1053 lines
892 KiB
Plaintext
1053 lines
892 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 37,
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"id": "a7ba08af-89d2-455a-9d9a-8eb9828b9d42",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Importowanie potrzebnych bibliotek\n",
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"import numpy as np\n",
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"import pandas as pd\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 import datasets\n",
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"from collections import defaultdict\n",
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"import warnings\n",
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"from sklearn.utils import Bunch\n",
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"warnings.simplefilter(\"ignore\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "52abc99b-2ab5-4185-b955-64f0069e7521",
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"metadata": {},
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"source": [
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"# Metryka euklidesowa z 3 klasami"
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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": 50,
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"id": "4755089e-f8bc-4cbc-88c5-606df2e03e1c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Liczba elementów w klasie 0: 39\n",
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"Liczba elementów w klasie 1: 61\n",
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"Liczba elementów w klasie 2: 50\n",
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"Centroidy:\n",
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"[6.85384615 3.07692308 5.71538462 2.05384615]\n",
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"[5.88360656 2.74098361 4.38852459 1.43442623]\n",
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"[5.006 3.428 1.462 0.246]\n",
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"Macierz pomyłek:\n",
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"[[ 0 0 50]\n",
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" [ 3 47 0]\n",
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" [36 14 0]]\n",
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"Dokładność modelu: 31.33%\n",
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"Recall: 31.33%\n",
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"Precyzja: 25.68%\n",
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"F1-score: 28.23%\n",
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"Klasyfikacja przy użyciu wbudowanego KMeans: [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
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" 1 1 1 1 1 1 1 1 1 1 1 1 1 0 2 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
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" 2 2 2 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 0 2 0 0 0 0 2 0 0 0 0\n",
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" 0 0 2 2 0 0 0 0 2 0 2 0 2 0 0 2 2 0 0 0 0 0 2 0 0 0 0 2 0 0 0 2 0 0 0 2 0\n",
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" 0 2]\n"
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]
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}
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],
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"source": [
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"iris=datasets.load_iris()\n",
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"\n",
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"X=pd.DataFrame(iris.data,columns=['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width'])\n",
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"y=pd.DataFrame(iris.target,columns=['Classes'])\n",
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"\n",
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"X['species'] = pd.Series(np.random.randn(150), index=X.index)\n",
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"X['species'][y['Classes']==0]='Setosa'\n",
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"X['species'][y['Classes']==1]='versicolor'\n",
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"X['species'][y['Classes']==2]='virginica'\n",
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"\n",
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"# Ustawienie liczby klastrów\n",
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"clusters=len(np.unique(y))\n",
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"\n",
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"# Funkcja obliczania odległości euklidesowej\n",
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"def euclidean_dis(x1, x2):\n",
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" return np.sqrt(np.sum((x1 - x2)**2))\n",
