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Diese Studie untersucht, inwiefern sich anhand dieser Daten mit einem statistischen Modell erkennen lässt, ob statt des gewöhnlichen Fahrers eine andere Person am Steuer sitzt -- was auf einen \emph{Diebstahl} hindeuten würde. Konkret soll ein Modell die Wahrscheinlichkeit $P(\text{Diebstahl})$ schätzen und bei Überschreiten eines Schwellwerts einen Alarm auslösen. Die \textbf{Zielvariable} (abhängige Variable) ist dabei binär (0 = kein Diebstahl, 1 = Diebstahl). Ein Diebstahl wird angenommen, wenn das Fahrverhalten signifikant vom üblichen Fahrerprofil abweicht. +Moderne Fahrzeuge erfassen in Echtzeit vielfältige Daten zum Fahrverhalten. Diese Studie untersucht, inwiefern sich anhand dieser Daten mit einem statistischen Modell erkennen +lässt, ob statt des gewöhnlichen Fahrers eine andere Person am Steuer sitzt -- was auf einen \emph{Diebstahl} hindeuten würde. Konkret soll ein Modell die +Wahrscheinlichkeit $P(\text{Diebstahl})$ schätzen und bei Überschreiten eines Schwellwerts einen Alarm auslösen. Die \textbf{Zielvariable} (abhängige Variable) ist +dabei binär (0 = kein Diebstahl, 1 = Diebstahl). Ein Diebstahl wird angenommen, wenn das Fahrverhalten signifikant vom üblichen Fahrerprofil abweicht. \noindent \newline -Für das Fachthema wurden insgesamt \textbf{8 Einflussgrößen} (potenziell erklärende Variablen) definiert, welche typische Aspekte des Fahrverhaltens und Umfelds repräsentieren. Davon sind 4 Variablen als tatsächlich erklärungsrelevant für die Zielvariable angenommen, während die übrigen 4 Variablen keinen Einfluss auf einen Fahrerwechsel (Diebstahl) haben. Zur Erfüllung der Aufgabenstellung sind unter den relevanten Prädiktoren zwei Variablen kategorial (ordinal) mit mindestens 3 Ausprägungen, die übrigen relevanten Variablen sind metrisch. Im Folgenden werden alle Variablen beschrieben: +Für das Fachthema wurden insgesamt \textbf{8 Einflussgrößen} (potenziell erklärende Variablen) definiert, welche typische Aspekte des Fahrverhaltens und Umfelds +repräsentieren. Davon sind 4 Variablen als tatsächlich erklärungsrelevant für die Zielvariable angenommen, während die übrigen 4 Variablen keinen Einfluss auf einen +Fahrerwechsel (Diebstahl) haben. Zur Erfüllung der Aufgabenstellung sind unter den relevanten Prädiktoren zwei Variablen kategorial (ordinal) mit mindestens 3 Ausprägungen, +die übrigen relevanten Variablen sind metrisch. Im Folgenden werden alle Variablen beschrieben: \begin{description} - \item[\textbf{Durchschnittsgeschwindigkeit} (metrisch, \textit{erklärend})] Mittlere Fahrgeschwindigkeit (z.\,B.~über einen Tag). Unterschiedliche Fahrer weisen charakteristische Tempoprofile auf. Ein deutlich höheres Durchschnittstempo kann somit auf einen fremden, ggf.\ aggressiveren Fahrer hinweisen. Typische Werte liegen um etwa 47~km/h mit großer Streuung (basierend auf natürlichen Fahrstudien der \cite{nhtsa2006}). - \item[\textbf{Schaltverhalten} (ordinal, \textit{erklärend})] Gewohnheiten beim Gangwechsel (bei Schaltgetriebe), insbesondere der Drehzahlbereich, bei dem hochgeschaltet wird. Es werden drei Ausprägungen unterschieden: \emph{früh} (sehr niedrige Drehzahlen, ökonomisch), \emph{normal} und \emph{spät} (hohe Drehzahlen, sportlich). Diese Variable ist relevant, da Fahrende ein individuelles Schaltmuster haben. Studien zeigen, dass das Schaltverhalten zur Fahreridentifikation genutzt werden kann (vgl. \cite{gearshift2023}). - \item[\textbf{Harte Bremsmanöver} (metrisch, \textit{erklärend})] Anzahl starker Bremsungen (mit Verzögerung $>0{,}3g$) pro Strecke (vgl. \cite{tesla_safety_score_2023}). Dieses Maß korreliert mit einer aggressiven Fahrweise -- viele Vollbremsungen deuten auf einen ungewohnten bzw.\ risikoreicheren Fahrer hin. g ist eine Einheit der Beschleunigung (1g $\approx 9{,}81$~m/s²). - \item[\textbf{Geschwindigkeitsüberschreitungen} (ordinal, \textit{erklärend})] Häufigkeit \newline bzw.\ Ausmaß von Tempoverstößen. Kategorisiert als \emph{selten}, \emph{manchmal} oder \emph{häufig} zu schnell. Ein fremder Fahrer könnte andere Risikoneigungen beim Schnellfahren zeigen. Laut einer AAA-Verkehrssicherheitsstudie geben rund 50\,\% der Fahrer an, in letzter Zeit auf Autobahnen mindestens 24~km/h über dem Limit gefahren zu sein (vgl.\ \cite{aaa2016}) -- was die allgemeine Relevanz dieser Variable für das Fahrverhalten unterstreicht. - \item[\textbf{Wetterbedingungen} (nominal)] Wetter während der Fahrt: \emph{trocken}, \emph{nass} oder \emph{winterlich} (Glätte/Schnee). Diese Kontextvariable dient zur Kontrolle äußerer Bedingungen. Erwartungsgemäß hat das Wetter keinen direkten Einfluss auf einen Fahrerwechsel. - \item[\textbf{Fahrstrecke} (metrisch)] Die zurückgelegte Distanz einer Fahrt (in km). Diese Variable (etwa lognormal verteilt um 16~km) repräsentiert typische Wege. Sie steht nicht in direktem Zusammenhang mit der Fahreridentität, sondern charakterisiert den Nutzungskontext (\cite{mobilitaet2017}). - \item[\textbf{Straßentyp} (nominal)] Vorherrschender Streckentyp: \emph{Autobahn}, \emph{Außerorts} oder \emph{Innerorts}. Diese Variable bietet Kontext (Stadtverkehr vs.\ Fernstraße), hat aber für sich genommen keinen Einfluss bezüglich eines Fahrerwechsels (\cite{openumwelt2024}) - \item[\textbf{Wochentag} (nominal)] Tag der Woche bzw.\ Kategorie \emph{Werktag} vs \emph{Wochenende} (vgl. \cite{mobilitaet2017}). Auch dies ist eine Kontextgröße ohne direkten Einfluss auf die Diebstahlwahrscheinlichkeit (daher ebenfalls nicht erklärend). + \item[\textbf{Durchschnittsgeschwindigkeit} (metrisch, \textit{erklärend})] Mittlere Fahrgeschwindigkeit (z.\,B.~über einen Tag). Unterschiedliche Fahrer weisen + charakteristische Tempoprofile auf. Ein deutlich höheres Durchschnittstempo kann somit auf einen fremden, ggf.\ aggressiveren Fahrer hinweisen. Typische Werte liegen um + etwa 47~km/h mit großer Streuung (\cite{nhtsa2006}). + \item[\textbf{Schaltverhalten} (ordinal, \textit{erklärend})] Gewohnheiten beim Gangwechsel (bei Schaltgetriebe), insbesondere der Drehzahlbereich, bei dem hochgeschaltet wird. + Es werden drei Ausprägungen unterschieden: \emph{früh} (sehr niedrige Drehzahlen, ökonomisch), \emph{normal} und \emph{spät} (hohe Drehzahlen, sportlich). Diese Variable ist + relevant, da Fahrende ein individuelles Schaltmuster haben. Studien zeigen, dass das Schaltverhalten zur Fahreridentifikation genutzt werden kann (\cite{gearshift2023}). + \item[\textbf{Harte Bremsmanöver} (metrisch, \textit{erklärend})] Anzahl starker Bremsungen (mit Verzögerung $>0{,}3g$) pro Strecke (\cite{tesla_safety_score_2023}). + Dieses Maß korreliert mit einer aggressiven Fahrweise -- viele Vollbremsungen deuten auf einen ungewohnten bzw.\ risikoreicheren Fahrer hin. g ist eine Einheit der + Beschleunigung (1g $\approx 9{,}81$~m/s²). + \item[\textbf{Geschwindigkeitsüberschreitungen} (ordinal, \textit{erklärend})] Häufigkeit \newline bzw.\ Ausmaß von Tempoverstößen. Kategorisiert als \emph{selten}, + \emph{manchmal} oder \emph{häufig} zu schnell. Ein fremder Fahrer könnte andere Risikoneigungen beim Schnellfahren zeigen. Laut einer AAA-Verkehrssicherheitsstudie geben rund + 50\,\% der Fahrer an, in letzter Zeit auf Autobahnen mindestens 24~km/h über dem Limit gefahren zu sein (\cite{aaa2016}), was die allgemeine Relevanz dieser Variable + für das Fahrverhalten unterstreicht. + \item[\textbf{Wetterbedingungen} (nominal)] Wetter während der Fahrt: \emph{trocken}, \emph{nass} oder \emph{winterlich} (Glätte/Schnee). Diese Kontextvariable dient zur Kontrolle + äußerer Bedingungen. Erwartungsgemäß hat das Wetter keinen direkten Einfluss auf einen Fahrerwechsel. + \item[\textbf{Fahrstrecke} (metrisch)] Die zurückgelegte Distanz einer Fahrt (in km). Diese Variable (etwa lognormal verteilt um 16~km) repräsentiert typische Wege. Sie steht + nicht in direktem Zusammenhang mit der Fahreridentität, sondern charakterisiert den Nutzungskontext (\cite{mobilitaet2017}). + \item[\textbf{Straßentyp} (nominal)] Vorherrschender Streckentyp: \emph{Autobahn}, \emph{Außerorts} oder \emph{Innerorts}. Diese Variable bietet Kontext (Stadtverkehr vs.\ + Fernstraße), hat aber für sich genommen keinen Einfluss bezüglich eines Fahrerwechsels (\cite{openumwelt2024}) + \item[\textbf{Wochentag} (nominal)] Tag der Woche bzw.\ Kategorie \emph{Werktag} vs \emph{Wochenende} (\cite{mobilitaet2017}). Auch dies ist eine Kontextgröße ohne direkten + Einfluss auf die Diebstahlwahrscheinlichkeit (daher ebenfalls nicht erklärend). \end{description} \noindent -Zusammenfassend stellen \emph{Durchschnittsgeschwindigkeit}, \emph{Schaltverhalten}, \emph{Harte Bremsmanöver} und \emph{Geschwindigkeitsüberschreitungen} die fachlich ausgewählten Schlüsselvariablen dar, die einen Fahrerwechsel anzeigen können. Die übrigen vier Größen (Wetter, Fahrstrecke, Straßentyp, Wochentag) dienen als Kontrolle und potentielle Störgrößen, besitzen aber per Annahme keinen Erklärungsgehalt für die Zielvariable. Dieses Setting erlaubt die Überprüfung, ob ein Statistical-Learning-Ansatz die relevanten Merkmale korrekt identifizieren kann. +Zusammenfassend stellen \emph{Durchschnittsgeschwindigkeit}, \emph{Schaltverhalten}, \emph{Harte Bremsmanöver} und \emph{Geschwindigkeitsüberschreitungen} die fachlich ausgewählten +Schlüsselvariablen dar, die einen Fahrerwechsel anzeigen können. Die übrigen vier Größen (Wetter, Fahrstrecke, Straßentyp, Wochentag) dienen als Kontrolle und potentielle Störgrößen, +besitzen aber per Annahme keinen Erklärungsgehalt für die Zielvariable. \section{Erzeugung der Grundgesamtheit} -Für die Simulation wurde eine \textbf{Grundgesamtheit} von $N = 1.000.000$ Fahrten generiert. Jede Fahrt hat einen binären Indikator (Zielvariable \emph{Diebstahl ja/nein}) und 8 zugehörige Merkmalswerte (die oben definierten Variablen). Die Werte wurden mittels geeigneter Zufallsverteilungen erzeugt, basierend auf realistischen Annahmen und empirischen Daten, jedoch so, dass die Abhängigkeitsstruktur kontrolliert vorgegeben ist. Die Zielvariable hängt funktional von genau 4 der 8 Variablen ab (den erklärenden), während die restlichen 4 keinen Einfluss auf den Diebstahl haben. Zudem wurden zwei inhaltlich sinnvolle Korrelationen zwischen ausgewählten Variablen eingeführt, ohne jedoch Multikollinearität zu verursachen (alle Varianzinflationsfaktoren $\text{VIF} < 5$). +Für die Simulation wurde eine \textbf{Grundgesamtheit} von $N = 1.000.000$ Fahrten generiert. Jede Fahrt hat einen binären Indikator (Zielvariable \emph{Diebstahl ja/nein}) und 8 +zugehörige Merkmalswerte (die oben definierten Variablen). Die Werte wurden mittels geeigneter Zufallsverteilungen erzeugt, basierend auf realistischen Annahmen und empirischen +Daten, jedoch so, dass die Abhängigkeitsstruktur kontrolliert vorgegeben ist. Die Zielvariable hängt funktional von genau 4 der 8 Variablen ab (den erklärenden), während die +restlichen 4 keinen Einfluss auf den Diebstahl haben. Zudem wurden zwei inhaltlich sinnvolle Korrelationen zwischen ausgewählten Variablen eingeführt, ohne jedoch Multikollinearität +zu verursachen (alle Varianzinflationsfaktoren $\text{VIF} < 5$). \subsubsection*{Verteilungen der Variablen} -Die Verteilungen der einzelnen Merkmale wurden in Anlehnung an empirische Daten gewählt. Insbesondere: \begin{itemize} - \item \textbf{Durchschnittsgeschwindigkeit:} Normalverteilung mit $\mu \approx 47$~km/h und $\sigma \approx 10$~km/h, begrenzt auf plausible Werte $[10,130]$~km/h (keine negativen oder extrem hohen Geschwindigkeiten). - \item \textbf{Schaltverhalten:} Kategorisch (ordinal) mit drei Stufen \emph{früh}, \emph{normal}, \emph{spät}. Es wurden 40\,\% für \emph{früh}, 40\,\% \emph{normal} und 20\,\% \emph{spät} simuliert, d.\,h. die meisten Fahrer schalten gewöhnlich früh oder durchschnittlich, während nur etwa jeder fünfte spät schaltet. - \item \textbf{Harte Bremsmanöver:} Poisson-verteilte Zufallsvariable mit $\lambda = 2$ pro definierter Strecke (z.\,B.~pro 100\,km). Diese Verteilung ergibt meist wenige bis keine harten Bremsungen pro Fahrt, aber mit einer gewissen Wahrscheinlichkeit auch Ausreißer mit mehreren Bremsmanövern (rechtsschiefe Verteilung). - \item \textbf{Geschwindigkeitsüberschreitungen:} Ordinal mit drei Stufen (\emph{selten}, \emph{manchmal}, \emph{häufig}). Diese Variable wurde \emph{nicht unabhängig} gezogen, sondern \textbf{abhängig von der Durchschnittsgeschwindigkeit} generiert: Fahrer mit höherer \texttt{avg\_speed} erhielten mit größerer Wahrscheinlichkeit die Kategorie \emph{häufig} zu schnell, während sehr langsame Fahrer überwiegend als \emph{selten} zu schnell eingeordnet wurden. Die Umsetzung erfolgte über eine gewichtete Zufallsauswahl (Softmax-Funktion auf Basis von \texttt{avg\_speed}). Dadurch besteht eine inhaltlich sinnvolle Korrelation zwischen \texttt{avg\_speed} und \texttt{speeding}, die jedoch moderat genug ist (VIF $\approx 2.57$ für \texttt{avg\_speed}, $< 2$ für \texttt{speeding}-Dummies), um Multikollinearität nicht zum Problem werden zu lassen. - \item \textbf{Wetterbedingungen:} Für jedes Fahrt wurde das Wetter zufällig gemäß empirischer Häufigkeiten zugeordnet: ca.~75\,\% \emph{trocken}, 20\,\% \emph{nass} (Regen) und 5\,\% \emph{winterlich} (Schnee/Eis). Dies entspricht etwa den in Mitteleuropa beobachteten Anteilen trockener bzw.\ widriger Fahrbedingungen. - \item \textbf{Fahrstrecke:} Lognormalverteilung mit $\mu_{\log} = \ln(16)$ und $\sigma_{\log} = 0{,}7$, um die Verteilung typischer Fahrtlängen abzubilden. Diese Parameter führen zu einer rechtsschiefen Verteilung: Viele kürzere Fahrten um einige wenige Kilometer, und seltener auch sehr lange Fahrten ($>50$~km). (Die Annahmen basieren auf Studien zur täglichen Weglänge \cite{mobilitaet2017}.) - \item \textbf{Straßentyp:} Diese Variable wurde \textbf{abhängig von der Fahrstrecke} generiert. Intuitiv werden sehr lange Fahrten eher auf Autobahnen stattfinden, während kurze Fahrten überproportional innerorts sind. Um dies zu modellieren, wurde ein stochastischer Zusammenhang erzeugt: Zunächst wurde \texttt{trip\_distance} auf $[0,1]$ skaliert; dann wurde mittels einer Softmax-Wahrscheinlichkeitsfunktion daraus der Straßentyp gezogen, sodass z.\,B.\ bei sehr großen Distanzen die Wahrscheinlichkeit für \emph{Autobahn} deutlich höher ist als für \emph{Innerorts}. Dadurch ergibt sich eine leichte Korrelation zwischen \texttt{trip\_distance} und \texttt{road\_type} (z.\,B.\ $VIF \approx 1.68$ für \texttt{trip\_distance}), ohne die beiden Größen stark redundant zu machen. - \item \textbf{Wochentag:} Festgelegte Verteilung von ca.~70\,\% \emph{Werktag} (Mo-Fr) und je 15\,\% \emph{Samstag} und \emph{Sonntag}. Damit wird berücksichtigt, dass die meisten Fahrten werktags stattfinden. Diese Variable wurde unabhängig von den übrigen generiert. + \item \textbf{Durchschnittsgeschwindigkeit:} Normalverteilung mit $\mu \approx 47$~km/h und $\sigma \approx 10$~km/h, begrenzt auf plausible Werte $[10,130]$~km/h (keine negativen + oder extrem hohen Geschwindigkeiten). + \item \textbf{Schaltverhalten:} Kategorisch (ordinal) mit drei Stufen \emph{früh}, \emph{normal}, \emph{spät}. Es wurden 40\,\% für \emph{früh}, 40\,\% \emph{normal} und + 20\,\% \emph{spät} simuliert, d.\,h. die meisten Fahrer schalten gewöhnlich früh oder durchschnittlich, während nur etwa jeder fünfte spät schaltet. + \item \textbf{Harte Bremsmanöver:} Poisson-verteilte Zufallsvariable mit $\lambda = 2$ pro definierter Strecke (z.\,B.~pro 100\,km). Diese Verteilung ergibt meist wenige + bis keine harten Bremsungen pro Fahrt, aber mit einer gewissen Wahrscheinlichkeit auch Ausreißer mit mehreren Bremsmanövern (rechtsschiefe Verteilung). + \item \textbf{Geschwindigkeitsüberschreitungen:} Ordinal mit drei Stufen (\emph{selten}, \emph{manchmal}, \emph{häufig}). Diese Variable wurde \emph{nicht unabhängig} gezogen, + sondern abhängig von der Durchschnittsgeschwindigkeit generiert: Fahrer mit höherer \texttt{avg\_speed} erhielten mit größerer Wahrscheinlichkeit die Kategorie \emph{häufig} zu + schnell, während sehr langsame Fahrer überwiegend als \emph{selten} zu schnell eingeordnet wurden. Die Umsetzung erfolgte über eine gewichtete Zufallsauswahl (Softmax-Funktion auf + Basis von \texttt{avg\_speed}). Dadurch besteht eine inhaltlich sinnvolle Korrelation zwischen \texttt{avg\_speed} und \texttt{speeding}, die jedoch moderat genug ist + (VIF $\approx 2.57$ für \texttt{avg\_speed}, $< 2$ für \texttt{speeding}-Dummies), um Multikollinearität nicht zum Problem werden zu lassen. + \item \textbf{Wetterbedingungen:} Für jedes Fahrt wurde das Wetter zufällig gemäß empirischer Häufigkeiten zugeordnet: ca.~75\,\% \emph{trocken}, 20\,\% \emph{nass} (Regen) + und 5\,\% \emph{winterlich} (Schnee/Eis). Diese Werte sind basierend auf Annahmen zu typischen Fahrbedingungen in Deutschland gewählt. + \item \textbf{Fahrstrecke:} Lognormalverteilung mit $\mu_{\log} = \ln(16)$ und $\sigma_{\log} = 0{,}7$, um die Verteilung typischer Fahrtlängen abzubilden. + Diese Parameter führen zu einer rechtsschiefen Verteilung: Viele kürzere Fahrten um einige wenige Kilometer, und seltener auch sehr lange Fahrten ($>50$~km). + (Die Annahmen basieren auf Studien zur täglichen Weglänge \cite{mobilitaet2017}.) + \item \textbf{Straßentyp:} Diese Variable wurde abhängig von der Fahrstrecke generiert. Intuitiv werden sehr lange Fahrten eher auf Autobahnen stattfinden, + während kurze Fahrten überproportional innerorts sind. Um dies zu modellieren, wurde ein stochastischer Zusammenhang erzeugt: Zunächst wurde \texttt{trip\_distance} + auf $[0,1]$ skaliert; dann wurde mittels einer Softmax-Wahrscheinlichkeitsfunktion daraus der Straßentyp gezogen, sodass z.\,B.\ bei sehr großen Distanzen die Wahrscheinlichkeit + für \emph{Autobahn} deutlich höher ist als für \emph{Innerorts}. Dadurch ergibt sich eine leichte Korrelation zwischen \texttt{trip\_distance} und \texttt{road\_type} + ($VIF \approx 1.68$ für \texttt{trip\_distance}). + \item \textbf{Wochentag:} Festgelegte Verteilung von ca.~70\,\% \emph{Werktag} (Mo-Fr) und je 15\,\% \emph{Samstag} und \emph{Sonntag}. Damit wird berücksichtigt, + dass die meisten Fahrten werktags stattfinden (\cite{mobilitaet2017}). Diese Variable wurde unabhängig von den übrigen generiert. \end{itemize} -Durch diese Vorgehensweise bilden die generierten Daten realistische Verteilungen und Beziehungen der Variablen ab, wobei genau zwei inhaltlich plausible Korrelationen eingebaut wurden (\texttt{speeding}-\texttt{avg\_speed} und \newline \texttt{road\_type}-\texttt{trip\_distance}). Beide Abhängigkeiten sind so kalibriert, dass im optimalen Regressionsmodell keine Multikollinearität auftritt ($\text{VIF} < 5$ in allen Fällen). +Durch diese Vorgehensweise bilden die generierten Daten nahezu realistische Verteilungen und Beziehungen der Variablen ab, wobei genau zwei inhaltlich plausible Korrelationen eingebaut +wurden. Beide Abhängigkeiten sind so kalibriert, dass im optimalen Regressionsmodell keine Multikollinearität auftritt ($\text{VIF} < 5$ in allen Fällen). \subsubsection*{Modellierung der Zielvariable und der $\pi$-Werte} -Die \textbf{Zielvariable} \texttt{Diebstahl} wurde als Zufallsvariable auf Basis eines logistischen Regressionsmodells generiert. Dazu wurde zunächst für jede Fahrt $i$ die Diebstahl-Wahrscheinlichkeit $\pi_i = P(Y_i=1)$ berechnet als +Die Zielvariable \emph{Diebstahl} wurde als Zufallsvariable auf Basis eines logistischen Regressionsmodells generiert. Dazu wurde zunächst für jede Fahrt $i$ die +Diebstahl-Wahrscheinlichkeit $p_i = P(Y_i=1)$ berechnet als \[ -\pi_i = \frac{1}{1 + \exp(- ( \beta_0 + \beta_1 x_{1i} + \cdots + \beta_8 x_{8i} + \varepsilon_i ) )}~, +p_i = \frac{1}{1 + \exp(- ( \beta_0 + \beta_1 x_{1i} + \cdots + \beta_8 x_{8i} + \varepsilon_i ) )}~, \] -wobei $x_{1},\dots,x_{8}$ die Merkmalswerte (erklärende Variablen) der Fahrt sind, $\beta_0,\dots,\beta_8$ die zugrunde liegenden Regressionskoeffizienten und $\varepsilon_i$ ein zufälliger Fehlerterm. Für den vorliegenden Klassifikationsfall entspricht $\varepsilon_i$ einem impliziten Rauschen, das die Nicht-Deterministik der Fahrerwechsel abbildet (nicht jeder ungewöhnliche Wert führt zwangsweise zum Diebstahl). In der Simulation wurde $\varepsilon_i \sim \mathcal{N}(0, 0.2^2)$ als additive Störung zur linearen Prädiktor-Summe gezogen. Anschließend wurde $Y_i$ durch einen Bernoulli-Zufall mit Parameter $\pi_i$ realisiert (d.\,h. \texttt{Diebstahl}~=~1 mit Wahrscheinlichkeit $\pi_i$). +wobei $x_{1},\dots,x_{8}$ die Merkmalswerte (erklärende Variablen) der Fahrt sind, $\beta_0,\dots,\beta_8$ die zugrunde liegenden Regressionskoeffizienten und $\varepsilon_i$ ein +zufälliger Fehlerterm. Für den vorliegenden Klassifikationsfall entspricht $\varepsilon_i$ einem impliziten Rauschen. In der Simulation wurde +$\varepsilon_i \sim \mathcal{N}(0, 0.2^2)$ addiert. +Anschließend wurde $Y_i$ durch einen Bernoulli-Zufall mit Parameter $p_i$ realisiert (d.\,h. \texttt{Diebstahl}~=~1 mit Wahrscheinlichkeit $p_i$). \noindent \newline -Die \textbf{gewählten Koeffizienten ($\beta$-Werte)} für das Generierungsmodell sind in Tabelle~\ref{tab:true_betas} aufgeführt. Diese wurden so festgelegt, dass sie plausible Einflussstärken der erklärenden Variablen widerspiegeln, ohne die Zielvariable extrem zu dominieren. Insbesondere wurde ein relativ niedriger Basiswert (\emph{Intercept} $\beta_0=-2$) gewählt, sodass bei unauffälligen Merkmalen die Diebstahl-Wahrscheinlichkeit sehr gering ist. Die erklärenden Variablen erhöhen bzw.\ verringern diese Grundwahrscheinlichkeit wie folgt: +Die \textbf{gewählten Koeffizienten ($\beta$-Werte)} für das Generierungsmodell sind in Tabelle~\ref{tab:true_betas} aufgeführt. Diese wurden so festgelegt, dass sie plausible +Einflussstärken der erklärenden Variablen widerspiegeln, ohne die Zielvariable extrem zu dominieren. Insbesondere wurde ein relativ niedriger Basiswert +(\emph{Intercept} $\beta_0=-2$) gewählt, sodass bei unauffälligen Merkmalen die Diebstahl-Wahrscheinlichkeit sehr gering ist. Die erklärenden Variablen erhöhen bzw.