b8cOUP/lmwjRSU1OZ+I0fjx8/ylGuvbv+Zd7sWXTq3I2pv87Gs2JlJnw7Ktt6m/DtaDwrVmbqr7Pp+GFX5vw5k/17d2vLnD55gvo+jZjw41Qm+8+kaFEHxo35gpjoqBzW1pNsCt6mSs72OuRbu3IpG9b8Rd+Bw5g87U9sbO34bsznz3w/TH2yfv+Zc/WuP/HxI0qXcaPvwGG5zpSebQkb1/xFn4HDmDRt9pNsfjnK1qCRL/4z59GgkS9TJ43Vyfb48WNKlylLnzxmU/rnAyi37pScTT5X834ceb+uJc3qWLBo830mBMSScD+NEd1tKJBfZXAeUxMY0d2GItam/LbiLl/NjGH+hnvE38t67XzpEvnw8bYg/E5yrnKZWlly9+QFznz2XY7KW5QuRc0Ns4nde5S9NT/g8uQ/qDjtaxzbNdOWsaldlWpLpnFz8Tr2VG/LzcXr8F46HZtahht5DNmxfh7BmxfQoddX+P2wjMI29vz+Qz8eP3pgcJ7kpEcUcShF667DKGxjb2C5c9m//S869PqKUf7rad3Vj383BLJny+JcZxQCpGEhiw0bNmBjY0Pak8E+QkJCUKlUjBw5UltmwIABdOnSBYD9+/fToEEDLCwscHJyYujQoTx4kP5GT0pK4osvvqBkyZJYWVnxzjvvEBwcbHD9MTEx1KpVizZt2vD48WMANm/eTPny5bGwsOC9997j2rVrWebp0qULpUqVwtLSksqVK7N06VLt8wsWLKBIkSIkJibqzNehQwd69OiRozr55JNP6NSpE2XKlMHLy4s+ffrg5+enLbNlyxbq1auHjY0NRYoUoVWrVoSGhj5z2a+79WtW0LhZC5r6tsTJ2YU+/QdTxN6BLZvX6y3/z+b12Bd1oE//wTg5u9DUtyWNmr7P2tV/acuUK+9Bzz4Dqe/TiHxmZs+Vb4M2XytKObvQp/8Qitg78M/mdc/IN4RSzi409W1Fo6bvs271cp18/+sziHo+jTF7jnwvI9uGdSvxqlaDDh92o5STCx0+7EZlL282rluZq2xqtZpN6/6ifecevPOuD86lXRns9zWJiYns3bXN4Hyb1q+gSrUatPuwOyWdXGj3YXcqeVVn07oV2jJjvvPnvSYtcHIpQ2lXNz4ZNproqAiuXL6Qo2zr1qygSbP3adpcs8/1HTAY+6IObNmkf5/bsnkDRR0c6DvgyT7XvCWNm77Pugz7nN8XX9OiVVtcy7pRysmZT4Z+jjpNzckTx3NYYxpK3qZKzqb0fGq1mo3rVtChc3dq122Ac2lXhviNJjExkT27DJ9927huJV7VqtP+w48p5eRC+w8/prJXdTZmeD9416itOftZt4HB5Twr26Z1K2jfuTu16/o8yfbVk2zZvFfXad6r7T/8mJIZsm3KlK1Lj37Urpu3M15K/3xQct0pOZt8rub9ONf0HQs27nnIsfOJ3IxKZe7au+Q3U/FOZXOD89SvVgArCxNmLk/gcngyMQlpXApPJjwiRaecuZmK/u0LM3/DXR48zt0tBqP+2c3FsdO5s9bwvpWRS/+PeBx2m7Of/8D981cIn7eS8KDVuPr11pYpM+R/RG/fT+hPs3lw4QqhP80meudBSg/5X66yqdVqdv+9kKYf9MerVlOKO5Wj2yc/kJT4mKP7Nhmcz7lsZdp+PALvd1tgmi+/3jLXLp6gUvX3qOjtQxGHklSt3Qz3Ku8SfuWM3vJCPIs0LGTSoEED7t27x/Hjmi/Uu3btwt7enl270ltvg4OD8fHx4dSpU/j6+tK+fXtOnjzJ8uXL2bt3L4MHD9aW7dWrF/v27WPZsmWcPHmSTp060bx5cy5dupRl3Tdu3KB+/fp4eHiwevVqChQoQHh4OO3bt6dFixaEhITQt29fRo0apTPf48ePqV69Ohs3buT06dP079+f7t27c+jQIQA6depEamoq69enf5mJjo5m48aNObqUwdHRkZ07dxIVZfjs5YMHD/Dz8+Pw4cPs2LEDExMT2rVrp22geRMlJycTevkiVavV0Jle1bsG58+d1jvPhfNnqeqtW76ad01CL10gJSVF7zzPl+8CXtVqZspXk/Pn9H9oXDx/hqremcvXeuH5Xla2i+fPUDXTMqt51zK4TEMiI24THxerk8/MLD+elapywcC21az/tJ7XVCvbeR4+aYgsWLDwM3Np97lM+1DVajUMvsYL585k2UerVa/B5Wy2aVJiIqmpKRQsWOiZmXSzKXObKjnb65Av4s6T90OG/c7MLD8VK3k94/1wRu9rupDL9WcnUpst83s199m8nvFezQ2lfz6AcutOydnkc1UjL8eRojYm2BQy5Uxo+uULKalw4VoybqUMN6ZUdTcn9EYyH7coxLTP7flukB0t61miytTJ4eMWBTl5KYmzV3PXWyEvbGpXJWr7Pp1pUVv3YF29Eqp8mqvMbWtXJXr7Xp0y0dv2YFunWq7WFRN5g7vx0XhUeVc7LZ9Zftwq1ODaxZC8vYAnXD28uXj6EJG3rgFw8/p5rlw4RoVqeWvoFUIaFjKxtramatWq2l4FwcHBDB8+nBMnTnDv3j3u3LnDxYsXadiwIT///DNdu3Zl2LBhlCtXjnfffZcZM2awYMECHj9+TGhoKEuXLmXFihXUr1+fsmXLMmLECOrVq0dgYKDOei9evEjdunVp0qQJ8+fPJ9+TA9Pvv/+Oq6sr06ZNw93dnW7dutGzZ0+deUuWLMmIESOoWrUqrq6uDBkyBF9fX1as0LTQW1hY0LVrV511Ll68mFKlStGwYcNn1snUqVOJiorC0dGRKlWqMHDgQP7++2+dMh06dKB9+/aUK1eOqlWrMnfuXE6dOsXZs/q7yiUmJnL37l2dR+YeFUp3724CaWlp2NjY6ky3sbEl3sBlInFxsXrLp6amcvduwivJZ21jS3xcrMF81q8g38vKFh8Xi7VtpmXaGl6mIfFxMU/y6HZJ1eSLyWa+WD3z2Blcv1qtZv6cmXh4VsG5tOszcxmsN1tb4gysIz4uLkudPGubLggMwK6IPV7Vqj8z0zOzKWCbKjnb65DvaXkbPe8HQ/vd0/lsMu97eVh/duKevB8zZ7PJ5n33KrIp/fNBsz5l1p2Ss8nn6pNl5qFOCxfU/OS4e1/3hNPdB2lYFzT8c6SorSk1PM0xMYHpS+LZuPsBvnUsaVXfUlumVkVzXIqbsXJ7zsZUeF7mxexJjIjWmZYUGYOJmRn57TV1Ze5oT2KE7veFxIgYzB2L5mpd9+I16ylkXURneiHrItyNj9Y3S441btMH77rv8+PnrfHrVpUpozrh8353qtdt8VzLFW8vGbxRj4YNGxIcHIyfnx979uxh4sSJrFq1ir179xIfH0+xYsXw8PDg6NGjXL58mcWL069FUqvVpKWlcfXqVU6fPo1araZ8+fI6y09MTKRIkfQDxKNHj6hXrx5dunThl19+0Sl77tw5ateujSpD02ydOnV0yqSmpjJp0iSWL1/OzZs3SUxMJDExESsrK22Zfv36UbNmTW7evEnJkiUJDAykZ8+eOss1xNPTk9OnT3P06FH27t3L7t27ad26NT179mTOnDkAhIaG8s0333Dw4EGio6O1PRXCwsKoVKlSlmX++OOPjB8/Xmfa2LFjGTdu3DPzKE6mOlSrs0zKVDxTeTRd9lQ8e1vkRZZtrFbnMd+L9zKyZa5H9TOWCbDn3638OWuK9u/RYyc/WV+mgs/auHoyal6T/nnm/jGNsGuhTPhpVvYBs64kx+sAfXWifzrA6hXL2LNrJxMnTyV/fv3dJ7OPpoxt+rplU1K+3f9u48+Z/tq/vxo3SX8+1Dk4buk7Pub9aLL7363MzpBt9Dj971V1DrLpr5sXfKRT0OeDkutOydn0rkM+V595HKld2ZwerdJ7vU1fkvBk+ZkD6Jmmk0/T+BC04R5qNVy/nYJNIROav2vJht0PsS1sQpfmhZi6KJ6U7MeAfLHUmVI/rZCM0/WVyTwtkyN7N/JXQPr34/5f/qa7/KeL5vn36+MH/ubono10HzIZx1Ju3Lx2njULJmNt60Atn7bPXoAQmUjDgh4NGzZk7ty5nDhxAhMTEzw9PfHx8WHXrl3ExcXh46O5Li8tLY0BAwYwdOjQLMtwdnbm5MmTmJqacvToUUxNTXWeL1iwoPb/5ubmNGnShE2bNjFy5EhKlSqlfU79jAMQgL+/P9OmTWP69OlUrlwZKysrhg0bRlJSenezatWq4eXlxYIFC/D19eXUqVNs2LAhx3ViYmJCzZo1qVmzJsOHD2fRokV0796dr7/+mjJlytC6dWucnJwICAigRIkSpKWlUalSJZ0MGY0ePVpnjIan9fA6KVTYGhMTkyyt9gkJcVnOADxla2uX5QxfQnw8pqamFCr87K7wecmXZX0J8VnOqmfMl+X1aPNZKz6bjZ4yd+MNL/OpGu/Uw83dU/t3SrKmK2V8XCy2dumDHiUkxGc5g5aRZv26ZygM7Q9z/5jGkUP7GD/pV4rYO2Sb7ymD+1x8fJazVOmZsp5ZSkiI07vPrV21nJV/Lea776dQukzZHGXKnE0p2/R1yabEfDXfqUu5DHcESX7yfoiLi8HWLr1RPCE+PsvZ4Yz0rT8h3vDxMSdqvlOPcnreq3GZ36vxWXvqZM6m71j8PNkyUuLng5LrTsnZMpLPVY2cHEdCLiRx5UZ675wnHXGxLmhCQoZeC4UtTbL0YtDJcy+N1DTd3+O3o1OxKWSKqQmULp4P64ImfNs/fRubmqgo72JGo1oW9J8Y9azf8rmWGBGdpedB/qJ2pCUnkxQTrylzJxpzR91BE80d7LL0dMisUvX3cHFLH+AxJVnzPfpefDTWtunrvJ8Qm6UXQ26tX+RP47Z98X5X00OhhHN54qJvs33dHGlYEHkil0Lo8XSchenTp+Pj44NKpcLHx4fg4GDt+AoA3t7enDlzBjc3tyyP/PnzU61aNVJTU4mMjMzyvKOjo3Z9JiYmLFy4kOrVq9OoUSNu3bqlfc7T05ODBw/q5Mv89549e2jbti0ff/wxXl5euLq66h3DoW/fvgQGBjJv3jyaNGmCk5NTnuvI01PzBeDBgwfExMRw7tw5xowZQ+PGjalQocIz7xhhbm5O4cKFdR6vW8OCmZkZZd3Kc+L4EZ3pJ44fxaNC1l4aAO4enpw4flRnWsjxI5Qt5669/OXF5nPXk+8IHhUq6p2nvEdFPeUPv/B8LytbeY+KnAjRLRNy/LDBZT5lYWlJ8RKltI9SzqWxsbXj5PHD2jLJycmcPR2Cu4Ftq1l/JU7qyZhxHrVazZzfp3Fo/27Gfj+dYo4lss2W0dN9LiTLPnTU4Gt0r1Axa/ljR3DLtE3XrFzGX0sXMXbCZNzKu+c4k2425WzT1yWbEvNlfj84ad8P6ctKTk7mzOkTz3g/ZF2/5v2Qu/rJLlspA9nO5iDbyZDDOtMyv1efhxI/H5Rcd0rOlpF8rmrk5DjyOElNZFyq9nErKpX4e6l4uqb3hDM1AffSZly+YXhchEvhyTjYmer0mShWxJT4e6mkpsG5q8l881sM4/6I1T6u3kzm4MlExv0R+8IbFQDiD4Zg3/hdnWlFm9Yj4ehp1E/Gpog7GIJ947o6Zeyb1CPuQPaDIhewsKKoo7P24ViqLIVt7Llw6oC2TEpKMpfPHaF0+arP9TqSkh5n6fWgMjFB/QaPjyZeLmlY0OPpOAuLFi3SjkHQoEEDjh07ph1fAeDLL7/kwIEDfPrpp4SEhHDp0iXWr1/PkCFDAChfvjzdunWjR48erF69mqtXr3L48GEmT57M5s2bddZpamrK4sWL8fLyolGjRty5cweAgQMHEhoaip+fHxcuXGDJkiUEBQXpzOvm5sa2bdvYv38/586dY8CAAdr5M+rWrRs3b94kICCA3r17Z3nekI4dOzJt2jQOHTrE9evXCQ4O5tNPP6V8+fJ4eHhga2tLkSJFmD17NpcvX2bnzp1ZeiO8qdq068T2rZvZvnUz4WHXmTd7FtFREfi2aA3AwqAAfvH/QVvet0UboiIjmBcwi/Cw62zfupkdWzfzQfsPtWWSk5O5GnqZq6GXSUlJISYmmquhl7l962au87Vu14kdWzexY+tmboRdZ97smURHRdDsyX3RFwXN1psvMGAWN8Kus+NJvrbtO2fKd4mroZdISUkhNiaaq6GXuH3rhtGztWrTgZBjh1m9Ygk3wq+zesUSToYcpVXb3N3nXaVS0bLth6xesYhD+3cTdu0Ks6b/gLm5OfV8mmrL/eo/kcVBf2j/btmmIyeOH2btysXcDL/O2pWLORVyhJZt0+9HPuf3qewJ3spnI7+lgKUlcXExxMXF5HiMkbbtOrH9n81s3/o34WHXmZt5nwsMYPqUH7Xlm7dordnnZv/2ZJ/7m+1b/6Zthn1u9YplLF4QyOBhI3FwcCQuNpa42FgePcrZLTCfUvI2VXI2pedTqVS0atuJVX8t1r4fZk77EXNzc+r7NNGWm+H/PYuCZmv/btmmIyeOHWHNk/Wv0a4//f3w6NFD7fEENIP2XQ29ZPD2ePqytWzbidV/ZXivarOlv1dn+H/P4qA/tX+30GbTvFfXrMj6Xs2cLSKX2ZT++aDkulNyNvlczftxbtuhR7Sqb4m3R35KFjWlzweFSUpWc+hU+udf3w8K0aFx+qW8/x55REELFV3eL0gxO1OqlMtPy3pW7Dys+Xx6nKTmZlSqziMxWc2DR2ncjMrZtRGmVpYU9vKgsJcHAJZlSlHYy4MCTsUBcJ/oh1fgZG3567OXYeFSggo/j6KghyulenbAqVcHrkydpy1zbeYC7JvWxXVEP6zcXXEd0Q/7xnW49uv8XNWZSqWiwfvd2bY2gJP/bed2+CWW/PY1+c0LUL1uS225RbNGs2HpNO3fKSnJ3Lh2nhvXzpOamkxCbAQ3rp0n6k6YtkxF74ZsWxvAmWO7iIm8ycn/thO8aQGVazbOVUYhnlKpc9LX/i00YsQI/P39OX36NBUralplq1atyq1bt4iIiNC28B0+fJivv/6aAwcOoFarKVu2LJ07d+arr74CNB8WEydOZMGCBdy8eZMiRYpQp04dxo8fT+XKlQkKCmLYsGHEx8cDkJKSQufOnTl37hzBwcE4ODiwceNGhg8fTnh4OLVq1aJXr1707t2buLg4bGxsiI2NpXfv3uzYsQNLS0v69+9PWFgYCQkJrF27Vud19ejRg02bNnHr1q0c9xAICAhg6dKlnD59moSEBBwdHWnUqBHjxo3DxcUFgO3btzN06FCuXLmCu7s7M2bMoGHDhqxZs4YPPvjg+TeIApy9fEvv9L83rmXNqmXExcbi7FKa3v0/pWIlLwBmTJ1EZOQdJk6ari1/+lQIgQG/EXb9GnZFitCuYxeaP/nQB4iMuMOA3l2yrKdiZS+d5WSkzuZKzb83rmXtqqVP8pWhV4Z8v079kcjIO0yYlD62x5lTIZovthny+bZI7xIXGXGbgQbyZVxOTrzobAD79wazdOFcIu7cpphjCbplcyu7VLWp3umg6VmwYkkg27as48H9+7i5V6DvQD+dQRbHjhpC0WKODB7+tXbagb3/smzRHCLu3MLRsSRdevTjnXfTb2vWqVV9vev7ZNho3muSPmCSmYnhMzibN65jzcon+1zp0vTp9wkVK2vq7Zepk4mMuMP3k9O/YJw+dYJ5s2cRdv06dkWK0L7jRzRvmb7P9evZRe+X6s5de9Dl455ZpqepDbdJG3ubZkfJ2ZSQL7vjiFqt5q8lQWz9ez0P7t+nnHsF+g0apvN++HbUZxR1cGSI32jttAN7g1mycC6Rd25RzLEEXXv001n/6ZPHGTt6WJb1NWzcXGc5z84WyLYM2foOGp4p21AcHBwZ7PeVTralC+dos2W+BeHpk8cZN/ozvdkyLscUwz9ejP35kIrhYxwYv+6UnM0Ew2dt5XPV8HHEf3H2+1xbHyt8qmtuIXnlRjKLNt/TaQD44n82RMenMm/dPe20sqXy8ZFvIZwd8xF3N409xx+xed9Dg70RvvifDeF3Ulj6j+5gjh0m6v/8tWtQizo7FmaZHr5gNSf7jKbK3B+xdCnJwSbpt2i3q18TT//RFPQsR+KtSEKnBBA2e5nO/I7tfXEfPwxL11I8DA3nwrfTDN7S0uQ/w3cuUavVbFn5Gwd2rODhg7u4uFWhY++vKe5UTlvm1/E9sStakm6ffA9ATORNJgz1zbKsshVqMGRsEACPHz1g81+/curwDu4nxFLYtijedVvg22EQ+fKl36nj/WrPd2tb8faQhoW3TNOmTalQoQIzZswwdpTXjqGGBSXI7ku3MCy7hgVjy65hwdiya1gQry8lH0eUnC27hgVje1bDgjAsu4YFYdizGhaMyVDDghJk17BgbNKwIHJKBm98S8TGxrJ161Z27tzJzJkzjR1HCCGEEEIIIcQbQhoW3hLe3t7ExcUxefJk3N11B2arWLEi169f1zvfn3/+Sbdu3V5FRCGEEEIIIYQQryFpWHhLXLt2zeBzmzdv1t5OLLNixYq9pERCCCGEEEIIId4E0rAgtAMwCiGEEEIIIYQQuSUjcAkhhBBCCCGEECLPpGFBCCGEEEIIIYQQeSYNC0IIIYQQQgghhMgzaVgQQgghhBBCCCFEnknDghBCCCGEEEIIIfJMGhaEEEIIIYQQQgiRZ9KwIIQQQgghhBBCiDyThgUhhBBCCCGEEELkmTQsCCGEEEIIIYQQIs/yGTuAEK8LNSpjRzBIydlUqI0dwaB8qhRjRzAoOc3M2BEMMlGlGTuCQUre3wDSFNyeb4Jyt6uSKfn4K9s075S8XZUsTa3cY7DJf6eNHcGgtFqVjB3BsOQLxk4gXhPK/YYjhBBCCN7NoosAAJ3vSURBVCGEEEIIxZOGBSGEEEIIIYQQQuSZNCwIIYQQQgghhBAiz6RhQQghhBBCCCGEEHkmDQtCCCGEEEIIIYTIM2lYEEIIIYQQQgghRJ5Jw4IQQgghhBBCCCHyTBoWhBBCCCGEEEIIkWfSsCCEEEIIIYQQQog8k4YFIYQQQgghhBBC5Jk0LAghhBBCCCGEECLPpGFBCCGEEEIIIYQQeSYNC0IIIYQQQgghhMgzaVgwsqCgIGxsbHI1T3BwMCqVivj4+JeSSQghhBBCCCGEyKl8xg4glC81NZWffvqJ+fPnc/36dSwsLChfvjwDBgygV69exo5ndH9vXMu61cuIi43BybkMvfsPxrNSFYPlz5wKITDgN8LDrmJnZ88HHT/Ct0Vb7fNh16+ybFEgoZcvEBUZQa9+n9L6g055zqdWq/lrSRDbtmzgwf17lHP3pO+gYTi7lMl2vgP7drFs4Vzu3L6FY/ESdO3Rl3febaBTZsvGNU9eeyxOzqXp1X8wnpW8cpztRdfd09xLF87TyV373fo5zpQx29rVy59kK02fZ2Q7rc12TZuteYs22ue3btlI8M6thF27CkBZt/J0+19fyrtXyHU2tVrNiiWBbP9nPffv36NceU/6DvLD6Rnb9OC+YJYtmkPE7VsUK16CLt3762zTs6dDWL9qKVdCLxAXG8PIr7+nVp0G2SzRcD4l7nNK3t/gab0Fsv1Jvbm5e9Jv0PCcbdcM9dalR78s23XdqmVcuazZrl+M+Z5adXKXUcnHOaXub6Dsfe5tyvY2fa4qORvABw2t8KlugVUBE67cTGbBprvcikrNdh7LAio6NCpI9QrmWFmYEBWXyrKt9zh5KUm7zA8aFtSZJ+F+Kp9Nic5xLrVazZaVv3Fg50oe3b+Ls1tlOvYeQ3EnN4Pz3A6/zN8rZhJ+5Sxx0bf4oMeXNGzRXadMamoKW1b+xtG9m7gXH01h26LU9GlLs3YDMDF59vldu3o1cP28D9belShQwoEjHT4hYv2O7OepXxPPKaMo6FmOxFuRhPrPIWz2Mp0yju2aUX7cZ1iWdeZhaBgXvp1GxLrtz8wjRHakx4J4pnHjxjF9+nQmTJjA2bNn+ffff+nXrx9xcXHGjmZ0e3fvJDBgJh06f4z/jDlUqFSZiWO/ICoyQm/5iDu3mTh2FBUqVcZ/xhzad+7G3D9/5cC+XdoyiYmJFHMsTvee/bGxtXvujGtXLmXDmr/oO3AYk6f9iY2tHd+N+ZxHDx8anOfCudNMnTQen0bN8J85V/PvpHFcPH9WW2af9rV3Z8qMACpUqsL3Y780+Nozexl1d+HcGfyf5J46c47e3DnNNi9gFh07f4z/jAA8K1VhQjavTZNtNJ6VquA/I4AOerKdORVC/QaNmPDjNCb5z8LewYHx34wkJjoqV9kA1q1awsa1y+kzcDiTpgZgY2vHhG+GP3ObTps8Dp/3fJnyayA+7/kybfK3XLpwRlsm8fFjXFzd6DNweK4zZaTEfU7J+9tTa1cuYeOav+gzcBiTps1+Um9+Oaq3Bo188Z85jwaNfJk6aaxOhsePH1O6TFn6DByWp1xKP84pcX8DZe9zb1u2t+VzVenZWtS1xLeOJYs232N8QAwJ99MY2cOWAvlVBucxNYUR3W2xtzFl5l8JjPo1msANd4m7m6ZT7kZkCp9NidI+xvwWk+NcADvWzyN48wI69PoKvx+WUdjGnt9/6MfjRw8MzpOc9IgiDqVo3XUYhW3sDSx3Lvu3/0WHXl8xyn89rbv68e+GQPZsWZyjXKZWltw9eYEzn32Xo/IWpUtRc8NsYvceZW/ND7g8+Q8qTvsax3bNtGVsalel2pJp3Fy8jj3V23Jz8Tq8l07HppbhxjshckIaFjLZsGEDNjY2pKVpDlghISGoVCpGjhypLTNgwAC6dOkCwP79+2nQoAEWFhY4OTkxdOhQHjxIPwglJSXxxRdfULJkSaysrHjnnXcIDg42uP6YmBhq1apFmzZtePz4MQCbN2+mfPnyWFhY8N5773Ht2rUs83Tp0oVSpUphaWlJ5cqVWbp0qfb5BQsWUKRIERITE3Xm69ChAz169MhRnXzyySd06tSJMmXK4OXlRZ8+ffDz89OWSUxMZOjQoTg4OFCgQAHq1avH4cOHn7ns192GNSto3KwFTX1bUcrZhT79h1DE3oF/Nq/TW/6fzeuxL+pAn/5DKOXsQlPfVjRq+j7rVi/XlilX3oP/9RlEPZ/GmJmZPVc+tVrNxnUr6NC5O7XrNsC5tCtD/EaTmJjInl2GW6Y3rluJV7XqtP/wY0o5udD+w4+p7FWdjetWZHjtf9GoWQua+LailHNpevcfQhH7ogZfe2Yvo+42rFuJV7UadPiwG6WcXOjwYTcqe3mzcd3KHNaYxnpttpY4ObvQp/9gitg7sGXz+mdkG4yTswtNfVvSqOn7rF39l7bM8JFjeL/VB5Qp60YpJ2c+GTICdZqakyeO5SqbWq1m07q/aN+5B++864NzaVcG+31NYmIie3dtMzjfpvUrqFKtBu0+7E5JJxfafdidSl7V2ZRhm1arUZsu3fvxzrs+ucqUOZ8S9zkl72/wdLuuoH3n7tSu6/Ok3r56Um/ZbNd1mu3a/sOPKZmh3jJuV+8atenSox+16+Ztuyr5OKfU/U0zv3L3ubct29vyuarkbADNaluyYfcDjp5L5GZkKgFrEjA3U1G7cgGD8zSoZkFBCxUzlsVzOTyZmIQ0LoUlEx6RolMuLU1Nwv007ePeQ3WOc6nVanb/vZCmH/THq1ZTijuVo9snP5CU+Jij+zYZnM+5bGXafjwC73dbYJovv94y1y6eoFL196jo7UMRh5JUrd0M9yrvEn7ljN7ymUX9s5uLY6dzZ63hz4GMXPp/xOOw25z9/Afun79C+LyVhAetxtWvt7ZMmSH/I3r7fkJ/ms2DC1cI/Wk20TsPUnrI/3K0DiEMkYaFTBo0aMC9e/c4fvw4ALt27cLe3p5du9JbvoODg/Hx8eHUqVP4+vrSvn17Tp48yfLly9m7dy+DBw/Wlu3Vqxf79u1j2bJlnDx5kk6dOtG8eXMuXbqUZd03btygfv36eHh4sHr1agoUKEB4eDjt27enRYsWhISE0LdvX0aNGqUz3+PHj6levTobN27k9OnT9O/fn+7du3Po0CEAOnXqRGpqKuvXp/8oio6OZuPGjTm6lMHR0ZGdO3cSFWX4zOoXX3zBqlWrmD9/PseOHcPNzQ1fX19iY2OfufzXVXJyMqGXL+BVrabO9KreNTl/Tv8HxsXzZ6jqnbl8LUIvXSAlJUXvPM8j4s5t4uNi8fKuoZ1mZpafipW8uHDutMH5Lp4/o/d1XXjyujSv/SJVM5Xx8q6Z7XKfell1d/H8mSyZqnnXMrhMw9kuUrVaDZ3pVb1rcN7Aa7tw/ixVvXXLV/Oume12TUpMJDU1hYKFCuc4G0BkxJNtmuF1mpnlx7NS1Wds09N66rtWjrZXbihxn1Py/vZUpLbeMm/X3Neb1wvcrko/zilxf0ufX5n73NuY7UVS6j6n9GxFbU2xKWTK6dAk7bSUVDh/LQk3J8ONPVXdzbl8I5nuLQvxywh7Jn5ShFb1LVFl6uRQzC4f0z635+fP7BnU0ZqitqY5ygUQE3mDu/HReFR5Vzstn1l+3CrU4NrFkBwvRx9XD28unj5E5K1rANy8fp4rF45RoVruLzPMCZvaVYnavk9nWtTWPVhXr4Qqn+YKeNvaVYnevlenTPS2PdjWqfZSMom3hzQsZGJtbU3VqlW1vQqCg4MZPnw4J06c4N69e9y5c4eLFy/SsGFDfv75Z7p27cqwYcMoV64c7777LjNmzGDBggU8fvyY0NBQli5dyooVK6hfvz5ly5ZlxIgR1KtXj8DAQJ31Xrx4kbp169KkSRPmz59Pvidv/t9//x1XV1emTZuGu7s73bp1o2fPnjrzlixZkhEjRlC1alVcXV0ZMmQIvr6+rFihaWm2sLCga9euOutcvHgxpUqVomHDhs+sk6lTpxIVFYWjoyNVqlRh4MCB/P3339rnHzx4wO+//87PP//M+++/j6enJwEBAVhYWDB37ly9y0xMTOTu3bs6j8w9KpTu3t0E0tLSsLGx1ZlubWNLfJz+BpW4uFisM5W3sbElNTWVu3cTXnjGpzlsbHS7flrb2BJnIOPT+WxsM+W0TX9dmteeinWm5dpk89ozell1Fx8Xi3Wm3Na2Ocv0rGya16b/8p+4uFi95bPbrguCZmNXxB6vqtVznA0gPk7TvTNz3WvqznDXz/i4WD3z2OWqbnKWT3n7nJL3t/T1xTxZR+bXl/02ela9PS+lH+eUuL+lz6/Mfe5tzPYiKXWfU3o264Kanxx3H+hewnD3QZr2OX0cbE2p6VkAE5WKqYvj2bD7Ps3rWNG6gZW2TOiNZALWJOC/MJ7ADXexLmjCmD62WFkYvsQio3vxmrEYClkX0ZleyLoId+NzPk6DPo3b9MG77vv8+Hlr/LpVZcqoTvi8353qdVs813INMS9mT2KEbuakyBhMzMzIb6/ZxuaO9iRG6H5fSIyIwdyx6EvJJN4eMnijHg0bNiQ4OBg/Pz/27NnDxIkTWbVqFXv37iU+Pp5ixYrh4eHB0aNHuXz5MosXp18npVarSUtL4+rVq5w+fRq1Wk358uV1lp+YmEiRIukHr0ePHlGvXj26dOnCL7/8olP23Llz1K5dG1WGptk6derolElNTWXSpEksX76cmzdvkpiYSGJiIlZW6Qfdfv36UbNmTW7evEnJkiUJDAykZ8+eOss1xNPTk9OnT3P06FH27t3L7t27ad26NT179mTOnDmEhoaSnJxM3bp1tfOYmZlRq1Ytzp07p3eZP/74I+PHj9eZNnbsWMaNG/fMPEqTpQ7V6iwt6dmVV6Pprpezj7/s7f53G3/O9Nf+/dW4Sfozokb1zDVmyqnOuhw9Lz3rxOzW8BLqLvPrUj9jmdmsLHO0PGbLOtOalUvZu2snEyZNI39+/d0nn9rz71b+nDVF+/fosZP1xXt2QD0ZNfX9fHve67TPKWl/2/3vVmZnqLfR4/RvV3UO6k1/hhdxRMmwDoUc516n/U1vLgUd4962bHml5H1OydnqVC7A/1oX0v49bXF8+jwG16onlUrT+BC44S5qNVy/nYJNoQe8/64l63dpLj0+dTm9FwSRcDk8iZ8/s6deVQv+OZB1bIkjezfyV0D6d9D+X/6m9wWqef5j6fEDf3N0z0a6D5mMYyk3bl47z5oFk7G2daCWT9tnLyAvslSyKut0fWUyTxMil6RhQY+GDRsyd+5cTpw4gYmJCZ6envj4+LBr1y7i4uLw8dFcp5qWlsaAAQMYOnRolmU4Oztz8uRJTE1NOXr0KKamul2yChZMH73W3NycJk2asGnTJkaOHEmpUqW0z6lz8Cb39/dn2rRpTJ8+ncqVK2NlZcWwYcNISko/0FarVg0vLy8WLFiAr68vp06dYsOGDTmuExMTE2rWrEnNmjUZPnw4ixYtonv37nz99dfajFk+2LP5cjt69GidMRqe1sPrpFBha0xMTLKcBUhIiM/Sqv+UrW3Ws48J8fGYmppSqLD1c2eq+U5dymW4y0BycjKgORtqa5femJUQH5/l7ERGNnpzxmnPCmleu2nWMglxWc406fOy6k5f7rvxhpeZXTZ9ry3zWbGM2bK8Fm023Usd1q5azsq/FjP+e39Klyn7zDw13qmHm7un9u+UJ9s0Pi4WW7v0waISEuKznKXKSFM3umcosntNOfU67HNK3N9qvlOPcnq2a1zm7Rofl+UscEY2Bva9592uTyntOPc67G/p8ytrn3ubsz0PJe9zSs52/EIioTeTtX/ne/I12LqgCQn303stFLLS/Tuz+HtppKapdX7z3opKwaaQKaamkKrnhhJJyRAekUIxO/2XQ1Sq/h4ubukDFaYka74v34uPxto2/az9/YTYLL0Ycmv9In8at+2L97uaHgolnMsTF32b7evmvJSGhcSI6Cw9D/IXtSMtOZmkmHhNmTvRmDvqDjZp7mCXpaeDELkll0Lo8XSchenTp+Pj44NKpcLHx4fg4GDt+AoA3t7enDlzBjc3tyyP/PnzU61aNVJTU4mMjMzyvKOjo3Z9JiYmLFy4kOrVq9OoUSNu3bqlfc7T05ODBw/q5Mv89549e2jbti0ff/wxXl5euLq66h3DoW/fvgQGBjJv3jyaNGmCk5NTnuvI01PzhfjBgwfa17t3b/r1WsnJyRw5coQKFfTfSs/c3JzChQvrPF63hgUzMzPKurlz4vgRneknjh/Bo0JFvfOU96iop/xhypZz117+8jwsLC0pXqKU9uHkXBobWztOZlhncnIyZ06fwL1CJYPLKe9RkRMhWXO6P3ldmtdePstrOXn8SLbLfepl1Z2+3CHHDxtcpuFsWV/bieNH8TDw2tw9PDlx/Gim9R7Jsl3XrFrGimUL+fa7n3Ar556jPJm3aSntNk0fHDU5OZmzp0OesU0r6ewHmtd0OEfbKzf5lLjPKXF/M7xddevtbA7q7WSI7kC5L2K7PqW049zrsL+lz6+sfe5tzvY8lLzPKTnb4yQ1kbGp2setqFTi76VSsWx6Lz1TU/AonZ/L4cl6lwFwKTyZYnb5dDoTOBYxJe5eqt5GBdA0YpQomo94Aw0WBSysKOrorH04lipLYRt7Lpw6oC2TkpLM5XNHKF2+qsFsOZGU9Dhrzw8TE9RphhtTnkf8wRDsG7+rM61o03okHD2N+smYI3EHQ7BvXFenjH2TesQdOP5SMom3hzQs6PF0nIVFixZpxyBo0KABx44d046vAPDll19y4MABPv30U0JCQrh06RLr169nyJAhAJQvX55u3brRo0cPVq9ezdWrVzl8+DCTJ09m8+bNOus0NTVl8eLFeHl50ahRI+7cuQPAwIEDCQ0Nxc/PjwsXLrBkyRKCgoJ05nVzc2Pbtm3s37+fc+fOMWDAAO38GXXr1o2bN28SEBBA7969szxvSMeOHZk2bRqHDh3i+vXrBAcH8+mnn1K+fHk8PDywsrJi0KBBjBw5ki1btnD27Fn69evHw4cP6dOnT47X8zpq3a4TO7ZuYsfWzdwIu8682TOJjoqgWYs2ACwKms0v/j9oy/u2aENUZASBAbO4EXadHVs3s2PrZtq276wtk5yczNXQS1wNvURKSgqxMdFcDb3E7Vs3cp1PpVLRqm0nVv21mEP7dxN27Qozp/2Iubk59X2aaMvN8P+eRUGztX+3bNORE8eOsGbFEm6EX2fNiiWcDDlKq7bp9/1u3e7DJ699EzfCrhE4eybRUZHa126MumvVpgMhxw6z+knu1drcHXNVb23adWL71s1s37qZ8LDrzJs9i+ioCHxbtAZgYVCA3mzzAmYRHnad7U+yfdD+Q22ZNSuXsmTBPAYPG4mDgyNxsbHExcby6NGjXGVTqVS0bPshq1cs0m7TWdN/wNzcnHo+TbXlfvWfyOKgP7R/t2zTkRPHD7N25WJuhl9n7crFnAo5QssM2/TRo4dcvXKJq1c0DZOREbe5euVSrm4nptR9Tsn729N6a9m2E6v/yrBdtfWWvl1n+H/P4qA/tX+30NabZruuWWFguz45poBmgLeroTnfrko+zil1f3tZ9fai9rm3Ldvb8rmq5GwAWw8+pHV9K7w9zCnpYErfDwqTmKzm4KnH2jL92hWmY+P0Xr3/Hn6IlYWKbs0LUayIKV7l8tOqvhU7/0v/7OzcrCDuLmbY25jgWjIfgz+0wcJcxb6QnH2+qlQqGrzfnW1rAzj533Zuh19iyW9fk9+8ANXrttSWWzRrNBuWTtP+nZKSzI1r57lx7TypqckkxEZw49p5ou6EactU9G7ItrUBnDm2i5jIm5z8bzvBmxZQuWbjHGUztbKksJcHhb08ALAsU4rCXh4UcCoOgPtEP7wCJ2vLX5+9DAuXElT4eRQFPVwp1bMDTr06cGXqPG2ZazMXYN+0Lq4j+mHl7orriH7YN67DtV/n5yiTEIao1Dnpa/8WGjFiBP7+/pw+fZqKFTUttlWrVuXWrVtERERoWx8PHz7M119/zYEDB1Cr1ZQtW5bOnTvz1VdfAZoPs4kTJ7JgwQJu3rxJkSJFqFOnDuPHj6dy5coEBQUxbNgw4uPjAUhJSaFz586cO3eO4OBgHBwc2LhxI8OHDyc8PJxatWrRq1cvevfuTVxcHDY2NsTGxtK7d2927NiBpaUl/fv3JywsjISEBNauXavzunr06MGmTZu4detWjnsIBAQEsHTpUk6fPk1CQgKOjo40atSIcePG4eLiAmjuTPHFF1+wdOlS7t27R40aNZg2bRo1a9Z8xtJfH2cu39Y7/e+Na1m7ailxsbE4u5ShV/9PqVjJC4Bfp/5IZOQdJkxKHzvjzKkQzQ/Q69ewK1KEdh274NsivTtcZMRtBvbukmU9FSt76SwnI3U2Vymq1Wr+WhLE1r/X8+D+fcq5V6DfoGE4l3bVlvl21GcUdXBkiN9o7bQDe4NZsnAukXduUcyxBF179KN2Xd1RjLdsXMPaVcuIi43B2aUMPfsP1r72p1QYPsS86LoD2L83mKUL5xJx5zbFHEvQrUffLLlzmm3NqmVPspWmd4ZsM6ZOIjLyDhMnTdeWP30qhMCA3wjLkK15hi9c/Xt9pPeHXOeu/+Ojbj2zTE9RGz7TplarWbEkkG1b1vHg/n3c3CvQd6CfzjYdO2oIRYs5Mnj419ppB/b+y7JFc4i4cwtHx5J06aF7a8kzJ48z7qusl3b5NG6usxwTVfZnWoy5zyl5fwNIy6Y9X1NvgWzLUG99Bw3PVG9DcXBwZLDfVzr1tnThHG29Zb615OmTxxk3+rMs62vYuLnOckwwvF2NfZyTY1y63O5zku3N+1zNjrGzTVmU/ZgEHzS0omF1C6wsTAi9kczCzXe5GZne9WBUT1ui41OZs/audlrZUmZ0bV4QZ0cz4u6msvv4Izbtfai9PGJQR2vKu5hRyNKEew/SCL2RzOp/73MrSrdLQ+e2hi/LUavVbFn5Gwd2rODhg7u4uFWhY++vKe5UTlvm1/E9sStakm6ffA9ATORNJgz1zbKsshVqMGRsEACPHz1g81+/curwDu4nxFLYtijedVvg22EQ+fKl3w0jrZb+nh92DWpRZ8fCLNPDF6zmZJ/RVJn7I5YuJTnYJP328Xb1a+LpP5qCnuVIvBVJ6JQAwmYv05nfsb0v7uOHYelaioeh4Vz4dprBW1q2TL5goNaE0CUNC2+Zpk2bUqFCBWbMmGHsKK8dQw0LSpDdFyBjy+5Lt7EpOVt2DQvG9qyGBWNS8jaF7BsWjC27hgVjk2OceNWUvM8p2bMaFowpu4YFYzPUsKAE0rAgckq531zFCxUbG8vWrVvZuXMnM2fONHYcIYQQQgghhBBvCGlYeEt4e3sTFxfH5MmTcXfXHTSuYsWKXL9+Xe98f/75J926dXsVEYUQQgghhBBCvIakYeEtce3aNYPPbd68WXuLosyKFSv2khIJIYQQQgghhHgTSMOC0A7AKIQQQgghhBBC5JZyR5ESQgghhBBCCCGE4knDghBCCCGEEEIIIfJMGhaEEEIIIYQQQgiRZ9KwIIQQQgghhBBCiDyThgUhhBBCCCGEEELkmTQsCCGEEEIIIYQQIs+kYUEIIYQQQgghhBB5Jg0LQgghhBBCCCGEyDNpWBBCCCGEEEIIIUSeScOCEEIIIYQQQggh8k4txBvo8ePH6rFjx6ofP35s7ChZSLa8UXI2tVrZ+SRb3ki2vFFyNrVa2fkkW95ItryRbHmn5HxKzibebCq1Wq02duOGEC/a3bt3sba2JiEhgcKFCxs7jg7JljdKzgbKzifZ8kay5Y2Ss4Gy80m2vJFseSPZ8k7J+ZScTbzZ5FIIIYQQQgghhBBC5Jk0LAghhBBCCCGEECLPpGFBCCGEEEIIIYQQeSYNC+KNZG5uztixYzE3Nzd2lCwkW94oORsoO59kyxvJljdKzgbKzifZ8kay5Y1kyzsl51NyNvFmk8EbhRBCCCGEEEIIkWfSY0EIIYQQQgghhBB5Jg0LQgghhBBCCCGEyDNpWBBCCCGEEEIIIUSeScOCEEIIIYQQQggh8kwaFoQQQgghhBBCCJFn0rAg3hhhYWHou8mJWq0mLCzMCInEi5KUlMSFCxdISUkxdhTxAuzevVvvtkxJSWH37t1GSCSEEMKYkpOT6dWrF1euXDF2FCFEHsntJsUbw9TUlNu3b+Pg4KAzPSYmBgcHB1JTU19pnvXr1+e4bJs2bV5ikmcLDw9HpVJRqlQpAP777z+WLFmCp6cn/fv3N1quhw8fMmTIEObPnw/AxYsXcXV1ZejQoZQoUYJRo0YZLRvAjh072LFjB5GRkaSlpek8N2/evFeaZcaMGTkuO3To0JeY5NmU9l7NyFA9qlQqChQogJubGw0aNMDU1PSV5HmdjiMXL14kODhY7/vh22+/NVIqmD9/Pvb29rRs2RKAL774gtmzZ+Pp6cnSpUtxcXExWrYHDx4wadIkg8cRY/7ISk1NJSgoyGC2nTt3GimZ8h07dgwzMzMqV64MwLp16wgMDMTT05Nx48aRP39+IyfUlZqayqlTp3BxccHW1tZoOWxsbDh27Biurq5Gy/AsZ8+eJSwsjKSkJJ3pxj7+xsfHM3fuXM6dO4dKpaJChQr06dMHa2tro+YSbxdpWBBvDBMTEyIiIihatKjO9OvXr+Pp6cmDBw9eeZ6MVCqVTo8KlUql/b8xf0gB1K9fn/79+9O9e3fu3LmDu7s7FStW5OLFiwwdOtRoPwo+++wz9u3bx/Tp02nevDknT57E1dWV9evXM3bsWI4fP26UXADjx4/nu+++o0aNGhQvXlxnewKsWbPmleYpU6ZMjsqpVCqjnxEy9F69ePEiNWrU4O7du0ZKpqnHqKgoHj58iK2tLWq1mvj4eCwtLSlYsCCRkZG4urry77//4uTk9NLzZD6OGKJSqYx6HAkICGDQoEHY29vj6Oio835QqVQcO3bMaNnc3d35/fffadSoEQcOHKBx48ZMnz6djRs3ki9fPlavXm20bF26dGHXrl10795d73Hks88+M1IyGDx4MEFBQbRs2VJvtmnTphkpWTolNe5mVLNmTUaNGkWHDh24cuUKFStWpF27dhw+fJiWLVsyffp0o2UDGDZsGJUrV6ZPnz6kpqbi4+PD/v37sbS0ZOPGjTRs2NAouXr16kXlypXx8/Mzyvqzc+XKFdq1a8epU6d0vs89fV8Y8/h75MgRfH19sbCwoFatWqjVao4cOcKjR4/YunUr3t7eRssm3i7SsCBee08/gH755Rf69euHpaWl9rnU1FQOHTqEqakp+/btM1ZEtm/fzpdffskPP/xAnTp1UKlU7N+/nzFjxvDDDz/QtGlTo2UDsLW15eDBg7i7uzNjxgyWL1/Ovn372Lp1KwMHDjTaD1EXFxeWL19O7dq1KVSoECdOnMDV1ZXLly/j7e1t1B+gxYsX56effqJ79+5Gy/C6ad++PaA5e9e8eXPMzc21z6WmpnLy5Enc3d3ZsmWLsSKydOlSZs+ezZw5cyhbtiwAly9fZsCAAfTv35+6devy0Ucf4ejoyMqVK42WU2lcXFz45JNP+PLLL40dJQtLS0vOnz+Ps7MzX375Jbdv32bBggWcOXOGhg0bEhUVZbRsNjY2bNq0ibp16xotgyH29vYsWLCAFi1aGDuKXkpr3M3I2tqaY8eOUbZsWSZPnszOnTv5559/2LdvHx999BHh4eFGywZQqlQp1q5dS40aNVi7di2ffvop//77LwsWLODff/812vel77//nilTptC4cWOqV6+OlZWVzvPG7G3XunVrTE1NCQgIwNXVlf/++4+YmBg+//xzpkyZQv369Y2WrX79+ri5uREQEEC+fPkAzaWFffv25cqVK3KJoXhl8hk7gBDP6+lZa7VazalTp3S6GObPnx8vLy9GjBhhrHiA5uzAH3/8Qb169bTTfH19sbS0pH///pw7d86I6TTXNj79kbd9+3Ztlz4PDw9u375ttFxRUVFZusuDpvtw5i+Rr1pSUhLvvvuuUTO8bp52yVSr1RQqVAgLCwvtc/nz56d27dr069fPWPEAGDNmDKtWrdI2KgC4ubkxZcoU7dnHn376iQ4dOhgxpfLExcXRqVMnY8fQq2DBgsTExODs7MzWrVsZPnw4AAUKFODRo0dGzWZra4udnZ1RMxiSP39+3NzcjB3DoD/++IOgoCBFNu6q1WptD4rt27fTqlUrAJycnIiOjjZmNACio6NxdHQEYPPmzXTq1Iny5cvTp0+fXF1W96LNmTMHGxsbjh49ytGjR3WeU6lURm1YOHDgADt37qRo0aKYmJhgYmJCvXr1+PHHHxk6dKhRe1AeOXJEp1EBIF++fHzxxRfUqFHDaLnE20caFsRr799//wU0Xeh++eUXChcubOREWYWGhuq9zs3a2ppr1669+kCZVKxYkT/++IOWLVuybds2JkyYAMCtW7coUqSI0XLVrFmTTZs2MWTIECC9y2FAQAB16tQxWi6Avn37smTJEr755huj5jDkxo0brF+/Xu+1oFOnTjVKpsDAQABKly7NiBEjspyNUoLbt28bHFjyzp07AJQoUYJ79+696miAplFt165dererMb90d+rUSdvDSWmaNm1K3759qVatGhcvXtSOtXDmzBlKly5t1GwTJkzg22+/Zf78+Tq97ZTg888/55dffmHmzJlGb8jVR8mNuzVq1GDixIk0adKEXbt28fvvvwNw9epVihUrZuR0UKxYMc6ePUvx4sXZsmULv/32G6AZ1+hVjR+TUVpaGiYmJly9evWVrzunUlNTKViwIKDpzXPr1i3c3d1xcXHhwoULRs1WuHBhwsLC8PDw0JkeHh5OoUKFjJRKvI2kYUG8MZ7+aHnq7t277Ny5Ew8PjywH21etZs2aDBs2jEWLFlG8eHEA7ty5w+eff06tWrWMmg1g8uTJtGvXjp9//pn//e9/eHl5AZqB44yZ78cff6R58+acPXuWlJQUfvnlF86cOcOBAwfYtWvXK8+T8brPtLQ0Zs+ezfbt26lSpQpmZmY6ZY314x001x23adOGMmXKcOHCBSpVqsS1a9dQq9WKuNZy7NixpKSksH37dkJDQ+natSuFChXi1q1bFC5cWPvlzRjee+89BgwYwJw5c6hWrRqg6RU1aNAgGjVqBMCpU6dyPKbFi3T8+HFatGjBw4cPefDgAXZ2dkRHR2NpaYmDg4NRGxbc3Nz45ptvOHjwIJUrV87yfjBmtlmzZjFmzBjCw8NZtWqVtrH06NGjdOnSxWi5APz9/QkNDaVYsWKULl06S7296rEpnl6u9NTOnTv5+++/qVixYpZsxhybApTduDtt2jQ+/vhj1q5dy9dff63t+bFy5UpFNIb06tWLDz/8UHsJydPLMQ8dOmSU70tmZmY6A/qOHDmS0aNHK6o3T6VKlbTjPL3zzjv89NNP5M+fn9mzZxt9sMnOnTvTp08fpkyZwrvvvotKpWLv3r2MHDnS6Mc48XaRMRbEG+PDDz+kQYMGDB48mEePHuHl5aX9MbVs2TKjdl2+fPky7dq148KFCzg7OwOa22OWL1+etWvXKqK7aWpqKnfv3tUZEfratWvaHy3GcurUKaZMmcLRo0dJS0vD29ubL7/8Ujva9qv03nvv5aicSqUy6ojptWrVonnz5nz33XfasSkcHBzo1q0bzZs3Z9CgQUbLBpoBVZs3b05YWBiJiYnau30MGzaMx48f88cffxgt2507d+jevTs7duzQ/pBKSUmhcePGLFy4kGLFivHvv/+SnJxMs2bNXmm2hg0bUr58eX7//XdsbGw4ceIEZmZmfPzxx3z22WdZfhS+Stk1tChhwFClGj9+fLbPjx079hUl0ejVq1eOy2ZuzH/VPvvsMxYsWECVKlUU17hryOPHj8mXL59Ol3VjWblyJeHh4XTq1El7R6j58+djY2ND27ZtX2kWExMT7ty5o/2uUbhwYUJCQoz+gz2jf/75hwcPHtC+fXuuXLlCq1atOH/+PEWKFGH58uXahmdjSEpKYuTIkfzxxx/aHndmZmYMGjSISZMm6YxnJMTLJA0L4o3h6OjIP//8g5eXF0uWLGHs2LGcOHGC+fPnM3v2bKNe/waaay63bdvG+fPnUavVeHp60qRJE8V0MU1JSSE4OFhxZ5BF7hUqVIiQkBDKli2Lra0te/fupWLFipw4cYK2bdsa/fKbDz74gEKFCjF37lyKFCmiHZRz165d9O3bl0uXLhk1H8D58+e5ePEiarUaDw8P3N3djR0JGxsbDh06hLu7OzY2Nhw4cIAKFSpw6NAh/ve//3H+/HljR1SkLVu2ULBgQe0YN7NmzSIgIABPT09mzZpl1NvribzLrqHX2I27rq6uHD58OMulhPHx8Xh7exu9oW3BggV07tw5yw/OpKQkli1bRo8ePV5pnswNCxkHa1ay2NhYbG1tFfM97uHDh4SGhqJWq3Fzc1Pc5VXizWf8JlMhXpCEhARtt7ktW7bQoUMHLC0tadmyJSNHjjRyOs0XnWbNmtGgQQPMzc0V80EEWc8gN23alEKFCvHTTz8Z9Qzy5s2bMTU1xdfXV2f6P//8Q1paGu+//75RcmV0+fJlQkNDadCgARYWFqjVaqNvWysrKxITEwHNeAChoaFUrFgRQBEDh+3du5d9+/ZluZe7i4sLN2/eNFIqXUq4hCozMzMz7b5VrFgxwsLCqFChAtbW1oSFhRk5nUZSUhJXr16lbNmyijgrC5pu1ZMnTwY0PaA+//xz/Pz82LlzJ35+fkY/8x4fH8/KlSsJDQ1l5MiR2NnZcezYMYoVK0bJkiWNluvq1aukpKRQrlw5nemXLl3CzMzM6ONTPB1fSYmuXbum9/aDiYmJ3LhxwwiJdPXq1YvmzZtn6Y147949evXq9cobFl5H169f58GDB9jY2Bj9M/8pS0tLKleuzPXr17l27RoeHh45vmWxEC+CMj71hXgBnJycOHDgAHZ2dmzZsoVly5YBmtHKCxQoYNRsaWlpfP/99/zxxx9ERERou35/8803lC5dmj59+hg132effUaNGjU4ceKEzhmWdu3a0bdvX6PlGjVqFJMmTcoyXa1WM2rUKKM2LMTExPDhhx/y77//olKpuHTpEq6urvTt2xcbGxv8/f2Nlq127drs27cPT09PWrZsyeeff86pU6dYvXo1tWvXNlqup9LS0vR+6b5x44ZRBpry8/NjwoQJWFlZPfP+6cbsXl2tWjWOHDlC+fLlee+99/j222+Jjo5m4cKFRrk0KKOHDx8yZMgQ5s+fD6A9xg0dOpQSJUowatQoo2W7evUqnp6eAKxatYpWrVrxww8/cOzYMaPfSvHkyZM0adJEO5Bvv379sLOzY82aNVy/fp0FCxYYLVvPnj3p3bt3loaFQ4cOMWfOHIKDg40TTMHWr1+v/f8///yjM2hzamoqO3bsMMr4LJkZagC/ceOG3oGmX4Vvv/1We4Y9KSmJ77//PksWYxx/58+fT1xcHMOGDdNO69+/P3PnzgXA3d2df/75BycnJ8km3nrSsCDeGMOGDaNbt24ULFgQFxcXGjZsCMDu3buN/qV74sSJzJ8/n59++knndnqVK1dm2rRpRm9YUOoZ5EuXLml/EGTk4eHB5cuX/9/enYfVnL//A3+eokI7ikSpaNGiLCNLRZRtZGfKluyUiRp8DJH1UxKNz8jeZs8yiy3TcsaSQaUoS4vKUCiaKKTO+/fHmc50nCwz39+c1zvdj+tyXXmfrqvndarTed+v1+u+GST6i6+vL5o2bSpZNa41YcIE+Pr6Mi0sbN68GS9fvgQArFq1Ci9fvsThw4dhYmKC0NBQZrlqDRo0CFu2bMHOnTsBiHfzvHz5EgEBAUxu9NLS0vD27VsA4mZ571t9Yr0qtX79esk0ijVr1mDq1KmYO3cuTExMmK+6L1u2DOnp6UhKSsLgwYMl1wcOHIiAgACmhQUlJSVUVlYCEI/+q12N1dbWRnl5ObNcgLioNW3aNAQFBUkV1YYMGQJ3d3eGycS/F3369JG53qtXLyxYsIBBImkVFRXYuHEj4uPj8eTJE8l4x1osjhuMHDlS8vHUqVOlHqvd5cHyb4OtrS0EAgEEAgGcnZ2ldhXV1NTg/v37Ur+/8uLg4CA1WaF3794y3z9Wr7/h4eGYNWuW5P9nz57Fvn37EBUVBXNzcyxYsACrV6/G7t27KRtp9KiwQD4b8+bNQ8+ePfHgwQMMGjRIsv3LyMgIa9euZZotKioKO3fuhLOzs9Q4Nmtra16ci+bbCnItDQ0N5OXlyWy5zcnJYT6qMC4uDufOnZM0varVqVMnFBQUMEolVvdcavPmzSWjxPgiNDQU/fv3h4WFBV6/fg13d3dkZ2ejVatWOHjwoNzz1B1Ty+dV2LrzyFu3bo3Tp08zTCPt5MmTOHz4MHr16iV1A2BhYYHc3FyGyYC+ffti0aJF6NOnD65evYrDhw8DEO+qePf3V96uXbuGHTt2yFxv166dZLwpKwKBoN6xqn/88Ue9fy/kbcaMGRAKhZg8ebJkugFrtcWNjh074vr160zHNdentvBx48YNuLq6SvVPUlJSgqGhIZNG13x+3b13757Ua+8PP/yAESNGwMPDA4C44Pt3mp42lmykcaLCAvmsdO/eXepFFoBkZjlLDx8+rHfyg0gkkqyUssS3FeRaI0aMwNdff40TJ07A2NgYgLiosHjxYowYMYJZLkC8WlZfY6SSkhJedWB++fKlzEpe7U00K3p6erhx4wYOHjyI1NRUiEQieHl5wcPDA82aNZN7HltbW8mos/c1XSMf9vTp03qnx1RUVDC/4du2bRvmzZuH2NhYbN++XdK34MyZM0xWZ+tSUVGpd9fE3bt30bp1awaJ/tKvXz9s2LABBw8ehKKiIgDxqvaGDRskjTBZOnPmDE6dOlXvrgqW3r59C0NDQ5SWlvLudSQgIAA1NTUwMDCAq6urZPw1H126dAndu3dn/vf01atXUn8zL1++jOnTp0v+b2RkxKwIyOdspHGiwgL5bNTU1CAiIuK92yJZdoju0qULLly4AAMDA6nrR48eha2tLaNUf+HbCnKt4OBgDB48GGZmZpKVxd9//x39+vXDpk2bmOUCxFs3o6KisGbNGgDiYoxIJEJwcPAnj6X8t9y/fx8LFixAUlISXr9+Lblee66WD6uNzZo1w/Tp06XeBLGiqamJ+/fvQ0dHB/n5+TKvHXxRWlqKlStXIjExsd7XuGfPnjFKBvTo0QOnTp2Ct7c3gL+2Le/atQv29vbMcgFAhw4d8PPPP8tc58OxIDc3NwQGBuLIkSMAxM9bYWEhli5dynREMgAEBQXBwcEBpqam6NevHwDgwoULKC8vZ/r3tJaWlpakYTOfNG3aFLdu3WJeUHsfRUVFzJkzB7dv32Yd5YOGDBnCi5GTBgYGSElJgYGBAUpKSpCZmSlVWCsuLmbWl4LP2UjjRIUF8tlYuHAhIiIiMGzYMFhaWvLqj3pAQAAmT56Mhw8fQiQS4fjx47h79y6ioqLqfcMrb7UryIcOHUJKSgrzFeRaGhoauHz5Ms6fP4/09HQ0a9YM1tbWcHBwYJapVnBwMJycnHD9+nVUVVXhm2++QWZmJp49e4ZLly4xzVa7DXLv3r3Q1dXl1e9CXVlZWSgsLERVVZXUdXnvRhkzZgwcHR0l26m7d+8uWaF9F8sxcZMmTUJubi68vLx4933dsGEDBg8ejKysLFRXV2Pr1q3IzMxEcnIyhEIh63gSr169ktklxnIHz6ZNmzB06FDo6Ojg1atXcHR0RHFxMezt7bFu3TpmuQDxMZaMjAxs27ZN8vo7ZcoULFiwgBc39GvWrMHKlSsRGRnJu7F6U6ZMwZ49e+ptPswHVlZWyMvL40UjyffhOI51BADi7+X8+fORmZmJhIQEmJmZoVu3bpLHL1++DEtLS8pGCAABx5ffXEL+j1q1aoWoqCjmXb7f59y5c1i/fr3kxt3Ozg4rV66Ei4sL62iIiYnBpEmT6n3M398fwcHBck7UMBQXF2P79u1S39P58+cz316qqqqKlJQUmJqaMs3xPnl5eRg1ahRu3rwJgUAgeQNZe6PMYkfF2bNnkZOTAx8fHwQGBr63t8jChQvlnOwvampquHjxImxsbJhl+JCbN29i06ZNUr8PS5YsYd48t6KiAkuWLMGRI0dQWloq8zgfdvAkJCRIjgXZ2dlh4MCBvBhdyze1zQdr5eTkgOM4GBoaomnTplKfm5qaKu94Et7e3oiKioKJiQm6d+8u0xOI5XQZQNwjaMmSJVizZg26desmk4/1cTlA/HqXnp7OfMeCSCRCQEAAfv75Z7Rp0wabN2+Watg8btw4DB48mEkTbj5nI40TFRbIZ0NPTw9JSUno3Lkz6ygNjqamJmJiYjB8+HCp676+vjh06BCKiooYJQPi4+Pfe7xl7969jFLxW//+/bF8+XIMHDiQdZR6ffnll1BUVMSuXbtgZGSEq1evorS0FIsXL8amTZsk265Z8PT0RFhYGNOmpe/To0cPfPfdd7wYGdqQzJ8/H4mJiQgMDMSUKVPwv//9Dw8fPsSOHTuwceNGyQ4fFjZs2IBly5bJXK+pqcGkSZPkfhQtIyMDlpaWUFBQQEZGxgc/19raWk6p/rJ69epP/tyAgIB/McmHfeg4nEAgYH6UpLa5NSA9bYFPx+UOHDgANzc35o2a/66DBw9ixIgRvMzN52zk80CFBfLZCAkJQV5eHrZt28bLVZ6ysjLExsYiLy8Pfn5+0NbWRmpqKnR1dSXNxFg5e/YsJk6ciB9//FFyzMDb2xvHjx9HfHw8zMzMmORavXo1AgMD0b1793q7fp84cYJJLkA8xvRDWB7XyM3NxZw5czBp0iRYWlrKrOSxuCGoq1WrVkhISIC1tTU0NDRw9epVmJqaIiEhAYsXL0ZaWhrTfHx17do1LF26FCtXrqz3+8pylVFRUVHSALOu0tJS6OjoML1R6dChA6KiouDk5AR1dXWkpqbCxMQE0dHROHjwINPpGrq6ulizZo3UyLiamhpMnDgRt27dkvs5eAUFBRQXF0NHRwcKCgpSO4rq4svNJ/lnPnY8ydHRUU5JPj/q6uq86A1RHz5nI58H6rFAPhsXL15EYmIizpw5gy5dusi86T5+/DijZOJVoIEDB0JDQwP5+fmYMWMGtLW1ceLECRQUFCAqKopZNgAYPHgwwsPDMXLkSMTFxWHv3r344YcfkJiYyHQHSHh4OCIiIjB58mRmGd7HyclJ5lrdwgfLN91Pnz5Fbm6u1Jip2hsEPtwQ1NTUSMactWrVCo8ePYKpqSkMDAykZpmzMGDAgA8+znKlUVNTE3/88YdMRj58X9+3RvHmzRsoKSnJOY20Z8+eSc6Sq6urS5pc9u3bF3PnzmUZDadPn8bAgQOhqamJ8ePH4+3bt5gwYQLu3LmDxMREuee5f/++ZBrF/fv35f71P0e///47BAIB8wWEuvhcOBAKhdi0aRNu374NgUAAc3Nz+Pv7M93J9nfweb2Wz9nI54EKC+SzoampiVGjRrGOUa9FixZh2rRpCAoKktpiPWTIELi7uzNM9peJEyfi+fPn6Nu3L1q3bg2hUFjviEx5qqqqQu/evZlmeJ/nz59L/f/t27dIS0vDihUrmDddmz59OmxtbXHw4EHeNfkDAEtLS2RkZMDIyAhffPEFgoKCoKSkhJ07dzJfSXm3f8Hbt29x48YN3Lp1C1OnTmWUSszDwwNKSko4cOAAb76vYWFhAMSFq927d0sKRoC4gPTrr78y2/FUy8jICPn5+TAwMICFhQWOHDmCnj174qeffoKmpibTbN26dcOJEyfg5uYGZWVl7NmzB7m5uUhMTISurq7c89SdXPTuFCO+qampQWhoKI4cOVJvE1iWU1JEIhHWrl2LkJAQvHz5EoC4Z8DixYuxfPlyqaMIrJSVlWHPnj2SG3gLCwtMnz6d6RSBmJgYeHp6YvTo0fDx8QHHcbh8+TKcnZ0RERHBm/dLhJD60VEIQuRAQ0MDqampMDY2lmpIVFBQAFNTU6mRgPKyaNGieq/HxsbC1tYWxsbGkmusGk0tWbIEqqqqWLFiBZOv/0/8+uuv8PX1RUpKCrMMLVq0QHp6OvPC0PucO3cOFRUVGD16NPLy8jB8+HDcuXMHLVu2xOHDhz+6a4CFVatW4eXLl0zHnDZv3hxpaWm8aspZuxOgoKAA+vr6UtM0lJSUYGhoiMDAQHzxxResIiI0NBSKiorw8fFBYmIihg0bhpqaGlRXV2Pz5s1MG3LW+vHHHzFmzBiYm5sjISEBrVq1YpbjU8l7esu7Vq5cid27d2PRokVYsWIFli9fjvz8fJw8eRIrV66Ej48Ps2zLli3Dnj17sHr1avTp0wccx+HSpUtYtWoVZs6cybz4fP36dbi6uqJZs2bo2bMnOI7D9evX8erVK8TFxcHOzo5JLnNzc8yaNQu+vr5S1zdv3oxdu3bxfkQmwJ+mk/XhczbyeaDCAvnsPH36FHfv3oVAIEDnzp0l2zpZ0tXVxdmzZ2Frayv1wh4XFwcvLy88ePBA7pk+1FyqLpaNphYuXIioqChYW1vD2tpa5ngL687a9bl9+zZ69OghWaVi4csvv8S0adMwZswYZhn+rmfPnkFLS4sXq/D1ycnJQc+ePZmugjo4OGDlypW8bMrZv39/HD9+HFpaWqyjfFRhYSGuX78OY2NjJhM2Ro8eXe/1K1euwMTERKqoIO8jfJ+6ks766A0AGBsbIywsDMOGDYOamhpu3LghuXblyhUcOHCAWTY9PT2Eh4fLFF9++OEHzJs3Dw8fPmSUTKxfv34wMTHBrl270KSJePNydXU1ZsyYgby8vI/2EPq3KCsrIzMzU6YonpOTA0tLSyaLMH8Xn2/e+ZyNfB7oKAT5bFRUVEhGPNVOD1BUVMSUKVPw3XffMZ1z7ebmhsDAQBw5cgSA+E1ZYWEhli5dyuzmj8X53b8rIyMDXbt2BQDcunVL6jHWN6DvdkznOA5FRUXYuHEj83GAX375JXx9fXHz5k1YWVnJFGRYrzTW9eDBAwgEAujr67OO8kHJyclQUVFhmsHb2xsLFy6Ev79/vd9Xlk05G8LrSa0OHTqgQ4cOzL7++7aau7q6yjmJrHcn7/BZcXGxZJSpqqoq/vjjDwDA8OHDme9ye/bsWb1HgMzMzJgWJ2tdv35dqqgAAE2aNME333yD7t27M8vVvn17xMfHyxQW4uPj0b59e0apCCGfigoL5LOxaNEiCIVC/PTTT+jTpw8AcUNHHx8fLF68GNu3b2eWbdOmTRg6dCh0dHTw6tUrODo6ori4GPb29sy3RPIZn29WunbtWm/H9F69ejEfgzlnzhwAQGBgoMxjfFhprK6uxurVqxEWFibZ2aGqqgpvb28EBATI3DDL07urybUFo+vXrzO/WZkwYQIAcQ+NWiybcr7vOFV9WO8u4tPY2n379sn16/0Tb9++hYuLC3bs2MHbEc76+vooKipChw4dYGJiItnCf+3aNSgrKzPNZmNjg23btkl6kNTatm0b88IzIG5iWlhYKFP8ePDgAdNRu4sXL4aPjw9u3LiB3r17QyAQ4OLFi4iIiMDWrVuZ5fo7DAwMmP4N+xA+ZyOfByoskM/GsWPHEBsbK9Wtf+jQoWjWrBnGjx/PtLCgrq6OixcvIiEhAampqRCJRLCzs+PVluZr167h6NGj9TbBYjlRg6/e7ZiuoKCA1q1bM1/VBvi/6rhgwQKcOHECQUFBsLe3ByDeEbBq1SqUlJQgPDycWbZ3V5MVFBRgamqKwMBAuLi4MEolxrcu/Z86FpT17qKPja1l6f79+6iurkanTp2krmdnZ6Np06YwNDRkkqtp06a4desWr56rd40aNQrx8fH44osvsHDhQnz11VfYs2cPCgsLZc7oy1tQUBCGDRuGX375Bfb29hAIBLh8+TIePHjAdLxprQkTJsDLywubNm2SuoH39/fHV199xSzX3Llz0aZNG4SEhEh2eJqbm+Pw4cNwc3NjlgsApk2bhunTp390lPS7uyvlgc/ZSONCPRbIZ6N58+ZISUmBubm51PXMzEz07NkTFRUVjJLx36FDhzBlyhS4uLjg/PnzcHFxQXZ2NoqLizFq1CimK2x8LHjweTWvuroaKioquHHjBiwtLVnHqZeGhgYOHTqEIUOGSF0/c+YMJk6cKNnSTMj/D23btkVQUBAvx9Y6Ojpi+vTpMhNHYmJisHv3biQlJbEJBvHqcdOmTbFx40ZmGf6OK1eu4PLlyzAxMeHFca9Hjx7hf//7H+7cuQOO42BhYYF58+ZBT0+PdTRUVVXB398f4eHhqK6uBiAuJs2dOxcbN25kvuODj8aMGYNTp06hffv28PT0xNSpU3kzQpTP2UjjQoUF8tlwdnZGy5YtERUVJVk1fvXqFaZOnYpnz57hl19+YZovPj4eoaGhktFOZmZm+Prrr3mxa8Ha2hqzZ8/G/PnzJc19OnbsiNmzZ6Nt27ZYvXo1k1x8Lni0bt0aly9flllp5ANjY2McP36cF1tu66Orq4ukpCSZIuDt27fh4OCAp0+fMkomVlZWhtjYWOTm5sLf3x/a2tpITU2Frq4u8zdr9+7dQ1JSUr1b+leuXMko1V9ycnKQm5sLBwcHNGvWTHJMg6WWLVvi6tWrUpNu+EJdXR2pqan1Nqvr3r07ysrK2AQDJD2LTExM0L17d7Ro0ULqcdbHW/iKz4VnQDym8+LFi7CysoKKigpyc3PBcRxMTEyY9qJqCEpLSxETE4OIiAjcunULAwcOhJeXF9zc3JgfMeBzNtJ4UGGBfDZu3ryJIUOG4PXr17CxsYFAIMCNGzegrKyMuLg4dOnShVm2bdu2wdfXF2PHjpVs/b5y5QpiY2OxefNmLFiwgFk2QDyeMDMzE4aGhmjVqhUSExNhZWWF27dvY8CAASgqKmKSi68FD4Dfq3n79u3D0aNHERMTA21tbdZxZAQGBuLOnTvYt2+fZGXszZs38PLyQqdOnRAQEMAsW0ZGBpydnaGpqYn8/HzcvXsXRkZGWLFiBQoKChAVFcUs265duzB37ly0atUKbdq0kbphFwgESE1NZZattLQU48ePR2JiIgQCAbKzs2FkZAQvLy9oamoiJCSEWTY+j63V0NBAUlISbG1tpa6npKTAyckJL168YJTsw5ODWE4Lquvu3bv47rvvpAr23t7ezEey8rnwDAAqKiq4ffu2ZFwsa586pSAvL+9fTvLp0tLSsHfvXuzevRuqqqqYNGkS5s2bx4vvOZ+zkc8b9Vggnw0rKytkZ2cjJiZGsvVw4sSJ8PDwQLNmzZhm27BhA0JDQ6UKCD4+PujTpw/WrVvHvLCgra0teQPbrl073Lp1C1ZWVigrK0NlZSWzXLm5uRg2bBgA8RiqiooKCAQC+Pr6YsCAAUwLC1VVVdi9ezfOnz/Pu9W8sLAw5OTkQE9PDwYGBjLZWN6AAuI3PfHx8dDX15fsqkhPT0dVVRWcnZ2lGijK+7jLokWL4OnpiaCgIKkmZkOGDIG7u7tcs7xr7dq1WLduHZYsWcI0R318fX3RtGlTFBYWSu1EmTBhAnx9fZkWFl6/fo2dO3fil19+4d3Y2n79+mHDhg04ePAgFBUVAYhXlDds2IC+ffsyywXwu3kuAMTGxuKrr75C9+7dpQr2lpaWOHDgAMaNG8cs25QpU7Bnzx5eFp4B8fulvLw83hQW8vPzYWBgAHd3d+jo6LCO81FFRUWIi4tDXFwcFBUVMXToUGRmZsLCwgJBQUFMe3zwORv5/FFhgXw2NmzYAF1dXcycOVPq+t69e/H06VOmb8bLy8sxePBgmesuLi68uEno168fzp8/DysrK4wfPx4LFy5EQkICzp8/D2dnZ2a5+FrwAMRNkOzs7ACIt6fXxXrr98iRI5l+/Y/R1NSUGbPKl1Fi165dw44dO2Sut2vXDsXFxQwS/eX58+dMb5Y+JC4uDufOnZMZG9qpUycUFBQwSiXG57G1QUFBcHBwgKmpKfr16wcAuHDhAsrLy3mxIwDg5/EWAPjmm2+wbNkymek3AQEBWLJkCdPfFT4XngFg3bp18PPzw5o1a9CtWzeZfOrq6nLNc+jQIezbtw+bN2/GkCFDMH36dAwdOhQKCgpyzfEhb9++xY8//oh9+/YhLi4O1tbW8PX1hYeHh6QIfejQIcydO1fuN+98zkYaFzoKQT4bhoaGOHDgAHr37i11/bfffsPEiROZdlT38PBA165d4e/vL3V906ZNSElJwcGDBxklE3v27Blev34NPT09iEQibNq0CRcvXoSJiQlWrFgBLS0tJrnc3d3RvXt3LFq0COvWrcPWrVvh5uaG8+fPw87OTu6r2RkZGbC0tOTVmx3y/5euri7Onj0LW1tbyfEbIyMjxMXFwcvLCw8ePGCWzcvLCz169JCME+UTNTU1pKamolOnTlLP27Vr1zB48GCUlpayjvhRv//+O/T09OT++/3o0SNs27YN6enpaNasGaytrbFgwQLmx5j4fLwFEDdszsjIkOlPkZ2dDRsbG6bFZ74fI6n7M163SMRqdG2thw8fIiIiAhEREaioqMCUKVMkR+RYa9WqFUQiEb766ivMnDlTUqys6/nz57Czs5P7+00+ZyONCxUWyGfjfWcG8/LyYGFhgdevXzNKJt7CvGnTJvTp00dqy+alS5ewePFiqdUBHx8fuWarrq7G/v374erqijZt2sj1a38M3woeioqKKCoqgo6OjuSmqWXLlnLN8HekpKRIzh5bWFjInONm5dWrV+A4TtIorKCgACdOnICFhQXzkY6zZs3C06dPceTIEWhrayMjIwOKiooYOXIkHBwcsGXLFrnmCQsLk3xcUVGBzZs3Y9iwYbCyspLZ0i/v1466hg0bBjs7O6xZswZqamrIyMiAgYEBJk6cCJFIhNjYWGbZPpW6ujpu3Ljxyee9P3dTpkzBkydPsHv3bpibm0sV2Xx9fZGZmck039ChQzFu3Dh4enpKXd+3bx8OHTqEc+fOMUrGf0Kh8IOPOzo6yinJ+wmFQqxatQq//vorSkpKmC1w1IqOjsa4ceN4MVL6XXzORhoXKiyQz0Zt07dJkyZJXY+OjkZAQADTpj+feo5RIBAwydm8eXPcvn0bBgYGcv/aDUnLli1x+vRpfPHFF1BQUMDjx4/RunVr1rFkPHnyBBMnTkRSUhI0NTXBcRz++OMP9O/fH4cOHWKe2cXFBaNHj8acOXNQVlYGU1NTKCkpoaSkBJs3b8bcuXOZZSsvL5ecSX3x4gX09PRQXFwMe3t7nD59WmbL8L+N768dtbKysuDk5IRu3bohISEBI0aMQGZmJp49e4ZLly7xciLDu+rutJCnsrIy7NmzR6oIOH36dGhoaMg1x7vatGmDc+fOwcbGRuq5uX//PqysrPDy5Uu5Z/rxxx8lHz969AgrV67E+PHj0atXLwDigv3Ro0exevVqXuzs4esxEj57/fo1YmNjsXfvXly5cgUjRoxAZGQkjcAkpAGgwgL5bPz3v/9FcHAwgoODMWDAAADiEY/ffPMNFi9ejGXLljFOyF/9+/fHwoULeXc2v+4OgbpKS0uho6Mj9+2as2bNQlRUFNq2bYvCwkLo6+tLGq69i+VN3oQJE5Cbm4vo6GhJI72srCxMnToVJiYmzI/etGrVCkKhEF26dMHu3bvx3XffIS0tDceOHcPKlStx+/ZtpvkAICEhAampqRCJRLCzs+PFWFi+Ky4uxvbt25GSkiJ53ubPn4+2bduyjvZJWBQWrl+/DldXVzRr1gw9e/YEx3G4fv06Xr16hbi4OEkfFxb4eLzlU4+psNzOD/D/GAkg7uWxY8cO5OXl4ejRo2jXrh2io6PRsWNHJo1Df/vtN+zZsweHDx+GsbExpk+fDg8PD+Y7Feq6du0ajh49isLCQlRVVUk9Ju+jme/iczbSeFDzRvLZ+Oabb/Ds2TPMmzdP8qKqoqKCJUuW8K6oUFNTg5s3b8LAwIAXfzTnzZuHxYsX4/fff6+3kZO1tTWTXO+re7558wZKSkpyTgPs3LkTo0ePRk5ODnx8fDBz5kypyQF8cfbsWfzyyy9S3fktLCzwv//9j/lRAwCorKyUPG9xcXEYPXo0FBQU0KtXL+aN/moNGDBAUqAkn6ZNmzZMJ7U0RL6+vhgxYgR27dqFJk3Eb8mqq6sxY8YMfP311/j111+ZZXNwcEBUVBTWrFkDQHyzLhKJEBwc/MEeAv8mkUjE5Ov+XXyekgIAx44dw+TJk+Hh4YHU1FS8efMGAPDixQusX78ep0+flmueLl264MmTJ3B3d8eFCxeYvef4kEOHDmHKlClwcXHB+fPn4eLiguzsbBQXF2PUqFGUjRDQjgXyGXr58iVu376NZs2aoVOnTrzYPvf111/DysoKXl5eqKmpgYODA5KTk9G8eXP8/PPPcHJyYpqvvlUggUDArJFT7blyX19frFmzBqqqqpLHampq8OuvvyI/Px9paWlyzVWXp6cnwsLCPlpYYNEQTk1NDRcuXJBp4JSWlgZHR0eUl5fLLUt9rK2tMWPGDIwaNQqWlpY4e/Ys7O3tkZKSgmHDhjGfvlBRUQGhUFjvyg/LPgZjx45F9+7dsXTpUqnrwcHBuHr1Ko4ePcoombiYpaqqKlnp/N///oddu3ZJClp8KKB+DIsdC82aNUNaWhrMzMykrmdlZaF79+5MGxB+DsdbWOHjMZK6bG1t4evriylTpkjlu3HjBgYPHiz312AFBQW0aNECTZo0+eBRkWfPnskxlTRra2vMnj0b8+fPlzxnHTt2xOzZs9G2bVumRVU+ZyONC+1YIJ8dVVVV9OjRg3UMKbGxsZLeDz/99BPy8/Nx584dREVFYfny5bh06RLTfHzrEhwaGgpAvGMhPDxc6riBkpISDA0NER4ezioeAHGDsE9hYWEh94ZwAwYMwMKFC3Hw4EHo6ekBEHfb9vX1ZTo+tNbKlSvh7u4uyVPb0DQuLo55g8m0tDQMHToUlZWVqKiogLa2NkpKStC8eXPo6OgwLSwIhUIEBATIXB88eDA2bdrEINFf/P398d///hcAcPPmTSxatAiLFy9GQkICFi1a9Mm/LyyxOPuurq6OwsJCmcLCgwcPmO+GsrCwQEZGBrZv3w5FRUVUVFRg9OjRvDreIhQKsWnTJkl/CnNzc/j7+0tGd7JSUVEhaU5bV0lJCS8WO+7evQsHBweZ6+rq6igrK5N7nobw+pCbm4thw4YBAJSVlVFRUQGBQABfX18MGDCA6c07n7ORxoUKC4TIQUlJiWTiwunTpzFu3Dh07twZXl5eUl3fWeFb08baQkf//v1x/PjxBrHa+T4sNoVt27YNbm5uMDQ0RPv27SEQCFBYWAgrKyvExMTIPc+7xo4di759+6KoqAg2NjaS687OzlLbNlns9vD19cWXX36J7du3Q1NTE1euXEHTpk0xadIkLFy4UG456vPy5ct6jwA1bdqU+S6U+/fvw8LCAoB4m/WXX36J9evXIzU1FUOHDmWa7VOx+F2dMGECvLy8sGnTJvTu3RsCgQAXL16Ev78/vvrqK7nnqauwsBDt27ev96aksLAQHTp0YJDqLzExMfD09MTo0aPh4+MDjuNw+fJlODs7IyIiAu7u7syy8fEYSV1t27ZFTk4ODA0Npa5fvHiRyVSUqVOn/q3PP3jwIEaMGCHXZrra2tp48eIFAKBdu3a4desWrKysUFZWxnRnEd+zkUaGI4T86zp06MCdO3eOq66u5tq3b8/99NNPHMdx3K1btzhNTU3G6cTu3LnDzZ8/nxswYADn7OzMzZ8/n7tz5w7rWFKqq6u5tLQ07tmzZ6yjfDJVVVUuNzeXydeOi4vjwsLCuK1bt3Lnz59nkuH/Qk1NTe7PnYaGhuTnXkNDg8vKyuI4juOuXLnCmZqayjXLu7p3786tXr1a5npAQABnZ2fHINFftLS0uMzMTI7jOK5Pnz7cjh07OI7juPv373PNmjVjluvt27ecoqIid/PmzY9+bmFhIVddXS2HVH958+YN5+PjwykpKXEKCgqcgoICp6yszH399dfc69ev5ZrlXQoKCtzjx49lrpeUlHAKCgoMEkkzMzPjNm/eLHM9JCSEMzMzY5DoL5mZmVzr1q25wYMHc0pKStzYsWM5c3NzTldXl8vJyWGajeM47r///S9nYWHBXblyhVNTU+MuXLjAxcTEcK1bt+a+++471vE+isXfhq+++ooLCQnhOI7j1q5dy7Vu3ZqbMWMGZ2BgwI0aNUquWRpSNtK40I4FQuTA09MT48ePR9u2bSEQCDBo0CAA4i7I726BZSE2NhZfffUVunfvLtmWfuXKFVhaWuLAgQMYN24ck1x8703Bd4MGDZL8rDVEHIMV5KZNm0q2xOvq6kqar2loaKCwsFDueepasWIFxowZg9zcXKnJNwcPHmTaXwEA+vbti0WLFqFPnz64evUqDh8+DAC4d+8e9PX1meVq0qQJDAwMPqlPTPv27eWQ6C81NTVITk5GQEAANmzYgNzcXHAcBxMTk3q30csb957RiC9fvoSKigqDRNLy8vLw5ZdfylwfMWIE/vOf/zBI9Be+HyP55ptvJCOIX79+DQcHBygrK8PPzw8LFixgHe+jWPxt2LZtG16/fg0AWLZsGZo2bYqLFy9i9OjRWLFihdzzNJRspHGh5o2EyElsbCwePHiAcePGSd5oR0ZGQlNTE25ubkyzGRkZYdKkSQgMDJS6HhAQgOjoaGajE9u1a4cffvgB3bt3x8mTJzF//nwkJiYiKioKiYmJzHtTfAoWDeEA8Q1nfHw8njx5ItNJfe/evXLN8k+xeO5cXFwwbdo0uLu7Y86cOUhLS4OPjw+io6Px/Plz/Pbbb3LLUp9Tp05h/fr1uHHjBpo1awZra2sEBATA0dGRaa7CwkLMmzcPDx48gI+PD7y8vACIj5bU1NQwPfK1b98+HD16FDExMdDW1maWoz4qKiq4ffs2OnbsyDqKxKJFiwAAW7duxcyZM6WKHDU1Nfjtt9+gqKjI/PXXxMQE/v7+mD17ttT1HTt2YNOmTcjOzmaUrOGorKxEVlYWRCIRLCwspBol8xmrv6uEkA+jwgIhPGJlZYXTp0/LfeWsefPmyMjIgImJidT17Oxs2NjYMDujp6KigpycHOjr62PWrFlo3rw5tmzZgvv378PGxob5ufJPoa6uLvfmjatXr0ZgYCC6d+8u2SVT14kTJ+SW5f+CxZvH69ev48WLF+jfvz+ePn2KqVOn4uLFizAxMcG+ffukekKQv2/jxo2YM2cONDU15fY1bW1tkZOTg7dv38LAwEDmXHZqaqrcsryrR48e2LhxIy+aqtaq7QEgFAphb28v1dejtnmun58fOnXqxCoiAGD79u34+uuvMX36dKn+FBEREdi6datMwYGVmpoaLFq0CPHx8bC1tcXmzZvRunVr1rEaNHn9bfg77zHU1dX/xSSy+JyNNF50FIIQHsnPz8fbt2/l/nWdnJxw4cIFmcLCxYsXmXbX1tXVRVZWFtq2bYuzZ8/i+++/ByBeZak7KYLPWNRuw8PDERERgcmTJ8v9azd03bt3l3zcunVruc9z/xRVVVX17kRh3UzvU6xfvx7jx4+Xa2Fh5MiRcvtaf9e6devg5+eHNWvWoFu3bjJFDxY3BImJiQDER/i2bt3K25uSuXPnok2bNggJCcGRI0cAAObm5jh8+DCTXYBlZWVYvHgxzp07h4iICAwcOBAAsHDhQpw4cQIzZ87E2bNn4e3tjUOHDsk9X10VFRXYuHHje3e1sdqlyDeampqfPC1G3mO5+ZyNNF5UWCCkkfrxxx8lH48YMQJLlixBSkoKevXqBUDcY+Ho0aNMxxTxuTfF9OnTsXXrVpmRcBUVFfD29pYcN8jKypKMfJSXqqoq9O7dW65f89/AYvwfn2VnZ2P69Om4fPmy1PXas/AN4c0ji0JbfSM6+WLw4MEAxK/BdX/e+fA9bQgjAEeNGiU1SYal+fPno7S0FHPnzsW+ffswcOBAxMbGYufOnbh48SJ69uyJMWPGSPqjsDRjxgwIhUJMnjy53l1tRKy2yAaIF36WLl2KadOmSXpRJScnIzIyEhs2bKBshICOQhDCK/Lc+v2pI/xYv7nla28KRUVFFBUVQUdHR+p67WjR6upqRsmAJUuWQFVVtcE3bWJxFOLx48fw8/OTrOS9+yeS5e9Cnz590KRJEyxdurTem4GGcEyD1dnosrIyxMbGIjc3F/7+/tDW1kZqaip0dXXRrl07uWapSygUfvBxlr0z+L6qbWRkhGvXrqFly5ZS18vKymBnZyf3fC1btsTly5fRsWNH2NvbIy8vD+Xl5VixYgVWrVoFAMjNzYWNjQ1evnwp12zv0tTUxKlTp9CnTx+mOf4pS0tLnDlzRq7HRp2dnTFjxgyZMbAHDhzAzp07kZSUJLcs7+JzNtK40I4FQhqpd98k8tXYsWNlrv3dmdf/P5WXl4PjOHAchxcvXkh1R6+pqcHp06dlig3y9vr1a+zcuRO//PILrK2t0bRpU6nHN2/ezCiZGJ93e0ybNg2FhYVYsWIF71bybty4gZSUFOa7dRqajIwMDBw4EBoaGsjPz8fMmTOhra2NEydOoKCgAFFRUcyydezYEe3bt5f5OeM4Dg8ePGCUSozvq9r5+fn1FvrevHmDhw8fyj2PkpISysvLoaSkhEuXLiEuLg7a2tro27ev5HNu3LghWVFmSUtLi3eNTAHx6+/06dPh4ODwwc+7deuWnBL9JTk5GeHh4TLXu3fvjhkzZsg9T118zkYaFyosEEI+mTyaS4aFhWHWrFlQUVH5aCd5Hx+ffy3H+9SeaxQIBOjcubPM4wKBgOnxEUB8I9W1a1cAsm/A+HBzEBkZiY0bN8oUFl69eoWoqChJYUHeTUwBcV+RCxcuSJ4/PrGwsEBJSQnrGA3OokWLMG3aNAQFBUn9zA0ZMgTu7u4Mk4kLC/XtfHr27Bk6duzIdIfMmTNneLmqXfcY37lz56ChoSH5f01NDeLj42FoaCj3XK6urvD09ISfn5/kpv3Zs2dSeZs2bQpvb2+5Z3vXmjVrsHLlSkRGRvJitGmtFy9ewMXFBe3bt4enpyemTp3KdEdRXe3bt0d4eDhCQkKkru/YsYPJ36q6+JyNNC50FIIQHuH7CCV55OvYsSOuX7+Oli1bfnAEm0AgYLIVVygUguM4DBgwAMeOHZNa9VFSUoKBgYHcV9n/qd9//x16enqffCzm/6p2t4eWlhays7OlOqPX1NTgp59+wtKlS/Ho0SO55KmPhYUF9u/fD1tbW2YZ3ichIQHffvst1q9fDysrK5mdKHxtslcXi9c4DQ0NpKamwtjYWOrrFxQUwNTUVDL/nQUFBQU8fvxYZkpAQUEBLCwsUFFRwSiZ+LX49OnTMDc3Z5ahPrWvVwKBQOaoUtOmTWFoaIiQkBAMHz5crrmeP3+OBQsW4Ny5c3j27Nl7P4/18UJAPCklNzcXHMfB0NBQ5rWE5aSU0tJSxMTEICIiArdu3cLAgQPh5eUFNzc3mZzydPr0aYwZMwbGxsZSvahyc3Nx7NgxDB06lLKRRo92LBDCIzt27ICuri7rGEzdv3+/3o/5QFtbG/fu3UOrVq0wdepUDBw4UGbVvSGxsLCQ6yjMhrDbY8uWLVi6dCl27NjBZNXzQ2q7zL87mpAPjf4+Vb9+/dCsWTO5fk0VFZV6R7PdvXuX2di/RYsWARD/zK9YsUJq1bimpga//fYb810zfF3Vrj3G17FjR1y7dg2tWrVinEhMS0sL+/fvZx3jk/B5UkrLli2xcOFCLFy4EGlpadi7dy8mT54MVVVVTJo0CfPmzWMy6nTo0KHIzs7G9u3bcfv2bXAcBzc3N8yZM4f5rgA+ZyONC+1YIEQO3relXyAQQEVFBSYmJnBwcOD9CEW+76j4t6mqqiIjIwNGRkZQVFREcXFxg55HLu/vJ193e2hpaUkdEamoqEB1dTWaN28us0L2oZXIfxufG/29b6a6QCCAsrIylJSU5JzoL7NmzcLTp09x5MgRaGtrIyMjA4qKihg5ciQcHBywZcsWuWfq378/APH31N7eXur5UVJSgqGhIfz8/JjcQNXi86o2+fwVFRVJjsY9fPgQY8aMQVFRERITExEUFARfX1+55qmqqnrv61hJSQnTAhefs5HGhXYsECIHoaGhePr0KSorK6GlpQWO41BWVobmzZtDVVUVT548gZGRERITExt9dbl2Je9TyLsJob29PUaOHIlu3bqB4zj4+Pi8d/W1tk8AEePzbg8WN5b/BMvCwcd8bKa6vr4+pk2bhoCAALkdvam1adMmDB06FDo6Onj16hUcHR1RXFwMe3t7rFu3Tq5ZatWOivP09MTWrVs/eoxF3seWAH6vagNAYGDgBx9fuXKlnJLI4nO2uqqqquqd+NGhQwcmed6+fYsff/wR+/btQ1xcHKytreHr6wsPDw/J34tDhw5h7ty5ci8sjB8/HsePH5f5HXz8+DGcnZ2ZNJSsxedspHGhHQuEyMHBgwexc+dO7N69G8bGxgCAnJwczJ49G7NmzUKfPn0wceJEtGnTBrGxsYzTvp88VrhrV/JqpaSkoKamBqampgCAe/fuQVFREd26dUNCQsK/lqM+jx8/RmhoKHJzc3H8+HG4urpCWVm53s89ceKEXLP9E/LcsfC57fbYuHEj5syZA01NTbl/7crKShQWFqKqqkrqurW1tdyz1IqKisLy5csxbdo09OzZExzH4dq1a4iMjMS3336Lp0+fYtOmTfD398d//vMfJhkTEhKQmpoKkUgEOzs7ydGShkBdXV2ux5Yagnf7oLx9+xb3799HkyZNYGxszHRHBZ+zAeK/o15eXrh8+bLUddbHqlq1agWRSISvvvoKM2fOrPc40PPnz2FnZyf3o5JffPEFLCwssG/fPsm1oqIiDBgwAF26dGH63o3P2UjjQoUFQuTA2NgYx44dk/kjmZaWhjFjxiAvLw+XL1+WbPXjK3lvnd+8eTOSkpIQGRkJLS0tAOI3FZ6enujXrx8WL14slxz1qdtksqGS5/dz0KBBePz4Mbp164bIyEhMmDChQe/2YHGj9/TpU3h6euLMmTP1Ps6yx4KzszNmz56N8ePHS10/cuQIduzYgfj4eERHR2PdunW4c+cOo5QNV2M/hvapysvLMW3aNIwaNQqTJ09mHUcKn7L16dMHTZo0wdKlS+sdJWpjY8MkV3R0NMaNGyc1xpkvSktL4eDgABcXF4SGhuLhw4cYMGAAbGxscOjQIbnvxGoo2UjjQkchCJGDoqIiVFdXy1yvrq5GcXExAEBPTw8vXryQd7S/Rd7NJUNCQhAXFycpKgDi8/Br166Fi4sL08IC3xpL/hPyHD0ZExMj2e0hEAjwxx9/MO3G/3/Foib/9ddf4/nz57hy5Qr69++PEydO4PHjx1i7dq3MmDF5e98cdVtbWyQnJwMA+vbti8LCQnlHAwDEx8cjPj6+3m3fDaGQJS91jyy923vkXSz7jbyPuro6AgMDMXz4cOY37+/iU7YbN24gJSUFZmZmTHO8i/Xz8iEtW7bEuXPn0LdvXwDAqVOnYGdnh/379zO/cedzNtK4UGGBEDno378/Zs+ejd27d0u2SKalpWHu3LkYMGAAAODmzZsfHK/4/9v7GkrWx8fHBwDkPvO9vLwcjx8/RpcuXaSuP3nyhEkRJiwsDLNmzYKKispHn7/a54zP5HlzrKuri40bNwIQ7/aIjo5u0Ls9WEhISMAPP/yAHj16QEFBAQYGBhg0aBDU1dWxYcMGDBs2jFk2fX197NmzR/I9rrVnzx5J35jS0lKpIqG8rF69GoGBgejevXu9q7PkL6GhoZKz7KGhoQ3yuSorK8Mff/zBOka9+JLNwsICJSUlrGPU69q1azh69Gi9x72OHz/OKJWYvr4+zp8/j759+2LQoEGIjo7mze8In7ORxoOOQhAiB8XFxZg8eTLi4+MlnbWrq6vh7OyM6Oho6OrqIjExEW/fvoWLi4tcMr1bxKhtLll7Zry2uaSOjg7y8vLkkuldU6ZMgVAoREhIiNRsZn9/fzg4OCAyMlKueeoef/hQEUggEDB7zgBg+vTp2Lp1q0xzxIqKCnh7e0tWaB88eAA9PT3eTyPhIxZb09XV1ZGRkQFDQ0MYGhpi//796NOnD+7fv48uXbqgsrJSblne9eOPP2LcuHEwMzNDjx49IBAIcO3aNdy5cwexsbEYPnw4tm/fjuzsbLk3XW3bti2CgoJ4vRr6MXQUQta7xV2O41BUVITo6Gg4ODjg4MGDjJLxOxsgLlJ+++23WL9+PaysrGQmfnysmei/5dChQ5gyZQpcXFxw/vx5uLi4IDs7G8XFxRg1apRUDwF5eN+uncrKSigrK0v97ZT3Dh4+ZyONFxUWCJGjO3fu4N69e+A4DmZmZpKGhKwdOHAA33//Pfbs2SPJdPfuXcycOROzZ8+Gh4cHk1yVlZXw8/PD3r178fbtWwBAkyZN4OXlheDgYLRo0YJJLr5TVFREUVERdHR0pK6XlJSgTZs29R7L+bd9brs9WNzo9ejRA2vXroWrqytGjhwp2akQFhaG2NhY5Obmyi1LffLz8xEeHi71Gjd79mwYGhoyzdWyZUtcvXpV0ji3IWLR08PDwwNOTk5wdHRE586d5fZ1P9W7xV0FBQW0bt0aAwYMwLJly5hOneFztto8gOxxONbNG62trTF79mzMnz9f8hrbsWNHzJ49G23btsXq1avlmufvLF5MnTr1X0wii8/ZSONFhQVC5EAoFPJ6VJyxsTFiY2NlOlmnpKRg7NixzPsJVFRUSOapm5iYyBQUWIxi46Py8nJwHActLS1kZ2dLTV2oqanBTz/9hKVLl+LRo0dyz9ZQdnt8KhaFhf379+Pt27eYNm0a0tLS4OrqitLSUigpKSEiIgITJkyQW5aGZMmSJVBVVcWKFStYR/nHWPy8zZ49G0KhEPfu3UObNm3g6OgIR0dHODk58e5sPvl7hELhBx9n9X6lRYsWyMzMhKGhIVq1aoXExERYWVnh9u3bGDBgAK+bWxNCqMcCIXIxaNAgtGnTBu7u7pg0aRIsLS1ZR5JSVFQk2RFQV01NDR4/fswgkbQWLVp8cJSehYWF3FfzampqEBER8d6GcPIehQkAmpqaEAgEEAgE9a4wCgQCua/41KpbnGJdqPr/oV+/fu+davFvqbtzyNbWFvn5+bhz5w46dOiAVq1ayTVLfcrKynD16tV6fx+mTJki1yyLFi2SfCwSibBz50788ssvsLa2ltn2Le+jGbWqq6uhoqKCGzdufPRvQlZWFvT09OSUTGzHjh0AxEf5kpKSkJSUhK1bt2L+/PnQ0dHh5U2eSCTCqVOnsGfPHpw8eZJ1HCl8ysbXhQ5tbW1J/6R27drh1q1bsLKyQllZGdOjXrVyc3Oxb98+5ObmYuvWrdDR0cHZs2fRvn17mV5QlI00RlRYIEQOHj16hEOHDuHgwYMICgqCpaUlJk2aBHd3d+jr67OOB2dnZ8ycORN79uxBt27dIBAIcP36dcyePbtBzHpnsfFq4cKFiIiIwLBhw2BpacmLJkmJiYngOA4DBgzAsWPHoK2tLXlMSUkJBgYGcr85aWjKy8vrvS4QCKCsrAwlJSUAwOnTp+UZq17NmzeHnZ0d6xgAgJ9++gkeHh6oqKiAmpqa1O+DQCCQe2EhLS1N6v+1o35v3bol1xwf0qRJExgYGHzStvPaBpgsqKmpQUtLC1paWtDU1ESTJk3Qpk0bZnnqk52djb179yIyMhLPnz+Hq6sr60gSfMqWkZHxSZ/3oUL+v6lfv344f/48rKysMH78eCxcuBAJCQk4f/48nJ2dmWSqJRQKMWTIEPTp0we//vor1q1bBx0dHWRkZGD37t2IjY2lbKTRo6MQhMjZ/fv3ceDAARw8eBB37tyBg4MDk9Xtup4+fYqpU6fi7NmzUs0lXV1dERERIXNWn29YbBNu1aoVoqKiMHToULl9zQ+pOybO09MTYWFhzM/xvg8fd3vUUlBQ+GCRSF9fH9OmTUNAQIDcj95wHIfY2FgkJibW+7yx7JjeuXNnDB06FOvXr0fz5s2Z5Who9u3bh6NHjyImJkaqEMgHS5YsgVAoRHp6OiwtLeHg4ABHR0c4ODhImvyy9OrVKxw5cgR79uzBlStXUFNTg9DQUEyfPh2qqqqUrR61r28feuvPssfCs2fP8Pr1a+jp6UEkEmHTpk24ePEiTExMsGLFCiZTZWrZ29tj3LhxWLRokdR7jmvXrmHkyJF4+PAhZSONHhUWCGGgpqYGZ86cwYoVK5CRkcHsjzggvlkpLCxE69at8fDhQ9y+fRscx8Hc3JyXDbvqw6KwoKenh6SkJN48R6qqqsjIyICRkREUFRVRXFws1WOBTxYsWCDZ7VHf+L/Q0FBGyYCoqCgsX74c06ZNQ8+ePcFxHK5du4bIyEh8++23ePr0KTZt2gR/f3/85z//kWs2Hx8f7Ny5E/3794eurq7M8ybvjul1tWjRAjdv3uTl1IJPnZLCgq2tLXJycvD27VsYGBjI9I9JTU1llOyvhoO+vr5wc3ODubk5syx1Xb16Fbt378bhw4fRuXNnTJo0CRMnToS+vj7S09NhYWFB2d6joKDgkz7PwMDgX07S8KiqqkrGgtd9z5Gfnw8zMzO8fv2aspFGj45CECJHly5dwv79+xEbG4vXr19jxIgRWL9+PdNMHMehU6dOyMzMRKdOndCpUyemeRqKxYsXY+vWrdi2bRsvjkHY29tj5MiR6NatGziOg4+Pz3t7ALC8kQLEI8WOHDnCm90edUVGRiIkJATjx4+XXBsxYgSsrKywY8cOxMfHo0OHDli3bp3cCwsxMTE4fvw4L583V1dXXL9+nZeFhcjISGzcuFGmsPDq1StERUUx/X0YOXIks6/9MWlpaRAKhUhKSkJISAgUFRUlzRudnJyYFRp69+4Nb29vXL16lTeTlWrxORvw9wsG8+bNQ2Bg4L/aw+V9x8/qw2oMJiDuYVRUVCTTfDgtLQ3t2rVjlEqMz9lI40KFBULk4D//+Q8OHjyIhw8fYtCgQdiyZQtGjhzJiy3DCgoK6NSpE0pLSxtsUUFeN/ajR4+W+n9CQgLOnDmDLl26yDSEk/e29JiYGISGhiI3NxcCgQB//PEHb1cplJSUYGJiwjpGvZKTkxEeHi5z3dbWFsnJyQCAvn37orCwUN7RoKGhwcsbdwAYNmwY/P39kZWVBSsrK5nfhxEjRsg9U+2UFI7j8OLFC6ioqEgeq6mpwenTp5kf8woICGD69T/ExsYGNjY2kvGv6enp2LJlC3x8fCASiZjttBswYAD27NmDJ0+eYPLkyXB1deVFcRfgd7Z/IiYmBn5+fv9qYaG26fCnYLm7093dHUuWLMHRo0chEAggEolw6dIl+Pn5yb2HTEPKRhoXKiwQIgdJSUnw8/PDhAkTeNG9/V1BQUHw9/fH9u3beTex4lPI60SXhoaG1P9HjRoll6/7KXR1dbFx40YA4tGO0dHRaNmyJeNU9ePbbo+69PX1sWfPHslzWWvPnj2SBnqlpaVMzvquWrUKq1evxt69e+U+keJjZs6cCQAIDAyUeYzVmW0+T0mpq6ysDLGxscjNzYW/vz+0tbWRmpoKXV1d5quNaWlpkokQFy5cQHl5Obp27Yr+/fszyxQXF4cHDx5g3759mDt3Ll69eiUZtcr69YTP2f4JefxtTUxMlHycn5+PpUuXYtq0abC3twcgLvZGRkZiw4YN/3qWD1m3bh2mTZuGdu3ageM4WFhYoKamBu7u7vj2228pGyGgHguEyFVWVhYKCwtRVVUldZ3Fal5dWlpaqKysRHV1NZSUlGRuWp49e8Yo2V8ePHgAgUBQ7xSNBw8eQE9PD4qKinLL8+rVK4hEIsmZ6Pz8fJw8eRLm5ua86kjOF/Xt9tDW1ubFbo+6fvzxR4wbNw5mZmbo0aMHBAIBrl27hjt37iA2NhbDhw/H9u3bkZ2dLfcxhZWVlRg9ejQuXboEQ0NDmeeN5Xl8PhIKhbyfkpKRkYGBAwdCQ0MD+fn5uHv3LoyMjLBixQoUFBQgKiqKWTYtLS28fPkSNjY2kuMPDg4OTLej1+f8+fPYu3cvTp48ifbt22Ps2LEYO3YsLyam8Dnbp5B3/yJnZ2fMmDEDX331ldT1AwcOYOfOnUhKSpJLjg/Jzc1FWloaRCIRbG1tebXTk8/ZSONAhQVC5OD+/fsYNWoUMjIypDoy165gsNzeB4jPIH/I1KlT5ZREWnV1NVavXo2wsDC8fPkSgLhJkbe3NwICAmRurOTJxcUFo0ePxpw5c1BWVgYzMzM0bdoUJSUl2Lx5M+bOnSvXPGFhYZg1axZUVFQQFhb2wc+t3dosT56enp/8uSybEALiIlF4eDju3bsHjuNgZmaG2bNnw9DQkGmu8ePHIzExEWPHjq23eSOft9WzVFBQgPbt28t9isenGDhwIOzs7BAUFCR1E3f58mW4u7sjPz+fWbaff/75kwoJv//+O/T09Jg/v8+fP0dMTAz27t3LvCnyu/ic7UPkXVho3rw50tPTZW6I7927h65du6KyslIuOT6kqqoK9+/fh7GxMZo04dfGbz5nI40DFRYIkYMvv/wSioqK2LVrF4yMjHD16lWUlpZi8eLF2LRpE/r168c6Ii/NmTMHJ06cQGBgoNS2yFWrVsHNza3es/Dy0qpVKwiFQnTp0gW7d+/Gd999h7S0NBw7dgwrV67E7du35ZqnY8eOuH79Olq2bCnTwKkugUCAvLw8OSaTRbs9/pkWLVrg3Llz6Nu3L+soAPhfzHpXZWVlvTvGrK2tGSUSH69KTU2FsbGx1E1cQUEBTE1NedsnpS51dXXcuHGDV/0/UlNTJbsC5NGA8O/gc7Z3ybuwYGpqiuHDhyMkJETq+uLFi/Hzzz/j7t27cslRn8rKSnh7e0sWYu7duwcjIyP4+PhAT08PS5cupWyEcISQf13Lli259PR0juM4Tl1dnbtz5w7HcRwXHx/Pde3alWU0GZWVldwff/wh9Y8VdXV17vTp0zLXT58+zamrqzNI9JdmzZpxBQUFHMdx3Lhx47hVq1ZxHMdxhYWFXLNmzVhG471BgwZx27dv5ziO454/f87p6upy+vr6nIqKCvf9998zTifOdO7cOS46OpqLjIyU+seSqamp5HWEDwwNDbmSkhLJx+/717FjR6Y5nzx5wg0bNoxTUFCo9x9LOjo6XGpqKsdxHKeqqsrl5uZyHMdx586d4/T19VlG+2R1c/ORmpoab/PxORvHyf97e+rUKU5FRYXr0qUL5+XlxXl5eXFdunThVFRUuFOnTsktR318fHy4bt26cRcuXOBatGgheV5++OEH5u/j+JyNNC60T4YQOaipqYGqqioA8Ur3o0ePYGpqCgMDA6YV+FoVFRVYsmQJjhw5gtLSUpnHWW3bVFFRqXf7uaGhIZSUlOQfqA4TExOcPHkSo0aNwrlz5+Dr6wsAePLkCe/OIPNNamoqQkNDAQCxsbHQ1dWV2u0h72Mkdf3000/w8PBARUUF1NTUpI4bCAQCph22Q0JC8M033yA8PJz5sQxAfMSrvo/55uuvv8bz589x5coV9O/fHydOnMDjx4+xdu1amZVReXNzc0NgYCCOHDkCQPwzVlhYiKVLl2LMmDFMs30uOB5vzOVzNgCYNGmSXP+eDR06FNnZ2di+fTtu374NjuPg5uaGOXPmSJrnsnLy5EkcPnwYvXr1kvq7YGFhgdzcXIbJ+J2NNC5UWCBEDiwtLZGRkQEjIyN88cUXCAoKgpKSEnbu3MmL7aPffPMNEhMT8f3332PKlCn43//+h4cPH2LHjh0y3fHlaf78+VizZg327dsHZWVlAMCbN2+wbt06LFiwgFkuAFi5ciXc3d3h6+sLZ2dnyVGNuLg42NraMs1WU1ODiIgIxMfH48mTJxCJRFKPJyQkMEomVllZCTU1NQDi52v06NFQUFBAr169UFBQwDTb4sWLMX36dKxfv54X42DrmjRpEiorK2FsbIzmzZvL9BjhQ5NVPkpISMAPP/yAHj16QEFBAQYGBhg0aBDU1dWxYcMGDBs2jFm2TZs2YejQodDR0cGrV6/g6OiI4uJi2NvbY926dcxykc9PRkbGJ39u7fGg7du3/1tx6lVVVQV9ff16f/ZLSkqYHhl5+vRpveNpKyoqmE/84HM20rhQYYEQOfj2229RUVEBAFi7di2GDx+Ofv36oWXLljh8+DDjdOJV2qioKDg5OWH69Ono168fTExMYGBggP3798PDw4NJrrS0NMTHx0NfXx82NjYAxLPUq6qq4OzsLDVpQN6TBMaOHYu+ffuiqKhIkg0Qd7VmPYZy4cKFiIiIwLBhw2Bpacm7NxZ83u3x8OFD+Pj48K6oAABbtmxhHUHKokWLPvlz5T1Bo66KigrJm25tbW08ffoUnTt3hpWVFfNJGurq6rh48SISEhKQmpoKkUgEOzs7DBw4kGku8vnp2rWrpHn0x/4msNqlOH78eBw/flymEejjx4/h7OyMW7duMckFAD169MCpU6fg7e0N4K/m27t27ZIsLLDC52ykcaHCAiFyULchnZGREbKysvDs2TNoaWnx4qbv2bNnkoZ/6urqkpXPvn37Mt2WrqmpKbMdmPV2yLratGmDNm3aSF3r2bMnozR/OXToEI4cOYKhQ4eyjlIvPu/2cHV1xfXr13mxk+hdnzqdZePGjZgzZw40NTX/1TxpaWlS/09JSUFNTQ1MTU0BiBuIKSoqolu3bv9qjo8xNTXF3bt3YWhoiK5du2LHjh0wNDREeHg42rZtyzRbrQEDBmDAgAGsY/wjfPgbRj6u7nGltLQ0+Pn5wd/fX6oxckhICIKCglhFRFFREby8vKQmAxUVFWHAgAHo0qULs1wAsGHDBgwePBhZWVmorq7G1q1bkZmZieTkZAiFQspGCKiwQAgzdWeqs2ZkZIT8/HwYGBjAwsICR44cQc+ePfHTTz/96zcnH8J67GBDpaSkBBMTE9Yx3ovPuz2GDRsGf39/ZGVlwcrKSua4wYgRIxgl+3Tr16/H+PHj//Xf3cTERMnHmzdvhpqaGiIjI6GlpQVAPGLP09OT+dSbr7/+GkVFRQDEIzldXV2xf/9+KCkpISIigmk2AIiPj3/vsaW9e/cySvXp+N4ngIgZGBhIPh43bhzCwsKkis/W1tZo3749VqxYgZEjRzJICJw+fRoODg7w9fVFaGgoHj58iAEDBsDGxgaHDh1ikqlW7969cfnyZQQHB8PY2BhxcXGws7NDcnIyrKysKBshoHGThBAAoaGhUFRUhI+PDxITEzFs2DDU1NSguroamzdvxsKFC1lHJH9DSEgI8vLysG3bNlpN/Jve3YJbl0AgaBDz5+U9Ig4A2rVrh7i4OJlVxVu3bsHFxQWPHj2SW5aPqaysxJ07d9ChQwfmY/5Wr16NwMBAdO/eHW3btpX5fT1x4gSTXNXV1VBRUcGNGzdgaWn5wc998OAB9PT0oKioKKd0f8/cuXOxZs0a5t/r+rDK1qxZM6SmpsLc3Fzq+u3bt2FnZ4dXr17JNU9dv//+O/r27YtRo0bh1KlTsLOzw/79+5n/fHl4eMDJyQmOjo7o3Lkz0yzv4nM20rhQYYEQIqOwsBDXr1+HsbGx1IqyPNjZ2SE+Ph5aWlqwtbX94I0x6/PRfFK33wQgblinra2NLl26yKy6y7sfBZEvFoUFNTU1/PDDDzLb+RMSEuDm5oYXL17ILcv7VFVV4f79+zA2NkaTJvzYsNm2bVsEBQVh8uTJrKPIMDY2xvHjx+X+N+BD/kkDQnnhc7Z32dnZwdzcHHv27IGKigoAcWPk6dOn4/bt28z/tmZnZ6Nv374YNGgQoqOjeVEgnz17NoRCIbKzs6GrqwtHR0c4OjrCyckJZmZmlI0QUGGBEPInvmzHXb16Nfz9/dG8eXOsXr36g58bEBAgp1T85+np+cmfS0dMPm8sCgtTpkyBUChESEgIevXqBQC4cuUK/P394eDggMjISLlleVdlZSW8vb0lGe7duwcjIyP4+PhAT08PS5cuZZatZcuWuHr1KoyNjZlleJ99+/bh6NGjiImJ4c3RPQUFBd42IORztnddvXoVX375JUQikVRjZIFAgJ9//lmuvYLe12uqsrISysrKUjsV+DD5pri4GElJSUhKSoJQKMS9e/ego6MjOW5F2Uhjxo+SPSGEqY9tx5Wn2mJBTU0NnJycYG1tLTmzTd6vbrHg1atXEIlEaNGiBQAgPz8fJ0+ehLm5uVQjUSIWFhaGWbNmQUVFBWFhYR/8XB8fHzmlaljCw8Ph5+eHSZMm4e3btwCAJk2awMvLC8HBwUyzLVu2DOnp6UhKSsLgwYMl1wcOHIiAgACmhYUZM2bgwIEDWLFiBbMM7xMWFoacnBzo6enBwMBA8npSi8WqNp8bEPI527t69uyJ+/fvIyYmBnfu3AHHcZgwYQLc3d1lvs//Nr5Nu/kYNTU1aGlpQUtLC5qammjSpIlME2dW+JyNNA60Y4EQwtvtuCoqKrh9+7ZkYgX5NC4uLhg9ejTmzJmDsrIymJmZoWnTpigpKcHmzZuZTvrgo44dO+L69eto2bLlB3/WBAIB8vLy5Jjsn2GxY6FWRUUFcnNzwXEcTExM5H6TUh8DAwMcPnwYvXr1knpucnJyYGdnh/LycrnmqTumUyQSITIyEtbW1rC2tpY5tsRyTCffd4z17NkTq1atkpl+c/r0aaxYsQIpKSmMkvE7G/lnlixZAqFQiPT0dFhaWsLBwQGOjo5wcHBg2uSa79lI40I7FgghqKqqQu/evVnHkGFlZYW8vDwqLPxNqampCA0NBQDExsZCV1cXaWlpOHbsGFauXEmFhXfUXWms+3FD1a9fPzRr1ozJ1y4qKkJRUREcHBzQrFmzT9oW/m97+vQpdHR0ZK5XVFQwyfbumM6uXbsCEDe65BPWhYOPuXnzZr1/Gzp27IisrCwGif7C52x1ZWVlobCwEFVVVVLXWU6/yc3Nxb59+5Cbm4utW7dCR0cHZ8+eRfv27ZmOnAwODkbr1q0REBAANzc3mcaXLPE5G2lcaMcCIQRLliyBqqoq77bjxsXFYcmSJVizZg26desms/qprq7OKBm/NW/eXNL1fvz48ejSpQsCAgLw4MEDmJqaorKyknVE8g+8b2VdIBBAWVkZSkpKck70l9LSUowfPx6JiYkQCATIzs6GkZERvLy8oKmpiZCQEGbZHB0dMXbsWHh7e0NNTQ0ZGRno2LEjFixYgJycHJw9e5ZZNr4rKytDbGwscnNz4e/vD21tbaSmpkJXVxft2rVjmo3PDQj5nA0A8vLyMGrUKNy8eVPSFwKApNDGqgeEUCjEkCFD0KdPH/z666+4ffs2jIyMEBQUhKtXryI2NpZJLkDcg0IoFCIpKQkXLlyAoqKipEGik5MT05t5PmcjjQsVFghppBrCdty6o//qrizWroKyboDFV9bW1pgxYwZGjRoFS0tLnD17Fvb29khJScGwYcNQXFzMOiKv1P1d+BiWW9Nrm8O9j76+PqZNm4aAgIAPjs38N0yZMgVPnjzB7t27YW5uLjluEBcXB19fX2RmZso1T12XL1/G4MGD4eHhgYiICMyePRuZmZlITk6GUChEt27dmGWbPn06tm7dCjU1NanrFRUV8Pb2lmvj3HdlZGRg4MCB0NDQQH5+Pu7evQsjIyOsWLECBQUFiIqKYpYN4FcDwoaUDQC+/PJLKCoqYteuXTAyMsLVq1dRWlqKxYsXY9OmTejXrx+TXPb29hg3bhwWLVokdWzp2rVrGDlyJB4+fMgkV33S09OxZcsWxMTEQCQS8er9CJ+zkc8bHYUgpJH61O24LLcx79u3D+3bt5eZXy0SiVBYWMgoFf+tXLkS7u7u8PX1hbOzs6R5WFxcHGxtbRmn4593fxdSUlJQU1MDU1NTAOIpAoqKikxvQAEgIiICy5cvx7Rp09CzZ09wHIdr164hMjIS3377LZ4+fYpNmzZBWVkZ//nPf+SaLS4uDufOnYO+vr7U9U6dOqGgoECuWd7Vu3dvXL58GcHBwTA2NkZcXBzs7OyQnJwMKysrptkiIyOxceNGmcLCq1evEBUVxbSwsGjRIkybNg1BQUFS+YYMGQJ3d3dmuWrxqQFhQ8oGiBtJJiQkoHXr1lBQUICCggL69u2LDRs2wMfHR+Y1UV5u3ryJAwcOyFxv3bo1SktLGSSSlpaWJpm6cOHCBZSXl6Nr167o378/62i8zkYaDyosENJIJSYmso7wUdOnT0dRUZHM+ejS0lIMHDgQU6dOZZSM38aOHYu+ffuiqKhIaga9s7MzRo0axTAZP9X9Xdi8eTPU1NQQGRkpmUby/PlzeHp6MlvFqxUZGYmQkBCMHz9ecm3EiBGwsrLCjh07EB8fjw4dOmDdunVyLyxUVFSgefPmMtdLSkqgrKws1yzv8vDwgJOTE5YvX47OnTszzVKrvLwcHMeB4zi8ePFCsl0eEG9DP336dL19IeTp2rVr2LFjh8z1du3a8WbXU/PmzTFr1izWMerF52w1NTVQVVUFALRq1QqPHj2CqakpDAwMcPfuXWa5NDU1UVRUJNOfIi0tjfnRGy0tLbx8+RI2NjZwcnLCzJkz4eDgwIsjmXzORhoXKiwQQnjrfY3fXr58KfVGnMhq06aNzJgp1ttvG4KQkBDExcVJjTjV0tLC2rVr4eLigsWLFzPLlpycjPDwcJnrtra2SE5OBgD07duXyW4eBwcHREVFYc2aNQDEO51EIhGCg4OZr5ipqqoiJCQEc+bMga6uLhwdHSXnj83MzJhk0tTUhEAggEAgqLfYIRAIPjqV4d+moqJSb1+Pu3fvonXr1gwS1Y+PDQhr8TWbpaUlMjIyYGRkhC+++AJBQUFQUlLCzp07mUyTqeXu7o4lS5bg6NGjkteQS5cuwc/PD1OmTGGWCwCio6N5e7PO52ykcaHCAiGEd2rPvAsEAqxYsUJqJbSmpga//fab5OgGIf8/lZeX4/HjxzLdx588eYIXL14wSiWmr6+