2488 lines
359 KiB
Plaintext
2488 lines
359 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<div style=\"\n",
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" border: 2px solid #4CAF50; \n",
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" padding: 15px; \n",
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" background-color: #f4f4f4; \n",
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" border-radius: 10px; \n",
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" align-items: center;\">\n",
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"\n",
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"<h1 style=\"margin: 0; color: #4CAF50;\">Unsupervised ML Modelle: K-Means und Affinity Propagation</h1>\n",
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"<h2 style=\"margin: 5px 0; color: #555;\">DSAI</h2>\n",
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"<h3 style=\"margin: 5px 0; color: #555;\">Jakob Eggl</h3>\n",
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"\n",
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"<div style=\"flex-shrink: 0;\">\n",
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" <img src=\"https://www.htl-grieskirchen.at/wp/wp-content/uploads/2022/11/logo_bildschirm-1024x503.png\" alt=\"Logo\" style=\"width: 250px; height: auto;\"/>\n",
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"</div>\n",
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"<p1> © 2024/25 Jakob Eggl. Nutzung oder Verbreitung nur mit ausdrücklicher Genehmigung des Autors.</p1>\n",
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"</div>\n",
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"<div style=\"flex: 1;\">\n",
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"</div> "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## k-Means Clustering"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Wir sehen uns nun den $k$-Means Algorithmus an. Er ist ein sehr effizienter und weit verbreiteter *Clustering* Algorithmus, welcher häufig im Unsupervised Learning Bereich eingesetzt wird."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Info:** Als **Clustering** bezeichnet man jenen Vorgang in Machine Learning, der Datenpunkte, je nach Ähnlichkeit, in Gruppen (=Cluster) zuweist."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Der $k$-means Algorithmus $\\ldots$\n",
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"* $\\ldots$ wird zum Clustering verwendet\n",
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"* $\\ldots$ arbeitet iterativ um den Datenpunkten den entsprechenden Cluster zuzuordnen\n",
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"* $\\ldots$ besitzt hauptsächlich die Hpyerparameter $k$ als die Anzahl der Cluster $k$ und die Distanzfunktion $d(x,y)$."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n",
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"\n",
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"(von https://blog.flyingwhales.io/machinelearning/2021/05/05/kmeans-clustering)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Beschreibung des Algorithmus"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Wir erklären den Algorithmus nun anhand von einer beispielhaften Visualisierung mit 17 2-dimensionalen Datenpunkten und $k=3$ gewünschte Cluster. Der Algorithmus funktioniert natürlich auch für andere Kombinationen genau gleich. In diesem Beispiel wird außerdem die euklidische Distanz, sprich der Betrag vom Richtungsvektor zweier Punkte verwendet."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Ausgangssituation:** Wir haben (in diesem Fall 17) Datenpunkte, welche in dieser Grafik rot dargestellt sind. Diese Datenpunkte sind in diesem Fall 2-dimensional (2 Features). \n",
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"\n",
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"**Schritt 1:** Nun werden 3 Puntke ($k=3$) zufällig platziert. Sie haben hier die Farben \"Gelb, Grün und Blau\".\n",
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"\n",
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"**Schritt 2:** Im nächsten Schritt wird von jedem Datenpunkt der Abstand zu den jeweiligen Clustern berechnet. Anschließend wird jedem Datenpunkt die Farbe (entspricht dem Cluster) zugewiesen, der dem Punkt am nähesten ist. Zur Distanzberechnung wird die *Distanz-Funktion* verwendet.\n",
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"\n",
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"**Schritt 3:** Die 3 platzierten Cluster werden nun verschoben. Dabei ist die neue Position die mittlere Position der bisherigen Punkte, welche diesem Cluster angehören.\n",
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"\n",
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"**Schritt 4-5:** Nun wird wie in Schritt $2$ erneut berechnet, wie groß der Abstand von jedem Punkt zu den 3 Cluster-Punkten ist. Die Punkte werden umgefärbt, falls ein neuer Cluster nun näher ist. Dies ist bei den beiden, rot eingekreisten, blauen Punkten der Fall. Die Cluster-Punkte werden nun erneut verschoben, genauso wie in Schritt $3$.\n",
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"\n",
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"**Ende:** Da beim erneuten Distanzausrechnen keine Punkte mehr umgefärbt werden müssen, ist können wir den Algorithmus beenden."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Wichtig:** In der Praxis kann es vorkommen, dass der Algorithmus nicht (früh genug) konvergiert. Sprich es verschieben sich die Clusterpunkte nach wie vor bei jeder Iteration. In diesem Fall bricht der Algorithmus nach einer gewissen Anzahl an Iterationen automatisch ab. Dieser Wert ist standardmäßig in `scikit-learn` auf 300 gesetzt, kann aber von uns geändert werden. "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Eine weitere, sehr informative, interaktive Demonstration kann hier gefunden werden:\n",
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"http://alekseynp.com/viz/k-means.html"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Der Hyperparameter $k$"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"* Legt fest, wie viele Cluster wir wollen.\n",
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"* Bestimmt somit die Anzahl der zufälligen Punkte, die am Anfang des Algorithmus verwendet werden."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Die Distanz Funktion"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Ähnlich wie die meisten Algorithmen hier, wird zu den $k$ Cluster-Zentren der Abstand, sprich die Distanz (vergleiche zum Beispiel kNN), berechnet. Je nach gewählter Distanzfunktion ergibt dies ein anderes Verhalten des Algorithmus. Standardmäßig wird die euklidische Distanz verwendet, sprich der Betrag des Richtungsvektors zwischen den beiden Punkte.\n",
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"\n",
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"Wiederholung: Welche anderen Distanzfunktionen gibt es?"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Vorteile und Nachteile von k-Means"
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]
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},
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{
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"cell_type": "markdown",
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||
"metadata": {},
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"source": [
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"**Vorteile:**\n",
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"* Sehr schnell und effizient für große Datenmengen\n",
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"* Leicht verständlich und einfach zu implementieren\n",
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"* Wahl von $k$\n",
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"\n",
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"**Nachteile:**\n",
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"* Wahl von $k$\n",
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"* Funktionert nur für numerische Attribute (gut)\n",
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"* Stark abhängig von der gewählten Distanz Funktion, somit auch Distanz-Funktion $d(x,y)$ entscheidend\n",
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"* Sensibel gegenüber Ausreißer (neues Cluster-Zentrum sehr verzerrt)\n",
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"* Clustering basiert auf Distanz und nicht auf semantische Unterschiede. Warum ist das ein Nachteil? Wo ist dies ein Nachteil (Word Embeddings)?"
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||
]
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||
},
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||
{
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||
"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Tipps und Tricks"
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||
]
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},
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{
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"cell_type": "markdown",
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||
"metadata": {},
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||
"source": [
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||
"* Es gibt noch weitere Parameter, welche die Initialisierung der zufälligen Punkte beinflussen. Viele verwenden *k-means++*, default ist *auto*\n",
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"* Standardisieren der Daten ist notwendig (Ausreißer), ansonsten kann es sein, dass ungewünschte Ergebnisse resultieren\n",
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"* Bei kleinen Datasets kann, ähnlich wie bei *kNN* der Wert von $k$ probiert werden. Dazu benötigt man Metriken, welche die Qualität bewerten. Eine weitere Methode ist zum Beispiel die Elbow-Method (siehe Referate)."
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||
]
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||
},
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||
{
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||
"cell_type": "markdown",
|
||
"metadata": {},
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||
"source": [
|
||
"## k-Means in Python"
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||
]
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||
},
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||
{
|
||
"cell_type": "code",
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||
"execution_count": 1,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
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||
"import matplotlib.pyplot as plt\n",
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||
"from sklearn.datasets import load_iris, load_wine\n",
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||
"from sklearn.cluster import KMeans\n",
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||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"import pandas as pd"
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||
]
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||
},
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||
{
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||
"cell_type": "code",
|
||
"execution_count": 2,
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||
"metadata": {},
|
||
"outputs": [],
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||
"source": [
|
||
"X, y_true = load_iris(return_X_y=True, as_frame=False)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
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||
"text/plain": [
|
||
"array([[5.1, 3.5, 1.4, 0.2],\n",