\ +verringern diese Grundwahrscheinlichkeit wie folgt: \begin{itemize} - \item \textbf{Durchschnittsgeschwindigkeit:} $\beta_{avg\_speed} = 0{,}015$. Ein leicht positiver Koeffizient - eine ungewöhnlich hohe Durchschnittsgeschwindigkeit erhöht also geringfügig die Diebstahl-Wkt., da sie auf einen anderen (rasanteren) Fahrstil hindeuten kann. - \item \textbf{Harte Bremsmanöver:} $\beta_{hard\_brakes} = 0{,}1$. Häufige Vollbremsungen haben einen deutlichen positiven Effekt auf $P(\text{Diebstahl})$, da sie ein Indiz für einen risikoreicheren Fahrstil sind. - \item \textbf{Schaltverhalten:} Das Schaltverhalten ist ein starker Prädiktor für den Fahrer. Für die kategorialen Ausprägungen wurden Dummy-Variablen erstellt: $\beta_{\text{früh}} = -0{,}3$, $\beta_{\text{normal}} = -0{,}3$ und $\beta_{\text{spät}} = +0{,}5$. Besonders \emph{spät} schalten wird mit einem hohen positiven Beta gewichtet, da es einen aggressiveren Fahrstil beschreibt. \emph{Früh} und \emph{normal} schalten erhalten einen negativen Einfluss, da sie auf einen defensiveren oder gewohnten Fahrstil hindeuten. - \item \textbf{Geschwindigkeitsüberschreitungen:} Die Häufigkeit von Geschwindigkeitsüberschreitungen ist ein wichtiger Indikator für den Fahrstil. Die Koeffizienten wurden festgelegt als: $\beta_{\text{selten}} = -0{,}3$, $\beta_{\text{manchmal}} = -0{,}3$ und $\beta_{\text{häufig}} = +0{,}5$. Besonders \emph{häufig} zu schnell fahren ist ein starkes Signal für einen Fahrerwechsel, da dies ein sehr auffälliges Verhalten darstellt. \emph{Selten} und \emph{manchmal} werden negativ gewichtet, da sie auf einen defensiveren Fahrstil hindeuten. - \item \textbf{Kontextvariablen (Wetter, Strecke, Straßentyp, Wochentag):} Diese wurden \emph{alle mit $\beta=0$} angesetzt, d.\,h.\ sie haben per Konstruktion keinen Einfluss auf die Diebstahl-Wahrscheinlichkeit. + \item \textbf{Durchschnittsgeschwindigkeit:} $\beta_{avg\_speed} = 0{,}015$. Ein leicht positiver Koeffizient - eine ungewöhnlich hohe Durchschnittsgeschwindigkeit erhöht also + geringfügig die Diebstahl-Wkt., da sie auf einen anderen (rasanteren) Fahrstil hindeuten kann. + \item \textbf{Harte Bremsmanöver:} $\beta_{hard\_brakes} = 0{,}1$. Häufige Vollbremsungen haben einen deutlichen positiven Effekt auf $P(\text{Diebstahl})$, da sie ein Indiz für + einen risikoreicheren Fahrstil sind. + \item \textbf{Schaltverhalten:} Das Schaltverhalten ist ein starker Prädiktor für den Fahrer. Für die kategorialen Ausprägungen wurden Dummy-Variablen erstellt: + $\beta_{\text{früh}} = -0{,}3$, $\beta_{\text{normal}} = -0{,}3$ und $\beta_{\text{spät}} = +0{,}5$. Besonders \emph{spät} schalten wird mit einem hohen positiven Beta gewichtet, + da es einen aggressiveren Fahrstil beschreibt. \emph{Früh} und \emph{normal} schalten erhalten einen negativen Einfluss, da sie auf einen defensiveren oder gewohnten Fahrstil + hindeuten. + \item \textbf{Geschwindigkeitsüberschreitungen:} Die Häufigkeit von Geschwindigkeitsüberschreitungen ist ein wichtiger Indikator für den Fahrstil. Die Koeffizienten wurden + festgelegt als: $\beta_{\text{selten}} = -0{,}3$, $\beta_{\text{manchmal}} = -0{,}3$ und $\beta_{\text{häufig}} = +0{,}5$. Besonders \emph{häufig} zu schnell fahren ist ein + starkes Signal für einen Fahrerwechsel, da dies ein sehr auffälliges Verhalten darstellt. \emph{Selten} und \emph{manchmal} werden negativ gewichtet, da sie auf einen defensiveren + Fahrstil hindeuten. + \item \textbf{Kontextvariablen (Wetter, Strecke, Straßentyp, Wochentag):} Diese wurden \emph{alle mit $\beta=0$} angesetzt, d.\,h.\ sie haben per Konstruktion keinen Einfluss auf + die Diebstahl-Wahrscheinlichkeit. \end{itemize} -\begin{table}[h] +\begin{table}[H] \centering -\caption{Übersicht der verwendeten Regressionskoeffizienten im Generierungsmodell der Zielvariable. Kategorische Effekte beziehen sich auf die jeweilige Referenzkategorie (in Klammern).} +\caption{Wahre $\beta$-Werte des Regressionsmodells} \label{tab:true_betas} -\begin{tabular}{lcl} +\begin{tabular}{lr} \toprule -\textbf{Variable} & \textbf{Kategorie (Ref.)} & \textbf{Beta-Wert}\\ +\textbf{Variable} & \textbf{Beta-Wert}\\ \midrule -Intercept & -- & $-2{,}0$ \\ -Durchschnittsgeschwindigkeit & -- & $+0{,}015$ \\ -Harte Bremsmanöver & -- & $+0{,}10$ \\ -Schaltverhalten & früh & $-0{,}30$ \\ -~ & normal & $-0{,}30$ \\ -~ & spät (Ref.) & $+0{,}00$ \\ -Geschwindigkeitsüberschreitung & selten & $-0{,}30$ \\ -~ & manchmal & $-0{,}30$ \\ -~ & häufig (Ref.) & $+0{,}00$ \\ -Wetter & trocken, nass, winterlich (alle) & $0{,}00$ \\ -Straßentyp & Autobahn, Außerorts, Innerorts (alle) & $0{,}00$ \\ -Wochentag & Mo–Fr, Sa, So (alle) & $0{,}00$ \\ +Intercept & $-2{,}0$ \\ +Durchschnittsgeschwindigkeit & $+0{,}015$ \\ +Harte Bremsmanöver & $+0{,}10$ \\ +Schaltverhalten (früh) & $-0{,}30$ \\ +Schaltverhalten (normal) & $-0{,}30$ \\ +Schaltverhalten (spät) & $+0{,}00$ \\ +Geschwindigkeitsüberschreitung (selten) & $-0{,}30$ \\ +Geschwindigkeitsüberschreitung (manchmal) & $-0{,}30$ \\ +Geschwindigkeitsüberschreitung (häufig) & $+0{,}00$ \\ +Wetter (alle Kategorien) & $0{,}00$ \\ +Straßentyp (alle Kategorien) & $0{,}00$ \\ +Wochentag (alle Kategorien) & $0{,}00$ \\ \bottomrule \end{tabular} \end{table} \noindent -Durch die gewählten $\beta$-Gewichte resultiert eine \textbf{Verteilung der Zielvariable}, bei der Diebstahl relativ selten vorkommt, aber nicht vernachlässigbar: Im generierten Datensatz sind etwa 23\,\% der Fahrten als Diebstahl deklariert worden (d.\,h. $Y=1$ in 229.150 von 1.000.000 Fällen). Diebstähle sollen deutlich seltener als normale Fahrten sein, jedoch häufig genug, um ein Modell daran zu trainieren. Die zugrunde liegenden individuellen Diebstahl-Wahrscheinlichkeiten $\pi_i$ waren überwiegend niedrig: Für eine typische Fahrt (alle Merkmale unauffällig) lag $\pi\approx 0,1$, während extreme Kombinationen (z.\,B.\ sehr hohe Geschwindigkeit, viele harte Bremsungen, spät geschaltet und häufig zu schnell zugleich) $\pi$-Werte von bis zu $\sim 0,8$ erreichten. Im Mittel ergaben sich $\bar{\pi} \approx 0,23$ und eine rechtsschiefe Verteilung der $\pi_i$ über alle Fahrten. Insgesamt war also gewährleistet, dass das generierte Modell weder trivial (zu seltene 1-Fälle) noch stark im Ungleichgewicht war. +Durch die gewählten $\beta$-Gewichte resultiert eine Verteilung der Zielvariable, bei der Diebstahl relativ selten vorkommt, aber nicht vernachlässigbar: Im generierten +Datensatz sind etwa 23\,\% der Fahrten als Diebstahl deklariert worden (d.\,h. $Y=1$ in 229.973 von 1.000.000 Fällen), was in Abb.~\ref{fig:target_dist} dargestellt ist. +Diebstähle sollen deutlich seltener als normale Fahrten sein, +jedoch häufig genug, um ein Modell daran zu trainieren. +\newline +\newline +Die zugrunde liegenden individuellen Diebstahl-Wahrscheinlichkeiten $p_i$ waren überwiegend niedrig: Im Mittel ergaben sich $\bar{p} \approx 0,23$ und eine rechtsschiefe Verteilung +der $p_i$ über alle Fahrten (siehe Abb.~\ref{fig:fig_pi_dist}). + +\begin{figure}[H] +\centering +\includegraphics[width=0.8\textwidth]{fig_pi_dist.png} +\caption{Verteilung der Diebstahl-Wahrscheinlichkeiten $p_i$} +\label{fig:fig_pi_dist} +\end{figure} + +\begin{figure}[H] +\centering +\includegraphics[width=0.8\textwidth]{fig_target_variable_dist.png} +\caption{Verteilung der Zielvariable in der Grundgesamtheit} +\label{fig:target_dist} +\end{figure} \section{Simulation der Perspektive des Data Scientist} -In einem realen Anwendungsszenario würde ein Data Scientist lediglich einen \emph{Stichprobendatensatz} der Grundgesamtheit vorfinden - ohne Kenntnis des zugrundeliegenden wahren Modells. Um diese Perspektive zu simulieren, wurde aus den 1.000.000 generierten Fällen eine \textbf{Zufallsstichprobe} von $n = 20.000$ Fahrten gezogen. Der Data Scientist würde zunächst eine explorative Datenanalyse und Vorverarbeitung durchführen, dann ein geeignetes Regressionsmodell (logistische Regression im Klassifikationsfall) schätzen und mittels Variablenselektion optimieren. Abschließend würde er die Modellgüte bewerten. All diese Schritte wurden in der Simulation nachvollzogen. +Es wurde aus den 1.000.000 generierten Fällen eine Zufallsstichprobe von $n = 20.000$ Fahrten gezogen. Der Data Scientist würde +zunächst eine explorative Datenanalyse und Vorverarbeitung durchführen, dann ein geeignetes Regressionsmodell (logistische Regression im Klassifikationsfall) schätzen und mittels +Variablenselektion optimieren. Abschließend würde er die Modellgüte bewerten. \subsection*{Datenexploration und Aufbereitung} -Zunächst wurde die Stichprobe auf Vollständigkeit und Ausreißer geprüft. Es traten keine fehlenden Werte auf (da simuliert). Ein \emph{Abgleich der Verteilungen} bestätigte, dass die Stichprobe die Grundgesamtheits-Charakteristika widerspiegelt. Beispielsweise sind die Verteilungen der metrischen Variablen in Abb.~\ref{fig:dist_vars} dargestellt (Histogramme und Boxplots): Man erkennt die angenommene Normalverteilung der \texttt{avg\_speed} (mit Mittelwert ca.~47~km/h), die rechtschiefe Poisson-Verteilung von \texttt{hard\_brakes} (häufig 0 oder 1 harte Bremsung, selten mehr) und die ausgeprägte Schiefe der lognormal verteilten \texttt{trip\_distance} (viele kurze Fahrten, wenige sehr lange). Die Boxplots zeigen, dass es bei \texttt{trip\_distance} einige Ausreißer (sehr lange Fahrten) gibt, während \texttt{avg\_speed} symmetrisch verteilt ist. Diese Beobachtungen stimmen mit der Datengenerierung überein und geben dem Data Scientist keinen Hinweis auf Anomalien im Datensatz. - -\begin{figure}[h] +Zunächst wurde die Stichprobe auf Vollständigkeit und Ausreißer geprüft. Es traten keine fehlenden Werte auf (da simuliert). Ein \emph{Abgleich der Verteilungen} bestätigte, +dass die Stichprobe die Grundgesamtheits-Charakteristika widerspiegelt. Beispielsweise sind die Verteilungen der metrischen Variablen in Abb.~\ref{fig:dist_vars} dargestellt +(Histogramme und Boxplots): Man erkennt die angenommene Normalverteilung der \texttt{avg\_speed} (mit Mittelwert ca.~47~km/h), die rechtschiefe Poisson-Verteilung von +\texttt{hard\_brakes} (häufig 0 oder 1 harte Bremsung, selten mehr) und die ausgeprägte Schiefe der lognormal verteilten \texttt{trip\_distance} (viele kurze Fahrten, wenige sehr +lange). Die Boxplots zeigen, dass es bei \texttt{trip\_distance} einige Ausreißer (sehr lange Fahrten) gibt, während \texttt{avg\_speed} symmetrisch verteilt ist. +\begin{figure}[H] \centering \includegraphics[width=\textwidth]{fig_variable_distributions.png} -\caption{Verteilungen der metrischen Variablen in der Stichprobe ($n=20.000$): Durchschnittsgeschwindigkeit, harte Bremsmanöver und Fahrstrecke (oben: Histogramme, unten: Boxplots). Die erwartete Normalverteilung von \texttt{avg\_speed}, Poisson-Verteilung von \texttt{hard\_brakes} und Rechtsschiefe von \texttt{trip\_distance} sind deutlich zu erkennen.} +\caption{Verteilungen der metrischen Variablen} \label{fig:dist_vars} \end{figure} \noindent \newline -Für die kategorialen Merkmale (Schaltverhalten, Geschwindigkeitsüberschreitungen, Wetter, Straßentyp, Wochentag) wurden Häufigkeitstabellen und Balkendiagramme betrachtet (nicht abgebildet). Auch diese entsprachen den Simulationseinstellungen (z.\,B.\ 20\,\% der Fahrer in der Stichprobe hatten \emph{spätes} Schaltverhalten; ca.~5\,\% der Fahrten fanden bei \emph{winterlichem} Wetter statt etc.). Zur Vorbereitung der Regression wurde ein \textbf{One-Hot-Encoding} der kategorialen Variablen durchgeführt (mit geeigneten Referenzkategorien, z.\,B.\ \emph{spät} für Schaltverhalten, \emph{häufig} für Speeding, etc.) und eine Konstante für den Interzept ergänzt. Anschließend standen 1 Zielvariable und 19 Regressoren (inkl.~Dummies und Interzept) für die Modellierung bereit. +Für die kategorialen Merkmale (Schaltverhalten, Geschwindigkeitsüberschreitungen, Wetter, Straßentyp, Wochentag) wurden Balkendiagramme betrachtet +(siehe Abb.~\ref{fig:fig_barplots_cats}). +Auch diese entsprachen den Simulationseinstellungen. Zur Vorbereitung der Regression wurde ein One-Hot-Encoding der kategorialen Variablen durchgeführt und eine Konstante für den +Interzept ergänzt. + +\begin{figure}[H] +\centering +\includegraphics[width=\textwidth]{fig_barplots_cats.png} +\caption{Balkendiagramme der kategorialen Variablen} +\label{fig:fig_barplots_cats} +\end{figure} + +\noindent +\newline +Daraufhin wurde die lineare Korrelation zwischen den Variablen untersucht. Die Korrelationen der Variablen sind in Abb.~\ref{fig:fig_corr_matrix} dargestellt. +Die Korrelationen sind erwartungsgemäß gering, da die Variablen weitgehend unabhängig sind. Lediglich die Dummykodierten Variablen weisen untereinander moderate Korrelationen auf, +weil sie sich gegenseitig ausschließen (z.\,B. eine 0 für \emph{früh} impliziert eine 1 für \emph{normal} oder \emph{spät}). + +\begin{figure}[H] +\centering +\includegraphics[width=0.8\textwidth]{fig_corr_matrix.png} +\caption{Korrelationsmatrix der Variablen} +\label{fig:fig_corr_matrix} +\end{figure} + \subsection*{Logistische Regression und Variablenselektion} -Ohne Kenntnis des wahren generativen Modells würde der Data Scientist zunächst alle verfügbaren Variablen als Prädiktoren in ein Modell einbeziehen. Daher wurde auf den Trainingsdaten (z.\,B.\ 70\,\% der Stichprobe) eine \textbf{logistische Regressionsanalyse} mit allen 8 ursprünglichen Merkmalen (entsprechend 1 Interzept + 3 Dummies für Schaltverhalten + 2 Dummies für Speeding + 2 Dummies für Wetter + 2 Dummies für Straßentyp + 2 Dummies für Wochentag + 3 metrische Variablen = 1 + 3+2+2+2+2+3 = 15 Parametern) durchgeführt. Dieses initiale Modell zeigte erwartungsgemäß, dass einige Prädiktoren keinen signifikanten Einfluss besitzen. Ein schrittweises Selektionsverfahren (\textbf{Backward Elimination}) wurde angewandt, um ein optimales Modell zu finden: beginnend mit dem vollen Modell wurden die am wenigsten signifikanten Variablen sukzessive entfernt, bis nur noch prädiktive (p-Wert $<0,05$) Variablen verblieben. +Ohne Kenntnis des wahren generativen Modells würde der Data Scientist zunächst alle verfügbaren Variablen als Prädiktoren in ein Modell einbeziehen. Daher wurde auf den +Trainingsdaten eine logistische Regressionsanalyse mit allen 8 ursprünglichen Merkmalen +durchgeführt. Dieses initiale Modell zeigte erwartungsgemäß, dass einige Prädiktoren keinen signifikanten Einfluss besitzen. Ein schrittweises Selektionsverfahren +(Backward Selection) wurde angewandt, um ein optimales Modell zu finden: beginnend mit dem vollen Modell wurden die am wenigsten signifikanten Variablen sukzessive +entfernt, bis nur noch prädiktive (p-Wert $<0,05$) Variablen verblieben. \noindent \newline -Das Ergebnis der Variablenselektion war, dass genau die 4 fachlich erwarteten Einflüsse im Modell verblieben, während die irrelevanten Merkmale entfernt wurden. Konkret blieben \texttt{avg\_speed}, \texttt{hard\_brakes}, \texttt{shift\_behavior} (mit zwei Dummy-Variablen) und \texttt{speeding} (ebenfalls zwei Dummies) im finalen Modell. Ausgeschlossen wurden dagegen \texttt{weather}, \texttt{trip\_distance}, \texttt{road\_type} und \texttt{weekday}, da deren Effekt auf die Zielvariable statistisch insignifikant war (p-Werte weit über 0,1). Dieses Resultat deckt sich mit der Konstruktion der Daten: Die irrelevanten Kontextvariablen bieten keine Erklärungskraft und wurden richtigerweise vom modellselektiven Ansatz eliminiert. +Das Ergebnis der Variablenselektion war, dass genau die 4 fachlich erwarteten Einflüsse im Modell verblieben, während die irrelevanten Merkmale entfernt wurden. Konkret +blieben \texttt{avg\_speed}, \texttt{hard\_brakes}, \texttt{shift\_behavior} (mit zwei Dummy-Variablen) und \texttt{speeding} (ebenfalls zwei Dummies) im finalen Modell. +Ausgeschlossen wurden dagegen \texttt{weather}, \texttt{trip\_distance}, \texttt{road\_type} und \texttt{weekday}, da deren Effekt auf die Zielvariable statistisch insignifikant +war (p-Werte weit über 0,1). Dieses Resultat deckt sich mit der Konstruktion der Daten: Die irrelevanten Kontextvariablen bieten keine Erklärungskraft und wurden richtigerweise +vom modellselektiven Ansatz eliminiert. \noindent \newline -Das \textbf{finale Regressionsmodell} wurde anschließend neu auf der gesamten Stichprobe ($n=20.000$) geschätzt. Die geschätzten Regressionskoeffizienten (im Folgenden $\hat{\beta}$) stimmten weitgehend mit den wahren Werten aus dem Generierungsmodell überein. Insbesondere waren alle verbleibenden Prädiktoren hochsignifikant ($p<0,001$). Ein Vergleich der Koeffizienten zeigt nur geringe Abweichungen aufgrund von Stichprobenfluktuation: +Das \textbf{finale Regressionsmodell} wurde anschließend neu auf der gesamten Stichprobe ($n=20.000$) geschätzt. Die geschätzten Regressionskoeffizienten +(im Folgenden $\hat{\beta}$) stimmten weitgehend mit den wahren Werten aus dem Generierungsmodell überein. Insbesondere waren alle verbleibenden Prädiktoren hochsignifikant +($p<0,001$). Ein Vergleich der Koeffizienten zeigt nur geringe Abweichungen aufgrund von Stichprobenfluktuation: \begin{itemize} - \item Für \texttt{avg\_speed} ergab sich $\hat{\beta}_{avg\_speed} \approx 0{,}0174$ (gegenüber wahr $0{,}015$). Dieser leichte Überschätzung ist mit dem Stichprobenzufall erklärbar, liegt aber in derselben Größenordnung. - \item Für \texttt{hard\_brakes} wurde $\hat{\beta}_{hard\_brakes} \approx 0{,}0817$ geschätzt (wahr $0{,}10$). Auch hier ist der Unterschied gering; das Vorzeichen und die Effektstärke (positiv, deutlicher Einfluss) wurden korrekt erkannt. - \item Die Dummyeffekte für das Schaltverhalten wurden zu $\hat{\beta}_{frueh} \approx -0{,}815$ und $\hat{\beta}_{normal} \approx -0{,}808$ geschätzt (Referenz \emph{spät} mit $\hat{\beta}_{spaet} = 0$). Die wahren Unterschiede (Früh/Normal vs.~Spät) betrugen $-0{,}8$. Somit liegen die Schätzungen praktisch genau auf den erwarteten Werten. - \item Für die Speeding-Kategorien ergaben sich $\hat{\beta}_{selten} \approx -0{,}834$ und \newline $\hat{\beta}_{manchmal} \approx -0{,}834$ (Referenz \emph{häufig}). Die wahren Unterschiede zu \emph{häufig} waren $-0{,}8$. Auch hier stimmen Richtung und Größe nahezu überein. - \item Den Interzept schätzte das Modell mit $\hat{\beta}_{0} \approx -1{,}05$ (wahr $-2$). Diese Abweichung erklärt sich durch die Dummy-Kodierung der Kategorien: Im generativen Modell hatten die Referenzkategorien \emph{spät} und \emph{häufig} jeweils einen positiven Beitrag von $+0{,}5$, welcher im geschätzten Modell im Interzept aufgefangen wird. Berücksichtigt man diese Verschiebung, liegt der Interzept im Rahmen. + \item Für \texttt{avg\_speed} ergab sich $\hat{\beta}_{avg\_speed} \approx 0{,}0174$ (gegenüber wahr $0{,}015$). Dieser leichte Überschätzung ist mit dem Stichprobenzufall + erklärbar, liegt aber in derselben Größenordnung. + \item Für \texttt{hard\_brakes} wurde $\hat{\beta}_{hard\_brakes} \approx 0{,}0817$ geschätzt (wahr $0{,}10$). Auch hier ist der Unterschied gering; das Vorzeichen und die + Effektstärke (positiv, deutlicher Einfluss) wurden korrekt erkannt. + \item Die Dummyeffekte für das Schaltverhalten wurden zu $\hat{\beta}_{frueh} \approx -0{,}815$ und $\hat{\beta}_{normal} \approx -0{,}808$ geschätzt (Referenz \emph{spät} + mit $\hat{\beta}_{spaet} = 0$). Die wahren Unterschiede (Früh/Normal vs.~Spät) betrugen $-0{,}8$. Somit liegen die Schätzungen praktisch genau auf den erwarteten Werten. + \item Für die Speeding-Kategorien ergaben sich $\hat{\beta}_{selten} \approx -0{,}834$ und \newline $\hat{\beta}_{manchmal} \approx -0{,}834$ (Referenz \emph{häufig}). + Die wahren Unterschiede zu \emph{häufig} waren $-0{,}8$. Auch hier stimmen Richtung und Größe nahezu überein. + \item Den Interzept schätzte das Modell mit $\hat{\beta}_{0} \approx -1{,}05$ (wahr $-2$). Diese Abweichung erklärt sich durch die Dummy-Kodierung der Kategorien: + Im generativen Modell hatten die Referenzkategorien \emph{spät} und \emph{häufig} jeweils einen positiven Beitrag von $+0{,}5$, + welcher im geschätzten Modell im Interzept aufgefangen wird. Berücksichtigt man diese Verschiebung, liegt der Interzept im Rahmen. \end{itemize} -Somit hat der Data Scientist durch das systematische Vorgehen tatsächlich \textbf{das zugrunde liegende Modell (bis auf Zufallsschwankungen) wiederentdeckt}. Insbesondere wurden keine falschen Variablen im Endmodell behalten und keine echten Einflüsse übersehen. Die logistische Regressionsgleichung aus der Stichprobe stimmt inhaltlich mit der zur Datengenerierung überein. +Somit hat der Data Scientist durch das systematische Vorgehen tatsächlich \textbf{das zugrunde liegende Modell (bis auf Zufallsschwankungen) wiederentdeckt}. +Insbesondere wurden keine falschen Variablen im Endmodell behalten und keine echten Einflüsse übersehen. Die logistische Regressionsgleichung aus der Stichprobe stimmt inhaltlich +mit der zur Datengenerierung überein. \noindent \newline -Abschließend wurde die Güte des Modells anhand der Testdaten (30\,\% der Stichprobe) überprüft. Die Vorhersagen der Diebstahlwahrscheinlichkeit zeigten eine gute Trennschärfe zwischen Diebstahl- und Normalfahrten (AUC $>0,8$; ca.~77\,\% richtige Klassifikationen bei geeignetem Schwellenwert). Dies verdeutlicht, dass die identifizierten Merkmale tatsächlich die Variation in der Zielvariable erklären können. +Abschließend wurde die Güte des Modells anhand der Testdaten (30\,\% der Stichprobe) überprüft. Die Vorhersagen der Diebstahlwahrscheinlichkeit zeigten eine gute Trennschärfe +zwischen Diebstahl- und Normalfahrten (AUC $>0,8$; ca.