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"\n",
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"# Implementacja KMeans\n",
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"class KMeans:\n",
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" def __init__(self, data, k, max_ite):\n",
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" self.data = data\n",
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" self.k = k\n",
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" self.max_ite = max_ite\n",
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" \n",
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" def predict(self):\n",
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" centroids = defaultdict(int)\n",
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" for i in range(self.k):\n",
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" centroids[i] = self.data[i]\n",
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" \n",
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" for _ in range(self.max_ite):\n",
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" classes = defaultdict(list)\n",
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" for key in range(self.k):\n",
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" classes[key] = []\n",
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" \n",
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" for datapoint in self.data:\n",
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" distances = [euclidean_dis(datapoint, centroids[j]) for j in range(self.k)]\n",
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" min_distance_index = distances.index(min(distances))\n",
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" classes[min_distance_index].append(datapoint)\n",
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" \n",
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" old_centroids = dict(centroids)\n",
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" for t in range(self.k):\n",
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" centroids[t] = np.mean(classes[t], axis=0)\n",
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" \n",
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" if all(np.sum((centroids[t] - old_centroids[t])/old_centroids[t] * 100) <= 0.001 for t in range(self.k)):\n",
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" break\n",
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" \n",
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" return classes, centroids\n",
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"\n",
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"# Predykcja z użyciem implementacji KMeans\n",
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"kmeans = KMeans(iris.data[:, :4], clusters, 10000)\n",
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"classes, centroids = kmeans.predict()\n",
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"\n",
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"# Drukowanie wyników\n",
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"for i in range(0, 3):\n",
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" classes[i] = np.array(classes[i]).tolist()\n",
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" print(f\"Liczba elementów w klasie {i}: {len(classes[i])}\")\n",
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"\n",
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"print(\"Centroidy:\")\n",
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"for centroid in centroids.values():\n",
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" print(centroid)\n",
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"\n",
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"# Funkcja do tworzenia macierzy pomyłek\n",
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"def subset(array1, array2):\n",
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" return any(np.array_equal(array2, elem) for elem in array1)\n",
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"\n",
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"def confusion_matrix(a, b, c, classes):\n",
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" cm = [[0] * len(np.unique(y)) for _ in range(len(np.unique(y)))]\n",
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" for idx, data_class in enumerate([a, b, c]):\n",
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" for element in data_class:\n",
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" for i, cluster in enumerate(classes.values()):\n",
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" if subset(cluster, element):\n",
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" cm[idx][i] += 1\n",
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" return cm\n",