|
||
" [4.9, 3. , 1.4, 0.2],\n",
|
||
" [4.7, 3.2, 1.3, 0.2],\n",
|
||
" [4.6, 3.1, 1.5, 0.2],\n",
|
||
" [5. , 3.6, 1.4, 0.2],\n",
|
||
" [5.4, 3.9, 1.7, 0.4],\n",
|
||
" [4.6, 3.4, 1.4, 0.3],\n",
|
||
" [5. , 3.4, 1.5, 0.2],\n",
|
||
" [4.4, 2.9, 1.4, 0.2],\n",
|
||
" [4.9, 3.1, 1.5, 0.1],\n",
|
||
" [5.4, 3.7, 1.5, 0.2],\n",
|
||
" [4.8, 3.4, 1.6, 0.2],\n",
|
||
" [4.8, 3. , 1.4, 0.1],\n",
|
||
" [4.3, 3. , 1.1, 0.1],\n",
|
||
" [5.8, 4. , 1.2, 0.2],\n",
|
||
" [5.7, 4.4, 1.5, 0.4],\n",
|
||
" [5.4, 3.9, 1.3, 0.4],\n",
|
||
" [5.1, 3.5, 1.4, 0.3],\n",
|
||
" [5.7, 3.8, 1.7, 0.3],\n",
|
||
" [5.1, 3.8, 1.5, 0.3],\n",
|
||
" [5.4, 3.4, 1.7, 0.2],\n",
|
||
" [5.1, 3.7, 1.5, 0.4],\n",
|
||
" [4.6, 3.6, 1. , 0.2],\n",
|
||
" [5.1, 3.3, 1.7, 0.5],\n",
|
||
" [4.8, 3.4, 1.9, 0.2],\n",
|
||
" [5. , 3. , 1.6, 0.2],\n",
|
||
" [5. , 3.4, 1.6, 0.4],\n",
|
||
" [5.2, 3.5, 1.5, 0.2],\n",
|
||
" [5.2, 3.4, 1.4, 0.2],\n",
|
||
" [4.7, 3.2, 1.6, 0.2],\n",
|
||
" [4.8, 3.1, 1.6, 0.2],\n",
|
||
" [5.4, 3.4, 1.5, 0.4],\n",
|
||
" [5.2, 4.1, 1.5, 0.1],\n",
|
||
" [5.5, 4.2, 1.4, 0.2],\n",
|
||
" [4.9, 3.1, 1.5, 0.2],\n",
|
||
" [5. , 3.2, 1.2, 0.2],\n",
|
||
" [5.5, 3.5, 1.3, 0.2],\n",
|
||
" [4.9, 3.6, 1.4, 0.1],\n",
|
||
" [4.4, 3. , 1.3, 0.2],\n",
|
||
" [5.1, 3.4, 1.5, 0.2],\n",
|
||
" [5. , 3.5, 1.3, 0.3],\n",
|
||
" [4.5, 2.3, 1.3, 0.3],\n",
|
||
" [4.4, 3.2, 1.3, 0.2],\n",
|
||
" [5. , 3.5, 1.6, 0.6],\n",
|
||
" [5.1, 3.8, 1.9, 0.4],\n",
|
||
" [4.8, 3. , 1.4, 0.3],\n",
|
||
" [5.1, 3.8, 1.6, 0.2],\n",
|
||
" [4.6, 3.2, 1.4, 0.2],\n",
|
||
" [5.3, 3.7, 1.5, 0.2],\n",
|
||
" [5. , 3.3, 1.4, 0.2],\n",
|
||
" [7. , 3.2, 4.7, 1.4],\n",
|
||
" [6.4, 3.2, 4.5, 1.5],\n",
|
||
" [6.9, 3.1, 4.9, 1.5],\n",
|
||
" [5.5, 2.3, 4. , 1.3],\n",
|
||
" [6.5, 2.8, 4.6, 1.5],\n",
|
||
" [5.7, 2.8, 4.5, 1.3],\n",
|
||
" [6.3, 3.3, 4.7, 1.6],\n",
|
||
" [4.9, 2.4, 3.3, 1. ],\n",
|
||
" [6.6, 2.9, 4.6, 1.3],\n",
|
||
" [5.2, 2.7, 3.9, 1.4],\n",
|
||
" [5. , 2. , 3.5, 1. ],\n",
|
||
" [5.9, 3. , 4.2, 1.5],\n",
|
||
" [6. , 2.2, 4. , 1. ],\n",
|
||
" [6.1, 2.9, 4.7, 1.4],\n",
|
||
" [5.6, 2.9, 3.6, 1.3],\n",
|
||
" [6.7, 3.1, 4.4, 1.4],\n",
|
||
" [5.6, 3. , 4.5, 1.5],\n",
|
||
" [5.8, 2.7, 4.1, 1. ],\n",
|
||
" [6.2, 2.2, 4.5, 1.5],\n",
|
||
" [5.6, 2.5, 3.9, 1.1],\n",
|
||
" [5.9, 3.2, 4.8, 1.8],\n",
|
||
" [6.1, 2.8, 4. , 1.3],\n",
|
||
" [6.3, 2.5, 4.9, 1.5],\n",
|
||
" [6.1, 2.8, 4.7, 1.2],\n",
|
||
" [6.4, 2.9, 4.3, 1.3],\n",
|
||
" [6.6, 3. , 4.4, 1.4],\n",
|
||
" [6.8, 2.8, 4.8, 1.4],\n",
|
||
" [6.7, 3. , 5. , 1.7],\n",
|
||
" [6. , 2.9, 4.5, 1.5],\n",
|
||
" [5.7, 2.6, 3.5, 1. ],\n",
|
||
" [5.5, 2.4, 3.8, 1.1],\n",
|
||
" [5.5, 2.4, 3.7, 1. ],\n",
|
||
" [5.8, 2.7, 3.9, 1.2],\n",
|
||
" [6. , 2.7, 5.1, 1.6],\n",
|
||
" [5.4, 3. , 4.5, 1.5],\n",
|
||
" [6. , 3.4, 4.5, 1.6],\n",
|
||
" [6.7, 3.1, 4.7, 1.5],\n",
|
||
" [6.3, 2.3, 4.4, 1.3],\n",
|
||
" [5.6, 3. , 4.1, 1.3],\n",
|
||
" [5.5, 2.5, 4. , 1.3],\n",
|
||
" [5.5, 2.6, 4.4, 1.2],\n",
|
||
" [6.1, 3. , 4.6, 1.4],\n",
|
||
" [5.8, 2.6, 4. , 1.2],\n",
|
||
" [5. , 2.3, 3.3, 1. ],\n",
|
||
" [5.6, 2.7, 4.2, 1.3],\n",
|
||
" [5.7, 3. , 4.2, 1.2],\n",
|
||
" [5.7, 2.9, 4.2, 1.3],\n",
|
||
" [6.2, 2.9, 4.3, 1.3],\n",
|
||
" [5.1, 2.5, 3. , 1.1],\n",
|
||
" [5.7, 2.8, 4.1, 1.3],\n",
|
||
" [6.3, 3.3, 6. , 2.5],\n",
|
||
" [5.8, 2.7, 5.1, 1.9],\n",
|
||
" [7.1, 3. , 5.9, 2.1],\n",
|
||
" [6.3, 2.9, 5.6, 1.8],\n",
|
||
" [6.5, 3. , 5.8, 2.2],\n",
|
||
" [7.6, 3. , 6.6, 2.1],\n",
|
||
" [4.9, 2.5, 4.5, 1.7],\n",
|
||
" [7.3, 2.9, 6.3, 1.8],\n",
|
||
" [6.7, 2.5, 5.8, 1.8],\n",
|
||
" [7.2, 3.6, 6.1, 2.5],\n",
|
||
" [6.5, 3.2, 5.1, 2. ],\n",
|
||
" [6.4, 2.7, 5.3, 1.9],\n",
|
||
" [6.8, 3. , 5.5, 2.1],\n",
|
||
" [5.7, 2.5, 5. , 2. ],\n",
|
||
" [5.8, 2.8, 5.1, 2.4],\n",
|
||
" [6.4, 3.2, 5.3, 2.3],\n",
|
||
" [6.5, 3. , 5.5, 1.8],\n",
|
||
" [7.7, 3.8, 6.7, 2.2],\n",
|
||
" [7.7, 2.6, 6.9, 2.3],\n",
|
||
" [6. , 2.2, 5. , 1.5],\n",
|
||
" [6.9, 3.2, 5.7, 2.3],\n",
|
||
" [5.6, 2.8, 4.9, 2. ],\n",
|
||
" [7.7, 2.8, 6.7, 2. ],\n",
|
||
" [6.3, 2.7, 4.9, 1.8],\n",
|
||
" [6.7, 3.3, 5.7, 2.1],\n",
|
||
" [7.2, 3.2, 6. , 1.8],\n",
|
||
" [6.2, 2.8, 4.8, 1.8],\n",
|
||
" [6.1, 3. , 4.9, 1.8],\n",
|
||
" [6.4, 2.8, 5.6, 2.1],\n",
|
||
" [7.2, 3. , 5.8, 1.6],\n",
|
||
" [7.4, 2.8, 6.1, 1.9],\n",
|
||
" [7.9, 3.8, 6.4, 2. ],\n",
|
||
" [6.4, 2.8, 5.6, 2.2],\n",
|
||
" [6.3, 2.8, 5.1, 1.5],\n",
|
||
" [6.1, 2.6, 5.6, 1.4],\n",
|
||
" [7.7, 3. , 6.1, 2.3],\n",
|
||
" [6.3, 3.4, 5.6, 2.4],\n",
|
||
" [6.4, 3.1, 5.5, 1.8],\n",
|
||
" [6. , 3. , 4.8, 1.8],\n",
|
||
" [6.9, 3.1, 5.4, 2.1],\n",
|
||
" [6.7, 3.1, 5.6, 2.4],\n",
|
||
" [6.9, 3.1, 5.1, 2.3],\n",
|
||
" [5.8, 2.7, 5.1, 1.9],\n",
|
||
" [6.8, 3.2, 5.9, 2.3],\n",
|
||
" [6.7, 3.3, 5.7, 2.5],\n",
|
||
" [6.7, 3. , 5.2, 2.3],\n",
|
||
" [6.3, 2.5, 5. , 1.9],\n",
|
||
" [6.5, 3. , 5.2, 2. ],\n",
|
||
" [6.2, 3.4, 5.4, 2.3],\n",
|
||
" [5.9, 3. , 5.1, 1.8]])"
|
||
]
|
||
},
|
||
"execution_count": 3,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"X"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(150, 4)"
|
||
]
|
||
},
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"X.shape"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# normalisieren der Daten\n",
|
||
"X = StandardScaler().fit_transform(X)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 33,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# k-means clustering\n",
|
||
"kmeans = KMeans(n_clusters=2, random_state=42)\n",
|
||
"kmeans_labels = kmeans.fit_predict(X) # fit_predict führt fit und predict zusammen aus"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([1, 2, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
||
" 1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 2, 2, 1,\n",
|
||
" 1, 2, 1, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0,\n",
|
||
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
||
" 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
||
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
||
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)"
|
||
]
|
||
},
|
||
"execution_count": 30,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"kmeans_labels"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/vnd.microsoft.datawrangler.viewer.v0+json": {
|
||
"columns": [
|
||
{
|
||
"name": "index",
|
||
"rawType": "int64",
|
||
"type": "integer"
|
||
},
|
||
{
|
||
"name": "Sepal length",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "Sepal width",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "Petal length",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "Petal width",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "kmeans_labels",
|
||
"rawType": "int32",
|
||
"type": "integer"
|
||
},
|
||
{
|
||
"name": "true_labels",
|
||
"rawType": "int64",
|
||
"type": "integer"
|
||
}
|
||
],
|
||
"conversionMethod": "pd.DataFrame",
|
||
"ref": "e31bee49-0d4b-45e5-862b-0cef04b83a6d",
|
||
"rows": [
|
||
[
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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[
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||
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||
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||
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||
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||
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||
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||
[
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||
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||
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||
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||
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||
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||
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||
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||
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||
[
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||
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||
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||
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||
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||
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||
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||
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||
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||
[
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||
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||
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||
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||
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||
"-1.3154442950077407",
|
||
"1",
|
||
"0"
|
||
],
|
||
[
|
||
"12",
|
||
"-1.2641847816287635",
|
||
"-0.1319794793216258",
|
||
"-1.3402265266227635",
|
||
"-1.4470764792980415",
|
||
"1",
|
||
"0"
|
||
],
|
||
[
|
||
"13",
|
||
"-1.87002413384702",
|
||
"-0.1319794793216258",
|
||
"-1.5107388073635728",
|
||
"-1.4470764792980415",
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||
"1",
|
||
"0"
|
||
],
|
||
[
|
||
"14",
|
||
"-0.052506077192250644",
|
||
"2.169988183264839",
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||
"-1.4539013804499696",
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||
"-1.3154442950077407",
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||
"1",
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||
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|
||
],
|
||
[
|
||
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|
||
"-0.1736739476359015",
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||
"3.0907752482994253",
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||
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||
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||
"1",
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||
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||
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||
[
|
||
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||
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||
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||
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||
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||
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||
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||
],
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||
[
|
||
"17",
|
||
"-0.9006811702978099",
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||
"1.0190043519716065",
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||
"-1.3402265266227635",
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||
"-1.18381211071744",
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||
"1",
|
||
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|
||
],
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||
[
|
||
"18",
|
||
"-0.1736739476359015",
|
||
"1.7095946507475455",
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||
"-1.169714245881954",
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||
"-1.18381211071744",
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||
"1",
|
||
"0"
|
||
],
|
||
[
|
||
"19",
|
||
"-0.9006811702978099",
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||
"1.7095946507475455",
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||
"-1.2833890997091604",
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||
"-1.18381211071744",
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||
"1",
|
||
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||
],
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||
[
|
||
"20",
|
||
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||