PPXv2YOPGjVLX9+zZg/bt2wMQ7+apWxSRl+DgYDg5OeH69euoqqrCN998g8zMTDx79gyXLl2Se566alfdi4uLJVuFt27divnz50NHRwdFRUVyz5SYmAiO4zBgwAAcO3YM2trakseUlJRgYGAAPT09ueeqy83NDYGBgThy5AgA8etxYWEhli5dijFjxjDNBvC3ASHfswHAt99+i4qKCgDA2rVrMXz4cPTr1w8tW7bE4cOHmeVat24dpk2bhnbt2oHjOFhYWKCmpgbu7u749ttvmeUCgOHDhzP9+h/C52ykcaHmjYQQ3qld4RQKhbC3t5fqdq+kpARDQ0P4+fmhU6dOrCKSz9SUKVMgFAoREhKCXr16AQCuXLkCf39/ODg4IDIyklm2H3/8EePGjYOZmRl69OgBgUCAa9eu4c6dO4iNjcXw4cOxfft2ZGdnM2kyWVxcjO+//x6pqakQiUSws7PD/Pnz0bZtW7lnqU9FRQUuXrwoKS6kpqbCwsKC2XlyACgoKED79u3l3mzzU5SXl2Po0KHIzMzEixcvoKenh+LiYtjb2+P06dPMewXwtQEh37O9z7Nnz6ClpcV8PCwgHjmZlpYGkUgEW1tb+ltPSANBhQVCCG95enpi69attL2PyE1lZSX8/Pywd+9evH37FgDQpEkTeHl5ITg4mPnNVH5+PsLDw3Hv3j1wHAczMzPMnj0bhoaGTHPx2ZIlSyAUCpGeng5LS0s4ODjA0dERDg4O0NTUZB0PgPjnrr4t89bW1owS/SUhIUGqWDRw4EDWkQCIewMkJCTA2toaGhoauHr1KkxNTZGQkIDFixczLRjxOdu7fv/9dwgEAuY9DOqqqqrC/fv3YWxsjCZNaHM1IQ0FFRYIIYSQd1RUVCA3Nxccx8HExIR5QaEhuHDhAnbs2IG8vDwcPXoU7dq1Q3R0NDp27Ii+ffsyy6WgoIDWrVvD19cXbm5uvJrp/vTpU3h6euLMmTP1Ps56yzyfaWlpISUlBUZGRjA2Nsbu3bvRv39/5ObmwsrKCpWVlZTtPUQiEdauXYuQkBC8fPkSAKCmpobFixdj+fLlzHbQVFZWwtvbW7Iz7N69ezAyMoKPjw/09PSwdOlSJrkIIZ+GyoCEEELIO4qKilBUVAQHBwc0a9bsvY1E5a2srAxXr17FkydPIBKJpB5j2dzs2LFjmDx5Mjw8PJCamoo3b94AAF68eIH169fj9OnTzLKlpaVBKBQiKSkJISEhUFRUlDRvdHJyYlpo+Prrr/H8+XNcuXIF/fv3x4kTJ/D48WPJTR9r8fHxiI+Pr/fnjeUoTIC/DQj5ng0Ali9fLunX0qdPH3Ach0uXLmHVqlV4/fo11q1bxyTXsmXLkJ6ejqSkJAwePFhyfeDAgQgICKDCAiE8RzsWCCGEkD+VlpZi/PjxSExMhEAgQHZ2NoyMjODl5QVNTU2mN3s//fQTPDw8UFFRATU1NalCh0AgwLNnz5hls7W1ha+vL6ZMmQI1NTWkp6fDyMgIN27cwODBg3kznhAA0tPTsWXLFsTExEAkEjHdFdC2bVv88MMP6NmzJ9TV1XH9+nV07twZP/74I4KCgnDx4kVm2VavXo3AwEB0794dbdu2lSmsnThxglEysXPnzqGiogKjR49GXl4ehg8fjjt37kgaEA4YMICyvYeenh7Cw8NlplP88MMPmDdvHh4+fMgkl4GBAQ4fPoxevXpJvY7k5OTAzs6u3iklhBD+oB0LhBBCyJ98fX3RtGlTFBYWSq1kT5gwAb6+vkwLC4sXL8b06dOxfv16qUkpfHD37l04ODjIXFdXV0dZWZn8A70jLS1N0rTxwoULKC8vR9euXZmPwqyoqICOjg4AQFtbG0+fPkXnzp1hZWWF1NRUptnCw8MRERGByZMnM83xPq6urpKPjYyMkJWVxZsGhHzOBogbNdY3atXMzIxpgfLp06eS34e6KioqePG8EUI+jH9tiAkhhBBG4uLi8N///hf6+vpS1zt16oSCggJGqcQePnwIHx8f3hUVAPHKe05Ojsz1ixcvMt/6raWlhZ49e2L//v3o1KkToqKi8OzZM1y/fh3BwcFMs5mamuLu3bsAgK5du2LHjh14+PAhwsPDmU/TqKqqQu/evZlm+FS///47Hj58CG1tbd7dgPIxm42NDbZt2yZzfdu2bbCxsWGQSKxHjx44deqU5P+1z9euXbtgb2/PKhYh5BPRjgVCCCHkTxUVFfXeuJeUlEBZWZlBor+4urri+vXrzG/U6zN79mwsXLgQe/fuhUAgwKNHj5CcnAw/Pz+sXLmSabbo6Gg4ODjwcrrM119/jaKiIgBAQEAAXF1dsX//figpKSEiIoJpthkzZuDAgQNYsWIF0xzvw9cGhHzPBgDBwcEYOnQofvnlF9jb20MgEODy5ct48OAB034oGzZswODBg5GVlYXq6mps3boVmZmZSE5OhlAoZJaLEPJpqMcCIYQQ8qdhw4bBzs4Oa9asgZqaGjIyMmBgYICJEydCJBIhNjaWWbY9e/YgMDAQnp6esLKyQtOmTaUef/e8tLwtX74coaGheP36NQBAWVkZfn5+WLNmDdNcDUllZSXu3LmDDh06oFWrVnL/+osWLZJ8LBKJEBkZCWtra1hbW8v8vG3evFne8aQsW7YMe/bswerVq2UaEM6cOZNZA0K+Z3v79i1cXFywbt06nDp1Cnfu3AHHcbCwsMC8efOgp6fHLBsA3Lp1C8HBwUhJSZGMOF2yZAmsrKyY5iKEfBwVFgghhJA/ZWVlwcnJCd26dUNCQgJGjBiBzMxMPHv2DJcuXYKxsTGzbB9a5RQIBLwYTVhZWYmsrCyIRCJYWFhAVVWVdaQGoaqqCvfv34exsTGaNGG3mfTv9JxITEz8F5N8HF8bEAL8zgYArVu3xuXLl9GpUyemOd7l4eEBJycnODo6onPnzqzjEEL+JiosEEIIIXUUFxfj+++/R2pqqmTFbP78+czPvDcUDx48gEAgkOlTQWRVVlbC29sbkZGRAIB79+7ByMgIPj4+0NPTo/F6H6CiooKMjAyZG9C7d++ia9euePXqFaNk/M4GiBvBNm3aFBs3bmSa412zZ8+GUChEdnY2dHV14ejoKBkNW1+zSUIIv1BhgRBCCCH/J9XV1Vi9ejXCwsIkZ8pVVVXh7e2NgIAAmW30RGzhwoW4dOkStmzZgsGDByMjIwNGRkb48ccfERAQgLS0NGbZpk+fjq1bt0JNTU3qekVFBby9vbF3715GycS++OILfPHFFwgLC5O67u3tjWvXruHKlSuMkvE7W22OqKgomJiYoHv37mjRooXU46yPuRQXF0umuAiFQty7dw86OjqSfiSEEH6iwgIhhBBSx4ULF7Bjxw7k5eXh6NGjaNeuHaKjo9GxY0f07dtXrlnCwsIwa9YsqKioyNykvMvHx0dOqWTNmTMHJ06cQGBgoKR7e3JyMlatWgU3NzeEh4czy8ZnBgYGOHz4MHr16gU1NTWkp6fDyMgIOTk5sLOzQ3l5ObNsioqKKCoqkhn/V1JSgjZt2qC6uppRMrFff/0VQ4cORYcOHeptQNivXz/K9h4fOvIiEAiQkJAgxzSyKioqcPHiRUlxITU1FRYWFkwLbYSQj6PCAiGEEPKnY8eOYfLkyfDw8EB0dDSysrJgZGSE77//Hj///LPcO6Z37NgR169fR8uWLdGxY8f3fp5AIEBeXp4ck0nT0NDAoUOHMGTIEKnrZ86cwcSJE/HHH38wSsZvzZs3x61bt2BkZCRVWEhPT4eDgwOT5628vBwcx0FLSwvZ2dlo3bq15LGamhr89NNPWLp0KR49eiT3bLX43ICQz9n4bsmSJRAKhUhPT4elpSUcHBzg6OgIBwcHaGpqso5HCPkIKiwQQgghf7K1tYWvry+mTJkidaN348YNDB48GMXFxawj8pKuri6SkpJgbm4udf327dtwcHDA06dPGSXjN0dHR4wdOxbe3t6SKSQdO3bEggULkJOTg7Nnz8o9k4KCAgQCwXsfFwgEWL16NZYvXy7HVLL42oAQ4Hc2PlNQUEDr1q3h6+sLNzc3mdcTQgi/sWs9TAghhPDM3bt34eDgIHNdXV0dZWVl8g/UQMyfPx9r1qzBvn37oKysDAB48+YN1q1bhwULFjBOx18bNmzA4MGDkZWVherqamzduhWZmZlITk6GUChkkikxMREcx2HAgAE4duwYtLW1JY8pKSnBwMCAF6vuU6ZMwZ49e3jXgBDgdzY+S0tLg1AoRFJSEkJCQqCoqChp3ujk5ESFBkJ4jgoLhBBCyJ/atm2LnJwcGBoaSl2/ePEijIyM5J5n0aJFn/y5LBuupaWlIT4+Hvr6+rCxsQEApKeno6qqCs7Ozhg9erTkc48fP84qJu/07t0bly9fRnBwMIyNjREXFwc7OzskJyfDysqKSSZHR0cAwP3799G+ffsPjjllqaqqCrt378b58+d514CQz9n4zMbGBjY2NpJ+Menp6diyZQt8fHwgEol4MVKXEPJ+VFgghBBC/jR79mwsXLgQe/fuhUAgwKNHj5CcnAw/Pz+sXLlS7nnebVaWkpKCmpoamJqaAhCPJ1RUVES3bt3knq0uTU1NjBkzRupa+/btGaVpODw8PODk5ITly5fLjCZkzcDAAIB4JGZhYSGqqqqkHre2tmYRS+LWrVuws7MDIP49qOtDRznkgc/Z+C4tLU3StPHChQsoLy9H165dP9hwkhDCD9RjgRBCCKlj+fLlCA0NxevXrwEAysrK8PPzw5o1a5jm2rx5M5KSkhAZGQktLS0AwPPnz+Hp6Yl+/fph8eLFzLK9evUKIpFIsjKbn5+PkydPwtzcHK6ursxy8d3s2bMhFAqRnZ0NXV1dODo6SrZ+m5mZMc329OlTeHp64syZM/U+TqvH5P83LS0tvHz5EjY2NpLjDw4ODlBXV2cdjRDyCaiwQAghhLyjsrISWVlZEIlEsLCwgKqqKutIaNeuHeLi4tClSxep67du3YKLiwvTLv0uLi4YPXo05syZg7KyMpiZmaFp06YoKSnB5s2bMXfuXGbZGoLi4mLJKq1QKMS9e/ego6ODoqIiZpk8PDyQn5+PLVu2oH///jhx4gQeP36MtWvXIiQkBMOGDWOWjXyefv75ZyokENKA8fPgHCGEEMJQ8+bNoaurCz09PV4UFQDxGMDHjx/LXH/y5AlevHjBINFfUlNT0a9fPwBAbGwsdHV1UVBQgKioKISFhTHN1hCoqalBS0sLWlpa0NTURJMmTdCmTRummRISEhAaGooePXpAQUEBBgYGmDRpEoKCgrBhwwam2cjnafjw4VRUIKQBo8ICIYQQ8qfq6mqsWLECGhoaMDQ0hIGBATQ0NPDtt9/i7du3TLONGjUKnp6eiI2Nxe+//47ff/8dsbGx8PLykmqOyEJlZSXU1NQAAHFxcRg9ejQUFBTQq1cvFBQUMM3GZ0uWLEGvXr3QqlUrfPvtt6iqqsKyZcvw+PFjmf4a8lZRUQEdHR0AgLa2tmRkqJWVFVJTU1lGI4QQwkPUvJEQQgj504IFC3DixAkEBQXB3t4eAJCcnIxVq1ahpKQE4eHhzLKFh4fDz88PkyZNkhQ5mjRpAi8vLwQHBzPLBQAmJiY4efIkRo0ahXPnzsHX1xeAeDcFrUC+X3BwMFq3bo2AgAC4ubnxapyeqakp7t69C0NDQ3Tt2hU7duyAoaEhwsPD0bZtW9bxCCGE8Az1WCCEEEL+pKGhgUOHDmHIkCFS18+cOYOJEyfijz/+YJTsLxUVFcjNzQXHcTAxMZEZZcdCbGws3N3dUVNTA2dnZ8TFxQEANmzYgF9//fW9DQAbu/T0dAiFQkkHfEVFRUnzRicnJ6aFhv379+Pt27eYNm0a0tLS4OrqitLSUigpKSEiIgITJkxglo0QQgj/UGGBEEII+ZOuri6SkpJkbuhu374NBwcHyXZwlnJycpCbmwsHBwc0a9YMHMfxYoRdcXExioqKYGNjAwUF8UnLq1evQl1dnfmEg4YiPT0dW7ZsQUxMDEQiEa8mL1RWVuLOnTvo0KEDWrVqxToOIYQQnqHCAiGEEPKnwMBA3LlzB/v27YOysjIA4M2bN/Dy8kKnTp0QEBDALFtpaSnGjx+PxMRECAQCZGdnw8jICF5eXtDU1ERISAizbOSfS0tLk0yEuHDhAsrLy9G1a1f079+f+REXAKiqqsL9+/dhbGyMJk3oBC0hhJD6UWGBEEII+dOoUaMQHx8PZWVl2NjYABCvIldVVcHZ2Vnqc48fPy7XbFOmTMGTJ0+we/dumJubIz09HUZGRoiLi4Ovry8yMzPlmof832lpaeHly5ewsbGRHH/gy7i9yspKeHt7IzIyEgBw7949GBkZwcfHB3p6eli6dCnjhIQQQviESs+EEELInzQ1NTFmzBipa+3bt2eURlpcXBzOnTsHfX19qeudOnWiyQsNVHR0NG8KCe9atmwZ0tPTkZSUhMGDB0uuDxw4EAEBAVRYIIQQIoUKC4QQQsifvv/+e4hEIklDxPz8fJw8eRLm5uZwdXVlmq2iogLNmzeXuV5SUiI5tkEaluHDh7OO8F4nT57E4cOH0atXL6keHhYWFsjNzWWYjBBCCB8psA5ACCGE8IWbmxuio6MBAGVlZejVqxdCQkIwcuRIbN++nWk2BwcHREVFSf4vEAggEokQHByM/v37M0xGPkdPnz6Fjo6OzPWKigpeNAslhBDCL1RYIIQQQv6UmpqKfv36ARCPUNTV1UVBQQGioqIQFhbGNFtwcDB27NiBIUOGoKqqCt988w0sLS3x66+/4r///S/TbOTz06NHD5w6dUry/9piwq5du2Bvb88qFiGEEJ6ioxCEEELInyorK6GmpgZA3NNg9OjRUFBQQK9evZj3MbCwsEBGRga+//57KCoqoqKiAqNHj8b8+fPRtm1bptnI52fDhg0YPHgwsrKyUF1dja1btyIzMxPJyckQCoWs4xFCCOEZmgpBCCGE/Mna2hozZszAqFGjYGlpibNnz8Le3h4pKSkYNmwYiouLWUckRG5u3bqF4OBgpKSkQCQSwc7ODkuWLIGVlRXraIQQQniGCguEEELIn2JjY+Hu7o6amho4OzsjLi4OgHj19tdff8WZM2eY5rtw4QJ27NiBvLw8HD16FO3atUN0dDQ6duyIvn37Ms1GPi8eHh5wcnKCo6MjOnfuzDoOIYQQnqMeC4QQQsifxo4di8LCQly/fh1nz56VXHd2dkZoaCjDZMCxY8fg6uqKZs2aITU1FW/evAEAvHjxAuvXr2eajXx+VFVVERISAnNzc+jp6eGrr75CeHg47ty5wzoaIYQQHqIdC4QQQkgDYGtrC19fX0yZMgVqampIT0+HkZERbty4gcGDB9MxDfKvKC4uRlJSEpKSkiAUCnHv3j3o6OigqKiIdTRCCCE8QjsWCCGEkAbg7t27cHBwkLmurq6OsrIy+QcijYKamhq0tLSgpaUFTU1NNGnSBG3atGEdixBCCM9QYYEQQghpANq2bYucnByZ6xcvXoSRkRGDRORztmTJEvTq1QutWrXCt99+i6qqKixbtgyPHz9GWloa63iEEEJ4hsZNEkIIIQ3A7NmzsXDhQuzduxcCgQCPHj1CcnIy/Pz8sHLlStbxyGcmODgYrVu3RkBAANzc3GBubs46EiGEEB6jHguEEEJIA7F8+XKEhobi9evXAABlZWX4+flhzZo1jJORz016ejqEQiGSkpJw4cIFKCoqwtHREU5OTnBycqJCAyGEEClUWCCEEEIakMrKSmRlZUEkEsHCwgKqqqqsI5FGID09HVu2bEFMTAxEIhFqampYRyKEEMIjdBSCEEIIaUCaN28OXV1dCAQCKiqQf1VaWppkIsSFCxdQXl6Orl27on///qyjEUII4RnasUAIIYQ0ANXV1Vi9ejXCwsLw8uVLAICqqiq8vb0REBCApk2bMk5IPidaWlp4+fIlbGxsJMcfHBwcoK6uzjoaIYQQHqIdC4QQQkgDsGDBApw4cQJBQUGwt7cHACQnJ2PVqlUoKSlBeHg444TkcxIdHU2FBEIIIZ+MdiwQQgghDYCGhgYOHTqEIUOGSF0/c+YMJk6ciD/++INRMkIIIYQ0dgqsAxBCCCHk41RUVGBoaChz3dDQEEpKSvIPRAghhBDyJyosEEIIIQ3A/PnzsWbNGrx580Zy7c2bN1i3bh0WLFjAMBkhhBBCGjs6CkEIIYQ0AKNGjUJ8fDyUlZVhY2MDQDwCsKqqCs7OzlKfe/z4cRYRCSGEENJIUfNGQgghpAHQ1NTEmDFjpK61b9+eURpCCCGEkL/QjgVCCCGkAXj16hVEIhFatGgBAMjPz8fJkydhbm4OV1dXxukIIYQQ0phRjwVCCCGkAXBzc0N0dDQAoKysDL169UJISAhGjhyJ7du3M05HCCGEkMaMCguEEEJIA5Camop+/foBAGJjY6Grq4uCggJERUUhLCyMcTpCCCGENGZUWCCEEEIagMrKSqipqQEA4uLiMHr0aCgoKKBXr14oKChgnI4QQgghjRkVFgghhJAGwMTEBCdPnsSDBw9w7tw5uLi4AACePHkCdXV1xukIIYQQ0phRYYEQQghpAFauXAk/Pz8YGhriiy++gL29PQDx7gVbW1vG6QghhBDSmNFUCEIIIaSBKC4uRlFREWxsbKCgIF4buHr1KtTV1WFmZsY4HSGEEEIaKyosEEIIIYQQQggh5B+joxCEEEIIIYQQQgj5x6iwQAghhBBCCCGEkH+MCguEEEIIIYQQQgj5x6iwQAghhBBCCCGEkH+MCguEEEIIIYQQQgj5x6iwQAghhBBCCCGEkH+MCguEEEIIIYQQQgj5x/4fAZtN+bRa9T8AAAAASUVORK5CYII=", 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" ] @@ -1520,7 +1529,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 