4221 lines
507 KiB
Plaintext
4221 lines
507 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 20,
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"id": "e6e27297",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"import random\n",
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"import time\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"from sklearn.preprocessing import MinMaxScaler\n",
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"from sklearn.metrics import silhouette_score\n",
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"from sklearn.decomposition import PCA\n",
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"from IPython.display import Image\n",
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"from sklearn.datasets import make_classification, make_blobs"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e1e5a2b7",
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"metadata": {
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"# Analiza skupień metodą k-medoids "
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]
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},
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{
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"cell_type": "markdown",
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"id": "80d5deaf",
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"metadata": {
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"### Co to jest klasteryzacja? "
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]
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},
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{
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"cell_type": "markdown",
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"id": "4040df16",
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"metadata": {
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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" Analiza skupień lub klasteryzacja to zadanie polegające na grupowaniu zbioru obiektów w taki sposób, aby obiekty w tej samej grupie lub klastrze były do siebie bardziej podobne niż obiekty w innych grupach lub klastrach. "
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]
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},
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{
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"cell_type": "markdown",
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"id": "493a0d16",
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"metadata": {},
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"source": [
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" Idea algorytmu klastrowania:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 99,
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"id": "e84b8c18",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<IPython.core.display.Image object>"
|
|
]
|
|
},
|
|
"execution_count": 99,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"Image(\"algorithm.png\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "5813bd1a",
|
|
"metadata": {},
|
|
"source": [
|
|
" Sama analiza skupień nie jest jednym konkretnym algorytmem, lecz ogólnym zadaniem do rozwiązania. Można je zrealizować za pomocą różnych algorytmów (algorytm $k-średnich$, algorytm $k-medoid$), które różnią się znacznie w rozumieniu tego, czym jest klaster i jak skutecznie je znaleźć. Popularne pojęcia klastrów obejmują grupy o małych odległościach między elementami klastra. Klastrowanie można zatem sformułować jako wieloprzedmiotowy problem optymalizacyjny. Wybór odpowiedniego algorytmu grupowania i ustawień parametrów zależy od indywidualnego zbioru danych i przeznaczenia wyników. Analiza skupień jako taka nie jest zadaniem automatycznym, lecz iteracyjnym procesem odkrywania wiedzy lub interaktywnej optymalizacji wieloprzedmiotowej, który wymaga prób i błędów. Często konieczne jest modyfikowanie wstępnego przetwarzania danych i parametrów modelu, aż do uzyskania pożądanych właściwości."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "3dc57d21",
|
|
"metadata": {
|
|
"pycharm": {
|
|
"name": "#%% md\n"
|
|
}
|
|
},
|
|
"source": [
|
|
" W naszym projekcie przedstawimy metodę $k-medoid$ i porównamy ją z metodą $k-średnich$."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "2e28109b",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Algorytm k-średnich"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "01b24fd7",
|
|
"metadata": {},
|
|
"source": [
|
|
" W uczeniu maszynowym występują głównie dwa rodzaje algorytmów uczenia. Są to algorytm uczenia nadzorowanego i algorytm uczenia nienadzorowanego. \n",
|
|
"Klastrowanie metodą $k-średnich$ jest algorytmem uczenia nienadzorowanego, który grupuje nieoznakowane zbiory danych w różne klastry lub grupy. W algorytmie $k-średnich$ $k$ określa liczbę wstępnie zdefiniowanych klastrów lub grup, które należy utworzyć w danym zbiorze danych. Na przykład jeśli $k = 3$, powstaną trzy klastry, dla $k = 5$ będzie pięć klastrów itd."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "7f4d7284",
|
|
"metadata": {},
|
|
"source": [
|
|
" Algorytm $k-średnich$ pomaga grupować dane w różne klastry w sposób konwencjonalny, umożliwiając samodzielne odkrywanie kategorii grup w nieoznakowanym zbiorze danych bez konieczności uczenia. Algorytm klasteryzacji $k-średnich$ jest algorytmem opartym na centroidach, w którym każdy klaster jest powiązany z centroidem. Głównym celem tego algorytmu jest zminimalizowanie sumy odległości wewnętrznych punktów danych w odpowiadających im klastrach. Algorytm przyjmuje jako dane wejściowe nieuporządkowany zbiór danych i dzieli go na $k$ grup, a następnie powtarza ten proces aż do uzyskania najlepszych klastrów. Wartość $k$ jest zawsze określona z góry."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "22642017",
|
|
"metadata": {},
|
|
"source": [
|
|
" Główne zadanie algorytmu $k-średnich$ jest następujące:\n",
|
|
"\n",
|
|
"  $1.$ Wybierz liczbę klastrów $k$. \n",
|
|
"  $2.$ Wybierz losowo $k$ punktów z zestawu danych jako centroidy. \n",
|
|
"  $3.$ Przypisz każdy punkt danych do najbliższego mu centroida. \n",
|
|
"  $4.$ Oblicz wariancję i umieść nowy centroid dla każdego klastra. \n",
|
|
"  $5.$ Powtórz $krok 3$, czyli ponownie przypisz każdy punkt danych do nowego najbliższego centroidu."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "15d683c2",
|
|
"metadata": {},
|
|
"source": [
|
|
" **Rozwiązanie**: Implementacja algorytmu $k-średnich$ przy użyciu Pythona. Do wykonania algorytmu $k-średnich$ potrzebne jest wstępne przetwarzanie danych. W naszym rozwiązaniu przeprowadziliśmy wstępne przetwarzanie danych w celu zaimplementowania algorytmu. Dodatkowo oceniliśmy jaka jest jakość naszego grupowania. Posłużyliśmy się tzw. sylwetką (ang. silhouette) $s(x_i)$ obliczaną dla każdego obiektu $x_i$. Najpierw dla $x_i$ znajduje się jego średnią odległość $a(x_i)$ od pozostałych obiektów grupy, do której został przydzielony, a następnie wybiera się minimalną wartość $b(x_i)$ spośród obliczonych odległości od $x_i$ do każdej spośród pozostałych grup osobno. Odległość $x_i$ od danej grupy oblicza się jako średnią odległość od $x_i$ do wszystkich elementów tej grupy. Obie wielkości zestawia się we wzorze: "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e276f546",
|
|
"metadata": {},
|
|
"source": [
|
|
"<h1><center>$s(x_i) = \\frac{b(x_i)-a(x_i)}{max(a(x_i),b(x_i))}$</center></h1>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "9af1d9d2",
|
|
"metadata": {},
|
|
"source": [
|
|
"otrzymując wartość sylwetki dla danego obiektu $x_i$. Jej zbiór wartości to $[-1, 1]$. Zatem ma ona prostą interpretację: obiekty, dla których wskaźnik jest bliski $1$, zostały trafnie zgrupowane, pozostałe (o wartości ok. $0$ i $ujemnej$) prawdopodobnie trafiły do złych grup."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"id": "409d4187",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"class TrainModel_means:\n",
|
|
" def __init__(self, data, k_value, max_iteration):\n",
|
|
" self.data = data\n",
|
|
" scaler = MinMaxScaler()\n",
|
|
" self.data = scaler.fit_transform(self.data)\n",
|
|
" self.k_value = k_value\n",
|
|
" self.max_iteration = max_iteration\n",
|
|
" self.centroids = []\n",
|
|
" self.final_clusters, self.silhouette = self.data_cluster()\n",
|
|
"\n",
|
|
" def generate_column(self, col, data):\n",
|
|
" values = []\n",
|
|
" for i in range(len(data)):\n",
|
|
" values.append(data[i][col])\n",
|
|
" return values\n",
|
|
"\n",
|
|
" def calculateDistance(self, x, y):\n",
|
|
" return np.linalg.norm(x-y)\n",
|
|
"\n",
|
|
" def get_closest_centroid(self, points, centroids):\n",
|
|
" closest_centroids = []\n",
|
|
" for i in points:\n",
|
|
" distance = []\n",
|
|
" for c in centroids:\n",
|
|
" dis = self.calculateDistance(i, c)\n",
|
|
" distance.append(dis)\n",
|
|
" closest_centroids.append(np.argmin(distance))\n",
|
|
" return closest_centroids\n",
|
|
"\n",
|
|
" def calculate_new_centroids(self, clusters, X):\n",
|
|
" new_centroids = []\n",
|
|
" new_df = pd.concat([pd.DataFrame(X), pd.DataFrame(clusters, columns=['cluster'])], axis=1)\n",
|
|
" for c in set(new_df['cluster']):\n",
|
|
" current_cluster = new_df[new_df['cluster'] == c][new_df.columns[:-1]]\n",
|
|
" cluster_mean = current_cluster.mean(axis=0)\n",
|
|
" new_centroids.append(cluster_mean)\n",
|
|
" return new_centroids\n",
|
|
"\n",
|
|
" def get_clustered_data(self, points, centroids):\n",
|
|
" closest_centroids = self.get_closest_centroid(points, centroids)\n",
|
|
" clustered_data = {}\n",
|
|
" for i in range(self.k_value):\n",
|
|
" clustered_data[i] = []\n",
|
|
" for i in range(len(points)):\n",
|
|
" clustered_data[closest_centroids[i]].append(points[i])\n",
|
|
" return clustered_data\n",
|
|
"\n",
|
|
" def get_clusters_label(self, data_points, clusters):\n",
|
|
" labels = []\n",
|
|
" for i in range(len(data_points)):\n",
|
|
" labels.append(0)\n",
|
|
" for i in clusters.keys():\n",
|
|
" cluster = clusters[i]\n",
|
|
" for j in range(len(cluster)):\n",
|
|
" for k in range(len(data_points)):\n",
|
|
" if (cluster[j] == data_points[k]).all():\n",
|
|
" labels[k] = i\n",
|
|
" break\n",
|
|
" return labels\n",
|
|
"\n",
|
|
" def data_cluster(self):\n",
|
|
" centroid_points = random.sample(range(0, len(self.data)), self.k_value)\n",
|
|
" for i in centroid_points:\n",
|
|
" self.centroids.append(self.data[i])\n",
|
|
" for i in range(self.max_iteration):\n",
|
|
" closest_centroids = self.get_closest_centroid(self.data, self.centroids)\n",
|
|
" self.centroids = self.calculate_new_centroids(closest_centroids, np.array(self.data))\n",
|
|
" final_clusters = self.get_clustered_data(self.data, self.centroids)\n",
|
|
" cluster_labels = self.get_clusters_label(self.data, final_clusters)\n",
|
|
" silhouette_avg = silhouette_score(self.data, cluster_labels)\n",
|
|
" print(\"Sylwetka (ang.silhouette) dla metody k-medoid i dla k =\", self.k_value, round(silhouette_avg,2))\n",
|
|
" \n",
|
|
" return final_clusters, round(silhouette_avg,2)\n",
|
|
" \n",
|
|
" def return_values(self):\n",
|
|
" return self.centroids, self.final_clusters, self.silhouette"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 55,
|
|
"id": "b42a9194",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 3 0.44\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# iris\n",
|
|
"dataset = pd.read_csv('iris.csv')\n",
|
|
"dataset = dataset.iloc[: , 1:-1]\n",
|
|
"dataset = dataset.values\n",
|
|
"model = TrainModel_means(dataset, 3, 10)\n",
|
|
"centroids, final_clusters, silhouette = model.return_values()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 60,
|
|
"id": "15541704",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 4 0.28\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# glass\n",
|
|
"dataset2= pd.read_csv('glass.csv')\n",
|
|
"dataset2 = dataset2.iloc[:,:-1]\n",
|
|
"dataset2 = dataset2.values\n",
|
|
"model2 = TrainModel_means(dataset2, 4, 10)\n",
|
|
"centroids2, final_clusters2, silhouette2 = model2.return_values()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 70,
|
|
"id": "c29dca2b",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 3 0.3\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# wine\n",
|
|
"dataset3= pd.read_csv('wine.csv')\n",
|
|
"dataset3 = dataset3.values\n",
|
|
"model3 = TrainModel_means(dataset3, 3, 10)\n",
|
|
"centroids3, final_clusters3, silhouette3 = model3.return_values()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "f7c684c9",
|
|
"metadata": {
|
|
"pycharm": {
|
|
"name": "#%% md\n"
|
|
}
|
|
},
|
|
"source": [
|
|
"### Algorytm k-medoid"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "af45d7c7",
|
|
"metadata": {
|
|
"pycharm": {
|
|
"name": "#%% md\n"
|
|
}
|
|
},
|
|
"source": [
|
|
"$1.$ Inicjalizacja: wybierz $k$ losowych punktów spośród $n$ punktów danych jako medoidy. \n",
|
|
"$2.$ Przyporządkuj każdy punkt danych do najbliższego medoidu, używając dowolnych popularnych metod metryki odległości. \n",
|
|
"$3.$ Podczas gdy koszt maleje: \n",
|
|
"  Dla każdej medoidy $m$, dla każdego punktu danych $o$, który nie jest medoidą: \n",
|
|
"    $i.$ Zamień punkty $m$ i $o$, przyporządkuj każdy punkt danych do najbliższej medoidy, ponownie oblicz koszt. \n",
|
|
"    $ii.$ Jeśli całkowity koszt jest większy niż w poprzednim kroku, cofnij zamianę."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "d8f6dd1e",
|
|
"metadata": {
|
|
"pycharm": {
|
|
"name": "#%% md\n"
|
|
}
|
|
},
|
|
"source": [
|
|
" **Rozwiązanie**: Implementacja algorytmu k-medoid w Pythonie. Do wykonania algorytmu k-medoidy potrzebne jest wstępne przetworzenie danych. W naszym rozwiązaniu przeprowadziliśmy wstępne przetwarzanie danych w celu zaimplementowania algorytmu k-medoid. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 52,
|
|
"id": "73cffc81",
|
|
"metadata": {
|
|
"pycharm": {
|
|
"name": "#%%\n"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"class TrainModel_medoids:\n",
|
|
" def __init__(self, data, k_value):\n",
|
|
" self.data = data\n",
|
|
" scaler = MinMaxScaler()\n",
|
|
" self.data = scaler.fit_transform(self.data)\n",
|
|
" self.k_value = k_value\n",
|
|
" self.medoids, self.res_cluster, self.cluster_labels, self.silhouette = self.kmedoids(self.data)\n",
|
|
"\n",
|
|
" def get_random_medoids(self, data):\n",
|
|
" points = random.sample(range(0, len(data)), self.k_value)\n",
|
|
" medoids = []\n",
|
|
" for i in points:\n",
|
|
" medoids.append(data[i])\n",
|
|
" return medoids\n",
|
|
"\n",
|
|
" def get_closest_medoids(self, sample_point, medoids):\n",
|
|
" min_distance = float('inf')\n",
|
|
" closest_medoid = None\n",
|
|
" for i in range(len(medoids)):\n",
|
|
" distance = self.calculateDistance(sample_point, medoids[i])\n",
|
|
" if distance < min_distance:\n",
|
|
" min_distance = distance\n",
|
|
" closest_medoid = i\n",
|
|
" return closest_medoid\n",
|
|
"\n",
|
|
" def get_clusters(self, data_points, medoids):\n",
|
|
" clusters = [[] for _ in range(self.k_value)]\n",
|
|
" for i in range(len(data_points)):\n",
|
|
" x = self.get_closest_medoids(data_points[i], medoids)\n",
|
|
" clusters[x].append(data_points[i])\n",
|
|
" return clusters\n",
|
|
"\n",
|
|
" def calculate_cost(self, data_points, clusters, medoids):\n",
|
|
" cost = 0\n",
|
|
" for i in range(len(clusters)):\n",
|
|
" for j in range(len(clusters[i])):\n",
|
|
" cost += self.calculateDistance(medoids[i], clusters[i][j])\n",
|
|
" return cost\n",
|
|
"\n",
|
|
" def get_non_medoids(self, data_points, medoids):\n",
|
|
" non_medoids = []\n",
|
|
" for sample in data_points:\n",
|
|
" flag = False\n",
|
|
" for m in medoids:\n",
|
|
" if (sample == m).all():\n",
|
|
" flag = True\n",
|
|
" if flag == False:\n",
|
|
" non_medoids.append(sample)\n",
|
|
" return non_medoids\n",
|
|
"\n",
|
|
" def get_clusters_label(self, data_points, clusters):\n",
|
|
" labels = []\n",
|
|
" for i in range(len(data_points)):\n",
|
|
" labels.append(0)\n",
|
|
" for i in range(len(clusters)):\n",
|
|
" cluster = clusters[i]\n",
|
|
" for j in range(len(cluster)):\n",
|
|
" for k in range(len(data_points)):\n",
|
|
" if (cluster[j] == data_points[k]).all():\n",
|
|
" labels[k] = i\n",
|
|
" break\n",
|
|
" return labels\n",
|
|
" \n",
|
|
" def plot_results(self, clusters, medoids):\n",
|
|
" colors = ['b', 'g', 'r', 'c', 'm', 'k']\n",
|
|
" X = []\n",
|
|
" Y = []\n",
|
|
" plt.figure(figsize=(10,8))\n",
|
|
" for i in range(self.k_value):\n",
|
|
" X.append(np.squeeze(clusters[i])[:, 0])\n",
|
|