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"\n",
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"# Obliczanie metryk wydajności\n",
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"class Metrics:\n",
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" def __init__(self, confusion_m):\n",
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" self.confusion_m = np.array(confusion_m)\n",
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" self.total = np.sum(confusion_m)\n",
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" self.diagonal = np.sum(np.diag(confusion_m))\n",
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"\n",
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" def accuracy(self):\n",
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" return self.diagonal / self.total\n",
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"\n",
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" def recall(self):\n",
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" return np.mean(np.diag(self.confusion_m) / np.sum(self.confusion_m, axis=1))\n",
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"\n",
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" def precision(self):\n",
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" return np.mean(np.diag(self.confusion_m) / np.sum(self.confusion_m, axis=0))\n",
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"\n",
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" def f1_score(self, precision, recall):\n",
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" return (2 * precision * recall) / (precision + recall)\n",
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"\n",
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"# Obliczenie metryk i wyświetlenie wyników\n",
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"class0 = np.array([iris.data[i] for i in range(len(iris.target)) if iris.target[i] == 0])\n",
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"class1 = np.array([iris.data[i] for i in range(len(iris.target)) if iris.target[i] == 1])\n",
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"class2 = np.array([iris.data[i] for i in range(len(iris.target)) if iris.target[i] == 2])\n",
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"\n",
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"matrix = confusion_matrix(class0, class1, class2, classes)\n",
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"performance = Metrics(matrix)\n",
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"\n",
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"accuracy = performance.accuracy()\n",
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"recall = performance.recall()\n",
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"precision = performance.precision()\n",
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"f1_score = performance.f1_score(precision, recall)\n",
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"\n",
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"print('Macierz pomyłek:')\n",
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"print(np.array(matrix))\n",
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"\n",
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"print(f\"Dokładność modelu: {accuracy * 100:.2f}%\")\n",
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"print(f\"Recall: {recall * 100:.2f}%\")\n",
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"print(f\"Precyzja: {precision * 100:.2f}%\")\n",
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"print(f\"F1-score: {f1_score * 100:.2f}%\")\n",
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"\n",
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"# Wykorzystanie wbudowanej funkcji KMeans ze scikit-learn\n",
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"from sklearn.cluster import KMeans as SklearnKMeans\n",
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"\n",
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"kmeans_sklearn = SklearnKMeans(n_clusters=3, random_state=42)\n",
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"y_kmeans = kmeans_sklearn.fit_predict(iris.data)\n",
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"\n",
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"print(\"Klasyfikacja przy użyciu wbudowanego KMeans:\", y_kmeans)\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4cf6521d-c29f-4c55-8947-4556a783c9cf",
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"metadata": {},
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"source": [
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"# Metryka L1 z 3 klasami"
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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": 51,