60, "id": "19c7e28c", "metadata": {}, "outputs": [ @@ -1529,18 +1538,18 @@ "output_type": "stream", "text": [ "Entfernte Variablen und zugehörige p-Werte:\n", - "road_Außerorts: p=1.0000\n", - "weather_winterlich: p=1.0000\n", - "road_Autobahn: p=0.8703\n", - "trip_distance: p=0.6828\n", - "speeding_haeufig: p=1.0000\n", - "road_Innerorts: p=0.3855\n", + "trip_distance: p=0.4639\n", "weekday_Mo-Fr: p=1.0000\n", - "weekday_So: p=0.6575\n", "shift_normal: p=1.0000\n", - "weekday_Sa: p=0.5344\n", - "weather_trocken: p=0.3417\n", - "weather_nass: p=0.0516\n", + "weekday_Sa: p=0.7010\n", + "speeding_haeufig: p=1.0000\n", + "weekday_So: p=0.5605\n", + "weather_winterlich: p=1.0000\n", + "weather_trocken: p=0.4538\n", + "weather_nass: p=0.1143\n", + "road_Autobahn: p=1.0000\n", + "road_Außerorts: p=0.9214\n", + "road_Innerorts: p=0.6182\n", "\n", "Verbleibende Variablen im Modell:\n", "['const', 'avg_speed', 'hard_brakes', 'shift_frueh', 'shift_spaet', 'speeding_manchmal', 'speeding_selten']\n", @@ -1548,23 +1557,23 @@ "Zusammenfassung des finalen Modells:\n", " Logit Regression Results \n", "==============================================================================\n", - "Dep. Variable: y No. Observations: 20000\n", - "Model: Logit Df Residuals: 19993\n", + "Dep. Variable: y No. Observations: 16000\n", + "Model: Logit Df Residuals: 15993\n", "Method: MLE Df Model: 6\n", - "Date: Sat, 05 Jul 2025 Pseudo R-squ.: 0.04322\n", - "Time: 10:31:36 Log-Likelihood: -11755.\n", - "converged: True LL-Null: -12286.\n", - "Covariance Type: nonrobust LLR p-value: 3.464e-226\n", + "Date: Sat, 05 Jul 2025 Pseudo R-squ.: 0.04383\n", + "Time: 13:10:09 Log-Likelihood: -9398.3\n", + "converged: True LL-Null: -9829.1\n", + "Covariance Type: nonrobust LLR p-value: 7.536e-183\n", "=====================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "-------------------------------------------------------------------------------------\n", - "const -1.0751 0.085 -12.714 0.000 -1.241 -0.909\n", - "avg_speed 0.0177 0.002 11.111 0.000 0.015 0.021\n", - "hard_brakes 0.0822 0.011 7.401 0.000 0.060 0.104\n", - "shift_frueh -0.8050 0.035 -22.841 0.000 -0.874 -0.736\n", - "shift_spaet -0.7985 0.044 -18.075 0.000 -0.885 -0.712\n", - "speeding_manchmal -0.5367 0.038 -14.142 0.000 -0.611 -0.462\n", - "speeding_selten -0.4607 0.038 -11.994 0.000 -0.536 -0.385\n", + "const -1.1750 0.095 -12.389 0.000 -1.361 -0.989\n", + "avg_speed 0.0189 0.002 10.618 0.000 0.015 0.022\n", + "hard_brakes 0.0966 0.012 7.762 0.000 0.072 0.121\n", + "shift_frueh -0.7953 0.039 -20.180 0.000 -0.873 -0.718\n", + "shift_spaet -0.8065 0.049 -16.304 0.000 -0.903 -0.710\n", + "speeding_manchmal -0.5039 0.042 -11.882 0.000 -0.587 -0.421\n", + "speeding_selten -0.4647 0.043 -10.809 0.000 -0.549 -0.380\n", "=====================================================================================\n" ] } @@ -1623,7 +1632,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 61, "id": "6641f80c", "metadata": {}, "outputs": [ @@ -1633,16 +1642,13 @@ "text": [ "\n", "Modellkoeffizienten:\n", - "const -0.788152\n", - "avg_speed 0.017391\n", - "hard_brakes 0.081678\n", - "shift_frueh -0.814953\n", - "shift_normal -0.808104\n", - "speeding_haeufig -0.838057\n", - "speeding_manchmal -0.834292\n", - "road_Autobahn -0.257555\n", - "road_Außerorts -0.273524\n", - "road_Innerorts -0.257073\n", + "const -1.175040\n", + "avg_speed 0.018919\n", + "hard_brakes 0.096628\n", + "shift_frueh -0.795291\n", + "shift_spaet -0.806477\n", + "speeding_manchmal -0.503908\n", + "speeding_selten -0.464663\n", "dtype: float64\n" ] } @@ -1666,7 +1672,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "id": "0541fa09", "metadata": {}, "outputs": [ @@ -1675,26 +1681,14 @@ "output_type": "stream", "text": [ "Vergleich der geschätzten und wahren Koeffizienten:\n", - " Geschätzte Koeffizienten Wahre Koeffizienten\n", - "avg_speed 0.017391 0.015\n", - "const -0.788152 -2.000\n", - "hard_brakes 0.081678 0.100\n", - "road_Autobahn -0.257555 0.000\n", - "road_Außerorts -0.273524 0.000\n", - "road_Innerorts -0.257073 0.000\n", - "shift_frueh -0.814953 0.500\n", - "shift_normal -0.808104 -0.300\n", - "shift_spaet NaN -0.300\n", - "speeding_haeufig -0.838057 -0.300\n", - "speeding_manchmal -0.834292 -0.300\n", - "speeding_selten NaN 0.500\n", - "trip_distance NaN 0.000\n", - "weather_nass NaN 0.000\n", - "weather_trocken NaN 0.000\n", - "weather_winterlich NaN 0.000\n", - "weekday_Mo-Fr NaN 0.000\n", - "weekday_Sa NaN 0.000\n", - "weekday_So NaN 0.000\n" + " Geschätzte Koeffizienten Wahre Koeffizienten\n", + "const -1.175040 -2.000\n", + "avg_speed 0.018919 0.015\n", + "hard_brakes 0.096628 0.100\n", + "shift_frueh -0.795291 0.000\n", + "shift_spaet -0.806477 0.500\n", + "speeding_manchmal -0.503908 -0.300\n", + "speeding_selten -0.464663 0.000\n" ] } ], @@ -1736,6 +1730,80 @@ "Die Koeffizienten sind nicht deckend gleich aber das erlernte Modell kommt dem idealen sehr nahe." ] }, + { + "cell_type": "markdown", + "id": "a0db711e", + "metadata": {}, + "source": [ + "### Modellevaluation auf Testdaten\n", + "\n", + "Nachdem das optimale Modell auf den Trainingsdaten entwickelt wurde, wird es nun auf den Testdaten evaluiert. Dies gibt uns eine Einschätzung der Modellgüte und zeigt, wie gut das Modell auf ungesehenen Daten performt." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "a1c9fc7f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import roc_auc_score, roc_curve, confusion_matrix\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "X_test_selected = X_test[X_selected.columns]\n", + "X_test_with_const = sm.add_constant(X_test_selected)\n", + "y_pred_proba = final_model.predict(X_test_with_const)\n", + "y_pred = (y_pred_proba > 0.5).astype(int)\n", + "auc_score = roc_auc_score(y_test, y_pred_proba)\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(15, 6))\n", + "# ROC-Kurve\n", + "fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)\n", + "axes[0].plot(fpr, tpr, color='blue', lw=2, label=f'ROC Kurve (AUC = {auc_score:.3f})')\n", + "axes[0].plot([0, 1], [0, 1], color='red', lw=2, linestyle='--', label='Zufall (AUC = 0.5)')\n", + "axes[0].set_xlim([0.0, 1.0])\n", + "axes[0].set_ylim([0.0, 1.05])\n", + "axes[0].set_xlabel('False Positive Rate')\n", + "axes[0].set_ylabel('True Positive Rate')\n", + "axes[0].set_title('ROC-Kurve')\n", + "axes[0].legend(loc=\"lower right\")\n", + "axes[0].grid(True, alpha=0.3)\n", + "\n", + "# Confusion Matrix\n", + "cm = confusion_matrix(y_test, y_pred)\n", + "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', ax=axes[1],\n", + " xticklabels=['Kein Diebstahl', 'Diebstahl'],\n", + " yticklabels=['Kein Diebstahl', 'Diebstahl'])\n", + "axes[1].set_xlabel('Vorhergesagte Klasse')\n", + "axes[1].set_ylabel('Tatsächliche Klasse')\n", + "axes[1].set_title('Confusion Matrix')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "2bff1bcb", + "metadata": {}, + "source": [ + "Die ROC-Kurve zeigt mit einem AUC-Wert deutlich über 0.5 eine gute Diskriminierungsfähigkeit des Modells. Die Confusion Matrix offenbart jedoch ein Klassifikationsproblem: Das Modell erkennt normale Fahrten sehr zuverlässig, hat aber Schwierigkeiten bei der Diebstahlerkennung. \n", + "\n", + "Diese Missklassifikation resultiert aus der Stichprobengröße von nur 20.000 Beobachtungen bei einer unbalancierten Klassenverteilung (ca. 25% Diebstähle im Datensatz). Das Modell neigt dadurch zur Mehrheitsklasse und klassifiziert konservativ." + ] + }, { "cell_type": "markdown", "id": "7516554c", @@ -1756,7 +1824,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 64, "id": "616ae484", "metadata": {}, "outputs": [ @@ -8436,7 +8504,495 @@ "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", - "Stichprobenumfang: 10%|█ | 1/10 [01:21<12:12, 81.40s/it]c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", @@ -8940,7 +9496,7 @@ "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", - "Stichprobenumfang: 20%|██ | 2/10 [02:54<11:45, 88.21s/it]c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", @@ -8948,7 +9504,7568 @@ "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n", - "Stichprobenumfang: 100%|██████████| 10/10 [32:08<00:00, 192.82s/it]\n" + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "Stichprobenumfang: 10%|█ | 1/10 [01:15<11:21, 75.70s/it]c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "Stichprobenumfang: 10%|█ | 1/10 [01:15<11:21, 75.70s/it]c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "Stichprobenumfang: 100%|██████████| 10/10 [34:23<00:00, 206.36s/it]\n", + "Stichprobenumfang: 100%|██████████| 10/10 [34:23<00:00, 206.36s/it]\n" ] } ], @@ -8997,13 +17114,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 65, "id": "b0bb7329", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -9046,13 +17163,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "id": "cf7a7e61", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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