" Y.append(np.squeeze(clusters[i])[:, 1])\n",
|
|
"\n",
|
|
" for i in range(len(X)):\n",
|
|
" plt.scatter(X[i], Y[i], c=colors[i])\n",
|
|
"\n",
|
|
" mx = []\n",
|
|
" my = []\n",
|
|
"\n",
|
|
" for m in medoids:\n",
|
|
" mx.append(m[0])\n",
|
|
" my.append(m[1])\n",
|
|
"\n",
|
|
" plt.scatter(mx, my, c='yellow', marker='*')\n",
|
|
" plt.xlabel(\"X\")\n",
|
|
" plt.ylabel(\"Y\")\n",
|
|
" plt.title(f\"K-medoids. Number of clusters: {self.k_value}\")\n",
|
|
" plt.show()\n",
|
|
" time.sleep(4)\n",
|
|
"\n",
|
|
" def kmedoids(self, data):\n",
|
|
" medoids = self.get_random_medoids(data)\n",
|
|
" clusters = self.get_clusters(data, medoids)\n",
|
|
" initial_cost = self.calculate_cost(data, clusters, medoids)\n",
|
|
" while True:\n",
|
|
" best_medoids = medoids\n",
|
|
" lowest_cost = initial_cost\n",
|
|
" for i in range(len(medoids)):\n",
|
|
" non_medoids = self.get_non_medoids(data, medoids)\n",
|
|
" for j in range(len(non_medoids)):\n",
|
|
" new_medoids = medoids.copy()\n",
|
|
" for k in range(len(new_medoids)):\n",
|
|
" if (new_medoids[k] == medoids[i]).all():\n",
|
|
" new_medoids[k] = non_medoids[j]\n",
|
|
" new_clusters = self.get_clusters(data, new_medoids)\n",
|
|
" new_cost = self.calculate_cost(data, new_clusters, new_medoids)\n",
|
|
" if new_cost < lowest_cost:\n",
|
|
" lowest_cost = new_cost\n",
|
|
" best_medoids = new_medoids\n",
|
|
" if lowest_cost < initial_cost:\n",
|
|
" initial_cost = lowest_cost\n",
|
|
" medoids = best_medoids\n",
|
|
" else:\n",
|
|
" break\n",
|
|
" final_clusters = self.get_clusters(data, medoids)\n",
|
|
" cluster_labels = self.get_clusters_label(data, final_clusters)\n",
|
|
" silhouette_avg = silhouette_score(data, cluster_labels)\n",
|
|
" self.plot_results(final_clusters, medoids)\n",
|
|
"\n",
|
|
" print(\"Sylwetka (ang.silhouette) dla metody k-medoid i dla k =\", self.k_value, round(silhouette_avg,2))\n",
|
|
"\n",
|
|
" res_cluster = []\n",
|
|
" for i in range(0, self.k_value):\n",
|
|
" res_cluster.append([data[s] for s in range(0, len(data)) if cluster_labels[s] == i])\n",
|
|
" return medoids, res_cluster, cluster_labels, round(silhouette_avg,2)\n",
|
|
"\n",
|
|
" def calculateDistance(self, x, y):\n",
|
|
" return np.linalg.norm(x-y)\n",
|
|
"\n",
|
|
" def return_values(self):\n",
|
|
" return self.medoids, self.res_cluster, self.cluster_labels, self.silhouette"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "c5290f06",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### Uruchomienie algorytmu k-medoid dla zbioru danych iris"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 117,
|
|
"id": "4c1f8423",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 3 0.48\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
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"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
|
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" vertical-align: middle;\n",
|
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" }\n",
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"\n",
|
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" .dataframe tbody tr th {\n",
|
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" vertical-align: top;\n",
|
|
" }\n",
|
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"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: center;\">\n",
|
|
" <th></th>\n",
|
|
" <th>Długość kielicha</th>\n",
|
|
" <th>Szerokość kielicha</th>\n",
|
|
" <th>Długość płatka</th>\n",
|
|
" <th>Szerokość płatka</th>\n",
|
|
" <th>Wartość medoidu 0</th>\n",
|
|
" <th>Wartość medoidu 1</th>\n",
|
|
" <th>Wartość medoidu 2</th>\n",
|
|
" <th>Medoid</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>0.17</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>0.17</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>0.75</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.63</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.59</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>0.72</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>0.66</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.12</td>\n",
|
|
" <td>0.51</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>0.61</td>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.61</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>0.39</td>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.59</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>0.56</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>0.63</td>\n",
|
|
" <td>0.63</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>0.17</td>\n",
|
|
" <td>0.17</td>\n",
|
|
" <td>0.39</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>10</th>\n",
|
|
" <td>0.64</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>0.61</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>11</th>\n",
|
|
" <td>0.25</td>\n",
|
|
" <td>0.29</td>\n",
|
|
" <td>0.49</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>12</th>\n",
|
|
" <td>0.19</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>13</th>\n",
|
|
" <td>0.44</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>14</th>\n",
|
|
" <td>0.47</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.51</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>15</th>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>0.63</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>16</th>\n",
|
|
" <td>0.36</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>0.44</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>17</th>\n",
|
|
" <td>0.67</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>18</th>\n",
|
|
" <td>0.36</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.59</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>19</th>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.29</td>\n",
|
|
" <td>0.53</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>20</th>\n",
|
|
" <td>0.53</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.59</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>21</th>\n",
|
|
" <td>0.36</td>\n",
|
|
" <td>0.21</td>\n",
|
|
" <td>0.49</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>22</th>\n",
|
|
" <td>0.44</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.64</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>23</th>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.51</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>24</th>\n",
|
|
" <td>0.56</td>\n",
|
|
" <td>0.21</td>\n",
|
|
" <td>0.66</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>25</th>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.63</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>26</th>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>0.56</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>27</th>\n",
|
|
" <td>0.64</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>28</th>\n",
|
|
" <td>0.69</td>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.64</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>29</th>\n",
|
|
" <td>0.47</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>0.59</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>30</th>\n",
|
|
" <td>0.39</td>\n",
|
|
" <td>0.25</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>31</th>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.17</td>\n",
|
|
" <td>0.47</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>32</th>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.17</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>33</th>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.29</td>\n",
|
|
" <td>0.49</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>34</th>\n",
|
|
" <td>0.47</td>\n",
|
|
" <td>0.29</td>\n",
|
|
" <td>0.69</td>\n",
|
|
" <td>0.63</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>35</th>\n",
|
|
" <td>0.31</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.59</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>36</th>\n",
|
|
" <td>0.47</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.59</td>\n",
|
|
" <td>0.63</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>37</th>\n",
|
|
" <td>0.67</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>0.63</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>38</th>\n",
|
|
" <td>0.56</td>\n",
|
|
" <td>0.12</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>39</th>\n",
|
|
" <td>0.36</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.53</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>40</th>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.21</td>\n",
|
|
" <td>0.51</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>41</th>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.25</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>42</th>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.61</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>43</th>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.25</td>\n",
|
|
" <td>0.51</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>44</th>\n",
|
|
" <td>0.19</td>\n",
|
|
" <td>0.12</td>\n",
|
|
" <td>0.39</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>45</th>\n",
|
|
" <td>0.36</td>\n",
|
|
" <td>0.29</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>46</th>\n",
|
|
" <td>0.39</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>47</th>\n",
|
|
" <td>0.39</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>48</th>\n",
|
|
" <td>0.53</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>0.56</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>49</th>\n",
|
|
" <td>0.22</td>\n",
|
|
" <td>0.21</td>\n",
|
|
" <td>0.34</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>50</th>\n",
|
|
" <td>0.39</td>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.53</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>51</th>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.29</td>\n",
|
|
" <td>0.69</td>\n",
|
|
" <td>0.75</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>52</th>\n",
|
|
" <td>0.17</td>\n",
|
|
" <td>0.21</td>\n",
|
|
" <td>0.59</td>\n",
|
|
" <td>0.67</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>53</th>\n",
|
|
" <td>0.39</td>\n",
|
|
" <td>0.21</td>\n",
|
|
" <td>0.68</td>\n",
|
|
" <td>0.79</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>54</th>\n",
|
|
" <td>0.47</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.68</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>55</th>\n",
|
|
" <td>0.36</td>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.66</td>\n",
|
|
" <td>0.79</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>56</th>\n",
|
|
" <td>0.56</td>\n",
|
|
" <td>0.29</td>\n",
|
|
" <td>0.66</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>57</th>\n",
|
|
" <td>0.53</td>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.64</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>58</th>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.66</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>59</th>\n",
|
|
" <td>0.56</td>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.69</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>60</th>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.25</td>\n",
|
|
" <td>0.78</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>61</th>\n",
|
|
" <td>0.47</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.64</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>62</th>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.29</td>\n",
|
|
" <td>0.69</td>\n",
|
|
" <td>0.75</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>63</th>\n",
|
|
" <td>0.56</td>\n",
|
|
" <td>0.21</td>\n",
|
|
" <td>0.68</td>\n",
|
|
" <td>0.75</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>64</th>\n",
|
|
" <td>0.44</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.69</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>65</th>\n",
|
|
" <td>0.67</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.68</td>\n",
|
|
" <td>0.67</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>66</th>\n",
|
|
" <td>0.56</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>0.85</td>\n",
|
|
" <td>1.00</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>67</th>\n",
|
|
" <td>0.78</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.83</td>\n",
|
|
" <td>0.83</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>68</th>\n",
|
|
" <td>0.56</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>0.78</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>69</th>\n",
|
|
" <td>0.61</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.81</td>\n",
|
|
" <td>0.88</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>70</th>\n",
|
|
" <td>0.92</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.95</td>\n",
|
|
" <td>0.83</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>71</th>\n",
|
|
" <td>0.83</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>0.90</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>72</th>\n",
|
|
" <td>0.67</td>\n",
|
|
" <td>0.21</td>\n",
|
|
" <td>0.81</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>73</th>\n",
|
|
" <td>0.81</td>\n",
|
|
" <td>0.67</td>\n",
|
|
" <td>0.86</td>\n",
|
|
" <td>1.00</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>74</th>\n",
|
|
" <td>0.61</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.69</td>\n",
|
|
" <td>0.79</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>75</th>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.29</td>\n",
|
|
" <td>0.73</td>\n",
|
|
" <td>0.75</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>76</th>\n",
|
|
" <td>0.69</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.76</td>\n",
|
|
" <td>0.83</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>77</th>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.69</td>\n",
|
|
" <td>0.96</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>78</th>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.73</td>\n",
|
|
" <td>0.92</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>79</th>\n",
|
|
" <td>0.61</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.76</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>80</th>\n",
|
|
" <td>0.94</td>\n",
|
|
" <td>0.75</td>\n",
|
|
" <td>0.97</td>\n",
|
|
" <td>0.88</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>81</th>\n",
|
|
" <td>0.94</td>\n",
|
|
" <td>0.25</td>\n",
|
|
" <td>1.00</td>\n",
|
|
" <td>0.92</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>82</th>\n",
|
|
" <td>0.72</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.80</td>\n",
|
|
" <td>0.92</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>83</th>\n",
|
|
" <td>0.94</td>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.97</td>\n",
|
|
" <td>0.79</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>84</th>\n",
|
|
" <td>0.67</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>0.80</td>\n",
|
|
" <td>0.83</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>85</th>\n",
|
|
" <td>0.81</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.85</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>86</th>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.78</td>\n",
|
|
" <td>0.83</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>87</th>\n",
|
|
" <td>0.81</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.81</td>\n",
|
|
" <td>0.63</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>88</th>\n",
|
|
" <td>0.86</td>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.86</td>\n",
|
|
" <td>0.75</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>89</th>\n",
|
|
" <td>1.00</td>\n",
|
|
" <td>0.75</td>\n",
|
|
" <td>0.92</td>\n",
|
|
" <td>0.79</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>90</th>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.78</td>\n",
|
|
" <td>0.88</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>91</th>\n",
|
|
" <td>0.94</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.86</td>\n",
|
|
" <td>0.92</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>92</th>\n",
|
|
" <td>0.56</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.78</td>\n",
|
|
" <td>0.96</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>93</th>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>0.76</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>94</th>\n",
|
|
" <td>0.72</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>0.75</td>\n",
|
|
" <td>0.83</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>95</th>\n",
|
|
" <td>0.67</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>0.78</td>\n",