~77\,\% richtige Klassifikationen bei geeignetem Schwellenwert). Dies verdeutlicht, dass die identifizierten Merkmale +tatsächlich die Variation in der Zielvariable erklären können. \section{Güte der Modellparameter} -In einem letzten Schritt wurde untersucht, wie verlässlich die Modellschätzung bei unterschiedlicher Datenmenge ist. Insbesondere stellt sich die Frage, welchen Einfluss der Stichprobenumfang $n$ auf die Präzision der geschätzten Regressionskoeffizienten hat. Hierzu wurde ein Monte-Carlo-Simulationsansatz gewählt: Aus der Grundgesamtheit wurden für verschiedene Umfangswerte $n$ jeweils $k = 1000$ Zufallsstichproben gezogen, darauf jeweils das optimale Modell aus Abschnitt~3 (mit den 4 relevanten Variablen) erneut trainiert, und die Verteilungen der resultierenden $\hat{\beta}$-Koeffizienten analysiert. +In einem letzten Schritt wurde untersucht, wie verlässlich die Modellschätzung bei unterschiedlicher Datenmenge ist. Insbesondere stellt sich die Frage, welchen Einfluss der +Stichprobenumfang $n$ auf die Präzision der geschätzten Regressionskoeffizienten hat. Hierzu wurde ein Monte-Carlo-Simulationsansatz gewählt: Aus der Grundgesamtheit wurden für +verschiedene Umfangswerte $n$ jeweils $k = 1000$ Zufallsstichproben gezogen, darauf jeweils das optimale Modell aus Abschnitt~3 (mit den 4 relevanten Variablen) erneut trainiert, +und die Verteilungen der resultierenden $\hat{\beta}$-Koeffizienten analysiert. \noindent \newline -Untersucht wurde exemplarisch der Koeffizient $\beta_{avg\_speed}$, der Effekt der Durchschnittsgeschwindigkeit. Die Ergebnisse sind in Abbildung~\ref{fig:beta_dist} dargestellt, welche die Verteilungen von $\hat{\beta}_{avg\_speed}$ für drei verschiedene Stichprobenumfänge gegenüberstellt. Man erkennt deutlich, dass \textbf{mit wachsendem $n$ die Verteilung der Koeffizientenschätzungen immer schmaler wird} und sich enger um den wahren Wert konzentriert. Für kleine Stichproben ($n=1000$) schwanken die geschätzten $\beta$ noch sehr stark -- die Verteilung ist breit und umfasst Werte von nahe 0 bis etwa 0,03. Bei mittlerer Stichprobe ($n=11000$) ist die Streuung bereits deutlich geringer. Im Fall $n=46000$ liegen nahezu alle Schätzungen dicht bei $\beta\approx0{,}015$; die Kurve ist stark konzentriert. Diese Beobachtung entspricht der erwarteten Verbesserung der Schätzgenauigkeit: Je mehr Daten zur Verfügung stehen, desto weniger zufällig streuen die geschätzten Parameter um den wahren Wert. +Untersucht wurde exemplarisch der Koeffizient $\beta_{avg\_speed}$, der Effekt der Durchschnittsgeschwindigkeit. Die Ergebnisse sind in Abbildung~\ref{fig:beta_dist} dargestellt, +welche die Verteilungen von $\hat{\beta}_{avg\_speed}$ für drei verschiedene Stichprobenumfänge gegenüberstellt. Man erkennt deutlich, dass +\textbf{mit wachsendem $n$ die Verteilung der Koeffizientenschätzungen immer schmaler wird} und sich enger um den wahren Wert konzentriert. Für kleine Stichproben ($n=1000$) +schwanken die geschätzten $\beta$ noch sehr stark -- die Verteilung ist breit und umfasst Werte von nahe 0 bis etwa 0,03. Bei mittlerer Stichprobe ($n=11000$) ist die Streuung +bereits deutlich geringer. Im Fall $n=46000$ liegen nahezu alle Schätzungen dicht bei $\beta\approx0{,}015$; die Kurve ist stark konzentriert. Diese Beobachtung entspricht der +erwarteten Verbesserung der Schätzgenauigkeit: Je mehr Daten zur Verfügung stehen, desto weniger zufällig streuen die geschätzten Parameter um den wahren Wert. \begin{figure}[h] \centering \includegraphics[width=0.8\textwidth]{fig_beta_distribution.png} -\caption{Verteilungen des geschätzten Koeffizienten $\hat{\beta}_{avg\_speed}$ aus 1000 Simulationen für drei Stichprobenumfänge ($n=1000$, $n=11000$, $n=46000$). Mit größerem $n$ wird die Verteilung deutlich schmaler und konzentriert sich stärker um den wahren Wert ($\beta_{avg\_speed}=0{,}015$, gestrichelte Linie).} +\caption{Verteilungen des geschätzten Koeffizienten $\hat{\beta}_{avg\_speed}$ aus 1000 Simulationen für drei Stichprobenumfänge ($n=1000$, $n=11000$, $n=46000$). Mit größerem $n$ +wird die Verteilung deutlich schmaler und konzentriert sich stärker um den wahren Wert ($\beta_{avg\_speed}=0{,}015$, gestrichelte Linie).} \label{fig:beta_dist} \end{figure} \noindent \newline -Zur Quantifizierung wurde für jede Stichprobengröße $n$ die \textbf{empirische Standardabweichung} der $\hat{\beta}_{avg\_speed}$-Schätzungen aus den 1000 Wiederholungen bestimmt. Abbildung~\ref{fig:std_n} zeigt die Entwicklung dieser Streuung in Abhängigkeit von $n$. Deutlich ist ein abnehmender Verlauf erkennbar. Die Kurve folgt näherungsweise der theoretischen Proportionalität $\sigma(\hat{\beta}) \sim \frac{1}{\sqrt{n}}$ (rot eingezeichnet). Die in der Simulation gemessenen Werte (blaue Punkte) liegen dicht auf der $1/\sqrt{n}$-Linie, was die theoretische Erwartung bestätigt. +Zur Quantifizierung wurde für jede Stichprobengröße $n$ die \textbf{empirische Standardabweichung} der $\hat{\beta}_{avg\_speed}$-Schätzungen aus den 1000 Wiederholungen bestimmt. +Abbildung~\ref{fig:std_n} zeigt die Entwicklung dieser Streuung in Abhängigkeit von $n$. Deutlich ist ein abnehmender Verlauf erkennbar. Die Kurve folgt näherungsweise der +theoretischen Proportionalität $\sigma(\hat{\beta}) \sim \frac{1}{\sqrt{n}}$ (rot eingezeichnet). Die in der Simulation gemessenen Werte (blaue Punkte) liegen dicht auf der +$1/\sqrt{n}$-Linie, was die theoretische Erwartung bestätigt. \begin{figure}[h] \centering \includegraphics[width=0.8\textwidth]{fig_std_vs_n.png} -\caption{Standardabweichung von $\hat{\beta}_{avg\_speed}$ in Abhängigkeit des Stichprobenumfangs $n$. Gezeigte Punkte basieren auf $k=1000$ Simulationen je Umfang; die rote Kurve visualisiert ein $1/\sqrt{n}$-Gesetz. Man erkennt, dass die Streuung der Schätzungen mit wachsendem $n$ deutlich abnimmt und sich näherungsweise nach $\propto n^{-1/2}$ verhält.} +\caption{Standardabweichung von $\hat{\beta}_{avg\_speed}$ in Abhängigkeit des Stichprobenumfangs $n$. Gezeigte Punkte basieren auf $k=1000$ Simulationen je Umfang; die rote Kurve +visualisiert ein $1/\sqrt{n}$-Gesetz. Man erkennt, dass die Streuung der Schätzungen mit wachsendem $n$ deutlich abnimmt und sich näherungsweise nach $\propto n^{-1/2}$ verhält.} \label{fig:std_n} \end{figure} \noindent \newline -Diese Ergebnisse illustrieren den wichtigen Zusammenhang zwischen Datenmenge und \textbf{Modelllernqualität}. Bereits zwischen $n=1000$ und $n=10000$ verbessert sich die Präzision der Koeffizientenschätzung erheblich. Noch größere Datenmengen führen zu weiter sinkender Unsicherheit, allerdings mit abnehmendem Grenznutzen (die Kurve flacht ab). Insgesamt zeigt die Simulation, dass zur zuverlässigen Identifikation kleiner Effekte (wie $\beta_{avg\_speed}\approx0{,}015$) eine ausreichend große Stichprobe notwendig ist. Mit $n \to 50.000$ nähert sich die Streuung einem Wert an, der durch die inhärente Ergebnisvarianz (bedingt durch den Rauschterm $\varepsilon$) begrenzt ist. Für das vorliegende Problem bedeutet dies: Je mehr Fahrten der Data Scientist zur Verfügung hat, desto genauer kann er die wahren Fahrerwechsel-Einflüsse schätzen und desto vertrauenswürdiger sind die vom Modell ausgegebenen Diebstahlwahrscheinlichkeiten. +Diese Ergebnisse illustrieren den wichtigen Zusammenhang zwischen Datenmenge und \textbf{Modelllernqualität}. Bereits zwischen $n=1000$ und $n=10000$ verbessert sich die +Präzision der Koeffizientenschätzung erheblich. Noch größere Datenmengen führen zu weiter sinkender Unsicherheit, allerdings mit abnehmendem Grenznutzen (die Kurve flacht ab). +Insgesamt zeigt die Simulation, dass zur zuverlässigen Identifikation kleiner Effekte (wie $\beta_{avg\_speed}\approx0{,}015$) eine ausreichend große Stichprobe notwendig ist. +Mit $n \to 50.000$ nähert sich die Streuung einem Wert an, der durch die inhärente Ergebnisvarianz (bedingt durch den Rauschterm $\varepsilon$) begrenzt ist. Für das vorliegende +Problem bedeutet dies: Je mehr Fahrten der Data Scientist zur Verfügung hat, desto genauer kann er die wahren Fahrerwechsel-Einflüsse schätzen und desto vertrauenswürdiger sind die +vom Modell ausgegebenen Diebstahlwahrscheinlichkeiten. \clearpage \printbibliography[title=Literaturverzeichnis] diff --git a/1_data_science/simulationsstudie/yann_ahlgrim_simulationsstudie.ipynb b/1_data_science/simulationsstudie/yann_ahlgrim_simulationsstudie.ipynb index d586495..139fea7 100644 --- a/1_data_science/simulationsstudie/yann_ahlgrim_simulationsstudie.ipynb +++ b/1_data_science/simulationsstudie/yann_ahlgrim_simulationsstudie.ipynb @@ -64,7 +64,6 @@ " - *nass* (~20–25 %) \n", " - *winterlich* (<5 %) \n", " → Modelliert als Multinomial. Kontextvariable. \n", - " Die Einteilung basiert auf Daten des Statistischen Bundesamtes: Etwa 70–75 % aller Fahrten finden unter trockenen Bedingungen statt, 20–25 % bei Nässe und unter 5 % bei Schnee oder Glätte.\n", "\n", "6. **Fahrstrecke (metrisch):** \n", " Länge der Fahrt (z.B. lognormalverteilt um 16 km). Kontextvariable, nicht direkt erklärend. \n", @@ -93,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 1, "id": "97ba29a9", "metadata": {}, "outputs": [], @@ -121,11 +120,7 @@ }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": null, -======= - "execution_count": 16, ->>>>>>> 07c8b4df4e7972c89d948d9a0aef7e3a594dfbc1 + "execution_count": 2, "id": "61247da5", "metadata": {}, "outputs": [], @@ -185,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 3, "id": "f8e0efb0", "metadata": {}, "outputs": [ @@ -228,7 +223,7 @@ " 2\n", " manchmal\n", " trocken\n", - " 30.449962\n", + " 40.599949\n", " Innerorts\n", " Mo-Fr\n", " \n", @@ -239,7 +234,7 @@ " 3\n", " haeufig\n", " trocken\n", - " 11.911103\n", + " 15.881471\n", " Autobahn\n", " Mo-Fr\n", " \n", @@ -250,7 +245,7 @@ " 2\n", " selten\n", " trocken\n", - " 6.086055\n", + " 8.114740\n", " Autobahn\n", " Mo-Fr\n", " \n", @@ -261,7 +256,7 @@ " 5\n", " haeufig\n", " trocken\n", - " 5.732684\n", + " 7.643579\n", " Innerorts\n", " Mo-Fr\n", " \n", @@ -272,7 +267,7 @@ " 2\n", " selten\n", " trocken\n", - " 7.256758\n", + " 9.675677\n", " Außerorts\n", " Mo-Fr\n", " \n", @@ -282,11 +277,11 @@ ], "text/plain": [ " avg_speed shift_behavior hard_brakes speeding weather trip_distance \\\n", - "0 64.494547 frueh 2 manchmal trocken 30.449962 \n", - "1 44.139270 frueh 3 haeufig trocken 11.911103 \n", - "2 42.154349 spaet 2 selten trocken 6.086055 \n", - "3 20.466814 normal 5 haeufig trocken 5.732684 \n", - "4 46.917154 spaet 2 selten trocken 7.256758 \n", + "0 64.494547 frueh 2 manchmal trocken 40.599949 \n", + "1 44.139270 frueh 3 haeufig trocken 15.881471 \n", + "2 42.154349 spaet 2 selten trocken 8.114740 \n", + "3 20.466814 normal 5 haeufig trocken 7.643579 \n", + "4 46.917154 spaet 2 selten trocken 9.675677 \n", "\n", " road_type weekday \n", "0 Innerorts Mo-Fr \n", @@ -296,7 +291,7 @@ "4 Außerorts Mo-Fr " ] }, - "execution_count": 17, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -326,7 +321,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 4, "id": "6bfb199a", "metadata": {}, "outputs": [ @@ -357,7 +352,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 5, "id": "1d3df64a", "metadata": {}, "outputs": [ @@ -388,7 +383,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 6, "id": "49045ee5", "metadata": {}, "outputs": [ @@ -431,7 +426,7 @@ " 2\n", " manchmal\n", " trocken\n", - " 30.449962\n", + " 40.599949\n", " Innerorts\n", " Mo-Fr\n", " \n", @@ -442,7 +437,7 @@ " 3\n", " haeufig\n", " trocken\n", - " 11.911103\n", + " 15.881471\n", " Autobahn\n", " Mo-Fr\n", " \n", @@ -453,7 +448,7 @@ " 2\n", " selten\n", " trocken\n", - " 6.086055\n", + " 8.114740\n", " Autobahn\n", " Mo-Fr\n", " \n", @@ -464,7 +459,7 @@ " 5\n", " haeufig\n", " trocken\n", - " 5.732684\n", + " 7.643579\n", " Innerorts\n", " Mo-Fr\n", " \n", @@ -475,7 +470,7 @@ " 2\n", " selten\n", " trocken\n", - " 7.256758\n", + " 9.675677\n", " Außerorts\n", " Mo-Fr\n", " \n", @@ -485,11 +480,11 @@ ], "text/plain": [ " avg_speed shift_behavior hard_brakes speeding weather trip_distance \\\n", - "0 64.494547 frueh 2 manchmal trocken 30.449962 \n", - "1 44.139270 frueh 3 haeufig trocken 11.911103 \n", - "2 42.154349 spaet 2 selten trocken 6.086055 \n", - "3 20.466814 normal 5 haeufig trocken 5.732684 \n", - "4 46.917154 spaet 2 selten trocken 7.256758 \n", + "0 64.494547 frueh 2 manchmal trocken 40.599949 \n", + "1 44.139270 frueh 3 haeufig trocken 15.881471 \n", + "2 42.154349 spaet 2 selten trocken 8.114740 \n", + "3 20.466814 normal 5 haeufig trocken 7.643579 \n", + "4 46.917154 spaet 2 selten trocken 9.675677 \n", "\n", " road_type weekday \n", "0 Innerorts Mo-Fr \n", @@ -499,7 +494,7 @@ "4 Außerorts Mo-Fr " ] }, - "execution_count": 20, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -518,7 +513,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 7, "id": "4ad287b1", "metadata": {}, "outputs": [ @@ -570,7 +565,7 @@ " 1.0\n", " 64.494547\n", " 2.0\n", - " 30.449962\n", + " 40.599949\n", " 1.0\n", " 0.0\n", " 0.0\n", @@ -592,7 +587,7 @@ " 1.0\n", " 44.139270\n", " 3.0\n", - " 11.911103\n", + " 15.881471\n", " 1.0\n", " 0.0\n", " 0.0\n", @@ -614,7 +609,7 @@ " 1.0\n", " 42.154349\n", " 2.0\n", - " 6.086055\n", + " 8.114740\n", " 0.0\n", " 0.0\n", " 1.0\n", @@ -636,7 +631,7 @@ " 1.0\n", " 20.466814\n", " 5.0\n", - " 5.732684\n", + " 7.643579\n", " 0.0\n", " 1.0\n", " 0.0\n", @@ -658,7 +653,7 @@ " 1.0\n", " 46.917154\n", " 2.0\n", - " 7.256758\n", + " 9.675677\n", " 0.0\n", " 0.0\n", " 1.0\n", @@ -681,11 +676,11 @@ ], "text/plain": [ " const avg_speed hard_brakes trip_distance shift_frueh shift_normal \\\n", - "0 1.0 64.494547 2.0 30.449962 1.0 0.0 \n", - "1 1.0 44.139270 3.0 11.911103 1.0 0.0 \n", - "2 1.0 42.154349 2.0 6.086055 0.0 0.0 \n", - "3 1.0 20.466814 5.0 5.732684 0.0 1.0 \n", - "4 1.0 46.917154 2.0 7.256758 0.0 0.0 \n", + "0 1.0 64.494547 2.0 40.599949 1.0 0.0 \n", + "1 1.0 44.139270 3.0 15.881471 1.0 0.0 \n", + "2 1.0 42.154349 2.0 8.114740 0.0 0.0 \n", + "3 1.0 20.466814 5.0 7.643579 0.0 1.0 \n", + "4 1.0 46.917154 2.0 9.675677 0.0 0.0 \n", "\n", " shift_spaet speeding_haeufig speeding_manchmal speeding_selten \\\n", "0 0.0 0.0 1.0 0.0 \n", @@ -709,7 +704,7 @@ "4 1.0 0.0 1.0 0.0 0.0 " ] }, - "execution_count": 21, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -744,28 +739,20 @@ }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": null, -======= - "execution_count": 22, ->>>>>>> 07c8b4df4e7972c89d948d9a0aef7e3a594dfbc1 + "execution_count": 8, "id": "75318d77", "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "C:\\Users\\yann\\AppData\\Local\\Temp\\ipykernel_18236\\1239231251.py:39: FutureWarning: \n", - "\n", - "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", - "\n", - " sns.countplot(x=target, palette='viridis')\n" + "Anzahl Diebstähle: 229973\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -824,6 +811,46 @@ "plt.show()" ] }, + { + "cell_type": "code", + "execution_count": 23, + "id": "76948d4a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mittelwert der pi-Werte: 0.2302\n", + "Standardabweichung der pi-Werte: 0.1027\n" + ] + }, + { + "data": { + "image/png": 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xmF2OiIiIiIiYxGazcfLkSaKionBzO3c/mAJZLTpy5AjR0dFmlyEiIiIiIi7i4MGDtGrV6pznFchqUUBAAGD86IGBgSZXIyIiIiIiZsnNzSU6OtqREc5FgawW2YcpBgYGKpCJiIiIiMgFH2XSpB4iIiIiIiImUSATERERERExiQKZiIiIiIiISRTIRERERERETKJAJiIiIiIiYhIFMhEREREREZMokImIiIiIiJhEgUxERERERMQkCmQiIiIiIiImUSATERERERExiQKZiIiIiIiISRTIRERERERETKJAJiIiIiIiYhIFMhEREREREZMokImIiIiIiJhEgUxERERERMQkCmQiIiIiIiImUSATERERERExiYfZBYg0FKmpqWRmZp63TUmJhQULoli5MpRrr81h/PijdOpUQGhoKDExMfVUqYiIiIg0FApkItWQmppKx46dKCg4dZ5W7YB3gHAAEhODSUwMBr7Ey+vP/PjjGwplIiIiIuJEgUykGjIzMykoOMXNNy+jRYtOlc7//HNzvv66NSUl7nh7l3L11UdIT2/Gzz8HYbNdT3HxtWzY8BPKYyIiIiJyNgUykRpo0aITkZE9nI6lp8N//2vsx8TAmDEeWK1G8srJgX/9K5fDhwP5299iuPVWcHev76pFRERExFVpUg+RS2QPYx07wt13g9V65pzVCv37HwBy2LnTn5dfNqVEEREREXFRCmQil2D/fvjpJ3Bzg8GDjdeK/P1LgD8B8NhjcPBgvZYoIiIiIi5MgUzkItlsZ3rHevSA4ODztX6dK6/MIy8PJk82PisiIiIiokAmcpH27oVDh8DDA6677kKtbTzxRCqenrBqFfz73/VRoYiIiIi4OgUykYtQXg5ffmns9+oFAQEX/sxllxXyyCPG/rPP1l1tIiIiItJwKJCJXITt2+HYMfDxgb59q/+5hx82etQ2bTKuISIiIiJNmwKZyEX49lvjtU8f8PWt/ufCw+Gmm4z9N96o/bpEREREpGFRIBOpoZwc49kxgG7dav75++4zXpcuhcLCWitLRERERBogLQwtUkO7dhmvrVtX79kxu5SUFABCQiAi4grS0715/vl93HBD1jk/ExoaSkxMzKWUKyIiIiIuTIFMpIZ27jReO3euXvu8vDTAwvjx4886+hTwNI8/vo/HHx94zs/6+vqxe3eKQpmIiIhII6VAJlIDJ096cfiwsV/dQFZYmA3YGDBgAbGx8QDk5XmyfLkNuJ7bbtuB1VpU6XMZGSmsXDmezMxMBTIRERGRRkqBTKQGfvmlOQBt2kCzZjX7bFDQ5URG9nC8v/xy+OknOHz4Cjp2rL0aRURERKTh0KQeIjXwyy9BQPV7x86nx+lstm0blJVd+vVEREREpOFRIBOptjZkZPhjsUCnTpd+tfbtwc8P8vIgNfXSryciIiIiDY8CmUi1/Rq4uOGKVXF3N0IZwJ49l349EREREWl4TA1kpaWlPPHEE7Rt2xZfX18uu+wy/vrXv1JeXu5oY7PZmDFjBlFRUfj6+tK/f3922qe5O62oqIiHH36Y0NBQ/P39GTVqFIfsC0WdlpWVRUJCAlarFavVSkJCAtnZ2U5tUlNTGTlyJP7+/oSGhjJlyhSKi4vr7PtLQzMWqJ3hinb2QLZ3L9hstXddEREREWkYTA1k//jHP3j11VdZsGABKSkpzJkzh2effZb58+c72syZM4fnn3+eBQsWsGXLFiIiIhg8eDAnT550tJk6dSorV65kxYoVrFu3jry8PEaMGEHZWQ/mjBs3jm3btpGYmEhiYiLbtm0jISHBcb6srIzhw4eTn5/PunXrWLFiBR988AHTpk2rnx9DXNrhw17AVVgstloZrmjXrp3RU5aVBZmZtXddEREREWkYTJ1lccOGDdx0000MHz4cgDZt2vDOO+/w7bffAkbv2Lx583j88ccZM2YMAEuWLCE8PJzly5fzwAMPkJOTw6JFi1i6dCmDBg0CYNmyZURHR/PFF18wdOhQUlJSSExMZOPGjfTq1QuAhQsXEh8fz549e+jQoQNJSUns2rWLgwcPEhUVBcDcuXOZMGECM2fOJDAwsL5/HnEhW7YYK0CHheXj718L4xVP8/IyhkD+/LPRS9aiRa1dWkREREQaAFN7yH71q1/x3//+l7179wLw/fffs27dOm688UYA9u3bR3p6OkOGDHF8xtvbm379+rF+/XoAkpOTKSkpcWoTFRVFXFyco82GDRuwWq2OMAbQu3dvrFarU5u4uDhHGAMYOnQoRUVFJCcnV1l/UVERubm5Tps0Tlu3GiEsMjKv1q/doYPxevr/BiIiIiLShJgayP70pz9xxx130LFjRzw9PenevTtTp07ljjvuACA9PR2A8PBwp8+Fh4c7zqWnp+Pl5UVQUNB524SFhVW6f1hYmFObivcJCgrCy8vL0aai2bNnO55Js1qtREdH1/QnkAYiOdkIZFFRJy/Qsubsz5EdPAinTtX65UVERETEhZkayN59912WLVvG8uXL2bp1K0uWLOG5555jyZIlTu0sFovTe5vNVulYRRXbVNX+Ytqc7dFHHyUnJ8exHTx48Lw1ScO0fz+kp3sDpYSH59f69a1WCA83JvX48cdav7yIiIiIuDBTA9kf//hH/vznP3P77bfTpUsXEhIS+P3vf8/s2bMBiIiIAKjUQ3Xs2DFHb1ZERATFxcVkZWWdt83Ro0cr3T8jI8OpTcX7ZGVlUVJSUqnnzM7b25vAwECnTRqfr7+2732Lp2f5+ZpetLNnWxQRERGRpsPUQHbq1Cnc3JxLcHd3d0x737ZtWyIiIli9erXjfHFxMWvXrqVPnz4A9OzZE09PT6c2aWlp7Nixw9EmPj6enJwcNm/e7GizadMmcnJynNrs2LGDtLQ0R5ukpCS8vb3p2bNnLX9zaUjWrnXs1dk97M+R/fQTnDU5qIiIiIg0cqbOsjhy5EhmzpxJTEwMV1xxBd999x3PP/889957L2AMIZw6dSqzZs0iNjaW2NhYZs2ahZ+fH+PGjQPAarUyceJEpk2bRkhICMHBwUyfPp0uXbo4Zl3s1KkTN9xwA5MmTeK1114D4P7772fEiBF0OP0n4SFDhtC5c2cSEhJ49tlnOXHiBNOnT2fSpEnq+WrinAPZ4Dq5R1SUsdh0Xp4xRLJduzq5jYiIiIi4GFMD2fz583nyySeZPHkyx44dIyoqigceeICnnnrK0eaRRx6hoKCAyZMnk5WVRa9evUhKSiIgIMDR5oUXXsDDw4OxY8dSUFDAwIEDWbx4Me7u7o42b7/9NlOmTHHMxjhq1CgWLFjgOO/u7s4nn3zC5MmT6du3L76+vowbN47nnnuuHn4JcVWHDxtT0ru52SgvX1dn97FYIDYWvvvOGLaoQCYiIiLSNFhsNpvN7CIai9zcXKxWKzk5OepVaySWL4c774ROnfJJSWnG/fcnExnZo0bX2L79bf797/GMGZNIly5Dz9kuJQXeew9CQ+GhhyAtbSuvv96T5ORkevSo2T1FRERExFzVzQamPkMm4urswxV79Kj99ccqatPGeM3MBC1pJyIiItI0KJCJnIc9kPXsWfeBzNcXIiON/X376vx2IiIiIuICFMhEziE9HfbsMZ7v6t697gMZQNu2xuv+/fVyOxERERExmQKZyDl8843x2qULBAbWz1z0l11mvP7yi7FQtIiIiIg0bgpkIudgH67Yr1/93TMmBtzcjGfIcnO96+/GIiIiImIKBTKRc9iwwXi99tr6u6enJ0RHG/uHDwecv7GIiIiINHgKZCJVKCmBHTuM/Z496/fe9ufIjhxRIBMRERFp7BTIRKqwaxcUF4PVeiYg1Rf7/YweMkv93lxERERE6pUCmUgVtm41Xrt3N2ZZrE8tWxpDF4uKPICu9XtzEREREalXCmQiVfjuO+O1e/f6v7e7O7RubX93ff0XICIiIiL1RoFMpAr2HrIePcy5/5lhkgPNKUBERERE6oUCmUgF5eXw/ffGvhk9ZHBmPTK4jpISc2oQERERkbqnQCZSwU8/QV4e+PhAhw7m1BAeDt7epUAAu3b5m1OEiIiIiNQ5BTKRCuzDFa+8Ejw8zKnBYoHIyDwAvvuumTlFiIiIiEidUyATqcDMCT3OFhl5EoCtWxXIRERERBorBTKRClwnkBk9ZN9/709Zmbm1iIiIiEjdUCATOYvNZv4Mi3bBwQVALnl5Hmzfbm4tIiIiIlI3FMhEznLoEBw/bqwFFhdnbi1ubgD/A+Drr00tRURERETqiAKZyFnswxU7dzZmWTSfkcQUyEREREQaJwUykbPYhyua/fzYGWcCmc1mcikiIiIiUutMmtRbxDypqalkZmZWee6rry4DmtOixUG2bs1wHE9JSamn6ir6Fm/vcjIy3Ni717x10URERESkbiiQSZOSmppKx46dKCg4dY4WB4DmzJ17J3PnflPpbF7eyTqtr7Ji4uLySU4O4OuvFchEREREGhsFMmlSMjMzKSg4xc03L6NFi05O5woL3fnnP2MAmDDhJby8yh3nfvzxU9aseZLCwsJ6rRege/c8RyCbNKneby8iIiIidUiBTJqkFi06ERnpPK/9L78Yr0FB0Lp1N6dzmZlmDVmEHj2M9cg0sYeIiIhI46NJPUROyzj9yFhYmLl1VNS1az4eHpCaCgcOmF2NiIiIiNQmBTKR0+zzfISGmltHRb6+5Y5Fqr+p/FibiIiIiDRgCmQip9kDWYsW5tZRleuuM141bFFERESkcVEgEznNVXvIAK691nhVD5mIiIhI46JAJgIUFkKeMXeGSwayPn2M19274fhxc2sRERERkdqjQCbCmQk9AgLA29vcWqoSGnpmDbING8ytRURERERqjwKZCK49XNGub1/j9X//M7cOEREREak9CmQiKJCJiIiIiDkUyERw7RkW7eyBbMsWKC42txYRERERqR0KZCI0jB6y9u2N+goLYetWs6sRERERkdqgQCZNXmkpZGUZ+64cyCyWM7MtatiiiIiISOOgQCZN3okTYLMZsys2a2Z2Neen58hEREREGhcFMmny7FPeh4YavVCu7OxAZrOZW4uIiIiIXDoFMmnyGsKEHnY9e4KXFxw7Bj//bHY1IiIiInKpFMikyWsIE3rY+fjAVVcZ+xq2KCIiItLweZhdgIjZXD2QpaSkOL2//PKWrF8fzocfZtKlS2qVnwkNDSUmJqY+yhMRERGRS6BAJk2azea6gSwvLw2wMH78+ApnRgEfsnLlUVau7FnlZ319/di9O0WhTERERMTFmTpksU2bNlgslkrbQw89BIDNZmPGjBlERUXh6+tL//792blzp9M1ioqKePjhhwkNDcXf359Ro0Zx6NAhpzZZWVkkJCRgtVqxWq0kJCSQnZ3t1CY1NZWRI0fi7+9PaGgoU6ZMoVir7zZ6OTnGtPfu7hAUZHY1zgoLswEbAwYs4P77kx1bQsLfTre4grvu2uZ07v77k7n55mUUFJwi0540RURERMRlmdpDtmXLFsrKyhzvd+zYweDBg/n1r38NwJw5c3j++edZvHgx7du35+9//zuDBw9mz549BAQEADB16lQ+/vhjVqxYQUhICNOmTWPEiBEkJyfj7u4OwLhx4zh06BCJiYkA3H///SQkJPDxxx8DUFZWxvDhw2nRogXr1q3j+PHj3H333dhsNubPn1+fP4nUM/sMiyEh4OaiT1QGBV1OZGQPp2MhIXD8OJSUXEnbtiYVJiIiIiKXzNRA1qLCtHbPPPMM7dq1o1+/fthsNubNm8fjjz/OmDFjAFiyZAnh4eEsX76cBx54gJycHBYtWsTSpUsZNGgQAMuWLSM6OpovvviCoUOHkpKSQmJiIhs3bqRXr14ALFy4kPj4ePbs2UOHDh1ISkpi165dHDx4kKioKADmzp3LhAkTmDlzJoGBgfX4q0h9ctXhihfSqpURyA4fhvbtza5GRERERC6Wy/QJFBcXs2zZMu69914sFgv79u0jPT2dIUOGONp4e3vTr18/1q9fD0BycjIlJSVObaKiooiLi3O02bBhA1ar1RHGAHr37o3VanVqExcX5whjAEOHDqWoqIjk5ORz1lxUVERubq7TJg1LQw5kABVG54qIiIhIA+Mygew///kP2dnZTJgwAYD09HQAwsPDndqFh4c7zqWnp+Pl5UVQhYd/KrYJCwurdL+wsDCnNhXvExQUhJeXl6NNVWbPnu14Ls1qtRIdHV2DbyyuoKEHssOHtUC0iIiISEPmMoFs0aJFDBs2zKmXCsBisTi9t9lslY5VVLFNVe0vpk1Fjz76KDk5OY7t4MGD561LXM+JE8ZrSIi5ddRUWBh4ekJR0ZlQKSIiIiINj0sEsgMHDvDFF19w3333OY5FREQAVOqhOnbsmKM3KyIiguLiYrKyss7b5ujRo5XumZGR4dSm4n2ysrIoKSmp1HN2Nm9vbwIDA502aTiKiyEvz9gPDja3lppyc4OWLY19DVsUERERabhcIpC99dZbhIWFMXz4cMextm3bEhERwerVqx3HiouLWbt2LX369AGgZ8+eeHp6OrVJS0tjx44djjbx8fHk5OSwefNmR5tNmzaRk5Pj1GbHjh2kpaU52iQlJeHt7U3PnlWv8yQNnz3H+/qCj4+5tVwMBTIRERGRhs/0haHLy8t56623uPvuu/HwOFOOxWJh6tSpzJo1i9jYWGJjY5k1axZ+fn6MGzcOAKvVysSJE5k2bRohISEEBwczffp0unTp4ph1sVOnTtxwww1MmjSJ1157DTCmvR8xYgQdOnQAYMiQIXTu3JmEhASeffZZTpw4wfTp05k0aZJ6vRoxeyBztfXHqksTe4iIiIg0fKYHsi+++ILU1FTuvffeSuceeeQRCgoKmDx5MllZWfTq1YukpCTHGmQAL7zwAh4eHowdO5aCggIGDhzI4sWLHWuQAbz99ttMmTLFMRvjqFGjWLBggeO8u7s7n3zyCZMnT6Zv3774+voybtw4nnvuuTr85mI2+/NjDW24op09kB07ZjxL5u1tbj0iIiIiUnOmB7IhQ4ZgO8c0cRaLhRkzZjBjxoxzft7Hx4f58+efdwHn4OBgli1bdt46YmJiWLVqVbVqlsbBHsgaag9Zs2bQvDlkZ8ORI2iBaBEREZEGyCWeIRMxQ0Mfsghnesk0waeIiIhIw6RAJk2WPZA11CGLcGZij8OHza1DRERERC6OApk0SeXlxlA/aBw9ZIcOaYFoERERkYZIgUyapLw8L2w28PCAs+aIaXAiIsDdHU6dOtPjJyIiIiINhwKZNEm5ucaUhEFBYLGYXMwl8PCAyEhjX9Pfi4iIiDQ8CmTSJJ0dyBo6LRAtIiIi0nApkEmT1JgCmf05Mk3sISIiItLwKJBJk2QPZA15hkU7eyBLT4eSEnNrEREREZGaUSCTJqkx9ZBZrcYi0eXlkJZmdjUiIiIiUhMKZNIk5eZ6AY2jh8xicZ7+XkREREQaDgUyaYLCKS11x2KB5s3NrqV2aIFoERERkYZJgUyaoHYABAYaa3g1BuohExEREWmYFMikCTICWWMYrmgXFWUMXczNhbw8T7PLEREREZFqUiCTJsgIZI1hQg87Ly8IDzf2jx3zN7cYEREREak2BTJpghpfIIMzz5EpkImIiIg0HApk0gQ1zkBmf45MgUxERESk4VAgkyao8T1DBmcCWUaGH+Bhai0iIiIiUj0KZNKk5Oe7AWFA4+shCwkBHx8oK3MDuppdjoiIiIhUgwKZNCmHDnkD4O1dio+PycXUsrMXiIZ4M0sRERERkWpSIJMmxR7IAgOLTK6kbpwJZL3NLENEREREqkmBTJqUI0e8AAUyEREREXENCmTSpKSlGYGsWbNikyupG/ap7+FysrI0sYeIiIiIq1MgkyYlPd0IZAEBjTOQ+fhA8+YFAGzfrunvRURERFydApk0KfYhi421hwwgLCwfgB07/EyuREREREQuRIFMmhT7kMXG2kMGEB5uBLIfflAPmYiIiIirUyCTJiM7G/LyjOeqGnMPmT2Q7dzpT1mZycWIiIiIyHkpkEmTceCAfS8DT89yM0upU82bFwK5nDrlzs6dZlcjIiIiIuejQCZNxplAduB8zRo8NzeAzQBs2GBqKSIiIiJyAQpk0mTs3+/YM6+IemMksY0bTS5DRERERM5LgUyajKbSQ2Ywkph6yERERERcmwKZNBlnesiaTiDbswdOnDC5FBERERE5JwUyaTLO9JDtN7GK+nKCmJhCADZtMrkUERERETknBTJpMprWkEXo0sWY/l7DFkVERERclwKZNAn5+ZCZaX/XtAKZJvYQERERcV0KZNIk2HvHmjUrBXJMraW+dO1qBLJNm6C88S67JiIiItKgKZBJk2APZJGRxeYWUo/atSvA3x9ycyElxexqRERERKQqCmTSJNhnWIyKajqBzMMDrr7a2NdzZCIiIiKuSYFMmgR7D1lERNMJZADx8carniMTERERcU0KZNIk2HvImtKQRYDevY1X9ZCJiIiIuCYFMmkS7D1kTWnIIpwJZLt2QXa2qaWIiIiISBUUyKRJaIqTegCEhcFllxn7mzebW4uIiIiIVGZ6IDt8+DDjx48nJCQEPz8/unXrRnJysuO8zWZjxowZREVF4evrS//+/dm5c6fTNYqKinj44YcJDQ3F39+fUaNGcejQIac2WVlZJCQkYLVasVqtJCQkkF2hyyA1NZWRI0fi7+9PaGgoU6ZMobi4af0BvjEqLIS0NGM/MrLI3GJMYH+OTMMWRURERFyPqYEsKyuLvn374unpyWeffcauXbuYO3cuzZs3d7SZM2cOzz//PAsWLGDLli1EREQwePBgTp486WgzdepUVq5cyYoVK1i3bh15eXmMGDGCsrIyR5tx48axbds2EhMTSUxMZNu2bSQkJDjOl5WVMXz4cPLz81m3bh0rVqzggw8+YNq0afXyW0jdOXjQePXzg+bNy87fuBHSxB4iIiIirsvDzJv/4x//IDo6mrfeestxrE2bNo59m83GvHnzePzxxxkzZgwAS5YsITw8nOXLl/PAAw+Qk5PDokWLWLp0KYMGDQJg2bJlREdH88UXXzB06FBSUlJITExk48aN9OrVC4CFCxcSHx/Pnj176NChA0lJSezatYuDBw8SFRUFwNy5c5kwYQIzZ84kMDCwnn4VqW32CT1atwaLxdRSTGF/jmzjRmOBaDfT+8VFRERExM7UP5p99NFHXHXVVfz6178mLCyM7t27s3DhQsf5ffv2kZ6ezpAhQxzHvL296devH+vXrwcgOTmZkpISpzZRUVHExcU52mzYsAGr1eoIYwC9e/fGarU6tYmLi3OEMYChQ4dSVFTkNITybEVFReTm5jpt4nrsz4+dlfWblK5dwdfXmNRj716zqxERERGRs5kayH755RdeeeUVYmNj+fzzz3nwwQeZMmUK//znPwFIT08HIDw83Olz4eHhjnPp6el4eXkRFBR03jZhYWGV7h8WFubUpuJ9goKC8PLycrSpaPbs2Y5n0qxWK9HR0TX9CaQe2ANZ69bm1mEWT0+46ipjX8+RiYiIiLgWUwNZeXk5PXr0YNasWXTv3p0HHniASZMm8corrzi1s1QYZ2az2Sodq6him6raX0ybsz366KPk5OQ4toP2h5XEpZw9ZLGp0nNkIiIiIq7J1EAWGRlJ586dnY516tSJ1NRUACIiIgAq9VAdO3bM0ZsVERFBcXExWVlZ521z9OjRSvfPyMhwalPxPllZWZSUlFTqObPz9vYmMDDQaRPX09SHLIIWiBYRERFxVaYGsr59+7Jnzx6nY3v37qX16a6Mtm3bEhERwerVqx3ni4uLWbt2LX369AGgZ8+eeHp6OrVJS0tjx44djjbx8fHk5OSw+ayFmDZt2kROTo5Tmx07dpBmnx8dSEpKwtvbm549e9byN5f61NSHLMKZQLZjB+hRRxERERHXYWog+/3vf8/GjRuZNWsWP/30E8uXL+f111/noYceAowhhFOnTmXWrFmsXLmSHTt2MGHCBPz8/Bg3bhwAVquViRMnMm3aNP773//y3XffMX78eLp06eKYdbFTp07ccMMNTJo0iY0bN7Jx40YmTZrEiBEj6NChAwBDhgyhc+fOJCQk8N133/Hf//6X6dOnM2nSJPV8NWBlZXD4sLEfE2NuLWaKjDQCqc0GW7aYXY2IiIiI2JkayK6++mpWrlzJO++8Q1xcHH/729+YN28ed955p6PNI488wtSpU5k8eTJXXXUVhw8fJikpiYCAAEebF154gdGjRzN27Fj69u2Ln58fH3/8Me7u7o42b7/9Nl26dGHIkCEMGTKErl27snTpUsd5d3d3PvnkE3x8fOjbty9jx45l9OjRPPfcc/XzY0idSEszQpmHB5weAdtkaYFoEREREddj6jpkACNGjGDEiBHnPG+xWJgxYwYzZsw4ZxsfHx/mz5/P/Pnzz9kmODiYZcuWnbeWmJgYVq1adcGapeGwz7MSFQVn5fMmKT4eVqzQxB4iIiIirsT0QCZSl+yBrCmuSJCSkuL0vnlzP6Aj69aVkpz8Q5WLZIeGhhLTlMd2ioiIiNQzBTJp1E5P2Nmknh/Ly0sDLIwfP77CGU8gl5wcH6666jbgp0qf9fX1Y/fuFIUyERERkXqiQCaNWlPsISsszAZsDBiwgNjYeKdzH35YytGj0L//atq3P+F0LiMjhZUrx5OZmalAJiIiIlJPFMikUWuKgcwuKOhyIiN7OB277DI4ehRyc9sQGdnGnMJERERExMHUWRZF6lpTDmRVsXd82X8XERERETGXApk0agpkzuy/Q0YGnDplbi0iIiIiokAmjVhRkTE8D5rWpB7n4+8PoaHGvnrJRERERMynQCaN1qFDxquPD4SEmFuLK7GHU/sMlCIiIiJiHgUyabTOHq5Y1ZpbTZUCmYiIiIjrUCCTRkvPj1XNHsiOHIGSEnNrEREREWnqFMik0VIgq1rz5tCsGZSXG6FMRERERMyjQCaNlgJZ1SwWDVsUERERcRUKZNJo2QOZZlisTIFMRERExDUokEmjZQ8b6iGr7OwFosvLza1FREREpClTIJNGS0MWzy08HLy8jLXaMjLMrkZERESk6VIgk0YpLw+ys419BbLK3NzO/C4HDphbi4iIiEhTpkAmjZK9dyww0NikMnsgs/9WIiIiIlL/FMikUdKEHhdm/20OHACbzdxaRERERJoqBTJplPT82IW1amUMXTx58szwThERERGpXwpk0ihphsUL8/SEli2N/f37TS1FREREpMlSIJNGST1k1dOmjfGqiT1EREREzKFAJo2SAln12APZ/v16jkxERETEDApk0igpkFWP/TmynBw4edLL7HJEREREmhwFMml0bDbNslhdXl5nniNLS2tmbjEiIiIiTZACmTQ6WVlw6pSx36qVubU0BPZhi0eOBJhah4iIiEhTpEAmjY59hsUWLcDHx9xaGgJ7IFMPmYiIiEj9UyCTRkfPj9WM/TmyvDxvoI3Z5YiIiIg0KQpk0ugokNXM2c+RQX8TKxERERFpejzMLkDkYqWmppKZmVnp+LffRgER+PgcY+vWQ07nUlJS6qm6hqVNG3uQ7W9uISIiIiJNjAKZNEipqal07NiJgoJTVZxdAtzFu+/O5d1351T5+by8k3VaX0PTpg188w1Af2y24yZXIyIiItJ0KJBJg5SZmUlBwSluvnkZLVp0cjq3alUsR47A9dc/yOWX3+Z07scfP2XNmicpLCysz3JdnvEcWTnl5a05cuQkPXuaXZGIiIhI06BAJg1aixadiIzs4XTMnrViYtoSGdnW6VxmpoYsVsXLC1q0OMXRo8349tsARo40uyIRERGRpkGTekijYrNBbq6xHxhobi0NTVSUMYxzyxatRyYiIiJSXxTIpFEpKIDSUmM/QLmiRlq2NALZpk0BlJebXIyIiIhIE6FAJo2KvXfMzw88NCC3RsLD84E8Tpzw5IcfzK5GREREpGlQIJNGRcMVL567uw1YA0BSkrm1iIiIiDQVCmTSqCiQXarVgAKZiIiISH1RIJNGRYHsUhlJbN06OFXVEm8iIiIiUqsUyKRROXl6vWcFsou1h/DwYoqK7AtFi4iIiEhdUiCTRkU9ZJeud2/jR9SwRREREZG6p0AmjUpOjvGqQHbxevc2uhkVyERERETqngKZNBpaFLp2XH11LhYL7NgBR46YXY2IiIhI42ZqIJsxYwYWi8Vpi4iIcJy32WzMmDGDqKgofH196d+/Pzt37nS6RlFREQ8//DChoaH4+/szatQoDh065NQmKyuLhIQErFYrVquVhIQEsrOzndqkpqYycuRI/P39CQ0NZcqUKRQXF9fZd5faV1QEJSXGvgLZxQsKKqNnT2P/iy/MrUVERESksTO9h+yKK64gLS3NsW3fvt1xbs6cOTz//PMsWLCALVu2EBERweDBgzlpn7kBmDp1KitXrmTFihWsW7eOvLw8RowYQVlZmaPNuHHj2LZtG4mJiSQmJrJt2zYSEhIc58vKyhg+fDj5+fmsW7eOFStW8MEHHzBt2rT6+RGkVth7x3x9wdPT3FoauiFDjFcNWxQRERGpWxcVyO69916nUGSXn5/PvffeW6NreXh4EBER4dhatGgBGL1j8+bN4/HHH2fMmDHExcWxZMkSTp06xfLlywHIyclh0aJFzJ07l0GDBtG9e3eWLVvG9u3b+eL0X+2npKSQmJjIG2+8QXx8PPHx8SxcuJBVq1axZ88eAJKSkti1axfLli2je/fuDBo0iLlz57Jw4UJy7X/Kr0JRURG5ublOm5hHwxVrz+DBxuvq1VBebm4tIiIiIo3ZRQWyJUuWUFBQUOl4QUEB//znP2t0rR9//JGoqCjatm3L7bffzi+//ALAvn37SE9PZ4j9r+oBb29v+vXrx/r16wFITk6mpKTEqU1UVBRxcXGONhs2bMBqtdKrVy9Hm969e2O1Wp3axMXFERUV5WgzdOhQioqKSE5OPmfts2fPdgyDtFqtREdH1+i7S+1SIKs9ffpAs2Zw7Bhs3Wp2NSIiIiKNV40CWW5uLjk5OdhsNk6ePOnUM5SVlcWnn35KWFhYta/Xq1cv/vnPf/L555+zcOFC0tPT6dOnD8ePHyc9PR2A8PBwp8+Eh4c7zqWnp+Pl5UVQUNB521RVU1hYmFObivcJCgrCy8vL0aYqjz76KDk5OY7t4MGD1f7uUvsUyGqPlxcMHWrsf/ihubWIiIiINGYeNWncvHlzx+Qb7du3r3TeYrHw9NNPV/t6w4YNc+x36dKF+Ph42rVrx5IlS+jdu7fjmmez2WyVjlVUsU1V7S+mTUXe3t54e3uftxapP5ryvnaNGgUffAAffQR/+5vZ1YiIiIg0TjUKZGvWrMFms3H99dfzwQcfEBwc7Djn5eVF69atnYb91ZS/vz9dunThxx9/ZPTo0YDRexUZGeloc+zYMUdvVkREBMXFxWRlZTn1kh07dow+ffo42hw9erTSvTIyMpyus2nTJqfzWVlZlJSUVOo5E9dlf6xRgax2DB8Obm7www+wfz+0aWN2RSIiIiKNT42GLPbr14/+/fuzb98+Ro8eTb9+/RxbfHz8JYUxMCbJSElJITIykrZt2xIREcHq1asd54uLi1m7dq0jbPXs2RNPT0+nNmlpaezYscPRJj4+npycHDZv3uxos2nTJnJycpza7Nixg7S0NEebpKQkvL296Wmf/1tcnoYs1q6QEPjVr4z9jz4ytxYRERGRxqraPWQ//PADcXFxuLm5kZOT4zQ9fUVdu3at1jWnT5/OyJEjiYmJ4dixY/z9738nNzeXu+++G4vFwtSpU5k1axaxsbHExsYya9Ys/Pz8GDduHABWq5WJEycybdo0QkJCCA4OZvr06XTp0oVBgwYB0KlTJ2644QYmTZrEa6+9BsD999/PiBEj6NChAwBDhgyhc+fOJCQk8Oyzz3LixAmmT5/OpEmTCNSf7hsMBbLaN2oUfP21EcimTDG7GhEREZHGp9qBrFu3bo4JMrp164bFYsFms1VqZ7FYnNYAO59Dhw5xxx13kJmZSYsWLejduzcbN26kdevWADzyyCMUFBQwefJksrKy6NWrF0lJSQQEBDiu8cILL+Dh4cHYsWMpKChg4MCBLF68GHd3d0ebt99+mylTpjhmYxw1ahQLFixwnHd3d+eTTz5h8uTJ9O3bF19fX8aNG8dzzz1X3Z9HTFZUZGygQFabRo2C6dNh7VrIzobmzc2uSERERKRxqXYg27dvn2ONsH379tXKzVesWHHe8xaLhRkzZjBjxoxztvHx8WH+/PnMnz//nG2Cg4NZtmzZee8VExPDqlWrzttGXJe9d8zHx5ghUGpHbCx06gQpKZCYCLffbnZFIiIiIo1LtQOZvdeq4r6IK9BwxbozapQRyD78UIFMREREpLZd1MLQAEuXLqVv375ERUVx4MABAObNm8eHWrRITKBAVndGjTJeP/sMiovNrUVERESksanRtPd2r7zyCk899RRTp05l5syZjmfGmjdvzrx587jppptqtUiRC7EHsrMeL5SLlJKS4vTe0xOCg7tw4oQnb775I9dcc7LSZ0JDQ4mJiamvEkVEREQajYsKZPPnz2fhwoWMHj2aZ555xnH8qquuYvr06bVWnEh1qYfs0uXlpQEWxo8fX8XZN4CJ/OY3iUDl6RZ9ff3YvTtFoUxERESkhi4qkO3bt4/u3btXOu7t7U1+fv4lFyVSUwpkl66wMBuwMWDAAmJj453OpaYGkpgIvr4PcuedfXE7a7BzRkYKK1eOJzMzU4FMREREpIYuKpC1bduWbdu2VZrc47PPPqNz5861UphITSiQ1Z6goMuJjOzhdCwszJj6vqDAk6KiHlx2mUnFiYiIiDQyFxXI/vjHP/LQQw9RWFiIzWZj8+bNvPPOO8yePZs33nijtmsUuSB7ILNaza2jsXJ3N6a/37oVtm9HgUxERESkllxUILvnnnsoLS3lkUce4dSpU4wbN46WLVvy4osvcrvmxZZ6VlwMhYXGvib1qDtduhiBLCUFhg8Hj4v6t4eIiIiInO2ipr3Pzs5m0qRJHDhwgGPHjpGens7BgweZOHEiP/30U23XKHJe9t4xLy9jYWipGzExRuAtKgL931xERESkdlxUILvxxhspPN0lERoaSlhYGAB79uyhf//+tVacSHXo+bH64eYGV1xh7O/YYW4tIiIiIo3FRQWyoKAgRo8eTWlpqeNYSkoK/fv355Zbbqm14kSqQ8+P1Z8uXYzXPXu0SLSIiIhIbbioQPbBBx+Qn5/PuHHjsNls7Nixg/79+3PHHXfw4osv1naNIuelRaHrT2QkBAdDaSns3m12NSIiIiIN30UFMh8fH1atWsWPP/7Ir3/9awYOHMhdd93F888/X9v1iVyQhizWH4sF4uKMfQ1bFBEREbl01Q5kubm5TpvFYuHdd99l8+bN3HLLLTz55JOOcyL1SUMW65c9kP38M5w6ZW4tIiIiIg1dtSeubt68ORaLpdJxm83Gq6++ymuvvYbNZsNisVBWVlarRYqcj3rI6leLFsbQxbQ0+P57aNPG7IpEREREGq5qB7I1a9bUZR0iF02BrP716AGffALJydC6tdnViIiIiDRc1Q5k/fr1q8s6RC5KaamFggJjX4Gs/nTpAklJcPw4pKc3M7scERERkQar2oHsbD/88EOVxy0WCz4+PsTExODt7X1JhYlUR16eF2AsCq1/5OqPt7fxLNl330FKSqjZ5YiIiIg0WBcVyLp161bl82R2np6e3Hbbbbz22mv4+PhcdHEiF5Kf7wkYvWPn+UdS6sBVVxmBbN++5kCw2eWIiIiINEgXNe39ypUriY2N5fXXX2fbtm189913vP7663To0IHly5ezaNEivvzyS5544onarlfESX6+0UOm4Yr1LzISIiKgrMwNuMvsckREREQapIvqIZs5cyYvvvgiQ4cOdRzr2rUrrVq14sknn2Tz5s34+/szbdo0nnvuuVorVqQi+5BFBbL6Z7FAz57G5B5wPzZbgdkliYiIiDQ4F9VDtn37dlpXMbVa69at2b59O2AMa0xLS7u06kQu4Owhi1L/unQBD48yoBPffafJPURERERq6qICWceOHXnmmWcoLi52HCspKeGZZ56hY8eOABw+fJjw8PDaqVLkHDRk0Vze3nD55VkAvP++JvcQERERqamLGrL4f//3f4waNYpWrVrRtWtXLBYLP/zwA2VlZaxatQqAX375hcmTJ9dqsSIVqYfMfJ07Z7B7dyirVwexbx+0bWt2RSIiIiINx0UFsj59+rB//36WLVvG3r17sdls3HrrrYwbN46AgAAAEhISarVQkaroGTLzhYYWAJ9TXj6UuXNhwQKzKxIRERFpOC4qkAE0a9aMBx98sDZrEakhH4qKjH+EFcjM9g9gKIsWwVNPQViY2fWIiIiINAzVDmQfffQRw4YNw9PTk48++ui8bUeNGnXJhYlcWEsAPD1By92ZbQ1XXJHPzp3+zJ8Pf/ub2fWIiIiINAzVDmSjR48mPT2dsLAwRo8efc52FouFsrKy2qhN5AKiAS0K7SomTDjKH/94GQsWwCOPwOnRyyIiIiJyHtWeZbG8vJyw0+OQysvLz7kpjEn9aQVouKKr6Ncvm/btITsbFi40uxoRERGRhqHagSw4OJjMzEwA7r33Xk6ePFlnRYlUjxHIrFaTyxAA3N2NnjGA55+Hs1bFEBEREZFzqHYgKy4uJjc3F4AlS5ZQWFhYZ0WJVI8xZFFD41zH+PEQFQWHD8Mrr5hdjYiIiIjrq/YzZPHx8YwePZqePXtis9mYMmUKvr6+VbZ98803a61AkXPTkEVX4+0NM2bA/ffD009DQgIEB5tdlYiIiIjrqnYP2bJly7jxxhvJy8vDYrGQk5NDVlZWlZtI/dCQRVd0773QpQtkZRmhTERERETOrdo9ZOHh4TzzzDMAtG3blqVLlxISElJnhYlc2JlZFsV1uLsbz5ANHgwvvwyTJ0OHDmZXJSIiIuKaqt1DdrZ9+/YpjImpioosQAtAgcwVDRoEI0ZAaSlMn252NSIiIiKuq9o9ZGf761//et7zTz311EUVI1Jdx455AuDhUYaPj7vJ1UhVnnsOEhNh1Sr44gsjpImIiIiIs4sKZCtXrnR6X1JSwr59+/Dw8KBdu3YKZFLnjh3zAsDfvwSLRYHMFaSkpFQ6duutrVixIoz77ivk7bd34+tb7nQ+NDSUmJiY+ipRRERExOVcVCD77rvvKh3Lzc1lwoQJ3HzzzZdclMiFHD1qD2TFgI+5xTRxeXlpgIXx48dXcbY5sIMDB1ryq19tBH7jdNbX14/du1MUykRERKTJuqhAVpXAwED++te/MmLECBISEmrrsiJVOnrUGLLYrFmJyZVIYWE2YGPAgAXExsZXOn/48Ck++QTgQYYMGUybNjkAZGSksHLleDIzMxXIREREpMmqtUAGkJ2dTU5OTm1eUqRK9mfIjB4ycQVBQZcTGdmj0vHISDhxAjZsgHXr2tGlCzRrZkKBIiIiIi7oogLZSy+95PTeZrORlpbG0qVLueGGG2qlMJHzOTNkUT1kDcH118Mvv8DRo/DhhzBunNkViYiIiLiGi5r2/oUXXnDaXnrpJb766ivuvvtuXn/99YsqZPbs2VgsFqZOneo4ZrPZmDFjBlFRUfj6+tK/f3927tzp9LmioiIefvhhQkND8ff3Z9SoURw6dMipTVZWFgkJCVitVqxWKwkJCWRnZzu1SU1NZeTIkfj7+xMaGsqUKVMoLlbvi6uyB7JmzfS/UUPg4QFjxhivP/0Ea9aYXZGIiIiIa7ioHrJ9+/bVahFbtmzh9ddfp2vXrk7H58yZw/PPP8/ixYtp3749f//73xk8eDB79uwhICAAgKlTp/Lxxx+zYsUKQkJCmDZtGiNGjCA5ORl3d2P2vXHjxnHo0CESExMBuP/++0lISODjjz8GoKysjOHDh9OiRQvWrVvH8ePHufvuu7HZbMyfP79Wv6vUjjNDFtVD1lCEhcGNN8JHH8E330B5eajZJYmIiIiY7qJ6yGpTXl4ed955JwsXLiQoKMhx3GazMW/ePB5//HHGjBlDXFwcS5Ys4dSpUyxfvhyAnJwcFi1axNy5cxk0aBDdu3dn2bJlbN++nS+++AIwpuJOTEzkjTfeID4+nvj4eBYuXMiqVavYs2cPAElJSezatYtly5bRvXt3Bg0axNy5c1m4cCG5ubn1/6PIeRUVwYkTeoasIereHfr3N/b/979oQLOyioiISNN20YFsy5YtPPLII9x+++2MGTPGaauJhx56iOHDhzOowqqx+/btIz09nSFDhjiOeXt7069fP9avXw9AcnIyJSUlTm2ioqKIi4tztNmwYQNWq5VevXo52vTu3Rur1erUJi4ujqioKEeboUOHUlRURHJy8jlrLyoqIjc312mTunf4sH3vFN7eZWaWIhfhuuugZ08AC7Cc5GTN8CEiIiJNV7UD2UsvvURhYSEAK1asoG/fvuzatYuVK1dSUlLCrl27+PLLL7FardW++YoVK9i6dSuzZ8+udC49PR2A8PBwp+Ph4eGOc+np6Xh5eTn1rFXVJiwsrNL1w8LCnNpUvE9QUBBeXl6ONlWZPXu247k0q9VKdHT0hb6y1IIzjwgewmIxsxK5GBaLMXSxTZtswIcpU9rx0UdmVyUiIiJijmo/Q/bCCy9w55134uPjw6xZs3jhhRd46KGHCAgI4MUXX6Rt27Y88MADREZGVut6Bw8e5He/+x1JSUn4+Jx7YV9LhT9x22y2SscqqtimqvYX06aiRx99lD/84Q+O97m5uQpl9eDsQGYsPCwNjZsbXH/9Pt58M53CwmHcfDO89BI89JDZlZ1hsxmzQu7YAV9/ncXevSXk57tz6pQbp065ExhYSkxMETExRbRtW0jHjqdwq/BXXKGhoVpjTURERM6r2oHs7Ik8fv75Z2688UbAGEaYn5+PxWLh97//Pddffz1PP/30Ba+XnJzMsWPH6GmMXQKMyTW+/vprFixY4Hi+Kz093SnkHTt2zNGbFRERQXFxMVlZWU69ZMeOHaNPnz6ONkePHq10/4yMDKfrbNq0yel8VlYWJSUllXrOzubt7Y23t/cFv6vUroMHHXsokDVcHh42YBSjR6fxn/+E8tvfwr598I9/wOn5eOqNzQYHDsDWrZCcbGxbt0JGhr1F0Pk+ftph4APgX8D/ABu+vn7s3p2iUCYiIiLnVO0hi9dff71jqvigoCDy8vIAaNmyJTt27ACMhaFPnTpVresNHDiQ7du3s23bNsd21VVXceedd7Jt2zYuu+wyIiIiWL16teMzxcXFrF271hG2evbsiaenp1ObtLQ0duzY4WgTHx9PTk4OmzdvdrTZtGkTOTk5Tm127NhBWlqao01SUhLe3t5OgVFcg3MPmTRspTzxRCozZxrv5s6FPn3ghx/q5m6pqakkJ2/l4493MGfOL9xzTzq9e+cSFFRK27Zwyy0waxZ8/rkRxtzcbERE5AIf0L79Dq655jB9+6bSv/9+evU6RMeOmURGnsTTswxoCUwBviEgoICuXTdTUACZmZl182VERESkUah2D9mVV16Jp6cxs92vfvUrvvzyS7p06cLYsWP53e9+x5dffsnq1asZOHBgta4XEBBAXFyc0zF/f39CQkIcx6dOncqsWbOIjY0lNjaWWbNm4efnx7jTq8parVYmTpzItGnTCAkJITg4mOnTp9OlSxfHJCGdOnXihhtuYNKkSbz22muAMe39iBEj6NChAwBDhgyhc+fOJCQk8Oyzz3LixAmmT5/OpEmTCAwMrO5PJPVEgaxxsVjgscfgssvggQdg82Zj0o8//QmeeALOM6K5WkpL4bvvYOXKLP7xj12Ul18NhFTRshjYASQ7tvLyHaSnG8/OXnXVV8TGxlXxOeMev/wCu3bB7t1w8qQ3P/xwNZDKK68U88wzEFLVLUVERKTJq9EzZHYvvfQSBQUFgPEclaenJ+vWrWPMmDE8+eSTtVbcI488QkFBAZMnTyYrK4tevXqRlJTkWIPMXpeHhwdjx46loKCAgQMHsnjxYscaZABvv/02U6ZMcczGOGrUKBYsWOA47+7uzieffMLkyZPp27cvvr6+jBs3jueee67WvovUnjNDFhXIGpPbb4drr4WHH4aVK2HmTHjzTbjnHrj3XmjXrnrXKSuDbduMxae/+spY88yYADUIuAEAN7dygoMLCA0tIDT0FC1anCI4uOD0UMmepzfDjz9+ypo1TzomNaqKhwe0b29sJSXG/b/5poiTJ0N44w147z2YPh2mToWz/vUlIiIiUrOFoe3Tuvv4+ODj4+N4/+CDD/Lggw9ecjFfffWV03uLxcKMGTOYMWPGOT/j4+PD/Pnzz7uAc3BwMMuWLTvvvWNiYli1alVNyhWTnOkhO3i+ZtJApKSkOL1/4gno06c5c+a0Ii3Ni1mzjGGEPXue5IorThETU0hkZB5Wqxv5+e7k5bmTleXBjz/6kpLix969vhQWOj+E1qxZKe3bp7F16/PcfPP9XHFFJ9zd/QH/C9aXmZlywTZn8/SEq6+GqKidvPHGbGJjl/Djj3489ZQxccljjxmTl3h51eiyIiIi0kjVKJA1b978gjMcgjE5h0hdKC42Zr4zqIesIcvLSwMsjB8//hwtPIFRwH3AEJKTA0hOrm73UjbwNfAV8BV5ed+zdWs5AL6+o3F373QppVeLMePi+zz55GgOH+7DK69Ekprqwx/+AC+8UMjUqYfp1y+nyqUbNDujiIhI01GjQLZmzRrHvs1m48Ybb+SNN96gZcuWtV6YSFXsi0J7e5dTVHTc3GLkkhQWZgM2BgxYQGxs/Hnbnjy5i9TUQHJyfDhyJJcTJ8DLKwI/P3e8vMrx9i6lefNCWrQ4RWjoKQIDi3BzawWMP71Vb+hhbbIHzrvusgdOd+Ae4G8cPBjBtGntgC+B3wLOvXCanVFERKTpqFEg69evn9N7d3d3evfuzWWXXVarRYmci324YlhY8VnPkklDFhR0OZGRPc7bJjLSeD4LYPv21fz73+MZMSKRLl2GntXq/IvS13To4aU6V+AsLk5n2zbYvj2MsrLrcXPbQffu6XTrdhR3dxsZGSmsXDmezMxMBTIREZEmoEaBTMRsZwJZiQKZNAhVBc7WraFfP/jsM9i7143k5CgOHoxi1Cho0cKkQkVERMQU1V6HTMQV2ENYeHixuYWIXKLmzY2ZJW+5Bfz84NgxWLQItm9XIhMREWlKLjmQVWeSD5HaYu8hCw8vMbcQkVpgsUBcnDHr4hVXgM0GGzZEA+9w6pT+vkxERKQpqNGQxTFjxji9Lyws5MEHH8Tf33nq6H//+9+XXplIFc5+hkyksfDzM3rKoqPh889t2Gy3c/fdBXz22Zln50RERKRxqlEgs1qdH5o/93TVInVDPWTSWFks0KsXeHnt5aOPmvHLLy3p2xc+/xx6nH/OExEREWnAahTI3nrrrbqqQ6Ra9AyZNHYREfnAdXTu/Au7dvnTvz98/LExCYiIiIg0PnpIQRqMsxeFVg+ZNG7HeOWVH+nfH06ehKFD4aOPzK5JRERE6oICmTQYaWnGpAfe3tC8eanZ5YjUqWbNyvnsMxg1CoqKYMwYWLnS7KpERESktimQSYNhH67YqpXxvI1IY+fjAx98AOPHQ1kZ3HYbfPqp2VWJiIhIbdLC0NJg2Cf0aNXK3DpE6kNKSopjf8oUOHasDUlJwdx8czkvvvgz11xzssrPhYaGEhMTU19lioiIyCVSIJMGQ4FMmoK8vDTAUsUsth7AvyguHs1vftMSGAqsq/R5X18/du9OUSgTERFpIBTIpMGwD1mMjja3DpG6VFiYDdgYMGABsbHxTufKyiwkJeVw8KAVL681jBz5IyEhBY7zGRkprFw5nszMTAUyERGRBkKBTBoM9ZBJUxIUdDmRkZUXIEtIgGXLIDXVg88/78TEidC8ef3XJyIiIrVDk3pIg6FAJgKennD77RAWBnl5Rjg7dcrsqkRERORiKZBJg6EhiyIGX1+4806wWuH4cVi+3FinT0RERBoeBTJpEEpKID3d2FcPmQgEBhrT4fv6wuHD8K9/QXm52VWJiIhITSmQSYNw5IixKLSXF4SGml2NiGsIDYVx48DDA376CdaubW12SSIiIlJDCmTSIKSmGq8xMeCmf2pFHFq1gl//2lgs/ccfQ4BnzC5JREREakB/tJUG4cAB41UzeYtU1r49jBplf/cnli9vYWY5IiIiUgMKZNIgnN1DJiKVdesG11xzGIAXXmhFYqK59YiIiEj1KJBJg2APZK31iIzIOV155VHgDcrLLdx+O+zda3ZFIiIiciEKZNIgqIdM5MIsFoCHuPLKPHJy4KabIDfX7KpERETkfBTIpEFQIBOprmLmzPmFli1h925jvTJNhy8iIuK6FMjE5dlsmtRDpCZCQ0v5z3/AxwdWrYLZs82uSERERM5FgUxcXnY25OUZ+9HRppYi0mBcdRW88oqx/5e/wIYN5tYjIiIiVVMgE5dnH67YogX4+ppbi0hDcvfdxsLRZWVwxx3GX26IiIiIa/EwuwCRC9HzYyI1k5KS4th/4AE31q7txIED3tx22wlmzdp/evIPZ6GhocTo/2QiIiL1ToFMXJ6mvBepnry8NMDC+PHjK5y5BlhHUlIwSUl/BN6s9FlfXz92705RKBMREalnCmTi8jShh0j1FBZmAzYGDFhAbGy807lt246xeXNLPDxe59Zbf09gYLHjXEZGCitXjiczM1OBTEREpJ4pkInL05BFkZoJCrqcyMgeTsciIuDoUThwwJ2NG+O46y6qHLooIiIi9UuTeojLUyATuXQWC4waBZ6esH8/fPut2RWJiIgIKJBJA6BAJlI7goNh4EBjf/VqyMoytx4RERFRIBMXV1ICR44Y+wpkIpfummuMCXJKSuCjj4yF10VERMQ8CmTi0g4dMv7A6O1trEMmIpdGQxdFRERciwKZuLSzhyu66Z9WkVpRcehibq6XuQWJiIg0Yfojrrg0PT8mUjfOHrq4dm1rQFMuioiImEGBTFyaAplI3Th76GJaWgDwoNkliYiINEkKZOLSFMhE6s7ZQxdhDocPa+iiiIhIfTM1kL3yyit07dqVwMBAAgMDiY+P57PPPnOct9lszJgxg6ioKHx9fenfvz87d+50ukZRUREPP/wwoaGh+Pv7M2rUKA4dOuTUJisri4SEBKxWK1arlYSEBLKzs53apKamMnLkSPz9/QkNDWXKlCkUFxfX2XeX6jlwwHhVIBOpG9dcA5GRJ4FmPP10a8rLza5IRESkaTE1kLVq1YpnnnmGb7/9lm+//Zbrr7+em266yRG65syZw/PPP8+CBQvYsmULERERDB48mJMnTzquMXXqVFauXMmKFStYt24deXl5jBgxgrKyMkebcePGsW3bNhITE0lMTGTbtm0kJCQ4zpeVlTF8+HDy8/NZt24dK1as4IMPPmDatGn192NIldRDJlK3LBbo1+8AkE9ycgBvvGF2RSIiIk2LqYFs5MiR3HjjjbRv35727dszc+ZMmjVrxsaNG7HZbMybN4/HH3+cMWPGEBcXx5IlSzh16hTLly8HICcnh0WLFjF37lwGDRpE9+7dWbZsGdu3b+eLL74AICUlhcTERN544w3i4+OJj49n4cKFrFq1ij179gCQlJTErl27WLZsGd27d2fQoEHMnTuXhQsXkpuba9rv09TZbGcCWevW5tYi0pgFBhYDjwPwxz8ay02IiIhI/XCZZ8jKyspYsWIF+fn5xMfHs2/fPtLT0xkyZIijjbe3N/369WP9+vUAJCcnU1JS4tQmKiqKuLg4R5sNGzZgtVrp1auXo03v3r2xWq1ObeLi4oiKinK0GTp0KEVFRSQnJ5+z5qKiInJzc502qT1ZWZCfb+y3amVuLSKN33zi4vLJzYXf/EYLRouIiNQX0wPZ9u3badasGd7e3jz44IOsXLmSzp07k56eDkB4eLhT+/DwcMe59PR0vLy8CAoKOm+bsLCwSvcNCwtzalPxPkFBQXh5eTnaVGX27NmO59KsVivR0dE1/PZyPvbesbAw8PU1txaRxq+cp546gKcnrFoFK1aYXY+IiEjTYHog69ChA9u2bWPjxo385je/4e6772bXrl2O8xaL89o4Nput0rGKKrapqv3FtKno0UcfJScnx7EdPHjwvHVJzWhCD5H61a5dIU8+aexPmQIZGebWIyIi0hSYHsi8vLy4/PLLueqqq5g9ezZXXnklL774IhEREQCVeqiOHTvm6M2KiIiguLiYrKys87Y5evRopftmZGQ4tal4n6ysLEpKSir1nJ3N29vbMUOkfZPaowk9ROrfn/4EXbpAZiZMnWp2NSIiIo2f6YGsIpvNRlFREW3btiUiIoLVq1c7zhUXF7N27Vr69OkDQM+ePfH09HRqk5aWxo4dOxxt4uPjycnJYfPmzY42mzZtIicnx6nNjh07SEtLc7RJSkrC29ubnj171un3lXNTIBOpf15e8Oab4OYGy5cbwxdFRESk7niYefPHHnuMYcOGER0dzcmTJ1mxYgVfffUViYmJWCwWpk6dyqxZs4iNjSU2NpZZs2bh5+fHuHHjALBarUycOJFp06YREhJCcHAw06dPp0uXLgwaNAiATp06ccMNNzBp0iRee+01AO6//35GjBhBhw4dABgyZAidO3cmISGBZ599lhMnTjB9+nQmTZqkXi8T7d9vvGqGRZH6ddVVMG0aPPssPPgg7NwJVqvZVYmIiDROpgayo0ePkpCQQFpaGlarla5du5KYmMjgwYMBeOSRRygoKGDy5MlkZWXRq1cvkpKSCAgIcFzjhRdewMPDg7Fjx1JQUMDAgQNZvHgx7u7ujjZvv/02U6ZMcczGOGrUKBYsWOA47+7uzieffMLkyZPp27cvvr6+jBs3jueee66efgmpij2QtW1rahkiTUZKSopj/6abLKxY0YmDB32YODGDxx6r+hnZ0NBQYtSNLSIictFMDWSLFi0673mLxcKMGTOYMWPGOdv4+Pgwf/585s+ff842wcHBLFu27Lz3iomJYZXG5riUffuM1zZtTC1DpNHLy0sDLIwfP77CmeuAtXzwQQs++ODXwNpKn/X19WP37hSFMhERkYtkaiATAUhNTSUzM9PpWH6+G8ePdwMgO3sbW7eWO50/+2/yReTSFBZmAzYGDFhAbGy807lvvskgJaUFgYGJ3HprCh4eZxYoy8hIYeXK8WRmZiqQiYiIXCQFMjFVamoqHTt2oqDgVIUzccB24AT9+3c/5+fz8k7WZXkiTUpQ0OVERvZwOnbTTXD4MOTm+rB3b3cGDjSpOBERkUZKgUxMlZmZSUHBKW6+eRktWnRyHD9wwMrnn0NoqA9jxiRX+tyPP37KmjVPUlhYWJ/lijQ53t4wbBi8+y6sX29MiR8WZnZVIiIijYcCmbiEFi06Of3NvH3K+xYt/Cr9jT1AZqaGLIrUl44doUMH2LPHmAb/nnvAYjG7KhERkcbB5dYhEwHIzjZemzc3swoRsRs2DDw94eBB2LrV7GpEREQaDwUycUkKZCKuxWqF66839r/4AvLyzK1HRESksVAgE5ekQCbieq65BiIjobAQkpLMrkZERKRxUCATl5SVZbwqkIm4Djc3GDHCeH5s+3Y4dCjA7JJEREQaPAUycTmFhVBUZOwrkIm4lqgoo6cM4JtvYgAfU+sRERFp6BTIxOXYe8f8/cHLy9xaRKSyAQMgIABOnvQGnjC7HBERkQZNgUxcjp4fE3Ft3t5w4432d4/w88/qJRMREblYCmTichTIRFxfx47QunU24MnMmTGUl5tdkYiISMOkQCYuR4FMpGHo2/cgkMf33zdj8WKzqxEREWmYFMjE5SiQiTQMzZqVAH8B4E9/ghMnzK1HRESkIVIgE5ejQCbSkLzEZZcVkJkJT2h+DxERkRpTIBOXYrOdmWUxKMjcWkSkOkr5058OAvDqq/DttyaXIyIi0sAokIlLKSiAkhJj32o1txYRqZ6rrsrjzjuNv1CZPBlN8CEiIlIDCmTiUuy9YwEB4OFhbi0iUn3PPmv8/3bLFli0yOxqREREGg4FMnEpen5MpGGKjIS//tXY//OfITPT3HpEREQaCgUycSkKZCIN129/C126GLMtPvaY2dWIiIg0DApk4lIUyEQaLg8P+L//M/bfeAM2bTK3HhERkYZAT+mIS1EgE2l4UlJSHPv+/jB8eGs++SSECRNO8c9/7sbdvfJnQkNDiYmJqccqRUREXJMCmbgUeyDTlPciri8vLw2wMH78+ApnwoA97N7dnGuuWQi8Wumzvr5+7N6dolAmIiJNngKZuAybTT1kIg1JYWE2YGPAgAXExsY7ndux4yTr1zfHy2s+t932G3x9Sx3nMjJSWLlyPJmZmQpkIiLS5CmQicvIz4fSUrBYIDDQ7GpEpLqCgi4nMrKH07HwcPjlF0hP92D79q7cdJNJxYmIiLg4TeohLuPECeM1MJAqnzkRkYbDzQ1uvNHY37YNDh0ytRwRERGXpUAmLsMeyEJCzK1DRGpHdDRceaWx/+mnUF5ubj0iIiKuSIFMXIY9kGlCD5HGY9Ag8PaGtDTYutXsakRERFyPApm4jKws4zU42Nw6RKT2NGsGAwYY+19+CadOmVuPiIiIq1EgE5dh7yFTIBNpXK6+2pjko6AA/vtfs6sRERFxLQpk4jIUyEQaJzc3GDbM2N+6FTIy/MwtSERExIUokIlLKCx0p7DQ2NczZCKNT+vW0LWrsb9uXTRgMbUeERERV6FAJi4hN9cbgIAA8PQ0uRgRqRODBoGXF2Rk+AP3ml2OiIiIS1AgE5dgD2QarijSeAUEQP/+9nfPkJOjBQdFREQUyMQl5OQokIk0BddcA0FBBUAoL78cZXY5IiIiplMgE5dg7yHT82MijZu7O/TtexCADz4I5dtvTS5IRETEZApk4hI0ZFGk6YiKygPexmazcN99UFJidkUiIiLmUSATl6BAJtLU/B6rtZTvv4e5c82uRURExDwKZOICAigoMKZWVCATaSoymDbtEAAzZsDeveZWIyIiYhYFMnEB7QDw9wdvb5NLEZF6c+ONJxgyBIqK4P77obzc7IpERETqnwKZuIDLAfWOiTQ1Fgu89hr4+cHatfDGG2ZXJCIiUv9MDWSzZ8/m6quvJiAggLCwMEaPHs2ePXuc2thsNmbMmEFUVBS+vr7079+fnTt3OrUpKiri4YcfJjQ0FH9/f0aNGsWhQ4ec2mRlZZGQkIDVasVqtZKQkEB2drZTm9TUVEaOHIm/vz+hoaFMmTKF4uLiOvnucjYFMpGmqk0bmDnT2J8+HQ4cMLUcERGRemdqIFu7di0PPfQQGzduZPXq1ZSWljJkyBDy8/MdbebMmcPzzz/PggUL2LJlCxEREQwePJiTJ0862kydOpWVK1eyYsUK1q1bR15eHiNGjKCsrMzRZty4cWzbto3ExEQSExPZtm0bCQkJjvNlZWUMHz6c/Px81q1bx4oVK/jggw+YNm1a/fwYTZoRyDTlvUjT9PDD0LcvnDwJEyZo6KKIiDQtHmbePDEx0en9W2+9RVhYGMnJyVx33XXYbDbmzZvH448/zpgxYwBYsmQJ4eHhLF++nAceeICcnBwWLVrE0qVLGTRoEADLli0jOjqaL774gqFDh5KSkkJiYiIbN26kV69eACxcuJD4+Hj27NlDhw4dSEpKYteuXRw8eJCoKGOx0rlz5zJhwgRmzpxJYGBgPf4yTY3xDJl6yESaJnd3WLIErrwSvvoKXnoJpk41uyoREZH6YWogqygnJweA4NN/Mt+3bx/p6ekMGTLE0cbb25t+/fqxfv16HnjgAZKTkykpKXFqExUVRVxcHOvXr2fo0KFs2LABq9XqCGMAvXv3xmq1sn79ejp06MCGDRuIi4tzhDGAoUOHUlRURHJyMgMGDKhUb1FREUVFRY73ubm5tfdjNCkasijSFKWkpDi9nzIllNmzY/jzn8uJjt5N27aFlT4TGhpKTExMfZUoIiJS51wmkNlsNv7whz/wq1/9iri4OADS09MBCA8Pd2obHh7OgdMPGqSnp+Pl5UVQhfFu4eHhjs+np6cTFhZW6Z5hYWFObSreJygoCC8vL0ebimbPns3TTz9d068qZykosACtAAUykaYiLy8NsDB+/Pgqzn5GUdEN3HprPtAHKHU66+vrx+7dKQplIiLSaLhMIPvtb3/LDz/8wLp16yqds1gsTu9tNlulYxVVbFNV+4tpc7ZHH32UP/zhD473ubm5REdHn7cucXb4sDHPvbd3Kb6+LvOPo4jUocLCbMDGgAELiI2NdzqXn+/J+++XUlR0Nd27p3L11WmOcxkZKaxcOZ7MzEwFMhERaTRc4k/ADz/8MB999BFff/01rVq1chyPiIgAjN6ryMhIx/Fjx445erMiIiIoLi4mKyvLqZfs2LFj9OnTx9Hm6NGjle6bkZHhdJ1NmzY5nc/KyqKkpKRSz5mdt7c33lo465IcPGj8foGBRbjIP44iUk+Cgi4nMrJHpeMjRsAHH8C2bZF06xaJspeIiDRmps6yaLPZ+O1vf8u///1vvvzyS9q2bet0vm3btkRERLB69WrHseLiYtauXesIWz179sTT09OpTVpaGjt27HC0iY+PJycnh82bNzvabNq0iZycHKc2O3bsIC3tzN/GJiUl4e3tTc+ePWv/ywtQMZCJiEBcnDHBh80G//43FFZ+lExERKTRMLVL4qGHHmL58uV8+OGHBAQEOJ7Vslqt+Pr6YrFYmDp1KrNmzSI2NpbY2FhmzZqFn58f48aNc7SdOHEi06ZNIyQkhODgYKZPn06XLl0csy526tSJG264gUmTJvHaa68BcP/99zNixAg6dOgAwJAhQ+jcuTMJCQk8++yznDhxgunTpzNp0iTNsFiHDh1SIBORyoYNg9RUyMqCTz+F0xPtioiINDqm9pC98sor5OTk0L9/fyIjIx3bu+++62jzyCOPMHXqVCZPnsxVV13F4cOHSUpKIiAgwNHmhRdeYPTo0YwdO5a+ffvi5+fHxx9/jLu7u6PN22+/TZcuXRgyZAhDhgyha9euLF261HHe3d2dTz75BB8fH/r27cvYsWMZPXo0zz33XP38GE3UgQM+AFitCmQicoa3N9x8M1gssH07/PCD2RWJiIjUDVN7yGw22wXbWCwWZsyYwYwZM87ZxsfHh/nz5zN//vxztgkODmbZsmXnvVdMTAyrVq26YE1Se/bvN3rImjfXmCQRcRYdDf36GWuTffIJjBnjZXZJIiIitc7UHjJp2nJyIDPT+AOWApmIVOXaa41gVlwMa9a0Adwv9BEREZEGRYFMTLN7t33vMF5e5WaWIiIuys3NeH7M2xuOHm0GPGZ2SSIiIrVKgUxMk5Li2DOzDBFxcc2bw4032t89xQ8/+JtYjYiISO1SIBPTnOkh232+ZiIidO0Kl19+AvDgiSfakJtrdkUiIiK1Q4FMTKMeMhGpiV/9KhXYz+HD3jz8sNnViIiI1A4FMjHNmUCmHjIRuTDjWdM7cXOz8c9/wooVZlckIiJy6RTIxBRFRfDLL/Z36iETkepaz8SJ6QA8+CAcOGByOSIiIpdIgUxM8dNPUFYG/v5lQJrZ5YhIA3LffWn07m0snZGQYPy7REREpKFSIBNT2Cf0aNNG64+JSM14eMCyZdCsGXzzDTzzjNkViYiIXDwFMjGF/fkxBTIRuRjt2sH//Z+x/5e/wObN5tYjIiJysRTIxBT2HrK2bRXIROTiJCTAbbcZQxbHjYO8PLMrEhERqTkFMjGFeshE5FJZLPDKKxAdDT//DFOmmF2RiIhIzXmYXYA0PeXl6iETkYuXkuI8M+tTTzXj/vtjeestCx06/MLgwdmVPhMaGkpMTEw9VSgiIlJ9CmRS7w4dglOnjAfzW7YsMrscEWkg8vLSAAvjx4+v4uzfgcf585+D+POf+wGHnM76+vqxe3eKQpmIiLgcBTKpd/besdhY8PQ0txYRaTgKC7MBGwMGLCA2Nt7pXHk5fPhhPhkZQYSHpzBy5F7cTg/Kz8hIYeXK8WRmZiqQiYiIy1Egk3pnH23UsaO5dYhIwxQUdDmRkT0qHb/9dnj9dTh6tBk7d/ZgyBATihMREakhTeoh9c7eQ9apk7l1iEjjEhwMN91k7G/YcOYvf0RERFyZApnUO/WQiUhd6dQJ4k+PZvzwQzhxwtx6RERELkSBTOqdeshEpC4NHGhMhV9UBO+9ByUl+k+diIi4Lv1XSupVVhYcPWrsd+hgbi0i0ji5u8Ott4K/v/HvmzVrWgMWs8sSERGpkgKZ1Ktdu4zXVq0gIMDcWkSk8QoMhLFjjXC2f38QxrT4IiIirkeBTOrV998br126mFuHiDR+MTEwapT93WN8/HGwmeWIiIhUSYFM6pU9kF15pbl1iEjT0LUrdO+eBsDf/x7D11+bXJCIiEgFCmRSr+yBrFs3U8sQkSbkqqvSgH9RWurGqFGwbZvZFYmIiJyhQCb1pqwMtm839tVDJiL1xWIBuIvu3U+SkwNDh8LevWZXJSIiYlAgk3rz009w6hT4+kJsrNnViEjTUsgLL/xM9+5w7BgMHgyHDpldk4iIiAKZ1CP7cMW4OGPmMxGR+hQQUE5iIrRvD6mpRihLTze7KhERaeoUyKTe6PkxETFbWBisXm0sHL17N/TvD0eOmF2ViIg0ZR5mFyBNh2ZYFBEzpaSkOPbnz/fiwQdj2bPHm969C3n11R+JiCip9JnQ0FBiYmLqs0wREWliFMik3thnNlMgE5H6lJeXBlgYP358hTOtgTUcPNiW4cP9geuBA04tfH392L07RaFMRETqjAKZ1Ivjx+HwYWO/a1dzaxGRpqWwMBuwMWDAAmJj453O5eXlsWpVIbm5l+Hnt5dhw34mJKQAgIyMFFauHE9mZqYCmYiI1BkFMqkX9uGKl10GgYHm1iIiTVNQ0OVERvaodHziRFi2DDIyvPj4407cdpvx7yoREZH6oEk9pF7o+TERcVWBgXDvvdC6NRQXw9tvww8/mF2ViIg0FQpkUi8UyETElfn4wPjxcMUVUF4OK1fCtm3hZpclIiJNgAKZ1AtN6CEirs7DA265BXr3Nt5v3twSmE9ZmalliYhII6dAJnWuuBh27TL2FchExJVZLDB0qLGBDfgtf/rTZRQUmFyYiIg0WgpkUud274aSEuM5jTZtzK5GROTCeveGQYP2AYWsWdOcAQMgPd3sqkREpDFSIJM6d/bzYxaLubWIiFTXZZdlA4MJDCxl0ya45poz/z4TERGpLQpkUuf0/JiINFzrWLx4D+3bw8GD0LcvfPSR2TWJiEhjokAmdU6BTEQastati9i4EQYOhPx8GD0a5swBm83sykREpDHQwtBSp8rKYPNmY//qq82tRUTkYqSkpAAwcyYEBUXz/vst+NOfYN264zz2WCpeXpWTWWhoKDExMfVdqoiINEAKZFKndu6EvDxo1gzi4syuRkSk+vLy0gAL48ePr3DmIeBFPv44hI8/3gWMATKdWvj6+rF7d4pCmYiIXJCpQxa//vprRo4cSVRUFBaLhf/85z9O5202GzNmzCAqKgpfX1/69+/Pzp07ndoUFRXx8MMPExoair+/P6NGjeLQoUNObbKyskhISMBqtWK1WklISCA7O9upTWpqKiNHjsTf35/Q0FCmTJlCcXFxXXztJmXDBuP1mmvA3d3cWkREaqKwMBuwMWDAAu6/P/ms7V5uvPEXvLxKgWtp1uwQo0enOM7ffPMyCgpOkZmZeYE7iIiImNxDlp+fz5VXXsk999zDLbfcUun8nDlzeP7551m8eDHt27fn73//O4MHD2bPnj0EBAQAMHXqVD7++GNWrFhBSEgI06ZNY8SIESQnJ+N+OgGMGzeOQ4cOkZiYCMD9999PQkICH3/8MQBlZWUMHz6cFi1asG7dOo4fP87dd9+NzWZj/vz59fRrNHypqamV/gCyalVrIIS2bdPYujWt0mfsQ4FERFxVUNDlREb2cDoWGWks47FiBZw44c1HH3Vk6FANzRYRkZozNZANGzaMYcOGVXnOZrMxb948Hn/8ccaMGQPAkiVLCA8PZ/ny5TzwwAPk5OSwaNEili5dyqBBgwBYtmwZ0dHRfPHFFwwdOpSUlBQSExPZuHEjvXr1AmDhwoXEx8ezZ88eOnToQFJSErt27eLgwYNERUUBMHfuXCZMmMDMmTMJDAysssaioiKKiooc73Nzc2vtt2loUlNT6dixEwUFpyqc2Q2EsGjRRBYt+uycn8/LO1mn9YmI1LYWLWDSJGPWxZQU+OwzSE2Fq6/WfFkiIlJ9LvsM2b59+0hPT2fIkCGOY97e3vTr14/169fzwAMPkJycTElJiVObqKgo4uLiWL9+PUOHDmXDhg1YrVZHGAPo3bs3VquV9evX06FDBzZs2EBcXJwjjAEMHTqUoqIikpOTGTBgQJU1zp49m6effroOvn3Dk5mZSUHBKW6+eRktWnQCoLDQnX/+swMAd901Gx+fv1f63I8/fsqaNU9SWFhYr/WKiNQGHx/49a9h0yZYvdp4bnb//s7AQLNLExGRBsJlA1l6ejoA4eHhTsfDw8M5cOCAo42XlxdBQUGV2tg/n56eTlhYWKXrh4WFObWpeJ+goCC8vLwcbary6KOP8oc//MHxPjc3l+jo6Op+xUapRYtOjqE9e/cax0JCoG3bque8z8zUkEURadgsFujdG1q2hJUrISvLC/iC2bMzeOstY1IjERGRc3H5cRUWi8Xpvc1mq3Ssooptqmp/MW0q8vb2JjAw0GmTMw4eNF6beEYVkSYiOhoefBA6d84A4P33WxAXB++/rzXLRETk3Fw2kEVERABU6qE6duyYozcrIiKC4uJisrKyztvm6NGjla6fkZHh1KbifbKysigpKanUcybVZ5/sslUrc+sQEakvXl7wq18dBAYSEVHEgQPGkMbrr4cffjC7OhERcUUuG8jatm1LREQEq1evdhwrLi5m7dq19OnTB4CePXvi6enp1CYtLY0dO3Y42sTHx5OTk8Nm++rEwKZNm8jJyXFqs2PHDtLSzswCmJSUhLe3Nz179qzT79lYlZfD4cPGvnrIRKTp+ZL339/FU08Zz5l99RV07w4TJsBPP5ldm4iIuBJTnyHLy8vjp7P+y7Rv3z62bdtGcHAwMTExTJ06lVmzZhEbG0tsbCyzZs3Cz8+PcePGAWC1Wpk4cSLTpk0jJCSE4OBgpk+fTpcuXRyzLnbq1IkbbriBSZMm8dprrwHGtPcjRoygQwdjwokhQ4bQuXNnEhISePbZZzlx4gTTp09n0qRJGoZ4kY4dg5IS8PY2ZiITEWlq9u/fxU032bjmGi/mzWvJF18EsWQJLFtm48YbTzBxYjrR0UVOnwkNDdVi0iIiTYypgezbb791msHQPkHG3XffzeLFi3nkkUcoKChg8uTJZGVl0atXL5KSkhxrkAG88MILeHh4MHbsWAoKChg4cCCLFy92rEEG8PbbbzNlyhTHbIyjRo1iwYIFjvPu7u588sknTJ48mb59++Lr68u4ceN47rnn6vonaLTsz4+1bGk88C4i0lTk5aUBFsaPH1/hzFXADMrKhvPxxyF8/HEQ8CEwD/gaAF9fP3bvTlEoExFpQkwNZP3798d2niedLRYLM2bMYMaMGeds4+Pjw/z588+7gHNwcDDLli07by0xMTGsWrXqgjVL9ej5MRFpqgoLswEbAwYsIDY2vtL5Y8d2k5wcycGDVuBm4GZCQk7Rps02kpMHkJmZqUAmItKEuOy099KwaYZFEWnqgoIudywDcrbISLjySsjIMNYv+/57OH7cj+PH+wAHeP31cv76V6hixRYREWmEXHZSD2m48vPBPvGleshERKrWogWMGAG//70xC6OfXzEQwWuvRdGqlTE742efQVmZ2ZWKiEhdUiCTWpeaary2aGHMLiYiIufm5wfXXgvjxu0A7uCKK/IpKTHWL7vxRmjdGp54An7+2exKRUSkLmjIotQ6+x8a2rY1tw4RkYbEzQ1gBY8+OgI3tx589FEIn30WzOHDHsycCTNnQs+eJxk58jj9+2cTEFDu+KxmZxQRabgUyKRW2Wxn1ti5/HJzaxERaUiqnp3RCxgF3AsMJTk5gOTkAKAI+Bx4D1iFr2+JZmcUEWmgFMikVmVn+5CTA+7uxjAbERGpngvNzpiXt5O9e0P46acgsrN9MYLaKCyWcgoK1vOPf3gwcSJ062bvbRMRkYZAgUxq1cGDxkLarVuDl5fJxYiINEDnmp0RIDbWeD12DHbuhJQUyMhwA37Fyy/Dyy9DeDgMHQrDhsHAgcbzvCIi4rr0d2hSqw4dMgKZhiuKiNSdsDAYMAAmT4Y77tgBPEC/ftk0awZHj8I//wl33GG069gR7rsPFi82hpSfZ/lPERExgXrIpBb5kpbWDFAgExGpLwEBxcDrTJp0Hf/4R2e++86fDRusrF8fyE8/+bJnD+zZA4sWGe1DQkro1i2Prl3z6dvXk2HDwvHzM/UriIg0aQpkUov6U1bmhtUKoaFm1yIi0jRUPRmIXRDQB/gVcC1wNcePe/Hf/wbx3/8G8cIL4O5uIy7OwjXX4Ng6dwYP/QlBRKRe6F+3UotuAKBdO7BYTC5FRKSJuNBkIGcrLd1JRoYf6enNOHSonLQ0D8rKIvn+e/j+e1i40Gjn41NGp06n6Nz5FHFxp7jiinyiooqd/t2uqfZFRGqHApnUIiOQabiiiEj9O99kIGeLjjZef/zxE5YvHwlEAdec3q4GrqawMJDvvgvgu+8CzvpkBrDZsfn47GTPnnUKZSIil0iBTGrFwYNeQHssFhuXXabuMRERV3emZ+1Rp541m+0nsrN9yMjw49gxfzIy/Dh+3Jfy8hbA8NMbFBZCnz5F/OpXZ4Y6du8O/v5mfBsRkYZLgUxqxYYNxuyKERF5eHsHXKC1iIi4iqp61qKinNuUlkJ6Ohw+DEeOwIEDheTk+HD4sDfvvgvvvmu0s1hsREUV065dAZddVki7doVcdlkBbdoU4uNjTO+ooY4iIs4UyKRW/O9/VgCio3MBBTIRkcbEwwNatTI2gB9//C/Ll48HenJmuGMvbLZIDh/25vBhb77++uwrlAH7gX24u//A1Kmj6datOW3aQJs2EBkJ7u71+IVERFyIAplcshMnYNMmI4QZgayluQWJiEidMoY7ZjNgwM1nDXdMo6Agg6wsH06c8CUry+f05ktRkQfQDmhHWRnMnet8PXd3G6GhJYSFFRMWVnJ6K6ZFixIiIopp06aQ2Ngg9ayJSKOkQCaXbMUKKClxA74nJKTM7HJERKSeVGciEZsN8vPh+HHYs2cbGzb8G2gNtAXaANGUlXly9KgXR496nedKx+jdu5BevXy4+mrjmbXLL9esviLS8CmQySVbssSxB1S1Do6IiDRVFgs0a2Zsubk7gb+dnqK/OZBNeXk2p055kp/vSX6+l9N+fr4nJ096kZfnDYSxcSNs3Hjm2gEBpXTufIorrjhF5875XHHFKcLCSpzur2fWRMTVKZDJJdm9GzZvNoablJW9jQKZiIhcSHWn6LdLSUnkvfeeBDoDPTCeWevOyZM+bNoUyKZNgWe1PoIxNf8WYAs+PrvYs2e9QpmIuCwFMrkk9t6x+Phc1q07Zm4xIiLSKJWWHge+ZcCACY5n1srLd3HihK/T9PxZWb7YbFHA6NObMT3/r35VSHw8dOp0ZmvfHnx8TPpCIiJnUSCTi1ZWBkuXGvsjRhxn3Tpz6xERkcatYs9aywpzSJWUQFramen5U1MLyc314eBBHw4edG7r5mYjKqqItm2LaNu2kLZtC2jbtpA2bQoJCCjXUEcRqTcKZHLRvvzS+I9eUBBcd12O2eWIiEgT5+kJMTHGBvbp+RMwpufvCnRybOXlwRw65MOhQz588421wpUO4+a2l4SEIOLjA+jVC+LijOn/RURqm/7VIhfNPlzx9tvB29tmbjEiIiIVGNPzZzFgwOizpucHm20/BQWHyM42pubPzvZx7J865QW0pLy8JUuWnPlvnZ8fXH01xMdD797GFh5uxrcSkcZGgUwuSm4u/Pvfxv7dd5tbi4iIyPnUZBKRwkLYs2c3//nPM4wY8UcyMqLZscOf/Hx31q6FtWvPtG3ZsoiuXfO58so8unXL57LLCggP11BHEakZBTK5KEuXQkEBdOhgrAXz3XdmVyQiInLpfHzAz+9n4J+sWmVf18WCMdSxNxB/+rUzhw97c/iwN599Fny6XQ5ubpuZOjWQ4cOb06sX+PvX/3cQkYZFgUxqrLAQZs0y9h9+WItyiohI42IMdbSdXi8tvooWxRQX/8CxY/6kpzfj6FF/jh71p7TUSnn5YJ5/Hp5/HtzdoVs36Nv3zFZxIhIREQUyqbHXXzdmr4qOhvvuM7saERGRunGhoY6tW5/ZLy+HnTtT+Pe/X6Z372ns2xfF0aNeJCdDcjK89JLRLjKyiCuvNIY5du2az+WXF+DhoQWsRZoyBTKpkVOnYPZsY/+JJ8Db29x6REREXIGbG/j4/AL8Hxs3Ljh9NBroe3rrA1xJWpo3aWneJCbahznmAZvx8FjN66/fw6hRYYSE1H/9ImIeBTKpkVdfhfR0aNMGJkwwuxoRERHXcaGhjsXF2zl2zN8xxPHo0WaUlDQDrqe09Hruvddo16ED9OljbPYFrd3c6vObiEh9UiCTasvLg2eeMfaffBK8vMytR0RExBWdb6jj2cMcbTbIyIAdOw7wzTdfEhk5hrQ0K3v2wJ498NZbRrtmzUrp0iWfrl2NLS4un2bNygENdRRpDBTIpNr+7/+M/3C0awcJCWZXIyIi0rBZLBAWBtHRO4CJpKXdCwRjzOLY5/R2DXl5/mzYYGXDBvsC1uXADmAjnp47effdRxk8OIJmzUz5GiJyiRTIpFoOHDjz7Nhf/gKenubWIyIi0licb6hjefkeTpzwdQxxPHrUn5MnvYGuQFdKSmDMGCPcxcYaszp26wZXXgldukCrVpoNWcTVKZDJBZWWwrhxkJNjjGUfN87sikRERBqfcw11bNnSCFd2eXlw8CDs3ZvOtm3JBAUNICvLj717Ye9eeO+9M20DAkpp166Qdu0KuPzyAmJjC2jXrpDLLgvSUEcRF6FAJpWkpqaSmZnpeP/qq5GsXx+Jv38Zjz2WwvffF1f6TEpKSn2WKCIi0mQ1a2ZM9OHhkcy2bSPJyrIBLYBuZ21XAh04edKDbduasW1bxfGMh+nXr4BrrvGlSxeIizOu6eNTn99ERECBTCpITU2lY8dOFBScOn3kWmANAPn5dzJy5Lvn/Xxe3sm6LVBERESAC83qWERZ2Xays304ccKHEyd8OXHCl6wsH/LyvIGWrF0La9ee+YS7u43o6CIuv7zgrB61Qlq2LHLM8qhJRERqnwKZOMnMzKSg4BQ337yMgIA4PvigE/n57rRvf5z+/R8BHqnycz/++Clr1jxJYWFh/RYsIiLSxJ1vVsdWrSof27nzc95//2/AFUAXx1ZWFsz+/T7s3+/DF18EnfWJfGAXsB1Pzz0sXvwHrr8+nPBwPZ8mUhsUyKRK/v5d+PzzruTnQ3Aw3HJLCF5e516pMjNTQxZFREQagvLyTOB/DBhwh6NnzWbbz6lThx29aVlZZ3rUysr8gauBqykpgTvvNK4TGopjuGOXLsakIm3bGiHQ3d2sbyfS8CiQSRWi+fjj9uTkgL8/3Hab1hwTERFpbM7Xs2ZXXg4nTsCxY/Dzz2ls3bqeiIjBHD0aQGamhTVrYM0a5894eJQTGVlMy5bFtGxZRMuWxXTs6EWvXi1o2xaCgqq+l0hTpUAmTvbv9wbWkZPjg9UKd91l9JCJiIhI0+PmZvSEhYaCp+dWtm79NenpNsAX6IQx3DHu9HYZ0IbSUi8OHvTh4MGqZwhp3hwuu8zY2raFNm0gJubMZrVqKKQ0LQpkAhhT27/8Mjz2WEfAnebNC7nnHh8CA82uTERERFzB+ScRAcijvHwHp055kpvrzcmTXpw86U1GRiEHD6ZhtXYnJ8eX7GzYutXYquLvX0ZERLFja9fOk65dmxMdDZGREBFhzDSp0CaNhQKZ8L//weTJ8MMPAO7A/xg5MoDAwK4mVyYiIiKupjpDHc/244+fsHz5SHJybIAf0AajN82+xZy1tSA/352ff/bl5599z3lNH58yQkJKCQ0tISSk5PRrKSEhxvvg4FLatg2kS5co9biJy1Mgq+Dll1/m2WefJS0tjSuuuIJ58+Zx7bXXml1WrcvJgfffh6VLz0x5GxwMv/nNAWbOvBZf32/NLVBEREQahQv3rNkdpLT0EHl5Xo7t0KF0fvklBSOsRQMRQACFhe4cPuzO4cPeF7y/u7sNq7X09FaG1VpK8+bGZrWWERBQir9/Oc2aldGsWRmtWgXSsWMkVqvRE2ef8l+kriiQneXdd99l6tSpvPzyy/Tt25fXXnuNYcOGsWvXrga/5kZ2Nnz7LWzZAhs3QlIS2Geot1jg3nvhmWcgNfU4M2faTK1VREREGp+a9qwBbN++jl9+ued0mGsOnKSkxI2CAo//b+/eY5q6/z6Av0tvIIpMFGRg8DLF6fZDAdECsyBIspglLnOaGW+bzvAsLCpzCxskgsujmVFRJxrJvBCdaOZ0MxNFMgFRdJk81TDHwOtPjGVeULkMirbf5w+ho8Nbse3pOO9XckLPt+fbvttPDvXjOT3gr7/U1qVjvaXl0XpTkxmtrR4AvGE2K1Bfr0Z9vdruzAqFQK9ej5o1b28ztFoLtFoBrdYCT0+Ldd3T0wJfXy0CAnzg5YUui1Zru3h6PnlMo+ERPblhQ9bJ2rVrMX/+fCxYsAAAsG7dOhQWFmLz5s1YuXKlxOme35UrwFdfARcutOD6dYE//9Tg/v2upR4ypAVTptTjzTfrMXDgA1y7BlRV8fL1RERE5F7sbeYqK7/F/v2zMHHiZgwaFIPWVhVaW5UwmVTtt1UwmZRobVWhrU2JtjYlHjxQorm5DS0tDwD0BaCBEAo0NyvR3Oza6/ir1RZoNAJq9aOGT622QK0WUCofHfFTqQQ8PMxQqxVQKh+Nq1TCet8/xzqP9+njhX79fKBSPfrzBArFo8XD4+/bT1scvV3n5tPe20+67/XXgZEjHVcPZ2ND1q6trQ0VFRVIS0uzGU9KSkJ5eflj55hMJphMJuv6/fv3AQANDQ3OC/ocbt8GtmzpPPKw/ecVAP/XvpThypVz2LgR2Lix62NcvnwCbW1Nz/2ct25Vtf+sxH//++RzvuU2T4rn5Dx5zpPiOTlPnvOkeE7Ok+c8RzynEE1Qq+9CrQb69Hn2vEuXjqKs7H/xn//8DwICXoPZrMaDB2o8fKjBw4dqWCweMJtVsFhUMJuVMJtVMJs90Nh4F3V1vwPQ4tEVKD3bf3bc1gLQtN/WtK9r/3H7bw8ePFqerfNZTYr25XlI+29VZ1u2DEhNlTrF3z2BEE8/+0whnrWFTNy4cQNBQUE4efIkoqOjreMrVqxAXl4eqquru8zJzMxEVlaWK2MSEREREdG/SG1tLYKDg594P4+Q/YPiH8dBhRBdxjp8/vnnSO3UflssFtTX18PPz++Jc+j5NDQ0YNCgQaitrYUPr70vOdbD/bAm7oc1cS+sh/thTdwPa+JcQgg0Njbi5Zdffup2bMja9e/fH0qlEnV1dTbjN2/eREBAwGPnaLVaaLW2h5h9fX2dFVGWfHx8+AvCjbAe7oc1cT+siXthPdwPa+J+WBPn6du37zO34YU822k0GkRERKCoqMhmvKioyOYURiIiIiIiIkfhEbJOUlNTMXv2bERGRkKn0yE3NxfXrl1DcnKy1NGIiIiIiKgHYkPWyYwZM3Dnzh0sX74cRqMRr732GgoKChASEiJ1NNnRarVYtmxZl1NCSRqsh/thTdwPa+JeWA/3w5q4H9bEPfAqi0RERERERBLhd8iIiIiIiIgkwoaMiIiIiIhIImzIiIiIiIiIJMKGjIiIiIiISCJsyEgSmzZtwpAhQ+Dp6YmIiAiUlZU9cVuj0YiZM2ciNDQUHh4eWLx4seuCyog9Ndm/fz8mT56MAQMGwMfHBzqdDoWFhS5MKw/21OTEiROIiYmBn58fvLy8MHLkSGRnZ7swrTzYU5POTp48CZVKhTFjxjg3oMzYU4+SkhIoFIouyx9//OHCxD2fvfuIyWRCeno6QkJCoNVqMWzYMGzbts1FaeXBnprMmzfvsfvJ6NGjXZhYftiQkcvt3bsXixcvRnp6OgwGA9544w28+eabuHbt2mO3N5lMGDBgANLT0xEWFubitPJgb02OHz+OyZMno6CgABUVFYiPj8dbb70Fg8Hg4uQ9l7018fb2RkpKCo4fP46qqipkZGQgIyMDubm5Lk7ec9lbkw7379/HnDlzkJCQ4KKk8tDdelRXV8NoNFqX4cOHuyhxz9edmkyfPh0///wztm7diurqauTn52PkyJEuTN2z2VuT9evX2+wftbW16NevH959910XJ5cZQeRiUVFRIjk52WZs5MiRIi0t7Zlz9Xq9WLRokZOSydeL1KTDqFGjRFZWlqOjyZYjavL222+LWbNmOTqabHW3JjNmzBAZGRli2bJlIiwszIkJ5cXeehQXFwsA4u7duy5IJ0/21uTw4cOib9++4s6dO66IJ0sv+lly4MABoVAoxNWrV50Rj9rxCBm5VFtbGyoqKpCUlGQznpSUhPLycolSyZsjamKxWNDY2Ih+/fo5I6LsOKImBoMB5eXl0Ov1zogoO92tyfbt23Hp0iUsW7bM2RFl5UX2kbFjxyIwMBAJCQkoLi52ZkxZ6U5NDh48iMjISKxatQpBQUEYMWIEli5dipaWFldE7vEc8VmydetWJCYmIiQkxBkRqZ1K6gAkL7dv34bZbEZAQIDNeEBAAOrq6iRKJW+OqMmaNWvQ3NyM6dOnOyOi7LxITYKDg3Hr1i08fPgQmZmZWLBggTOjykZ3anLhwgWkpaWhrKwMKhU/bh2pO/UIDAxEbm4uIiIiYDKZsHPnTiQkJKCkpAQTJ050RewerTs1uXz5Mk6cOAFPT08cOHAAt2/fxkcffYT6+np+j8wBXvTz3Wg04vDhw9i9e7ezIlI7fkKQJBQKhc26EKLLGLlWd2uSn5+PzMxM/Pjjj/D393dWPFnqTk3KysrQ1NSE06dPIy0tDa+88gree+89Z8aUleetidlsxsyZM5GVlYURI0a4Kp7s2LOPhIaGIjQ01Lqu0+lQW1uL1atXsyFzIHtqYrFYoFAo8O2336Jv374AgLVr12LatGnIycmBl5eX0/PKQXc/33fs2AFfX19MnTrVScmoAxsycqn+/ftDqVR2+Z+ZmzdvdvkfHHKNF6nJ3r17MX/+fHz33XdITEx0ZkxZeZGaDBkyBADw+uuv488//0RmZiYbMgewtyaNjY04c+YMDAYDUlJSADz6x6cQAiqVCkePHsWkSZNckr0nctRnyYQJE7Br1y5Hx5Ol7tQkMDAQQUFB1mYMAF599VUIIXD9+nVecOUFvch+IoTAtm3bMHv2bGg0GmfGJPAqi+RiGo0GERERKCoqshkvKipCdHS0RKnkrbs1yc/Px7x587B7925MmTLF2TFlxVH7iRACJpPJ0fFkyd6a+Pj4oLKyEmfPnrUuycnJCA0NxdmzZzF+/HhXRe+RHLWPGAwGBAYGOjqeLHWnJjExMbhx4waampqsYzU1NfDw8EBwcLBT88rBi+wnpaWluHjxIubPn+/MiNRBoouJkIzt2bNHqNVqsXXrVvH777+LxYsXC29vb+sVfNLS0sTs2bNt5hgMBmEwGERERISYOXOmMBgM4vz581LE75Hsrcnu3buFSqUSOTk5wmg0Wpd79+5J9RJ6HHtrsnHjRnHw4EFRU1MjampqxLZt24SPj49IT0+X6iX0ON353dUZr7LoWPbWIzs7Wxw4cEDU1NSI3377TaSlpQkA4vvvv5fqJfQ49taksbFRBAcHi2nTponz58+L0tJSMXz4cLFgwQKpXkKP093fW7NmzRLjx493dVzZYkNGksjJyREhISFCo9GI8PBwUVpaar1v7ty5Qq/X22wPoMsSEhLi2tA9nD010ev1j63J3LlzXR+8B7OnJhs2bBCjR48WvXr1Ej4+PmLs2LFi06ZNwmw2S5C857L3d1dnbMgcz556fPXVV2LYsGHC09NTvPTSSyI2NlYcOnRIgtQ9m737SFVVlUhMTBReXl4iODhYpKamir/++svFqXs2e2ty79494eXlJXJzc12cVL4UQggh0cE5IiIiIiIiWeN3yIiIiIiIiCTChoyIiIiIiEgibMiIiIiIiIgkwoaMiIiIiIhIImzIiIiIiIiIJMKGjIiIiIiISCJsyIiIiIiIiCTChoyIiIiIiEgibMiIiEj2Bg8ejHXr1kkdg4iIZIgNGRERyd6vv/6KhQsXPva+I0eOQKFQoK6uzmZ84MCBGDRokM3Y9evXoVAocPTo0W5nyczMxJgxY7o9n4iI/l3YkBERkewNGDAAvXr1eux9sbGxUKlUKCkpsY5VVVWhtbUVDQ0NuHjxonW8uLgYarUaMTExdmcQQuDhw4d2zyMion83NmRERNTjxcXFISUlBSkpKfD19YWfnx8yMjIghADw9FMWe/fujXHjxtk0ZCUlJYiNjUVsbGyX8aioKHh7e0MIgVWrVmHo0KHw8vJCWFgY9u3bZ7OtQqFAYWEhIiMjodVqsXPnTmRlZeHcuXNQKBRQKBTYsWMHAOD+/ftYuHAh/P394ePjg0mTJuHcuXOOfquIiMjF2JAREZEs5OXlQaVS4ZdffsGGDRuQnZ2Nb7755rnmxsfHo7i42LpeXFyMuLg46PX6LuPx8fEAgIyMDGzfvh2bN2/G+fPnsWTJEsyaNQulpaU2j/3ZZ59h5cqVqKqqQlJSEj755BOMHj0aRqMRRqMRM2bMgBACU6ZMQV1dHQoKClBRUYHw8HAkJCSgvr7eAe8OERFJRSV1ACIiIlcYNGgQsrOzoVAoEBoaisrKSmRnZ+PDDz985ty4uDisWLECRqMRgYGBKC0txaeffgqLxYL169cDAGpra3HlyhXEx8ejubkZa9euxbFjx6DT6QAAQ4cOxYkTJ7Blyxbo9XrrYy9fvhyTJ0+2rvfu3RsqlQoDBw60jh07dgyVlZW4efMmtFotAGD16tX44YcfsG/fvid+/42IiNwfGzIiIpKFCRMmQKFQWNd1Oh3WrFkDs9lss11ycjJ27dplXW9qakJMTAw0Gg1KSkoQFhaGlpYWhIeHQwiBhoYGXLhwAadOnYJWq0V0dDQqKyvR2tpq02gBQFtbG8aOHWszFhkZ+czsFRUVaGpqgp+fn814S0sLLl269NzvARERuR82ZERERJ0sX74cS5cutRnr1asXoqKiUFxcjPr6esTGxkKpVAIAoqOjUVxcjFOnTkGn08HT0xMWiwUAcOjQIQQFBdk8VscRrg7e3t7PzGSxWBAYGGjzfbUOvr6+drw6IiJyN2zIiIhIFk6fPt1lffjw4dbGqoO/vz/8/f27zI+Pj8eePXtw9+5dxMXFWcf1ej1KSkpw6tQpvP/++wCAUaNGQavV4tq1azanJz4PjUbT5ahdeHg46urqoFKpMHjwYLsej4iI3Bsv6kFERLJQW1uL1NRUVFdXIz8/H19//TUWLVr03PPj4+Nx4cIFHDlyxKbJ0uv1+Omnn3D16lXrBT369OmDpUuXYsmSJcjLy8OlS5dgMBiQk5ODvLy8pz7P4MGDceXKFZw9exa3b9+GyWRCYmIidDodpk6disLCQly9ehXl5eXIyMjAmTNnuveGEBGRW+ARMiIikoU5c+agpaUFUVFRUCqV+Pjjj+26GIZOp7OebhgREWEdHzduHMxmM7y8vDB+/Hjr+Jdffgl/f3+sXLkSly9fhq+vL8LDw/HFF1889Xneeecd7N+/H/Hx8bh37x62b9+OefPmoaCgAOnp6fjggw9w69YtDBw4EBMnTkRAQICd7wQREbkThej4IyxEREQ9VFxcHMaMGfPEvzVGREQkFZ6ySEREREREJBE2ZERERERERBLhKYtEREREREQS4REyIiIiIiIiibAhIyIiIiIikggbMiIiIiIiIomwISMiIiIiIpIIGzIiIiIiIiKJsCEjIiIiIiKSCBsyIiIiIiIiibAhIyIiIiIiksj/A0AEGsqbFwUIAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Verteilung der pi-Werte\n", + "mean_pi = np.mean(pi)\n", + "std_pi = np.std(pi)\n", + "print(f\"Mittelwert der pi-Werte: {mean_pi:.4f}\")\n", + "print(f\"Standardabweichung der pi-Werte: {std_pi:.4f}\")\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "sns.histplot(pi, bins=50, kde=True, color='blue')\n", + "plt.title('Verteilung der pi-Werte')\n", + "plt.xlabel('pi-Werte')\n", + "plt.ylabel('Häufigkeit')\n", + "plt.show()" + ] + }, { "cell_type": "markdown", "id": "825cc812", @@ -889,7 +916,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 9, "id": "706ff5c6", "metadata": {}, "outputs": [], @@ -912,7 +939,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 10, "id": "9f12d325", "metadata": {}, "outputs": [ @@ -924,7 +951,7 @@ "Anteil Zielvariable (Diebstahl):\n", "0 0.77085\n", "1 0.22915\n", - "Name: proportion, dtype: float64\n" + "dtype: float64\n" ] }, { @@ -997,7 +1024,7 @@ " 1.0\n", " 46.955431\n", " 1.987300\n", - " 15.300650\n", + " 20.400866\n", " 0.396750\n", " 0.405100\n", " 0.198150\n", @@ -1019,7 +1046,7 @@ " 0.0\n", " 9.983939\n", " 1.406321\n", - " 12.277946\n", + " 16.370594\n", " 0.489236\n", " 0.490924\n", " 0.398616\n", @@ -1041,7 +1068,7 @@ " 1.0\n", " 10.000000\n", " 0.000000\n", - " 0.764512\n", + " 1.019350\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", @@ -1063,7 +1090,7 @@ " 1.0\n", " 40.210704\n", " 1.000000\n", - " 7.442576\n", + " 9.923435\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", @@ -1085,7 +1112,7 @@ " 1.0\n", " 46.933676\n", " 2.000000\n", - " 11.922107\n", + " 15.896143\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", @@ -1107,7 +1134,7 @@ " 1.0\n", " 53.726714\n", " 3.000000\n", - " 19.153933\n", + " 25.538578\n", " 1.000000\n", " 1.000000\n", " 0.000000\n", @@ -1129,7 +1156,7 @@ " 1.0\n", " 81.665638\n", " 10.000000\n", - " 171.637620\n", + " 228.850160\n", " 1.000000\n", " 1.000000\n", " 1.000000\n", @@ -1153,13 +1180,13 @@ "text/plain": [ " const avg_speed hard_brakes trip_distance shift_frueh \\\n", "count 20000.0 20000.000000 20000.000000 20000.000000 20000.000000 \n", - "mean 1.0 46.955431 1.987300 15.300650 0.396750 \n", - "std 0.0 9.983939 1.406321 12.277946 0.489236 \n", - "min 1.0 10.000000 0.000000 0.764512 0.000000 \n", - "25% 1.0 40.210704 1.000000 7.442576 0.000000 \n", - "50% 1.0 46.933676 2.000000 11.922107 0.000000 \n", - "75% 1.0 53.726714 3.000000 19.153933 1.000000 \n", - "max 1.0 81.665638 10.000000 171.637620 1.000000 \n", + "mean 1.0 46.955431 1.987300 20.400866 0.396750 \n", + "std 0.0 9.983939 1.406321 16.370594 0.489236 \n", + "min 1.0 10.000000 0.000000 1.019350 0.000000 \n", + "25% 1.0 40.210704 1.000000 9.923435 0.000000 \n", + "50% 1.0 46.933676 2.000000 15.896143 0.000000 \n", + "75% 1.0 53.726714 3.000000 25.538578 1.000000 \n", + "max 1.0 81.665638 10.000000 228.850160 1.000000 \n", "\n", " shift_normal shift_spaet speeding_haeufig speeding_manchmal \\\n", "count 20000.000000 20000.000000 20000.000000 20000.000000 \n", @@ -1202,7 +1229,7 @@ "max 1.000000 1.000000 " ] }, - "execution_count": 24, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -1219,7 +1246,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 11, "id": "30bfcdde", "metadata": {}, "outputs": [ @@ -1264,12 +1291,12 @@ "source": [ "### Feature Engineering\n", "\n", - "Für die Modellierung werden kategoriale Variablen in numerische Dummy-Variablen umgewandelt. Dies ist notwendig, damit die Regressionsmodelle die Informationen nutzen können. In unserem Fall sind die kategorischen Variablen schon aus dem vorherigen Schritt One-Hot Encoded, weil wir aus den Variablen das logistische Regressionsproblem erstellt haben. Grundsätzlich sollte eine Aufteilung in Trainings- und Testdaten durchgeführt werden, jedoch habe ich mich aufgrund des kleinen Samples (20.000 Instanzen) und weil es die Aufgabe nicht vorgibt dagegen entschieden (siehe Parameter **test_size**)" + "Für die Modellierung werden kategoriale Variablen in numerische Dummy-Variablen umgewandelt. Dies ist notwendig, damit die Regressionsmodelle die Informationen nutzen können. Außerdem werden die Daten in Trainings- und Testdaten aufgeteilt, um eine objektive Modellbewertung zu ermöglichen. In unserem Fall sind die kategorischen Variablen schon aus dem vorherigen Schritt One-Hot Encoded, weil wir aus den Variablen das logistische Regressionsproblem erstellt haben. Für die Aufteilung in Test und Trainingsdaten wählen wir einen Split von 70 zu 30." ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 12, "id": "b71ab517", "metadata": {}, "outputs": [ @@ -1277,7 +1304,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Trainingsdaten: (20000, 19)\n" + "Trainingsdaten: (14000, 19)\n", + "Testdaten: (6000, 19)\n" ] } ], @@ -1287,15 +1315,13 @@ "# Zielvariable\n", "y = target_sample\n", "\n", - "# One-Hot-Encoding (eigentlich schon oben gemacht)\n", + "# One-Hot-Encoding für kategoriale Variablen\n", "X_encoded = pd.get_dummies(X_sample, drop_first=True)\n", "\n", "# Aufteilung in Trainings- und Testdaten\n", - "# X_train, X_test, y_train, y_test = train_test_split(X_encoded, y, test_size=0.0, random_state=22)\n", - "X_train = X_encoded\n", - "y_train = y\n", + "X_train, X_test, y_train, y_test = train_test_split(X_encoded, y, test_size=0.3, random_state=42)\n", "print(\"Trainingsdaten:\", X_train.shape)\n", - "# print(\"Testdaten:\", X_test.shape)" + "print(\"Testdaten:\", X_test.shape)" ] }, { @@ -1316,44 +1342,13 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 13, "id": "b748cb80", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\yann\\AppData\\Local\\Temp\\ipykernel_18236\\658816552.py:15: FutureWarning: \n", - "\n", - "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", - "\n", - " sns.barplot(x=counts.index, y=counts.values, palette='viridis', ax=axes[i])\n", - "C:\\Users\\yann\\AppData\\Local\\Temp\\ipykernel_18236\\658816552.py:15: FutureWarning: \n", - "\n", - "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", - "\n", - " sns.barplot(x=counts.index, y=counts.values, palette='viridis', ax=axes[i])\n", - "C:\\Users\\yann\\AppData\\Local\\Temp\\ipykernel_18236\\658816552.py:15: FutureWarning: \n", - "\n", - "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", - "\n", - " sns.barplot(x=counts.index, y=counts.values, palette='viridis', ax=axes[i])\n", - "C:\\Users\\yann\\AppData\\Local\\Temp\\ipykernel_18236\\658816552.py:15: FutureWarning: \n", - "\n", - "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", - "\n", - " sns.barplot(x=counts.index, y=counts.values, palette='viridis', ax=axes[i])\n", - "C:\\Users\\yann\\AppData\\Local\\Temp\\ipykernel_18236\\658816552.py:15: FutureWarning: \n", - "\n", - "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", - "\n", - " sns.barplot(x=counts.index, y=counts.values, palette='viridis', ax=axes[i])\n" - ] - }, { "data": { - "image/png": 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", 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", 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" ] @@ -1395,7 +1390,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 14, "id": "3ffbfe79", "metadata": {}, "outputs": [ @@ -1405,19 +1400,19 @@ "text": [ "Deskriptive Statistiken für metrische Variablen:\n", " avg_speed hard_brakes trip_distance\n", - "count 20000.000000 20000.000000 20000.000000\n", - "mean 46.955431 1.987300 15.300650\n", - "std 9.983939 1.406321 12.277946\n", - "min 10.000000 0.000000 0.764512\n", - "25% 40.210704 1.000000 7.442576\n", - "50% 46.933676 2.000000 11.922107\n", - "75% 53.726714 3.000000 19.153933\n", - "max 81.665638 10.000000 171.637620\n" + "count 14000.000000 14000.000000 14000.000000\n", + "mean 47.020561 1.978714 20.412017\n", + "std 10.045785 1.399225 16.335114\n", + "min 10.000000 0.000000 1.019350\n", + "25% 40.185504 1.000000 9.900877\n", + "50% 46.982260 2.000000 15.927706\n", + "75% 53.850891 3.000000 25.556148\n", + "max 81.665638 9.000000 228.850160\n" ] }, { "data": { - "image/png": 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" ] @@ -1487,13 +1482,13 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 15, "id": "8a320b4c", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -1530,7 +1525,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 16, "id": "19c7e28c", "metadata": {}, "outputs": [ @@ -1539,42 +1534,42 @@ "output_type": "stream", "text": [ "Entfernte Variablen und zugehörige p-Werte:\n", - "weekday_Mo-Fr: p=1.0000\n", - "speeding_selten: p=1.0000\n", - "road_Außerorts: p=1.0000\n", - "trip_distance: p=0.9301\n", - "weekday_So: p=0.8271\n", "weather_winterlich: p=1.0000\n", - "weekday_Sa: p=0.8550\n", - "road_Innerorts: p=0.7525\n", + "speeding_selten: p=1.0000\n", "shift_spaet: p=1.0000\n", - "road_Autobahn: p=0.8026\n", - "weather_trocken: p=0.1308\n", - "weather_nass: p=0.6593\n", + "trip_distance: p=0.6894\n", + "weather_nass: p=0.2317\n", + "weather_trocken: p=0.6194\n", + "weekday_Mo-Fr: p=1.0000\n", + "weekday_So: p=0.6089\n", + "weekday_Sa: p=0.4513\n", "\n", "Verbleibende Variablen im Modell:\n", - "['const', 'avg_speed', 'hard_brakes', 'shift_frueh', 'shift_normal', 'speeding_haeufig', 'speeding_manchmal']\n", + "['const', 'avg_speed', 'hard_brakes', 'shift_frueh', 'shift_normal', 'speeding_haeufig', 'speeding_manchmal', 'road_Autobahn', 'road_Außerorts', 'road_Innerorts']\n", "\n", "Zusammenfassung des finalen Modells:\n", " Logit Regression Results \n", "==============================================================================\n", - "Dep. Variable: y No. Observations: 20000\n", - "Model: Logit Df Residuals: 19993\n", - "Method: MLE Df Model: 6\n", - "Date: Thu, 03 Jul 2025 Pseudo R-squ.: 0.04762\n", - "Time: 14:03:27 Log-Likelihood: -10252.