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"id": "36eeb4d3-65eb-42c3-9b1e-4c1dbbc8dd2e",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Liczba elementów w klasie 0: 63\n",
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"Liczba elementów w klasie 1: 37\n",
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"Liczba elementów w klasie 2: 50\n",
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"Centroidy:\n",
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"[5.9047619 2.74603175 4.41269841 1.43333333]\n",
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"[6.87027027 3.08648649 5.74594595 2.08918919]\n",
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"[5.006 3.428 1.462 0.246]\n",
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"Macierz pomyłek:\n",
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"[[ 0 0 50]\n",
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" [48 2 0]\n",
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" [15 35 0]]\n",
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"Dokładność modelu: 1.33%\n",
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"Recall: 1.33%\n",
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"Precyzja: 1.80%\n",
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"F1-score: 1.53%\n",
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"Klasyfikacja przy użyciu wbudowanego KMeans: [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
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" 1 1 1 1 1 1 1 1 1 1 1 1 1 0 2 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
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" 2 2 2 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 0 2 0 0 0 0 2 0 0 0 0\n",
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" 0 0 2 2 0 0 0 0 2 0 2 0 2 0 0 2 2 0 0 0 0 0 2 0 0 0 0 2 0 0 0 2 0 0 0 2 0\n",
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" 0 2]\n"
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]
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}
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],
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"source": [
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|
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"# Odległość Manhatan\n",
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"def manhattan_dis(x1, x2):\n",
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" return np.sum(np.abs(x1 - x2))\n",
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"\n",
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"# Implementacja KMeans\n",
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"class KMeans:\n",
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" def __init__(self, data, k, max_ite):\n",
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" self.data = data\n",
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" self.k = k\n",
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" self.max_ite = max_ite\n",
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" \n",
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" def predict(self):\n",
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" centroids = defaultdict(int)\n",
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" for i in range(self.k):\n",
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" centroids[i] = self.data[i]\n",
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" \n",
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" for _ in range(self.max_ite):\n",
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" classes = defaultdict(list)\n",
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" for key in range(self.k):\n",
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" classes[key] = []\n",
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" \n",
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" for datapoint in self.data:\n",
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" distances = [manhattan_dis(datapoint, centroids[j]) for j in range(self.k)]\n",
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" min_distance_index = distances.index(min(distances))\n",
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" classes[min_distance_index].append(datapoint)\n",
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" \n",
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" old_centroids = dict(centroids)\n",
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" for t in range(self.k):\n",
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" centroids[t] = np.mean(classes[t], axis=0)\n",
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" \n",
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" if all(np.sum((centroids[t] - old_centroids[t])/old_centroids[t] * 100) <= 0.001 for t in range(self.k)):\n",
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" break\n",