|
|
" <td>0.96</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>96</th>\n",
|
|
" <td>0.72</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>0.69</td>\n",
|
|
" <td>0.92</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>97</th>\n",
|
|
" <td>0.69</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.83</td>\n",
|
|
" <td>0.92</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>98</th>\n",
|
|
" <td>0.67</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>0.80</td>\n",
|
|
" <td>1.00</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>99</th>\n",
|
|
" <td>0.67</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>0.92</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>100</th>\n",
|
|
" <td>0.61</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>0.79</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>101</th>\n",
|
|
" <td>0.53</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.75</td>\n",
|
|
" <td>0.92</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>102</th>\n",
|
|
" <td>0.22</td>\n",
|
|
" <td>0.62</td>\n",
|
|
" <td>0.07</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>103</th>\n",
|
|
" <td>0.17</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.07</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>104</th>\n",
|
|
" <td>0.11</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>105</th>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>106</th>\n",
|
|
" <td>0.19</td>\n",
|
|
" <td>0.67</td>\n",
|
|
" <td>0.07</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>107</th>\n",
|
|
" <td>0.31</td>\n",
|
|
" <td>0.79</td>\n",
|
|
" <td>0.12</td>\n",
|
|
" <td>0.12</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>108</th>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.07</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>109</th>\n",
|
|
" <td>0.19</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>110</th>\n",
|
|
" <td>0.03</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>0.07</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>111</th>\n",
|
|
" <td>0.17</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>112</th>\n",
|
|
" <td>0.31</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>113</th>\n",
|
|
" <td>0.14</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.10</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>114</th>\n",
|
|
" <td>0.14</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.07</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>115</th>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.02</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>116</th>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.83</td>\n",
|
|
" <td>0.03</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>117</th>\n",
|
|
" <td>0.39</td>\n",
|
|
" <td>1.00</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.12</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>118</th>\n",
|
|
" <td>0.31</td>\n",
|
|
" <td>0.79</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>0.12</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>119</th>\n",
|
|
" <td>0.22</td>\n",
|
|
" <td>0.62</td>\n",
|
|
" <td>0.07</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>120</th>\n",
|
|
" <td>0.39</td>\n",
|
|
" <td>0.75</td>\n",
|
|
" <td>0.12</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>121</th>\n",
|
|
" <td>0.22</td>\n",
|
|
" <td>0.75</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>122</th>\n",
|
|
" <td>0.31</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.12</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>123</th>\n",
|
|
" <td>0.22</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.12</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>124</th>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.67</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>125</th>\n",
|
|
" <td>0.22</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>0.12</td>\n",
|
|
" <td>0.17</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>126</th>\n",
|
|
" <td>0.14</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.15</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>127</th>\n",
|
|
" <td>0.19</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.10</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>128</th>\n",
|
|
" <td>0.19</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.10</td>\n",
|
|
" <td>0.12</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>129</th>\n",
|
|
" <td>0.25</td>\n",
|
|
" <td>0.62</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>130</th>\n",
|
|
" <td>0.25</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.07</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>131</th>\n",
|
|
" <td>0.11</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.10</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>132</th>\n",
|
|
" <td>0.14</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>0.10</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>133</th>\n",
|
|
" <td>0.31</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.12</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>134</th>\n",
|
|
" <td>0.25</td>\n",
|
|
" <td>0.87</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>135</th>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.92</td>\n",
|
|
" <td>0.07</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>136</th>\n",
|
|
" <td>0.19</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.03</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>137</th>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.62</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>138</th>\n",
|
|
" <td>0.03</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>139</th>\n",
|
|
" <td>0.22</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>140</th>\n",
|
|
" <td>0.19</td>\n",
|
|
" <td>0.62</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>141</th>\n",
|
|
" <td>0.06</td>\n",
|
|
" <td>0.12</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>142</th>\n",
|
|
" <td>0.03</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>143</th>\n",
|
|
" <td>0.19</td>\n",
|
|
" <td>0.62</td>\n",
|
|
" <td>0.10</td>\n",
|
|
" <td>0.21</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>144</th>\n",
|
|
" <td>0.22</td>\n",
|
|
" <td>0.75</td>\n",
|
|
" <td>0.15</td>\n",
|
|
" <td>0.12</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>145</th>\n",
|
|
" <td>0.14</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.07</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>146</th>\n",
|
|
" <td>0.22</td>\n",
|
|
" <td>0.75</td>\n",
|
|
" <td>0.10</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>147</th>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.07</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>148</th>\n",
|
|
" <td>0.28</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>149</th>\n",
|
|
" <td>0.19</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>0.07</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Długość kielicha \\\n",
|
|
"0 0.17 \n",
|
|
"1 0.17 \n",
|
|
"2 0.75 \n",
|
|
"3 0.58 \n",
|
|
"4 0.72 \n",
|
|
"5 0.33 \n",
|
|
"6 0.61 \n",
|
|
"7 0.39 \n",
|
|
"8 0.56 \n",
|
|
"9 0.17 \n",
|
|
"10 0.64 \n",
|
|
"11 0.25 \n",
|
|
"12 0.19 \n",
|
|
"13 0.44 \n",
|
|
"14 0.47 \n",
|
|
"15 0.50 \n",
|
|
"16 0.36 \n",
|
|
"17 0.67 \n",
|
|
"18 0.36 \n",
|
|
"19 0.42 \n",
|
|
"20 0.53 \n",
|
|
"21 0.36 \n",
|
|
"22 0.44 \n",
|
|
"23 0.50 \n",
|
|
"24 0.56 \n",
|
|
"25 0.50 \n",
|
|
"26 0.58 \n",
|
|
"27 0.64 \n",
|
|
"28 0.69 \n",
|
|
"29 0.47 \n",
|
|
"30 0.39 \n",
|
|
"31 0.33 \n",
|
|
"32 0.33 \n",
|
|
"33 0.42 \n",
|
|
"34 0.47 \n",
|
|
"35 0.31 \n",
|
|
"36 0.47 \n",
|
|
"37 0.67 \n",
|
|
"38 0.56 \n",
|
|
"39 0.36 \n",
|
|
"40 0.33 \n",
|
|
"41 0.33 \n",
|
|
"42 0.50 \n",
|
|
"43 0.42 \n",
|
|
"44 0.19 \n",
|
|
"45 0.36 \n",
|
|
"46 0.39 \n",
|
|
"47 0.39 \n",
|
|
"48 0.53 \n",
|
|
"49 0.22 \n",
|
|
"50 0.39 \n",
|
|
"51 0.42 \n",
|
|
"52 0.17 \n",
|
|
"53 0.39 \n",
|
|
"54 0.47 \n",
|
|
"55 0.36 \n",
|
|
"56 0.56 \n",
|
|
"57 0.53 \n",
|
|
"58 0.50 \n",
|
|
"59 0.56 \n",
|
|
"60 0.50 \n",
|
|
"61 0.47 \n",
|
|
"62 0.42 \n",
|
|
"63 0.56 \n",
|
|
"64 0.44 \n",
|
|
"65 0.67 \n",
|
|
"66 0.56 \n",
|
|
"67 0.78 \n",
|
|
"68 0.56 \n",
|
|
"69 0.61 \n",
|
|
"70 0.92 \n",
|
|
"71 0.83 \n",
|
|
"72 0.67 \n",
|
|
"73 0.81 \n",
|
|
"74 0.61 \n",
|
|
"75 0.58 \n",
|
|
"76 0.69 \n",
|
|
"77 0.42 \n",
|
|
"78 0.58 \n",
|
|
"79 0.61 \n",
|
|
"80 0.94 \n",
|
|
"81 0.94 \n",
|
|
"82 0.72 \n",
|
|
"83 0.94 \n",
|
|
"84 0.67 \n",
|
|
"85 0.81 \n",
|
|
"86 0.58 \n",
|
|
"87 0.81 \n",
|
|
"88 0.86 \n",
|
|
"89 1.00 \n",
|
|
"90 0.58 \n",
|
|
"91 0.94 \n",
|
|
"92 0.56 \n",
|
|
"93 0.58 \n",
|
|
"94 0.72 \n",
|
|
"95 0.67 \n",
|
|
"96 0.72 \n",
|
|
"97 0.69 \n",
|
|
"98 0.67 \n",
|
|
"99 0.67 \n",
|
|
"100 0.61 \n",
|
|
"101 0.53 \n",
|
|
"102 0.22 \n",
|
|
"103 0.17 \n",
|
|
"104 0.11 \n",
|
|
"105 0.08 \n",
|
|
"106 0.19 \n",
|
|
"107 0.31 \n",
|
|
"108 0.08 \n",
|
|
"109 0.19 \n",
|
|
"110 0.03 \n",
|
|
"111 0.17 \n",
|
|
"112 0.31 \n",
|
|
"113 0.14 \n",
|
|
"114 0.14 \n",
|
|
"115 0.00 \n",
|
|
"116 0.42 \n",
|
|
"117 0.39 \n",
|
|
"118 0.31 \n",
|
|
"119 0.22 \n",
|
|
"120 0.39 \n",
|
|
"121 0.22 \n",
|
|
"122 0.31 \n",
|
|
"123 0.22 \n",
|
|
"124 0.08 \n",
|
|
"125 0.22 \n",
|
|
"126 0.14 \n",
|
|
"127 0.19 \n",
|
|
"128 0.19 \n",
|
|
"129 0.25 \n",
|
|
"130 0.25 \n",
|
|
"131 0.11 \n",
|
|
"132 0.14 \n",
|
|
"133 0.31 \n",
|
|
"134 0.25 \n",
|
|
"135 0.33 \n",
|
|
"136 0.19 \n",
|
|
"137 0.33 \n",
|
|
"138 0.03 \n",
|
|
"139 0.22 \n",
|
|
"140 0.19 \n",
|
|
"141 0.06 \n",
|
|
"142 0.03 \n",
|
|
"143 0.19 \n",
|
|
"144 0.22 \n",
|
|
"145 0.14 \n",
|
|
"146 0.22 \n",
|
|
"147 0.08 \n",
|
|
"148 0.28 \n",
|
|
"149 0.19 \n",
|
|
"\n",
|
|
" Szerokość kielicha \\\n",
|
|
"0 0.46 \n",
|
|
"1 0.46 \n",
|
|
"2 0.50 \n",
|
|
"3 0.50 \n",
|
|
"4 0.46 \n",
|
|
"5 0.12 \n",
|
|
"6 0.33 \n",
|
|
"7 0.33 \n",
|
|
"8 0.54 \n",
|
|
"9 0.17 \n",
|
|
"10 0.38 \n",
|
|
"11 0.29 \n",
|
|
"12 0.00 \n",
|
|
"13 0.42 \n",
|
|
"14 0.08 \n",
|
|
"15 0.38 \n",
|
|
"16 0.38 \n",
|
|
"17 0.46 \n",
|
|
"18 0.42 \n",
|
|
"19 0.29 \n",
|
|
"20 0.08 \n",
|
|
"21 0.21 \n",
|
|
"22 0.50 \n",
|
|
"23 0.33 \n",
|
|
"24 0.21 \n",
|
|
"25 0.33 \n",
|
|
"26 0.38 \n",
|
|
"27 0.42 \n",
|
|
"28 0.33 \n",
|
|
"29 0.38 \n",
|
|
"30 0.25 \n",
|
|
"31 0.17 \n",
|
|
"32 0.17 \n",
|
|
"33 0.29 \n",
|
|
"34 0.29 \n",
|
|
"35 0.42 \n",
|
|
"36 0.58 \n",
|
|
"37 0.46 \n",
|
|
"38 0.12 \n",
|
|
"39 0.42 \n",
|
|
"40 0.21 \n",
|
|
"41 0.25 \n",
|
|
"42 0.42 \n",
|
|
"43 0.25 \n",
|
|
"44 0.12 \n",
|
|
"45 0.29 \n",
|
|
"46 0.42 \n",
|
|
"47 0.38 \n",
|
|
"48 0.38 \n",
|
|
"49 0.21 \n",
|
|
"50 0.33 \n",
|
|
"51 0.29 \n",
|
|
"52 0.21 \n",
|
|
"53 0.21 \n",
|
|
"54 0.08 \n",
|
|
"55 0.33 \n",
|
|
"56 0.29 \n",
|
|
"57 0.33 \n",
|
|
"58 0.42 \n",
|
|
"59 0.33 \n",
|
|
"60 0.25 \n",
|
|
"61 0.42 \n",
|
|
"62 0.29 \n",
|
|
"63 0.21 \n",
|
|
"64 0.42 \n",
|
|
"65 0.42 \n",
|
|
"66 0.54 \n",
|
|
"67 0.42 \n",
|
|
"68 0.38 \n",
|
|
"69 0.42 \n",
|
|
"70 0.42 \n",
|
|
"71 0.38 \n",
|
|
"72 0.21 \n",
|
|
"73 0.67 \n",
|
|
"74 0.50 \n",
|
|
"75 0.29 \n",
|
|
"76 0.42 \n",
|
|
"77 0.33 \n",
|
|
"78 0.50 \n",
|
|
"79 0.42 \n",
|
|
"80 0.75 \n",
|
|
"81 0.25 \n",
|
|
"82 0.50 \n",
|
|
"83 0.33 \n",
|
|
"84 0.54 \n",
|
|
"85 0.50 \n",
|
|
"86 0.33 \n",
|
|
"87 0.42 \n",
|
|
"88 0.33 \n",
|
|
"89 0.75 \n",
|
|
"90 0.33 \n",
|
|
"91 0.42 \n",
|
|
"92 0.58 \n",
|
|
"93 0.46 \n",
|
|
"94 0.46 \n",
|
|
"95 0.46 \n",
|
|
"96 0.46 \n",
|
|
"97 0.50 \n",
|
|
"98 0.54 \n",
|
|
"99 0.42 \n",
|
|
"100 0.42 \n",
|
|
"101 0.58 \n",
|
|
"102 0.62 \n",
|
|
"103 0.42 \n",
|
|
"104 0.50 \n",
|
|
"105 0.46 \n",
|
|
"106 0.67 \n",
|
|
"107 0.79 \n",
|
|
"108 0.58 \n",
|
|
"109 0.58 \n",
|
|
"110 0.38 \n",
|
|
"111 0.46 \n",
|
|
"112 0.71 \n",
|
|
"113 0.58 \n",
|
|
"114 0.42 \n",
|
|
"115 0.42 \n",
|
|
"116 0.83 \n",
|
|
"117 1.00 \n",
|
|
"118 0.79 \n",
|
|
"119 0.62 \n",
|
|
"120 0.75 \n",
|
|
"121 0.75 \n",
|
|
"122 0.58 \n",
|
|
"123 0.71 \n",
|
|
"124 0.67 \n",
|
|
"125 0.54 \n",
|
|
"126 0.58 \n",
|
|
"127 0.42 \n",
|
|
"128 0.58 \n",
|
|
"129 0.62 \n",
|
|
"130 0.58 \n",
|
|
"131 0.50 \n",
|
|
"132 0.46 \n",
|
|
"133 0.58 \n",
|
|
"134 0.87 \n",
|
|
"135 0.92 \n",
|
|
"136 0.50 \n",
|
|
"137 0.62 \n",
|
|
"138 0.42 \n",
|
|
"139 0.58 \n",
|
|
"140 0.62 \n",
|
|
"141 0.12 \n",
|
|
"142 0.50 \n",
|
|
"143 0.62 \n",
|
|
"144 0.75 \n",
|
|
"145 0.42 \n",
|
|
"146 0.75 \n",
|
|
"147 0.50 \n",
|
|
"148 0.71 \n",
|
|
"149 0.54 \n",
|
|
"\n",
|
|
" Długość płatka \\\n",
|
|
"0 0.08 \n",
|
|
"1 0.08 \n",
|
|
"2 0.63 \n",
|
|
"3 0.59 \n",
|
|
"4 0.66 \n",
|
|
"5 0.51 \n",
|
|
"6 0.61 \n",
|
|
"7 0.59 \n",
|
|
"8 0.63 \n",
|
|
"9 0.39 \n",
|
|
"10 0.61 \n",
|
|
"11 0.49 \n",
|
|
"12 0.42 \n",
|
|
"13 0.54 \n",
|
|
"14 0.51 \n",
|
|
"15 0.63 \n",
|
|
"16 0.44 \n",
|
|
"17 0.58 \n",
|
|
"18 0.59 \n",
|
|
"19 0.53 \n",
|
|
"20 0.59 \n",
|
|
"21 0.49 \n",
|
|
"22 0.64 \n",
|
|
"23 0.51 \n",
|
|
"24 0.66 \n",
|
|
"25 0.63 \n",
|
|
"26 0.56 \n",
|
|
"27 0.58 \n",
|
|
"28 0.64 \n",
|
|
"29 0.59 \n",
|
|
"30 0.42 \n",
|
|
"31 0.47 \n",
|
|
"32 0.46 \n",
|
|
"33 0.49 \n",
|
|
"34 0.69 \n",
|
|
"35 0.59 \n",
|
|
"36 0.59 \n",
|
|
"37 0.63 \n",
|
|
"38 0.58 \n",
|
|
"39 0.53 \n",
|
|
"40 0.51 \n",
|
|
"41 0.58 \n",
|
|
"42 0.61 \n",
|
|
"43 0.51 \n",
|
|
"44 0.39 \n",
|
|
"45 0.54 \n",
|
|
"46 0.54 \n",
|
|
"47 0.54 \n",
|
|
"48 0.56 \n",
|
|
"49 0.34 \n",
|
|
"50 0.53 \n",
|
|
"51 0.69 \n",
|
|
"52 0.59 \n",
|
|
"53 0.68 \n",
|
|
"54 0.68 \n",
|
|
"55 0.66 \n",
|
|
"56 0.66 \n",
|
|
"57 0.64 \n",
|
|
"58 0.66 \n",
|
|
"59 0.69 \n",
|
|
"60 0.78 \n",
|
|
"61 0.64 \n",
|
|
"62 0.69 \n",
|
|
"63 0.68 \n",
|
|
"64 0.69 \n",
|
|
"65 0.68 \n",
|
|
"66 0.85 \n",
|
|
"67 0.83 \n",
|
|
"68 0.78 \n",
|
|
"69 0.81 \n",
|
|
"70 0.95 \n",
|
|
"71 0.90 \n",
|
|
"72 0.81 \n",
|
|
"73 0.86 \n",
|
|
"74 0.69 \n",
|
|
"75 0.73 \n",
|
|
"76 0.76 \n",
|
|
"77 0.69 \n",
|
|
"78 0.73 \n",
|
|
"79 0.76 \n",
|
|
"80 0.97 \n",
|
|
"81 1.00 \n",
|
|
"82 0.80 \n",
|
|
"83 0.97 \n",
|
|
"84 0.80 \n",
|
|
"85 0.85 \n",
|
|
"86 0.78 \n",
|
|
"87 0.81 \n",
|
|
"88 0.86 \n",
|
|
"89 0.92 \n",
|
|
"90 0.78 \n",
|
|
"91 0.86 \n",
|
|
"92 0.78 \n",
|
|
"93 0.76 \n",
|
|
"94 0.75 \n",
|
|
"95 0.78 \n",
|
|
"96 0.69 \n",
|
|
"97 0.83 \n",
|
|
"98 0.80 \n",
|
|
"99 0.71 \n",
|
|
"100 0.71 \n",
|
|
"101 0.75 \n",
|
|
"102 0.07 \n",
|
|
"103 0.07 \n",
|
|
"104 0.05 \n",
|
|
"105 0.08 \n",
|
|
"106 0.07 \n",
|
|
"107 0.12 \n",
|
|
"108 0.07 \n",
|
|
"109 0.08 \n",
|
|
"110 0.07 \n",
|
|
"111 0.08 \n",
|
|
"112 0.08 \n",
|
|
"113 0.10 \n",
|
|
"114 0.07 \n",
|
|
"115 0.02 \n",
|
|
"116 0.03 \n",
|
|
"117 0.08 \n",
|
|
"118 0.05 \n",
|
|
"119 0.07 \n",
|
|
"120 0.12 \n",
|
|
"121 0.08 \n",
|
|
"122 0.12 \n",
|
|
"123 0.08 \n",
|
|
"124 0.00 \n",
|
|
"125 0.12 \n",
|
|
"126 0.15 \n",
|
|
"127 0.10 \n",
|
|
"128 0.10 \n",
|
|
"129 0.08 \n",
|
|
"130 0.07 \n",
|
|
"131 0.10 \n",
|
|
"132 0.10 \n",
|
|
"133 0.08 \n",
|
|
"134 0.08 \n",
|
|
"135 0.07 \n",
|
|
"136 0.03 \n",
|
|
"137 0.05 \n",
|
|
"138 0.05 \n",
|
|
"139 0.08 \n",
|
|
"140 0.05 \n",
|
|
"141 0.05 \n",
|
|
"142 0.05 \n",
|
|
"143 0.10 \n",
|
|
"144 0.15 \n",
|
|
"145 0.07 \n",
|
|
"146 0.10 \n",
|
|
"147 0.07 \n",
|
|
"148 0.08 \n",
|
|
"149 0.07 \n",
|
|
"\n",
|
|
" Szerokość płatka \\\n",
|
|
"0 0.00 \n",
|
|
"1 0.00 \n",
|
|
"2 0.54 \n",
|
|
"3 0.58 \n",
|
|
"4 0.58 \n",
|
|
"5 0.50 \n",
|
|
"6 0.58 \n",
|
|
"7 0.50 \n",
|
|
"8 0.63 \n",
|
|
"9 0.38 \n",
|
|
"10 0.50 \n",
|
|
"11 0.54 \n",
|
|
"12 0.38 \n",
|
|
"13 0.58 \n",
|
|
"14 0.38 \n",
|
|
"15 0.54 \n",
|
|
"16 0.50 \n",
|
|
"17 0.54 \n",
|
|
"18 0.58 \n",
|
|
"19 0.38 \n",
|
|
"20 0.58 \n",
|
|
"21 0.42 \n",
|
|
"22 0.71 \n",
|
|
"23 0.50 \n",
|
|
"24 0.58 \n",
|
|
"25 0.46 \n",
|
|
"26 0.50 \n",
|
|
"27 0.54 \n",
|
|
"28 0.54 \n",
|
|
"29 0.58 \n",
|
|
"30 0.38 \n",
|
|
"31 0.42 \n",
|
|
"32 0.38 \n",
|
|
"33 0.46 \n",
|
|
"34 0.63 \n",
|
|
"35 0.58 \n",
|
|
"36 0.63 \n",
|
|
"37 0.58 \n",
|
|
"38 0.50 \n",
|
|
"39 0.50 \n",
|
|
"40 0.50 \n",
|
|
"41 0.46 \n",
|
|
"42 0.54 \n",
|
|
"43 0.46 \n",
|
|
"44 0.38 \n",
|
|
"45 0.50 \n",
|
|
"46 0.46 \n",
|
|
"47 0.50 \n",
|
|
"48 0.50 \n",
|
|
"49 0.42 \n",
|
|
"50 0.50 \n",
|
|
"51 0.75 \n",
|
|
"52 0.67 \n",
|
|
"53 0.79 \n",
|
|
"54 0.58 \n",
|
|
"55 0.79 \n",
|
|
"56 0.71 \n",
|
|
"57 0.71 \n",
|
|
"58 0.71 \n",
|
|
"59 0.58 \n",
|
|
"60 0.54 \n",
|
|
"61 0.71 \n",
|
|
"62 0.75 \n",
|
|
"63 0.75 \n",
|
|
"64 0.71 \n",
|
|
"65 0.67 \n",
|
|
"66 1.00 \n",
|
|