"0.7888075857129598",
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||
"-1.169714245881954",
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||
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||
"1",
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||
"0"
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||
],
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||
[
|
||
"21",
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||
"-0.9006811702978099",
|
||
"1.4793978844888998",
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||
"-1.2833890997091604",
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||
"-1.052179926427139",
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||
"1",
|
||
"0"
|
||
],
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||
[
|
||
"22",
|
||
"-1.5065205225160663",
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||
"1.2492011182302531",
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||
"-1.567576234277176",
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||
"-1.3154442950077407",
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||
"1",
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||
"0"
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||
],
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||
[
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||
"23",
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||
"-0.9006811702978099",
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||
"0.5586108194543131",
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||
"-1.169714245881954",
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||
"-0.9205477421368382",
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||
"1",
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||
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||
],
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||
[
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||
"24",
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||
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||
"0.7888075857129598",
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||
"-1.056039392054748",
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||
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||
"1",
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||
"0"
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||
],
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||
[
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||
"25",
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||
"-1.0218490407414607",
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||
"-0.1319794793216258",
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||
"-1.2265516727955572",
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||
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||
"1",
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||
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||
],
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||
[
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||
"26",
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||
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||
"0.7888075857129598",
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||
"-1.2265516727955572",
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||
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||
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||
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||
],
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||
[
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||
"27",
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||
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||
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||
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||
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||
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||
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||
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||
[
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
[
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||
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||
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||
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||
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||
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||
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||
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||
],
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||
[
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||
"31",
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||
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||
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||
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||
"-1.052179926427139",
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||
"1",
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||
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||
],
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||
[
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||
"32",
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||
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||
"2.400184949523484",
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||
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||
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||
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||
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||
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||
[
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||
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||
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||
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||
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||
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||
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||
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||
],
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||
[
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||
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||
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||
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||
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||
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||
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||
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||
],
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||
[
|
||
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||
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||
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||
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||
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||
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||
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|
||
],
|
||
[
|
||
"36",
|
||
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|
||
"1.0190043519716065",
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||
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||
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||
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|
||
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|
||
],
|
||
[
|
||
"37",
|
||
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|
||
"1.2492011182302531",
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||
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||
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|
||
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||
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|
||
],
|
||
[
|
||
"38",
|
||
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||
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||
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||
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||
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|
||
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|
||
],
|
||
[
|
||
"39",
|
||
"-0.9006811702978099",
|
||
"0.7888075857129598",
|
||
"-1.2833890997091604",
|
||
"-1.3154442950077407",
|
||
"1",
|
||
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|
||
],
|
||
[
|
||
"40",
|
||
"-1.0218490407414607",
|
||
"1.0190043519716065",
|
||
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|
||
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|
||
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|
||
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|
||
],
|
||
[
|
||
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|
||
"-1.6276883929597172",
|
||
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||
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||
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|
||
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||
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||
],
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||
[
|
||
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|
||
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|
||
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||
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||
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||
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||
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||
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||
[
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||
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||
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||
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||
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||
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||
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||
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||
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||
[
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||
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||
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||
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||
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||
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||
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||
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||
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||
[
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||
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||
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||
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||
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||
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||
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||
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||
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||
[
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||
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||
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||
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||
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||
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||
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||
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||
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|