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" \n",
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" return classes, centroids\n",
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"\n",
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"# Predykcja z użyciem implementacji KMeans\n",
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"kmeans = KMeans(iris.data[:, :4], clusters, 10000)\n",
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"classes, centroids = kmeans.predict()\n",
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"\n",
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"# Drukowanie wyników\n",
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"for i in range(0, 3):\n",
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" classes[i] = np.array(classes[i]).tolist()\n",
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" print(f\"Liczba elementów w klasie {i}: {len(classes[i])}\")\n",
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"\n",
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"print(\"Centroidy:\")\n",
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"for centroid in centroids.values():\n",
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" print(centroid)\n",
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"\n",
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"# Funkcja do tworzenia macierzy pomyłek\n",
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"def subset(array1, array2):\n",
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" return any(np.array_equal(array2, elem) for elem in array1)\n",
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"\n",
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"def confusion_matrix(a, b, c, classes):\n",
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" cm = [[0] * len(np.unique(y)) for _ in range(len(np.unique(y)))]\n",
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" for idx, data_class in enumerate([a, b, c]):\n",
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" for element in data_class:\n",
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" for i, cluster in enumerate(classes.values()):\n",
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" if subset(cluster, element):\n",
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" cm[idx][i] += 1\n",
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" return cm\n",
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"\n",
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"# Obliczanie metryk wydajności\n",
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"class Metrics:\n",
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" def __init__(self, confusion_m):\n",
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" self.confusion_m = np.array(confusion_m)\n",
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" self.total = np.sum(confusion_m)\n",
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" self.diagonal = np.sum(np.diag(confusion_m))\n",
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"\n",
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" def accuracy(self):\n",
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" return self.diagonal / self.total\n",
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"\n",
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" def recall(self):\n",
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|
|
" return np.mean(np.diag(self.confusion_m) / np.sum(self.confusion_m, axis=1))\n",
|
||
|
|
"\n",
|
||
|
|
" def precision(self):\n",
|
||
|
|
" return np.mean(np.diag(self.confusion_m) / np.sum(self.confusion_m, axis=0))\n",
|
||
|
|
"\n",
|
||
|
|
" def f1_score(self, precision, recall):\n",
|
||
|
|
" return (2 * precision * recall) / (precision + recall)\n",
|
||
|
|
"\n",
|
||
|
|
"# Obliczenie metryk i wyświetlenie wyników\n",
|
||
|
|
"class0 = np.array([iris.data[i] for i in range(len(iris.target)) if iris.target[i] == 0])\n",
|
||
|
|
"class1 = np.array([iris.data[i] for i in range(len(iris.target)) if iris.target[i] == 1])\n",
|
||
|
|
"class2 = np.array([iris.data[i] for i in range(len(iris.target)) if iris.target[i] == 2])\n",
|
||
|
|
"\n",
|
||
|
|
"matrix = confusion_matrix(class0, class1, class2, classes)\n",
|
||
|
|
"performance = Metrics(matrix)\n",
|
||
|
|
"\n",
|
||
|
|
"accuracy = performance.accuracy()\n",
|
||
|
|
"recall = performance.recall()\n",
|
||
|
|
"precision = performance.precision()\n",
|
||
|
|
"f1_score = performance.f1_score(precision, recall)\n",
|
||
|
|
"\n",
|
||
|
|
"print('Macierz pomyłek:')\n",
|
||
|
|
"print(np.array(matrix))\n",
|
||
|
|
"\n",
|
||
|
|
"print(f\"Dokładność modelu: {accuracy * 100:.2f}%\")\n",
|
||
|
|
"print(f\"Recall: {recall * 100:.2f}%\")\n",
|
||
|
|
"print(f\"Precyzja: {precision * 100:.2f}%\")\n",
|
||
|
|
"print(f\"F1-score: {f1_score * 100:.2f}%\")\n",