"67 0.83 \n",
|
|
"68 0.71 \n",
|
|
"69 0.88 \n",
|
|
"70 0.83 \n",
|
|
"71 0.71 \n",
|
|
"72 0.71 \n",
|
|
"73 1.00 \n",
|
|
"74 0.79 \n",
|
|
"75 0.75 \n",
|
|
"76 0.83 \n",
|
|
"77 0.96 \n",
|
|
"78 0.92 \n",
|
|
"79 0.71 \n",
|
|
"80 0.88 \n",
|
|
"81 0.92 \n",
|
|
"82 0.92 \n",
|
|
"83 0.79 \n",
|
|
"84 0.83 \n",
|
|
"85 0.71 \n",
|
|
"86 0.83 \n",
|
|
"87 0.63 \n",
|
|
"88 0.75 \n",
|
|
"89 0.79 \n",
|
|
"90 0.88 \n",
|
|
"91 0.92 \n",
|
|
"92 0.96 \n",
|
|
"93 0.71 \n",
|
|
"94 0.83 \n",
|
|
"95 0.96 \n",
|
|
"96 0.92 \n",
|
|
"97 0.92 \n",
|
|
"98 1.00 \n",
|
|
"99 0.92 \n",
|
|
"100 0.79 \n",
|
|
"101 0.92 \n",
|
|
"102 0.04 \n",
|
|
"103 0.04 \n",
|
|
"104 0.04 \n",
|
|
"105 0.04 \n",
|
|
"106 0.04 \n",
|
|
"107 0.12 \n",
|
|
"108 0.08 \n",
|
|
"109 0.04 \n",
|
|
"110 0.04 \n",
|
|
"111 0.00 \n",
|
|
"112 0.04 \n",
|
|
"113 0.04 \n",
|
|
"114 0.00 \n",
|
|
"115 0.00 \n",
|
|
"116 0.04 \n",
|
|
"117 0.12 \n",
|
|
"118 0.12 \n",
|
|
"119 0.08 \n",
|
|
"120 0.08 \n",
|
|
"121 0.08 \n",
|
|
"122 0.04 \n",
|
|
"123 0.12 \n",
|
|
"124 0.04 \n",
|
|
"125 0.17 \n",
|
|
"126 0.04 \n",
|
|
"127 0.04 \n",
|
|
"128 0.12 \n",
|
|
"129 0.04 \n",
|
|
"130 0.04 \n",
|
|
"131 0.04 \n",
|
|
"132 0.04 \n",
|
|
"133 0.12 \n",
|
|
"134 0.00 \n",
|
|
"135 0.04 \n",
|
|
"136 0.04 \n",
|
|
"137 0.04 \n",
|
|
"138 0.04 \n",
|
|
"139 0.04 \n",
|
|
"140 0.08 \n",
|
|
"141 0.08 \n",
|
|
"142 0.04 \n",
|
|
"143 0.21 \n",
|
|
"144 0.12 \n",
|
|
"145 0.08 \n",
|
|
"146 0.04 \n",
|
|
"147 0.04 \n",
|
|
"148 0.04 \n",
|
|
"149 0.04 \n",
|
|
"\n",
|
|
" Wartość medoidu 0 \\\n",
|
|
"0 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"1 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"2 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"3 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"4 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"5 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"6 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"7 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"8 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"9 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"10 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"11 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"12 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"13 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"14 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"15 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"16 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"17 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"18 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"19 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"20 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"21 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"22 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"23 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"24 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"25 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"26 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"27 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"28 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"29 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"30 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"31 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"32 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"33 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"34 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"35 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"36 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"37 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"38 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"39 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"40 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"41 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"42 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"43 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"44 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"45 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"46 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"47 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"48 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"49 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"50 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"51 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"52 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"53 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"54 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"55 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"56 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"57 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"58 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"59 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"60 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"61 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"62 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"63 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"64 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"65 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"66 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"67 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"68 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"69 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"70 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"71 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"72 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"73 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"74 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"75 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"76 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"77 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"78 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"79 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"80 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"81 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"82 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"83 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"84 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"85 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"86 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"87 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"88 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"89 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"90 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"91 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"92 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"93 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"94 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"95 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"96 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"97 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"98 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"99 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"100 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"101 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"102 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"103 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"104 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"105 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"106 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"107 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"108 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"109 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"110 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"111 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"112 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"113 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"114 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"115 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"116 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"117 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"118 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"119 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"120 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"121 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"122 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"123 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"124 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"125 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"126 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"127 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"128 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"129 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"130 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"131 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"132 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"133 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"134 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"135 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"136 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"137 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"138 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"139 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"140 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"141 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"142 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"143 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"144 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"145 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"146 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"147 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"148 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"149 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"\n",
|
|
" Wartość medoidu 1 \\\n",
|
|
"0 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"1 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"2 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"3 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"4 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"5 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"6 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"7 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"8 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"9 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"10 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"11 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"12 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"13 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"14 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"15 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"16 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"17 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"18 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"19 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"20 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"21 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"22 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"23 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"24 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"25 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"26 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"27 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"28 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"29 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"30 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"31 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"32 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"33 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"34 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"35 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"36 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"37 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"38 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"39 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"40 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"41 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"42 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"43 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"44 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"45 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"46 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"47 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"48 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"49 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"50 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"51 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"52 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"53 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"54 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"55 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"56 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"57 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"58 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"59 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"60 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"61 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"62 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"63 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"64 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"65 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"66 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"67 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"68 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"69 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"70 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"71 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"72 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"73 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"74 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"75 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"76 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"77 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"78 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"79 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"80 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"81 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"82 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"83 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"84 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"85 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"86 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"87 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"88 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"89 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"90 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"91 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"92 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"93 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"94 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"95 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"96 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"97 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"98 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"99 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"100 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"101 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"102 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"103 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"104 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"105 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"106 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"107 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"108 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"109 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"110 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"111 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"112 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"113 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"114 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"115 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"116 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"117 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"118 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"119 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"120 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"121 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"122 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"123 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"124 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"125 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"126 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"127 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"128 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"129 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"130 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"131 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"132 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"133 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"134 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"135 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"136 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"137 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"138 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"139 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"140 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"141 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"142 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"143 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"144 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"145 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"146 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"147 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"148 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"149 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"\n",