\n", - "converged: True LL-Null: -10765.\n", - "Covariance Type: nonrobust LLR p-value: 2.930e-218\n", + "Dep. Variable: y No. Observations: 14000\n", + "Model: Logit Df Residuals: 13991\n", + "Method: MLE Df Model: 8\n", + "Date: Fri, 04 Jul 2025 Pseudo R-squ.: 0.04892\n", + "Time: 15:26:24 Log-Likelihood: -7127.2\n", + "converged: True LL-Null: -7493.9\n", + "Covariance Type: nonrobust LLR p-value: 4.917e-153\n", "=====================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "-------------------------------------------------------------------------------------\n", - "const -1.0447 0.094 -11.071 0.000 -1.230 -0.860\n", - "avg_speed 0.0171 0.002 9.839 0.000 0.014 0.021\n", - "hard_brakes 0.0824 0.012 6.788 0.000 0.059 0.106\n", - "shift_frueh -0.8005 0.045 -17.983 0.000 -0.888 -0.713\n", - "shift_normal -0.7930 0.044 -17.902 0.000 -0.880 -0.706\n", - "speeding_haeufig -0.8291 0.041 -20.057 0.000 -0.910 -0.748\n", - "speeding_manchmal -0.8153 0.043 -19.171 0.000 -0.899 -0.732\n", + "const -0.7882 nan nan nan nan nan\n", + "avg_speed 0.0174 0.002 8.369 0.000 0.013 0.021\n", + "hard_brakes 0.0817 0.015 5.579 0.000 0.053 0.110\n", + "shift_frueh -0.8150 0.053 -15.278 0.000 -0.920 -0.710\n", + "shift_normal -0.8081 0.053 -15.239 0.000 -0.912 -0.704\n", + "speeding_haeufig -0.8381 0.050 -16.908 0.000 -0.935 -0.741\n", + "speeding_manchmal -0.8343 0.051 -16.291 0.000 -0.935 -0.734\n", + "road_Autobahn -0.2576 nan nan nan nan nan\n", + "road_Außerorts -0.2735 nan nan nan nan nan\n", + "road_Innerorts -0.2571 nan nan nan nan nan\n", "=====================================================================================\n" ] } @@ -1633,7 +1628,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 17, "id": "6641f80c", "metadata": {}, "outputs": [ @@ -1643,13 +1638,16 @@ "text": [ "\n", "Modellkoeffizienten:\n", - "const -1.044708\n", - "avg_speed 0.017141\n", - "hard_brakes 0.082381\n", - "shift_frueh -0.800488\n", - "shift_normal -0.792985\n", - "speeding_haeufig -0.829066\n", - "speeding_manchmal -0.815301\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", "dtype: float64\n" ] } @@ -1673,7 +1671,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 18, "id": "0541fa09", "metadata": {}, "outputs": [ @@ -1683,17 +1681,17 @@ "text": [ "Vergleich der geschätzten und wahren Koeffizienten:\n", " Geschätzte Koeffizienten Wahre Koeffizienten\n", - "avg_speed 0.017141 0.015\n", - "const -1.044708 -2.000\n", - "hard_brakes 0.082381 0.100\n", - "road_Autobahn NaN 0.000\n", - "road_Außerorts NaN 0.000\n", - "road_Innerorts NaN 0.000\n", - "shift_frueh -0.800488 0.500\n", - "shift_normal -0.792985 -0.300\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 -0.829066 -0.300\n", - "speeding_manchmal -0.815301 -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", @@ -1709,8 +1707,8 @@ "# Tatsächliche Betas aus der Generierung (siehe oben)\n", "true_betas = pd.Series([\n", " -2, # konstante\n", - " 0.015, # avg_speed (erklärend)\n", - " 0.1, # hard_brakes (erklärend)\n", + " 0.015, # avg_speed\n", + " 0.1, # hard_brakes\n", " 0., # trip_distance\n", " 0.5, # shift_frueh (erklärend)\n", " -0.3, # shift_normal (erklärend)\n", @@ -1765,7 +1763,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 19, "id": "616ae484", "metadata": {}, "outputs": [ @@ -8445,5159 +8443,7 @@ "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", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation 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 [01:10<10:35, 70.63s/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", - "Stichprobenumfang: 10%|█ | 1/10 [01:10<10:35, 70.63s/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", + "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", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", @@ -14101,7 +8947,7 @@ "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", - "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\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", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", @@ -14109,8 +8955,7 @@ "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: 100%|██████████| 10/10 [25:43<00:00, 154.36s/it]\n", - "Stichprobenumfang: 100%|██████████| 10/10 [25:43<00:00, 154.36s/it]\n" + "Stichprobenumfang: 100%|██████████| 10/10 [32:08<00:00, 192.82s/it]\n" ] } ], @@ -14159,13 +9004,13 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 20, "id": "b0bb7329", "metadata": {}, "outputs": [ { "data": { - "image/png": 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ZsqXHbUaNGmUsFou5/fbbzSeffGK++OIL87e//c28/PLL7jItW7Y0zZs3Nx07djRvvfWWWbx4sfnd735nJJmVK1e6y1Xmvd23b18TFRVlEhMTzfTp083y5cvNypUrTVpammnWrJlp0qSJefXVV82iRYvMPffcYySZP/3pTx6PietYv/3tb828efPMBx98YH7zm98Yu91uVq9e7S5b0deS61zcqlUrc9NNN5nPP//cvPfee6ZFixambdu2pqioyOMxqcz5vWXLlmb06NFm0aJF5tVXXzVhYWGmf//+ZsCAAeaBBx4wS5YsMc8++6yxWq3m3nvvNcYYk5eXZ1JSUkzXrl1N69atS33PuOWWW8zrr79ukpOTzZIlS8r8HKjM8+eL73/V/Y6DMyPAo1x9+/Y1MTExHl9MJkyYYCSZn376yRjjDIthYWFmz549Hrd9/vnnjST3m9l1wm3Tpo3H/owxZu3ataU+wF3OPfdc07VrV/eXfpehQ4ea+Ph4U1xcbIzxXYC/6667PMpNnTrVSDIHDhwwxpz8h8ZDDz3kUc71ZfZsAX7mzJlGkvnkk088tt9xxx2lHpOKPhadOnUyw4YNO+Nxy/K73/3OhIaGeoSx4uJi07FjR48T+C+//GJsNpv7g8YlMzPTxMXFmREjRhhjjDl8+LCRZKZNm1buMX/++WdjtVrNTTfddMa69e3b10gya9as8djesWNHM2jQoHJvV1RUZAoLC83YsWNN165dPa4LDQ096/NjjDH33HOPsdlsZuHChe5tzz33XKkPNWMq/tgY4/xwk2T+85//eJQdMmSIad++/Vnr5XpMTv9p166d+f777z3KDho0yDRv3tz94X/qfQsKCjJHjx41xpz5vXi6oqIik5WVZUJDQz2+8JVn2rRpRpL7y9ULL7xg4uPjjTHGbN++3UgyW7duNcYYM3nyZCPJbN++3Rjj2/NMRV6n5XGdJ9auXeuxvbJf8Pr06VPpYxvjfH8WFhaaJ5980kRHR5uSkhJjjDGFhYUmNjbW3HjjjR7lH3zwQRMYGGgOHz5sjDHm888/N5LMzJkzPcpNmTKl0gH+nnvuMY0aNTpjGdfjde2113ps/+abb4wk89e//tUYY0x2draJiooyV111Van7e/7555uLLrrIva2ir+2HHnrIWCwWs3HjRo9yAwYMqFSAv//++z22v/vuu0aSeeedd4wxlTsHVCTAG2PM8OHDTfPmzd3neGOMWbhwoZFkFixYYIyp3GPmOu7jjz/uUdYf5+yyAry3n9OqPDZTp071KHvXXXeZoKAg93ssJSXFSDIvvPCCR7m9e/ea4OBg8+CDD1b6sSjPrFmzTFhYmPscHx8fb2655Rbz1VdfeZT74IMPyn0tnx7gv/rqKyOp1D+GTteyZUsTFBTkce7Nzc01UVFR5o9//KN7W0Xf28acfDy+/PJLj7IPP/xwmY/Tn/70J2OxWMyPP/5ojDn5mklISDC5ubnuchkZGSYqKspcfvnl7m0VfS25zsVDhgzxKPef//zHSDIpKSkej0llzu+nv+7GjRtnJHn8c9MYY4YNG2aioqJK7fO8884rdaxTlfc54KprRZ4/b3//M6b633FwZnShR7nGjh2rw4cPu7tTFRUV6Z133tEll1yitm3bSpI+++wz9e/fXwkJCSoqKnL/uLrXnzpDqiRdffXVpbr2lmfnzp364Ycf3GN/Tt3/kCFDdODAgVJdm7zt6quv9vi7S5cukuTuNuy6fyNGjPAod/3111doDOvy5csVHh5e6jg33nijx9+VeSwuuugiffHFF3r44Ye1YsUK5ebmVui+rly5UpdeeqliYmLc2wICAkrdt8WLF6uoqEi33HKLRz2CgoLUt29fdxevqKgotWnTRs8995xefPFFbdiwoVQ3uOTkZBUXF+vuu+8+a/3i4uJKjTPu0qVLqS7cH3zwgXr37q2wsDDZbDbZ7XbNmjVL33//fYUeh1M988wzmjFjhl599VX3a/pMKvrYuFgsFl111VVnvU/ladOmjdauXau1a9cqJSVFc+bMUXBwsC677DLt2LFDkpSXl6cvv/xS1157rUJCQkq9dvLy8vTtt9+e9VhZWVl66KGHdM4558hms8lmsyksLEzZ2dkej+2p+y8qKnJ36Tt1HLzrd9++fSVJHTp0UNOmTd3d6FesWKHY2Fh16NBBkm/PMxV5nfraddddV+Gyy5Yt0+WXX67IyEhZrVbZ7XY9/vjjOnLkiNLS0iRJNptNN998sz766CN3t9/i4mK9/fbbuuaaaxQdHS2p/PPXyJEjK30fLrroIh0/flwjR47UJ598csbZ2k8fz9mrVy+1bNnS/fyvXr1aR48e1a233urxfJeUlOiKK67Q2rVrlZ2dXanX9vLly3Xeeefp/PPP9zj26efaszm97iNGjJDNZnPXvbLngIoYM2aM9u3bp6VLl7q3vfHGG4qLi3O/Byr6mJ3q9Nedv87Zp/LFc1qVx6asz/68vDz3e+yzzz6TxWLRzTff7LHPuLg4nX/++aWe56o8Fi633Xab9u3bpzlz5ui+++5TYmKi3nnnHfXt21fPPffcWW9fli+++EKSKvQ8XnDBBWrRooX776CgILVr167Mup/tve3SuHFj9wRsLsuWLVPHjh1LPU6jR4+WMUbLli3z2D58+HAFBQW5/w4PD9dVV12lr776SsXFxVX67Dvbd76qOH0Wf9fn2ukTN3bo0EFHjx51d6M/k4p8DrhU5Pnz9vc/l+p+x0H5CPAo1/XXX6/IyEi98cYbkqSFCxfq4MGDHpPXHTx4UAsWLJDdbvf4Oe+88ySp1Je48sZxlcW1RN0DDzxQav+uyfW8uaRPWVxfdF0cDockuUOxa3za6bPx22y2Urcty5EjR8qcyT8uLs7j78o8Fq+88ooeeughffzxx+rfv7+ioqI0bNgwd6CrbF1O3+aqy29+85tSdXn//ffd9bBYLPryyy81aNAgTZ06VRdeeKGaNGmi++67zz1G0TV2tCITOZX1eDocDo9/UHz00UcaMWKEmjVrpnfeeUcpKSlau3atbrvtNuXl5Z31GKd655139Mgjj+jxxx+v8ISNFX1sXEJCQjy+gLjuU0Xr6hr31717d1188cUaOXKkvvjiCx04cECPP/64JOfzWlRUpOnTp5eq05AhQyRV7H104403asaMGbr99tu1ePFi/fe//9XatWvVpEkTj+fg9GO8+eabkqTOnTsrJiZGy5cvd49/dwV4yTkB34oVK5Sfn6+UlBSP2ed9eZ6pyOvU1ypa3//+97/uiQn//e9/65tvvtHatWv16KOPSpLH8+B6zc+dO1eS84vXgQMHPCbCOnLkiGw2m6KiojyOU5XVRUaNGqXXX39de/bs0XXXXaemTZuqR48eSk5OLlX29POba5vrfOp6H11//fWlnvNnn31WxhgdPXq0Uq/tI0eOlHvcyji9vOtcf3rdK3oOqIjBgwcrPj7e/Vl87Ngxffrpp7rllltktVo9jnu2x+xUp7/u/HHOPp0vntOqPDZn++w/ePCgjDGKjY0ttc9vv/221PNclcfiVJGRkRo5cqRefvllrVmzRps3b1ZsbKweffTRKq2GcujQIVmt1gq9/itT97O9t13KOucdOXKkzO0JCQnu6ytyrIKCAmVlZVXps+9sz3tVnH5+DQwMPOP2s33+V+ZzQKrY8+ft738u1f2Og/IxzS3KFRwcrJEjR+rf//63Dhw4oNdff13h4eHuiVIkKSYmRl26dNHf/va3MvfhOvG6VGbNW9d/AidOnKjhw4eXWaZ9+/YV3p/kPHGUNXnG6R8MFeU6MR48eFDNmjVzby8qKqrQPqOjo/Xf//631PbTJ7GrzGMRGhqqyZMna/LkyTp48KC7Nf6qq64qtVTX6XVxnZwrUpcPP/xQLVu2PMO9k1q2bOme2Oinn37Sf/7zH02aNEkFBQV69dVX3bPF79u3T4mJiWfcV0W88847SkpK0vvvv+/xWqvshCnJycm67bbbNHr0aE2ePLnCt6vMY+Mr8fHxiomJ0aZNmyQ5WzqsVqtGjRpVbmtLUlLSGfeZnp6uzz77TE888YQefvhh9/b8/PxSX3zXrl1b5r4tFov69u2rRYsW6b///a+OHz/uEeD79u2rSZMmKSUlRXl5eR4B3pfnGensr9PKCgoKKvM1d/jwYY8WjsrWd+7cubLb7frss888vhSVtRyiqyXrjTfe0B//+Ee98cYbSkhI8FiZIDo6WkVFRTp69KjHl8nyJtE8mzFjxmjMmDHKzs7WV199pSeeeEJDhw7VTz/95PF+KGv/qampOueccySdfB9Nnz693NmVY2NjVVRUVOHXdnR0dLnHrYzU1NQyz/WuzwJfnANc9/GVV17R8ePHNWfOHOXn53v8M6aij9mpynrd1fQ5+3SVOV9V9DmtymNzNjExMbJYLPr666/dIe9UZW3zpvPOO0+///3vNW3aNP3000+lWq3PpkmTJiouLlZqamql/uF5Nmd7b7uU9dqLjo7WgQMHSm13TRx8+rmzvGMFBgYqLCxMdru92p99Zans+d3bKvM5UFG++P4H3yLA44zGjh2rV199Vc8995wWLlyo0aNHeyz1MXToUC1cuFBt2rRR48aNq3SM8v7D2b59e7Vt21abNm3S008/XfU7cYpWrVpp8+bNHtuWLVtWoS5LZenTp48k54ycp87s/OGHH1Zo6ar+/fvrP//5jz799FOPrltz5szxKFfVxyI2NlajR4/Wpk2bNG3atDMu1dK3b18tXLjQ40OopKREH3zwgUe5QYMGyWaz6X//+1+luv62a9dOf/nLXzRv3jx99913kqSBAwfKarVq5syZ6tmzZ4X3VR6LxaLAwECPLwepqan65JNPSpUtrwVh48aNuu6663TppZfqX//6V5nHKe81W9XHxpv27dunw4cPq2PHjpKc/wHv37+/NmzYoC5durj/y1+W8u6XxWKRMabUl9LXXntNxcXFHtu6d+9e7v779++vefPm6bnnnlPTpk3dXQkl5+vvyJEjmj59urusiy/PM6cr63VaWWWdZ3766Sf9+OOP1fqC51peztXqKjnvz9tvv11m+TFjxuhPf/qTVq1apQULFmj8+PEet+3bt6+mTp2q999/X3/605/c212t9lUVGhqqwYMHq6CgQMOGDdO2bds8vuy9++67Hu+P1atXa8+ePbr99tslSb1791ajRo20fft23XPPPeUeJzAwsMKv7f79+2vq1KnatGmTR5fr08+1Z/Puu++qW7du7r//85//qKioyD37tK/OAWPGjNHUqVP13nvvafbs2erZs6fHsosVfcwqoybO2aerzPmqos+pLx6boUOH6plnntGvv/5aqpuxNx05ckTh4eFlPg6uf8i7/oFZmdbiwYMHa8qUKZo5c6aefPJJr9X3bO/tM7nssss0ZcoUfffddx7fp9566y1ZLBaPzwTJ2ePuueeec4fYzMxMLViwQJdccomsVmulXkuV4avze0VV9nOgInz9/Q/eR4DHGXXv3l1dunTRtGnTZIwp1ZX4ySefVHJysnr16qX77rtP7du3V15ennbv3q2FCxfq1VdfPWtXuzZt2ig4OFjvvvuuOnTooLCwMCUkJCghIUH//Oc/NXjwYA0aNEijR49Ws2bNdPToUX3//ff67rvvSp1czmbUqFF67LHH9Pjjj6tv377avn27ZsyY4V56q7LOO+88jRw5Ui+88IKsVqsuvfRSbdu2TS+88IIiIyPdS7yU55ZbbtFLL72kW265RX/729/Utm1bLVy4UIsXLy5VtqKPRY8ePTR06FB16dJFjRs31vfff6+3335bPXv2POM6q48++qgWLFigyy67TI8++qiCg4P16quvuscGuu5Lq1at9OSTT+rRRx/Vzz//rCuuuEKNGzfWwYMH9d///tfdA2Dz5s2655579Lvf/U5t27ZVYGCgli1bps2bN7tbcVu1aqVHHnlETz31lHJzc93L9mzfvl2HDx+uVOu35PxS9dFHH+muu+7S9ddfr7179+qpp55SfHx8qSEEnTt31ooVK7RgwQLFx8crPDxc8fHxGjJkiIKDg/XAAw9o3bp1Hrfp2LGjIiIi1LlzZ0nSyy+/rFtvvVV2u13t27ev8GPjLbm5ue4xfMXFxdq1a5emTp0qSRo3bpy73Msvv6zf/va3uuSSS/SnP/1JrVq1UmZmpnbu3KkFCxa4xxae6b3Yp08fPffcc4qJiVGrVq20cuVKzZo1S40aNapwfV1fwObPn6/rr7/e47pOnTopOjpa8+fPV7NmzdzzbEi+Pc8cPnz4rK/Tyho1apRuvvlm3XXXXbruuuu0Z88eTZ061d16WVVXXnmlXnzxRd144436wx/+oCNHjuj5558vt7Vv5MiRGj9+vEaOHKn8/PxSSx9dccUV6t27tyZMmKCMjAx169ZNKSkp7mUXz3b+OtUdd9yh4OBg9e7dW/Hx8UpNTdWUKVMUGRnpXsLPZd26dbr99tv1u9/9Tnv37tWjjz6qZs2auYcDhYWFafr06br11lt19OhRXX/99WratKkOHTqkTZs26dChQ5o5c6akir+2x40bp9dff11XXnml/vrXvyo2NlbvvvvuGXslleWjjz6SzWbTgAEDtG3bNj322GM6//zz3SHOV+eAc889Vz179tSUKVO0d+/eUv9crMxjVh5/nLPL4u3n1BuPzel69+6tP/zhDxozZozWrVunPn36KDQ0VAcOHNCqVavUuXNnj3+KVdXy5cv15z//WTfddJN69eql6OhopaWl6b333tOiRYt0yy23uM99nTp1kiT961//Unh4uIKCgpSUlFRmF+pLLrlEo0aN0l//+lcdPHhQQ4cOlcPh0IYNGxQSEqJ77723SvU923v7TO6//3699dZbuvLKK/Xkk0+qZcuW+vzzz/WPf/xDf/rTn9SuXTuP8larVQMGDND48eNVUlKiZ599VhkZGR6vwYq+lirDV+f3iqrs50BFePv7H2qA/+bPQ13x8ssvG0mmY8eOZV5/6NAhc99995mkpCRjt9tNVFSU6datm3n00UdNVlaWMebkrKHPPfdcmft47733zLnnnmvsdnup2Y83bdpkRowYYZo2bWrsdruJi4szl156qXn11VfdZSo6C31+fr558MEHTWJiogkODjZ9+/Y1GzduLHcW+tNnly7rOHl5eWb8+PGmadOmJigoyFx88cUmJSXFREZGlpqxuCz79u0z1113nQkLCzPh4eHmuuuuM6tXry5zxuyKPBYPP/yw6d69u2ncuLFxOBymdevW5v7773fPPH0mX3/9tenRo4dxOBwmLi7O/N///Z959tlnjSRz/Phxj7Iff/yx6d+/v4mIiDAOh8O0bNnSXH/99e4leQ4ePGhGjx5tzj33XBMaGmrCwsJMly5dzEsvveSxJIsxxrz11lvmN7/5jQkKCjJhYWGma9euHve9vJlYy1oa55lnnjGtWrUyDofDdOjQwfz73/8u87WwceNG07t3bxMSEmIkmb59+7pfp+X9nPq8T5w40SQkJJiAgIBS153tsXHVPTQ0tNR9qujM1KfPQh8QEGASEhLM4MGDzYoVK0qV37Vrl7nttttMs2bNjN1uN02aNDG9evXymB3YmPLfi67XaePGjU14eLi54oorzNatW8udkbc8cXFxRpKZMWNGqeuGDRtmJJU5w7WvzjOVeZ2errzzRElJiZk6dapp3bq1CQoKMt27dzfLli0rd5biDz744GwPm9vrr79u2rdv735vT5kyxcyaNavMVRGMMebGG280kkzv3r3L3N/Ro0fNmDFjTKNGjUxISIgZMGCA+fbbb42kCq0u4PLmm2+a/v37m9jYWBMYGGgSEhLMiBEjzObNm91lXI/XkiVLzKhRo0yjRo3cyznu2LGj1D5XrlxprrzyShMVFWXsdrtp1qyZufLKK0s9XhV9bW/fvt0MGDDABAUFmaioKDN27Fj3knsVnYV+/fr15qqrrnKfr0eOHGkOHjxYqnxFzgEVfa+7/Otf/zKSTHBwcKlZtV0q8pi5jnv68o/+OGeXNQu9a7u3n9PqPDau1+7p77HXX3/d9OjRw4SGhprg4GDTpk0bc8stt5h169ZV+rEoy969e81f/vIX07t3bxMXF2dsNpsJDw83PXr0MNOnTy/1vEybNs0kJSUZq9Xq8biWdazi4mLz0ksvmU6dOpnAwEATGRlpevbs6V7ZwBjnLOZXXnllqXqdfi6rzHv7TDOr79mzx9x4440mOjra2O120759e/Pcc895rMDges08++yzZvLkyaZ58+YmMDDQdO3a1SxevLjUPivyWirvXFzW67O65/fyPjfKeu2V91hV9HOgos+fMd79/mdM9b/j4MwsxpyYIhiA16xevVq9e/fWu+++W+lZjmubgQMHavfu3frpp5/8XRUANWDOnDm66aab9M0336hXr15e2+/s2bM1ZswYrV279oxDLQDULby36ye+/9VedKEHqik5OVkpKSnq1q2bgoODtWnTJj3zzDNq27ZtuRPO1Vbjx49X165dlZiYqKNHj+rdd99VcnKye1IjAPXLe++9p19//VWdO3dWQECAvv32Wz333HPq06ePV8M7AKD24vtf3UKAB6opIiJCS5Ys0bRp05SZmamYmBj3BDGnL59R2xUXF+vxxx9XamqqLBaLOnbsqLfffls333yzv6sGwAfCw8M1d+5c/fWvf1V2drbi4+M1evRo/fWvf3WXOduEnAEBAZUaLw8AqF34/le30IUeAACUaffu3WddaumJJ57QpEmTaqZCAAA0cLTAAwCAMiUkJGjt2rVnLQMAAGoGLfAAAAAAANQBDFoDAAAAAKAOoAu9pJKSEu3fv1/h4eGyWCz+rg4AAAAAoJ4zxigzM1MJCQkVnhCWAC9p//79SkxM9Hc1AAAAAAANzN69e9W8efMKlSXAy7mMjuR84CIiIvxcGwAAAABAfZeRkaHExER3Hq0IArzk7jYfERFBgAcAAAAA1JjKDONmEjsAAAAAAOoAAjwAAAAAAHUAAR4AAAAAgDqAMfAAAAAAUAsZY1RUVKTi4mJ/VwVVYLVaZbPZvLpUOQEeAAAAAGqZgoICHThwQDk5Of6uCqohJCRE8fHxCgwM9Mr+CPAAAAAAUIuUlJRo165dslqtSkhIUGBgoFdbceF7xhgVFBTo0KFD2rVrl9q2bauAgOqPYCfAAwAAAEAtUlBQoJKSEiUmJiokJMTf1UEVBQcHy263a8+ePSooKFBQUFC198kkdgAAAABQC3mjxRb+5e3nkFcEAAAAAAB1AAEeAAAAAIA6gAAPAAAAAEAdQIAHAAAAANRKf/7zn9WtWzc5HA5dcMEFZZbZsmWL+vbtq+DgYDVr1kxPPvmkjDEeZVauXKlu3bopKChIrVu31quvvlpqP/PmzVPHjh3lcDjUsWNHzZ8/3xd3qVoI8AAAAACAWskYo9tuu0033HBDmddnZGRowIABSkhI0Nq1azV9+nQ9//zzevHFF91ldu3apSFDhuiSSy7Rhg0b9Mgjj+i+++7TvHnz3GVSUlJ0ww03aNSoUdq0aZNGjRqlESNGaM2aNT6/j5VhMaf/a6IBysjIUGRkpNLT0xUREeHv6gAAAABowPLy8rRr1y4lJSW5lx4zxii3sLjG6xJst1ZqDfp+/fqpS5cuCgoK0muvvabAwEDdeeedmjRpUrXqMWnSJH388cfauHGjx/aZM2dq4sSJOnjwoBwOhyTpmWee0fTp07Vv3z5ZLBY99NBD+vTTT/X999+7b3fnnXdq06ZNSklJkSTdcMMNysjI0BdffOEuc8UVV6hx48Z67733qlzvsp5Ll6rkUNaBBwAAAIBaLrewWB0fX1zjx93+5CCFBFYuNr755psaP3681qxZo5SUFI0ePVq9e/fWgAEDNHjwYH399ddnvH1WVlaFj5WSkqK+ffu6w7skDRo0SBMnTtTu3buVlJSklJQUDRw40ON2gwYN0qxZs1RYWCi73a6UlBTdf//9pcpMmzatwnWpCQR4AAAAAIDXdOnSRU888YQkqW3btpoxY4a+/PJLDRgwQK+99ppyc3O9dqzU1FS1atXKY1tsbKz7uqSkJKWmprq3nVqmqKhIhw8fVnx8fLllUlNTvVZXbyDAAwAAAEAtF2y3avuTg/xy3Mrq0qWLx9/x8fFKS0uTJDVr1swr9TrV6V38XaPET91e1TKVGT5QEwjwAACgVlpzYI0CrYHq2rSrv6sCAH5nsVgq3ZXdX+x2u8ffFotFJSUlkuT1LvRxcXGlWsld/yxwtaiXV8Zmsyk6OvqMZU5vlfe3uvEKAAAADcpX+77S3V/eLVuATR9f87FaRrT0d5UAAF7g7S70PXv21COPPKKCggIFBgZKkpYsWaKEhAR31/qePXtqwYIFHrdbsmSJunfv7v5nQ8+ePZWcnOwxDn7JkiXq1auX1+rqDQR4AABQ67y25TVJUlFJkWZvm60nej7h5xoBALyhsl3od+7cqaysLKWmpio3N9c9C33Hjh0VGBioG2+8UZMnT9bo0aP1yCOPaMeOHXr66af1+OOPu7u/33nnnZoxY4bGjx+vO+64QykpKZo1a5bH7PJ//vOf1adPHz377LO65ppr9Mknn2jp0qVatWqV1+67NxDgAQBArZJTmKMth7e4/96YttF/lQEA+NXtt9+ulStXuv/u2tU5rGrXrl1q1aqVIiMjlZycrLvvvlvdu3dX48aNNX78eI0fP959m6SkJC1cuFD333+//v73vyshIUGvvPKKrrvuOneZXr16ae7cufrLX/6ixx57TG3atNH777+vHj161NydrQDWgRfrwAMAUJt8e+Bb3bHkDgVYAlRiShRgCdC3N36rYFuwv6sGADXiTGuHo27x9jrwAb6oJAAAQFX9ePRHSdJlLS5TVFCUSkyJfjr2k59rBQCA/xHgAQBArbIrfZckqXVka53T6BxJ0i8Zv/izSgAA1AqMgQcAALWKK8AnRSYpNdu5pM+B7AP+rBIAALUCLfAAAKBW2Z2xW5IzwCeEJUiS9mft92ONAACoHQjwAACg1sgqyNLRvKOSpJYRLRUfGi9J7pZ4AAAaMgI8AACoNfZnO1vaIx2RCrWHKj4s3mM7AAANGQEeAADUGgeynGPdE0KdXefjQuIkSQezD/qtTgAA1BYEeAAAUGv8mvWrJLnHvkcHR0uScopylFuU67d6AQBQGxDgAQBAreGabd419j3MHiZ7gF2SdCzvmN/qBQBAbUCABwAAtYZrsjpXgLdYLIoKipIk9+R2AAA0VAR4AABQaxzKPSRJahrS1L2NAA8ADdef//xndevWTQ6HQxdccEGp6/Py8jR69Gh17txZNptNw4YNK3M/K1euVLdu3RQUFKTWrVvr1VdfLVVm3rx56tixoxwOhzp27Kj58+eXKvOPf/xDSUlJCgoKUrdu3fT1119X9y5WCgEeAADUGodynAE+JjjGvS0qmAAPAA2VMUa33XabbrjhhjKvLy4uVnBwsO677z5dfvnlZZbZtWuXhgwZoksuuUQbNmzQI488ovvuu0/z5s1zl0lJSdENN9ygUaNGadOmTRo1apRGjBihNWvWuMu8//77GjdunB599FFt2LBBl1xyiQYPHqxffvnFu3f6DGw1diQAAIAzMMaU2QIfHeScyI4AD6BBM0YqzKn549pDJIulwsX79eunLl26KCgoSK+99poCAwN15513atKkSVU6/CuvvCJJOnTokDZv3lzq+tDQUM2cOVOS9M033+j48eOlyrz66qtq0aKFpk2bJknq0KGD1q1bp+eff17XXXedJGnatGkaMGCAJk6cKEmaOHGiVq5cqWnTpum9996TJL344osaO3asbr/9dvdtFi9erJkzZ2rKlClVun+VRYAHAAC1QnZhtnumeY8W+BNd6I/kHvFLvQCgVijMkZ5OqPnjPrJfCgyt1E3efPNNjR8/XmvWrFFKSopGjx6t3r17a8CAARo8ePBZu51nZWVVp8alpKSkaODAgR7bBg0apFmzZqmwsFB2u10pKSm6//77S5Vxhf6CggKtX79eDz/8sEeZgQMHavXq1V6t75kQ4AEAQK3gan0PtYcqxB7i3h7piJQkpeen+6VeAIDK6dKli5544glJUtu2bTVjxgx9+eWXGjBggF577TXl5tbssqCpqamKjY312BYbG6uioiIdPnxY8fHx5ZZJTXVOrnr48GEVFxefsUxNIMADAIBa4XDuYUlSk+AmHtsjAiMkSekFBHgADZg9xNka7o/jVlKXLl08/o6Pj1daWpokqVmzZl6pVmVZThsGYIwptb2sMqdvq0gZXyLAAwCAWiEtx/nlrknIaQHe4QzwGfkZNV4nAKg1LJZKd2X3F7vd7vG3xWJRSUmJJPmlC31cXFypVvK0tDTZbDZFR0efsYyrxT0mJkZWq/WMZWoCAR4AANQKrhb4U8e/S1JkoLMLfUYBAR4A6jp/dKHv2bOnFixY4LFtyZIl6t69u/ufDT179lRycrLHOPglS5aoV69ekqTAwEB169ZNycnJuvbaa91lkpOTdc0119TAvXAiwAMAgFrBtYRc0+CmHttpgQeA+qOyXeh37typrKwspaamKjc3Vxs3bpQkdezYUYGBgZKk7du3q6CgQEePHlVmZqa7jGvd+DvvvFMzZszQ+PHjdccddyglJUWzZs1yzy4vOdeb79Onj5599lldc801+uSTT7R06VKtWrXKXWb8+PEaNWqUunfvrp49e+pf//qXfvnlF915551Vf0AqiQAPAABqhbTcsrvQ0wIPAA3X7bffrpUrV7r/7tq1qyTn2u6tWrWSJA0ZMkR79uwpVcY1zj0pKUkLFy7U/fffr7///e9KSEjQK6+84l5CTpJ69eqluXPn6i9/+Ysee+wxtWnTRu+//7569OjhLnPDDTfoyJEjevLJJ3XgwAF16tRJCxcuVMuWLX12/09nMa571YBlZGQoMjJS6enpioiI8Hd1AABokG5bfJvWpq7VM5c8oytbX+nenlGQod7v9ZYkrbt5nRxWh7+qCAA1Ii8vT7t27VJSUpKCgoL8XR1Uw5mey6rk0ABfVBIAAKCy3F3oQzy70IfZw2SRc4ZfutEDABoyvwb4mTNnqkuXLoqIiFBERIR69uypL774wn396NGjZbFYPH4uvvhij33k5+fr3nvvVUxMjEJDQ3X11Vdr3759NX1XAABANbnWgT99ErsAS8DJcfB0owcANGB+DfDNmzfXM888o3Xr1mndunW69NJLdc0112jbtm3uMldccYUOHDjg/lm4cKHHPsaNG6f58+dr7ty5WrVqlbKysjR06FAVFxfX9N0BAABVlFOYo+zCbEml14GXpHB7uCQCPACgYfPrJHZXXXWVx99/+9vfNHPmTH377bc677zzJEkOh0NxcXFl3j49PV2zZs3S22+/rcsvv1yS9M477ygxMVFLly7VoEGDfHsHAACAV7ha34NtwQq1l17nOCwwTJLcIR8AgIao1oyBLy4u1ty5c5Wdna2ePXu6t69YsUJNmzZVu3btdMcddygtLc193fr161VYWKiBAwe6tyUkJKhTp05avXp1ucfKz89XRkaGxw8AAPAf1/j3JsFNZLFYSl3vCvVZhVk1Wi8AAGoTvwf4LVu2KCwsTA6HQ3feeafmz5+vjh07SpIGDx6sd999V8uWLdMLL7ygtWvX6tJLL1V+fr4kKTU1VYGBgWrcuLHHPmNjY5WamlruMadMmaLIyEj3T2Jiou/uIAAAOKvDuYcllV5CzsUV4HMKc2qsTgAA1DZ+Xwe+ffv22rhxo44fP6558+bp1ltv1cqVK9WxY0fdcMMN7nKdOnVS9+7d1bJlS33++ecaPnx4ufs0xpT533uXiRMnavz48e6/MzIyCPEAAPhRWs6JNeDLGP8undICX0ALPACg4fJ7gA8MDNQ555wjSerevbvWrl2rl19+Wf/85z9LlY2Pj1fLli21Y8cOSVJcXJwKCgp07Ngxj1b4tLQ09erVq9xjOhwOORysIQsAQG3haoE/fQZ6lzA7Y+ABAPB7F/rTGWPcXeRPd+TIEe3du1fx8fGSpG7duslutys5Odld5sCBA9q6desZAzwAAKhdXJPYnb4GvIurBZ4ADwBoyPzaAv/II49o8ODBSkxMVGZmpubOnasVK1Zo0aJFysrK0qRJk3TdddcpPj5eu3fv1iOPPKKYmBhde+21kqTIyEiNHTtWEyZMUHR0tKKiovTAAw+oc+fO7lnpAQBA7eeaxK68FngmsQMAwM8t8AcPHtSoUaPUvn17XXbZZVqzZo0WLVqkAQMGyGq1asuWLbrmmmvUrl073XrrrWrXrp1SUlIUHh7u3sdLL72kYcOGacSIEerdu7dCQkK0YMECWa1WP94zAABQGa4W+PImsaMLPQA0bEeOHFHz5s1lsVh0/Phxj+uMMXr++efVrl07ORwOJSYm6umnn/Yos3LlSnXr1k1BQUFq3bq1Xn311VLHmDdvnjp27CiHw6GOHTtq/vz5pcr84x//UFJSkoKCgtStWzd9/fXXXr2fZ+PXFvhZs2aVe11wcLAWL1581n0EBQVp+vTpmj59ujerBgAAapCrBb5pMF3oAQCljR07Vl26dNGvv/5a6ro///nPWrJkiZ5//nl17txZ6enpOnz4sPv6Xbt2aciQIbrjjjv0zjvv6JtvvtFdd92lJk2a6LrrrpMkpaSk6IYbbtBTTz2la6+9VvPnz9eIESO0atUq9ejRQ5L0/vvva9y4cfrHP/6h3r1765///KcGDx6s7du3q0WLFjXyOPh9EjsAANCw5RXlKbMwU5IUE3LmLvQEeAANlTFGuUW5NX7cYFvwGVf4Ol2/fv3UpUsXBQUF6bXXXlNgYKDuvPNOTZo0qcp1mDlzpo4fP67HH39cX3zxhcd133//vWbOnKmtW7eqffv2Zd7+1VdfVYsWLTRt2jRJUocOHbRu3To9//zz7gA/bdo0DRgwQBMnTpTkXLls5cqVmjZtmt577z1J0osvvqixY8fq9ttvd99m8eLFmjlzpqZMmVLl+1cZBHgAAOBXru7zDqtD4fbwMsu4utAzBh5AQ5VblKsec3rU+HHX3LhGIfaQSt3mzTff1Pjx47VmzRqlpKRo9OjR6t27twYMGKDBgweftdt5VtbJc/327dv15JNPas2aNfr5559LlV2wYIFat26tzz77TFdccYWMMbr88ss1depURUVFSXK2rg8cONDjdoMGDdKsWbNUWFgou92ulJQU3X///aXKuEJ/QUGB1q9fr4cfftijzMCBA7V69eoKPzbVRYAHAAB+deoa8OW18ri+PNICDwC1X5cuXfTEE09Iktq2basZM2boyy+/1IABA/Taa68pN7diPQny8/M1cuRIPffcc2rRokWZAf7nn3/Wnj179MEHH+itt95ScXGx7r//fl1//fVatmyZJCk1NVWxsbEet4uNjVVRUZEOHz6s+Pj4csukpqZKkg4fPqzi4uIzlqkJBHgAAOBXrgBf3hJyEpPYAUCwLVhrblzjl+NWVpcuXTz+jo+PV1qa81zfrFmzCu9n4sSJ6tChg26++eZyy5SUlCg/P19vvfWW2rVrJ8k511q3bt30448/urvVn/4PYmNMqe1llTl9W0XK+BIBHgAA+JUrwMeGxJZbJiyQAA+gYbNYLJXuyu4vdrvd42+LxaKSkhJJqlQX+mXLlmnLli368MMPJZ0M3TExMXr00Uc1efJkxcfHy2azucO75BzjLkm//PKL2rdvr7i4uFKt5GlpabLZbIqOjpakcsu4WtxjYmJktVrPWKYmEOABAIBfVaQF3vWltbCkUAXFBQq0BtZI3QAA3lWZLvTz5s3zKLt27Vrddttt+vrrr9WmTRtJUu/evVVUVKT//e9/7m0//fSTJKlly5aSpJ49e2rBggUe+16yZIm6d+/u/mdDz549lZyc7DEOfsmSJerVq5ckKTAwUN26dVNycrKuvfZad5nk5GRdc801lXoMqoMADwAA/KoiAT7UFuq+nFWYpShrlM/rBQDwvsp0oXcFchfX0nAdOnRQo0aNJEmXX365LrzwQt12222aNm2aSkpKdPfdd2vAgAHuVvk777xTM2bM0Pjx43XHHXcoJSVFs2bNcs8uLzmXouvTp4+effZZXXPNNfrkk0+0dOlSrVq1yl1m/PjxGjVqlLp3766ePXvqX//6l3755RfdeeedVX04Ki2gxo4EAABQBneADy0/wFsDrO5xmHSjBwC4BAQEaMGCBYqJiVGfPn105ZVXqkOHDpo7d667TFJSkhYuXKgVK1boggsu0FNPPaVXXnnFvYScJPXq1Utz587VG2+8oS5dumj27Nl6//333WvAS9INN9ygadOm6cknn9QFF1ygr776SgsXLnS39NcEi3ENJGjAMjIyFBkZqfT0dEVERPi7OgAANChXzLtCv2b9qrcGv6WuTbuWW67/f/rrcO5hfXDVBzo36twarCEA1Ky8vDzt2rVLSUlJCgoK8nd1UA1nei6rkkNpgQcAAH5jjNGhHOc68GfqQi+dshZ8AWvBAwAaJgI8AADwm+P5x1VQUiDJuQ78mYTanePgc4pyfF4vAABqIwI8AADwG9f498aOxmedWd4V4GmBBwA0VAR4AADgNxWZgd7FHeALCfAAgIaJAA8AAPymMgHeNQY+p5Au9AAaBuYbr/u8/RwS4AEAgN9UJsCH2EMkSdlFLCMHoH6z2+2SpJwc/mFZ17meQ9dzWl02r+wFAACgCg7mHJQkxYbEnrWsax343MJcn9YJAPzNarWqUaNGSktz/pMzJCREFovFz7VCZRhjlJOTo7S0NDVq1EhWq9Ur+yXAAwAAv6lMC7w7wBcR4AHUf3FxcZLkDvGomxo1auR+Lr2BAA8AAPymUl3obc4u9CwjB6AhsFgsio+PV9OmTVVYWOjv6qAK7Ha711reXQjwAADAb2iBB4Azs1qtXg+BqLuYxA4AAPhFQXGBjuUfk1TBMfB2AjwAoGEjwAMAAL9wtb4HBgQq0hF51vK0wAMAGjoCPAAA8ItTu89XZHZlAjwAoKEjwAMAAL9wBfjY0LN3n5cI8AAAEOABAIBfuNaAr8gEdhLrwAMAQIAHAAB+4QrwFZnATqIFHgAAAjwAAPCLg9mVC/CnrgNvjPFZvQAAqK0I8AAAwC8qswa8dHIZuWJTrMKSQp/VCwCA2ooADwAA/KKqk9hJdKMHADRMBHgAAFDjSkzJyQBfwS709gC7bAE2SQR4AEDDRIAHAAA17mjeURWZIllkUXRwdIVv52qFzynK8VXVAACotQjwAACgxrlmoI8JjpE9wF7h2zETPQCgISPAAwCAGpeWXbkJ7FxcM9GzFjwAoCEiwAMAgBpX2TXgXWiBBwA0ZAR4AABQ4yq7hJwLAR4A0JAR4AEAQI1zt8BXcAk5F9da8ExiBwBoiAjwAACgxlW1C717DDwt8ACABogADwAAahxd6AEAqDwCPAAAqHEHs5nEDgCAyiLAAwCAGpVTmOMew94kpEmlbusO8CwjBwBogAjwAACgRh3JPSJJCrIGuce0VxQt8ACAhowADwAAatSRPGeAjw6OlsViqdRtmcQOANCQEeABAECNcgf4oOhK35YWeABAQ0aABwAANcrVhT4qOKrSt2UdeABAQ0aABwAANYoWeAAAqoYADwAAatTR3KOSpKigyrfAB1mDJEl5RXlerRMAAHUBAR4AANSoUyexq6wg24kAX0yABwA0PAR4AABQo1xj4KvThZ4WeABAQ0SABwAANeponrMLfVVa4B1WhyQpvzjfq3UCAKAu8GuAnzlzprp06aKIiAhFRESoZ8+e+uKLL9zXG2M0adIkJSQkKDg4WP369dO2bds89pGfn697771XMTExCg0N1dVXX619+/bV9F0BAAAVVJ0WeFcXeiaxAwA0RH4N8M2bN9czzzyjdevWad26dbr00kt1zTXXuEP61KlT9eKLL2rGjBlau3at4uLiNGDAAGVmZrr3MW7cOM2fP19z587VqlWrlJWVpaFDh6q4uNhfdwsAAJSjoLhAmYXOz/GqtMCf2oXeGOPVugEAUNtZTC379IuKitJzzz2n2267TQkJCRo3bpweeughSc7W9tjYWD377LP64x//qPT0dDVp0kRvv/22brjhBknS/v37lZiYqIULF2rQoEFlHiM/P1/5+Se73mVkZCgxMVHp6emKiIjw/Z0EAKCBSs1O1YAPB8hmsWn9qPUKsFSuLSGrIEs93+spSVp38zp3l3oAAOqajIwMRUZGViqH1pox8MXFxZo7d66ys7PVs2dP7dq1S6mpqRo4cKC7jMPhUN++fbV69WpJ0vr161VYWOhRJiEhQZ06dXKXKcuUKVMUGRnp/klMTPTdHQMAAG6u7vNRQVGVDu+S5LCdDOxMZAcAaGj8HuC3bNmisLAwORwO3XnnnZo/f746duyo1NRUSVJsbKxH+djYWPd1qampCgwMVOPGjcstU5aJEycqPT3d/bN3714v3ysAAFAW1xJyUcGVXwNekuwBdtksNkkEeABAw2PzdwXat2+vjRs36vjx45o3b55uvfVWrVy50n29xWLxKG+MKbXtdGcr43A45HDQ5Q4AgJqWnp8uSWrkaFTlfQTZgpRVmMVa8ACABsfvLfCBgYE655xz1L17d02ZMkXnn3++Xn75ZcXFxUlSqZb0tLQ0d6t8XFycCgoKdOzYsXLLAACA2sNbAV6iBR4A0PD4PcCfzhij/Px8JSUlKS4uTsnJye7rCgoKtHLlSvXq1UuS1K1bN9ntdo8yBw4c0NatW91lAABA7XE8/7gkKdIRWeV9uCauowUeANDQ+LUL/SOPPKLBgwcrMTFRmZmZmjt3rlasWKFFixbJYrFo3Lhxevrpp9W2bVu1bdtWTz/9tEJCQnTjjTdKkiIjIzV27FhNmDBB0dHRioqK0gMPPKDOnTvr8ssv9+ddAwAAZfBGgD91KTkAABoSvwb4gwcPatSoUTpw4IAiIyPVpUsXLVq0SAMGDJAkPfjgg8rNzdVdd92lY8eOqUePHlqyZInCw8Pd+3jppZdks9k0YsQI5ebm6rLLLtPs2bNltVr9dbcAAEA5MvIzJFWzC72VLvQAgIbJrwF+1qxZZ7zeYrFo0qRJmjRpUrllgoKCNH36dE2fPt3LtQMAAN7mjRZ41xj43OJcb1QJAIA6o9aNgQcAAPVXekH1J7FzrQWfX5TvjSoBAFBnEOABAECNcc1CX60x8FbGwAMAGiYCPAAAqDHuLvSB1e9Czyz0AICGhgAPAABqRGFJobILsyV5Zx343CLGwAMAGhYCPAAAqBGu7vMWWRQeGH6W0uVzzUKfX8wYeABAw0KABwAANcIV4MMDw2UNqPpyr+4u9IyBBwA0MAR4AABQI1wBvjrd56WTLfB0oQcANDQEeAAAUCNcE9hVO8AziR0AoIEiwAMAgBrhaoGPcERUaz/uMfCsAw8AaGAI8AAAoEZ4rQu9axb6YrrQAwAaFgI8AACoEV7vQs8kdgCABoYADwAAakRGQYYkVWsJOUkKtgVLIsADABoeAjwAAKgRWQVZkqof4B1WhyTWgQcANDwEeAAAUCMyCzMlVT/Au8fAs4wcAKCBIcADAIAakVlwIsDbqxngrYyBBwA0TAR4AABQI7zVhZ514AEADRUBHgAA1AhXC3xYYFi19sM68ACAhooADwAAaoR7DHx1u9CfaIEvMkUqLCmsdr0AAKgrCPAAAMDnCksK3ZPOeWsZOYlx8ACAhoUADwAAfC67INt9OTQwtFr7sgfYZZFFEgEeANCwEOABAIDPuca/B9uCZQ+wV2tfFouFiewAAA0SAR4AAPict8a/u7i60dMCDwBoSAjwAADA57y1hJwLa8EDABoiAjwAAPA5by0h5+KwOSTRhR4A0LAQ4AEAgM+5utB7K8DTAg8AaIgI8AAAwOdcLfAR9giv7M89Bp4WeABAA0KABwAAPucaA++1FngbLfAAgIaHAA8AAHwuoyBDkvcmsXNYGQMPAGh4CPAAAMDnsgq9PAs9LfAAgAaIAA8AAHzOPQu93Ttd6FkHHgDQEBHgAQCAz/lqHfjcolyv7A8AgLqAAA8AAHzOtYyc18bAn1gHPr843yv7AwCgLiDAAwAAn3N1ofdWgA+20oUeANDwEOABAIDPuZeR89IYePckdsxCDwBoQAjwAADAp4wxXm+BdwV4xsADABoSAjwAAPCpvOI8FZkiSd6fxI4x8ACAhoQADwAAfMrV+h5gCVCILcQr+2QdeABAQ0SABwAAPnXq+HeLxeKVfbpa4AnwAICGhAAPAAB8KqMgQ5L3us9LJ5eRYxI7AEBDQoAHAAA+lVXobIH3ZoAPtrGMHACg4SHAAwAAn3KNgffWEnISXegBAA0TAR4AAPiUt5eQk+hCDwBomAjwAADAp3zShd5KF3oAQMNDgAcAAD7lmoU+1B7qtX26WuALSgpUXFLstf0CAFCbEeABAIBPZRdmS/LNGHhJyi/O99p+AQCozQjwAADAp1wBPsQe4rV9BtlOBnjGwQMAGgoCPAAA8ClftMAHWAIUGBAoiXHwAICGw68BfsqUKfrNb36j8PBwNW3aVMOGDdOPP/7oUWb06NGyWCwePxdffLFHmfz8fN17772KiYlRaGiorr76au3bt68m7woAACiHK8B7cwy8dLIVnhZ4AEBD4dcAv3LlSt1999369ttvlZycrKKiIg0cOFDZ2dke5a644godOHDA/bNw4UKP68eNG6f58+dr7ty5WrVqlbKysjR06FAVFzOpDQAA/ubzAE8LPACggbD58+CLFi3y+PuNN95Q06ZNtX79evXp08e93eFwKC4ursx9pKena9asWXr77bd1+eWXS5LeeecdJSYmaunSpRo0aJDv7gAAADgrX3Shl05OZMckdgCAhqJWjYFPT0+XJEVFRXlsX7FihZo2bap27drpjjvuUFpamvu69evXq7CwUAMHDnRvS0hIUKdOnbR69eoyj5Ofn6+MjAyPHwAA4BuudeB91QKfW5Tr1f0CAFBb1ZoAb4zR+PHj9dvf/ladOnVybx88eLDeffddLVu2TC+88ILWrl2rSy+9VPn5zv+2p6amKjAwUI0bN/bYX2xsrFJTU8s81pQpUxQZGen+SUxM9N0dAwCggaMLPQAA3uHXLvSnuueee7R582atWrXKY/sNN9zgvtypUyd1795dLVu21Oeff67hw4eXuz9jjCwWS5nXTZw4UePHj3f/nZGRQYgHAMAHjDG+C/B0oQcANDC1ogX+3nvv1aeffqrly5erefPmZywbHx+vli1baseOHZKkuLg4FRQU6NixYx7l0tLSFBsbW+Y+HA6HIiIiPH4AAID35RblyshIogUeAIDq8muAN8bonnvu0UcffaRly5YpKSnprLc5cuSI9u7dq/j4eElSt27dZLfblZyc7C5z4MABbd26Vb169fJZ3QEAwNm5Wt8DLAEKtgV7dd+uFnjGwAMAGgq/dqG/++67NWfOHH3yyScKDw93j1mPjIxUcHCwsrKyNGnSJF133XWKj4/X7t279cgjjygmJkbXXnutu+zYsWM1YcIERUdHKyoqSg888IA6d+7snpUeAAD4h3sCO1touUPbqsrVAk8XegBAQ+HXAD9z5kxJUr9+/Ty2v/HGGxo9erSsVqu2bNmit956S8ePH1d8fLz69++v999/X+Hh4e7yL730kmw2m0aMGKHc3Fxddtllmj17tqxWa03eHQAAcJqcwhxJUog9xOv7drXA04UeANBQ+DXAG2POeH1wcLAWL1581v0EBQVp+vTpmj59ureqBgAAvMDVAu/tNeAlyWFzSJJyi+lCDwBoGGrFJHYAAKB+8tUM9NIps9AX0YUeANAwEOABAIDP+DLAuybFyyumCz0AoGEgwAMAAJ/xZYB3WE90oWcWegBAA0GABwAAPuOehd4XXehtdKEHADQsBHgAAOAzrlnowwK9P4kdXegBAA0NAR4AAPiMqwU+xOb9ZeRcXehZRg4A0FAQ4AEAgM+4xsD7ogXe1YWeFngAQENBgAcAAD7jnsTO5sNZ6GmBBwA0EAR4AADgM+4AH+i7Wejzi5nEDgDQMBDgAQCAz/iyBd7VhZ5l5AAADQUBHgAA+Iwvx8AHW+lCDwBoWAjwAADAZ9yz0Nt9MAu97WQXemOM1/cPAEBtQ4AHAAA+426Bt/tuFvpiU6yikiKv7x8AgNqGAA8AAHyixJQopzBHkhRq98Es9Ce60EtSbjHj4AEA9R8BHgAA+ERuUa6MnF3bfRHgbQE2BVicX2Xyi5iJHgBQ/xHgAQCAT7i6z1stVgVZg7y+f4vF4t4vE9kBABoCAjwAAPCJUyews1gsPjmGeyk5utADABoAAjwAAPAJ1/h3X0xg5+JqgacLPQCgISDAAwAAn3C1wPti/LuLqwU+r5gu9ACA+o8ADwAAfMI1Bt6XAd5hda4Fn1tEF3oAQP1HgAcAAD5REwE+2OZcSi6/mC70AID6jwAPAAB8oiYCvLsLPbPQAwAaAAI8AADwiZrsQs8YeABAQ0CABwAAPuEK8D6dhZ4WeABAA0KABwAAPpFVcHIdeF9xjYEnwAMAGgICPAAA8ImcIt+vA08XegBAQ0KABwAAPuFqgWcSOwAAvIMADwAAfCK7qAaWkbPShR4A0HAQ4AEAgE9kF/h+EjuHjS70AICGgwAPAAB8IqvQ95PYBVnpQg8AaDgI8AAAwCdyCn0/iZ17DDwt8ACABoAADwAAfMLVAu/TSexOtMDnF+X77BgAANQWBHgAAOB1JabEvYxcTcxCn1uc67NjAABQWxDgAQCA17m6z0s10wLPGHgAQENAgAcAAF6XXeicgd5msclhdfjsOK4W+PxiutADAOo/AjwAAPA6V4APsYfIYrH47DjuLvRFdKEHANR/BHgAAOB1rgDvyxnopVMmsaMFHgDQAFQrwO/cuVOLFy9Wbq7zv97GGK9UCgAA1G01sQa8dMoycoyBBwA0AFUK8EeOHNHll1+udu3aaciQITpw4IAk6fbbb9eECRO8WkEAAFD31MQa8JLnGPgSU+LTYwEA4G9VCvD333+/bDabfvnlF4WEnPzP+g033KBFixZ5rXIAAKBucq8BH+i7Geilk13oJbrRAwDqP1tVbrRkyRItXrxYzZs399jetm1b7dmzxysVAwAAdZdrDHyozbcB/tQZ7vOK8hRsC/bp8QAA8KcqtcBnZ2d7tLy7HD58WA6H75aKAQAAdYN7ErtA33ahtwZYFRgQKIlx8ACA+q9KAb5Pnz5666233H9bLBaVlJToueeeU//+/b1WOQAAUDe5l5Gz+XYSO0ly2JyNB3nFBHgAQP1WpS70zz33nPr166d169apoKBADz74oLZt26ajR4/qm2++8XYdAQBAHeMaA+/rFnhJCrYGK1OZtMADAOq9KrXAd+zYUZs3b9ZFF12kAQMGKDs7W8OHD9eGDRvUpk0bb9cRAADUMa5Z6H09Bl46ZSk5WuABAPVclVrgf/nlFyUmJmry5MllXteiRYtqVwwAANRdNTULvXQywOcW5vr8WAAA+FOVWuCTkpJ06NChUtuPHDmipKSkalcKAADUbTXZAu8aZ59bRIAHANRvVQrwxhhZLJZS27OyshQUFFTGLQAAQENSo2PgTywdl1OU4/NjAQDgT5XqQj9+/HhJzlnnH3vsMY+l5IqLi7VmzRpdcMEFFd7flClT9NFHH+mHH35QcHCwevXqpWeffVbt27d3lzHGaPLkyfrXv/6lY8eOqUePHvr73/+u8847z10mPz9fDzzwgN577z3l5ubqsssu0z/+8Y9S69QDAICaUZOz0LsCPC3wAID6rlIt8Bs2bNCGDRtkjNGWLVvcf2/YsEE//PCDzj//fM2ePbvC+1u5cqXuvvtuffvtt0pOTlZRUZEGDhyo7Oxsd5mpU6fqxRdf1IwZM7R27VrFxcVpwIAByszMdJcZN26c5s+fr7lz52rVqlXKysrS0KFDVVxcXJm7BwAAvKSm1oGXpGA7AR4A0DBUqgV++fLlkqQxY8bo5ZdfVkRERLUOvmjRIo+/33jjDTVt2lTr169Xnz59ZIzRtGnT9Oijj2r48OGSpDfffFOxsbGaM2eO/vjHPyo9PV2zZs3S22+/rcsvv1yS9M477ygxMVFLly7VoEGDqlVHAABQee5J7Ow1NwaeLvQAgPquSmPg33jjjWqH97Kkp6dLkqKioiRJu3btUmpqqgYOHOgu43A41LdvX61evVqStH79ehUWFnqUSUhIUKdOndxlTpefn6+MjAyPHwAA4B3FJcXu1vCaCPDuLvTMQg8AqOeqtIxcdna2nnnmGX355ZdKS0tTSUmJx/U///xzpfdpjNH48eP129/+Vp06dZIkpaamSpJiY2M9ysbGxmrPnj3uMoGBgWrcuHGpMq7bn27KlCllLoEHAACq79SW8DA7k9gBAOAtVQrwt99+u1auXKlRo0YpPj6+zBnpK+uee+7R5s2btWrVqlLXnb7/8mbBr2iZiRMnuifkk6SMjAwlJiZWodYAAOB0rvHvtgCbAq2BPj9eiJ1l5AAADUOVAvwXX3yhzz//XL179/ZKJe699159+umn+uqrrzxmjo+Li5PkbGWPj493b09LS3O3ysfFxamgoEDHjh3zaIVPS0tTr169yjyew+GQw+HwSt0BAIAn9wR2NdD6LjELPQCg4ajSGPjGjRu7x6lXhzFG99xzjz766CMtW7ZMSUlJHtcnJSUpLi5OycnJ7m0FBQVauXKlO5x369ZNdrvdo8yBAwe0devWcgM8AADwHVeAr4nx7xJd6AEADUeVWuCfeuopPf7443rzzTc91oKvrLvvvltz5szRJ598ovDwcPeY9cjISAUHB8tisWjcuHF6+umn1bZtW7Vt21ZPP/20QkJCdOONN7rLjh07VhMmTFB0dLSioqL0wAMPqHPnzu5Z6QEAQM2pyRnoJSaxAwA0HBUO8F27dvUYU75z507FxsaqVatWstvtHmW/++67Cu1z5syZkqR+/fp5bH/jjTc0evRoSdKDDz6o3Nxc3XXXXTp27Jh69OihJUuWKDw83F3+pZdeks1m04gRI5Sbm6vLLrtMs2fPltVqrejdAwAAXpJT6GwJr6kA71pGji70AID6rsIBftiwYV4/uDHmrGUsFosmTZqkSZMmlVsmKChI06dP1/Tp071YOwAAUBV+a4EnwAMA6rkKB/gnnnjCl/UAAAD1RI2PgbczBh4A0DBUaRK7tWvXas2aNaW2r1mzRuvWrat2pQAAQN1V07PQ04UeANBQVCnA33333dq7d2+p7b/++qvuvvvualcKAADUXa4A71qf3deYxA4A0FBUKcBv375dF154YantXbt21fbt26tdKQAAUHf5ax34IlOkwuLCGjkmAAD+UKUA73A4dPDgwVLbDxw4IJutSivTAQCAeqKmx8C7utBLjIMHANRvVQrwAwYM0MSJE5Wenu7edvz4cT3yyCMaMGCA1yoHAADqnpqehd5utcsW4GxAYBw8AKA+q1Jz+QsvvKA+ffqoZcuW6tq1qyRp48aNio2N1dtvv+3VCgIAgLqlpteBl5zd6DMLMmmBBwDUa1UK8M2aNdPmzZv17rvvatOmTQoODtaYMWM0cuRI2e12b9cRAADUITXdAi+dDPC0wAMA6rMqD1gPDQ3VH/7wB2/WBQAA1AP+aIF3LyXHTPQAgHqswgH+008/1eDBg2W32/Xpp5+esezVV19d7YoBAIC6yV8t8BKT2AEA6rcKB/hhw4YpNTVVTZs21bBhw8otZ7FYVFxc7I26AQCAOqimZ6GXTlkLni70AIB6rMIBvqSkpMzLAAAALsUlxe4QXaNd6O0nutAT4AEA9Vilx8CXlJRo9uzZ+uijj7R7925ZLBa1bt1a1113nUaNGiWLxeKLegIAgDrg1C7sYfawGjuuuwt9IV3oAQD1V6XWgTfG6Oqrr9btt9+uX3/9VZ07d9Z5552n3bt3a/To0br22mt9VU8AAFAHuLrP2wJsCrQG1thx6UIPAGgIKtUCP3v2bH311Vf68ssv1b9/f4/rli1bpmHDhumtt97SLbfc4tVKAgCAusEV4Guy9V06OQs9k9gBAOqzSrXAv/fee3rkkUdKhXdJuvTSS/Xwww/r3Xff9VrlAABA3eKPCexOPR5d6AEA9VmlAvzmzZt1xRVXlHv94MGDtWnTpmpXCgAA1E3+WEJOksICwzyODwBAfVSpAH/06FHFxsaWe31sbKyOHTtW7UoBAIC6ydUCXuMB/kSXfVcPAAAA6qNKBfji4mLZbOUPm7darSoqKqp2pQAAQN3krxZ41/GyCmiBBwDUX5WaxM4Yo9GjR8vhcJR5fX5+vlcqBQAA6iZ/jYF3tcDThR4AUJ9VKsDfeuutZy3DDPQAADRc/pqFnjHwAICGoFIB/o033vBVPQAAQD3gCvAh9pAaPa57DHwBY+ABAPVXpcbAAwAAnAld6AEA8B0CPAAA8Bp/daEPDTyxDnxRjopLimv02AAA1BQCPAAA8BpXC7i/utBLzhAPAEB9RIAHAABe41oHvqZb4AOtgbIH2CWxlBwAoP4iwAMAAK/x1zrwEuPgAQD1HwEeAAB4jasF3i8B/sRScq5x+AAA1DcEeAAA4DW0wAMA4DsEeAAA4DX+Wkbu1GMS4AEA9RUBHgAAeEVxSbFyi3IleTnAGyMV5p61mKsFPruALvQAgPrJ5u8KAACA+uHU5du8Mgv9L2ukr1+Q/rdMKimUYjtLvx0ndb6+zOKuteBpgQcA1FcEeAAA4BWu7vO2AJsCrYFV35Ex0vKnpa+ek2RObj+4RZo3VvrlW2nIc5LF4nEzxsADAOo7utADAACvcAX4arW+l5RIC/4sfTVVkpHOv1G661tpwo9S34ckS4C09t/SNy+Xuqk7wLMOPACgnqIFHgAAeIVXZqBf/lfpuzedQf3q6VLXm09e1/8RKSRG+uL/pC8nS+dcJsV1dl/NMnIAgPqOFngAAOAV1Z6BfutHzjHvknT1DM/w7nLRHVLHayRTIn3xkLO7/QnMQg8AqO8I8AAAwCuqFeAz9kufjXNe7j1O6npT2eUsFmng3yRbsLTnG2lHsvsq9yz0tMADAOopAjwAAPAKV3AOsYdU7oYlJdLHd0l56VLChdKlfzlz+UaJ0m/GOi+nTHdvdv3jILMgs3LHBwCgjiDAAwAAr6jyJHZrX5N+Xu5sVR/+L8lqP/ttetwpWazSrq+kA5slSY0cjSRJ6fnplTs+AAB1BAEeAAB4RZW60GemOiekk6QBT0oxbSt2u0aJ0nnDnJe/e9O5KaiRJOlY/rGKHx8AgDqEAA8AALyiSgF+6WSpIEtq1k36ze2VO+AFJ8bJb50nFRW4W+AzCzJVVFJUuX0BAFAHEOABAIBXVDrA71snbZrjvDx4qhRQya8lrftJYXFS7jFpxxJFBEbIIoskutEDAOonAjwAAPCKSo2BLymRFv6f8/IFN0nNu1f+gAFWqcvvnJe3zpMtwKYIR4Qk6Xj+8crvDwCAWo4ADwAAvMK1/nqFZqHf/rG0/zspMFy67ImqH7TjMOfvHclSUb4aOxpLko7lMQ4eAFD/EOABAIBX5BTmSKpAC3xxkbT8aeflXvdI4bFVP2jChc5u9AWZ0u6vmYkeAFCvEeABAIBXuFrgzzoGfssH0pEdUnBj6eK7qnfQgACp/RXOyz8sdAd4ZqIHANRHBHgAAOAVrhb4Mwb4ogJpxRTn5d7jpKCI6h+4/ZXO3zuS3QGeMfAAgPqIAA8AALyiQi3wm+ZIx/dIoU2li+7wzoFb9ZasgVL6L2psnJsYAw8AqI/8GuC/+uorXXXVVUpISJDFYtHHH3/scf3o0aNlsVg8fi6++GKPMvn5+br33nsVExOj0NBQXX311dq3b18N3gsAACBVYBm5kmLpm5edl387TgqsxHrxZxIYKiX2kCQ1yjokiRZ4AED95NcAn52drfPPP18zZswot8wVV1yhAwcOuH8WLlzocf24ceM0f/58zZ07V6tWrVJWVpaGDh2q4uJiX1cfAACcUFxSrNyiXElnCPDfL5CO/iwFNZIuvNW7FWhzqSSp0ZFdkgjwAID6yebPgw8ePFiDBw8+YxmHw6G4uLgyr0tPT9esWbP09ttv6/LLL5ckvfPOO0pMTNTSpUs1aNAgr9cZAACUllOU475c5iz0xkjfTHNevugPkqMCa8VXRpv+0peT1SjtJyk6TMfzjnt3/wAA1AK1fgz8ihUr1LRpU7Vr10533HGH0tLS3NetX79ehYWFGjhwoHtbQkKCOnXqpNWrV5e7z/z8fGVkZHj8AACAqnN1n7cF2BRoDSxdYNdX0v4Nki1Y6vFH71cgrovkiFDjfOc4fGahBwDUR7U6wA8ePFjvvvuuli1bphdeeEFr167VpZdeqvz8fElSamqqAgMD1bhxY4/bxcbGKjU1tdz9TpkyRZGRke6fxMREn94PAADqu7OOf3eNfe96sxQa4/0KBFilxIvUqKREkmiBBwDUS7U6wN9www268sor1alTJ1111VX64osv9NNPP+nzzz8/4+2MMbJYLOVeP3HiRKWnp7t/9u7d6+2qAwDQoLhmoC+z+/zhndL/vpRkkXre7btKtOipRsXOAJ9ZmKnCkkLfHQsAAD+o1QH+dPHx8WrZsqV27NghSYqLi1NBQYGOHfPsJpeWlqbY2Nhy9+NwOBQREeHxAwAAqs7VAh9iDyl95bpZzt/tBklRSb6rRIueiigpkdU415JjKTkAQH1TpwL8kSNHtHfvXsXHx0uSunXrJrvdruTkZHeZAwcOaOvWrerVq5e/qgkAQIPjCvClWuALsqUN7zov/+Z231ai2YWyBtgVfWIlmrSctLPcAACAusWvs9BnZWVp586d7r937dqljRs3KioqSlFRUZo0aZKuu+46xcfHa/fu3XrkkUcUExOja6+9VpIUGRmpsWPHasKECYqOjlZUVJQeeOABde7c2T0rPQAA8L1yW+C3fCjlp0uNW0ltLvNtJezBUrMLFVu0W2k2mw7mHFQndfLtMQEAqEF+DfDr1q1T//793X+PHz9eknTrrbdq5syZ2rJli9566y0dP35c8fHx6t+/v95//32Fh4e7b/PSSy/JZrNpxIgRys3N1WWXXabZs2fLarXW+P0BAKChKrMF3hhp7b+dl7uPlQJqoONfi4vVdMf/JNECDwCof/wa4Pv16ydzYpxaWRYvXnzWfQQFBWn69OmaPn26N6sGAAAqocxZ6H/9TkrdItmCnLPP14QWvdT0+7ckSYdyDtXMMQEAqCF1agw8AAConVyz0HsE+E3vOX+fO1QKiaqZiiRepKYnxsAfzPilZo4JAEANIcADAIBqyynMkXRKgC8qkLZ+6Lx8wciaq0hIlGJDnCvRpB7dUXPHBQCgBhDgAQBAtZVaB37HEin3mBQWJ7Xuf4Zbel/zmA6SpF9zUmv0uAAA+BoBHgAAVFupWehd3ee7jJACanZi2WYJF0mSDhTnqLCksEaPDQCALxHgAQBAtXnMQp9zVPrpxES059dg9/kTYlr1kaOkRCWSUjMP1PjxAQDwFQI8AACoNo9Z6LfOk0oKpbguUmzHGq9LQNPzlFBcIkna++uaGj8+AAC+QoAHAADV5u5CbwuRtn/i3NhlhH8qY7Wppc05Fn/3/m/9UwcAAHyAAA8AAKrN3YW+uEja841zY4er/Vafc8ISJUk7j2z3Wx0AAPA2AjwAAKg2dxf6PWskUyLFXyA1bum3+pzTtLMkaQcz0QMA6hECPAAAqJbikmLlFuVKkkL/t8y5scNVfqyR1Lalc+m6nSqQKczza10AAPAWAjwAAKiWnKIc9+XQXSe6z3e8xk+1cUpq1ks2Y5QVEKADu1f6tS4AAHgLAR4AAFSLq/u8zRKgwJJCqcm5Ukxbv9bJbgtUK0uQJGnnLyv8WhcAALyFAA8AAKolqyBLkhRqLLJIfp287lRtQ+IkST+lbfZzTQAA8A4CPAAAqJasQmeADysqcG7w8/h3l3Yx50mSfsre5+eaAADgHQR4AABQLa4AH15cLIUnSHGd/Vwjpw4t+kmStitfyj3u17oAAOANBHgAAFAtri70YSUlUtvLJYvFzzVy6tjsYknSHrtdmb+s9nNtAACoPgI8AAColszCTEknAvw5A/xcm5MaBzVWgiVQkvTD7mV+rg0AANVHgAcAANWSnb5XkhRmJLXu59e6nK5jaDNJ0vZDm/xcEwAAqo8ADwAAqiXz4FZJUlhoUykows+18dSxyfmSpG3Zv0rG+Lk2AABUDwEeAABUS9bRnZKksMZt/FyT0jq27C9J2m4tkdKZjR4AULcR4AEAQNUV5ikr64AkKaxpRz9XprSOsV0lnZjIbs8qP9cGAIDqIcADAICq+2W1slQiSQqvhS3wjYMaKyEgSJL0/S8r/FsZAACqiQAPAACq7ucVygpwfp0IDQzzc2XK1jGshSRp++Ftfq4JAADVQ4AHAABVt+srZQU4130PDwz3c2XK1v5EN/oduQel4iI/1wYAgKojwAMAgKrJPSbt3+hugQ+z184W+DbxF0mSfrZZpLTtfq4NAABVR4AHAABVs3uVJKMsq12SFGoP9W99ytH6xNj8XXa7zL61fq4NAABVR4AHAABV8/NKSXK3wNfWLvQtwlvIKouyAwJ0cO9qf1cHAIAqI8ADAICq2bVSBZIKTsxCH1ZLJ7GzW+1q5oiSJO09uNnPtQEAoOoI8AAAoPIyDkiHf1KW1ebeFGqrnV3oJalZZCtJ0q/Zv0p5Gf6tDAAAVUSABwAAlbfrK0lSVmxHSVKILUTWAKs/a3RGCScC/H6bTdq/wb+VAQCgigjwAACg8nY5x79nNrtAUu3tPu/SLKyZJOlXm1X6dZ2fawMAQNUQ4AEAQOUY457ALjv2PEm1dwk5l5MB3ibtW+/n2gAAUDUEeAAAUDlHf5Yy9knWQGVGtZRU+1vgE8ISJEn77TZnC7wxfq4RAACVR4AHAACV8/MK5+/mFymrpECSFG6vnUvIubha4A9arSrMOihl/OrnGgEAUHkEeAAAUDknJrBTUh9lFWZJkkLttXcGekmKCY6Rw+pQicWigzartG+tv6sEAEClEeABAEDFlZScDPCt+yqrwBngwwNrdwu8xWJRfGi8pBPj4H/51s81AgCg8gjwAACg4g5ulXKPSoFhUrNuyi7MllT7J7GTTpvIbs9qP9cGAIDKI8ADAICKO7F8nFr2kqx2ZRZmSpJCA2t3F3pJiguNkyRnF/qDW6W8DD/XCACAyiHAAwCAijuxfJyS+krSyS70tXwSO0lqEtJEknQ4pJFkSqR9//VvhQAAqCQCPAAAqJiigpNdz1ufCPAnJrGr7cvISVJMUIwk6VBolHPDnhQ/1gYAgMojwAMAgIr5db1UmC2FREtNz5N0sgW+LoyBjwlxBvgjdodzwy8EeABA3UKABwAAFeMa/97qEinA+RWiLrXANwl2dqE/pELnhn3rpKJ8P9YIAIDKIcADAICKcY1/P9F9XjoZ4OvCGPiYYGcL/OH8dJnQJlJxvrR/g59rBQBAxRHgAQDA2RVkS/vWOi8nnRLgT3ShD7XX/lnoXQG+sKRQGYndnRtZTg4AUIcQ4AEAwNntSZFKCqXIRCmqtSSpxJScXAe+DnShD7QGKtIRKUk6FNfJuXHPN36sEQAAlUOABwAAZ7frlOXjLBZJUk5hjoyMJCk8sPZ3oZdOzkR/uEkb54Y9qxkHDwCoMwjwAADg7HaVHv+eUZAhSQoMCJTD6vBHrSrNNRP9IUeoFNpUKsyR9rIePACgbvBrgP/qq6901VVXKSEhQRaLRR9//LHH9cYYTZo0SQkJCQoODla/fv20bds2jzL5+fm69957FRMTo9DQUF199dXat29fDd4LAADquZyj0oHNzstJfdybXQE+whHhj1pViWsm+sN5R6TW/Zwbf17uvwoBAFAJfg3w2dnZOv/88zVjxowyr586dapefPFFzZgxQ2vXrlVcXJwGDBigzMxMd5lx48Zp/vz5mjt3rlatWqWsrCwNHTpUxcXFNXU3AACo33Z9JclIMe2l8Dj35swC5+dxRGDdCfDumehzD0tt+js3/o8ADwCoG2z+PPjgwYM1ePDgMq8zxmjatGl69NFHNXz4cEnSm2++qdjYWM2ZM0d//OMflZ6erlmzZuntt9/W5ZdfLkl65513lJiYqKVLl2rQoEE1dl8AAKi3diY7f59zmcfmjHxnC3xdGf8unQzwh3IPSe1vcm7cv8HZyyAkyo81AwDg7GrtGPhdu3YpNTVVAwcOdG9zOBzq27evVq92Lvmyfv16FRYWepRJSEhQp06d3GXKkp+fr4yMDI8fAABQBmOkHUudl9sO8LjK3YW+DrbAH8k9IkUkSE3OlWRO9DIAAKB2q7UBPjU1VZIUGxvrsT02NtZ9XWpqqgIDA9W4ceNyy5RlypQpioyMdP8kJiZ6ufYAANQTqVukrFTJHiK17O1xlSvA16UW+MZBzu8MR/OOOje0PtGNnnHwAIA6oNYGeBfLiaVqXIwxpbad7mxlJk6cqPT0dPfP3r17vVJXAADqHVf3+aS+ks1zpvm62AIfFeTsJn88/7hzA+PgAQB1SK0N8HFxzklyTm9JT0tLc7fKx8XFqaCgQMeOHSu3TFkcDociIiI8fgAAQBl2nAjwbS8vdZVrDHxdmoW+scPZAn8877iMMc5eBQF26fge6dBPfq4dAABnVmsDfFJSkuLi4pScnOzeVlBQoJUrV6pXr16SpG7duslut3uUOXDggLZu3eouAwAAqij3+Mk10s8ZUOrqzMK6Nwu9qwt9kSly9iBwhJ1cGu/Hz/1YMwAAzs6vs9BnZWVp586d7r937dqljRs3KioqSi1atNC4ceP09NNPq23btmrbtq2efvpphYSE6MYbb5QkRUZGauzYsZowYYKio6MVFRWlBx54QJ07d3bPSg8AAKro5+WSKXYuH9e4Zamr3S3wdSjAB1oDFWoPVXZhto7lHVOkI1I6d4j0vy+lHz6Xfnu/v6sIAEC5/Brg161bp/79+7v/Hj9+vCTp1ltv1ezZs/Xggw8qNzdXd911l44dO6YePXpoyZIlCg8/OVnOSy+9JJvNphEjRig3N1eXXXaZZs+eLavVWuP3BwCAeuWnJc7fbUu3vkt1cwy85OxGn12YrWP5x9RKraT2Q6TPJ0j71kmZqR5r3QMAUJv4NcD369fPOf6sHBaLRZMmTdKkSZPKLRMUFKTp06dr+vTpPqghAAANVHGR9NMi5+V2V5RZJLPA2YW+Ls1CLzknstuXtU/H8k7MoRORICVcKO3/TvrxC6n7GP9WEACActTaMfAAAMCPflkt5R6VQqKlFj3LLOJuga9Dk9hJUqOgRpJ0MsBL0rlXOn//uLDmKwQAQAUR4AEAQGnfL3D+bj9YspbdYa+utsC7ZqI/ll9GgP95hZSfWfOVAgCgAgjwAADAU0mJ9P1nzsvnXlVmkfzifOUX50uqe2PgXWvBH807enJjk3OlqNZScYG0c6mfagYAwJkR4AEAgKf9G6TM/VJgmNS6X5lFXDPQB1gCFGoPrcHKVZ9rKTmPLvQWi9ThxD8rts7zQ60AADg7AjwAAPD0w4nu820HSPagMou4us+H2cMUYKlbXyfKDPCS1Pl3zt8/LZZyj9dspQAAqIC69YkLAAB8y5iT4987lN19Xqq7S8hJ5YyBl6TYTlKTDs5u9N9/6oeaAQBwZgR4AABw0sGt0pGdktUhnVP2+u9S3Z2BXjpDC7zFInW+3nl5ywc1XCsAAM6OAA8AAE5yBdd2A6Wg8sN5en66pDraAl9egJdOdqPf9bWUsb8GawUAwNkR4AEAgFNJibT1I+dlV5Atx/H845JOdkevS1yz0OcV5ymnMMfzysYtpcSLJRkmswMA1DoEeAAA4LR3jZS+VwoMl9oOPGNRV+t1pCOyJmrmVSG2ENkD7JLKGAcvnexGv2muc04AAABqCQI8AABw2vqh83eHqyR78BmLurrQu7qj1yUWi8Vd7+N5x0sX6HSdcw6Ag1ulX7+r2coBAHAGBHgAACAVF0rb5jsvd77urMVdLdd1sQVeOtmN/mje0dJXhkRJHa9xXv5uds1VCgCAsyDAAwAA6eeVUs4RKSRGSup31uLuFvg6OAZeOsNSci7dRjt/b5kn5WXUTKUAADgLAjwAADg5+3yn4ZLVdtbiruDbyNHIh5XynTPORC9JLXtJ0W2lwuyTQwsAAPAzAjwAAA1dYa70w2fOy52ur9BNXGPHGwU18k2dfMwV4MvsQi8514Tvdqvz8vo3a6hWAACcGQEeAICG7qdFUkGW1KiFlHjRWYsbY+r0MnLSKV3oy2uBl6Tzb5SsgdKBjdL+jTVSLwAAzoQADwBAQ7flRBfxTtc5W57PIqcoR4UlhZLq7iR27i705Y2Bl6TQaOeM/JL0Ha3wAAD/I8ADANCQ5R6XdixxXq5o9/kTre8Oq0PBtjMvN1dbnXEW+lO5JrPb/IGUn+XbSgEAcBYEeAAAGrIfPpOKC6QmHaTY8yp0E9f490hHpCwVaLGvjVwB/oxd6CWp1SVSVGupIFPaOq8GagYAQPkI8AAANGSu7vOdK9Z9XlKdH/8uVWAWeheL5WQr/Po3fFspAADOggAPAEBDlXlQ2rXSebnTdRW+WV1fQk462QKfVZilguKCMxe+4CbnZHb7N0i/flcDtQMAoGwEeAAAGqrtH0umRGrW3dlNvILq+hJykhQeGC6rxSqpAq3woTFSx2ucl2mFBwD4EQEeAICGyt19vmKT17m4utDX5Rb4AEuAu/5nnchOkrqNcf7eMk/KS/ddxQAAOAMCPAAADdGx3dK+/0qWAOm8ayt1U1eAr6tLyLlEBVdwIjtJatlLimkvFWZLm//j45oBAFA2AjwAAA2Ra0b1VpdI4XGVuqmrxTo6KNrbtapRUY4TS8nlV6AF3mKRut/mvLzuDckYH9YMAICyEeABAGiItpwI8JXsPi9Jh3IOSZJigmO8WaMaV+GZ6F3Ov0GyBUlp26S9//VhzQAAKBsBHgCAhubgdmcIDbBLHa6q9M0P5x6W1AADfHDjk7P1M5kdAMAPCPAAADQ0W09MXtd2gDOUVoIxRkfyjkiq+wHetZRchSaxc3F1o9/6kZRTidsBAOAFBHgAABoSY06Of69C9/mcohzlFuVKaqABvlk3Ka6zVJwvbXrPRzUDAKBsBHgAABqSX9c7Z6C3h0rtBlf65q7u8yG2EIXYQ7xcuZpV6S70knMyO9eScuteZzI7AECNIsADANCQbPnA+fvcIVJg5QN4fRn/LkmNHScCfH4lArwkdRkh2UOkIzulfWt9UDMAAMpGgAcAoKEoKZa2zXde7vy7Ku2iPgV41zrwlepCL0mOcKnD1c7LdKMHANQgAjwAAA3F7q+lrIPOieta96/SLlwBPjq4bq8BL51cBz6zIFOFxYWVu/EFI52/t86TivK9XDMAAMpGgAcAoKFwdZ/veI1kC6zSLo7k1o8Z6CUpwhEhq8UqqQrd6FtdIkU0k/LSpR+/8EHtAAAojQAPAEBDUJQvbV/gvFzF7vNS/epCH2AJUKQjUlIlJ7KTpACrcyy8JG2a6+WaAQBQNgI8AAANwc6lUn66FJ4gtehV5d3UpwAvVXEpOZfzT3Sj35ksZR3yYq0AACgbAR4AgIbA1X2+03ApoOof//U1wFe6BV6SmrSXEi6USopOTg4IAIAPEeABAKjv8rOkHxc5L3e6rlq7OpTrbGmuLwHetRZ8lVrgJanz9c7f2z/2ToUAADgDAjwAAPXdjwulolwpqo2U0LXKuykoLnC3wMeHxnurdn7lWgu+ygG+4zXO33tWSxkHvFQrAADKRoAHAKC+c3Wf73y9ZLFUeTcHsw9KkoKsQWrkaOSFivmfazm8Kgf4yOZSYg9JRvr+U+9VDACAMhDgAQCoz3KOSv9b5rzc6fpq7Wp/9n5JUlxonCzV+EdAbeIaCuAaGlAl513r/M04eACAjxHgAQCoz7Z/7JxkLa6L1KRdtXZ1INvZRTwhLMELFasdmgQ3kXRycr4q6XC18/cvKVLGfi/UCgCAshHgAQCoz7Z86PzduXqt75J0IMsZ4OvL+HdJiglxtsAfzqlGgI9sJiVe7Ly8/RMv1AoAgLIR4AEAqK+O75X2fOO8fN7wau/O1QJfnwK8qwX+SN4RFZcUV31Hrm70BHgAgA8R4AEAqK+2nmh9b/lbqVFitXfnGgMfH1Z/AnxUUJQssqjYFOtYfhXWgnc590rn771rpOxqtOYDAHAGBHgAAOojY6RN7zsvdxnhlV2mZqdKql8t8LYAm3st+CO5R6q+o0aJznkGTIn00yIv1Q4AAE8EeAAA6qPULdKh7yWr4+Ra5dVgjKmXY+Clk93oqzUTvXSyFf6HhdWsEQAAZSPAAwBQH20+0fre/gopuFG1d3ck74gKSgpkkUWxIbHV3l9t4prI7lBONQN8+yHO3/9bJhXmVrNWAACUVqsD/KRJk2SxWDx+4uLi3NcbYzRp0iQlJCQoODhY/fr107Zt2/xYYwAAaoGS4pOzz3e5wSu73Je5T5LUNKSp7Fa7V/ZZW8QEnZiJvjpLyUlSXGcpMlEqypV+XlH9igEAcJpaHeAl6bzzztOBAwfcP1u2bHFfN3XqVL344ouaMWOG1q5dq7i4OA0YMECZmZl+rDEAAH626yspK1UKbiydM8Aru9ydsVuS1CqylVf2V5s0CfHCWvCSZLFI7Qc7L//weTVrBQBAabU+wNtsNsXFxbl/mjRxfsgaYzRt2jQ9+uijGj58uDp16qQ333xTOTk5mjNnjp9rDQCAH7m6z593rWQL9Moud6fvliS1imjllf3VJjHBJ7rQV3cMvHSyG/1Pi5w9IQAA8KJaH+B37NihhIQEJSUl6fe//71+/vlnSdKuXbuUmpqqgQMHuss6HA717dtXq1evPuM+8/PzlZGR4fEDAEC9UJAtfb/AedlL3eclaU/GHkn1M8C7JrGrdgu8JLX6reSIlLIPSb+ur/7+AAA4Ra0O8D169NBbb72lxYsX69///rdSU1PVq1cvHTlyRKmpzqVsYmM9J9KJjY11X1eeKVOmKDIy0v2TmFj9tXEBAKgVfvxCKsiSGrWQEnt4bbf1uQt905CmkqS0nLTq78xql9qeGLZAN3oAgJfZ/F2BMxk8eLD7cufOndWzZ0+1adNGb775pi6++GJJksVi8biNMabUttNNnDhR48ePd/+dkZFBiAcA1A8b3nb+7vJ755jsKjLG6HhOodIy83UgPVu7050t8F9tk77e/L0y84qUlVekrHzn74y8QuUXlaiopERFxUZFJUbFJUaFxSUqLjn5tyRZ5KyaRRbn71Mvy/nZbjlRMMBikd1qkS0gQDarRYFW529bQIBzuzVAtgCL7Kdsd9gDFGy3KiTQqmC7VUGuy4EnL4cH2RURZFNEsF0241wH/mDOQZWYEgVYqtm+ce4QaeuH0o8LpQGTq7cvAABOUasD/OlCQ0PVuXNn7dixQ8OGDZMkpaamKj7+5Hq0aWlppVrlT+dwOORwOHxZVQAAat6x3SdmP7dIXW8+a3FjjPYdy9VPBzO163C29hzJ0S9HnT+/HstVQXGJJMliP6qwcwplSmz617JjktK9VGHjpf1UV7HCzrWoqKRIFz0zX5GB0YoItisiyH7it02RwXZFhQa6f6JDHYoKC1R0aKCC7FbP3Z1zuRRglw7/JB3eKcWc45+7BQCod+pUgM/Pz9f333+vSy65RElJSYqLi1NycrK6du0qSSooKNDKlSv17LPP+rmmAAD4wYZ3nL9b95Mat/S4qrjEaEdapjb+clyb9qXrh9QM/ZSaqeyCM0+01jjEroioLB2VFBoQpyu7t1R4kE1hQTaFOWyKCLK7LwcHWmULcLaCWwOcLefWgJOt59YAZ48AYyQjc+K3VHKiZf707cYYlRi5W/ULik+07heXqLDkxO9i476+8MTfBUXFyi0sUW5BkXILi5VTUKzcwmLlnbick1+szPwiZeQWKiOvUJl5kimKkMWerqP5h3Q4PahSD3tIoFVRoYFqGu5QfGSw4iKDNCaym5of+1Z7v/1Q6nWfYiOCFGir1SMXAQB1QK0O8A888ICuuuoqtWjRQmlpafrrX/+qjIwM3XrrrbJYLBo3bpyefvpptW3bVm3bttXTTz+tkJAQ3Xjjjf6uOgAANau4SNrwrvPyhbcor7BY3/1yTN/+fFT/3XVEm/elK6eMsG63WtSmSZjaNA1Ty6gQtYwOUWJUiBIbh6hphEMOm1Vvb39bU9dKvVqcq2f7d6nhO+Z7xSVGNy+co61HNuvRa+LUMeJiZeQVngj4zqCfnluoo9kFOppdoCPZBTqana+j2QUqLDbOfwoU5GrfsVxJxyVJBdZz9ZT9Wx3470casaqzJKlJuEOtokPUKjpUrWJClRQTqnaxYWoVHSqblXAPADi7Wh3g9+3bp5EjR+rw4cNq0qSJLr74Yn377bdq2dLZqvDggw8qNzdXd911l44dO6YePXpoyZIlCg8P93PNAQCoWWbnUlky9yvXFqnbV0Vr7dwlKigq8SgTGmhVl+aNdH5iI52XEKFz48LVKiZU9rOExx+O/iBJate4nc/q70/WAIuahcdr65HNsgdmqGeb6ArdzhijzPwiHc1yhvq0jDwdSM9Takae8g8Pln6erW4BOxRrzdLB4jAdyszXocx8rd19zGM/gbYAndMkTOfGheu8ZpG64MTzU6prPgCgwavVAX7u3LlnvN5isWjSpEmaNGlSzVQIAIBaJLegWF/tOKRl36dp8LYX1E/Su3m99M3uLElS03CHeraJ1sWto9WtZWO1aRLm7sZeGd8f/V6SdG7UuV6sfe0SH+qcTyc158wr2ZzKYrE4x8kH2dUqJvS0aztIr3aRNXWzvr2+UEfbXq59x3K1+0i2dh/O0Z4j2frf4WztOJipnIJibT+Qoe0HMvTRhl8lOXtGdIiP0IUtGqtXm2j1aB2tyGC7t+4uAKCOqtUBHgAAeMrMK9SyH9K0eFuqlv9wSLmFxUrQYT3tWCtZpD0tr9dTnTupd5toJcWEnnVllrPJL87Xz8d/liR1iO7gjbtQK8WFxkmSUrMrHuDPqv0QKXWzLD9+oeiuNys6zKHzExt5FCkpcU4k+ENqhn5IzdTmfce1ce9xHc4q0OZ96dq8L12zV+9WgEXq1CxSvc+J0WXnNlXXFo2r9M8YAEDdRoAHAKCWKywu0dc7Dmned78qeftBj67xzRoF65lG62RNNSppeYmeGnO9V4+989hOFZtiNXI0UmzImVd5qcviQnwQ4M8dIq18RvrfMqkwV7IHlyoSEGBRi+gQtYgO0cDznHVwrQ6wYe9x/XfXEa3+3xH9fCjbHehnrvifokID1a99Ew3oEKt+7ZsqOJDu9gDQEBDgAQCohYwx2rY/Qx9996s+3fSrDmcVuK9r3SRUgzvF6Yrz4tUpNlCWl26XJAVc/Eev1+PU7vPVbc2vzVwt8AeyD3hxp12kiOZSxj5p11dSu0EVupnFYnFOJBgVoqvPT5AkpabnafX/Duurnw5p+Y+HdDS7QB9996s++u5XhQRadXmHWF19foL6tGvCbPcAUI8R4AEAqEUOZuTp4w3OYPbjwUz39ujQQF1zQTMNv7CZzkuIOBmmN74n5RxxBsV2g71eH9cEdh2i6m/3eelkgD+Se0SFxYWyW70w3txikdoPltb+W/rh8woH+DLrFxmk4Rc21/ALm6uouETr9hzT0u0HtXh7qvYezdWnm/br0037FRFk09DzEzSie6LObx5Zr//pAgANEQEeAAA/Kykx+nrnYb21ereW/5imE8uiK9AWoAEdY3Xdhc10SdsmpWeLN0b67z+dl39zm2T1/sf6lsNbJEkdozt6fd+1SVRQlAIDAlVQUqDU7FQlRiR6Z8fnDnEG+J8WSSUlUkD1W8dt1gBd3No5OeGjV3bQpn3p+nTjfn22eb/SMvM1Z80vmrPmF7VtGqYR3RN17YXNFBPm8MKdAQD4GwEeAAA/ycgr1Lz1+/R2yh79fDjbvf03rRpr+IXNNaRz/JlnHt/7X2n/BsnqkC681ev1yyrIcrfAd23a1ev7r00sFouahTfTrvRd2pu113sBvuVvJUeElHVQ2v+d1Ly7d/Z7gsVi0QWJjXRBYiM9emUHrfn5iD5Yv08LtxzQjrQs/W3h93pu8Y+6sku8bunZUl1bNPbq8QEANYsADwBADdtxMFNvpezRR9/tU3ZBsSQp3GHT9d2ba9TFLdW6SVjFdrT6FefvLr+TQmO8Xs9NhzapxJSoWVgzxYbW3wnsXBLDE7UrfZf2Ze7z3k5tgdI5l0vbPnJ2o/dygD+VNcCiXufEqNc5MZp8zXlasGm//rN2rzbtS9f8Db9q/oZfdX7zSN3Ss5WGnh8vh42J7wCgriHAAwBQA4pLjJZ+f1BvpezWNzuPuLe3bRqmW3q10vCuzRTqqMTH8qGfnIFQknr92cu1dVp/cL0kqVtsN5/sv7ZpHtZckrQvy4sBXnIuJ7ftI+n7BdJljzvHxvtYRJBdN/VoqZt6tNSmvcf1ZspufbbpgDbtS9eEDzbp6YXfa+RFLXTTxS0UH1l6dnzg/9u77/Coqvzx4++ZTGbSe0J6BRJ6J4QiINUKLiuoqMjq7qqra1nWZX/qFxQF1921rKsoiMCigiKoqIgiAtJLSABJKIGEBEhIQnrPZM7vj0kGhiTUTAr5vJ5nnpl77rn3nJuT9rmnXCFE6yQBvBBCCGFD+aVVrNiTwcc7T3K6oBwArQbGdO3AtLhw4qK8r22hsR3vAAqibwPfzk1b6VrtLYAPcTUPm2/SHngwL15nZ4Bzx+Dsr+Dfo2nPfxm9Qjx4I6Q3z9/axfK9mFlYwX83pjB/83HGdevA74ZE0C/MUxa9E0KIVk4CeCGEEMIGfj1dyNLtaazZf4bK2ue2ezrZc8/AUKbGhhLs6XTtJy/Ogv0rzJ+H2Kb3vay6zLKAXV+/vjYpo7UJdq3tgW/qAN7BDTqNgcPfwq+rmz2Ar+PtYuBPIzvyx5siWZ90lqU70th5Io+1B7NYezCLXiEePDI0glu6+6O7eMFEIYQQrYIE8EIIIUQTqTKa+P7XTP634yTxJ/Mt6d2D3JgWF84dvQJxsG+Cecc73oWaKggZBKGx13++BuzK3EW1qZoglyDC3MJsUkZrU9cDn1GcgVKqaXuju0+qDeBXNdsw+sbo7LTc0iOAW3oEcCSrmMXbUlmdcJr9GQU8uTyBIA9HHhoczpSBIbg5NMHj9IQQQjQZCeCFEEKI65RdVMEnu9L5dHc6OcWVANjbabi1RwAPxoXTN9Sj6YLB0lzY86H589BnmuacDdh8ajMAw4OHt5th1UEuQQCUVJdQWFmIh4NH05288ziwd4KCk+bV6INax7SEaH9XXpvUkxnjovlkZzrLdqZxuqCcV9cm89ZPR5kyIJTpQ8IJ8bqOESNCCCGajATwQgghxDVQSrEvPZ8l20/y/cFMjLUPb/dzNTA1Nox7B4bg5+bQ9AVv/w9Ul0FAb3NQaANKKX459QtgDuDbCwedA36OfmSXZ3Oq5FTTBvB6Z4i+xdwD/+vqVhPA1/FxMfDU6E78cXgkaxLP8OHWExw9W8JH21JZsj2V8d39eXhoJP3C5DF0QgjRkiSAF0IIIa5CRXUNa/afYen2NA6dKbKk9w/zZNrgcMZ180evs9H84dJc2L3Q/HnE3202DDs5L5mc8hwcdY7097fdY89ao2DXYLLLs8kozqC7T/emPXm335wP4MfMAW3rm2fuYG/H5AEh3N0/mC3Hcvlwayq/HM2xzJPvE+rBI0MjGdetg8yTF0KIFiABvBBCCHEFTuWX8fHOdD7bk05+WTUABp2WCb0DeTAunO5B7ravRDP0vgNszjAPnx8cOBi9nd5m5bRGoW6h7MveR1pRWtOfvONoMLhD8RlI3QxRI5u+jCai0Wi4qbMvN3X25UhWMYu2nuCrhDMkpBfwp0/3EeThyPQh4UwZEIKrzJMXQohmIwG8EEII0QhjjYmNR3L4dNdJNh3NQZlHyRPk4cgDcWFM6R+Cp3MzBbhFmc3S+w7W89/bmwj3CABSC1Ob/uT2DtDzbvMaBvGLW3UAf6Fof1de/20v/jouhmU7T1oeifjKd8m89dMx7hkQwkNDwq/vyQpCCCGuiATwQgghxEUyC8tZsTuDz/dmkFlYYUkf0tGbaXHhjOrSATttMy/stmmeufc9JNamve/ZZdkcOncIDRqGBQ+zWTmtVYSbOYBPK0yzTQH9ppsD+MPfQfFZcO1gm3JswNfVwLNjOvP4iCi+TDjNoq2ppGSX8OHWVBZvT2N8d38eGRpBn1CZJy+EELYiAbwQQggB1JgUm49m8+mudH4+nE3tmnR4Oev5bb9g7h0YSoSPc8tULucIJCwzfx7zsk173zdlbAKgh28PfBx9bFZOa1XXA59WlIZJmdBqmniet393CB4Ip3ab2/SmGU17/mbgYG/HvQNDmdI/hM3Hcli0JZWtKbl8dyCT7w5k0i/Mk0eGRjC2m3/z3+gSQogbnATwQggh2rUTOSV8lXiGVfGnOF1QbkmPjfDivthQxnf3x6Brgme3X4+fZoMyQfRtEDrIpkXVBfAjQ9rG8O6mFuQahE6jo9xYTnZZNv7O/k1fSP/fmQP4fUvNjwLUtvD31zXSajWMjPZjZLQfyZlFfLQ1la8TzxB/Mp/4k/mEeDkyfXAEkweE4GKQfzmFEKIpyG9TIYQQ7U5uSSXf7D/DV4ln2J9RYEn3cLJnUl9zb3tHP5eWq+CFTu6AI2tBYwejZ9u0qLLqMnZl7gJgRPAIm5bVWtlr7QlxCyG1MJUThSdsE8B3mwjrZkJBOqRsgM5jm76MZtYlwI1/3t2Lv46PZtkO8zz5jLxyXv42iTfXH+Xe2FCmDQ4nyMOxpasqhBBtmgTwQggh2oWyKiM/HjrLV4mn2XIsl5raMfJ2Wg03dfJhYp8gxnXzx8G+FfWGKgXrXzR/7vsA+Ha2aXE7zuygylRFsEswUR5RNi2rNYtwiyC1MJXUwlQGBw5u+gLsHaH3VNj5Lux454YI4Ov4uTrwl7HRPD6iI6sTTrFoayonckpZ8MsJPtxygptjOnD/oFBu6uSLVobXCyHEVZMAXgghxA2rtNLIL0dzWHcoi/VJZymrqrHs6xXiwV29A7m9VyA+LoYWrOUlJH0Np/aAvZN55Xkb25ixEYARISPQ2HCefWsX4R4BGTZaib7OoMdg9weQ+gtk7IGQAbYrqwU46u2YGhvGvQNC2XQ0mw+3pLL9+Dl+Sj7LT8lnCfVy4r7YUCb3D8GruZ7kIIQQNwAJ4IUQQtxQsosr2JCczfqks2xNyaXKaLLsC/N2YmLvICb2CWq5BemuVFUZ/PiC+fPgJ8HVBkO5L6CUYuvprYA5gG/PIj0iAThecNx2hXiEQM8pkPgJbH4N7l9lu7JakFar4eaYDtwc04GU7BI+2XWSL+JPkZ5XxmvfH+aNH49yaw9/HogLo2+oZ7u+cSSEEFdCAnghhBBt3vGcEtYnneXHQ1kkZBRYntcO5qB9bNcOjO8eQN9Qj7YTIGx9EwozwD0Ehjxt8+JSClI4V3EOBzsH+vj1sXl5rVlnT/NUhSP5R1BK2e57Zthf4MBnkPITpG2F8KG2KaeV6Ojnwqw7uvHcuBi+2X+Gj3ed5MCpQr5KNK9HEePvyj0DQpjQOwhP6ZUXQogGSQAvhBCizSksr2bH8Vx+OZbL1mO5pOeVWe3vFezOmK4dGNvNn05+Lm0naK+TdwK2vW3+PG4u6J1sXmTd4nV9O/RFb9e+g6dI90h0Gh3FVcWcLTtrm4XsALyjoO802LsIfnwRHtkA2iZ+bF0r5Ki3Y/KAECYPCGF/RgEf7zzJmv1nOJxVzOxvkpi79jBjunXg7n7BDOvkK4+iE0KIC0gAL4QQotWrrjGRkF7A1mM5/HIslwOnCizPaQewt9MQF+XDmK4dGNOlA/7uDi1X2aaw7v9BTSVEjoQudzRLkXUBfGxAbLOU15rp7fSEu4eTUpDC0fyjtgvgAYb/DQ6uhDP7IOF/0O8h25XVCvUK8aBXiAcv3NaVLxNOsTL+FIfOFFmeKR/g7sCkvsH8tl8w4a192osQQjQDCeCFEEK0OsYaE4fOFLEr9Ry7TuSx88Q5Si9YgA4gyteZYZ18GdbJh9hI7xvnOdNHf4Cj34NWB7e8Ds0wesBoMrL37F4ABgXY9jnzbUVnz86WAP6m4JtsV5BrBxj5/8yPlVs/CzqOAfcg25XXSrk72fPQkAgeGhLBr6cL+SL+FF8lniazsIL/bkzhvxtTGBjhxW/7BTO+uz9uDvYtXWUhhGgRN8h/O0IIIdqy6hoTB04VWgL2+JP5lFQarfJ4OesZ0tGHYZ18GNrRh8Ab8XnSVWXw/d/Mnwc9bvPHxtU5dO4QJdUluBvcifGKaZYyW7vOnp1Zm7qWI3lHbF/YgN+b58KfSYDVf4Bpa0Dbih5n2My6B7nTPcidv98aw09J2Xy+N4NfjuWwOzWP3al5vPDVr9wc7ceE3oGMjPFrXY9+FEIIG5MAXgghRLOrNNaQmF7Artp/yONP5lNebd3D7uagY2CEF7ER3sRFedM1wO3Gf270prmQnwqugTD8uWYrdueZnQAM9B+IVnPjz8G+EtFe0YB5ITubs9PBpEXw/jA4uRW2vgE3/dX25bZyBp0dt/UM4LaeAWQWlrN632m+TDhNSnYJ6w5lse5QFq4GHeO6+zOhdyBxkd7o7OT7VwhxY5MAXgghhM1VVNewLz3fMhw+IaPA6vFuYO5hHxjuZQ7aI72I8XdrX4tXnY6HHe+aP9/+Jhhcm63oXVm189/9Zf57nbqRCCeLTlJaXYqzvY3nX3tHwW3/gq8eg43zIGQQRAyzbZltSIC7I38a2ZHHR0SRnFnM1/tP8+3+TE4XlPNF/Cm+iD+Fj4uB23sGcGuPAPqFebav3x9CiHZDAnghhBBNrqK6hn0n89l54hw7U/NITC+gqsY6YPdxMRAb6cWgCC9iI73p6Oty4/ewN8ZYBV8/AcoEPe6G6PHNVnS5sZzE7EQABgXK/Pc6Po4+BDgHkFmaya+5vzbP4n697oXjG+Hg5/DZVPjdj+AnUxoupNFo6BroRtdAN/42Lob49Hy+TjzN2oNZ5JZUsmR7Gku2p+HjomdMV3/Gd/cnLtIbvU565oUQNwYJ4IUQQly38ipzD/vOE+Y57IkZ9QP2Dm4GBkV6ExvhTWykF5E+zm3v8W62suXfkJ0ETj4w/h/NWnRCdgLVpmr8nf0JdQ1t1rJbu16+vcgszeRAzoHmCeA1GrjzP1BwEjJ2wSe/hUd+AlcbroLfhmm1GgaEezEg3ItZd3Rja0ou3+w/w4bkbHJLqli+O53lu9NxddAxuksHxnXzZ3hnXxz1MmdeCNF2SQAvhBDiqpVVGdl3ssDcw37iHPtPFVBdo6zy+Ls5MCjSi0GR3gyK9CbM20kC9oacPWQO4AFufR2cvZu1+J2Z5vnvsf6x0j4X6enbk3Vp6ziQc6D5CrV3hHuWw6IxkHcc/jfRvKidi1/z1aENsrfTMjLaj5HRflTXmNh1Io91hzL54dBZcoor+TLBPH/ewV7LsE6+3BxjztvmHzkphGh3JIAXQghxWaWVRuJP5rMr9Rw7T+SxP6MAo8k6YA9wdyAu0ty7PijSm1AvCdgvq7ocVj0CpmqIvg26/abZqyDPf29cT9+eABzIPYBSqvm+n5294f4vYPGtkJMMS243B/HSE39F7O20DO3kw9BOPrx8Z3cSMvJZ92sW3/+axan8ctYnnWV90lkAuga4MaqLHyNj/OgV7CHz5oUQrZ5GKaUun+3GVlRUhLu7O4WFhbi5ubV0dYQQosWVVhrZe7JuSPw5DpwqrBewB7o7MCjKm0ER5h72EC9HCdiv1tq/wu4F4OwLj21v9l7WwspChq0YhkLx890/4+vk26zlt3ZVNVUM+nQQ1aZqvrvrO0LdmnmKwbnjsPQOKDoN3h1h2jfgFti8dbiBKKVIyixiQ3I2Px/OZv+pAi78L9jLWc+Izr6MjPFjaEcfPJ31LVdZIUS7cC1xqPTACyGEoKTSyN60PHaeyGNXqjlgr7koYA/ycKwdDm/uYQ/2lID9uhxZZw7eASa+3yJDpHdn7UahiHKPkuC9AXo7PT18erAvex87M3c2fwDvHQUPfWcO4s+lwJLb4IGvwDOseetxg9BoNHQLdKdboDt/HtWJ3JJKNh/J4ecj2fxyNIe80ipWJ5xmdcJpNBroHujO0E4+DOvoQ79wTww6mTsvhGh5EsALIUQ7VFxRbelh33kij19P1w/Ygz0dLfPXYyO8CPFyaqHa3oDy0+CrR82fB/0JOo1ukWrUPf9dhs83bljwMPZl72PLqS1Mjp7c/BXwiqgN4m+HvBPmufFTv4CAns1flxuMj4uBSf2CmdQvmOoaE3vT8tl4JJtNR7I5eraEg6cLOXi6kPmbjuNgr2VghDfDOpqH5sf4u8oNTCFEi5Ah9MgQeiHEjS+3pJK9aXnsSs1jT1oeSWeKuCheJ8TL0TIcPjbSi2BPCdhtoqoUFo2Fs79CUD+Y/j3oDM1ejRpTDaNWjuJcxTneG/Uew4LlmeMNOZx3mLu/uRtHnSNb7tmCwa752wqAojPw8W8h+xDoXWHKMoga2TJ1aQfOFlWw9VguW1PMr5ziSqv93s56BkZ4EVv7GMzoDq7t9zGYQohrdi1xqATwSAAvhLjxnC4oZ3fqOXan5rM79RzHc0rr5Qn1crIMh4+N9CbIw7EFatrOKAWrHoZfV5nnvf9hM7gHtUhV9mTt4Xc//A43vRubJm/C3s6+RerR2imlGLVyFDnlOXww5gMGBw5uucpUFMKKqZC2BbQ6uPO/0PvelqtPO6GU4ujZErYcy2FrSi67TuRRXl1jlcfDyZ4B4eaAflCkN10C3GRBPCHEZckceCGEaIdMJsWJ3FL2pOWxO9X8Ol1QXi9fdAdXBkZ4MSDCi4HhXvL4pJaw4WVz8K7VweT/tVjwDvBD2g8AjAodJcH7JWg0GoYFD2P1sdWsP7m+ZQN4B3e4fxV89Zj5++irR8098qNfAq3Mz7YVjUZDtL8r0f6uPDIskiqjiQOnCtiVmsfOE+eIP5lPQVm11er2rgYdvUM96BPqSZ9QD3oHe8iieEKIJiE98EgPvBCibckrrSIxI5/E9AISMgrYn1FAUYXRKo+dVkP3IHdiI7wYEO5F/zBP+eexpe14F374f+bPd74DfR9ssapU1lQyZuUY8ivzmT96PkODhrZYXdqCXZm7eOTHR3C1d2XD5A046lp4tIrJBBtfhS3/Mm9H3Qy//QgcPVu2Xu1UdY2JX08Xsis1j10nzrE3LZ/iSmO9fJE+zvQO8aBPbWAf7e+KvZ22BWoshGgtZAj9NZIAXgjRWpVUGjmSVcT+jEISMwpIzCggPa+sXj6DTkvvEA9iI7wYGOFNn1APnA0yyKrV2Pc/WPOk+fOo/4Nhf2nR6qw6uorZO2bj7+zP2t+sxV4rPfCXYlImbl19K6dLTvN87PPcE3NPS1fJ7NCX8NXjUF0GXpFw9xII6NXStWr3akyK5MwiEjMKSEgvICE9nxO59acx6XVaoju40jXAjW5BbnQNcKNLgJv87haiHZEA/hpJAC+EaGlKKc4WVZKUWUjSmSKSMotIOlPEybwyGvotHeXrTO8QT/MQzRAP6clpzbb9B9a/aP486HEYNxdacPXq0upS7vzqTrLLspnRfwbTuk1rsbq0JcsPL2furrn4OvryzV3f4Gzv3NJVMss8YJ4XX5gOdnoYPRtiHwOt/D5oTfJLq0g8dT6gT8wooLiifi+9RgPh3s50DXCja6AbHf1c6OjnQqiXk/yOF+IGJAH8NZIAXgjRXJRSZBdXcjy7hJScEo5nl3Asu4TkzCLyy6obPKaDm4Fuge6WoZc9gz1wd5Qe01avxggbZsP2d8zbg/8MY15u0eC9rLqMv/3yNzad2kSQSxBfT/y65VZVb2Mqayq56+u7yCjO4PbI23l16KtoNa0koCrLg6+fgCPfmbc7joYJ74Frh5atl2iUyaTIyC8j6UwRhy64aZtVVNFgfns7DWHezkT5OluC+ihfFyJ8nHF1kL8HQrRVEsBfIwnghRBNSSlFYXk1GXnlZOSXkZpbyvHaYP14TiklDcyNBPO89Sjf8z0vXQPc6RLgireLBFhtTnEWfPEwnNxq3h49G4Y+03LVqSrmsyOfsSxpGXkVedhr7flo3Ef09uvdYnVqi/Zk7eGRHx/BpEyMDBnJ87HP08G5lQTJSsHej8zrLBgrzPPhx8yBPve36E0jcXVySypJrg3mkzOLam/0ltZb9f5Cnk72hHg5mV+eToR6mV8hXo4EejhKz70QrZgE8NdIAnghxNUwmRTnSqs4W1RBVmEFGflllmA9I6+M0/nlDS5gVEerwdKTEuXnQpSPC10C3OjUwQUHe1lJuk1TCg58Dj8+D6U5oHeBO96GHr9tkerkV+TzcfLHLE9eTnF1MQBBLkHMHjybQQGDWqRObd03x7/h/7b9H0ZlxGBnYFKnSUzvPh1/Z/+WrppZ9mFY/XvIOmDeDh0M416FoL4tWy9xzUwmRWZRhXnkVnYJx3POv+eWVF3yWK0G/Fwd8Hd3IMD9wndH87ubA35uBgw6+dsjREuQAP4aSQAvhFBKUVxpJL+0inOlVeSXVpFXWkVOSSVnCys4W1RJVlEF2UUVZBdXYjRd/lenr6uBEE9HQr2cLMMdO/q5EOrtJP8s3WiUgvSdsOElSN9hTvPrBpOXgk+nZq9OVmkWy5KWsfLoSsqN5kcKRrpH8kiPR7gl4hZ0Wlkk63ocyTvCKztfITEnEQCdVsfEjhN5qs9TeDh4tGjdAPP0jV3zYeNc8wJ3AN3ugpuegw5dW7ZuokmVVBrJyCsjPc98A9nyOb+cjLwyKo2mKzqPq4MOHxcDPi56vJ0N+LjWvrvo8XEx4F23z8WAm4MOjYzqEKJJSAB/jSSAF+LGUWNSlFQYKaqoprjCSHHde2U1ReVG8moD87yyKvJKqsgvM2/nl1VRXXPlvw41GvBxMdDBzUCwh3moYt3wxRAvR4I9naQ3vT2oLIbDa2H3Aji915xm7wQ3/RXi/gQ6201/UEpRWVNJSXUJJVUl5JTnkHQuiS2nt7A7czcK8/dzF68u/KHnH7g59ObWM2f7BqCUYlfWLhYeWMjurN0AeDl48Xzs84wJG9M6ApyCDPj5FTjwGdR+PxA5EgY8Yp4nb+/QotUTtmUyKXJLKsksrCCzsIKswnIya0eOmbfNr6qaKwvy6+jttLg52uPuqKt9t8fNwR43R53ls7ujfb19jno7nPQ6HO3tsNO2gp8PIVqBdh3Av/fee/zzn/8kMzOTbt268dZbbzFs2LArOlYCeCGaX41JUWmsocpoospoory6htLKGsqrjZRV1VBWVUN57XtZldH8ubouzZzHKkCv/Vxa1fg8wSvhpLfDy1mPl7MeTyc93i56/N0c6FD78nd3oIObAV8XAzqZV9j+mGrg7K9wcjukboGUn6Cm0rzPzgC97oHhz4F7cJMUl1GcwY4zOziaf5S0ojQKKgrMAXt1CaVVpRhV41M1BvgP4Hfdf8eQwCGtI5i8gcWfjWfOjjkcLzwOwIiQEcwcOJMgl6AWrlmtrIOw+XU4/C2o2mDN4Aadx0H4UAiNA++OoJWbju2NUoqiciM5JZWcK6kkt6SKc6WV5BZXkltadT6t9r2xNVyulkGnxakuoNfb4aS3w8HerjbNDkd7HY56LXo7O/Q6rfllp6l916LXNZR+Ps3eToOhNk1np8Hezpymq32312rRyk0E0Qq02wD+s88+44EHHuC9995jyJAhfPDBB3z44YckJSURGhp62eMlgBdNRSmFSYFJKdTF79S+m8zvVmmqLs18jou3L3xv6Nx157LkMTV8bpNSmEwKo0lRY3k3Yay5cPuCdJOipqaR9LrtGkVVjTkIrzTWUGms+3w+7fz+8+81VzAE/XoYdFpcHexxc9Dh6qAzf3bUmYNyZz2etUF6XaBe91l6zQUAlSVQnAmFGZB7DHIOQ85R87ziyiLrvF5R0HMy9H8YXHyvq9jqmmris+PZcmoLW05vIbUw9bLHaNDgYu+Cm8GNGK8Yevj0YHzE+NYTPLYTVTVVLDiwgEUHF2FURhzsHJgcPZnJ0ZMJcwtr6eqZ5afBng/h4CooPmO9z05vfpa8W5B5ATwnL/O7wdW8lkPdu94ZDC6gd619r02Tm0TtQkV1DedKqygsq6awvJqiimqKyus+GykqN28XVdSmlRstecqqaxp8LGpLsdNq0GkvCu61Gux12gvStdY3ALRay2fLPq0We515n772WJ2d+caCzs68bU6/6LgLz9PgvguPrTuneb9Oq5EbszeIdhvAx8bG0rdvX+bPn29J69KlCxMnTmTevHmXPb6tBPCf7DpJYbn5MVMXtlpdwAeWAXIohWX4pPnz+R2N5ak7/vzn8wfWBYcXn6+hc1jqxIVBaF1weT6Q5KLAsu5cJtP5dLgg8KwX3DYe8FrKNl107saC63oB8fn6KmoD4gv30XBwLa6NVgOO9nY46nXn777r7Sx35y+8I3/hPlcH+9rgXIer4YLPDvboddI73u7tWQTl+eZec5MRTNW173XbRqipgooiqCg0B+YVhVB6DioLGz+vwQ1CBpp7LTuNBf8eVx287Du7j71n91JaXUppdSnFVcWcLDrJicITljnrAHYaO3r79aa3b28i3CPwcfTBRe+Ci7355ap3xUHnIEPjW5HjBcd5Zecr7D2715IW5BJER4+O+Dv742TvhJPOic6enbk59OaWqaTJBBm7zCNI0neap38YG3582ZXRWAf6htrg3uBqDvT1TqDV1b7sLvhsb/7Z0WjO/yyJG5ZSikqjyXpkXd2IuwZG31VUn+8AqHtV15iovCitqsacXnVhB8IFacbajoYbzfkbCvUD/7p9OjtzoK/VgJ1Gg1ajQaMBrUaDndb6s1bD+bzauuM02NXmqb+P2uPqn1OD+W9i3Z/Gur+Q57cb3l+XcKn89fZd9Pf3wmMifZ0Z162VLDDaiGuJQ9v8KjZVVVXEx8czc+ZMq/SxY8eyffv2Bo+prKyksrLSsl1YaP5HraioqMH8rcV/fzjA6fzr+QMrWqO6X3pajXlDgzmg1damowEtGsu2xvKLEssvVwCttu48GssvOKvt2ndd7S9YnVaLnVaDnZ0GOy3otLXpmtr0uu3aO8CWNDtN7TnM6VottcPZtOffdeeHtxnszHem9XZaDPbm4WwGnfkPjF6nxaDTNuFQ9Bow1VBRVon8pAg2/BsKT1378XpXcPE390r6dASfzuAbDX5drIcaFxdf9ak3HdvEh79+2OA+T4MngwMHExcYx8CAgbjpG/mDXgPGciMllFx1+cJ2fLW+vBX3FtvPbGflkZXsPbuX9PJ00nPSrfKNDh1Nf4/+LVRLwLMbDOgGA54y39QqPA15J6A023zjqzwfKgqgshSqSqGq2PxeWQrVJbXvpdTe4q+96XWJG1+XM94ZnFrJSAVhUzrAzQ7cHAFHLaAFbPsse6XOjxysqqkdeVhjDvSNJoXRZKLaWPteY8JYA0aTorqmbrtunzmt5oLPRpOyHFt3/pra/FV16TUmqk0KY+25Lcdd+F5jPqfxgrTqC0ZCXqyy9iUaN6arH3EhTi1djUuqiz+vpk+9zffAnzlzhqCgILZt28bgwYMt6XPnzmXp0qUcOXKk3jGzZ8/mpZdeas5qCiGEEEIIIYQQ9WRkZBAcfGXr57T5Hvg6Fw+fUEo1Ojfk73//O88++6xl22QykZeXh7e3d5ufT1JUVERISAgZGRmtejqAaBrS3u2HtHX7Iu3dvkh7ty/S3u2HtHX7ci3trZSiuLiYwMDAKy6nzQfwPj4+2NnZkZWVZZWenZ1Nhw4dGjzGYDBgMFg/2sfDw8NWVWwRbm5u8ouiHZH2bj+krdsXae/2Rdq7fZH2bj+krduXq21vd3f3qzp/m1/1Rq/X069fP9avX2+Vvn79eqsh9UIIIYQQQgghRFvW5nvgAZ599lkeeOAB+vfvT1xcHAsWLCA9PZ1HH320pasmhBBCCCGEEEI0iRsigJ8yZQrnzp3j5ZdfJjMzk+7du7N27VrCwtrfaqYGg4FZs2bVmyIgbkzS3u2HtHX7Iu3dvkh7ty/S3u2HtHX70lzt3eZXoRdCCCGEEEIIIdqDNj8HXgghhBBCCCGEaA8kgBdCCCGEEEIIIdoACeCFEEIIIYQQQog2QAJ4IYQQQgghhBCiDZAAvo3Jz8/ngQcewN3dHXd3dx544AEKCgoueYxSitmzZxMYGIijoyMjRozg0KFDlv15eXk8+eSTREdH4+TkRGhoKH/+858pLCy08dWIy7FFewMsWLCAESNG4Obmhkajuew5hW289957RERE4ODgQL9+/diyZcsl82/evJl+/frh4OBAZGQk77//fr08q1atomvXrhgMBrp27cqXX35pq+qLq9TU7X3o0CEmTZpEeHg4Go2Gt956y4a1F1ejqdt64cKFDBs2DE9PTzw9PRk9ejS7d++25SWIq9DU7b169Wr69++Ph4cHzs7O9O7dm2XLltnyEsRVsMXf7jorVqxAo9EwceLEJq61uFZN3d5LlixBo9HUe1VUVFx5pZRoU8aPH6+6d++utm/frrZv3666d++ubr/99kse89prrylXV1e1atUqdfDgQTVlyhQVEBCgioqKlFJKHTx4UP3mN79Ra9asUSkpKWrDhg2qU6dOatKkSc1xSeISbNHeSin15ptvqnnz5ql58+YpQOXn59v4SsTFVqxYoezt7dXChQtVUlKSeuqpp5Szs7M6efJkg/lPnDihnJyc1FNPPaWSkpLUwoULlb29vfriiy8sebZv367s7OzU3LlzVXJyspo7d67S6XRq586dzXVZohG2aO/du3erGTNmqOXLlyt/f3/15ptvNtPViEuxRVvfd9996t1331UJCQkqOTlZTZ8+Xbm7u6tTp04112WJRtiivTdu3KhWr16tkpKSVEpKinrrrbeUnZ2dWrduXXNdlmiELdq7TlpamgoKClLDhg1TEyZMsPGViCthi/ZevHixcnNzU5mZmVavqyEBfBuSlJSkAKt/xnfs2KEAdfjw4QaPMZlMyt/fX7322muWtIqKCuXu7q7ef//9Rsv6/PPPlV6vV9XV1U13AeKqNEd7b9y4UQL4FjJw4ED16KOPWqXFxMSomTNnNpj/ueeeUzExMVZpf/zjH9WgQYMs25MnT1bjx4+3yjNu3Dh1zz33NFGtxbWyRXtfKCwsTAL4VsLWba2UUkajUbm6uqqlS5def4XFdWmO9lZKqT59+qgXXnjh+iorrput2ttoNKohQ4aoDz/8UE2bNk0C+FbCFu29ePFi5e7ufl31kiH0bciOHTtwd3cnNjbWkjZo0CDc3d3Zvn17g8ekpqaSlZXF2LFjLWkGg4Hhw4c3egxAYWEhbm5u6HS6prsAcVWas71F86qqqiI+Pt6qnQDGjh3baDvt2LGjXv5x48axd+9eqqurL5lH2r5l2aq9RevTXG1dVlZGdXU1Xl5eTVNxcU2ao72VUmzYsIEjR45w0003NV3lxVWzZXu//PLL+Pr68vDDDzd9xcU1sWV7l5SUEBYWRnBwMLfffjsJCQlXVTcJ4NuQrKws/Pz86qX7+fmRlZXV6DEAHTp0sErv0KFDo8ecO3eOOXPm8Mc//vE6ayyuR3O1t2h+ubm51NTUXFU7ZWVlNZjfaDSSm5t7yTzS9i3LVu0tWp/mauuZM2cSFBTE6NGjm6bi4prYsr0LCwtxcXFBr9dz22238c477zBmzJimvwhxxWzV3tu2bWPRokUsXLjQNhUX18RW7R0TE8OSJUtYs2YNy5cvx8HBgSFDhnDs2LErrpsE8K3A7NmzG1zM4MLX3r17AdBoNPWOV0o1mH6hi/c3dkxRURG33XYbXbt2ZdasWddxVaIxram9Rcu62nZqKP/F6dL2rZct2lu0TrZs69dff53ly5ezevVqHBwcmqC24nrZor1dXV1JTExkz549vPrqqzz77LNs2rSp6SotrllTtndxcTH3338/CxcuxMfHp+krK65bU/98Dxo0iPvvv59evXoxbNgwPv/8czp37sw777xzxXWS8dGtwBNPPME999xzyTzh4eEcOHCAs2fP1tuXk5NT725PHX9/f8B8RyggIMCSnp2dXe+Y4uJixo8fj4uLC19++SX29vZXeyniCrSW9hYtx8fHBzs7u3p3cC/VTv7+/g3m1+l0eHt7XzKPtH3LslV7i9bH1m39r3/9i7lz5/LTTz/Rs2fPpq28uGq2bG+tVkvHjh0B6N27N8nJycybN48RI0Y07UWIK2aL9j506BBpaWnccccdlv0mkwkAnU7HkSNHiIqKauIrEVeiuf52a7VaBgwYID3wbY2Pjw8xMTGXfDk4OBAXF0dhYaHVo2N27dpFYWEhgwcPbvDcERER+Pv7s379ektaVVUVmzdvtjqmqKiIsWPHotfrWbNmjdzVt6HW0N6iZen1evr162fVTgDr169vtJ3i4uLq5f/xxx/p37+/5WZbY3mk7VuWrdpbtD62bOt//vOfzJkzh3Xr1tG/f/+mr7y4as35s62UorKy8vorLa6ZLdo7JiaGgwcPkpiYaHndeeedjBw5ksTEREJCQmx2PeLSmuvnWylFYmKiVcfbZV3XEnii2Y0fP1717NlT7dixQ+3YsUP16NGj3mPFoqOj1erVqy3br732mnJ3d1erV69WBw8eVPfee6/VY8WKiopUbGys6tGjh0pJSbF6pIHRaGzW6xPWbNHeSimVmZmpEhIS1MKFCxWgfvnlF5WQkKDOnTvXbNfW3tU9mmTRokUqKSlJPf3008rZ2VmlpaUppZSaOXOmeuCBByz56x5N8swzz6ikpCS1aNGieo8m2bZtm7Kzs1OvvfaaSk5OVq+99po8Rq6VsEV7V1ZWqoSEBJWQkKACAgLUjBkzVEJCgjp27FizX584zxZt/Y9//EPp9Xr1xRdfWP2NLi4ubvbrE9Zs0d5z585VP/74ozp+/LhKTk5W//73v5VOp1MLFy5s9usT1mzR3heTVehbD1u09+zZs9W6devU8ePHVUJCgpo+fbrS6XRq165dV1wvCeDbmHPnzqmpU6cqV1dX5erqqqZOnVrvEWCAWrx4sWXbZDKpWbNmKX9/f2UwGNRNN92kDh48aNlf9yixhl6pqanNc2GiQbZob6WUmjVrVoPtfeF5hO29++67KiwsTOn1etW3b1+1efNmy75p06ap4cOHW+XftGmT6tOnj9Lr9So8PFzNnz+/3jlXrlypoqOjlb29vYqJiVGrVq2y9WWIK9TU7Z2amtrgz/HF5xHNr6nbOiwsrMG2njVrVjNcjbicpm7v559/XnXs2FE5ODgoT09PFRcXp1asWNEclyKugC3+dl9IAvjWpanb++mnn1ahoaFKr9crX19fNXbsWLV9+/arqpNGqdqZ9UIIIYQQQgghhGi1ZA68EEIIIYQQQgjRBkgAL4QQQgghhBBCtAESwAshhBBCCCGEEG2ABPBCCCGEEEIIIUQbIAG8EEIIIYQQQgjRBkgAL4QQQgghhBBCtAESwAshhBBCCCGEEG2ABPBCCCGEEEIIIUQbIAG8EEKIG8KIESN4+umnL5knMTERjUZDWloas2fPpnfv3s1SN2F7Sin+8Ic/4OXlhUajITExETj/fVFQUIBGo2HTpk0tWs8bTVpamtXXWwghhG1JAC+EEOK6ZWVl8dRTT9GxY0ccHBzo0KEDQ4cO5f3336esrKylq2fRvXt3MjMzCQkJYcaMGWzYsMFq/5IlS/Dw8Liqc9r6RsCmTZvQaDSWl6OjI926dWPBggXXdJ6CgoLrqs/777+Pq6srRqPRklZSUoK9vT3Dhg2zyrtlyxY0Gg1Hjx695vIeeughJk6ceNl869atY8mSJXz77bdkZmbSvXt3AFavXs2cOXNwd3cnMzOTwYMHX3NdhBBCiJama+kKCCGEaNtOnDjBkCFD8PDwYO7cufTo0QOj0cjRo0f56KOPCAwM5M4772zpagKg0+nw9/cHwMXFBRcXlxau0XnV1dXY29s3uv/IkSO4ublRXl7ON998w2OPPUZUVBSjRo1qxlrCyJEjKSkpYe/evQwaNAgwB+r+/v7s2bOHsrIynJycAPNNg8DAQDp37nzV5dTU1KDRaK44//HjxwkICKgXoHt5eVk+17X9pVyuHYQQQoiWJD3wQgghrsvjjz+OTqdj7969TJ48mS5dutCjRw8mTZrEd999xx133GHJW1hYyB/+8Af8/Pxwc3Pj5ptvZv/+/Zb9+/fvZ+TIkbi6uuLm5ka/fv3Yu3evZf+2bdsYPnw4Tk5OeHp6Mm7cOPLz8y37TSYTzz33HF5eXvj7+zN79myruv7jH/+ge/fuODk5ERISwp/+9CdKSkoAc7A5ffp0CgsLLb3ds2fPrtcDXvd66KGHWLJkCS+99BL79++3pC9ZsuSKrrWu5/6jjz4iMjISg8GAUqrRr7Ofnx/+/v5ERETw5z//mfDwcPbt22fZr5Ti9ddfJzIyEkdHR3r16sUXX3wBmIc5jxw5EgBPT09L/cHccz106FA8PDzw9vbm9ttv5/jx443WIzo6msDAQKuh6Js2bWLChAlERUWxfft2q/S6cquqqnjuuecICgrC2dmZ2NhYq3PUjX749ttv6dq1KwaDgenTp7N06VK+/vpry9e3oSHwDz30EE8++STp6eloNBrCw8MBCA8P56233rLK27t3b6vvC41Gw/vvv8+ECRNwdnbmlVdeafC6P/74Y/r374+rqyv+/v7cd999ZGdnA+bvu+DgYN5//32rY/bt24dGo+HEiRMAHD58mKFDh+Lg4EDXrl356aef0Gg0fPXVV41+vetUVVXxxBNPEBAQgIODA+Hh4cybN8/qOubPn88tt9yCo6MjERERrFy50uocp0+fZsqUKXh6euLt7c2ECRNIS0uzyrN48WK6dOmCg4MDMTExvPfee1b7d+/eTZ8+fXBwcKB///4kJCRctu5CCCGajgTwQgghrtm5c+f48ccf+dOf/oSzs3ODeep6UZVS3HbbbWRlZbF27Vri4+Pp27cvo0aNIi8vD4CpU6cSHBzMnj17iI+PZ+bMmZbe0MTEREaNGkW3bt3YsWMHW7du5Y477qCmpsZS1tKlS3F2dmbXrl28/vrrvPzyy6xfv96yX6fT8d///pekpCSWLFnChg0beO655wAYPHgwb731Fm5ubmRmZpKZmcmMGTMYPHiwZTszM5Off/4ZBwcHbrrpJqZMmcJf/vIXunXrZtk/ZcqUK7pWgJSUFD7//HNWrVp1xXOIlVKsW7eOjIwMYmNjLekvvPACixcvZv78+Rw6dIhnnnmG+++/n82bNxMSEsKqVasAc09+ZmYmb7/9NgClpaU8++yz7Nmzhw0bNqDVarnrrrswmUyN1mHEiBFs3LjRsr1x40ZGjBjB8OHDLelVVVXs2LHDEsBPnz6dbdu2sWLFCg4cOMDdd9/N+PHjOXbsmOU8ZWVlzJs3jw8//JBDhw7xn//8h8mTJzN+/HjL17ehIfBvv/02L7/8MsHBwWRmZrJnz54r+lrWmTVrFhMmTODgwYP87ne/azBPVVUVc+bMYf/+/Xz11VekpqZaboJotVruuecePvnkE6tjPv30U+Li4oiMjMRkMjFx4kScnJzYtWsXCxYs4Pnnn7/iOv7nP/9hzZo1fP755xw5coSPP/7YcqOizosvvsikSZPYv38/999/P/feey/JycmA+Ws7cuRIXFxc+OWXX9i6dSsuLi6MHz+eqqoqABYuXMjzzz/Pq6++SnJyMnPnzuXFF19k6dKlgPl75fbbbyc6Opr4+Hhmz57NjBkzrvgahBBCNAElhBBCXKOdO3cqQK1evdoq3dvbWzk7OytnZ2f13HPPKaWU2rBhg3Jzc1MVFRVWeaOiotQHH3yglFLK1dVVLVmypMGy7r33XjVkyJBG6zJ8+HA1dOhQq7QBAwaov/3tb40e8/nnnytvb2/L9uLFi5W7u3uj+XNzc1VUVJR6/PHHLWmzZs1SvXr1ssp3Jdc6a9YsZW9vr7KzsxstTymlNm7cqADL11On0ymtVqteeeUVS56SkhLl4OCgtm/fbnXsww8/rO69916r8+Tn51+yvOzsbAWogwcPNppnwYIFytnZWVVXV6uioiKl0+nU2bNn1YoVK9TgwYOVUkpt3rxZAer48eMqJSVFaTQadfr0aavzjBo1Sv39739XSpm/9oBKTEy0yjNt2jQ1YcKES9ZZKaXefPNNFRYWZpUWFham3nzzTau0Xr16qVmzZlm2AfX0009f9vwX2717twJUcXGxUkqpffv2KY1Go9LS0pRSStXU1KigoCD17rvvKqWU+v7775VOp1OZmZmWc6xfv14B6ssvv7xseU8++aS6+eablclkanA/oB599FGrtNjYWPXYY48ppZRatGiRio6Otjq+srJSOTo6qh9++EEppVRISIj69NNPrc4xZ84cFRcXp5RS6oMPPlBeXl6qtLTUsn/+/PkKUAkJCZe9BiGEENdP5sALIYS4bhfPVd69ezcmk4mpU6dSWVkJQHx8PCUlJXh7e1vlLS8vtwzZfvbZZ3nkkUdYtmwZo0eP5u677yYqKgow98Dffffdl6xHz549rbYDAgIsw5zB3FM8d+5ckpKSKCoqwmg0UlFRQWlpaaMjCOpUV1czadIkQkNDLb3XjbmSawUICwvD19f3kueqs2XLFlxdXamsrGT37t088cQTeHl58dhjj5GUlERFRQVjxoyxOqaqqoo+ffpc8rzHjx/nxRdfZOfOneTm5lp63tPT0+nevTu33HILW7ZssdT30KFDjBw5ktLSUvbs2UN+fj6dO3fGz8+P4cOH88ADD1BaWsqmTZsIDQ0lMjKSlStXopSqNxe+srLS6muk1+vrtWFz6N+//2XzJCQkMHv2bBITE8nLy7P6OnXt2pU+ffoQExPD8uXLmTlzJps3byY7O5vJkycD5pEPISEhVvPwBw4ceMV1fOihhxgzZgzR0dGMHz+e22+/nbFjx1rliYuLq7ddN7IjPj6elJQUXF1drfJUVFRw/PhxcnJyyMjI4OGHH+b3v/+9Zb/RaMTd3R2A5ORkevXqZVnjoKEyhRBC2JYE8EIIIa5Zx44d0Wg0HD582Co9MjISAEdHR0uayWQiICCgwTnMdSu/z549m/vuu4/vvvuO77//nlmzZrFixQruuusuq3M15uLFxzQajSXQOnnyJLfeeiuPPvooc+bMwcvLi61bt/Lwww9TXV192XM/9thjpKens2fPHnS6S//5vJJrBS570+BCERERlmO7devGrl27ePXVV3nssccs1/jdd98RFBRkdZzBYLjkee+44w5CQkJYuHAhgYGBmEwmunfvbhlW/eGHH1JeXg6c//p27NiR4OBgNm7cSH5+PsOHDwewzNHftm0bGzdu5Oabb7Z8Pezs7IiPj8fOzs6q/AsXEnR0dLyqhesuR6vV1ltXoKG2vlw7lJaWMnbsWMaOHcvHH3+Mr68v6enpjBs3zvJ1AvMUkE8//ZSZM2fy6aefMm7cOHx8fADz1Ifruba+ffuSmprK999/z08//cTkyZMZPXq0ZZ2DxtSVaTKZ6NevX71h/gC+vr5UVFQA5mH0F07NACxtdvHXUgghRPOTAF4IIcQ18/b2ZsyYMfz3v//lySefvGQg1LdvX7KystDpdPXm7l6oc+fOdO7cmWeeeYZ7772XxYsXc9ddd9GzZ082bNjASy+9dE113bt3L0ajkX//+99oteYlYD7//HOrPHq93mpOfZ033niDzz77jB07dtTrVW/omCu91uthZ2dnCazrFn1LT0+3BNMX0+v1AFZ1PXfuHMnJyXzwwQeWR8Bt3brV6riLbwjUGTlyJJs2bSI/P5+//vWvlvThw4fzww8/sHPnTqZPnw5Anz59qKmpITs7u96j5i6nsTa5Er6+vmRmZlq2i4qKSE1NverzHD58mNzcXF577TVCQkIArBZXrHPffffxwgsvEB8fzxdffMH8+fMt+2JiYkhPT+fs2bN06NAB4Krn6ru5uTFlyhSmTJnCb3/7W8aPH09eXp5lpf2dO3fy4IMPWvLv3LnTMgKjb9++fPbZZ5ZFFS/m7u5OUFAQJ06cYOrUqQ2W37VrV5YtW0Z5ebnlhtrOnTuv6hqEEEJcH1nETgghxHV57733MBqN9O/fn88++4zk5GTLIluHDx+29N6NHj2auLg4Jk6cyA8//EBaWhrbt2/nhRdeYO/evZSXl/PEE0+wadMmTp48ybZt29izZw9dunQB4O9//zt79uzh8ccf58CBAxw+fJj58+eTm5t7RfWMiorCaDTyzjvvcOLECZYtW1Zv1fDw8HBKSkrYsGEDubm5lJWV8dNPP/Hcc8/xr3/9Cx8fH7KyssjKyqKwsNByTGpqKomJieTm5lJZWXnZa70W2dnZZGVlcfLkSVauXMmyZcuYMGECAK6ursyYMYNnnnmGpUuXcvz4cRISEnj33XctC5CFhYWh0Wj49ttvycnJoaSkxLIa+YIFC0hJSeHnn3/m2WefvaL6jBw5kq1bt5KYmGh102D48OEsXLiQiooKywJ2nTt3ZurUqTz44IOsXr2a1NRU9uzZwz/+8Q/Wrl17yXLCw8M5cOAAR44cITc394pGS9S5+eabWbZsGVu2bOHXX39l2rRp9UYAXInQ0FD0er3le2fNmjXMmTOnXr6IiAgGDx7Mww8/jNFotLQPwJgxY4iKimLatGkcOHCAbdu2WRaxu5Ke+TfffJMVK1Zw+PBhjh49ysqVK/H397ca0bFy5Uo++ugjjh49yqxZsyxTLcA8OsDHx4cJEyawZcsWUlNT2bx5M0899RSnTp0CzCNg5s2bx9tvv83Ro0c5ePAgixcv5o033gDMNyi0Wi0PP/wwSUlJrF27ln/9619X/fUUQghxHVp2Cr4QQogbwZkzZ9QTTzyhIiIilL29vXJxcVEDBw5U//znP60WvCoqKlJPPvmkCgwMVPb29iokJERNnTpVpaenq8rKSnXPPfeokJAQpdfrVWBgoHriiSdUeXm55fhNmzapwYMHK4PBoDw8PNS4ceMsi7INHz5cPfXUU1b1mjBhgpo2bZpl+4033lABAQHK0dFRjRs3Tv3vf/+rt7Dbo48+qry9vRWgZs2apWbNmqWAeq+681ZUVKhJkyYpDw8PBajFixdf9lqVanjxu4bULT5X99LpdCoiIkLNmDFDlZSUWPKZTCb19ttvq+joaGVvb698fX3VuHHj1ObNmy15Xn75ZeXv7680Go2l/uvXr1ddunRRBoNB9ezZU23atOmKFlZLTU1VgIqJibFKz8jIUICKioqySq+qqlL/93//p8LDw5W9vb3y9/dXd911lzpw4IBSqvEFBLOzs9WYMWOUi4uLAtTGjRsbrE9Di9gVFhaqyZMnKzc3NxUSEqKWLFnS4CJ2V7KI3KeffqrCw8OVwWBQcXFxas2aNQ0u3vbuu+8qQD344IP1zpGcnKyGDBmi9Hq9iomJUd98840C1Lp16y5b/oIFC1Tv3r2Vs7OzcnNzU6NGjVL79u2zuo53331XjRkzRhkMBhUWFqaWL19udY7MzEz14IMPKh8fH2UwGFRkZKT6/e9/rwoLCy15PvnkE9W7d2+l1+uVp6enuummm6wWqdyxY4fq1auX0uv1qnfv3mrVqlWyiJ0QQjQjjVIyoUkIIYQQorlt27aNoUOHkpKSYlms8VppNBq+/PJLJk6c2DSVE0II0SrJHHghhBBCiGbw5Zdf4uLiQqdOnUhJSeGpp55iyJAh1x28CyGEaD9kDrwQQgghRDMoLi7m8ccfJyYmhoceeogBAwbw9ddfAzB37lxcXFwafN1yyy0tXHMhhBCthQyhF0IIIYRoYXl5eeTl5TW4z9HRsdGnAQghhGhfJIAXQgghhBBCCCHaABlCL4QQQgghhBBCtAESwAshhBBCCCGEEG2ABPBCCCGEEEIIIUQbIAG8EEIIIYQQQgjRBkgAL4QQQgghhBBCtAESwAshhBBCCCGEEG2ABPBCCCGEEEIIIUQb8P8BmeGkjFMvU/QAAAAASUVORK5CYII=", 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", 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" ] @@ -14208,13 +9053,13 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 21, "id": "cf7a7e61", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -14248,7 +9093,7 @@ ], "metadata": { "kernelspec": { - "display_name": "hft_ml", + "display_name": "base", "language": "python", "name": "python3" }, @@ -14262,7 +9107,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.10" + "version": "3.10.9" } }, "nbformat": 4,