||
[
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||
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||
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||
"text/plain": [
|
||
" Sepal length Sepal width Petal length Petal width kmeans_labels \\\n",
|
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"0 -0.900681 1.019004 -1.340227 -1.315444 1 \n",
|
||
"1 -1.143017 -0.131979 -1.340227 -1.315444 1 \n",
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|
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||
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||
"1 0 \n",
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||
"2 0 \n",
|
||
"3 0 \n",
|
||
"4 0 \n",
|
||
".. ... \n",
|
||
"145 2 \n",
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||
"146 2 \n",
|
||
"147 2 \n",
|
||
"148 2 \n",
|
||
"149 2 \n",
|
||
"\n",
|
||
"[150 rows x 6 columns]"
|
||
]
|
||
},
|
||
"execution_count": 34,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Kombinieren der Clusterlabels mit den wahren Labels\n",
|
||
"df = pd.DataFrame(X, columns=['Sepal length', 'Sepal width', 'Petal length', 'Petal width'])\n",
|
||
"df['kmeans_labels'] = kmeans_labels\n",
|
||
"df['true_labels'] = y_true\n",
|
||
"df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Funktioniert das gut oder schlecht (und wieso)?"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 35,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
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||
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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Visualisierung der Ergebnisse\n",
|
||
"plt.figure(figsize=(10, 5))\n",
|
||
"plt.scatter(X[:, 0], X[:, 1], c=kmeans_labels) # WICHTIG: wir plotten nur die ersten beiden Dimensionen! -> Nächstes mal lernen wir, wie wir mit Downprojection eine besser Visualisierung erhalten\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# vergleichen wir das nun mit den echten Labels\n",
|
||
"plt.figure(figsize=(10, 5))\n",
|
||
"plt.scatter(X[:, 0], X[:, 1], c=y_true)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"**Wichtig** Den 2. Plot haben wir normalerweise *NICHT* zur Verfügung. Im Unsupervised Learning ist kein Label vorhanden!"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Was passiert, wenn wir die Anzahl der Cluster ändern?\n",
|
||
"Wie gut funktioniert das ganze bei dem *Wine* Dataset?"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Affinity Propagation"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Wir sehen uns nun den *Affinity-Propagation* Clustering Algorithmus an. Im Gegensatz zu $k$-Means ist dieser ein Clustering-Algorithmus, der **keine vorherige Festlegung der Clusteranzahl** benötigt. Stattdessen basiert er auf einem Message-Passing-Verfahren zur Identifikation der optimalen Clusterzentren."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Der Affinity Propagation Algorithmus $\\ldots$\n",
|
||
"* $\\ldots$ wird zur Clusterbildung verwendet\n",
|
||
"* $\\ldots$ nutzt ein Ähnlichkeitsmaß zwischen Datenpunkten, anstatt einer Distanzmetrik\n",
|
||
"* $\\ldots$ **wählt Datenpunkte** als Cluster Centers"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"\n",
|
||
"\n",
|
||
"(von https://python.plainenglish.io/the-mechanics-of-affinity-propagation-clustering-eb199cc7a7c2?gi=4a1093a86978)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Beschreibung des Algorithmus"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"1. **Punkte als mögliche Clusterzentren betrachten:** \n",
|
||
" Jeder Datenpunkt wird zunächst als potenzielles Clusterzentrum angesehen.\n",
|
||
" \n",
|
||
"2. **Austausch von \"Botschaften\" zwischen den Punkten:** \n",
|
||
" Die Punkte kommunizieren miteinander, um herauszufinden, welche Punkte gute Clusterzentren (sogenannte **Exemplare**) sein könnten. Dabei werden zwei Arten von Informationen ausgetauscht:\n",
|
||
" - **\"Verantwortung\"**: Ein Punkt teilt einem anderen Punkt mit, wie gut er sich als dessen Clusterzentrum eignet.\n",
|
||
" - **\"Verfügbarkeit\"**: Ein Punkt teilt mit, wie sehr er bereit ist, ein Clusterzentrum zu sein.\n",
|
||
"\n",
|
||
"3. **Iterativer Prozess:** \n",
|
||
" Diese Kommunikation wird in mehreren Runden durchgeführt, bis sich die Clusterzentren stabilisiert haben.\n",
|
||
"\n",
|
||
"4. **Zuordnung der Punkte:** \n",
|
||
" Am Ende ordnet der Algorithmus alle Punkte den endgültigen Clusterzentren zu."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Wir geben nun auch noch ein **Beispiel** an, um sich das besser vorstellen zu können."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"**Ein Beispiel zur Veranschaulichung**\n",
|
||
"\n",
|
||
"Stellen wir uns vor, du arbeitest in einem Unternehmen und möchtest Teams basierend auf den Fähigkeiten und Interessen der Mitarbeiter bilden.\n",
|
||
"\n",
|
||
"1. **Jeder Mitarbeiter ist ein potenzielles Team-Leader-Kandidat.** \n",
|
||
" Anfangs wird niemand als Teamleiter festgelegt – jeder könnte einer sein.\n",
|
||
"\n",
|
||
"2. **Mitarbeiter tauschen Informationen aus.** \n",
|
||
" Sie bewerten einander: \n",
|
||
" - *Verantwortung:* \"Passt Person X als Teamleiter zu mir?\" \n",
|
||
" - *Verfügbarkeit:* \"Bin ich überhaupt gut als Teamleiter geeignet?\" \n",
|
||
"\n",
|
||
"3. **Nach mehreren Feedback-Runden stehen die besten Teamleiter fest.** \n",
|
||
" Mitarbeiter, die am meisten Unterstützung als Teamleiter erhalten, werden zu Clusterzentren.\n",
|
||
"\n",
|
||
"4. **Alle anderen schließen sich dem nächstgelegenen Teamleiter an.** \n",
|
||
" Jetzt sind klare Teams entstanden, ohne dass vorher festgelegt wurde, wie viele es sein müssen."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"**Hinweis:** Die obige Beschreibung ist eher oberflächlich, jedoch würde die genaue Beschreibung dieses Algorithmus hier etwas über das Ziel hinaus schießen. "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Vorteile und Nachteile"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"**Vorteile**:\n",
|
||
"* Kein Wert $k$ als Anzahl der Clusterzentren notwendig\n",
|
||
"* Schnelle konvergenz\n",
|
||
"\n",
|
||
"**Nachteile:**\n",
|
||
"* Kein Wert $k$ als Anzahl der Clusterzentren notwendig\n",
|
||
"* Sehr teuer (rechenintensiv)\n",
|
||
"* Schwer zu interpretieren, was genau passiert"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Tipps und Tricks\n",
|
||
"\n",
|
||
"* Es können Präferenzwerte für gewisse Punkte vergeben werden, falls diese stärker/schwächer als potenzielle Cluster-Zentren herangezogen werden sollen\n",
|
||
"* Da auch dieser Algorithmus iterativ funktioniert, kann es bei schlechter Performance helfen, Parameter, die das Algorithmus- bzw. das Konvergenzverhalten beinflussen, zu ändern"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Affinity Propagation in Python"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"from sklearn.datasets import load_iris, load_wine\n",
|
||
"from sklearn.cluster import AffinityPropagation\n",
|
||
"from sklearn.preprocessing import StandardScaler\n",
|
||
"import pandas as pd\n",
|
||
"import numpy as np"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"X, y_true = load_iris(return_X_y=True, as_frame=False)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
||
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
||
" 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
||
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
||
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
|
||
" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
|
||
" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
|
||
]
|
||
},
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"y_true"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([[5.1, 3.5, 1.4, 0.2],\n",
|
||
" [4.9, 3. , 1.4, 0.2],\n",
|
||
" [4.7, 3.2, 1.3, 0.2],\n",
|
||
" [4.6, 3.1, 1.5, 0.2],\n",
|
||
" [5. , 3.6, 1.4, 0.2],\n",
|
||
" [5.4, 3.9, 1.7, 0.4],\n",
|
||
" [4.6, 3.4, 1.4, 0.3],\n",
|
||
" [5. , 3.4, 1.5, 0.2],\n",
|
||
" [4.4, 2.9, 1.4, 0.2],\n",
|
||
" [4.9, 3.1, 1.5, 0.1],\n",
|
||
" [5.4, 3.7, 1.5, 0.2],\n",
|
||
" [4.8, 3.4, 1.6, 0.2],\n",
|
||
" [4.8, 3. , 1.4, 0.1],\n",
|
||
" [4.3, 3. , 1.1, 0.1],\n",
|
||
" [5.8, 4. , 1.2, 0.2],\n",
|
||
" [5.7, 4.4, 1.5, 0.4],\n",
|
||
" [5.4, 3.9, 1.3, 0.4],\n",
|
||
" [5.1, 3.5, 1.4, 0.3],\n",
|
||
" [5.7, 3.8, 1.7, 0.3],\n",
|
||
" [5.1, 3.8, 1.5, 0.3],\n",
|
||
" [5.4, 3.4, 1.7, 0.2],\n",
|
||
" [5.1, 3.7, 1.5, 0.4],\n",
|
||
" [4.6, 3.6, 1. , 0.2],\n",
|
||
" [5.1, 3.3, 1.7, 0.5],\n",
|
||
" [4.8, 3.4, 1.9, 0.2],\n",
|
||
" [5. , 3. , 1.6, 0.2],\n",
|
||
" [5. , 3.4, 1.6, 0.4],\n",
|
||
" [5.2, 3.5, 1.5, 0.2],\n",
|
||
" [5.2, 3.4, 1.4, 0.2],\n",
|
||
" [4.7, 3.2, 1.6, 0.2],\n",
|
||
" [4.8, 3.1, 1.6, 0.2],\n",
|
||
" [5.4, 3.4, 1.5, 0.4],\n",
|
||
" [5.2, 4.1, 1.5, 0.1],\n",
|
||
" [5.5, 4.2, 1.4, 0.2],\n",
|
||
" [4.9, 3.1, 1.5, 0.2],\n",
|
||
" [5. , 3.2, 1.2, 0.2],\n",
|
||
" [5.5, 3.5, 1.3, 0.2],\n",
|
||
" [4.9, 3.6, 1.4, 0.1],\n",
|
||
" [4.4, 3. , 1.3, 0.2],\n",
|
||
" [5.1, 3.4, 1.5, 0.2],\n",
|
||
" [5. , 3.5, 1.3, 0.3],\n",
|
||
" [4.5, 2.3, 1.3, 0.3],\n",
|
||
" [4.4, 3.2, 1.3, 0.2],\n",
|
||
" [5. , 3.5, 1.6, 0.6],\n",
|
||
" [5.1, 3.8, 1.9, 0.4],\n",
|
||
" [4.8, 3. , 1.4, 0.3],\n",
|
||
" [5.1, 3.8, 1.6, 0.2],\n",
|
||
" [4.6, 3.2, 1.4, 0.2],\n",
|
||
" [5.3, 3.7, 1.5, 0.2],\n",
|
||
" [5. , 3.3, 1.4, 0.2],\n",
|
||
" [7. , 3.2, 4.7, 1.4],\n",
|
||
" [6.4, 3.2, 4.5, 1.5],\n",
|
||
" [6.9, 3.1, 4.9, 1.5],\n",
|
||
" [5.5, 2.3, 4. , 1.3],\n",
|
||
" [6.5, 2.8, 4.6, 1.5],\n",
|
||
" [5.7, 2.8, 4.5, 1.3],\n",
|
||
" [6.3, 3.3, 4.7, 1.6],\n",
|
||
" [4.9, 2.4, 3.3, 1. ],\n",
|
||
" [6.6, 2.9, 4.6, 1.3],\n",
|
||
" [5.2, 2.7, 3.9, 1.4],\n",
|
||
" [5. , 2. , 3.5, 1. ],\n",
|
||
" [5.9, 3. , 4.2, 1.5],\n",
|
||
" [6. , 2.2, 4. , 1. ],\n",
|
||
" [6.1, 2.9, 4.7, 1.4],\n",
|
||
" [5.6, 2.9, 3.6, 1.3],\n",
|
||
" [6.7, 3.1, 4.4, 1.4],\n",
|
||
" [5.6, 3. , 4.5, 1.5],\n",
|
||
" [5.8, 2.7, 4.1, 1. ],\n",
|
||
" [6.2, 2.2, 4.5, 1.5],\n",
|
||
" [5.6, 2.5, 3.9, 1.1],\n",
|
||
" [5.9, 3.2, 4.8, 1.8],\n",
|
||
" [6.1, 2.8, 4. , 1.3],\n",
|
||
" [6.3, 2.5, 4.9, 1.5],\n",
|
||
" [6.1, 2.8, 4.7, 1.2],\n",
|
||
" [6.4, 2.9, 4.3, 1.3],\n",
|
||
" [6.6, 3. , 4.4, 1.4],\n",
|
||
" [6.8, 2.8, 4.8, 1.4],\n",
|
||
" [6.7, 3. , 5. , 1.7],\n",
|
||
" [6. , 2.9, 4.5, 1.5],\n",
|
||
" [5.7, 2.6, 3.5, 1. ],\n",
|
||
" [5.5, 2.4, 3.8, 1.1],\n",
|
||
" [5.5, 2.4, 3.7, 1. ],\n",
|
||
" [5.8, 2.7, 3.9, 1.2],\n",
|
||
" [6. , 2.7, 5.1, 1.6],\n",
|
||
" [5.4, 3. , 4.5, 1.5],\n",
|
||
" [6. , 3.4, 4.5, 1.6],\n",
|
||
" [6.7, 3.1, 4.7, 1.5],\n",
|
||
" [6.3, 2.3, 4.4, 1.3],\n",
|
||
" [5.6, 3. , 4.1, 1.3],\n",
|
||
" [5.5, 2.5, 4. , 1.3],\n",
|
||
" [5.5, 2.6, 4.4, 1.2],\n",
|
||
" [6.1, 3. , 4.6, 1.4],\n",
|
||
" [5.8, 2.6, 4. , 1.2],\n",
|
||
" [5. , 2.3, 3.3, 1. ],\n",
|
||
" [5.6, 2.7, 4.2, 1.3],\n",
|
||
" [5.7, 3. , 4.2, 1.2],\n",
|
||
" [5.7, 2.9, 4.2, 1.3],\n",
|
||
" [6.2, 2.9, 4.3, 1.3],\n",
|
||
" [5.1, 2.5, 3. , 1.1],\n",
|
||
" [5.7, 2.8, 4.1, 1.3],\n",
|
||
" [6.3, 3.3, 6. , 2.5],\n",
|
||
" [5.8, 2.7, 5.1, 1.9],\n",
|
||
" [7.1, 3. , 5.9, 2.1],\n",
|
||
" [6.3, 2.9, 5.6, 1.8],\n",
|
||
" [6.5, 3. , 5.8, 2.2],\n",
|
||
" [7.6, 3. , 6.6, 2.1],\n",
|
||
" [4.9, 2.5, 4.5, 1.7],\n",
|
||
" [7.3, 2.9, 6.3, 1.8],\n",
|
||
" [6.7, 2.5, 5.8, 1.8],\n",
|
||
" [7.2, 3.6, 6.1, 2.5],\n",
|
||
" [6.5, 3.2, 5.1, 2. ],\n",
|
||
" [6.4, 2.7, 5.3, 1.9],\n",
|
||
" [6.8, 3. , 5.5, 2.1],\n",
|
||
" [5.7, 2.5, 5. , 2. ],\n",
|
||
" [5.8, 2.8, 5.1, 2.4],\n",
|
||
" [6.4, 3.2, 5.3, 2.3],\n",
|
||
" [6.5, 3. , 5.5, 1.8],\n",
|
||
" [7.7, 3.8, 6.7, 2.2],\n",
|
||
" [7.7, 2.6, 6.9, 2.3],\n",
|
||
" [6. , 2.2, 5. , 1.5],\n",
|
||
" [6.9, 3.2, 5.7, 2.3],\n",
|
||
" [5.6, 2.8, 4.9, 2. ],\n",
|
||