|
|
" Wartość medoidu 2 \\\n",
|
|
"0 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"1 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"2 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"3 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"4 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"5 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"6 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"7 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"8 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"9 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"10 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"11 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"12 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"13 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"14 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"15 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"16 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"17 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"18 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"19 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"20 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"21 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"22 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"23 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"24 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"25 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"26 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"27 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"28 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"29 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"30 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"31 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"32 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"33 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"34 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"35 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"36 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"37 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"38 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"39 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"40 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"41 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"42 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"43 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"44 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"45 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"46 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"47 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"48 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"49 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"50 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"51 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"52 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"53 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"54 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"55 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"56 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"57 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"58 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"59 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"60 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"61 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"62 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"63 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"64 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"65 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"66 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"67 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"68 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"69 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"70 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"71 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"72 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"73 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"74 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"75 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"76 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"77 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"78 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"79 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"80 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"81 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"82 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"83 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"84 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"85 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"86 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"87 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"88 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"89 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"90 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"91 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"92 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"93 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"94 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"95 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"96 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"97 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"98 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"99 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"100 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"101 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"102 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"103 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"104 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"105 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"106 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"107 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"108 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"109 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"110 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"111 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"112 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"113 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"114 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"115 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"116 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"117 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"118 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"119 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"120 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"121 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"122 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"123 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"124 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"125 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"126 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"127 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"128 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"129 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"130 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"131 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"132 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"133 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"134 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"135 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"136 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"137 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"138 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"139 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"140 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"141 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"142 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"143 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"144 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"145 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"146 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"147 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"148 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"149 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"\n",
|
|
" Medoid \n",
|
|
"0 2 \n",
|
|
"1 2 \n",
|
|
"2 2 \n",
|
|
"3 2 \n",
|
|
"4 2 \n",
|
|
"5 2 \n",
|
|
"6 2 \n",
|
|
"7 2 \n",
|
|
"8 2 \n",
|
|
"9 2 \n",
|
|
"10 2 \n",
|
|
"11 2 \n",
|
|
"12 2 \n",
|
|
"13 2 \n",
|
|
"14 2 \n",
|
|
"15 2 \n",
|
|
"16 2 \n",
|
|
"17 2 \n",
|
|
"18 2 \n",
|
|
"19 2 \n",
|
|
"20 2 \n",
|
|
"21 2 \n",
|
|
"22 2 \n",
|
|
"23 2 \n",
|
|
"24 2 \n",
|
|
"25 2 \n",
|
|
"26 2 \n",
|
|
"27 2 \n",
|
|
"28 2 \n",
|
|
"29 2 \n",
|
|
"30 2 \n",
|
|
"31 2 \n",
|
|
"32 2 \n",
|
|
"33 2 \n",
|
|
"34 0 \n",
|
|
"35 2 \n",
|
|
"36 2 \n",
|
|
"37 0 \n",
|
|
"38 2 \n",
|
|
"39 2 \n",
|
|
"40 2 \n",
|
|
"41 2 \n",
|
|
"42 2 \n",
|
|
"43 2 \n",
|
|
"44 2 \n",
|
|
"45 2 \n",
|
|
"46 2 \n",
|
|
"47 2 \n",
|
|
"48 2 \n",
|
|
"49 2 \n",
|
|
"50 0 \n",
|
|
"51 0 \n",
|
|
"52 0 \n",
|
|
"53 0 \n",
|
|
"54 0 \n",
|
|
"55 0 \n",
|
|
"56 0 \n",
|
|
"57 0 \n",
|
|
"58 0 \n",
|
|
"59 0 \n",
|
|
"60 0 \n",
|
|
"61 0 \n",
|
|
"62 0 \n",
|
|
"63 0 \n",
|
|
"64 0 \n",
|
|
"65 0 \n",
|
|
"66 0 \n",
|
|
"67 0 \n",
|
|
"68 0 \n",
|
|
"69 0 \n",
|
|
"70 0 \n",
|
|
"71 0 \n",
|
|
"72 0 \n",
|
|
"73 0 \n",
|
|
"74 0 \n",
|
|
"75 0 \n",
|
|
"76 0 \n",
|
|
"77 1 \n",
|
|
"78 0 \n",
|
|
"79 0 \n",
|
|
"80 0 \n",
|
|
"81 0 \n",
|
|
"82 0 \n",
|
|
"83 0 \n",
|
|
"84 0 \n",
|
|
"85 0 \n",
|
|
"86 0 \n",
|
|
"87 0 \n",
|
|
"88 0 \n",
|
|
"89 0 \n",
|
|
"90 0 \n",
|
|
"91 0 \n",
|
|
"92 0 \n",
|
|
"93 0 \n",
|
|
"94 0 \n",
|
|
"95 0 \n",
|
|
"96 0 \n",
|
|
"97 0 \n",
|
|
"98 0 \n",
|
|
"99 0 \n",
|
|
"100 1 \n",
|
|
"101 0 \n",
|
|
"102 1 \n",
|
|
"103 1 \n",
|
|
"104 1 \n",
|
|
"105 1 \n",
|
|
"106 0 \n",
|
|
"107 1 \n",
|
|
"108 1 \n",
|
|
"109 1 \n",
|
|
"110 1 \n",
|
|
"111 1 \n",
|
|
"112 1 \n",
|
|
"113 0 \n",
|
|
"114 1 \n",
|
|
"115 1 \n",
|
|
"116 1 \n",
|
|
"117 1 \n",
|
|
"118 1 \n",
|
|
"119 0 \n",
|
|
"120 1 \n",
|
|
"121 0 \n",
|
|
"122 1 \n",
|
|
"123 0 \n",
|
|
"124 1 \n",
|
|
"125 1 \n",
|
|
"126 0 \n",
|
|
"127 0 \n",
|
|
"128 1 \n",
|
|
"129 1 \n",
|
|
"130 1 \n",
|
|
"131 1 \n",
|
|
"132 1 \n",
|
|
"133 0 \n",
|
|
"134 0 \n",
|
|
"135 1 \n",
|
|
"136 1 \n",
|
|
"137 1 \n",
|
|
"138 0 \n",
|
|
"139 1 \n",
|
|
"140 1 \n",
|
|
"141 1 \n",
|
|
"142 0 \n",
|
|
"143 1 \n",
|
|
"144 1 \n",
|
|
"145 1 \n",
|
|
"146 0 \n",
|
|
"147 1 \n",
|
|
"148 1 \n",
|
|
"149 0 "
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# iris\n",
|
|
"model4 = TrainModel_medoids(dataset, 3)\n",
|
|
"medoids4, res_cluster4, cluster_labels4, silhouette4 = model4.return_values()\n",
|
|
"\n",
|
|
"res = res_cluster4[0] + res_cluster4[1] + res_cluster4[2]\n",
|
|
"\n",
|
|
"data = {'Długość kielicha': [round(res[i][0],2) for i in range(0, len(res))],\n",
|
|
" 'Szerokość kielicha': [round(res[i][1],2) for i in range(0, len(res))],\n",
|
|
" 'Długość płatka': [round(res[i][2],2) for i in range(0, len(res))],\n",
|
|
" 'Szerokość płatka': [round(res[i][3],2) for i in range(0, len(res))],\n",
|
|
" 'Wartość medoidu 0': [(round(medoids4[0][0],2),round(medoids4[0][1],2), round(medoids4[0][2],2), round(medoids4[0][3],2)) for i in range(150)],\n",
|
|
" 'Wartość medoidu 1': [(round(medoids4[1][0],2), round(medoids4[1][1],2), round(medoids4[1][2],2), round(medoids4[1][3],2)) for i in range(150)],\n",
|
|
" 'Wartość medoidu 2': [(round(medoids4[2][0],2), round(medoids4[2][1],2), round(medoids4[2][2],2), round(medoids4[2][3],2)) for i in range(150)],\n",
|
|
" 'Medoid': cluster_labels4}\n",
|
|
"df = pd.DataFrame(data)\n",
|
|
"df.to_csv('iris_data.csv')\n",
|
|
"\n",
|
|
"pd.set_option('display.max_rows', None)\n",
|
|
"pd.set_option('display.max_columns', None)\n",
|
|
"pd.set_option('display.width', 10)\n",
|
|
"pd.set_option('display.colheader_justify', 'center')\n",
|
|
"pd.set_option('display.precision', 5)\n",
|
|
"display(df)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "655da39a",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### Uruchomienie algorytmu k-medoid dla zbioru danych glass"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 36,
|
|
"id": "bb83f704",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 4 0.35\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# glass\n",
|
|
"model5 = TrainModel_medoids(dataset2, 4)\n",
|
|
"medoids5, res_cluster5, cluster_labels5, silhouette5 = model5.return_values()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "17f279ec",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### Uruchomienie algorytmu k-medoid dla zbioru danych wine"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 74,
|
|
"id": "6ffe3810",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 3 0.28\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# wine\n",
|
|
"model6 = TrainModel_medoids(dataset3, 3)\n",
|
|
"medoids6, res_cluster6, cluster_labels6, silhouette6 = model6.return_values()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "3db40fc5",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Algorytm k-medoid + PCA"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 39,
|
|
"id": "662d4cf5",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"pca = PCA(n_components=2)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "ab279486",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### Redukcja wymiaru z 4 do 2 przy pomocy PCA na zbiorze danych iris"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 40,
|
|
"id": "21f2332b",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"pca.fit(dataset)\n",
|
|
"dataset_pca = pca.transform(dataset)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 47,
|
|
"id": "71128af5",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 3 0.5\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# iris\n",
|
|
"model7 = TrainModel_medoids(dataset_pca, 3)\n",
|
|
