{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lernfall 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seed setzen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Verteilungen erstellen und visualisieren"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Normalverteilung erstellen\n",
    "normal_verteilung = np.random.normal(loc=0, scale=1, size=1000)\n",
    "\n",
    "# Histogramm erstellen\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.hist(normal_verteilung, bins=30, density=True, alpha=0.6, color='g')\n",
    "plt.title('Histogramm der Normalverteilung')\n",
    "plt.xlabel('Wert')\n",
    "plt.ylabel('Häufigkeit')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Stetige Gleichverteilung erstellen\n",
    "uniform_verteilung = np.random.uniform(low=0, high=1, size=1000)\n",
    "\n",
    "# Histogramm erstellen\n",
    "plt.hist(uniform_verteilung, bins=30, density=True, alpha=0.6, color='b')\n",
    "plt.title('Histogramm der Gleichverteilung')\n",
    "plt.xlabel('Wert')\n",
    "plt.ylabel('Häufigkeit')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Diskrete Gleichverteilung\n",
    "diskrete_gleichverteilung = np.random.choice([1, 2, 3, 4, 5], size=1000, p=[0.2, 0.2, 0.2, 0.2, 0.2])\n",
    "\n",
    "# Balkendiagramm erstellen\n",
    "plt.hist(diskrete_gleichverteilung, bins=np.arange(1, 7)-0.5, density=True, alpha=0.6, color='r')\n",
    "plt.xticks([1, 2, 3, 4, 5])\n",
    "plt.title('Balkendiagramm der diskreten Gleichverteilung')\n",
    "plt.xlabel('Wert')\n",
    "plt.ylabel('Häufigkeit')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Diskrete Verteilung erstellen mit Wahrscheinlichkeitsvektor\n",
    "diskrete_verteilung = np.random.choice([1, 2, 3, 4, 5], size=1000, p=[0.1, 0.2, 0.3, 0.2, 0.2])\n",
    "\n",
    "# Balkendiagramm erstellen\n",
    "plt.hist(diskrete_verteilung, bins=np.arange(1, 7)-0.5, density=True, alpha=0.6, color='y')\n",
    "plt.xticks([1, 2, 3, 4, 5])\n",
    "plt.title('Balkendiagramm der diskreten Gleichverteilung')\n",
    "plt.xlabel('Wert')\n",
    "plt.ylabel('Häufigkeit')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Je mehr man die Anzahl an Instanzen erhöht desto mehr nähern sich die IST-Wahrscheinlichkeiten den SOLL-Wahrscheinlichkeiten an"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}