" [7.7, 2.8, 6.7, 2. ],\n",
|
||
" [6.3, 2.7, 4.9, 1.8],\n",
|
||
" [6.7, 3.3, 5.7, 2.1],\n",
|
||
" [7.2, 3.2, 6. , 1.8],\n",
|
||
" [6.2, 2.8, 4.8, 1.8],\n",
|
||
" [6.1, 3. , 4.9, 1.8],\n",
|
||
" [6.4, 2.8, 5.6, 2.1],\n",
|
||
" [7.2, 3. , 5.8, 1.6],\n",
|
||
" [7.4, 2.8, 6.1, 1.9],\n",
|
||
" [7.9, 3.8, 6.4, 2. ],\n",
|
||
" [6.4, 2.8, 5.6, 2.2],\n",
|
||
" [6.3, 2.8, 5.1, 1.5],\n",
|
||
" [6.1, 2.6, 5.6, 1.4],\n",
|
||
" [7.7, 3. , 6.1, 2.3],\n",
|
||
" [6.3, 3.4, 5.6, 2.4],\n",
|
||
" [6.4, 3.1, 5.5, 1.8],\n",
|
||
" [6. , 3. , 4.8, 1.8],\n",
|
||
" [6.9, 3.1, 5.4, 2.1],\n",
|
||
" [6.7, 3.1, 5.6, 2.4],\n",
|
||
" [6.9, 3.1, 5.1, 2.3],\n",
|
||
" [5.8, 2.7, 5.1, 1.9],\n",
|
||
" [6.8, 3.2, 5.9, 2.3],\n",
|
||
" [6.7, 3.3, 5.7, 2.5],\n",
|
||
" [6.7, 3. , 5.2, 2.3],\n",
|
||
" [6.3, 2.5, 5. , 1.9],\n",
|
||
" [6.5, 3. , 5.2, 2. ],\n",
|
||
" [6.2, 3.4, 5.4, 2.3],\n",
|
||
" [5.9, 3. , 5.1, 1.8]])"
|
||
]
|
||
},
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"X"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(150, 4)"
|
||
]
|
||
},
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"X.shape"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Normalisieren der Daten\n",
|
||
"X = StandardScaler().fit_transform(X)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# affinity propagation clustering\n",
|
||
"affinity_prop = AffinityPropagation(random_state=42)\n",
|
||
"affinity_prop_labels = affinity_prop.fit_predict(X) # fit_predict führt fit und predict zusammen aus"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
|
||
" 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1,\n",
|
||
" 1, 0, 1, 0, 1, 0, 3, 3, 3, 2, 7, 4, 3, 2, 3, 4, 2, 4, 2, 7, 4, 3,\n",
|
||
" 4, 4, 2, 2, 7, 4, 7, 4, 3, 3, 3, 3, 7, 2, 2, 2, 4, 7, 4, 3, 3, 2,\n",
|
||
" 4, 2, 4, 7, 4, 2, 4, 4, 4, 4, 2, 4, 8, 7, 5, 7, 8, 5, 2, 5, 7, 6,\n",
|
||
" 8, 7, 8, 7, 7, 8, 3, 6, 5, 2, 8, 7, 5, 7, 8, 5, 7, 7, 7, 5, 5, 6,\n",
|
||
" 7, 7, 7, 5, 8, 3, 7, 8, 8, 8, 7, 8, 8, 8, 7, 8, 8, 7])"
|
||
]
|
||
},
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"affinity_prop_labels"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"9"
|
||
]
|
||
},
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Anzahl der Cluster in Affinity Propagation\n",
|
||
"n_clusters = len(np.unique(affinity_prop_labels))\n",
|
||
"n_clusters"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/vnd.microsoft.datawrangler.viewer.v0+json": {
|
||
"columns": [
|
||
{
|
||
"name": "index",
|
||
"rawType": "int64",
|
||
"type": "integer"
|
||
},
|
||
{
|
||
"name": "Sepal length",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "Sepal width",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "Petal length",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "Petal width",
|
||
"rawType": "float64",
|
||
"type": "float"
|
||
},
|
||
{
|
||
"name": "affinity_prop_labels",
|
||
"rawType": "int64",
|
||
"type": "integer"
|
||
},
|
||
{
|
||
"name": "true_labels",
|
||
"rawType": "int64",
|
||
"type": "integer"
|
||
}
|
||
],
|
||
"conversionMethod": "pd.DataFrame",
|
||
"ref": "92265ec4-3eff-4106-a1a7-a4590b2e6e30",
|
||
"rows": [
|
||
[
|
||
"0",
|
||
"-0.9006811702978099",
|
||
"1.0190043519716065",
|
||
"-1.3402265266227635",
|
||
"-1.3154442950077407",
|
||
"1",
|
||
"0"
|
||
],
|
||
[
|
||
"1",
|
||
"-1.1430169111851116",
|
||
"-0.1319794793216258",
|
||
"-1.3402265266227635",
|
||
"-1.3154442950077407",
|
||
"0",
|
||
"0"
|
||
],
|
||
[
|
||
"2",
|
||
"-1.3853526520724144",
|
||
"0.3284140531956675",
|
||
"-1.3970639535363667",
|
||
"-1.3154442950077407",
|
||
"0",
|
||
"0"
|
||
],
|
||
[
|
||
"3",
|
||
"-1.5065205225160663",
|
||
"0.09821728693702086",
|
||
"-1.2833890997091604",
|
||
"-1.3154442950077407",
|
||
"0",
|
||
"0"
|
||
],
|
||
[
|
||
"4",
|
||
"-1.0218490407414607",
|
||
"1.2492011182302531",
|
||
"-1.3402265266227635",
|
||
"-1.3154442950077407",
|
||
"1",
|
||
"0"
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|
||
"1 -1.143017 -0.131979 -1.340227 -1.315444 \n",
|
||
"2 -1.385353 0.328414 -1.397064 -1.315444 \n",
|
||
"3 -1.506521 0.098217 -1.283389 -1.315444 \n",
|
||
"4 -1.021849 1.249201 -1.340227 -1.315444 \n",
|
||
".. ... ... ... ... \n",
|
||
"145 1.038005 -0.131979 0.819596 1.448832 \n",
|
||
"146 0.553333 -1.282963 0.705921 0.922303 \n",
|
||
"147 0.795669 -0.131979 0.819596 1.053935 \n",
|
||
"148 0.432165 0.788808 0.933271 1.448832 \n",
|
||
"149 0.068662 -0.131979 0.762758 0.790671 \n",
|
||
"\n",
|
||
" affinity_prop_labels true_labels \n",
|
||
"0 1 0 \n",
|
||
"1 0 0 \n",
|
||
"2 0 0 \n",
|
||
"3 0 0 \n",
|
||
"4 1 0 \n",
|
||
".. ... ... \n",
|
||
"145 8 2 \n",
|
||
"146 7 2 \n",
|
||
"147 8 2 \n",
|
||
"148 8 2 \n",
|
||
"149 7 2 \n",
|
||
"\n",
|
||
"[150 rows x 6 columns]"
|
||
]
|
||
},
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Kombinieren der Clusterlabels mit den wahren Labels\n",
|
||
"df = pd.DataFrame(X, columns=['Sepal length', 'Sepal width', 'Petal length', 'Petal width'])\n",
|
||
"df['affinity_prop_labels'] = affinity_prop_labels\n",
|
||
"df['true_labels'] = y_true\n",
|
||
"df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Wie bewerten wir das?"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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AERKqNtrGYuFLtjXJ6JxkrF1moo+TEBFRoBBCICUsBQKeJ51RoJh2cX96RDsoXkoEBQrSw9v7MVHz8IiJKEBMPGdgo23GTO7t+yDUYvoN6YR2GfFQlIY/yBRVYOCILkhNj/dzMiIiMtPk5Elet+vQcUbyeD+lqW9S8kSvp9Lp0DEpeaIfEzUPixyiAPHHB85DRJTV4/aw8BD8ed7FfkxEp0sIgbsfuQiqRYWi1n+7VVUFkZFhuPXvM0xKR0REZpmYfIZ7PZyTRnNq/31R+gVIDUsxIxr62/phbOIYj9vHJY5FX1sfPyZqHq6TQxRAHA4H7rjqf9i7I6fe7Z26peDp929EaGioScnodOzbmYO3n/8RK5fsgJQSFouCM6b1x+w/TOIoDhFRG+XQnfgmez4W5/+ECpd7SuaM8HTMaHcuRiQMNzWbLnX8kPcjFuZ+j0JHIQAgMTQB09Km4szkiaYtCNqU2oBFDlEAcjgc2LBqP6QmMWh0ZxY3QaKyvAblZdWIjYtEWASfUyIicq+RU+IogUWxIMYSE1ALROtSR4mzFAAQG2Izrbip1ZTawOKnTETUBKGhoRg+rofZMaiFRUaHITI6zOwYREQUQFShIsGaYHaMBilCQXxonNkxmoXX5BARERERUVBhkUNEREREREGFRQ4REREREQUVXpNDFGDWLt+Fz97+FZvW7IeERN/BHXHB7DEYcUZPs6Nh5+Yj+PTN5Vi5dAdcTg2de6Th/CtHYdL0AVCUpn1n4nS68O0na/HVByuRfbAQoWEhOGNqP1xw9Rh07JLso0dAREREbQFnVyMKIB+8shRvPL0Iiiqga+6XpqII6LrElb+fiNl/ONO0bD8t2IjH//oJFCGgae5FwoQiIHWJSecOwN2PXGi40HE4XLjvpjexae1+AEDtu5CqKlBUBQ8/fzUGDO/sk8dBRERErVNTagOerkYUIHZuPoI3nl4EAHUFDgDouvv3d1/8CVvWHzAjGoqOluPJv38Kqcu6AgcA5LFsP87fiB+++s1wfx+/tgyb1h2AlMcLHADQNB0ul4Z/3vke7DXOFstPREREbQuLHKIA8fUHK6Gqnl+Sqqrgq/dX+THRcd99tq6u2GqIUAS+fG+lob40l4av3l9VVyCdTOoSFWU1+HnRlmZlJSIiImKRQxQgdmw+Um+U5GSapmPHpkN+THTc7m1Z8HZmq9Ql9u3Mga57zl+r6Gg5SosrvbZRLQp2b81uck4iIiIigEUOUcCwhKiNtgkJMWeuEEuICqWRFZgVVTG0SrPFyGOQxv4eRERERA1hkUMUIEZN7AVF8VwkKKrAqEm9/JjouGFju3s9XU1RFQwf18NQkRMbH4nM7qle22qajuHjuzcrKxERERGLHKIAcc5FwxASaoFooNARQkBVVUy/dIQJyYDxU/shISkaitpwYSJ1HRdeM8ZQX0IIXHb9GR5Pf1NVBV16paH/0Mxm5yUiIqK2jUUOUYBISrXhn89fjbCwEAgB4Fg9IYRAqNWCB5+9Cqnt40zJZg0Lwbz/XYu4+Ki6TIB7emtFEbjzoQvQd3Anw/2dMa0f5tw62d3HsckWakex2nVIwEPPzDY0KkRERETUEK6TQxRgysuq8cOXv2HT2n2QEug3pBPOmjkYMbERZkdDTbUDy77bjNXLdsHhcKJbr/aYduFQJKXamtXfkQNH8e2na3FoXz4iIq0YO7kPRk3sxetxiIiI6BRNqQ1Y5BARERERUcDjYqBERERERNRmscghIiIiIqKgwiKHiIiIiIiCijkrC1LAKiwox28r9sDpcKFLr3bo3qe92ZF8Zu+OHOzacgSWEBWDRnZFYkrzr/uy1zixZvkulBZXIjHFhiGjuvLieSIiCmi5NXnYVb4LANA9ujtSw1JMTkTUcljkEAD3Qfpz//oaP3z1W71FH7v0SsM9j16CjMwkE9O1rOzDhXj0Lx9h15asutuEIjDpnP649e8zERYR2qT+vvlwFV777/eoqrDX3RYbH4mb/zoD46b0bbHcRERELaHMWY6X972CzaVb6t3ez9YXv+t8PWJCok1KRtRyOLsaQUqJB255G2t+2Q150qr2iqogMsqK5z++GUmpseYEbEFFR8tx88XPobSkCrqm19umKAL9h2XiXy/NgaIYO5Pzmw9X4dlHvm54owAeeOpKjJrY63RjExERtQi7Zsc/tv4TuTV50HHS5yAUpIal4B997oNVtZqUkMgzzq5GTbJ1/UGs/nnXKQUOAOiajsoKOz5761cTkrW8L95dgdLiylMKHADQdYkNq/bht5V7DfVlr3Hitf9+77XN/578FgH8PQIREbUxvxauQHZNzikFDgDo0JFdk4NfC1eakIyoZbHIISz+ZgNU1fOuoGs6vv9yvR8T+c73X6yrdzreyRRVweJvNhjqa83yXfVOUTuFBLIPF2HX1izPbYiIiPzo54JfICA8bhcQ+LlguR8TEfkGixxCSVEltAZGNk5UWV4DXffepjUoL632ul3XdBQfrTDUV2lxpaF2JUXG2hEREflaqbMMEp6/7JOQKHWW+TERkW+wyCEkpdq8juQA7gvpjV6nEsjiE71fTKmqCpLTYg31lZhiM9QuKdVYOyIiIl9LsMY3OpKTaE3wYyIi32j9R6102s6aOdjrSI6iCJx90TA/JvKdcy4aBkXx/OauaTqmzhpiqK8ho7oiNj7S43ahCHTukYrMbpySk4iIAsOEpDMaHcmZkDTej4mIfINFDqFb73YeD+wVVUFSqg2zrhrt51S+cd7lI5GaHg+loZErAUw4pz96Dcgw1JclRMXNf50BCPf/W68rRUBRBP5wz3QI4bmoIiIi8qfh8UPRPapbg6M5AgLdo7phWPxQE5IRtSwWOQQAuO3+mbjqpkmIiDo+ZaRQBEZO6Il/v30jYmIjTEzXciKjw/Dvt36HsWf2rjeiExYRisuuPwN/evjCJhUl46b0xQNPXYl26fH1bs/sloLH/ncd+g7p1FLRiYiITptFseDuHndgQtJ4WMTxRastwoIJSeNxd487YFG4jCK1flwnh+qx1zixfeMhOB0aMrunIjEleP/uhQXl2LcjB5YQFb36ZzR5EdATSSmxa2sWSooqkZRqQ2a3FI7gEBFRQKtwVWB/5QEAQOfITERaPJ+CTRQImlIbsMghIiIiIqKAx8VAiYiIiIiozWKRQ0REREREQYVFDhERERERBRVOn0F0mirKqvG//1uI5Yu2wG53ISIyFJNnDMacW89EaFjTJzPYsHovXvn3dzi4Jw8AkJGZhOtun4qhY7o1uS9N07FyyQ4s/HQtco4UITYhCpNnDMTEcwbAGhbS5P4CWWV5DRZ9uR5Lvt2Eygo7OnZJxjmXDMOgEV04CQQREVEbw4kHiE5D1sGj+P2Fz8LpcJ2yLSomDK99c2eTpt9++Ylv8dnbvzS4bcalI3Dz32YY7svhcOGh29/F2uW7oSgCui4hhICUEh26JOPxV65DbEKU4f4CWfahQvzp2ldQdLQcte9oiqpA13RMmTUYtz9wPhSFA9dEREStWcBMPLBs2TLMmDED7dq1gxACX3zxhS/vjsjv7przvwYLHACoKKvBX+a+ariv9St2eyxwAODrD1fh1x+3Ge7vzacXYd0vewAAuu4+8q/9TuPIgaN47N6PDfcVyHRdx/23vI3iokqc+JWNrukAgO8/X4+v3l9lUjoiIiIyg0+LnMrKSgwYMADPPfecL++GyBQbVu9FSWGl1zb7d+chL6vYUH+v/uf7Rtu88fQPhvqqrrLjm49Ww9NAra7p+G3lXhzcm2+ov0C2fsVeHDlwtK6oacinby6HrnveTkRERMHFp0XO2WefjYcffhizZs3y5d0QmWLx1xsMtfvp242G2hkpOLIOHjXU155t2bDXOL22EQLYtGa/of4C2aY1+6BavL+VFeSWIj+n1E+JiIiIyGwBNfGA3W6H3W6v+3dZWZmJaYi8M3o5W0te9Wa0K+PtAvaSPMMM/30D9/JDIiIiamEBdSXuvHnzYLPZ6n4yMjLMjkTk0fip/Yy1m9LXULv2HRMabZOWHmeory490hAS6v07DCmBvoM6GuovkPUd3BGay/upaPGJ0UhKi/VPICIiIjJdQBU59957L0pLS+t+Dh8+bHYkIo+Gj+uBqJhwr23ad0xA+46Jhvqbc9tZjbaZffOZhvqKjA7DtAuGQFEanjpZURX0GdQRnXukGeovkA0d2x0p7WKhqA0/ViGAWVeNhqoG1NsdERER+VBAfepbrVbExMTU+yEKZI+/dp3H60HCIkLx+KtzDfc1akIvTLtgiMftk84dgAnT+hvub+4dU9F7YAcAgKgtdoT7oD851YZ7HrvEcF+BTFUVPPjMbERFh9cr6mp/H3dWX1xwzRiz4hEREZEJ/LZOjhACn3/+Oc4//3zD/w/XyaHWoDC/DC88+g1W/7wLTqcGa1gIxk/pixv/fA4io8Ka3N+y77fgzWcWIedIESCBlPZxmP2HMzHp3AFN7svpdGHpt5ux4JM1yM0qRmxcJM6aORhTZg1uVrZAVlJYgQWfrsGSBe7FQDt0TsK5lwzH6Em9uEYOERFREGhKbeDTIqeiogJ79rjX6Rg0aBD+/e9/Y+LEiYiPj0eHDh0a/f9Z5BAREREREdC02sCns6utXbsWEydOrPv3nXfeCQC45ppr8MYbb/jyromIiIiIqI3yaZEzYcIEw9PsEhERERERtQSeqE5EREREREEloBYDJfNpLg17NhyA0+5Eh17tERMfbXakOrquY/lnq1CSX4reo3ug68BMsyPVs3rheuTsyUNm/w7oP77PafVlr3Fi385cSEh07paKsIjQ0+ovL7sYBTmliImLQEZmEoRoeLplM+zfnYct6/YjOjYCYyf3hsXCt6XWzqlp2JlVAKemoXNKAmwRgTPJhZQ64NoJyEpA7QihJp1ef3oZ4NoNiBDA0gtChLRQUiIiOh08miAAgJQSnz01Hx8+/gWK80oBAJYQFROvGIvfP3kNYhLMLXaeueUVzH/5B2gure62uBQb/v7hHaddUJyuDx//Am89+DEc1Y662yJsEbj1mesw+aozmtSX0+nC28//iG8+WIWqSjsA91TU5148HNfcciZCrU07gNq3MwcvPbEAG1fvr7sts3sq5t4+BUPHdm9SXy1t28ZDePjO91FUUF53m6IqmDJzEG7/xywTk1FzSSnx1tL1eO2nNSiqqAYAhKgKpg/phbtmjDe92JHVX0FW/BfQatdgUyCtkyCi/wphSW9aX3opZNljQM2XAJzHuosHIq8HIq6DEDxRgojITH6bQro5OLua/7xwxxv47L/zT7ldURW075aGZ1Y8gkhbpAnJgH9e+m8s+3hFg9uEAP6z7J/oM6ann1O5vfWPD/H2Q5943P6n1/+AKddM9Lj9RJqm48Hb3sGa5btPuZZNKAIDhnXGIy9cDdWiGupv744c3Hn1y3A6XND14/25R3Ek/v7vKzDmzN6G+mppu7Zm4Y9XvOjxmr0RE3riwaev8nMqOl3zPv8J7y3fcMrtqiLQMTEO7/7xMkSFWf0fDICsfAuy/OEGtqiAYoNI+AxCbWesL70CsugywLUXgHZqg/AroNj+cTpxiYioAU2pDfhVE2H/lkMNFjgAoGs6snbn4LP/LvBzKrec/XkeCxwAkBJ49Oqn/ZjoOIfDiXce/tRrm2duedVwfyuX7MDqn3c1eOAvdYkNq/bi50VbDff3wqPfnFLgAO5v2yWApx/6Ei5nAwdofvDYPR95nZRk1ZIdOLgnz4+J6HTtzC5osMABAE2XOFBQ7HG7r0m9GLL8UQ9bNUAvhSz/r/EOq94FXHvQYIEDANXvQTqNv1aJiKjlscghLHz1R6gWz7uCrumY//IiPyY67pV73m20Te7+AhQcKfRDmvo+fvIrSN37QGhNpR0rvl5jqL9vP1kDRfF8rYyiCCz42Fhf2YcLsWX9wVMKnDoSKC2uxJrluwz115LKSqqQdbDx5+u1p83Z56h5Plu1BaqX/VeXEh/9usmPiU5Q/TU8FiSAe1vNN5B6paHuZNX7AHQvLVTI6o+bEJCIiFoaixxC7oF8aC5vH9hAYXYxdN17G1/I3Z9vqN3+LQd9nORUh7YdMdRu97p9htplHy7yXJQA0HWJ7MPGirm8rJJG2wghkJdVbKi/lnR4f4GhdvkmZKPmyyoqg9ZI0Z9XWmHKsgJSOwKgsdM8nYB+1FiHem4jDTTAdbiRNkRE5Esscggx8VFeR3IAIDw6DIri/90lOj7KULuUjsk+TnKq2ORYQ+0S0xMMtbPFRqCxSc9sccaui4q2hTfaRkqJ6NgIQ/21pKRUm6F2UTGNPwYKHLaIMK8jOQAQFRZqysx+QokFYKC4Ugxe+ykam4hFBZQ4Y30REZFPsMghTLxinNeRHNWiNHmWsJZy+T2Nz7IVFReJjr2aNjNSS7jsnpmNtlFUBVPmTDDU35kzBsLbl9xCAJNnDDLUV5eeaWjXIR7wcjwZarVg5Bn+n7AhOS0WtrjGi6tLrhvvhzTUUs4d3NPrSI6qCMwYYs5EFwg7F95