"medoids7, res_cluster7, cluster_labels7, silhouette7 = model7.return_values()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "7a82f399",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### Redukcja wymiaru z 9 do 2 przy pomocy PCA na zbiorze danych glass"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 42,
|
|
"id": "5c305f99",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"pca.fit(dataset2)\n",
|
|
"dataset2_pca = pca.transform(dataset2)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 43,
|
|
"id": "e4621daf",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 4 0.5\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# glass\n",
|
|
"model8 = TrainModel_medoids(dataset2_pca, 4)\n",
|
|
"medoids8, res_cluster8, cluster_labels8, silhouette8 = model8.return_values()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "14a5a5dd",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### Redukcja wymiaru z 13 do 2 przy pomocy PCA na zbiorze danych wine"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 48,
|
|
"id": "b63e355c",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"pca.fit(dataset3)\n",
|
|
"dataset3_pca = pca.transform(dataset3)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 49,
|
|
"id": "bf13c5e4",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 3 0.45\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# wine\n",
|
|
"model9 = TrainModel_medoids(dataset3_pca, 3)\n",
|
|
"medoids9, res_cluster9, cluster_labels9, silhouette9 = model9.return_values()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "ba1dd5c6",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Porównanie metody k-średnich, k-medoid, k-medoid + PCA"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "80537977",
|
|
"metadata": {},
|
|
"source": [
|
|
" W celu porównania przedstawionych powyżej trzech metod został stworzony wykres. Na wykresie zostały porównane wartości sylwetek trzech metod na trzech zbiorach danych (iris, glass, wine): "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 125,
|
|
"id": "c5909200",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"result_data = {'Iris': [silhouette, silhouette4, silhouette7],\n",
|
|
" 'Glass': [silhouette2, silhouette5, silhouette8],\n",
|
|
" 'Wine': [silhouette3, silhouette6, silhouette9]}\n",
|
|
"df_shoulette = pd.DataFrame(result_data)\n",
|
|
"df_shoulette.index = ['k-średnich', 'k-medoid', 'k-medoid + PCA']"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 126,
|
|
"id": "a8983527",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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nTwfg6aef5vHHH2fnnXdea30dsS1EkiSpB+nduzczZsxg2rRpXH755QBMmTKFefPmtQTrzjj00EO55pprePbZZ4HKFeRFixax//77c9ttt7F06VJee+01rr766pb3HHTQQVx55ZUATJ8+fbWe6q222ooBAwZw5513tszpSueccw5nnnkmy5YtA2DZsmVMnjyZ+fPn8/LLL/PUU0+xcOFCFi5cyDnnnNNS+/owXEuSJPUw/fr144YbbuA73/kOv/jFL9bpGEOHDuX888/n8MMPZ8899+Swww7jmWeeYdCgQTQ1NXHggQfykY98hH322aflPRMnTmTKlCnsueee/OxnP+OSSy5Z7bhTpkzhzDPP5MADD6Rv377r/Bk747Of/Swf+tCHGD58OLvvvjsjRozgbW97G1dccQVHH330W+aOHj2aK664Yr3PGZm53gfZEAwbNizvvffeepfR45x33nk9+iEyJe/g3tA03X475557br3LkN7ivPPO67HfO79zAnj00Ud53/ve17LtUnyNr+3fKUBEzM3MYe3Nt+dakiSpixiENz62hUiSJEmFGK4lSZKkQgzXkiRJBfWU+9m0bn+XhmtJkqRCNt98c5YuXWrA7gEyk6VLl7L55pvX9D5vaJQkSSpk8ODBLF68mCVLltS7FBWw+eabM3jw4JreY7iWJEkqZLPNNmPIkCH1LkN1ZFuIJEmSVIjhWpIkSSrEcC1JkiQVYriWJEmSCjFcS5IkSYUYriVJkqRCDNeSJElSIa5zLUmS1ENdMGECy5ub611Gl+jTvz9nn3VWvctYjeFakiSph1re3EzTiBH1LqNLNN1+e71LaJdtIZIkSVIhhmtJkiSpEMO1JEmSVIjhWpIkSSrEcC1JkiQVYriWJEmSCnEpvgImTLiA5ubl9S5DkiRJdWa4LqC5eTkjRjTVu4wucfvtTfUuQZIkqWHYFiJJkiQVYriWJEmSCjFcS5IkSYUYriVJkqRCDNeSJElSIYZrSZIkqRDDtSRJklSI4VqSJEkqxHAtSZIkFWK4liRJkgoxXEuSJEmFGK4lSZKkQgzXkiRJUiGGa0mSJKkQw7UkSZJUiOFakiRJKsRwLUmSJBViuJYkSZIKMVxLkiRJhRiuJUmSpEIM15IkSVIhhmtJkiSpEMO1JEmSVIjhWpIkSSrEcC1JkiQVYriWJEmSCjFcS5IkSYUYriVJkqRC6hKuI+KIiJgfEU9ExNkdzBseEa9HxDHdWZ8kSZK0Lro9XEdEL+BS4GPAUOCEiBi6hnnfAm7q3golSZKkdVOPK9f7AU9k5oLMXAFcCYxqZ97ngGuBZ7uzOEmSJGld1SNcbwc82Wp7cXVfi4jYDjgamNTRgSLijIi4NyLuXbJkSfFCJUmSpFrUI1xHO/uyzfZ3ga9m5usdHSgzJ2fmsMwcNnDgwFL1SZIkSetk0zqcczGwfavtwcDTbeYMA66MCIBtgCMjYmVmzuiWCiVJkqR1UI9wPQfYOSKGAE8BxwMntp6QmUNWvY6IqcANBmtJkiRt6Lo9XGfmyogYR2UVkF7ATzLz4YgYWx3vsM9akiRJ2lDV48o1mTkTmNlmX7uhOjNP646aJEmSpPXlExolSZKkQgzXkiRJUiGGa0mSJKkQw7UkSZJUiOFakiRJKsRwLUmSJBViuJYkSZIKMVxLkiRJhRiuJUmSpEIM15IkSVIhhmtJkiSpEMO1JEmSVIjhWpIkSSrEcC1JkiQVYriWJEmSCjFcS5IkSYUYriVJkqRCDNeSJElSIYZrSZIkqRDDtSRJklSI4VqSJEkqxHAtSZIkFWK4liRJkgoxXEuSJEmFGK4lSZKkQgzXkiRJUiGGa0mSJKkQw7UkSZJUiOFakiRJKsRwLUmSJBViuJYkSZIKMVxLkiRJhRiuJUmSpEIM15IkSVIhhmtJkiSpEMO1JEmSVMim9S5AkvSmCRdMoHl5c73LkCStI8O1JG1Ampc3M6JpRL3L6DK3N91e7xIkqUvZFiJJkiQVYriWJEmSCjFcS5IkSYUYriVJkqRCDNeSJElSIYZrSZIkqRDDtSRJklSI4VqSJEkqxHAtSZIkFdLhExoj4kjgzsxcVn3docycWawySZIkqcGs7fHnNwAHAPdUXycQa5ibQK9ypUmSJEmNZW3hegjwTKvXkiRJktagw3CdmYtabf4tM19d09yI2K5YVZIkSVIDquWGxusjond7AxHxHuDOMiVJkiRJjamWcP0u4JqIeMvV7ojYDbgDmF+yMEmSJKnR1BKuPwwMBS6PiE0AImIYcDtwN3BU+fIkSZKkxtHpcJ2ZTwEfAfYHfhoRI4FbgJuAYzJzRVcUKEmSJDWKmh4ik5kLqVzB/jCVYH1FZp6Uma93QW2SJElSQ1nbQ2QuXMPQXOBAYFmrOZmZXy1ZnCRJktRI1rbO9T93MPZSm/EEDNeSJEnaaK1tnWsfHCNJkiR1Uqd7riPilIjYeg1j/xARp5QrS5IkSWo8tdzQOAV49xrGhlTHJUmSpI1WLeE6OhjbGli2nrVIkiRJDW1tq4WMAka12vXvEbGkzbTNgQ8CcwrXJkmSJDWUta0Wsi2wR6vtdwPvaDNnBfBr4PyCdUmSJEkNZ22rhfwQ+CFARNwK/GtmPtodhUmSJEmNZm1Xrltk5odWvY6IAAYBz2bmyq4oTJIkSWo0NT3+PCKOjIi7gVeBJ4E9q/t/GBGf6oL6JEmSpIZR0zrXwPXAH4EzeOvqIY8Bny5bmiRJktRYarly/TXgosw8FbiszdjDwNBiVUmSJEkNqJZw/S7g5jWMvQpsuf7lSJIkSY2rlnD9JLD3GsaGAU+sfzmSJElS46olXP8YOLd642Lf6r6IiEOBr1Bdsk+SJEnaWHV6KT7gW8D2wE+B16v77gJ6AT/IzImFa5MkSZIaSi3rXCdwZkR8B/gwsA3wPPDbzHysi+qTJEmSGkYtV64ByMwnWM/+6og4AriEylXvH2XmBW3GRwH/AbwBrAS+kJl3rs85JUmSpK7W6XAdEYuBO4DfAb/LzIfW5YQR0Qu4FDgMWAzMiYjrM/ORVtNuAa7PzIyIPYH/AnZdl/NJkiRJ3aWWGxq/A7wN+AbwQEQsjYjrI+LLEXFARHQ2qO8HPJGZCzJzBXAlMKr1hMx8qdqGAtAPSCRJkqQNXC091xcDFwNExO7AwcAhwOeAC4BXgC06cajtqCzrt8piYP+2kyLiaGA8sC3w8fYOFBFnUHlaJDvssEMnP4kkSarFhAkX0Ny8vN5ldJn+/ftw1lln17sM9RA191xXvULlwTGvAsupPAr9L518b7Szb7Ur05n5c+DnEXEIlf7rj7QzZzIwGWDYsGFe3ZYkqQs0Ny9nxIimepfRZW6/vaneJagH6XRbSESMi4irIuJp4I/AmcALwJeBbTOzs48/X0xlSb9VBgNPr2lyZt4BvDsitulsrZIkSVI91HLleiKVK9Y/Bi7MzMXreM45wM4RMQR4CjgeOLH1hIh4D/Cn6g2N+wC9gaXreD5JkiSpW9QSrj8LfJDKzYf/GhEPUVk95A7gjsx8tjMHycyVETEOuInKUnw/ycyHI2JsdXwSMBo4JSJeoxLoj2t1g6MkSZK0QarlhsYfAD8AiIh3UQnaq/qhd4mIxzLzfZ081kxgZpt9k1q9/haVJ0JKkiRJDaOWpfha26rNT1BZBUSSJEnaaNXyEJmzqVytPgjYkkoP9J3AhVRaQ+7rigIlSZKkRlFLz/VYKk9n/CqVJzQ+2jUlSZIkSY2plp7rHbuwDkmSJKnhrWvPtSRJkqQ2OrxyHRFLaOfpiWuSmduud0WSJElSg1pbW8il1BCuJUmSpI1Zh+E6M5u6qQ5JkiSp4XW65zoivh0RQ7uyGEmSJKmR1XJD42jgwYi4JyLGRsSAripKkiRJakSdDteZOQT4CPBH4CLgmYi4PCI+0lXFSZIkSY2klofIkJm3ArdGxBbAccCpwE0RsRiYCvw0MxcUr1KSWpkw4QKam5fXuwxJPUS8Fpx33nn1LkM9RE3hepXMfAn4cUQsAM4DDgbOAb4WEb8CPp+Zi8qVKUlvam5ezogRTfUuo0vcfntTvUuQNjq5WTKiaUS9y+gStzfdXu8SNjo1P0QmInaMiHOrwfrXwEvAPwP9gaOAHYErSxYpSZIkNYJOX7mOiJOBMcAhwF+AKcCUzFzcatrMiHgZ+E3RKiVJkqQGUEtbyGTg58BHM/OWDuY9Bpy/XlVJkiRJDaiWcP3OzPz72iZl5jNU+rAlSZKkjUot4bo5IvpkZsst+hFxODAUuCMz/1C8OkmSJKmB1BKurwJeBE4HiIjPA98FlgO9IuKfMvOG4hVKkiRJDaKW1UIOAGa22v4ycHFm9gV+BHytZGGSJElSo6klXG8N/BUgIvYA3glMqo5dTaU9RJIkSdpo1RKu/0ZlDWuAI4BFmfmn6nZf4I2CdUmSJEkNp5ae66uBb0XE+6msd/29VmN7A4+XLEySJElqNLWE67OBZcBw4P8B/9lqbF8qNzxKkiRJG61Oh+vMXAl8Yw1j/1SsIkmSJKlB1dJzLUmSJKkDhmtJkiSpEMO1JEmSVIjhWpIkSSrEcC1JkiQVssZwHREfbrP9+Yi4YA1zx0fEuNLFSZIkSY2koyvX10fEWa22/xV4Yg1zH6uOS5IkSRutjsL1/sBJEXF5dftdrDlc/5k3H40uSZIkbZTWGK4z82HgAOBP1V1/B967hunvpfL0RkmSJGmj1eENjZn5Wmb+e3Xzl0BTROzRek5E7A6cC/yia0qUJEmSGkOnH38OnAMcBNwXEfcBzwCDgL2Bh4Czy5cnSZIkNY5OL8WXmc8Dw4EzqbSK9K3+/iywf2b+vUsqlCRJkhpELVeuycxXgR9UfyRJkiS10tE6133abG8bEUNabUdEnBER342I/9WVRUqSJEmNoKO2kDsj4p2ttqcCX2y1fR7wfeAI4OcRcVrx6iRJkqQG0lG4/gMwNyIOqm7vA/wWICI2odJr/X8yc1fgm8AXurBOSZIkaYPX0TrXnwE+DUyv7hoALK2+3hf4h1ZjvwXe00U1SpIkSQ1hbetcz6QSpAEWA0Orrz8O/DEzn6puDwBe7ZIKJUmSpAax1tVCqkvwAfwEuDAiPkIlXJ/TatoBwKPly5MkSZIaR6eX4svM8RHxFJW1rj9HJWyv8g/AjwrXJkmSJDWUWte5ngZMa2f/2GIVSZIkSQ2qpnAdEZsCo4GDqVytfh74HXBdZq4sX54kSZLUODodriNiW+DXwJ7AQuBvwIFUHod+f0QcnplLuqJISZIkqRF0uFpIGxOArYH9M3OnzDwwM3cC9q/un9AVBUqSJEmNopZwfSTw1cyc03pndfscKiuISJIkSRutWsJ1H6B5DWPNQO/1L0eSJElqXLWE698DX42Ifq13Vre/Wh2XJEmSNlq1rBbyv4FbgScj4tdUbmjcFvgoEMDI4tVJkiRJDaTTV64zcx6wMzAZGAgcRiVcTwJ2zsz7u6JASZIkqVHU+hCZ54Czu6gWSZIkqaF1+sp1RCyIiPevYWz3iFhQrixJkiSp8dRyQ+OOVFYMac/bgMHrXY0kSZLUwDpsC4mILYGtWu16R0Ts0Gba5sDxwFNlS5MkSZIay9p6rr8InAtk9efna5gXVFYTkSRJkjZaawvXlwP3UgnP1wNfAua3mbMCmJ+ZfylfniRJktQ4OgzXmfk48HhE9AFOAe7PzAe7pTJJkiSpwXTqhsbMXA78EPiHri1HkiRJaly1rBbyILBLVxUiSZIkNbpaHiLzRWBqRDwD/HdmruyimiRJkqSGVEu4nkFlPetfABkRf6eygkiLzNy2XGmSJElSY6klXF9KmzAtSZIk6U2dDteZ2dSFdUiSJEkNr5YbGiVJkiR1oJa2ECLiQODTVFYN2bzteGbuV6guSZIkqeF0+sp1RBwG3AEMBg4GlgAvAe8HtgYe6ooCJUmSpEZRS1vIN4BLgI9Xt/89Mz9M5Sr2a8BtZUuTJEmSGkst4XoocCPwBpVVQ/oBZOYioAn4WuniJEmSpEZSS7h+FdgkMxN4Bnh3q7FlVNpFJEmSpI1WLeH6fuC91de3AOdExGERMYJKy8iDnT1QRBwREfMj4omIOLud8ZMi4oHqz10R8f4a6pQkSZLqopZw/V3efIjM/wFeBm4CbgW2Bc7szEEioheVB9J8jEqryQkRMbTNtD8DIzJzT+A/gMk11ClJkiTVRYdL8UXEAcDczHwtM2eu2p+ZT0XEvsB7gL7AHzNzRSfPuR/wRGYuqJ7jSmAU8Eir49/Vav7vseVEkiRJDWBt61zfBbwaEfdWX98JzM7MpdXe68fX4ZzbAU+22l4M7N/B/E9TuZFSkiRJ2qCtLVx/FDgQOAj4DPAVICPicWBW9eeuzPxjDeeMdvZlO/uIiA9RCdcHr2H8DOAMgB122KGGEiRJkqTyOgzXmXkzcDNARASwG/ABKoH7g8AYKmH771RC9lGdOOdiYPtW24OBp9tOiog9gR8BH8vMpWuobzLVfuxhw4a1G9AlSZKk7tLpGxqz4qHM/EFmnkZl5ZAPU2nZeDtvPlxmbeYAO0fEkIjoDRwPXN96QkTsAFwHnJyZj3W2RkmSJKme1tYW0iIi+lHpjT6o+nMA0J/KjYg/BGZ35jiZuTIixlFZaaQX8JPMfDgixlbHJwFfp/JI9e9XLpizMjOHdbZWSZIkqR7WtlrIibwZpvcEXqCyesddwMXA3Zn5Uq0nra48MrPNvkmtXv8L8C+1HleSJEmqp7Vdub6MynrW06i0aDzc9SVJkiRJjWltPdcXAfcBpwH3RMQdEfGtiPhkRPxjl1cnSZIkNZC1rRbyVYCI2BTYmzeX5TsBGBwRf6bSaz2byvrXf+jaciVJkqQNV6duaMzMlVRW+ZgDTASIiO2oBO1TgUtqOZ4kSZLUE9UUhiOiDzCcN29yPBAYWB3+S9nSJEmSpMayttVC3smbQfogYC+gN7ASmAdcQfVJjZm52oNgJEmSpI3J2q5cL6byaPK/U+mrPo/KMnz3ZOYrXVybJEmS1FDWFq7/hcpjzf/YHcVIkiRJjWxtq4X8pLsKkSRJkhrd2ta5liRJktRJhmtJkiSpEMO1JEmSVIjhWpIkSSrEcC1JkiQVYriWJEmSCjFcS5IkSYUYriVJkqRCDNeSJElSIYZrSZIkqRDDtSRJklSI4VqSJEkqxHAtSZIkFWK4liRJkgoxXEuSJEmFGK4lSZKkQgzXkiRJUiGGa0mSJKkQw7UkSZJUiOFakiRJKsRwLUmSJBViuJYkSZIKMVxLkiRJhRiuJUmSpEIM15IkSVIhhmtJkiSpEMO1JEmSVIjhWpIkSSrEcC1JkiQVYriWJEmSCjFcS5IkSYUYriVJkqRCDNeSJElSIYZrSZIkqRDDtSRJklSI4VqSJEkqxHAtSZIkFWK4liRJkgoxXEuSJEmFGK4lSZKkQgzXkiRJUiGGa0mSJKkQw7UkSZJUiOFakiRJKsRwLUmSJBViuJYkSZIKMVxLkiRJhRiuJUmSpEIM15IkSVIhhmtJkiSpEMO1JEmSVIjhWpIkSSrEcC1JkiQVYriWJEmSCjFcS5IkSYUYriVJkqRCDNeSJElSIYZrSZIkqRDDtSRJklSI4VqSJEkqxHAtSZIkFWK4liRJkgoxXEuSJEmFGK4lSZKkQgzXkiRJUiF1CdcRcUREzI+IJyLi7HbGd42I2RGxPCK+VI8aJUmSpFpt2t0njIhewKXAYcBiYE5EXJ+Zj7Sa9jzweeCT3V2fJEmStK7qceV6P+CJzFyQmSuAK4FRrSdk5rOZOQd4rQ71SZIkSeukHuF6O+DJVtuLq/skSZKkhlaPcB3t7Mt1OlDEGRFxb0Tcu2TJkvUsS5IkSVo/9QjXi4HtW20PBp5elwNl5uTMHJaZwwYOHFikOEmSJGld1SNczwF2joghEdEbOB64vg51SJIkSUV1+2ohmbkyIsYBNwG9gJ9k5sMRMbY6Piki3gHcC2wJvBERXwCGZuay7q5XkiRJ6qxuD9cAmTkTmNlm36RWr/9KpV1EkiRJahg+oVGSJEkqxHAtSZIkFWK4liRJkgoxXEuSJEmFGK4lSZKkQgzXkiRJUiGGa0mSJKkQw7UkSZJUiOFakiRJKsRwLUmSJBViuJYkSZIKMVxLkiRJhRiuJUmSpEIM15IkSVIhhmtJkiSpEMO1JEmSVIjhWpIkSSrEcC1JkiQVYriWJEmSCjFcS5IkSYUYriVJkqRCDNeSJElSIYZrSZIkqRDDtSRJklSI4VqSJEkqxHAtSZIkFWK4liRJkgoxXEuSJEmFGK4lSZKkQgzXkiRJUiGGa0mSJKkQw7UkSZJUiOFakiRJKsRwLUmSJBViuJYkSZIKMVxLkiRJhRiuJUmSpEIM15IkSVIhhmtJkiSpEMO1JEmSVIjhWpIkSSrEcC1JkiQVYriWJEmSCjFcS5IkSYUYriVJkqRCDNeSJElSIYZrSZIkqRDDtSRJklSI4VqSJEkqxHAtSZIkFWK4liRJkgoxXEuSJEmFGK4lSZKkQgzXkiRJUiGGa0mSJKkQw7UkSZJUiOFakiRJKsRwLUmSJBViuJYkSZIKMVxLkiRJhRiuJUmSpEIM15IkSVIhhmtJkiSpEMO1JEmSVIjhWpIkSSrEcC1JkiQVYriWJEmSCjFcS5IkSYUYriVJkqRCDNeSJElSIYZrSZIkqRDDtSRJklSI4VqSJEkqxHAtSZIkFVKXcB0RR0TE/Ih4IiLObmc8ImJidfyBiNinHnVKkiRJtej2cB0RvYBLgY8BQ4ETImJom2kfA3au/pwB/L9uLVKSJElaB/W4cr0f8ERmLsjMFcCVwKg2c0YB07Li98BWETGouwuVJEmSalGPcL0d8GSr7cXVfbXOkSRJkjYokZnde8KIfwY+mpn/Ut0+GdgvMz/Xas6vgPGZeWd1+xbgK5k5t82xzqDSNgLwXmB+N3wEda1tgOfqXYS0EfE7J3Uvv3M9w7syc2B7A5t2dyVUrkJv32p7MPD0OswhMycDk0sXqPqJiHszc1i965A2Fn7npO7ld67nq0dbyBxg54gYEhG9geOB69vMuR44pbpqyAHAi5n5THcXKkmSJNWi269cZ+bKiBgH3AT0An6SmQ9HxNjq+CRgJnAk8ATwP8CY7q5TkiRJqlW391xLHYmIM6rtPpK6gd85qXv5nev5DNeSJElSIT7+XJIkSSrEcK1uFxEvdTB2V3fWIvU0EfGPEXF5RCyIiLkRMTsijo6IkRFxQ73rk3qaiPhORHyh1fZNEfGjVtsXR8TXI+LsuhSobme41gYhInoBZOZB9a5FalQREcAM4I7M3Ckz96WyItPguhYm9Wx3AQcBRMQmVNax3q3V+EHATZl5QR1qUx0YrlU31Stpt0bE5cCD1X0vVX8Piog7ImJeRDwUER+sa7FSY/gwsKK66hIAmbkoM/9v60kRsV9E3BUR91V/v7e6f7eIuKf6vXsgInaOiH4R8auIuL/6XTyumz+TtKGbRTVcUwnVDwHNEfH2iOgDvA94f0R8DyAipkbExOp3b0FEHLPqQBHx5YiYU/3+ndfdH0Rl1OMhMlJr+wG7Z+af2+w/kcq/9L9Zvar9tu4vTWo4uwF/6MS8PwKHVJdG/Qjwn8BoYCxwSWZOrz6HoBeVZVGfzsyPA0TEgK4pXWpMmfl0RKyMiB2ohOzZwHbAgcCLwAPAijZvGwQcDOxK5dke10TE4cDOVP5/MYDrI+KQzLyjez6JSjFcq97uaSdYQ+VhQz+JiM2AGZk5r3vLkhpfRFxK5f/AVwBfbjU0APhpROwMJLBZdf9s4GsRMRi4LjMfj4gHgW9HxLeAGzLzd933CaSGserq9UHABCrh+iAq4bq9e4lmZOYbwCMR8Y/VfYdXf+6rbm9BJWwbrhuMbSGqt5fb21n9l/ohwFPAzyLilG6tSmpMDwP7rNrIzDOBQ4GBbeb9B3BrZu4O/C9g8+r8y4GjgFeAmyLiw5n5GLAvldat8RHx9S7/FFLjWdV3vQeVtpDfU7lyfRCV4N3W8lavo9Xv8Zm5V/XnPZn54y6sWV3EcK0NUkS8C3g2M38I/JhWgUHSGv0W2DwiPttqX3stVQOo/MMV4LRVOyNiJ2BBZk6k8p+q94yIdwL/k5mXAd/G76LUnlnAJ4DnM/P1zHwe2IpKwJ7dyWPcBJweEVsARMR2EbFtVxSrrmVbiDZUI4EvR8RrwEuAV66ltcjMjIhPAt+JiK8AS6j816Gvtpl6IZW2kLOoBPJVjgM+Vf3e/RX4BjAcuCgi3gBeAz6LpLYepLJKyOVt9m2Rmc9VFvLpWGb+OiLeB8yuzn8J+BTwbPly1ZV8QqMkSZJUiG0hkiRJUiGGa0mSJKkQw7UkSZJUiOFakiRJKsRwLUmSJBViuJakBhER10TETRHRq961SJLaZ7iWpHUQESMjItfyc1p1blNEPNeJYy6MiG+vYexkYG/ghMx8vfBneS4imkoes6SIuC0irql3HZLUGT5ERpLWzR+oPH2tPZOAdwO/q/GYRwNL2+6MiMHAeODI6pPfJEkbKMO1JK2DzFwG/L7t/og4A3g/MCYz/1TjMe9bw/7FwODOHiciNs/MV2s5tySpDNtCJKmQiHgv8B3gqsyc2s74ByLiDxHxakTMi4iD24yv1hYSEcdGxIMRsTwinoyIb0bEpq3GT6u2oOxXbZ94BfhyBzUeEhH3V2uYGxEHtTPn4xFxc0Q8GxHLIuL3EXF4mzlN1XaSvavj/xMR90XEB1vNuSgiFkSbZz9HxJiIWBER21S3e0XEORHxWPVzLo6I9v78ToyIJ6o13Vi9oi9JGxTDtSQVEBGbAZcDzwFj25nyNuAyKi0j/wy8ANwYEe/o4JiHA1dRaUEZBfxf4EvA99qZfgVwA3Bk9Xd7x3sncCPwPHAM8ANgerW21oYAvwROBkYDd1Vr/UA7n+mn1eOMBpYDP4+IVcf7UfVYI9q87zTgl5m5qg/9B8B5wH8BnwD+N9CvzXv2B8ZVx84A9gEmt/c5JamebAuRpDLOp9IOMjIzX2hnvC/wtcy8HCAibgX+AnwBOHsNx/wGcFtmnlrd/u/qReDxEXF+tV1klYmZeclaavwC8Crw8cz8n2odL1MJ/S0ysyW8R8QmwK3AbsCngVltPtMXMvO31bnPAPcBhwD/nZnzI2IWMAa4rTpnJ+CDwFHV7V2rx/23zJzY6thXtal9y2rdf6++7x3AdyKib2a+spbPLUndxivXkrSeIuJDVK4ofzMz7+xg6s9XvcjMl4Cbgf3WcMxeVK7OXt1m6Coq/9vd9mbKX3Wi1P2Am1cF66rr2jn34Ij4aUQ8BawEXgMOB3ZpM/U1qqG56pHq79btGj8GRkfEFtXt04C/Af9d3f5Q9ffUtdQ+Z1WwbnOu7dbyPknqVoZrSVoPEfF2YBpwN/AfHUx9qZ0rrM8Cg9YwfxtgMypBtLVV2/+whv0deUf1nC2qNb20art6pfp64CDg61TC73Aq7SSbtznessx8o9WxVlRftp73X8AbwLHV3utTgGmZubI6vjXwcvUG0Y680Ga7vXNJUt3ZFiJJ62cylZaFk1oFxvZs0U4Lw7bAM2uY/xyVK8Pbttn/j9XfbZfky07U+te2x4uIvsAWrXa9h8p62h/LzP9uM69mmflyRFxJ5Yr1IuBdvPUq9VKgX0Rs2YmALUkbPK9cS9I6iohPU7kx8F8z88+deMvRrd67BXAYcE97E6sPiplL5ebH1o6lciV49jqUPAc4rNUNhwD/1GbOqhC9vFWt7wLa3sxYix9T6bNuAn6fmY+2Gvtt9fcp63F8SdpgeOVaktZBRLwb+C6Vta7/FBEHtDNtcaubDl8BvlkN1U9T6dHuDXR0E+K5wE0RMQW4EtiDSuvJD9vczNhZ3wXOBG6IiAnAO4FzqrWt8kdgMXBxRPw70J/KSh5PrcP5AMjMuyPiYeBg4DNtxuZHxOTq+bYF7gC2Ao7JzOPX9ZySVC+Ga0laNx+k0k5xAGu+inwelau1AP9D5ers/wXeRyXEHpmZa2oLITN/HRHHA/8fcBKVfumLqYTummXmUxFxJDARuBZ4FPgU8ItWc5ZHxD8BlwLXUAna3wRGAruvy3mrZgA7UflHQlv/SqVl5F+orJzyLJWbPSWp4URmZ9r0JEladxFxDzA/M0+udy2S1JW8ci1J6jIRMQz4MJUVR86sczmS1OUM15KkrjSHyjJ652TmnDrXIkldzrYQSZIkqRCX4pMkSZIKMVxLkiRJhRiuJUmSpEIM15IkSVIhhmtJkiSpEMO1JEmSVMj/D0hKzw/cKetRAAAAAElFTkSuQmCC\n",
|
|
"text/plain": [
|
|
"<Figure size 864x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"barWidth = 0.25\n",
|
|
"fig = plt.subplots(figsize =(12, 8))\n",
|
|
"\n",
|
|
"iris = [silhouette, silhouette2, silhouette3]\n",
|
|
"glass = [silhouette4, silhouette5, silhouette6]\n",
|
|
"wine = [silhouette7, silhouette8, silhouette9]\n",
|
|
"\n",
|
|
"br1 = np.arange(len(iris))\n",
|
|
"br2 = [x + barWidth for x in br1]\n",
|
|
"br3 = [x + barWidth for x in br2]\n",
|
|
"\n",
|
|
"plt.bar(br1, iris, color ='y', width = barWidth, edgecolor ='grey', label ='k-średnich')\n",
|
|
"plt.bar(br2, glass, color ='m', width = barWidth, edgecolor ='grey', label ='k-medoid')\n",
|
|
"plt.bar(br3, wine, color ='c', width = barWidth, edgecolor ='grey', label ='k-medoid + PCA')\n",
|
|
"\n",
|
|
"plt.xlabel('Zbiór danych', fontsize = 15)\n",
|
|
"plt.ylabel('Wartość sylwetki', fontsize = 15)\n",
|
|
"plt.xticks([r + barWidth for r in range(len(iris))], ['Iris', 'Glass', 'Wine'])\n",
|
|
"\n",
|
|
"plt.legend()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 127,
|
|
"id": "74fc7277",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: center;\">\n",
|
|
" <th></th>\n",
|
|
" <th>Iris</th>\n",
|
|
" <th>Glass</th>\n",
|
|
" <th>Wine</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>k-średnich</th>\n",
|
|
" <td>0.44</td>\n",
|
|
" <td>0.28</td>\n",
|
|
" <td>0.30</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>k-medoid</th>\n",
|
|
" <td>0.48</td>\n",
|
|
" <td>0.35</td>\n",
|
|
" <td>0.28</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>k-medoid + PCA</th>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.45</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Iris \\\n",
|
|
"k-średnich 0.44 \n",
|
|
"k-medoid 0.48 \n",
|
|
"k-medoid + PCA 0.50 \n",
|
|
"\n",
|
|
" Glass \\\n",
|
|
"k-średnich 0.28 \n",