|
||
|
|
"\n",
|
||
|
|
"# Wykorzystanie wbudowanej funkcji KMeans ze scikit-learn\n",
|
||
|
|
"from sklearn.cluster import KMeans as SklearnKMeans\n",
|
||
|
|
"\n",
|
||
|
|
"kmeans_sklearn = SklearnKMeans(n_clusters=3, random_state=42)\n",
|
||
|
|
"y_kmeans = kmeans_sklearn.fit_predict(iris.data)\n",
|
||
|
|
"\n",
|
||
|
|
"print(\"Klasyfikacja przy użyciu wbudowanego KMeans:\", y_kmeans)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "d68189cd-45d8-404a-8f55-9dccfe1eaf19",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# Metryka euklidesowa z 2 klasami"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 56,
|
||
|
|
"id": "5914c6ba-eb11-445f-b0ff-744df411f0c7",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Liczba elementów w klasie 0: 50\n",
|
||
|
|
"Liczba elementów w klasie 1: 50\n",
|
||
|
|
"Centroidy:\n",
|
||
|
|
"[5.006 3.428 1.462 0.246]\n",
|
||
|
|
"[5.936 2.77 4.26 1.326]\n",
|
||
|
|
"Macierz pomyłek:\n",
|
||
|
|
"[[50 0]\n",
|
||
|
|
" [ 0 50]]\n",
|
||
|
|
"Dokładność modelu: 100.00%\n",
|
||
|
|
"Recall: 100.00%\n",
|
||
|
|
"Precyzja: 100.00%\n",
|
||
|
|
"F1-score: 100.00%\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Usunięcie jednej z klas (np. klasy 2)\n",
|
||
|
|
"X = X[y['Classes'] != 2]\n",
|
||
|
|
"y = y[y['Classes'] != 2]\n",
|
||
|
|
"\n",
|
||
|
|
"# Zresetowanie indeksów po usunięciu klasy\n",
|
||
|
|
"X.reset_index(drop=True, inplace=True)\n",
|
||
|
|
"y.reset_index(drop=True, inplace=True)\n",
|
||
|
|
"\n",
|
||
|
|
"# Ustawienie liczby klastrów po usunięciu klasy\n",
|
||
|
|
"clusters = len(np.unique(y))\n",
|
||
|
|
"\n",
|
||
|
|
"# Funkcja obliczania odległości euklidesowej\n",
|
||
|
|
"def euclidean_dis(x1, x2):\n",
|
||
|
|
" return np.sqrt(np.sum((x1 - x2)**2))\n",
|
||
|
|
"\n",
|
||
|
|
"# Implementacja KMeans z metryką Manhattan\n",
|
||
|
|
"class KMeans:\n",
|
||
|
|
" def __init__(self, data, k, max_ite):\n",
|
||
|
|
" self.data = data\n",
|
||
|
|
" self.k = k\n",
|
||
|
|
" self.max_ite = max_ite\n",
|
||
|
|
" \n",
|
||
|
|
" def predict(self):\n",
|
||
|
|
" centroids = defaultdict(int)\n",
|
||
|
|
" for i in range(self.k):\n",
|
||
|
|
" centroids[i] = self.data[i]\n",
|
||
|
|
" \n",
|
||
|
|
" for _ in range(self.max_ite):\n",
|
||
|
|
" classes = defaultdict(list)\n",
|
||
|
|
" for key in range(self.k):\n",
|
||
|
|
" classes[key] = []\n",
|
||
|
|
" \n",
|
||
|
|
" for datapoint in self.data:\n",
|
||
|
|
" distances = [euclidean_dis(datapoint, centroids[j]) for j in range(self.k)]\n",
|
||
|
|
" min_distance_index = distances.index(min(distances))\n",
|
||
|
|
" classes[min_distance_index].append(datapoint)\n",
|
||
|
|
" \n",
|
||
|
|
" old_centroids = dict(centroids)\n",
|
||
|
|
" for t in range(self.k):\n",
|
||
|
|
" centroids[t] = np.mean(classes[t], axis=0)\n",
|
||
|
|
" \n",
|
||
|
|
" if all(np.sum((centroids[t] - old_centroids[t])/old_centroids[t] * 100) <= 0.001 for t in range(self.k)):\n",
|
||
|
|
" break\n",
|
||
|
|
" \n",
|
||
|
|
" return classes, centroids\n",
|
||
|
|
"\n",
|
||
|
|
"# Predykcja z użyciem zmodyfikowanej implementacji KMeans\n",
|
||
|
|
"kmeans = KMeans(X.values, clusters, 10000)\n",
|
||
|
|
"classes, centroids = kmeans.predict()\n",
|
||
|
|
"\n",
|
||
|
|
"# Drukowanie wyników\n",
|
||
|
|
"for i in range(clusters):\n",
|
||
|
|
" classes[i] = np.array(classes[i]).tolist()\n",
|
||
|
|
" print(f\"Liczba elementów w klasie {i}: {len(classes[i])}\")\n",
|
||
|
|
"\n",
|
||
|
|
"print(\"Centroidy:\")\n",
|
||
|
|
"for centroid in centroids.values():\n",
|
||
|
|
" print(centroid)\n",
|
||
|
|
"\n",
|
||
|
|
"# Obliczenie metryk i wyświetlenie wyników dla pozostałych klas\n",
|
||
|
|
"class0 = np.array([X.values[i] for i in range(len(y)) if y.iloc[i, 0] == 0])\n",
|
||
|
|
"class1 = np.array([X.values[i] for i in range(len(y)) if y.iloc[i, 0] == 1])\n",
|
||
|
|
"\n",
|
||
|
|
"matrix = confusion_matrix(class0, class1, [], classes)\n",
|
||
|
|
"performance = Metrics(matrix)\n",
|
||
|
|
"\n",
|
||
|
|
"accuracy = performance.accuracy()\n",
|
||
|
|
"recall = performance.recall()\n",
|
||
|
|
"precision = performance.precision()\n",
|
||
|
|
"f1_score = performance.f1_score(precision, recall)\n",
|
||
|
|
"\n",
|
||
|
|
"print('Macierz pomyłek:')\n",
|
||
|
|
"print(np.array(matrix))\n",
|
||
|
|
"\n",
|
||
|
|
"print(f\"Dokładność modelu: {accuracy * 100:.2f}%\")\n",
|
||
|
|
"print(f\"Recall: {recall * 100:.2f}%\")\n",
|
||
|
|
"print(f\"Precyzja: {precision * 100:.2f}%\")\n",
|
||
|
|
"print(f\"F1-score: {f1_score * 100:.2f}%\")\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "0e29854d-e26f-4250-b8f2-b075d7ed6306",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# Porównanie wyników klasyfikacji przy użyciu metryki euklidesowej\n",
|
||
|
|
"\n",
|
||
|
|
"## 1. Wyniki z metryką euklidesową dla wszystkich klas:\n",
|
||
|
|
"- **Liczba elementów w klasach**:\n",