PV1OA0DEQRguT8FnwPjKkQYTPMJ6PiIhaHIscwqBJfTFgQh8o6qm7g6IqsEZYcfFd5nxgD5jQBxk9vc94NPdfV/opTX1xybEYce5gr21m3DTV8LovZ84YiHYd4qE28DyoqoLktFhMmeX9/moJIXDd7VO9fnl92Q1nIDLanCl9b7jrbK/bU9PjMGycuVNcU9OM7NYBQzq3h9LASI0iBMJDQ3DNBGP7b0sTlo5A+MVouOoXABSIqD8a7y9yDiAi0XChowAhg4HQcc3KSkRELYNFDkEIgYe+/AtGzxwGCPe/awue1Mxk/N9PDyKtc4pp+V5c/wQy+3U45XahCMx56FJMv/EsE1K5PfTlXzB06oAGt029diJuefo6w32FR1jxxGvXo2d/96iUUETdRATderfDk29cj8go40XJ2Ml98Od5F9UVMorq7iskRMXsm8/E5TdMMNxXS5t83iBcf+dUiAZOb8ronITnP7nZhFR0OhRF4Nm5MzGxT2cA7pHH2oInI9GGN26+BO3jjZ2q6Asi5h9A+BVwf+wJ1BUoSgJE3MsQoQON96W2g4h/F1Br35dq+wRgnQQR9wrXySEiMhnXyaF6svfmYvW3v8Fpd6Hb4EwMmNDHlHPoG7Jv0wF89MRXqCipROcBHXHV3y9CaFio2bEAAPmHCvDOPz9FYU4R0jqn4up/XIyY+OYvoLp3Rw42rdkPCYl+QzqhW+/2ze7LYXdi5ZIdyMsugS0uAqMn9Q6Y610cNQ58+NrP2L0tGxGRVlx0zRh0PY3HSoHh0NESLN9xAE6Xhl7pyRjWJT1g3kekVgDYFwOyElAzAet4CNG8dbGllIBzDeDcDCDE3ZelU4vmJSKi45pSG7DIISIiIiKigMfFQImIiIiIqM1ikUNEREREREGFRQ4REREREQWV5l1tSUT17FyzBz+8vQwlR8uQ1D4eU6+diI69M5rVV02VHT+9vxyblm2DlBL9xvbCpCvGIjwqMCYLICK30so87Nz7EizaduiwQA0/A327XoUQizlTs7cWul4FVDwF2H8EpAaE9ACi74HCSRuIqAVx4gGi0+CwO/HY7Kex7JOVUC0KpC4BRUB36Zh5yzT84alroSjGB0x3rN6Nv507D2WF5XXTeOuajqi4SDz89b3oM7qHrx4KETXBxp1voXvkPISoGqQUkBKwqBK55TbI2NfQPqmf2REDkm5fCRRfiwYXZ42YCyXmL37PREStByceIPKT529/HT9/tgoAoLl06LqE7tIBAF8+uxAfPPqF4b6Kcotxz9SHUVFSAcBd3Oiau6/K0ircO+1hFBwpbNkHQERNtvfIMvSKfgQhqgZFAKoiYVHd3xcmRpZBKbkWNY4Kk1MGHl0v81zgAEDVq9CrP/drJiIKXixyiJqpOK8EC19d7B698eDjJ7+Co8ZhqL/5L/+A6vJq6Nqp/Uldwl7twDcvft/svETUMkoKXgAANLCWLSyKREp0GbbuedPPqVqB8ifhscCpa/OUP5IQURvAIoeomdZ+vxHasVEbTypKKrFtxS5D/f3y+WroXgomXdPx86crm5SRiFpez8SNsCieX6uaDqiOn/yYqJWwL2m8jZ7j8xhE1DawyCFqJke1sREaR43TULuaKnujbewG75OIfCdU9T4aoQhAEY2/ntscaey9UNe9f3lERGQEixyiZuo8oFOjbYQQ6NTX2Cxr3Yd0hmrx/JJULQq6DelsNB4R+cjhkmRoXo7DdSlQI7v6L1BrYWj2tNAmTdZCROQJ30mImqnn8K7I7Nehbha0k6kWBSOmD0ZyRqKh/s77w1Svp79pLh0zb57WrKxE1HKK9YsavB6nlhASHTJ+579ArUX0nxtvE3aO73MQUZvAIoeomYQQuPfdPyI8OuyUERhFVRCXEovbnrvBcH99x/bCZffMcv//JxxBiWO/X3jHdAyc2LcFkhPR6RjY6w/YlNcfUgInXkbn0t2v1Q1Hb0BqQi+T0gUuJXQQEHae5wYiCYh52H+BiCiocZ0cotOUsz8PHz72JRa9vRSOagcibRE4e+6ZuORP5yEuJbZJfUkpseyTlfj4ya+wc80eAEC3wZ1x0V0zMPGyMRDCy9fHROQ3mubE+m3/QWroZ2hvK4IugZ0FXYDI36FPl1lmxwtoesUrQOXLgCw5dosFsJ4F2B6DonAhVSLyrCm1AYscohai6zrs1Q6ERVhbpBhx2N0X6YZaQ067LyLynRpHBVTFghALD9CbQterADigKLFmRyGiVqIptYHFT5mIgp6iKAiPbLmDHBY3RK1DWGiU2RFaJUWJABBhdgwiClK8JoeIiIiIiIIKixwiIiIiIgoqLHJMUlVejcqyKgTiJVE1VXZUlFS2SDaXy4Wc/XmoKKlogWSBzelwoqyoHJrL+0KBRIFIly44tFLoBhdsJN9wOqtRWpkHl6tlFv6VegWkHvzvv1LqkHoppGyZRViltB/r7/QXJpVSoqy6BlX2wHxtVTrtqHRy8VoKPrwmx4+klFj87s/4+MmvsG/TQQBAx97puOjOGZh67UTTZ85a890GvD/vM2xeth0AkJSRgFm3noPzbzsbIaFNuz6kKLcYj1z2FDb/vL2uWLIlxWDOQ5dh+o1ntXh2Mx3ZnYP3HvkUP33wC1wOF6wRVkydMwGX//UCJLaLNzsekVc1rqPYWfIaDpR9CU1WQcCC9Kip6Bk3F9GhmWbHazOO5G9EXtbj6Ju8DtGqjqpCC7YXjkVm53uRaGva8yClBGq+gKx8FXDtct9m6Q4ReR0QNsv0z5qWJPUK9+OsevfYbG0C0joBIvImiNCBTe/PsRGy8gXA/hMACYhYyIgrICKvh1Cadu2VU9Pw3vINePfn35BTXA4AGJzZDnPPHI7xvcx9bUkp8eXhTXht96/YXZYPAOgWk4zruo3GzIz+QbWPUNvF2dX86KW738In//4aQhGQxxZXEEJASonpv5+C25673rQ3lq9f+A5P3/wKFFWBfsJS3kIRGHJWf/zzq3tgCTFWExflFmN2l1vgqG74m8iL7z4Pv3t8dovkNtueDftx5xn3w1HtqLeQp6IqsCXF4OlfH0Fqp2QTExJ5VuXKw5IjV8OuFULi+AikgApFhGJcu/8hPqyPiQnbhr1HliFFvwmhFhcsyvGPZJcuUFIdARn3IVLiuxvqS0oJWf4IUPUWAAGgtr9jv4fPhoj5e1AcxEq9ArLoimOF3IkjLioAQMQ+CxF2pvH+an6ELLn52L9OHJFXAEs3iPj3DRc6Lk3H7W98hWXb9uPEgyxFCOhS4p7zJ+DKcYMMZ2tJUkr8a9NCvLNvdUN7CGZ3HoF7+08Nin2Egk9TagOeruYnm3/ejk/+/TUA1BU4AOpGOb558Xus/W6DGdGQf6gAz976KgDUK3AAd9Z132/ENy8tMtzfPy54wmOBAwAfP/kVjmYXNS9sAJFS4tGrnoa9qn6BA7j/jmVHy/D0za+YlI6ocRsLHj2lwAEACQ26tGNN/l8D8pTaYKLrOiwVf4H1pAIHACyKRGx4FfIO3228Q8eqYwUOgHqH18d+r34bcPx6WpkDhax4DnDtRv0CB3AXKDpk6d2QepWxvvQqyNK7jvV18inHOuDaDVnxrOFsn63egqUnFTgAoB97PT325RIcKSw13F9LWlmwH+/sWw2gwT0Eb+9bhZUF+/2ei6ilscjxk69eWAjV4vnPragKvnr+Oz8mOm7BK4sBL9/YSABfPrfQUF81VTXYvnJ3o+1e+cs7RuMFrO0rd+HgtiOnFIa1NJeONQt/Q97BAj8nI2pctSsfOVVLTylwaknoqHQewtGadX5O1rbsPvQ9MmILoSoNF5MWRaJ38g7kFzX+vgoAsupd1I5kNEyFrHqv6UEDjJQOoPpDnFqQ1LUAZCVQs8BYhzXfutufUpbU0oHqj9z3a8D7yzfA2ziIgMAnKzcby9bC3t+/FqrwfDyiCgXv71/rx0REvsEix0/2/HbglG/7T6RrOvZuPOC/QCc4sOUQdN3LxZUSOLIz23ubY/ZvPmToPmuvSWrNDD1WCRzcdsT3YYiaqNyxH54P6GoJlDn2+CNOm1VWvgmNDZYpAigo3mSsQ9d2eD7wh3uba7vReIFLywdkYxMqWCCPXZPUGOnaiUYvU5YVgJZnqL99eUVeX126lNidc9RQXy1te0kONC8TKmhSx47SXD8mIvINFjl+Eh5pbbRNWETjbXzBGmGFonjfFSyhFkPn58bERxu6z9DwUEPtApnV4PNlDYLHSsFHVYwsXCuhCnPel9oKRQn3NpBeR1XDjXUoDLQz0ibQGXoMEjC6/4pwNF70AxDGFnwOtXgbTXNfmxMWas7cT+GWxj+TwlQuRk2tH4scPxl34SgIxfMnmaIqGH/xKD8mOm7M+cM9nnIFAKpFwdgLhhsqctp3S0N4VOMfAudcb/xi0EA1bNpAWEK8f5BFx0eh1yhjFwwT+VOctTesqvfZ/wQUpEaM81OitikzYxZcuvf31pLqcHRpP9FQfyJsGrx/tCsQYWcbDxighJoAWPrD+2PVIMKmGOsvbDK8j4ApgKUfhJpkqL/J/bpB9fKZr0uJM/t2NdRXS5vavjcULyfTKRCY1r63HxMR+QaLHD85+/pJiLJFQlFP/ZMrqoKwSCum/97Ym3FLGz1zGNK7p0Fp6JqhY++DF991nuH+Lv7TTK/bI2LCMe26SU2JGJBsiTGY/vspXou/y/5yPkKt/EaMAo8iQtAjdq6XFgIdo89HmCXRb5naoviYdGzIGw9vZwPvKb8QISEGR1/CLwVEJBr+eFfc28IvbU7UgCOi/oBTJx2opQIhIyBC+hnrK6QfEDoKnq9n0o/dnzFzJg6BgGiwlFAVgfQEGyb3N6fIuaTTEESGWKE08NmlCIHIECsu7jTEhGRELYtFjp/EJtnw+OL7EZvknu5OtSh1ExFExUXise/vM21NFUuIBY99fx/Su6Udy6ZCtSgQQsAaHor7P74b3Yd0Mdzf7PsuwuSrxje4LTwqDM+ve6zR0+NaixufvBqTrhgLwP2cKhalrpC9+K4ZuPhu48Uhkb91sV2O7rHXwX0ZtFr3AwDtI8/CgKS/mBuwjRjU92lsyhsMAHBpwv2jC+gSWJszDUP7/t1wX0JNhIh/E1Dijt1iQd2BuxILEf+G4dGIQCfCJkHEPAj341OO/ffYKWAhAyHijM+GBgAi9hkgpHZa59q/m7tfEfOPJk1H3aNdEv577QyEhVog4C5s1GOfe+kJsXjl9xci1GLO6WpJYVF4fcxsxIVGAHBPNFA7EUFcaAReHzMbSWFNWxOIKBBxnRw/c9id+PmTldi4ZCuklOg3rhfOuGQUrOHmn/euaRrWLtyAld+sg9PuQtfBmThr9nhE2iKb1d+BrYfx8p/fRtbuHFjDQzH12omYdds5QVPgnGjfpoNY/M4yFBeUIjk9EWddcwbad00zOxaRIZXOIzhY/jWqXDmwKnHIiD4HsdYeZsdqc/Zn/4qCvPehoBiaSEVG+zlol9S3WX1JaQdqFkI63FMFi9BhQNjZEEF4jZXUjgLVn0Fq+wAR6T5lL2Ros9Z5kVICzrWQNQsBWQmhdgbCL4BQmzeiWV5txzfrtmPbkXyEWBSM75WJcb0y6woeM9k1FxZmbcXao+6JgIYmdsS09n1gVblOPAWuptQGLHKIiIiIiCjgcTFQIiIiIiJqs1jkEBERERFRUGGRQ0REREREQYVXl1E9B7cfwer56+GwO9FtcGcMmdIfqup9LRh/yT98FL98vhpVZdVI79EOo84b2uzpmV0uFz564its+HELQqwWnD33TIydNaKFExMRGVdcUY1Fm3ahqLIaabHROKt/N0RYg3Mx4dXbf8ThnK8g4II1fACmDr0WlpDAOCQpKFyOqqIXoKASLqU70jP+jpBQXhdM1Npw4gECAFSWVmLeVU9j1fz1UFQFQgCaS0dyh0Tc99Gd6Dm8m2nZnA4nnrnlVSx89UdAAIqiQHNpiI6Pwp9evxmjZgxtUn8/ffgLHpv9DDRX/YXfouOj8Myqf6F9F86KRkT+I6XE89+twCs/roGm6VAVBS5dR1iIBX+aeQYuGdXf7Igt5sjRwzi09zqM7HgQmi4gJWBRJbJKo7Gv5n6cMcD7Omu+5HCUoOTIuUgIL6i7TQhAl8Dh6iuR2fkB07IRkRtnV6Mm0XUdd55xP7av3A1dq7+wmqIKWMOteGH946ZNifzEtc9h0dtLIfX6u6oQgFAUPLH4AfQfb2x15q2/7MDt4+7zuN0aYcVnRa8jNJQLeBKRf7y0aBWeXfirx+2PXnk2zh3c04+JfMPhtGP7lrPQKyUPFqX++7mmC9Q4Ldjreg0Du5gzql6wbyTiw4tw8szTtUdJR5y3o2MH4wuCElHL4+xq1CTrf9iMrb/sPKXAAQBdk3DUOPDxk1+bkAzI2pOD799cckqBAxz/4HnrHx8Z7u+ZW1/1ut1eZcc7D33cpIxERM1VUWPH/xav9trmv/OXQ2/gPbC1+X7t6+iXlntKgQMAqiJhDXEh6/B/TUgG5OR+jYSIUwscAHW3xcmX/BuKiE4LixzCTx8sh2rxvCtoLh2L313mx0THLf1oBRTVczZd07FxyVYU55ca6m/fxgONtln8zs9G4xERnZZl2/bD7nR5bZNTUo4th3P9lMh3wrTv4NI8L9BpUSTGZm7yY6LjXOWvwNt5LUIAUdZqlJbv8F8oIjotLHIIFcWV0FynjuKcqKbSDl333sYXKooroCiNr1pdWVJpqD8jJ2fWVNQY6ouI6HSVVdsNtSs32C6QhVuqYFG9vwlHhjqga5rXNr5gERWG2tXUZPk4CRG1FBY5hLTOKV5HcgAgMT0eiuL/3SWtS2qjBZgl1IL4tDhD/akhjc8UF5caa6gvIqLTlZFgM9SuvcF2gay4JsnrSI4ugZyyaCgmzOhpl+0bbSMlEB3Vxw9piKglsMghnH39mV4LCUURmH7jFD8mOm7iZWNgsXqeVlSxKJh0+VhERIcb6m/4tEGNtrn8rxcYzkdEdDpGdu+AZFsUPB36K0JgUKd26JRk7IucQBafdI3XkRwpgdVZY/2Y6Li4NM8T0gDubIXVyYgIT/VTIiI6XX4pcp577jl06tQJYWFhGDFiBFav9n6RJflXx17puORPDU/bqagKOvbJwKzbzvFzKreo2Ejc8t/rAADipCtCFYsCW0I05vzzMsP93fnqTbBGeF53IrNfB5x5xbjmhSUiaiJVUfDgJWdBCAHl5Pc4IRBqUfHXCyaalK5lje5zFubvGAng1FOHXbrAnqOJOGPI30xIBtiiuuFQxWgAp2aTEtClgBr3lP+DEVGz+bzI+fDDD3HnnXfigQcewPr16zFgwABMnToV+fn5vr5raoLrH70Stz57PRLTE+puCw0LwTnXn4l/L33I8EiJL5xzw2Tc//FdyOjZru42xaJg3AUj8OyqeUg6IXNjYhNj8MauZ5DZr0O924UiMOb84XjxtydaLDcRkRFje3bCKzddhH4djo8SCACjunfAO7ddhp7tk80L18LOHfc6Pt8+C/kVkXW31ThV/LB7MOLSP0GizbzHmtntDRyoPA8u/fihkZRASU0sSsPeREJc09ZkIyJz+XydnBEjRmDYsGF49tlnAbjXZMnIyMCtt96Ke+65x+v/y3Vy/E/TNBzangWn3Yn23dIQGRNhdqQ6Ukpk7c5BZVk1UjslwZZ4evtEUW4x1v+wCdYIK0ZMH8K1cYjIdFlFpSiuqEayLQrJtiiz4/iMw2nHul3L4HDVoHfH4UiKTTE7Uh1d05BXsBAuVwFibCNhi279axQRBYuAWQzU4XAgIiICn3zyCc4///y626+55hqUlJTgyy+/rNfebrfDbj8+g0xZWRkyMjJY5BARERERtXEBsxjo0aNHoWkaUlLqf0OTkpKC3NxT5/yfN28ebDZb3U9GRoYv4xERERERURAKqNnV7r33XpSWltb9HD582OxIRERERETUyniem7cFJCYmQlVV5OXl1bs9Ly8PqamnTsNotVphtVp9GYmIiIiIiIKcT4uc0NBQDBkyBIsXL667JkfXdSxevBi33HKLL++6xVSWVmLB/xZj4es/oSS/FEkZCTjn+smYeu0EWMObXpDtXLsXnz89H+t/2AxIiQET+2DWreeg96geLR8+iOxavw/P3/YadqzeDV2TCIsKw1mzx+PGf1/TrAkD1i3aiC+e/RbbV+5GSKgFI6cPwfm3nYOOvdKb3FdFSSUW/O8HLHz9J5QWlCEpIwHn3jAZU+Y0bx9pK3QpsWD3Try9aSP2FBUiMiQE07v3xOz+A5EWHW12PDy/ZhVe37AeRdVVEEKgS1w8/jJmHCZldmlyX2X2Gry/ZRM+2bYVRdVVSIuOwWV9+uGi3n0QZjF3wosqZyW+3vM/lDq/RURIKeyucFgwAed2uQmJEU2f6arccQB7Sz9AduVP0KUTcWF90MV2GVLCR58yDXxjdN2FzUX/xsGyr+GSlRBQEGvtjQGJf0Z8WN8mZ2tLdu9/E2VF76NDbA6EkDhUnIqouCvQvfOcJvdld7rw5dpt+GTFZuQUlyE2KhznD+uDi0b2gy0irMn9Hc77DbnZz6ND9DpYFA1Z5R2gRF6N3p0vbPKi01JqQM23kFXvAa69gIgAwqdDRFwJoQbXmjZljhp8dGAdPj+4AUWOSqSF23BJ5hDM6jAQVrXph3ObirLw9t5VWFmwHwAwMikTs7uMQP/4xhdGbcvyqsvw3r41mH9kC6pcDnSJTsRlnYdhWvveUEVT91+J5fl78c7eVdhSnI0QRcWktB6Y3WUEMqMTffQI2g6fz6724Ycf4pprrsFLL72E4cOH46mnnsJHH32EHTt2nHKtzsnMnl3taFYh7jzjAeQeyIfU3X8mIQAJoOvATDyx+AFExUZ67+QEC/73A/7z+5egqkrd4puqxf37zf+9DufferYvHkart/SjX/Hw5f9x/+FPEptsw9v7nkVYEz5oX733XXzw2BdQVAW6dvx5EELgvo/vwujzhhnu62hWIe4Yfz/yDhY0uI88+eMDiLQZ30faCpeu47Zvv8HCvbuhCAH92NuQKgQiQkLw9qyL0T/FnAMUXddx4cfvY2PeqdcNAsAtw0bgzlHGFyzMKS/HpZ98gKzysrpdWMC9j/RNSsY7F1yCGJNGsEtqivHhziuQGJkLCUAR7ilzJYAKexTGt3sVneO6G+4vt/JnrMy9CxI6JDQAgIAKCQ1dbVehX8Kdhgsdl16D7w+dhxqtoMHtQ5MfRofocw1na0vWbLgFg1K+h66LusU3XbqAIiR+y5uMYQOfN9xXRY0d17/4KbYezqvbbwH3+1yqLRpv3nIJ0uKMfz5v3v0+ukU8CEVIWJTj2SyKxNqcszB0wDOGCx0pXZAlfwTsi+A++752UWsFEJEQ8W9ChARHMZxTVYqrlr2OnOoyyGPPQt37SGw7vD72akSFGH8feX/fGvxz4wIoQoEmj30OCgW61HHfgHNweWfjn4NtydbibMxZ/haqNEfd51btZ9hZ7Xrh38MugsXw/ivx2Obv8ebelVCFgFb3OahAEQJPj7gEE1KNv/+2FQEz8QAAXHrppXjyySdx//33Y+DAgdiwYQMWLlzYaIETCB675lnkHTp+8AocWyRMAvs2HcRzf3zNcF/7txzCU79/GZCoK3CA478/98fXsHPt3hbLHiwcDif+deV/GyxwAKAkvxT3z3zccH+/frUGHzz2BQDUFTiA+3lwuTQ8fOl/UJxXYri/R2c/jfxDRz3vI7e/brivtuSNDevx3d7dAFD3QQEAmpSodDpxw9dfwKlppmT7vxW/eCxwAODZNauw42jDB94NueP7BcipKK+3C9f+vu1oAR5e9lPzgraAD3fcg4SIPAjhLnAA1P0eGVqJRYfuMNyXXSvGyrw/QYerrsABUPf7ntJ3kF35g+H+VuX+yWOBAwBr8++DS6823F9bse/QRxiU8j0UgboCBwAsioQigEEpP2DvwQ8M9/fkV8uw/Yh7Xbt6+7AE8ssq8Od3vjXcV0l5NrqGPwSLotcVOLXZAGBo2iJs2v2W4f5Q+Tpgr92n9BM26ICshCz+PaR0Gu8vgN295lPk1RwvcIAT3kdKc/DY5u8M97W9JBf/3LgAEqgrcHDsdwngnxsXYHuJ5/fAtsqpa/jDyg/qFTjA8c+wH7K34409Kwz3tyh7O97cuxIA6goc9+86XLqG21d9jGJ7VQulb5v8MvHALbfcgoMHD8Jut2PVqlUYMWKEP+72tBzcfgQbftwC3aU3uF3XdPz0/i8oKSg11N/Xz38HRfX8DaZqUfDls8Y/LNqKdx76uF4x0pANP26Bo8ZhqL/PnpoPRfWw20vA5XRhwSuLDfV1cNthbFyyzWM+XdPx47vLDe8jbYUuJV7bsM5T3QpdShRUVWLRvj1+zVXrnc0bGm0zb/lSQ33tKjyK1VlH6n2AnUiXEl/s3I7iav8frB+tykdc5FooSsPZVEUiKSob63ONfWgfLPsKunTA4zcSULC75F1Dfem6A3nVvzbSSmJbkfERibaiMP8t6LrnzxpdFyg++rahvkqravDV2m31DuhOpOkSGw5kY2e2saJ/174XEaJodQX1qf0JhDnfMdSXlBpk1ZvwvL/pgJ4P2H801F8g21mah/VFh72+j3x5eBNKHMbeR97dtxqKl9OqFKHgvX2rm5U1mP2UsxP5NeUeXw8SwNt7V9UrHL15c89KKGj4xSABOHQNnx38rZlpCQiw2dUCybZfdzbaRnNp2LV2n6H+Ni7dWm8E59S+dGxcstVwvrbit8WbG20jpcSWX3YY6m/bip1eiyapS2w12NfWX3c12kZzadi9ztg+0lbkVVQgt6LCaxuLomBtTrafEtVX7mi8YN5akG+or3UGHoNL17ElP6/Rdi1tS8FqWBTvH8ZSAjuLfjHUX6F9YyMtdBTZN8PIGdLFjp2o/818wwqq1xrK1pakRufUG8E5mUWVSI3KMdTX9iP5cDbyJRMAbDhg7LVqxSZ4OKYD4C6su8Qfhq4bOEjU89xFjFcWSMc6Q9kC2W9Fjc8069Q1bCsx9ryuOXrA64G4JnWsPnrAaLw2Y33hYVgaueYmv6YcedXljfYlpcTG4iPQPRbpACCxvpCzDJ8OFjkeePy234ftjPbVlhj9m1hCVGP9NXaurDDnuW9LVE9f457crokXqfuTp2/fTmln8DEoBv8mLUlVjF2oLISx15Yw8HEijP7dDM6JY+Q+2xopG/8bS2ns72b0tWp0P5dS9TzwcoxuIP+xezXYztj+G8iMvt8Yfc/0NorTlDZtjdG/r9F2Rt4Pjb62qGHciz0YMKGP12+cACA0LAS9RnQ11N+wqQO9HuwqFgXDpg1qSsQ2Yeysxk9tVCwKeo82NjvdoMn9oFg8Pw8CAoPP7G+orwETehvaR3qO6Gaov7YiKSISnWJjvf7pXLqO0Rkd/JbpRAnhEY22GdLO2OxDo9IzGv0YC7NYMCAlzVB/LWlA8mg4XN6LCSGA/kmTDPWXHD4c3o5gBVQkhQ8zNPGALbQHhIFCJy1yvKFsbUluRQe4vJyu5tIFciqMLbTdJyMF4QZmrxzRzdhr1amO8Pqe6dIFdhV2NTbxgJICqB3g/U3YBWEdbShbIBuRlNlomzA1BH3jjL0vjUvp6vVAXBUC41KMHdu0JaOSO8PlZQRMAOgQGY/ksMZnBxVCYGRSZqMF0ajkzk2NSSdgkeNBaqdkjJ013GNhIhSBc26YbHjmrBl/mOr+ttbD/iwAzLxlWjPTBq8L75iOEKv3g52xs0bAYjH2ze9Fd87weJ2VUAQiYsIxZc4EQ32lZaZgzEzv+8j0G6cgMqbxg+a2RAiBGwcP83g4rAqBTrGxOKNj4x/svnDjkMZnFfr7uAmG+upgi8Xkzl08fpAJCFzRdwCiQkObErFFxFhjUFFzBnQPT4SmC+SVd0XvxAGG+suIPhchSgw8faxIaOgWe42hvhRFQXrUVK9tBCzoETvXUH9tSfv0P0BK0eDzqkv3KYhp7X9vqK8IayguG90fno7DVEVgfK9MdEiMNdRfr65zUWG3QvNQhFkUCSXS2HMqhICIvB6eC2sVUDOB0DGG+gtkHaPiMSG1u/f3kcyhiLQYex+5PHMYhIdxBFHbH2dXO8Xo5C7IjErw+DxIAHO7GZ8q/9puoz1eZ6VAIDokDDMzjH3pSg1jkePFXa/8Ad0Guw+0ag9ka/87ZMoA3PD4bMN9pWWm4P5P7oYlxFLvoFhRFagWBfe+e3uz1mgJdoqi4LFF93ssJDr0Tsff3r/dcH8DzuiDm5++zn1a2gkjOkIRCIu04pH5f23StOB3v/YHdB3U6VhW9xtbbdahUwdi7qNXGu6rLbmkTz9cO3AwgOND++LYT1JEJF477wLThumvHzwU07p6Hn17ZNJZaN+EKe0fnzwNvZKSABw/9aD2MZ/RsRP+NNr4dNQt7ao+D6Og3D0KWnvgWXtwXFKdiAu7PWO4rxAlEmPSnoNFicCJ3+aIY6cL9Uu4EykRIw33NyTpIcSEeJo+VcGYtOegGDzlri1pnzoZO0qvgKYr9YoJTRfQdAXbSy5HRpr3AvJEt549Bmf0cn+bXHv6Wu1+3C0tEf+6wviXc1HhCShQnkK1MwS6fmwmSgAuzd3f2vzZ6NNlluH+EH4pEF77OVx7Wtqxx6wkQcT9DyJITruaN+R89IhxT6tfe/pa3ftIajf8sc+ZhvvKjE7Av4dfVDdVcS1FCKhCwb+HX4ROUQktmD44KELgxVFXIMkaXfd5BaBubZyrOg/HxZ0GG+5vdHJn/KXflHp9AO4iM8ISipdHX4nIJkwLTqfy+To5p8PsdXIA92xbyz9bhe/eXILinGKkdErG2XPPxPBzBjV50TIAyD9UgG9eWoT1P2yClMDACX0w/aYpSMsM/Cm1zVSUW4yX7n4Lqxash9PuQlyyDRffNePYCFnTn4cDWw/j6xe+w7YVuxASFoJR04di2txJiEu2Nbkvp8OJ5Z+txvdvtcw+0pasy8nCe5s3YWfhUUSFhOLc7j0wq2dvU0Y2Tvb93t34z8pfcbC0BIoQGJLWDn8bNwHdE5q+QJtD0/Dd3t34dNtWFFRVIj0mBpf26Y8JnTJNP+da0zUsPvgF9pZ8AotaCJcejdSIs3F256sQZmn6Qo92rRgHyr5ATuVS6NKOuLC+6BxzCWzW5p22ua/0Y+wqeRM12lEoCEFKxGj0T7wb4ZakZvXXVuQWLMfBQ08jzuq+cLm4JgMdMm5BWkrTT/HTdYnlOw/g05WbkVVUhsToCMwY2htT+ndDiKXp17wUlx/B7n0vw6b+AlVxotjeHcmpN6BjWvNmXpWOdZBVHwCuXYASDRF2DhA2E0IJrjXKHLqGRVnb8MWhjThqr0B6RBwu6jQY41K6Nut9JKuqBB/uX4sV+e7FQEclZ+LSzKFoHxHbwsmDS6XLga8ObcS3WVtR4bSjW0wyLs0cgsEJzTvFeldpHt7fvxabio7AqlowMa0HLuw4CPHW4Np/W0pTagMWOUREREREFPACajFQIiIiIiIif2KRQ0REREREQYVFDhERERERBRVOTWOCkoJSbF+5G1JK9BrRDXEpsWZHIqIGZJWXYWt+HkJUFUPT2iPa2vyZbjRdx7qcbBRVVyMtOhr9k1MMTzXa2jj1ShRWr4cOF2yhPRAZ0u60+itz7EO54wAsSgQSwwdDFc2fmMLucmFNdhYqnQ50jo1Ht4TTm0WqypmNEsdOKLAgIXwQQpSoZvel6To2HMhGcUU1UmOj0ScjcPYRKSW2HclDTnE5YiPDMSizHVROrEInkFJiS0k2cqrKEG+NwKCEjHqzhhH5G4scP6quqMbzt7+ORW8tg+bSALinMZ546Rjc+uxcw2vuEJFv5VdW4G8/LsKP+/fVrcIRZrFgdv+BuHvUWISoTZtRav6unfjX8qXIqSivu61LXDwenHCmaYue+oIuXdhW9Bz2lr4PTdqP3SqQEjEGg5PuQ7gluUn9lTn2Yn3Bwyiq2VB3W4gSjZ5x16OrbXaTCgApJV7fsB7PrF6JUntN3e2DUtPwrzOnoEcTZ82rduXjt4KHkVu1HLVrtajCis62y9An/mYoovFFNE+08LedePLrZcgrrai7rXNyPP56wUTDi236ypo9h/HIZz9ib15R3W0ptijcOX0czhnc08RkFChWFezHQxsXYF/50brbUsJi8Od+Z+Gc9L4mJqO2jLOr+YnL6cKfznwQ21bsgq7VX4xSURV0HdQJ/1n2T4SGmT91LlFbVlJTjfPefwc5FeWnLNQmAJzdrTuemTbd8AH2Fzu2487vF5xyu4B73YW3zr8Io4Kg0JFSYm3+33C4YiFOXqBRQEWYJQmT0t+DVY0z1F+F4yB+zLoKml4NCe2U7T3irkef+JsN5/vvql/x31UrTrldFQJhlhB8edmV6BwXb6gvh1aKH49cgWpXXgPZBNpHnoXhKY8a3ke+Xrcdf31v4Sm3C+HeR/5344UY1jXDUF8tbe3eI7jhxU+hSR0NHS08cvlUnDe0t/+DUcBYffQArlv+NnSpN7g066NDzsfMDsYWFSZqDGdXC0DLPlmJLct3nFLgAICu6di1dh9+fG+5CcmI6ERvbvwN2Q0UOID70H3B7l1Yl5NtqC+HpuGfy35scJsEoEuJh5b9hAD+rsmwYvsWHK74Fg2tQC+hocaVjz0l7xnub1vxix4LHADYWfwaql35hvrKr6zAM6tXNrhNkxI1Lif+s/IXw9n2lL6HKleuh2wSWZXfo8i+0VBfDpcLj32xpMFtUrqLx0e/WGLaPvL4l0uhS9lggQMAj32xBA6Xy7+hKGBIKTFv40JIKRsscABg3qbv4NC4j5D/scjxk4Wv/QhF8fytnlAEFryy2I+JiKghH2zZDN3LAaUqBD7ZtsVQX8sO7kdxTY3H7RLAzsKj2FF41GOb1uJg+VcQ8Hwan4SOA+WfGerLpVchq2KRxwKn1qHy+Yb6+2rnDq/bNSnx7Z7dKLPbvbardaDscwCnfmFVS0DFwbKvDPW1fMdBlFZ53kd0CezKOYrduYWG+mtJe3KPYntWvtfXQ1m1HT9vP+C/UBRQdpcXYEdZHhoew3ErdVZjWd4eP6YicmOR4yf5h45C1z2/CUhdouBw6z/QIWrtjlZVet2uSYmcigqvbWrlGm5X3nijAFftym+0KLFrRYZGJOxaSaN9CSio0YyN5ORWVDS6IrwuJYqqqwz1V6N5LzgkNFS78gz1lV9aASMnteWXGtuXWlJ+qffXAuA+pS7PhGwUGPKryxptIwDk1TTejqilscjxk4R2cRDeRnKEQEI7Y+eqE5HvxIWHe92uCoHkSGOThCQZbRfR+icdCVMTvI7kAECoYjN0nUqoGoPGP550WFVjkwUkRkR4HY0A3Adi8Y0897WsamwjfbmvQTIiITrCy3fgxyVGRxjqryUlGLhPKc3JRoEhwdr4bIISQJKBdkQtjUWOn0ydMxHS20gOJKZeO8mPiYioIZf07gfVy4G4JiUu6GnsQusJHTMRHep52mkB9yxrfZKaNutYIOoQPd3r6IuAgo4x5xvqK0SJQrvIiY2c/ibRIepsQ/2d16On1xEkVQhM7twFMdYwQ/11jJ4J4eXjU0JDh+jphvoa1ysTUV4mnBHCPctaj3bGiqaW1D0tEV1SEuCtLo20hmJ8787+C0UBpactBV2iE72ORkZZrBif2s1vmYhqscjxkwmXjUHXwZlQ1FP/5IqqoFPvDJx19RkmJCOiE80ZOBiJERENFjqKEJjQMRMj043NdGW1WHDv2PENbqvt/W/jJgTMWiinIyFsENIiJgANHO4IqAhV49DNdpXh/nrH3wRFhHgsJrrarkCEwfV32kXH4IbBQxvcpgiBEFXFHSPHGM7W1XYlrGq8hyJMQWrEOCSGDTHUV1iIBXdOH9fgttq/5J9mnmHKPiKEwJ9nngEB4fEg9s7pYxEWwtUo2iohBO7pN9X9u4c2d/edjDC1aVOqE7UEFjl+EmoNwRM/PIDR5w+r/2ElgOFnD8KTP/0DYRHNX2iQiFpGYkQEPr74cgxJa1/vdoui4JLeffHCuec16YDzsr79Me/MKYgNqz9KkBIVhZemz8SETpktkttsQggMT30MmTEXnnLwHxfWDxPav4Ewi/G1aGJCu2B8+1cRHVL/76OKMPSMuxH9Eu5sUr4/jxmP20eMRril/gF557h4vH/BJeiZaHykJMySgDPav4n4sP71bhdQ0Sl6FkakPNGkfeTiUf3xj4snwxZRfx9JtkXh6WvPw9ienQz31dJG9+iIZ647Dymx9U83igm34v6LzsQlozk1cFs3NqUrnht5OVLC60/nawsJx4MDp+PSzIa/YCDyNa6TY4L8QwXY/PMOSCnRZ0wPpGWmmB2JiBqwu7AQm/NzEaKqGJXeAYkRzb/2wKFp+OXwQRRWVSEtOhoj22cE7Yrxdq0IBdVroEkHYkN7wWbt2uy+pJQotm9FuWMfLEoEkiNGIURp/jVMlQ4Hfj50EJVOBzrHxmFgatppjZKUOfai2L4NCkKQFD4cYRZja+00xOnSsGLXQRRVVCMtLhpDu6QHzD6i6xJr9h5GTnE54qPCMbJ7B4RaOIJDx2lSx5qjB5FTVYo4awRGJ3dBqNK0hZOJGtOU2oBFDhERERERBTwuBkpERERERG0WixwiIiIiIgoqLHKIiIiIiCio8KpBIqIGVDqzsb/sUxTbt0BBCFIjx6FD9LkIUZq+qJ3D5cLza1fjk21bUO5wID48HHMGDMLs/gOhNOPC8hpXEQ6Wf4GC6jUAJBLDh6FT9PkIsyQ0ua9A9+2eXXh61QrkVJQjVFExpUtX/GXMOEQbXNPmRJp0ILtiMY5UfA+nXoGY0M7IjLkQNmv3ZmWrdGYd20e2nvY+ousO7Ch+DQfKv4BLr4RVjUNX2xXIjLmkBfYRIDF8aNDuIzuzC/DJys3Ym1eIKKsVUwZ0w5QB3TgxQitW4bTjq8ObsCR3F5yahn7x7XBJpyFIjwy+RdOPVBbjw/3rsKU4GyGqigmp3TEzoz8iQzjj7unixANERCc5UPYl1hc8BAFAQkftChChig1j272AWGtPw33lVVRgyjuvo9zhOGVbWlQ0Fl01BxGhnheDPKW/qpVYmXsHNOkAoB+7VYEiLBiZ8n9IjRxruK9Apus6Lv/sI6zJzjplm0VR8OGFl2JQmrF1cgCg2pWPn7N/hwrnQbhPYtAhoEJCQ4/Yuegdf3OTZlk7UPYF1hf808M+8iJirT2akK0Aiw7NgktWnrItXE3FWR0+h0UxXtTlVa3Aitw7oEsnju8jAooIxcjU/0NqhPE1gQKZlBLPLPwV//thNVRFQNMlFCGgS4mOibF45aaLkBobbXZMaqLtJbmY+8vbKHFUAQAk3Av2Sgk8OGg6Luo02NyALeij/evw4Ib5EMK90HTtO1BsaAReGzsbPW2ppuYLRJx4gIiomQprNmB9wYMA9GMHr4D7Y1bCqZdhefZNcOqnHox6cuFH7zVY4ABATkU5Zn/xieG+qpzZWJH7R2jSjuMHrwCgQ5dOrMy9E5XOI4b7C2T3/bS4wQIHAFy6jis++wi6rje4/WRSSvya88cT/jbu/09CAwDsLHkVh8q/NpytsHoD1hc8BG/7iEuvMtzfkiOzGyxwAKBay8Xy7JsM91XpzMaK3Nuh1yuC3fl06cDK3DtQ6Wz479rafL1uO/73w2oAgKa7v6/Vj31ve6SoFLe8+iUC+HtcakCly4Hrf3kbpY7qY68oN01K6JC4/7evse7oITMjtpi1Rw/iHxu+gQ4J7dh+WvuYSx3VuP6Xd1Dlavizg4xhkUNEdILdJe9AeHhrlNDh0EtwuPxbQ339lpON7Ipy721yc3C0ytgB8b6yTyClC8c/+uunk9Cxr/RjQ30FMl3X8cn2rV7b2DUNr25Yb6i/ozXrUOrYUVfUnEpgZ8nrhg+Id5W81cg+UozDFcb2kcLqDajW8ry2KbJvQI2r2FB/+8s+9r6PSB37ylr/PiKlxKuL18DT2JumS+zMLsDavcFR9LcV8w9vRpGjCnqD+y+gCAWv71nh51S+8druFVA8jB7rkCi0V2LBkS1+ThVcWOQQEZ0gt2q5l4NhABDIq/rVUF/vbdlkqN3nO7YZapdTueyEkYNTSWjIqVpqqK9AtqOwAE7d23PgNn/XDkP95VX9CgFvixJKVDgPoFrLN9Zf9S+N7iO5lb8Y6mt/2WeG2h2uWGCoXU7l0sb3kcrWv48UlFViX36Rh0NhN4ui4OcdB/wViVrAz3l7IDyWru4FR3/O2+PHRL4hpcTy/D11IzgNEUBQPFYzscghIjqBlI0dXEvocBrqy2XwdCqH5jLUTqLxdu7rMFo3u6vxAgcAXAZHXtzPaePX27hHQIz019jzKg09VwCgG7xP9+lnBtoZuF+jjzOQaQZfWy7NWDsKDJrUIb2Wru42waCxfVgChr7sIc9Y5BARnSDO2gfe3xoVxFv7GurrzMzOBtt1MdQu3jrA64iEgIqEsIGG+gpkvZKSDZQkwBCDEw/EhfVptOgIVWIRbkk21F+stRca20fc+1HjjE4UkRox3lC7BOvARveR+LABhvoKZEkxUYiLDPfaxqXr6NeBF263Jn3j2kHx8upXINAnNs2PiXxDCPfjaOyx9o9v78dUwYdFDhHRCbrGXg54Od1HQEGnmFmG+prevSciLCFe27SLjkbPxCRD/XWxXeL1NCkJDV1slxrqK5CFWSwYld7BaxsB4O5RxmYJaxc5EVY1Hp4/8hR0tl0KRXh/rmp1tV2BltpHOkSfA1V4P1iPUNNgsxorhDvbLm0T+4hFVXD52IHwNCGeIgTio8IxuV9X/waj03JRp8Eer1MB3NeqXN1lhB8T+c7sriM8Xnsk4N6HL+wYPDPJmYFFDhHRCdpHTkFmzEXH/nX8LdL97biCocn/NPyNPwC8PON8j9/WhSgK3j7/YsN9xYX1Qb+EO07Ig3q/94m/FfFh/Q33F8henD4TsWGep03+x4RJhtfKUUQIRqb+G6qwnjTK4X5eksKHokfcdYazpUdNRWb0hbW9n9CbCgEFw1IebtI+Mir1KXg6nU4gBGPavWC4r/iwPugbf3tdnhOzAUDf+D8iPqyf4f4C2dxJQzGym7sYPvGvpyoCoRYVT82ZgRCLt2uxKNAkh0Xj8aEXQBECqjj+2qp9D72k0xCck25sJD3QTU/vh4uOFTEnfkaoQoEQAk8MuwBJYU1fc4uO4zo5REQnkVIiu/IH7C39AMX2rRCwIC3yDHSNvRJx1t5N7m9X4VH8/ccfsD43G7qUsCgKxmZ0wCOTpiAtuunreORXrcae0nfrFnpMCh+KrrYrkBwxssl9BbIqhwP3/fQDFuzZBbumQQDonpCIv4+bgDEdOja5vwrnYewtfQ+HyxfCJasRFdIBXWyXomP0eYZHcWrV7iN7St9HiX3bae8jpfY92HD0ERTWbELtGj7J4aMwOPl+hFuMjfSdqK3sI05Nw5drtuGDXzbgQH4xwq0hOHtgT1w1fhA6JMaaHY+aaWtJDt7asxI/5uyES2robUvD7K4jMLVd7yatZxXopJRYmLUN7+xdhW2lOQgRKiam9cDVXUcGxWl5vtCU2oBFDhERERERBTwuBkpERERERG0WixwiIiIiIgoqLHKIiIiIiCioWMwOQES+V2a342hVJWzWMCRERJgdp9VwaBqyyssQqqhoFx0dUBe81jiqsCr3J+hSYlTaJISFBs7zKqVEtSsHunQiPCQNqgg1O5LP6NKJKmc2FBGCcEvaae8jdq0YDq0UVjUBoWrTJ6UgIiI3FjlEQexQaQme/HU5vt2zC9qxOUbGZnTAnaPGYmAqZ27xpMblxDOrV+LdzRtRZrcDADrFxuIPQ0fgwl59TC12ahxVeH3rHCRF74ZyLMY3h+5DQUUXXNv7LdOLnUPl32BH8SuocB4EAIQoUciMuQg9434Hi+J9PZjWRNNrsL34Zewv+xhOvQIAEBXSET1ir0OH6BlN3keKarZiW9FzyK9eCUBCQEG7yDPRJ/5mRIU2fSY5IqK2jrOrEQWpfcVFuPCj91HhsNcVOIB7gTFFCLw+8wKMyeDB08nsLhdmf/5J3XTPtQQACeCWYSNxp8FFKFuay+XCWzsmwRZW7s507Di6Nma5PRJX9PgRoRZzRk62F72E7cUv4vhfq5aCOGsfjG/3MlTF2No2gUzT7fg55/coOjbd83Hux90z7gb0jv+D4f4Kqtfil+w/QEKDPKE/ARUWJQJntH8DMaGdWyw/EVFrxdnViAgPLv3xlAIHAHQpoekSd3+/EJruedX2tuqDrZuwLierXoEDHD9kf3bNSuwqPOr/YADe3vYgYsPLIQTqrfRe+++YsEq8vfU+U7KVOw4cK3AAnLKKt45i+xbsK/vY37F8Yn/Zpyiq2Yj6BQ5Q+7h3FP8PZY69hvqSUse6/Aegw1WvwHH3psGlV2FDwaMtkJqIqG1hkUMUhLLKy7D80MFTCpxaEhJ5lRVYfuign5MFvrc3bfS6XRUCH2zd7Kc0J923ZTG8jb1LCYSGLvVfoBMcKPscAt5Xl99b+qGf0vjWvjLvj0NAxYGyzw31VVC9BlWubJxaGLpJaDhaswaVziNNjUlE1KaxyCEKQgdLSjwcMh2nCIH9JcV+ydOaHCwp9vq306TEvuIiv+U5UVhIDbxd6iEEEGZx+C/QCSqcByGheWkhUeXKRgCfIW2Yu+Dw/DgkNJQ7Dhjqq/bapcbbHTLUjoiI3FjkEAWhyNDGr8nQpUSUgXZtTUSI97+JIgRiQq1+SlOfriuNjuTo0pxJESxKZKMjOaoIC6gZ6ppLFd4nUBBQEKJGGerLokS2aDsiInJjkUMUhPomJSMtyvv0sxZFwaRMXsx8shk9ekL1ciCuS4mzu3X3Y6Ljiqsaf76KKjP9kORU7aMmex3JEVCRHjXVj4l8Jz16mteCTkJH+8izDPWVGjEOCkK8tglTkxBv7dukjEREbR2LHKIgpCqK1xnABIA5AwYhPjxw1lYJFHMHDUGoqkJpoNBRhUCPhERMzuxiQjJgcocHIYEGR3OkdJ9ANSH9fn/HAuA+WLeF9vBw8K9ACAu6x17t91y+0M02G4oIQUMfoQIqYkK7IS3yDEN9haox6Bo722ubXvG/hxDeR8mIiKg+FjlEQerCXn1w3/iJCFEUCAAhigJFCAgAs/sPxF/GjDc7YkDKjI3DW7MuQny4+5Qki6JAFe63yn4pqXhr1kUIUc054OyW0Aux6n3QpDuPlMcLHl0KRIu/oldSf1OyKcKCse2eR1yYe8RBQIU4thRbqGLD2LTnER1qzihTS4sO7YixaS/AqsYCAAQsdcVdnLUPxqa9AEUYX4auT/wf0CXmCgACAsqxv5uAgAV9E+5AZswFLf8giIiCHNfJIQpyxdXV+HrXDmSVlyEuLBwzuvdEe76eGuXUNCzatxeb8nMRqqiY0CkTg1JPf0X7luBwOfDetn/BLtcAkLCKIbii932mrY9zIikliu1bkFv1M3TpRKy1J9pFTjo28hFcdOlEduUSlNi3QQgLUiPGIt7av9n7SJUrF0fKF8KuFSMiJBXpUdNgVeNaODURUevVlNqARQ4REREREQU8LgZKRERERERtls+KnEceeQSjR49GREQEYmNjfXU3RERERERE9fisyHE4HLj44otx0003+eouiIiIiIiITmF8+pcmevDBBwEAb7zxhq/ugoionu0F+ViwZxcqHQ5kxsVjZo+eiLGGmR0LAJCVW4JFP29HSVkVkhOiMeWM3kiMM7Zg5Mk0XcdPB/ZhVdYRSAmMaJ+OiZmdYVGC7wxkp1aOwxXfotx5ABYRgfZRkxFr7Wl2LCKiFmHXXFiYtRVbS3IQoqg4I7UbhiV0DIhJblo7nxU5zWG322G32+v+XVZWZmIaImotqpxO/HHhfCzevxeqEBBCQNN1/OvnJXhk0lm4oFcf07Jpmo6nXl2Mz7/bCEURUISArku8+O7PuO7S0bjmwpFN+jDbXViIuV99hiPlZXVFzWsb1qFddDRePe8C9EhI9NVD8bvD5d9ifcFD0KT92BTNEjtLXkVK+BgMT30MIUqk2RGJiJptZcF+3L7qI5Q6a2ARCiSA13b/ij6xaXh+1OVIDvO+qDd5F1Bf+82bNw82m63uJyMjw+xIRNQK3L5wPn46sA8AoEkJl65DArBrGv60aCGWHthvWrYX312GL77bCADQdQmXpkOXErou8cr7v+Dz7zYY7qu4uhpXfPYhcirKAQAuXYdL1wEAeRUVuOLTD1FYVdXij8EM+VUrsSb/b9BkDQAJCRckNPe26hVYk3ePuQGJiE7DnrIC3Pjruyhzur/cd0kdmnS/n+8ozcPc5W/DqWtmRmz1mlTk3HPPPRDHviX19LNjx45mh7n33ntRWlpa93P48OFm90VEbcOOowX4Yf9e6B5mwxdC4L+rV/g5lVtZeTU+nr8e3ubpf/2jFXC5jH2QfbRtM4qqa6A18Fg1KVFqt+ODrZubmTawbC9+GUDDI1wSOnKrlqPE3vzPGyIiM722+1doUkI28AmhSR17ygvwU84uE5IFjyadrnbXXXdhzpw5Xtt07ty52WGsViusVmuz/38iansW7tkNVYgGD/wBQJcSG3JzUFBZiaRI/57etGL9frhcutc2xaVV2Lo7BwN6pTfa3ze7djb4gVhLlxLf7NqBm4eNaHLWQGLXilFY85vXNgIqsip+4PU5RNQqLczaWjdy0xBVCHyfvQ1T2vfyY6rg0qQiJykpCUlJSb7KQkTUZFVOh/ualkbWNa50OpAE/xY51TUOQ+2qqo21q3A03q7CYW+0TaDT9GoDrQRcMjhOzSOitkVKiRrN6bWNJiUqna3//dxMPrsm59ChQ9iwYQMOHToETdOwYcMGbNiwARUVFb66SyJqg7rExdddl+JJuMWClMjmzWR2OjqmJxhr1z7eULseCYlQvUxSoAqB7kEw8YDVkghVhHttI6EhOiTTT4mIiFqOEAIdo+I9nJDrpgqBzjEcWDgdPity7r//fgwaNAgPPPAAKioqMGjQIAwaNAhr16711V0SURs0vXtPRISEePywUIXARb37IjwkxK+5AGBg73S0T42FojScTlEEhvbrgHYpsYb6u7LfAI+n5QHub/6u7DewGUkDiypC0SlmFoSXjyhVWJERfbYfUxERtZwrOg/3ul2XEhd3GuynNMHJZ0XOG2+8ASnlKT8TJkzw1V0SURsUGRqKx86cCgBQThrlUIVAeowNt48YbUY0CCHw91vPhkVVTil0VEUgMsKKu353luH+xnboiEt693X33cD2WT17Y2Kn4Bjd6BV3IyJDOjRQ6CgABAYn3Y8Qxf+jc0RELeHSTkMwNLEjlJPezWv/dXvvSegUZexsAGqYkLKRE9lNVFZWBpvNhtLSUsTExJgdh4gC2IrDh/D06hVYlXUEABBuCcElffrituGjEBfu/dQnX9t9IB+vffArlq/dAykBi6rgzLE9MffS0YZHcWrpUuKdTRvwyvq1OFLuXkusfXQM5g4agqsHDDql0GvNHFoZthe/hANlX0A7dv1NQtgg9Ir7HZIjRpqcjojo9Ng1F17e9TPe37cWxQ73e1yPmBT8rsdYnJPe1+R0gakptQGLHCIKKsXV1ahyOpEYEQGrJaDWO0ZFpR3llTWIjQlHeFjoafWlS4n8ygpICaRERQVVcXMyTTpgdx2FqkTAqsaaHYeIqEW5dB35NeUIVVQkWCObtEB0W9OU2iCwjgCIiE5TXHi46SM3nkRFWhEV2TLT5CtCIDWqbayGrYpQRIS0MzsGEZFPWBQF7SJsZscIOj67JoeIiIiIiMgMLHKIiIiIiCiosMghIiIiIqKgwmtyqM2RUmLR8h34ZP467NibB4uqYOTgzrj8vKHo17O92fGw8rf9+PDrtfhty2FIAAN6tcelM4ZizNAuZkcLaIeyivDB12vxw887UGN3on1qLGZNG4iZUwbAGtq0tzopJRb9vB0fz1+Pnfvc+8ioIZ1x+XnD0LdH068NqXblY0/JuzhY/hUceinC1CRkxlyALrYrEKq2jetqgo2u69hYOA+Hyr+BJmsAAGFqEnrG3YjOtgtNThfYimo2YVfJ28itWgYpXbCF9kTX2MuREXUOhOB3r0TUMji7GrUpUko8+vx3mP/jFihCQD+2+6uqAl3X8debp+HsieZN2/j2Z6vw0rs/Q1EEdN2drfb3ay8ZhbmXjjEtWyDbuO0I7vjnJ9BcGrRjf7fayWn69WiP/9x/EaxWY4uBSikx77mFWPDT1ob3kVvOxtkT+hjOVu44iKVZc+DUyyGhnbBFQaSlPc5o/wbCLPGG+yPz6bqORYfPR6XrcIPbM6MvxKDkv/s5VetwqHwB1ub/HQLKCa8HBYCOjKhzMTT5IRY6RORRU2oDvpNQm/LD8h2Y/+MWAKg7eAUATdMhJTDv+e+Qd7TMlGzbdufgpXd/dmfTj2er/f31j1Zg4/YjpmQLZE6nhr898SVczuMFDgBI6f7ZsjMbb3yy0nB/i5bvwIKftgJoeB959LmFTdpH1uTd20CBAwA6qlzZ2Hh0nuG+KDBsLHzUY4EDAPvLP0VRzRY/Jmodql35WJf/AAB50utBBwAcrpiPQxXzTclGRMGHRQ61KR/PX9/oeiJfLdrkpzT1ffbtBqiK52yqIvDpgt/8mKh1WLZ6N0rKqusVJCfSpcQX32+E03lykdGwT+av87qPSADf/LDZUF/F9m0ocWxvoMCp7UtDduWPqHYVGOqPAsOh8m8abbO58CnfB2llDpR9DnmsoGmYgr0lH/gtDxEFNxY51Kbs3p/n8WAYcI+abN+T68dEx23dnV1vJOJkmi6xdXeOHxO1Djv25MKien8rK6+oQX5huaH+du7Lb7F9pLhmW6NtJHSUOXYb6o8CgyarG21T7tjnhyStS7F9G+C1yNFR4tiBAD6LnohaERY51Kaoqup1uxBAqMV7G18xcr8hJmULZBaLCiOHRBaLsbe7xgomIYCQEGPPgyKMXQdktB21HorgvD4nc+/n3kfSFWHhau9E1CJY5FCbMnpIZ6+nhEkJjBrS2Y+Jjhs7vCsUL9kURWD88K5+TNQ6jBrSGZrm+dthIYCO6fFITjA2i9mYoV0a3UdGG9xHUiJGobG3WYuIRLy1n6H+KDCEqcmNtkkOH+mHJK1LasQ4wMtXEgLqsTZERKePRQ61KZedNxSezoRQFIE4WwTOGtfLv6GOmTllAEJD1Aa/xRTCPcIwa9pA/wcLcP16tEPvbqkeCxMpgdmzRhj+dvjSGd73kfjYCEwe29NQX+GWZGREnQ3Pb7UCXWOvhKqEGeqPAkOv+BsbaSHQN+F2f0RpVdKjpsCqJkJ4eD1I6OgWe5WfUxFRsGKRQ21K725puO+P50BVlbpRk9pjX1t0OJ564GJEhIeaki05IRpP/O1ChFktOPF4XAjAGmrBY/degLRkmynZApkQAvP+Mgsd0xMAoO55rS16rr1kFKY1YcrnPt3T8PfbzoaqiFP2kdgY9z4SHmZ8HxmU9Dckhw9z9wO13n87RJ2LXnG/M9wXBYbMmAuQGXOxh60CI1Ke5LTgDbAo4RjX7kWEqrV/m2OvLygQUDEk+UEkhA00LR8RBReuk0NtUkFhOb5ZvBnb9+QixKJi5OBMTB7bs0kHr75SVlGDhUu2Yv2WQ4AEBvRJxzkT+8IWHW52tIDmcmn4Ze1e/LRiFyqrHOjQPg4zJvdHp2PFT1OdvI+MGtwZZ47t0ax9REqJgurVOFSxAHZXISJCUtEx+nzEWfvw+oNWrLhmGzYX/gdljr1QhAXJ4SPRN+EOhFnizI4W0Fx6NY5UfIfcquXQpQOx1t7oFDMLEZYUs6MRUYBrSm3AIoeIiIiIiAIeFwMlIiIiIqI2i0UOEREREREFFRY5REREREQUVLhaGbVZu/fnH7uoXMHQ/h2RZHAdFQpcOfmleP/LNaiotKNHlxRcfO5gKAq/yyEiImprOPEAtTlZuSX4x3++wfY9uXW3KULgrHG9cPeNkwNihjVqGpdLxy33f4AtO7Pr3a6qArddOwkXnj3IpGRERETUUjjxAJEHhcWVuOlv72HXvrx6t+tSYtHy7bjn0S+g6wFb95MH1//l7VMKHADQNIn/vLIY3y/bZkIqIiIiMguLHGpTPp6/DiVl1dAaKGR0XWLd5kNYu+mgCcmouTbvzMaeAwVe2zz9+hL/hCEiIqKAwCKH2pT5P27xOlKjKgLfLeW3/q3J6x/92mibkrIqZOUU+yENERERBQIWOdSmlFXUeN2u6RJFJZV+SkMtoaSsylC7rLxSHychIiKiQMEih9qUxLhIr9tVRSAliZNctCbJBmfF69g+3sdJiIiIKFCwyKE25byzBkARwuN2TZc4d1JfPyai03XjlWMbbZOSGM3ilYiIqA1hkUNtyoVnD0K71FioyqmFjgBw1tie6Nujnf+DUbNlZiRh+MBOXtvce/M0/4QhIiKigMAih9qUqEgrXnjkcowf2R3KCYVOeFgIZl84En+77RwILyM9FJie/NsFmDy2J05+6qIirHjs3lkY2r+jOcGIiIjIFFwMlNqso8UV2L0/HyEWFX26p3ER0CBQ43Bh/uLNKC2rRv9e7VncEBERBZGm1AYWP2UiCjiJcVFIjIsyOwa1oLBQCy48e5DZMYiIiMhkPF2NiIiIiIiCCoscIiIiIiIKKixyiIiIiIgoqPCanFZM0zSs/HodFrzyA3L25iE22Yazrj4Dk64YC2u41ex4bYbd4cLi5TuwYMkWFJVUITUpBjMm98O44d1gUfk9gr9UVNqx4Kct+GH5DlRW2ZGZkYCZUwZiaP8OnDGPfGLnvjx8tvA3bN2Zg5AQFWOHdcHMswYgMZ7X+hERmY2zq7VSDrsTD174BFYv+A2KqkDXdAghIKVEh17t8eRPDyIu2WZ2zKBXWl6NPz7wEfYcLKj7+yuKgK5LDOnXAY/fOwtWa4jZMYPekZxi3HLfhygsqUDtO5qqiLrFXf9y09R6U4YTna73vliN599eVrefAYCiCISGWPB/f78QA3qnm5yQiCj4NKU24NfMrdSb932ANQs3AAB0TQcA1NarR3blYN6V/zUrWpvyyLPfYt/howCO//31Ywc8v205jOffXmZatrZC1yX+/K/PUFxaiRO/sqk98Jz/4xZ8+u1vJqWjYLRm44G613btfga490WHw4U//eszlFXUmBWPiIjAIqdVqq6oxlcvfAepNzwIp2s6flu8GQe2HvZzsrYlK7cEv67dV1fUnEyXEl//sAnllTzY8aU1Gw/gUHZxvYPNk33w1RqPzxNRU73/1VqoHkYGdSlRXePAwiVb/ZyKiIhOxCKnFdrz2wHUVNq9thFCYCM/ZH3qNwNFpMOpYfvuXD+kabvWbzkMtZFrn/KOliPvaJmfElGw+23LIa9FtZTA+s2H/JiIiIhOxiKnFTJ6GVUAX24VHAz+efk8+JY0+ETweaCWYmRX0rm/ERGZikVOK9RlYCeEhnm/mF1KiX7jevkpUdvUr2e7RttYLAp6dk31Q5q2a0CvdGjHrkvzJCEuEimJnLyEWkb/Xu29TmQhBDCQEw8QEZmKRU4rFBkTgXOun+zxQ1a1KOgzuge6DOjk32BtTMf0BAzt38HjufmKIjDtjD6wRYf7OVnbMnJQJlKTYzy+HgSAS6YPafSUNiKjLp0x1OM1XkIAoSEWnDOpr59TERHRifip30rNffRK9BnbEwAgag/uhPtanMT0BPztgztMTNd23HfbuUhLicWJy7DUrsnSq2sqbrt2oknJ2g5VVfD4vbMQHWmFcsITUVv0TBjdA5fNGGpWPApCY4Z2wZyLRgJAvS85VEXAoqr4159nIjYmwqx4REQErpPTqjkdTiz54FfM/98PyN2fB1tiDKZcMwHTrpuISFuk2fHajKpqBxb8tAXzf9yC4pJKpCTF4Lyz+uOscb0QGsL1dv2luLQSXy7ahB9+3o6KKgc6pSdg1tQBGDe8G9fIIZ/4bethfLrgN2zdlY2QEBXjhnXFBWcPQvvUWLOjEREFpabUBixyiIiIiIgo4HExUCIiIiIiarNY5BARERERUVBhkUNEREREREGFV0UTEVGLqqlx4McVu2B3uDB8YCe0T4k1O1IdXZfYe6gAVdUOpKfGISGOk7QQEQUjFjlERNQidF3HPY99gRXr9uHEKW3S02Lxf3+7EO3T4swLB+D7Zdvwyge/IDuvFACgCIExQ7vgtusmIi3ZZmo2IiJqWTxdjYiIWsS1d7+FX9fWL3AA4EhOCa66/XXkF5abEwzAJwvW46H/LqgrcABAlxK/rtuLG/7yDnILykzLRkRELY9FDhERnbbFv+7A3oNHPW53unT869mFfkx0XGl5NZ59Y0mD2zRdoryiBq988It/QxERkU/5rMg5cOAA5s6di8zMTISHh6NLly544IEH4HA4fHWXRERkkrc/WdVom/VbDvkhyam+X7Ydmq573K7pEj8s346qan4+EREFC59dk7Njxw7ouo6XXnoJXbt2xZYtW3DDDTegsrISTz75pK/uloiITFBYUtloG12X0HUdiuLfkwhy80uhKgpcmudCx+XSUVRSiYjwUD8mIyIiX/FZkTNt2jRMmzat7t+dO3fGzp078cILL3gscux2O+x2e92/y8p4jjQRUWsQERaK4tKqRtv5u8ABgOjoMOgnXyjUULuoMD+kISIif/Drp01paSni4+M9bp83bx5sNlvdT0ZGhh/TERFRc00/s2+jbbp0TPRDklNNHtMTuu65yFEUgWEDOsIWHe7HVERE5Et+K3L27NmDZ555BjfeeKPHNvfeey9KS0vrfg4fPuyveEREdBounzkckRHeT/W684bJfkpTX3paHKaf2Q9CnLpNCAEhBOZeNsb/wYiIyGeaXOTcc889dR8Knn527NhR7//JysrCtGnTcPHFF+OGG27w2LfVakVMTEy9HyIiCnwWi4K3n7q2wcU1LaqCB++ajgG90k1I5nb37yZj1tSBUBQBIdyjNwAQZ4vAE3+9AH27tzMtGxERtTwhpYETlU9QUFCAwsJCr206d+6M0FD3N3rZ2dmYMGECRo4ciTfeeKNJ52OXlZXBZrOhtLSUBQ8RUSuxdtNBfPn9RtidLgzu0wGXTB9syrU4DSksrsTPa/agqtqBDu3iMXJwJixqYGQjIiLvmlIbNLnIaYqsrCxMnDgRQ4YMwTvvvANVVZv0/7PIISIiIiIioGm1gc9mV8vKysKECRPQsWNHPPnkkygoKKjblpqa6qu7JSIiIiKiNs5nRc6iRYuwZ88e7NmzB+np9c/D9uHgERERERERtXE+OxF5zpw5kFI2+ENEREREROQrvNqSiIiIiIiCCoscIiIiIiIKKixyiIiIiIgoqLDIISIiIiKioMIih4iIiIiIggqLHCIiIiIiCioscoiIiIiIKKiwyCEiIiIioqDCIoeIiIiIiIIKixwiIiIiIgoqLHKIiIiIiCiosMghIiIiIqKgwiKHiIiIiIiCCoscIiIiIiIKKixyiFqI06mhrLwaLk03OwoRERFRm2YxOwBRa3c4uxhvfroSi5fvgNOlIcxqwTkT++LqC0ciMT7K7HhEREREbQ5HcohOw+79+Zj757exaNk2OF0aAKDG7sKX32/EdX96Gzn5pSYnJCIiImp7WOQQNZOUEg8+NR81dic0XdbbpukSpWVV+L+XfzApHREREVHbxSKHqJm27srBgSOF0E8qcGppusTK3/Yjl6M5RERERH7FIoeomfYeLDDUbv+RQh8nISIiIqITscghaqYwa4ihdtZQzu9BRERE5E8scoiaacSgTrCo3l9CMVFh6NujnZ8SERERERHAIoeo2WJjIjBzygAI4bnNVbOGIzSEIzlERERE/sQih+g03DpnAs4a1wsAoCqi7gcALj9vKC6fOczMeERERERtkpBSNjw1VAAoKyuDzWZDaWkpYmJizI5D5NGeAwX4ftk2FJdWITkxGmdP6IP0tDizYxEREREFjabUBjyPhqgFdO2UhK6dzjA7BhERERGBp6sREREREVGQYZFDRERERERBhUUOEREREREFFRY5REREREQUVFjkEBERERFRUGGRQ0REREREQYVFDhERERERBRUWOUREREREFFRY5BARERERUVBhkUNEREREREHFYnYAb6SUAICysjKTkxARERERkZlqa4LaGsGbgC5yysvLAQAZGRkmJyEiIiIiokBQXl4Om83mtY2QRkohk+i6juzsbERHR0MIYfj/KysrQ0ZGBg4fPoyYmBgfJiTyjPshmY37IAUC7ocUCLgfBgcpJcrLy9GuXTsoiverbgJ6JEdRFKSnpzf7/4+JieGOTKbjfkhm4z5IgYD7IQUC7oetX2MjOLU48QAREREREQUVFjlERERERBRUgrLIsVqteOCBB2C1Ws2OQm0Y90MyG/dBCgTcDykQcD9sewJ64gEiIiIiIqKmCsqRHCIiIiIiartY5BARERERUVBhkUNEREREREGFRQ4REREREQUVFjlERERERBRUgr7IOXDgAObOnYvMzEyEh4ejS5cueOCBB+BwOMyORm3II488gtGjRyMiIgKxsbFmx6E24rnnnkOnTp0QFhaGESNGYPXq1WZHojZk2bJlmDFjBtq1awchBL744guzI1EbM2/ePAwbNgzR0dFITk7G+eefj507d5odi/wk6IucHTt2QNd1vPTSS9i6dSv+85//4MUXX8Rf//pXs6NRG+JwOHDxxRfjpptuMjsKtREffvgh7rzzTjzwwANYv349BgwYgKlTpyI/P9/saNRGVFZWYsCAAXjuuefMjkJt1NKlS3HzzTdj5cqVWLRoEZxOJ6ZMmYLKykqzo5EftMl1cp544gm88MIL2Ldvn9lRqI154403cPvtt6OkpMTsKBTkRowYgWHDhuHZZ58FAOi6joyMDNx666245557TE5HbY0QAp9//jnOP/98s6NQG1ZQUIDk5GQsXboU48ePNzsO+VjQj+Q0pLS0FPHx8WbHICLyCYfDgXXr1mHy5Ml1tymKgsmTJ2PFihUmJiMiMk9paSkA8BiwjWhzRc6ePXvwzDPP4MYbbzQ7ChGRTxw9ehSapiElJaXe7SkpKcjNzTUpFRGReXRdx+23344xY8agb9++ZschP2i1Rc4999wDIYTXnx07dtT7f7KysjBt2jRcfPHFuOGGG0xKTsGiOfsgERER+d/NN9+MLVu24IMPPjA7CvmJxewAzXXXXXdhzpw5Xtt07ty57vfs7GxMnDgRo0ePxssvv+zjdNQWNHUfJPKXxMREqKqKvLy8erfn5eUhNTXVpFREROa45ZZb8M0332DZsmVIT083Ow75SastcpKSkpCUlGSobVZWFiZOnIghQ4bg9ddfh6K02gEsCiBN2QeJ/Ck0NBRDhgzB4sWL6y701nUdixcvxi233GJuOCIiP5FS4tZbb8Xnn3+OJUuWIDMz0+xI5EettsgxKisrCxMmTEDHjh3x5JNPoqCgoG4bv9Ekfzl06BCKiopw6NAhaJqGDRs2AAC6du2KqKgoc8NRULrzzjtxzTXXYOjQoRg+fDieeuopVFZW4tprrzU7GrURFRUV2LNnT92/9+/fjw0bNiA+Ph4dOnQwMRm1FTfffDPee+89fPnll4iOjq67JtFmsyE8PNzkdORrQT+F9BtvvOHxQz3IHzoFkDlz5uDNN9885faffvoJEyZM8H8gahOeffZZPPHEE8jNzcXAgQPx9NNPY8SIEWbHojZiyZIlmDhx4im3X3PNNXjjjTf8H4jaHCFEg7e//vrrjZ5uTq1f0Bc5RERERETUtvDiFCIiIiIiCioscoiIiIiIKKiwyCEiIiIioqDCIoeIiIiIiIIKixwiIiIiIgoqLHKIiIiIiCiosMghIiIiIqKgwiKHiIiIiIiCCoscIiIiIiIKKixyiIiIiIgoqLDIISIiIiKioPL/rCK6jFeSUOwAAAAASUVORK5CYII=",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Visualisierung der Ergebnisse\n",
|
||
"plt.figure(figsize=(10, 5))\n",
|
||
"plt.scatter(X[:, 0], X[:, 1], c=affinity_prop_labels) # WICHTIG: wir plotten nur die ersten beiden Dimensionen! -> Nächstes mal lernen wir, wie wir mit Downprojection eine besser Visualisierung erhalten\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Vergleichen wir das nun mit den echten Labels\n",
|
||
"plt.figure(figsize=(10, 5))\n",
|
||
"plt.scatter(X[:, 0], X[:, 1], c=y_true)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 3000x500 with 3 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Vergleich von affinity propagation und k-means nebeneinander und echten Labels\n",
|
||
"\n",
|
||
"plt.figure(figsize=(30, 5))\n",
|
||
"plt.subplot(1, 3, 1)\n",
|
||
"plt.scatter(X[:, 0], X[:, 1], c=affinity_prop_labels)\n",
|
||
"plt.title(\"Affinity Propagation\")\n",
|
||
"plt.subplot(1, 3, 2)\n",
|
||
"plt.scatter(X[:, 0], X[:, 1], c=kmeans_labels)\n",
|
||
"plt.title(\"K-Means\")\n",
|
||
"plt.subplot(1, 3, 3)\n",
|
||
"plt.scatter(X[:, 0], X[:, 1], c=y_true)\n",
|
||
"plt.title(\"Echte Labels\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Was sind hier die Unterschiede?"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"---"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Sonstiges\n",
|
||
"\n",
|
||
"* Es gibt noch viele weitere Clustering Methoden\n",
|
||
"* Wir werden darauf aber nicht mehr genauer eingehen (Referat?)\n",
|
||
"* Haben alle ihre Vor- und Nachteile"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"---"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Aufgabe"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"* Verwende nun die folgenden Datasets und versuche die Aufgabenstellungen zu beantworten.\n",
|
||
"* Lade dazu das Dataset mit den bekannten Methoden (Laden mit Hilfe von `pd.read_csv`).\n",
|
||
"* Verwende für jede Aufgabe eigene Code-Zellen und dokumentiere für die verschiedenen Durchläufe die Ergebnisse.\n",
|
||
"\n",
|
||
"**Aufgaben**\n",
|
||
"1) HELP International konnte rund 10 Millionen Dollar aufbringen. Nun muss der Geschäftsführer der NRO entscheiden, wie er dieses Geld strategisch und effektiv einsetzen will. Der CEO muss also entscheiden, welche Länder die Hilfe am dringendsten benötigen. Ihre Aufgabe als Datenwissenschaftler besteht also darin, die Länder anhand einiger sozioökonomischer und gesundheitlicher Faktoren zu kategorisieren, die die Gesamtentwicklung des Landes bestimmen. Sie müssen also Länder vorschlagen, auf die sich der CEO am meisten konzentrieren soll.\n",
|
||
"\n",
|
||
" *Dataset:* Country `country.csv` (verwendet von https://www.kaggle.com/datasets/rohan0301/unsupervised-learning-on-country-data?select=Country-data.csv)\n",
|
||
"\n",
|
||
"2) Nach der HTL-Karriere beschließen Sie, für eine gewisse Zeit ins Ausland zu gehen. Dabei wollen Sie in ein Land ziehen, in welchem die Länder besonders glücklich sind. Versuchen Sie nun das nachfolgende Dataset zu gruppieren und herauszufinden, in welchen Ländern (Gruppen) die Menschen ähnlich glücklick zu sein scheinen.\n",
|
||
"\n",
|
||
" *Dataset:* Happiness 2017 `happiness_2017.csv` (verwendet von https://www.kaggle.com/datasets/unsdsn/world-happiness?select=2017.csv)"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "dsai",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.9.18"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|