|
|
"k-medoid 0.35 \n",
|
|
"k-medoid + PCA 0.50 \n",
|
|
"\n",
|
|
" Wine \n",
|
|
"k-średnich 0.30 \n",
|
|
"k-medoid 0.28 \n",
|
|
"k-medoid + PCA 0.45 "
|
|
]
|
|
},
|
|
"execution_count": 127,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df_shoulette"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "cf18c7b0",
|
|
"metadata": {},
|
|
"source": [
|
|
" W wyniku tego porównania można dojść do wniosku, że najlepszą z metod jest metoda k-medoid wraz z redukcją wymiaru zbiorów danych przy pomocy PCA. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "2c403633",
|
|
"metadata": {},
|
|
"source": [
|
|
"### K-medoids na innych zbiorach danych"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 53,
|
|
"id": "6692b36e",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Load datasets\n",
|
|
"\n",
|
|
"dataset = np.array([[5, 6], [4, 7], [4, 8], [4, 6], [5, 7], [5, 8], [7, 6], [8, 8], [7, 7], [7, 8]])\n",
|
|
"dataset3 = np.array(\n",
|
|
" [[4.5, 6], [4, 7], [4, 8], [4, 6], [4.5, 7], [4.5, 8], [7, 6], [5.5, 7], [5.5, 8], [5.5, 6], [8, 8], [7, 7],\n",
|
|
" [7, 8]])\n",
|
|
"\n",
|
|
"X1, Y1 = make_blobs(n_features=2, centers=4)\n",
|
|
"X, y = make_classification(n_samples=100, n_features=2, n_informative=2, n_redundant=0, n_repeated=0, n_classes=2,\n",
|
|
" n_clusters_per_class=2, class_sep=2, flip_y=0, weights=[0.5, 0.5], random_state=17)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 54,
|
|
"id": "3103aca8",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def print_sns_plot(data):\n",
|
|
" column_values = ['x', 'y']\n",
|
|
" df = pd.DataFrame(data=data, columns=column_values, index=None)\n",
|
|
" sns.set_theme(style='darkgrid')\n",
|
|
" plt.figure(figsize=(10,8))\n",
|
|
" plt.title(\"Dataset for clustering\")\n",
|
|
" sns.scatterplot(data=df, x='x', y='y')\n",
|
|
" plt.show()\n",
|
|
" time.sleep(4)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 55,
|
|
"id": "e0eb2616",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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AAKt4jDHGyRPk5OTolltuOe+jy2effVb79+8/bxE7fvy4EhMT9cYbbzg5TQAAAKs49k6aJDU2Nmr37t16/PHHz3vMK6+8ovvvvz+4XVdXp40bN+qee+6RJBljFBISclnnrak5oeZmR7unYmIiVV1d5+g5bEV2d2aX3J3fzdkld+cnuzuzS87nb9fOo+joiPOPO3ZmSfv371ePHj3k8/m+dtwYo3feeUcDBgwI7vP5fFq1apXefvttSdKaNWuUmJjo5DQBAACs42hJq6ioUGxsbIt9WVlZKisrk3Tmj93wer0KCwsLjoeEhGjJkiUqLCxUUlKS3nnnHeXl5Tk5TQAAAOs4/k5aa+Bxp7PI7s7skrvzuzm75O78ZHdndqmNP+4EAADAN0NJAwAAsBAlDQAAwEKUNAAAAAtR0gAAACxESQMAALAQJQ0AAMBClDQAAAALUdIAAAAsREkDAACwECUNAADAQpQ0AAAAC1HSAAAALERJAwAAsBAlDQAAwEKUNAAAAAtR0gAAACxESQMAALAQJQ0AAMBClDQAAAALUdIAAAAsREkDAACwECUNAADAQpQ0AAAAC1HSAAAALERJAwAAsBAlDQAAwEKUNAAAAAtR0gAAACxESQMAALAQJQ0AAMBClDQAAAALUdIAAAAsREkDAACwECUNAADAQpQ0AAAAC1HSAAAALERJAwAAsBAlDQAAwEKUNAAAAAtR0gAAACwU6tQXr1+/XmvWrAluHzhwQKmpqZo1a1Zw3/Lly7Vx40Z16tRJkjR+/HhNmDBBlZWVysvLU01Nja699lotWLBAHTt2dGqqAAAA1nGspKWnpys9PV2S9P777ys7O1sPPPBAi2PKy8u1aNEiDRgwoMX+2bNnKyMjQykpKVqxYoWKioqUl5fn1FQBAACsc0UedxYWFio3N1dRUVEt9peXl2vlypUaM2aM5syZo9OnT8vv92v37t0aOXKkJCktLU1btmy5EtMEAACwhmN30r6wa9cuNTQ0KCkpqcX+kydPqnfv3srLy9MPfvAD5efnq6ioSBMmTFBERIRCQ89MLSYmRocPH76sc0ZHR3xr87+QmJjIK3IeG5Hdvdyc383ZJXfnJ7t7tWZ+x0va2rVrde+9956zv2PHjnrqqaeC25mZmZo+fboyMjLk8XhaHPvV7YupqTmh5mbzzSZ8iWJiIlVdXefoOWxFdndml9yd383ZJXfnJ7s7s0vO52/XznPBG0uOPu5sbGzU7t27NXz48HPGKisrtWHDhuC2MUahoaGKiopSXV2dAoGAJKm6ulpdu3Z1cpoAAADWcbSk7d+/Xz169JDP5ztnLDw8XE888YQqKipkjNGzzz6rxMREeb1eDRo0SJs3b5Ykbdq0SQkJCU5OEwAAwDqOlrSKigrFxsa22JeVlaWysjJFRUVpzpw5mjJlikaNGiVjTPCxaEFBgdatW6fk5GTt2bNHU6dOdXKaAAAA1vEYY5x9easV8E6as8juzuySu/O7Obvk7vxkd2d2qY2/kwYAAIBvhpIGAABgIUoaAACAhShpAAAAFqKkAQAAWIiSBgAAYCFKGgAAgIUoaQAAABaipAEAAFiIkgYAAGAhShoAAICFKGkAAAAWoqQBAABYiJIGAABgIUoaAACAhShpAAAAFqKkAQAAWIiSBgAAYCFKGgAAgIUoaQAAABaipAEAAFiIkgYAAGAhShoAAICFKGkAAAAWoqQBAABYiJIGAABgIUoaAACAhShpAAAAFqKkAQAAWIiSBgAAYCFKGgAAgIUoaQAAABaipAEAAFiIkgYAAGAhShoAAICFKGkAAAAWoqQBAABYiJIGAABgIUoaAACAhShpAAAAFgp16ovXr1+vNWvWBLcPHDig1NRUzZo1K7jvlVde0bJly2SM0TXXXKN58+apc+fOKi4u1sKFCxUdHS1JGjp0qHJzc52aKgAAgHUcK2np6elKT0+XJL3//vvKzs7WAw88EBw/ceKECgsLtXHjRnXr1k1PPvmkli1bppkzZ6q8vFz5+fkaPXq0U9MDAACw2hV53FlYWKjc3FxFRUUF9/n9fhUUFKhbt26SpJ49e+rQoUOSpLKyMhUXF2vMmDGaNm2ajh8/fiWmCQAAYA2PMcY4eYJdu3Zp4cKF2rhx43mPaWhoUEZGhiZOnKhbb71V2dnZyszM1MCBA7Vo0SJVVlZq4cKFTk4TAADAKo6XtJycHN1yyy3nfXRZV1en7OxsXXPNNXrsscfOGT9+/LgSExP1xhtvXPI5a2pOqLnZ0ViKiYlUdXWdo+ewFdndmV1yd343Z5fcnZ/s7swuOZ+/XTuPoqMjzj/u2JklNTY2avfu3Ro+fPjXjldVVSkjI0M9e/bU3LlzJZ0pbc8880zwGGOMQkJCnJwmAACAdRwtafv371ePHj3k8/nOGQsEApo8ebKSkpI0Y8YMeTweSZLP59OqVav09ttvS5LWrFmjxMREJ6cJAABgHcd+ulOSKioqFBsb22JfVlaWcnJy9Nlnn+ndd99VIBDQyy+/LEnq27ev5s6dqyVLlqiwsFANDQ3q0aOH5s+f7+Q0AQAArOP4O2mtgXfSnEV2d2aX3J3fzdkld+cnuzuzS238nTQAAAB8M5Q0AAAAC1HSAAAALERJAwAAsBAlDQAAwEKUNAAAAAtR0gAAACxESQMAALAQJQ0AAMBClDQAAAALUdIAAAAsREkDAACwECUNAADAQpQ0AAAAC1HSAAAALERJAwAAsBAlDQAAwEKUNAAAAAtR0gAAACxESQMAALAQJQ0AAMBClDQAAAALUdIAAAAsREkDAACwECUNAADAQpQ0AAAAC1HSAAAALERJAwAAsBAlDQAAwEKUNAAAAAtR0gAAACxESQMAALAQJQ0AAMBClDQAAAALUdIAAAAsREkDAACwECUNAADAQpQ0AAAAC1HSAAAALERJAwAAsBAlDQAAwEKhTn3x+vXrtWbNmuD2gQMHlJqaqlmzZgX37du3TzNmzNDJkyc1aNAgzZ49W6GhoaqsrFReXp5qamp07bXXasGCBerYsaNTU708Hqm23q/PPqiWLyxUnTqESqa1JwXHse6A+3Ddo5V5jDGO/5Z7//33lZ2drbVr1yoqKiq4f/To0Xr00UcVFxen6dOnq2/fvsrIyND999+vsWPHKiUlRStWrFB9fb3y8vIu+Xw1NSfU3OxALI+079PjWrruLZ32BxTmDVHO+Dj1/n5nV124MTGRqq6ua+1pXDmse5Dr1v4sbs4uuTA/170kF677Vzidv107j6KjI84/frEv+Jd/+Rft2rXrb5pEYWGhcnNzWxS0gwcPqqGhQXFxcZKktLQ0bdmyRX6/X7t379bIkSNb7LdBbb0/eMFK0ml/QEvXvaXaen8rzwxOYt0B9+G6hw0u+rgzMTFRRUVFmj17tsaPH69x48bpO9/5ziWfYNeuXWpoaFBSUlKL/VVVVYqJiQlux8TE6PDhwzp27JgiIiIUGhraYv/luFAr/Vt89kF18IL9wml/QPX+gK77ftR5PtU2xcREtvYUrhjWvSU3rf1XuTm75K78XPdfctO6f53WzH/RkjZ27FiNHTtWH374oTZu3KjbbrtNAwYM0MSJE3XDDTdc9ARr167Vvffee87+5uZmeTye4LYxRh6PJ/jPs311+2KcetzpCwtVmDekxYUb5g2RzxviqtvBbrv9zbp/yW1rfzY3Z5fcl5/r/gy3rftXWf+4UzpTqD755BN9/PHHCgQCio6OVmFhoZYuXXrBzzU2Nmr37t0aPnz4OWOxsbGqrq4Obh85ckRdu3ZVVFSU6urqFAicuTCqq6vVtWvXS5mm4zp1CFXO+DiFeUMkKfiOQieft5VnBiex7oD7cN3DBhe9k7Z48WK98MIL+t73vqeMjAw9+eST8nq9qq+v17Bhw5STk3Pez+7fv189evSQz+c7Z6x79+4KCwvTm2++qRtvvFElJSVKSEiQ1+vVoEGDtHnzZo0ZM0abNm1SQkLC35by22Kk3t/vrN/88ieq9wfk84acuWBd9BKpK7HugPtw3cMCF72TdvToUT311FN67rnnNHr0aHm9Z/4vwufzaeHChRf8bEVFhWJjY1vsy8rKUllZmSRpwYIFmjdvnkaNGqX6+nrdddddkqSCggKtW7dOycnJ2rNnj6ZOnfpNsjnDSJ06eNXvuhh16sAF6xqsO+A+XPdoZVfkj+C40hz7IzjO4ubn9GR3Z3bJ3fndnF1yd36yuzO79HfyThoAAACuLEoaAACAhShpAAAAFqKkAQAAWIiSBgAAYCFKGgAAgIUoaQAAABaipAEAAFiIkgYAAGAhShoAAICFKGkAAAAWoqQBAABYiJIGAABgIUoaAACAhShpAAAAFqKkAQAAWIiSBgAAYCFKGgAAgIUoaQAAABaipAEAAFiIkgYAAGAhShoAAICFKGkAAAAWoqQBAABYiJIGAABgIUoaAACAhShpAAAAFqKkAQAAWIiSBgAAYCFKGgAAgIUoaQAAABaipAEAAFiIkgYAAGAhShoAAICFKGkAAAAWoqQBAABYiJIGAABgIUoaAACAhShpAAAAFqKkAQAAWCjUyS/ftm2bli9frlOnTmnw4MGaOXNmcGzfvn3Kz88Pbh89elSdO3fWSy+9pOLiYi1cuFDR0dGSpKFDhyo3N9fJqQIAAFjFsZJWUVGhgoICrV+/XtHR0br77ru1fft2DRkyRJLUu3dvlZSUSJJOnTql9PR0FRYWSpLKy8uVn5+v0aNHOzU9AAAAqzlW0rZu3ark5GTFxsZKkhYvXqywsLCvPXblypW66aabNGjQIElSWVmZPv74Y61cuVI9e/bUI488os6dOzs1VQAAAOt4jDHGiS8uKCiQ1+vVgQMHdOjQIQ0dOlRTp06Vx+NpcVxdXZ1Gjhyp//7v/w4+3szOzlZmZqYGDhyoRYsWqbKyUgsXLnRimgAAAFZy7E5aIBDQnj17tHr1avl8Pk2ZMkXFxcVKS0trcdyLL76oESNGBAuaJK1YsSL475MmTVJiYuJlnbum5oSamx3pnkExMZGqrq5z9By2Irs7s0vuzu/m7JK785Pdndkl5/O3a+dRdHTE+cedOnGXLl0UHx+vqKgohYeHa8SIEdq7d+85x73yyitKTk4ObtfV1emZZ54JbhtjFBIS4tQ0AQAArORYSRs2bJhKS0tVW1urQCCgHTt2qE+fPi2OMcbonXfe0YABA4L7fD6fVq1apbfffluStGbNmsu+kwYAAPD3zrHHnf3799ekSZOUkZEhv9+vwYMHa9y4ccrKylJOTo769euno0ePyuv1tviBgpCQEC1ZskSFhYVqaGhQjx49NH/+fKemCQAAYCXHfnCgNfFOmrPI7s7skrvzuzm75O78ZHdndqkNv5MGAACAb46SBgAAYCFKGgAAgIUoaQAAABaipAEAAFiIkgYAAGAhShoAAICFKGkAAAAWoqQBAABYiJIGAABgIUoaAACAhShpAAAAFqKkAQAAWIiSBgAAYCFKGgAAgIUoaQAAABaipAEAAFiIkgYAAGAhShoAAICFKGkAAAAWoqQBAABYiJIGAABgIUoaAACAhShpAAAAFqKkAQAAWIiSBgAAYCFKGgAAgIUoaQAAABaipAEAAFiIkgYAAGAhShoAAICFKGkAAAAWoqQBAABYiJIGAABgIUoaAACAhShpAAAAFqKkAQAAWIiSBgAAYCFKGgAAgIUoaQAAABaipAEAAFgo1Mkv37Ztm5YvX65Tp05p8ODBmjlzZovx5cuXa+PGjerUqZMkafz48ZowYYIqKyuVl5enmpoaXXvttVqwYIE6duzo5FQBAACs4tidtIqKChUUFKioqEgvvvii3n33XW3fvr3FMeXl5Vq0aJFKSkpUUlKiCRMmSJJmz56tjIwMbdmyRX379lVRUZFT0wQAALCSYyVt69atSk5OVmxsrLxerxYvXqz+/fu3OKa8vFwrV67UmDFjNGfOHJ0+fVp+v1+7d+/WyJEjJUlpaWnasmWLU9MEAACwkmOPOz/55BN5vV5NnjxZhw4d0tChQzV16tTg+MmTJ9W7d2/l5eXpBz/4gfLz81VUVKQJEyYoIiJCoaFnphYTE6PDhw9f1rmjoyO+zSjnFRMTeUXOYyOyu5eb87s5u+Tu/GR3r9bM71hJCwQC2rNnj1avXi2fz6cpU6aouLhYaWlpkqSOHTvqqaeeCh6fmZmp6dOnKyMjQx6Pp8V3fXX7YmpqTqi52fztIS4gJiZS1dV1jp7DVmR3Z3bJ3fndnF1yd36yuzO75Hz+du08F7yx5Njjzi5duig+Pl5RUVEKDw/XiBEjtHfv3uB4ZWWlNmzYENw2xig0NFRRUVGqq6tTIBCQJFVXV6tr165OTRMAAMBKjpW0YcOGqbS0VLW1tQoEAtqxY4f69OkTHA8PD9cTTzyhiooKGWP07LPPKjExUV6vV4MGDdLmzZslSZs2bVJCQoJT0wQAALCSYyWtf//+mjRpkjIyMpScnKzvfve7GjdunLKyslRWVqaoqCjNmTNHU6ZM0ahRo2SM0b333itJKigo0Lp165ScnKw9e/a0eJcNAADADTzGGGdf3moFvJPmLLK7M7vk7vxuzi65Oz/Z3ZldasPvpAEAAOCbo6QBAABYiJIGAABgIUoaAACAhShpAAAAFqKkAQAAWIiSBgAAYCFKGgAAgIUoaQAAABaipAEAAFiIkgYAAGAhShoAAICFKGkAAAAWoqQBAABYiJIGAABgIUoaAACAhShpAAAAFqKkAQAAWIiSBgAAYCFKGgAAgIUoaQAAABaipAEAAFiIkgYAAGAhShoAAICFKGkAAAAWoqQBAABYiJIGAABgIUoaAACAhShpAAAAFqKkAQAAWIiSBgAAYCFKGgAAgIUoaQAAABaipAEAAFiIkgYAAGAhShoAAICFKGkAAAAWoqQBAABYiJIGAABgIUoaAACAhUKd/PJt27Zp+fLlOnXqlAYPHqyZM2e2GH/llVe0bNkyGWN0zTXXaN68eercubOKi4u1cOFCRUdHS5KGDh2q3NxcJ6cKAABgFcdKWkVFhQoKCrR+/XpFR0fr7rvv1vbt2zVkyBBJ0okTJ1RYWKiNGzeqW7duevLJJ7Vs2TLNnDlT5eXlys/P1+jRo52aHgAAgNUce9y5detWJScnKzY2Vl6vV4sXL1b//v2D436/XwUFBerWrZskqWfPnjp06JAkqaysTMXFxRozZoymTZum48ePOzVNAAAAK3mMMcaJLy4oKJDX69WBAwd06NAhDR06VFOnTpXH4znn2IaGBmVkZGjixIm69dZblZ2drczMTA0cOFCLFi1SZWWlFi5c6MQ0AQAArOTY485AIKA9e/Zo9erV8vl8mjJlioqLi5WWltbiuLq6OmVnZ6tXr1669dZbJUkrVqwIjk+aNEmJiYmXde6amhNqbnakewbFxESqurrO0XPYiuzuzC65O7+bs0vuzk92d2aXnM/frp1H0dER5x936sRdunRRfHy8oqKiFB4erhEjRmjv3r0tjqmqqlJGRoZ69uypuXPnSjpT2p555pngMcYYhYSEODVNAAAAKzlW0oYNG6bS0lLV1tYqEAhox44d6tOnT3A8EAho8uTJSkpK0owZM4KPQX0+n1atWqW3335bkrRmzZrLvpMGAADw986xx539+/fXpEmTlJGRIb/fr8GDB2vcuHHKyspSTk6OPvvsM7377rsKBAJ6+eWXJUl9+/bV3LlztWTJEhUWFqqhoUE9evTQ/PnznZomAACAlRz7wYHWxDtpziK7O7NL7s7v5uySu/OT3Z3ZpTb8ThoAAAC+OUoaAACAhShpAAAAFqKkAQAAWIiSBgAAYCFKGgAAgIUoaQAAABaipAEAAFiIkgYAAGAhShoAAICFKGkAAAAWoqQBAABYiJIGAABgIUoaAACAhShpAAAAFqKkAQAAWIiSBgAAYCFKGgAAgIUoaQAAABaipAEAAFiIkgYAAGAhShoAAICFKGkAAAAWoqQBAABYiJIGAABgIUoaAACAhShpAAAAFqKkAQAAWIiSBgAAYCFKGgAAgIUoaQAAABaipAEAAFiIkgYAAGAhShoAAICFKGkAAAAWoqQBAABYiJIGAABgIUoaAACAhShpAAAAFqKkAQAAWMjRkrZt2zalpaUpKSlJjz766Dnj+/btU1pamkaOHKkZM2aoqalJklRZWakJEyZo1KhRmjJlik6ePOnkNAEAAKzjWEmrqKhQQUGBioqK9OKLL+rdd9/V9u3bWxyTl5enWbNm6eWXX5YxRuvWrZMkzZ49WxkZGdqyZYv69u2roqIip6Z5+TxS7Sm/yj6oVm1Dk+Rp7QnhimDdAffhukcrC3Xqi7du3ark5GTFxsZKkhYvXqywsLDg+MGDB9XQ0KC4uDhJUlpampYuXar09HTt3r1bK1asCO6/8847lZeX59RUL51H2vfpcS1d95ZO+wMK84YoZ3ycen+/s2Rae3JwDOsOuA/XPSzg2J20Tz75RIFAQJMnT1Zqaqqee+45de7cOTheVVWlmJiY4HZMTIwOHz6sY8eOKSIiQqGhoS3226C23h+8YCXptD+gpeveUm29v5VnBiex7oD7cN3DBo7dSQsEAtqzZ49Wr14tn8+nKVOmqLi4WGlpaZKk5uZmeTxf3js2xsjj8QT/ebavbl9MdHTE3x7ga3z2QXXwgv3CaX9A9f6Arvt+lCPntFVMTGRrT+GKYd1bctPaf5Wbs0vuys91/yU3rfvXac38jpW0Ll26KD4+XlFRZ34zjxgxQnv37g2WtNjYWFVXVwePP3LkiLp27aqoqCjV1dUpEAgoJCRE1dXV6tq162Wdu6bmhJqbv/370b6wUIV5Q1pcuGHeEPm8IaqurvvWz2ermJhIV+Vl3b/ktrU/m5uzS+7Lz3V/htvW/auczt+uneeCN5Yce9w5bNgwlZaWqra2VoFAQDt27FCfPn2C4927d1dYWJjefPNNSVJJSYkSEhLk9Xo1aNAgbd68WZK0adMmJSQkODXNy9KpQ6hyxscpzBsiScF3FDr5vK08MziJdQfch+seNvAYYxx7BXLDhg165pln5Pf7NXjwYM2cOVP333+/cnJy1K9fP7333nuaOXOmTpw4oT59+mjevHlq3769Dh48qPz8fNXU1Ojqq6/WokWLWrzPdjFO3UmTdOanfer9qvcH5POGnLlgXfYSqSv/z4p1l+TStf//3Jxdcml+rnt3rvtZWvtOmqMlrbU4WtL+Pzf/xiW7O7NL7s7v5uySu/OT3Z3ZpdYvafyNAwAAABaipAEAAFiIkgYAAGAhShoAAICFKGkAAAAWoqQBAABYiJIGAABgIUoaAACAhShpAAAAFqKkAQAAWIiSBgAAYCFKGgAAgIUoaQAAABYKbe0JOKFdO0+bOo+NyO5ebs7v5uySu/OT3b2czH+x7/YYY4xjZwcAAMA3wuNOAAAAC1HSAAAALERJAwAAsBAlDQAAwEKUNAAAAAtR0gAAACxESQMAALAQJQ0AAMBClDQAAAALUdIAAAAs1Cb/7s5v229+8xsdO3ZMjz/+eIv9+/bt04wZM3Ty5EkNGjRIs2fPVmho2/olPV/25cuXa+PGjerUqZMkafz48ZowYUJrTNEREydO1NGjR4PrOWfOHPXv3z843tbX/mL52/L6b9u2TcuXL9epU6c0ePBgzZw5s8V4W177i2Vvy+u+fv16rVmzJrh94MABpaamatasWcF9bXXtLyV7W157SSopKdFvf/tbSVJCQoIeeuihFuOttvYGF7Rr1y7zT//0T+ahhx46ZywlJcX8+c9/NsYY8/DDD5tnn332Cs/OWRfKfv/995s//elPrTAr5zU3N5ubb77Z+P3+8x7Tltf+UvK31fX/9NNPzc0332wOHTpkGhsbzR133GFee+21Fse01bW/lOxtdd2/6i9/+YtJTEw0NTU1Lfa31bU/2/myt+W1r6+vNzfddJOpqakxfr/f3HbbbWbnzp0tjmmttedx5wV8/vnnWrx4sSZPnnzO2MGDB9XQ0KC4uDhJUlpamrZs2XKFZ+icC2WXpPLycq1cuVJjxozRnDlzdPr06Ss8Q+f89a9/lSRlZmZq7NixLf4PU2r7a3+x/FLbXf+tW7cqOTlZsbGx8nq9Wrx4cYs7iG157S+WXWq76/5VhYWFys3NVVRUVHBfW177s31ddqltr30gEFBzc7NOnTqlpqYmNTU1KSwsLDjemmtPSbuAWbNmKTc3N3h792xVVVWKiYkJbsfExOjw4cNXcnqOulD2kydPqnfv3srLy1NxcbFqa2tVVFTUCrN0Rm1treLj47VixQo988wzWrt2rXbu3Bkcb+trf7H8bXn9P/nkEwUCAU2ePFmpqal67rnn1Llz5+B4W177i2Vvy+t+tl27dqmhoUFJSUkt9rfltf/C+bK39bWPiIjQgw8+qKSkJA0ZMkTdu3fXwIEDg+OtufaUtPNYv369rr76asXHx3/teHNzszweT3DbGNNi++/ZxbJ37NhRTz31lK677jqFhoYqMzNT27dvv8KzdM6AAQM0f/58RUZGKioqSrfddluLfG157aWL52/L6x8IBPT666/rscce0+9+9zvt3btXxcXFwfG2vPYXy96W1/1sa9eu1b333nvO/ra89l84X/a2vvbvvfeeNm7cqFdffVU7duxQu3bt9PTTTwfHW3PtKWnnsXnzZu3cuVOpqalaunSptm3bpsceeyw4Hhsbq+rq6uD2kSNH1LVr19aY6rfuYtkrKyu1YcOG4LYxpk28PPuFPXv26PXXXw9ufzVfW1576eL52/L6d+nSRfHx8YqKilJ4eLhGjBihvXv3Bsfb8tpfLHtbXvcvNDY2avfu3Ro+fPg5Y2157aULZ2/ra19aWqr4+HhFR0erffv2SktL0xtvvBEcb821p6Sdx3/913/ppZdeUklJiXJycjR8+HBNnz49ON69e3eFhYXpzTfflHTmJ0MSEhJaa7rfqotlDw8P1xNPPKGKigoZY/Tss88qMTGxFWf87aqrq9P8+fN1+vRpnThxQsXFxS3yteW1ly6evy2v/7Bhw1RaWqra2loFAgHt2LFDffr0CY635bW/WPa2vO5f2L9/v3r06CGfz3fOWFtee+nC2dv62vfq1Uu7du1SfX29jDHatm2b+vXrFxxvzbWnpF2mrKwslZWVSZIWLFigefPmadSoUaqvr9ddd93VyrNz1hfZo6KiNGfOHE2ZMkWjRo2SMeZrb5H/vRo2bJiGDBmin/3sZxo3bpzGjRunAQMGuGbtL5a/La9///79NWnSJGVkZCg5OVnf/e53NW7cOFes/cWyt+V1/0JFRYViY2Nb7HPD2ksXzt7W1/7mm29WSkqK0tLSNHbsWDU1Nem+++6zYu09xhhzRc4EAACAS8adNAAAAAtR0gAAACxESQMAALAQJQ0AAMBClDQAAAALUdIAAAAsREkDAACwECUNAM6juLhYI0aM0MmTJ1VfX6+kpCRt2rSptacFwCX4w2wB4AL+9V//VZGRkWpsbFRISIh+/etft/aUALgEJQ0ALuDEiRNKTU1VeHi4XnjhBYWFhbX2lAC4BI87AeACampqdPr0adXW1qqqqqq1pwPARbiTBgDn4ff7dfvtt+v2229Xc3Oz1q9fr+eff15er7e1pwbABbiTBgDnsWjRInXp0kXp6en6+c9/rquuukqLFy9u7WkBcAnupAEAAFiIO2kAAAAWoqQBAABYiJIGAABgIUoaAACAhShpAAAAFqKkAQAAWIiSBgAAYKH/B1ZTvLFpw2xEAAAAAElFTkSuQmCC\n",
|
|
"text/plain": [
|
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"<Figure size 720x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 720x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 2 0.33\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# 2 clusters\n",
|
|
"print_sns_plot(dataset)\n",
|
|
"model9 = TrainModel_medoids(dataset, 2)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 56,
|
|
"id": "208dd1f2",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 720x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 720x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 3 0.36\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print_sns_plot(dataset3)\n",
|
|
"model10 = TrainModel_medoids(dataset3, 3)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 57,
|
|
"id": "ddbf7f30",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 720x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 720x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 4 0.42\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print_sns_plot(dataset3)\n",
|
|
"model11 = TrainModel_medoids(dataset3, 4)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "a1c83b30",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Przykłady z syntetycznymi zbiorami danych"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 62,
|
|
"id": "0e04d49b",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 720x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Dataset with 4 centers\n",
|
|
"X1, Y1 = make_blobs(n_features=2, centers=4)\n",
|
|
"print_sns_plot(X1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 63,
|
|
"id": "8e86192b",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 720x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 2 0.64\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"model12 = TrainModel_medoids(X1, 2)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 64,
|
|
"id": "5c4e9183",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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2TlXJ8AG2B6PC/FzCFwAkAI9lWfZNurIZc8bcyWnt4ttm4sTVjSnJSa7qqXJam+BbtIvz0CbO5Mo5Y0AiYZsJAEBXEMaALmpvVWOo6wAAnIgwBnSRbxJ9uNcBADgRYQzoIraZAAB0hZFNX4FE4pukH+vVlACAxEAYA6KAbSYAAJ3FMCUAAIBBhDEAAACDCGMAAAAGEcYAAAAMIowBAAAYRBgDAAAwiDAGAABgEPuMAQ5TUVXt6A1k26tv/I/TTZcFAHGLMAYYECxwVVRV67kN23W8xSvp28PGn9uwXZIcEciC1dc7vYfyz8owWxwAxCnCGNAJXem9ChW41m7Z5b/uc7zFq7VbdjkijAWr7/kN2/TAzEJDVQFAfCOMARHqau9VqMBV19DU7s8Eux5rweo4cLAxxpUAQOJgAj8QoVBhKhyhAldm79R27wW7HmvB6ujXp2eMKwGAxEEYAyLU1d6rUIGrZPgApSQH/rNMSU5SyfABkRVpk2D1lY3+vqGKACD+EcaACHW19ypU4CrMz9W00Xn+z8rsnappo/McMV9MUtD6fnzRmYYrA4D4xZwxIEIlwwcEzBmTIuu98gWrYAsACvNzHRO+2uP0+gAg3hDGgAh1FKbC/YyuBBqn70UGAAgfYQzoBJO9Q07fiwwAEBnmjAFxpqurOQEAzkIYA+KM0/ciAwBEhmFKIExOmaeV2Tu13eAVzmpOp/wOAIB/oGcMCINvnpYvBPnmaVVUVce8ls7uReak3wEA8A+EMSAMTpqn1dm9yJz0OwAA/oFhSiAMTpun1ZnVnE77HQAA36JnDAiD08+MDEci/A4AkIgIY0AYnH5mZDgS4XcAgETEMCUQhmjsum9aIvwOAJCICGNAmBLhTMZE+B0AINEwTAkAAGAQYQwAAMAgwhgAAIBBhDEAAACDCGMAAAAGsZoScDAO9g4P3xOAeEYYAxzKd7C37zxJ38HekggaJ+B7AhDvGKYEHIqDvcPD9wQg3hHGAIfiYO/w8D0BiHcMU8L1nDrfKLN3aruBgoO9A/E9AYh3hDG4mpPnG5UMHxBQmxT5wd5ODZrRFI3vCQBMIozB1ULNN4pVaAkWmLp6sLeTg2Y0cQA6gHhHGIOrmZ5v1FFg6srB3k4ImrHCAegA4hlhDK7W1flGXR0GjDQwRfI800ETABAeVlPC1UqGD1BKcuA/g3DnG/l6tXzhxterVVFVHfbzIwlMkT4vWKBkYjsAOAthDK5WmJ+raaPz/AEls3eqpo3OC6t3Kxr7W0USmCJ9XleCJgAgdhimhKt0NFk+EtEYBoxkJWCkz2NiOwDEB8IYXCPaqwujsb9VR4HpxPCY5JG8Vvt1hPp8whcAOBthDK4R7dWF0drfKlhgahse2wtiDDsCQPwjjME1or260O5hwPbCoyR/DxnDjgCQGAhjcA07js2xcxgwWEj0WtL/mXeFLc8EAMQeYSxBuOHYm66Kt2NzOHMRANyBrS0SQDT2u3KDrmxjYQJbUwCAO9AzlgDcdOxNV8XD6sITezlP6Zms7skeHT3WSo8nACQowlgC4NibxNF2BeWRxhalJCdp+rjzCGFAAtpa/b5e27VRB5u+Vp/UDI0fMEo/yr3QdFmIMYYpEwDH3iSOaOzqDyA+bK1+X2u2v6yDTV9Lkg42fa0121/W1ur3zRaGmCOMJQDmFiUOejkB93ht10Y1e5sDrjV7m/Xaro2GKoIpDFMmAI69SRysoATcw9cjFu51JC7CWIKIh4np6Fi8bb8BoPP6pGa0G7z6pGbEvJZ4FI35dr7P+Lrpa2UYnLNHGAMchF5OwD3GDxilNdtfDhiq7J7UXeMHjDJYVSCnLDBoW8f5mXmqrH7P/9355ttJCrs+35y9rnxGtBDGAIehlxNwB98ffCeEnfY4Jay0V8f/fFl50vt88+3CrS3UnD3CGAAALvGj3AsdE77ackpYaa+OYCKZb+ekOXuspgQAACdxSliJ5HmRzLcL9l4Tc/YIYwAA4CROCSvhPi/S+XbjB4xS96TuXfqMaCGMAQCAkzglrASr47LvFPiDWp/UDF2bVxrR8OmPci/UtXml6pOaIU8nPyNamDMGAABO4pQFBnbW4Zuzl5WVrtraw13+vM4ijAEA4FCmt5ZwygIDp9RhF1uHKdevX68xY8ZoxIgRWr169Un3q6qqVFpaqvHjx2vmzJlqaGiwsxwAAOIGZ1e6h21hbP/+/VqxYoXWrFmjV199VS+++KJ27twZ8J6lS5dq1qxZeu2113T22WfrmWeesascAADiCmdXuodtYay8vFwFBQXKyMhQWlqaRo4cqY0bA/8H5PV6dfToUUlSY2OjevToYVc5AADEFadsLQH72TZnrKamRllZWf7X2dnZ+vDDDwPeM2/ePN14441atmyZevbsqZdeeimiZ2RmnhKVWkPJykq3/RmIHO3iPLSJM9EuzhNum/RL66sD39S3e512jT6T36ltYczr9crj8fhfW5YV8PrYsWNauHChnn32WQ0ZMkT//u//rrlz52rVqlVhP6Ou7oi8XiuqdZ/I9OoKtI92cR7axJloF+eJpE3GfndEu2dXjv3uCNo1yuz+t5KU5AnZgWRbGMvNzdW7777rf11bW6vs7Gz/6x07dig1NVVDhgyRJF199dV67LHH7CoHAIC44pStJUIxvdozUdgWxoqKirRy5UrV19erZ8+e2rx5s+677z7//f79+6u6ulq7d+/WOeecozfeeEODBw+2qxwAAOKOk7d0cMpB4onAtjCWk5Oj2bNnq6ysTM3NzZo0aZKGDBmi6dOna9asWRo8eLCWL1+uO+64Q5ZlKTMzU8uWLbOrHMAVKqqqtXbLLtU1NCmzd6pKhg9QYX6u6bIARJkTeqSccpB4IrB109dx48Zp3LhxAdeeeuop//89fPhwDR8+3M4SANeoqKrWcxu263iLV5JU19Ck5zZslyQCGZBAnNIj5aTVnk4Ip13B2ZRAgli7ZZc/iPkcb/Fq7ZZdhioCYAen7D/mlIPEE2FzXMIYoqqiqlqLn9msxtZ8LX5msyqqqk2X5Bp1DU0RXQcQn5zSI+WUg8SdEk67grMpETW+YbLCwW/rrNx9OueMcj23IUUSw2SxkNk7td3gldk71UA1AOzSJzWj3eBlR49UqOG/rqz2jOawolPCaVcQxhA1WZkztPreCiUnt0iSZl/zmFr++Tf6YEehpNfMFpegTpywf0rPZHXzSK0nbL2XkpykkuEDjNXEIgIg+sYPGNXu/mPR7pEKZ25aZ1Z7RnvOWyzDqV0YpkTU/Pv6q1VzMEstrd0kSS2t3VRzMFv/Z/3VhitLTL6eSF9v2JHGFnmSPOrV49vvP7N3qqaNzotpEGpbk28RAcPVQPT8KPdCXZtX6g8bfVIzdG1eadQnrNs1/Bftz3XKcGlX0DOGqDne/F2t3nSt5lz3b2ps6qHuyc1avekaNTd/13RpCam9CfstrZZO7ZWslXeYWaUcahEBvWNA9MRi/zG7hv+i/bnxsDluRwhjiJqS4QPUr8+v1HQ8VS+8/v9o8pUvadjQcuX3/5np0hKSEyfsO7EmAJ3TmeG/cOaC2TGs6OTNccNBGEPUFObnatve2Zr/m+P67Ks0/W3HCJVenkqPiE2cOGHfiTUB6NyE+UjnpoU7FyxWc97iCWEMUfX9s0bonmmmq3CHkuEDAjZ5leyfsN/R5HwTNQEIrbMT5iMd/gt3R/5EGFaMNsIYEKd8IShWKxdD7fA//sfpRmoC0LGuHFsUyfDfwaav1bN7k371k1e16I0JamxO9V/vyue6AWEMiGOF+bkxCzqhJueP//H3jNQEoGOx2IfLt9v9haft1ZmnHtSFp+3V23u//e9CPG0xYQphDIhjsdzTi8n5QHyKxT5cWRk/1/Olnyo5qVWSdOvFb2rmP23Ru198V4cano7ac8IRj+dUss8YEKdivadXsEn4TM4HnC0W+3D97sOhOnD0FLV6v40Vrd4k1R5N14sf/VNMg1C8nlNJGAPiVKwPBi8ZPkApyYH/yWByPuAMW6vf16K3l+nWN+/WoreXBYSPWGwSe7y5v178+J/ULcmrxuZkdUvy6qWPf6jjLf2j9oxwxOs5lQxTAnHmxKHJ9tg1bMjkfMCZ7Dq2KBLjB4xSZsYf1NTSXS9/cpFKz3tPRWd9przeC0+q1c4hxHg9p5IwBsSRtisa22PnsCGT8wHn6cpqyWj5Ue6F+v8O3aUlb1Rpb0OzPvzqh5rwvfyA50f7TMr2xOs5lYQxII60NzR5IoYNAfdxSm/QoFOv1dwfBr8fi9AYrxvKEsaAOBJqCJJhQ8Cd4qU3KBahMV43lCWMAXEk1HFDD91yiYGKAJgWL71BsQqN8bihLKspgTjCikYAbcVitWQ0xGKLjXhFzxgQR1jRCKA98dAbFK9DiLFAGAPiDCsaAcSreAiNJjBMCQAAYBBhDAAAwCDCGAAAgEHMGQMAAI5j99FJTkIYAwAAjhKLo5OchGFKAADgKKGOTkpEhDEAAOAoTjlvM1YIYwAAwFGCHZHktPM2o4U5Y4ZUVFWzizoAAO2Il/M2o4UwZkBFVbWe27Bdx1u8kqS6hiY9t2G7JBHIAAAJKZLVkW47OokwZsDaLbv8QczneItXa7fsIowBABJOZ1ZHuunoJMKYAXUNTRFdBwDAhGjt9RVqdaRbAlcoTOA3ILN3atB7c554WxVV1TGsBgCAk/l6s3wrGH29WVur34/4s9y2OjJS9IwZUDJ8QMCcsRM5Zf4YCwwAwN2i2ZvVJzWj3eCVqKsjI0UYM8AXanxhpy3T88dYYAAAiSmSYcdo9ma5bXVkpAhjhhTm56owP1c33v9mu/frGpo054m3jfRMscAAABJPpJPoo9mb5bbVkZEijBmW2Tu1wwn9se6ZYoEBACSeSIcdo92b5abVkZFiAr9hJcMHKCW542bw9UzFQrAFBqEWHgAAnC3SYccf5V6oa/NK/T1hfVIzdG1eKYHKBvSMGdZ2/lg4PWV2a2+BQUpykkqGD4jJ8wEA0deZYUd6s2KDMOYAvvljPr65Ym3FqmeqvYDIakoAiG9dHXaM1p5jOBlhzIGc0DPVNiACAOJbVybRd2YHfYSPMOZA9EwBAOzQ2WFHdtC3F2HMoeiZAgA4BTvo24vVlAAAIKRgk/zZQT866BlzCI4fAgA4FTvo24sw5gAcPwQAcDJ20LcXYcwBOH4IABBrkW5V4bQ9xxJpqw3CmAOY3uQVAOAu8b5VRbzX3xYT+B2A44cAALEUaquKeBDv9bdFz5gDhNrklYn9AIBoi/etKuK9/rYIYw4QbJNXSUzsBwBEXWfOqXSSeK+/LcKYQ7S3yeucJ95mYj8AIOrifauKeK+/LcKYgzGxHwBgh2hvVRHrlY2JttUGYczBMnunthu8nD6xn3luAOB80dqqwtTKRqdttdEVrKZ0sJLhA5SSHNhEvon9TuXbwNYXIn3z3Cqqqg1XBgCwQ6KtbDSBMOZghfm5mjY6z98Tltk7VdNG5zm6lynUBrYAgMSTaCsbTWCY0uHam9jvZMxzAwB3SbSVjSbQM4aoYgNbAHCX8QNGqXtS94Br8byy0QR6xhAVJ07ab8vp89wAAJ2XaCsbTSCMoct8k/bbzhWTxGpKAHCBRFrZaAJhDF3W3qR96dsg9tAtlxioCACA+EEYM8Rpe3F1pR4m7QMA0HmEMQPaDuuZPnPyd5u2678/+NL/OtJ64nVzWgAAnIDVlAY4aS+uiqrqgCDWmXricXNaAACcgp4xA5w0rBcqcIVbj6/3zEnDrgAAxAvCmAF2D+tFMv8rVOCKpJ5425wWAACnYJjSALuG9SqqqjXrsf9XT63/JOBsyKfWf6Lfbdre7s+EClwMMwIAYD96xmLM12t1vMWrJI/ktaKzF1eovb4k6b8/+FIDz8g46Rklwwe0+3OXX/AdW3u6QvXeOW2lKQAAdrI1jK1fv15PPvmkWlpaNG3aNE2ZMiXg/u7du7VkyRIdOnRIWVlZeuSRR3TqqafaWZJRbQOT1/pHj1hXw0awvb7avqftc0zM9wq1mlSSo1aaAgBgN9vC2P79+7VixQqtXbtWKSkpmjx5si6++GINHDhQkmRZln7+859r4cKFGjZsmB5++GGtWrVKc+bMsask40Ktouxq0Ahnsn2w98R6vldHq0nt+o4AAHAi2+aMlZeXq6CgQBkZGUpLS9PIkSO1ceNG//2qqiqlpaVp2LBhkqSbb775pJ6zRGPnKspwJts7Zd+vUN+Dk1aaAgAQC7b1jNXU1CgrK8v/Ojs7Wx9++KH/9d69e9WvXz8tWLBA27Zt0znnnKN77rknomdkZp4StXqDycpKj95n9emp2oON7V7v6nOuL87Xr//zb2pqbm33fmr3brq+OD+qv09nhfoeJIX1HTnh90Ag2sSZaBfnoU2cyWS72BbGvF6vPB6P/7VlWQGvW1patHXrVv3+97/X4MGD9eijj+r+++/X/fffH/Yz6uqOyOu1olr3ibKy0lVbezjgWlcml0+49OyTJsunJCdpwqVnn/ScSOWflaGyUYP8tZ3SM1mWZenosVZ/nflnZXT5OdEQ6nuQ1OF31F67wCzaxJloF+ehTZzJ7nZJSvKE7ECyLYzl5ubq3Xff9b+ura1Vdna2/3VWVpb69++vwYMHS5KKi4s1a9Ysu8qJiq4eY2T3ZPl42esrnO+B1ZQAALewLYwVFRVp5cqVqq+vV8+ePbV582bdd999/vsXXHCB6uvrtX37duXl5enNN99Ufn6+XeVERTQm4MdLYLJbqO+B7wgA4Ca2hbGcnBzNnj1bZWVlam5u1qRJkzRkyBBNnz5ds2bN0uDBg/Wb3/xGixYtUmNjo3Jzc/Xggw/aVU5EfEOR9Q1N6ntCzwyTywEAQLR5LMuyb9KVzeyYM9be5qkpyUmaNjrPP3TWVmbvVD10yyVRrQPBMefCeWgTZ6JdnIc2cSbTc8Y4DqmNUEORdh1jBAAA3IvjkNoINRRpYrd6AACQ2AhjbWT2Tg06FCkxuRwAAEQXYayN9g7ODncokgOuAQBApAhjbZw4FNl2NWUoXd2DLNTnEvAAAEhchLF2+IYiI1ldYcch4HYFPAAA4ByspowSO/YgCxXwAABAYiCMRYlvgn+418PBJrMAACQ+wliU2LEHmR0BDwAAOAthLEoK83M1bXSePyhl9k7VtNF5XZrbxSazAAAkPibwR1G09yBjk1kAABIfYczh2GQWAIDExjAlAACAQYQxAAAAgwhjAAAABhHGAAAADCKMAQAAGEQYAwAAMIitLeJcRVU1+5ABABDHCGNxrKKqWs9t2O4/TLyuoUnPbdguSQQyAADiBMOUcWztll3+IOZzvMWrtVt2GaoIAABEijAWx+oamiK6DgAAnIcwFsd8h5KHex0AADgPYSyOlQwfoJTkwCZMSU5SyfABhioCAACRYgJ/HPNN0mc1JQAA8YswFucK83MJXwAAxDGGKQEAAAwijAEAABjEMGUXsPs9AADoKsJYJ7H7PQAAiAaGKTuJ3e8BAEA0EMY6id3vAQBANAQNY4899pgsy4plLXGF3e8BAEA0BA1jlZWVKisrU21tbSzriRvsfg8AAKIhaBhbvXq1CgsLVVpaqr/85S+xrCkuFObnatroPH9PWGbvVE0bncfkfQAAEJGgqymTkpJ0yy236Mc//rEWLVqkN954Q2eddZb//g033BCTAp2M3e8BAEBXdbi1RVJSkjwej3bu3Kljx47FoiYAAADXCBrGLMvSb3/7Wz3zzDOaPXu2pkyZEsu6AAAAXCFoGJs8ebIaGxu1Zs0anXvuubGsCQAAwDWCTuDPz8/XH/7wB4IYAACAjYL2jC1evDiWdQAAALgSO/ADAAAYRBgDAAAwiDAGAABgEGEMAADAIMIYAACAQYQxAAAAgwhjAAAABhHGAAAADCKMAQAAGEQYAwAAMIgwBgAAYBBhDAAAwCDCGAAAgEGEMQAAAIMIYwAAAAYRxgAAAAwijAEAABhEGAMAADCIMAYAAGAQYQwAAMAgwhgAAIBBhDEAAACDCGMAAAAGEcYAAAAMIowBAAAYRBgDAAAwiDAGAABgEGEMAADAIMIYAACAQbaGsfXr12vMmDEaMWKEVq9eHfR9b731lq644go7SwEAAHCkZLs+eP/+/VqxYoXWrl2rlJQUTZ48WRdffLEGDhwY8L4DBw7ogQcesKsMAAAAR7OtZ6y8vFwFBQXKyMhQWlqaRo4cqY0bN570vkWLFum2226zqwwAAABHs61nrKamRllZWf7X2dnZ+vDDDwPe8/zzz+u8887TD37wg049IzPzlC7VGI6srHTbn4HI0S7OQ5s4E+3iPLSJM5lsF9vCmNfrlcfj8b+2LCvg9Y4dO7R582Y9++yzqq6u7tQz6uqOyOu1ulxrMFlZ6aqtPWzb56NzaBfnoU2ciXZxHtrEmexul6QkT8gOJNuGKXNzc1VbW+t/XVtbq+zsbP/rjRs3qra2VqWlpZoxY4Zqamp07bXX2lUOAACAI9kWxoqKilRRUaH6+no1NjZq8+bNGjZsmP/+rFmztGnTJq1bt06rVq1Sdna21qxZY1c5AAAAjmRbGMvJydHs2bNVVlamCRMmqLi4WEOGDNH06dP10Ucf2fVYAACAuOKxLMu+SVc2Y86YO9EuzkObOBPt4jy0iTMl7JwxAAAAdIwwBgAAYBBhDAAAwCDCGAAAgEGEMQAAAIMIYwAAAAYRxgAAAAwijAEAABhEGAMAADCIMAYAAGAQYQwAAMAgwhgAAIBBhDEAAACDCGMAAAAGEcYAAAAMIowBAAAYRBgDAAAwiDAGAABgEGEMAADAIMIYAACAQYQxAAAAgwhjAAAABhHGAAAADCKMAQAAGEQYAwAAMIgwBgAAYBBhDAAAwCDCGAAAgEGEMQAAAIMIYwAAAAYRxgAAAAwijAEAABhEGAMAADCIMAYAAGAQYQwAAMAgwhgAAIBBhDEAAACDCGMAAAAGEcYAAAAMIowBAAAYRBgDAAAwiDAGAABgULLpAgAAQOJqqCzXgbUvq6W+Tsl9M9WvpFS9C4pMl+UohDEAAFwmVgGpobJc+59/Vtbx45Kklvo67X/+WUkikJ2AYUoAAFzEF5Ba6usk/SMgNVSWR/1ZB9a+7A9iPtbx4zqw9uWoPyueEcYAAHCRWAYkX+AL97pbEcYAAHCRWAak5L6ZEV13K8IYAAAuEsuAlDZkyEnXPCkp6ldSGvVnxTPCGAAALtKvpFSelJSAa3YEpIbKch0uf/uk6+lFlzB5vw1WUwIA4CK+IGT3asr25qZJ0jcffhjV5yQCwhgAAC7Tu6DI9t4pJu+Hj2FKAAAQdUzeDx89YwAAJACn7XTfr6Q0YMNXicn7wRDGAACIc07c6T5Wc9MSAWEMAIA4F2ojV5PhJxZz0xIBc8YAAIhzTJaPb4QxAADiHJPl4xvDlAAAxDkmy4evvYUOWeNGGq2JMAYAQJxLxMnydqwODbbQIb13T3nOuyAaZXcKYQwAgASQSJPlu7o6NFiQC7bQYe/vVqv/csIYAABIANHo0erK6tBQQS7YgoamA2YXOjCBHwAARIUvCPlCjy8INVSWR/Q5XVkdGirIBVvQkNrP7EIHwhgAAIiKUEEoEl1ZHRoqyPUrKZUnJSXguiclRWdNnRJRfdFGGAMAAFERKgjt+Nn12n33nWH1kgULTeGsDg0V5HoXFCmn7Hr/e5L7Ziqn7HplDx/W4efaiTljAAAgKpL7ZoYcSgx3In5nV4c2VJartenYSddPDHJOXOhAGAMAAFHR3n5nbYU7ET/S0NR24r5faqpypk5zXAA7EWEMAABERdserWDsOKapvflqkqSmpqg/K9qYMwYAAKKmd0GRznnw33Tu08/G9JimUAEv0gUEsUYYAwAAtujKRPxIhQp4Tj8wnTAGAABsEWz1oh3zt0IFPKcfmG7rnLH169frySefVEtLi6ZNm6YpUwL38fjzn/+slStXyrIsnXHGGVq+fLlOPfVUO0sCAAAxFKvVi70LivTNzk/V8NZ/B1yPhwPTbesZ279/v1asWKE1a9bo1Vdf1YsvvqidO3f67x85ckS//OUvtWrVKr322msaNGiQVq5caVc5AAAgweVeN025P5sRk564aLKtZ6y8vFwFBQXKyMiQJI0cOVIbN27UbbfdJklqbm7WkiVLlJOTI0kaNGiQ1q9fb1c5AADABZy4j1hHbAtjNTU1ysrK8r/Ozs7Whx9+6H/dp08fXXnllZKkY8eOadWqVZo6dWpEz8jMPCU6xYaQlZVu+zMQOdrFeWgTZ6JdnIc2cSaT7WJbGPN6vfJ4PP7XlmUFvPY5fPiwbr31VuXl5WnixIkRPaOu7oi8XqvLtQaTlZWu2trDtn0+Ood2cR7axJloF+ehTZzJ7nZJSvKE7ECyLYzl5ubq3Xff9b+ura1VdnZ2wHtqamp00003qaCgQAsWLLCrFAAAkIAaKssjPjLJiWybwF9UVKSKigrV19ersbFRmzdv1rBh/ziIs7W1VTfffLNGjx6thQsXtttrBgAA0B7f8Ue+PcR8516GcxC509jWM5aTk6PZs2errKxMzc3NmjRpkoYMGaLp06dr1qxZqq6u1ieffKLW1lZt2rRJknT++edr6dKldpUEAAASRHvHH4V77qXT2LrP2Lhx4zRu3LiAa0899ZQkafDgwdq+fbudjwcAAAkq2K76Tt9tvz3swA8AAOJOLM+9tBthDAAAxJ1YnntpN1uHKQEAAOzgmxeWCKspCWMAACAuxeNu++0hjAEAgLAkyr5eTkMYAwAAHfLt6+XbTsK3r5ckAlkXMYEfAAB0KNS+XugawhgAAOhQIu3r5TSEMQAA0KFE2tfLaQhjAACgQ4m0r5fTMIEfAAB0KJH29XIawhgAAAhLR/t6NVSW6+CG/9B589bqk/tL1Gf0NTELa/G87QZhDAAAdJlv64vMgl1KO+uw0s/5xJatL9oLXZLietsNwhgAAOiyU/vdqu/+bo88yV5J0vfueFcDW97XwQ/+V5Y+iMozgu115klJCbrtRjyEMSbwAwCALvv7swPUVJsmq8UjSbJaPGqqSdPfnx0QtWcE2+vMe+RIu++Pl203CGMAAKDLWo73194158mTbKm1sZs8yZb2/sd5ajneP3rPiDBcxcu2G4QxAADQZf1KStXvsi/lbeqmvf9xnrxN3dTvsi+juvVFsHDl6dUrrrfdYM4YAADost4FRTq27S79bcFWHdvTqPq/DVFO6cVRnbPVr6Q0YM6Y9G3oyrlmiqT43XaDMAYAAKIi9fs36KxFN9j2+R3tdRYv4astwhgAAIgbHe11Fo+YMwYAAGAQYQwAAMAgwhgAAIBBzBkDAAAxEc/nR9qJMAYAAGwX7CgjKX5XQUYLw5QAAMB2wY4yOrD2ZUMVOQc9YwAAuFCshwyDHWUUL+dH2omeMQAAXMY3ZOgLQr4hw4bKctueGewoo3g5P9JOhDEAAFzGxJBhv5LSuD4/0k4MUwIA4DImhgw7OsrIzQhjAAC4THLfzHaDl91Dhol4lFE0MEwJAIDLMGToLPSMAQDgMgwZOgs9YwAAuFDvgiKd8+C/KfdnMyRJ1U+v0u6777R1RSXaR88YAAAuxa74zkAYAwDApUJtceELY5wnaT/CGAAALtXRFhf0nMUGc8YAAHCpjnbF5zzJ2CCMAQDgUh1tccF5krHBMCUAAC7V0RYXpjaHdRvCGAAALhZqV/x+JaUBc8YkNoe1A2EMAAC0i81hY4MwBgAAguI8SfsxgR8AAMAgesYAAIBRbt9YljAGAACMYWNZhikBAIBBbCxLGAMAAAaxsSxhDAAAGNTRkUxuQBgDAADGdHQkkxswgR8AAHRaV1dCsrEsYQwAAHRStFZCun1jWcIYAADoVA9XqJWQbg5XkSKMAQDgcp3t4WIlZHQwgR8AAJfr7F5frISMDsIYAAAu19keLlZCRgfDlAAAuFhDZbmUlCR5vSfd66iHi5WQ0UEYAwDApXxzxdoLYuH2cLl9JWQ0MEwJAIBLtTdXTJKUlKScsusJWTFCGAMAwKWCzgnzegliMUQYAwDApVgN6QyEMQAAXIrVkM7ABH4AAFyK1ZDOQBgDAMDFWA1pHsOUAAAABhHGAAAADCKMAQAAGEQYAwAAMIgwBgAAYBBhDAAAwCBbw9j69es1ZswYjRgxQqtXrz7p/rZt21RSUqKRI0dq4cKFamlpsbMcAAAAx7EtjO3fv18rVqzQmjVr9Oqrr+rFF1/Uzp07A94zZ84cLV68WJs2bZJlWXrppZfsKgcAANdoqCzX7rvv1I6fXa/dd9+phspy0yUhBNvCWHl5uQoKCpSRkaG0tDSNHDlSGzdu9N//4osvdOzYMQ0dOlSSVFJSEnAfAABErqGyXPuff9Z/CHhLfZ32P/8sgczBbAtjNTU1ysrK8r/Ozs7W/v37g97PysoKuA8AACJ3YO3Lso4fD7hmHT+uA2tfNlQROmLbcUher1cej8f/2rKsgNcd3Q9HZuYpXS+0A1lZ6bY/A5GjXZyHNnEm2sV57G6THQfr273ecrCe/z2EYPK7sS2M5ebm6t133/W/rq2tVXZ2dsD92tpa/+sDBw4E3A9HXd0Reb1W14sNIisrXbW1h237fHQO7eI8tIkz0S7OE4s2Se7T1z9E2fY6/3ton93tkpTkCdmBZNswZVFRkSoqKlRfX6/GxkZt3rxZw4YN898//fTTlZqaqvfee0+StG7duoD7AAAgcv1KSuVJSQm45klJUb+SUkMVoSO2hbGcnBzNnj1bZWVlmjBhgoqLizVkyBBNnz5dH330kSTp4Ycf1vLlyzVq1Ch98803Kisrs6scAABcoXdBkXLKrldy30xJUnLfTOWUXa/eBUWGK0MwHsuy7BvnsxnDlO5EuzgPbeJMtIvz0CbOlLDDlAAAAOgYYQwAAMAgwhgAAIBBhDEAAACDCGMAAAAGEcYAAAAMIowBAAAYRBgDAAAwiDAGAABgEGEMAADAIMIYAACAQYQxAAAAgwhjAAAABiWbLqArkpI8CfEMRI52cR7axJloF+ehTZzJznbp6LM9lmVZtj0dAAAAITFMCQAAYBBhDAAAwCDCGAAAgEGEMQAAAIMIYwAAAAYRxgAAAAwijAEAABhEGAMAADCIMAYAAGAQYQwAAMAgwpik9evXa8yYMRoxYoRWr1590v1t27appKREI0eO1MKFC9XS0mKgSvfpqF3+/Oc/66qrrtL48eN1yy236NChQwaqdJeO2sTnrbfe0hVXXBHDytyto3bZvXu3pk6dqvHjx+umm27i30oMdNQmVVVVKi0t1fjx4zVz5kw1NDQYqNJ9jhw5ouLiYn3++ecn3TP6t95yuerqauvyyy+3Dh48aB09etQaN26c9emnnwa8Z+zYsdYHH3xgWZZlzZ8/31q9erWBSt2lo3Y5fPiwdckll1jV1dWWZVnWo48+at13332mynWFcP6tWJZl1dbWWqNGjbIuv/xyA1W6T0ft4vV6rREjRlhbtmyxLMuyHnroIevBBx80Va4rhPNv5ZprrrHeeusty7Isa/ny5dYjjzxiolRX+etf/2oVFxdb+fn51r59+066b/Jvvet7xsrLy1VQUKCMjAylpaVp5MiR2rhxo//+F198oWPHjmno0KGSpJKSkoD7sEdH7dLc3KwlS5YoJydHkjRo0CB99dVXpsp1hY7axGfRokW67bbbDFToTh21S1VVldLS0jRs2DBJ0s0336wpU6aYKtcVwvm34vV6dfToUUlSY2OjevToYaJUV3nppZe0ZMkSZWdnn3TP9N9614exmpoaZWVl+V9nZ2dr//79Qe9nZWUF3Ic9OmqXPn366Morr5QkHTt2TKtWrdJPf/rTmNfpJh21iSQ9//zzOu+88/SDH/wg1uW5VkftsnfvXvXr108LFizQxIkTtWTJEqWlpZko1TXC+bcyb948LVq0SJdeeqnKy8s1efLkWJfpOkuXLtUPf/jDdu+Z/lvv+jDm9Xrl8Xj8ry3LCnjd0X3YI9zv/fDhw5oxY4by8vI0ceLEWJboOh21yY4dO7R582bdcsstJspzrY7apaWlRVu3btU111yjV155RWeeeabuv/9+E6W6RkdtcuzYMS1cuFDPPvus/vKXv+jaa6/V3LlzTZSK/8v033rXh7Hc3FzV1tb6X9fW1gZ0Yba9f+DAgXa7OBFdHbWL9O3/J3Pttddq0KBBWrp0aaxLdJ2O2mTjxo2qra1VaWmpZsyY4W8f2KujdsnKylL//v01ePBgSVJxcbE+/PDDmNfpJh21yY4dO5SamqohQ4ZIkq6++mpt3bo15nXiH0z/rXd9GCsqKlJFRYXq6+vV2NiozZs3++dWSNLpp5+u1NRUvffee5KkdevWBdyHPTpql9bWVt18880aPXq0Fi5cSG9lDHTUJrNmzdKmTZu0bt06rVq1StnZ2VqzZo3Bit2ho3a54IILVF9fr+3bt0uS3nzzTeXn55sq1xU6apP+/fururpau3fvliS98cYb/rAMM0z/rU+O2ZMcKicnR7Nnz1ZZWZmam5s1adIkDRkyRNOnT9esWbM0ePBgPfzww1q0aJGOHDmi/Px8lZWVmS474XXULtXV1frkk0/U2tqqTZs2SZLOP/98eshsFM6/FcReOO3ym9/8RosWLVJjY6Nyc3P14IMPmi47oYXTJsuXL9cdd9why7KUmZmpZcuWmS7blZzyt95jWZYVs6cBAAAggOuHKQEAAEwijAEAABhEGAMAADCIMAYAAGAQYQwAAMAgwhgAV6uqqtJFF12kjz76yH+tvr5eP/3pT/XWW2+ZKwyAa7C1BQDXe+GFF7Rq1Sq98sor6tWrl2688UYVFhbq5z//uenSALgAYQwAJM2ZM0fffPONzjrrLO3bt08rV67kZAcAMUEYAwBJ33zzjSZMmKCWlhatX79evXr1Ml0SAJdgzhgASPrss8909OhRNTQ0qKqqynQ5AFyEnjEArldfX69JkybpF7/4hZqamrRixQq98sorysrKMl0aABcgjAFwtdbWVt14440aOHCg7rnnHknS/PnztW/fPj333HPq1q2b4QoBJDqGKQG42oMPPqjGxkbNnTvXf23x4sU6dOiQHnnkEYOVAXALesYAAAAMomcMAADAIMIYAACAQYQxAAAAgwhjAAAABhHGAAAADCKMAQAAGEQYAwAAMOj/B7TJ/PSb5+17AAAAAElFTkSuQmCC\n",
|
|
"text/plain": [
|
|
"<Figure size 720x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 3 0.74\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"model13 = TrainModel_medoids(X1, 3)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 65,
|
|
"id": "f38c0210",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 720x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 4 0.74\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"model14 = TrainModel_medoids(X1, 4)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 66,
|
|
"id": "ff1666c4",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
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"text/plain": [
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"<Figure size 720x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 720x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# 2 clusters\n",
|
|
"X2, y2 = make_classification(n_samples=500, n_features=2, n_informative=2, n_redundant=0, n_repeated=0, n_classes=2,\n",
|
|
" n_clusters_per_class=1, class_sep=4, flip_y=0, weights=[0.5, 0.5], random_state=17)\n",
|
|
"print_sns_plot(X2)\n",
|
|
"\n",
|
|
"# 4 clusters\n",
|
|
"X3, y3 = make_classification(n_samples=500, n_features=2, n_informative=2, n_redundant=0, n_repeated=0, n_classes=2,\n",
|
|
" n_clusters_per_class=2, class_sep=3, flip_y=0, weights=[0.5, 0.5], random_state=17)\n",
|
|
"print_sns_plot(X3)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 67,
|
|
"id": "9f18cbdb",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 720x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 2 0.72\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"model15 = TrainModel_medoids(X2, 2)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 68,
|
|
"id": "75e364f5",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 720x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 4 0.77\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"model16 = TrainModel_medoids(X3, 4)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "7db02032",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"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": "3.8.13"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|