|
||
|
|
" - Klasa 0: 39\n",
|
||
|
|
" - Klasa 1: 61\n",
|
||
|
|
" - Klasa 2: 50\n",
|
||
|
|
"\n",
|
||
|
|
"- **Centroidy**:\n",
|
||
|
|
" - Klasa 0: [6.8538, 3.0769, 5.7154, 2.0538]\n",
|
||
|
|
" - Klasa 1: [5.8836, 2.7410, 4.3885, 1.4344]\n",
|
||
|
|
" - Klasa 2: [5.006, 3.428, 1.462, 0.246]\n",
|
||
|
|
"\n",
|
||
|
|
"- **Macierz pomyłek**:\n",
|
||
|
|
" - [[ 0, 0, 50], [ 3, 47, 0], [36, 14, 0]]\n",
|
||
|
|
"\n",
|
||
|
|
"- **Metryki wydajności**:\n",
|
||
|
|
" - Dokładność: 31.33%\n",
|
||
|
|
" - Recall: 31.33%\n",
|
||
|
|
" - Precyzja: 25.68%\n",
|
||
|
|
" - F1-score: 28.23%\n",
|
||
|
|
"\n",
|
||
|
|
"- **Komentarz**: Model wykazuje niską jakość klasyfikacji, szczególnie dla klasy 2, która została całkowicie pominięta w prognozach. Chociaż klasa 1 ma relatywnie wysoką precyzję, wyniki ogólne są słabe, co sugeruje problemy z klastrowaniem przy pełnym zbiorze danych."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "ea82dc2a-9c7f-46a5-a496-f40b808ef9ec",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## 2. Wyniki z metryką euklidesową po usunięciu jednej klasy:\n",
|
||
|
|
"- **Liczba elementów w klasach**:\n",
|
||
|
|
" - Klasa 0: 50\n",
|
||
|
|
" - Klasa 1: 50\n",
|
||
|
|
"\n",
|
||
|
|
"- **Centroidy**:\n",
|
||
|
|
" - Klasa 0: [5.006, 3.428, 1.462, 0.246]\n",
|
||
|
|
" - Klasa 1: [5.936, 2.770, 4.260, 1.326]\n",
|
||
|
|
"\n",
|
||
|
|
"- **Macierz pomyłek**:\n",
|
||
|
|
" - [[50, 0], [ 0, 50]]\n",
|
||
|
|
"\n",
|
||
|
|
"- **Metryki wydajności**:\n",
|
||
|
|
" - Dokładność: 100.00%\n",
|
||
|
|
" - Recall: 100.00%\n",
|
||
|
|
" - Precyzja: 100.00%\n",
|
||
|
|
" - F1-score: 100.00%\n",
|
||
|
|
"\n",
|
||
|
|
"- **Komentarz**: Po usunięciu jednej klasy model osiągnął perfekcyjne wyniki. Wszystkie punkty danych zostały poprawnie sklasyfikowane, co pokazuje, że zredukowanie liczby klas poprawiło znacząco wydajność algorytmu.\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "453b9ac5-9651-4846-8860-7428e91ce138",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## 3. Wyniki z metryką L1 dla wszystkich klas:\n",
|
||
|
|
"- **Liczba elementów w klasach**:\n",
|
||
|
|
" - Klasa 0: 63\n",
|
||
|
|
" - Klasa 1: 37\n",
|
||
|
|
" - Klasa 2: 50\n",
|
||
|
|
"\n",
|
||
|
|
"- **Centroidy**:\n",
|
||
|
|
" - Klasa 0: [5.9048, 2.7460, 4.4127, 1.4333]\n",
|
||
|
|
" - Klasa 1: [6.8703, 3.0865, 5.7459, 2.0892]\n",
|
||
|
|
" - Klasa 2: [5.006, 3.428, 1.462, 0.246]\n",
|
||
|
|
"\n",
|
||
|
|
"- **Macierz pomyłek**:\n",
|
||
|
|
" - [[ 0, 0, 50], [48, 2, 0], [15, 35, 0]]\n",
|
||
|
|
"\n",
|
||
|
|
"- **Metryki wydajności**:\n",
|
||
|
|
" - Dokładność: 1.33%\n",
|
||
|
|
" - Recall: 1.33%\n",
|
||
|
|
" - Precyzja: 1.80%\n",
|
||
|
|
" - F1-score: 1.53%\n",
|
||
|
|
"\n",
|
||
|
|
"- **Komentarz**: Podobnie jak w przypadku metryki euklidesowej z 3 klasami, model z tą metryką wykazuje bardzo niską dokładność. Klasyfikacja nie działa dobrze przy wykorzystaniu metryki L1 dla pełnego zbioru danych."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "7041caf0-71cb-4d7c-bf6e-003176854ee2",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Podsumowanie:\n",
|
||
|
|
"- **Metryka euklidesowa dla wszystkich klas**: Niska wydajność z punktami błędnie klasyfikowanymi. Klasa 2 została całkowicie pominięta.\n",
|
||
|
|
"- **Metryka euklidesowa po usunięciu klasy**: Perfekcyjne wyniki. Algorytm działa doskonale przy dwóch klasach.\n",
|
||
|
|
"- **Metryka L1 dla wszystkich klas**: Bardzo niska jakość klasyfikacji, co wskazuje na nieodpowiedniość metryki do struktury danych w tym przypadku.\n",
|
||
|
|
"\n",
|
||
|
|
"## Wnioski:\n",
|
||
|
|
"Zastosowanie metryki euklidesowej przy pełnym zbiorze danych wykazuje lepsze rezultaty w porównaniu do metryki L1, ale po usunięciu jednej klasy algorytm KMeans osiąga idealne wyniki. To podkreśla znaczenie doboru metryki oraz liczby klas w analizie klastrów."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 58,
|
||
|
|
"id": "6d49ca08-fe5b-40af-a98c-5d03e589d440",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"<class 'pandas.core.frame.DataFrame'>\n",
|
||
|
|
"RangeIndex: 150 entries, 0 to 149\n",
|
||
|
|
"Data columns (total 5 columns):\n",
|
||
|
|
" # Column Non-Null Count Dtype \n",
|
||
|
|
"--- ------ -------------- ----- \n",
|
||
|
|
" 0 sepal_length 150 non-null float64\n",
|
||
|
|
" 1 sepal_width 150 non-null float64\n",
|
||
|
|
" 2 petal_length 150 non-null float64\n",
|
||
|
|
" 3 petal_width 150 non-null float64\n",
|
||
|
|
" 4 species 150 non-null object \n",
|
||
|
|
"dtypes: float64(4), object(1)\n",
|
||
|
|
"memory usage: 6.0+ KB\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"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: right;\">\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" <th>sepal_length</th>\n",
|
||
|
|
" <th>sepal_width</th>\n",
|
||
|
|
" <th>petal_length</th>\n",
|
||
|
|
" <th>petal_width</th>\n",
|
||
|
|
" <th>species</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>0</th>\n",
|
||
|
|
" <td>5.1</td>\n",
|
||
|
|
" <td>3.5</td>\n",
|
||
|
|
" <td>1.4</td>\n",
|
||
|
|
" <td>0.2</td>\n",
|
||
|
|
" <td>setosa</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>1</th>\n",
|
||
|
|
" <td>4.9</td>\n",
|
||
|
|
" <td>3.0</td>\n",
|
||
|
|
" <td>1.4</td>\n",
|
||
|
|
" <td>0.2</td>\n",
|
||
|
|
" <td>setosa</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>2</th>\n",
|
||
|
|
" <td>4.7</td>\n",
|
||
|
|
" <td>3.2</td>\n",
|
||
|
|
" <td>1.3</td>\n",
|
||
|
|
" <td>0.2</td>\n",
|
||
|
|
" <td>setosa</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>3</th>\n",
|
||
|
|
" <td>4.6</td>\n",
|
||
|
|
" <td>3.1</td>\n",
|
||
|
|
" <td>1.5</td>\n",
|
||
|
|
" <td>0.2</td>\n",
|
||
|
|
" <td>setosa</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>4</th>\n",
|
||
|
|
" <td>5.0</td>\n",
|
||
|
|
" <td>3.6</td>\n",
|
||
|
|
" <td>1.4</td>\n",
|
||
|
|
" <td>0.2</td>\n",
|
||
|
|
" <td>setosa</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>5</th>\n",
|
||
|
|
" <td>5.4</td>\n",
|
||
|
|
" <td>3.9</td>\n",
|
||
|
|
" <td>1.7</td>\n",
|
||
|
|
" <td>0.4</td>\n",
|
||
|
|
" <td>setosa</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>6</th>\n",
|
||
|
|
" <td>4.6</td>\n",
|
||
|
|
" <td>3.4</td>\n",
|
||
|
|
" <td>1.4</td>\n",
|
||
|
|
" <td>0.3</td>\n",
|
||
|
|
" <td>setosa</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>7</th>\n",
|
||
|
|
" <td>5.0</td>\n",
|
||
|
|
" <td>3.4</td>\n",
|
||
|
|
" <td>1.5</td>\n",
|
||
|
|
" <td>0.2</td>\n",
|
||
|
|
" <td>setosa</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>8</th>\n",
|
||
|
|
" <td>4.4</td>\n",
|
||
|
|
" <td>2.9</td>\n",
|
||
|
|
" <td>1.4</td>\n",
|
||
|
|
" <td>0.2</td>\n",
|
||
|
|
" <td>setosa</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>9</th>\n",
|
||
|
|
" <td>4.9</td>\n",
|
||
|
|
" <td>3.1</td>\n",
|
||
|
|
" <td>1.5</td>\n",
|
||
|
|
" <td>0.1</td>\n",
|
||
|
|
" <td>setosa</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" sepal_length sepal_width petal_length petal_width species\n",
|
||
|
|
"0 5.1 3.5 1.4 0.2 setosa\n",
|
||
|
|
"1 4.9 3.0 1.4 0.2 setosa\n",
|
||
|
|
"2 4.7 3.2 1.3 0.2 setosa\n",
|
||
|
|
"3 4.6 3.1 1.5 0.2 setosa\n",
|
||
|
|
"4 5.0 3.6 1.4 0.2 setosa\n",
|
||
|
|
"5 5.4 3.9 1.7 0.4 setosa\n",
|
||
|
|
"6 4.6 3.4 1.4 0.3 setosa\n",
|
||
|
|
"7 5.0 3.4 1.5 0.2 setosa\n",
|
||
|
|
"8 4.4 2.9 1.4 0.2 setosa\n",
|
||
|
|
"9 4.9 3.1 1.5 0.1 setosa"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 58,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"iris = pd.read_csv(\"iris.csv\")\n",
|
||
|
|
"x = iris.iloc[:, [0, 1, 2, 3]].values\n",
|
||
|
|
"iris.info()\n",
|
||
|
|
"iris[0:10]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 59,
|
||
|
|
"id": "67c9ab0e-f9be-4745-a9a8-3948db8f6029",
|
||
|
|
"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: right;\">\n",
|
||
|
|
" <th>col_0</th>\n",
|
||
|
|
" <th>count</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>species</th>\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>setosa</th>\n",
|
||
|
|
" <td>50</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>versicolor</th>\n",
|
||
|
|
" <td>50</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>virginica</th>\n",
|
||
|
|
" <td>50</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
"col_0 count\n",
|
||
|
|
"species \n",
|
||
|
|
"setosa 50\n",
|
||
|
|
"versicolor 50\n",
|
||
|
|
"virginica 50"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 59,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"iris_outcome = pd.crosstab(index=iris[\"species\"], # Make a crosstab\n",
|
||
|
|
" columns=\"count\") # Name the count column\n",
|
||
|
|
"\n",
|
||
|
|
"iris_outcome"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 60,
|
||
|
|
"id": "f5de2e39-30dc-4fab-86bb-325d7abc2455",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"iris_setosa=iris.loc[iris[\"species\"]==\"Iris-setosa\"]\n",
|
||
|
|
"iris_virginica=iris.loc[iris[\"species\"]==\"Iris-virginica\"]\n",
|
||
|
|
"iris_versicolor=iris.loc[iris[\"species\"]==\"Iris-versicolor\"]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 61,
|
||
|
|
"id": "60299a20-6423-4f91-98bb-d8aa0aecfcd7",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEiCAYAAADwEwVaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAABP7ElEQVR4nO3deVxU9frA8c+sDIuCCO5bikslBYJiqbnkcjX369LyM/VmWRSp7eltsbKrLbfSNkvbl2vXqzfX0tzSq6GmFmqYmAgCIovsDLP+/hhmlADZhpkBnvfrNa/gzOGcZyDPc77L+T4Kq9VqRQghRJOjdHcAQggh3EMSgBBCNFGSAIQQoomSBCCEEE2UJAAhhGiiJAEIIUQTJQlACCGaKEkAQgjRRDX6BGC1WjGbzcjzbkIIUVajTwAWi4Vjx45hsVjcHUq1WCwWfv311wYTb03J52u4GuNnauoafQJoaKxWK0ajsdG2WOTzNVyN8TM1dZIAhBCiiZIEIIQQTZQkACGEaKIkAQghRBMlCUAIIZooSQBCCNFESQIQQogmShKAEEI0UZIAhBCiiVK7O4DGKrfYSL7eSDOdBn9vjbvDEUKIcqQFUE/y9UaOp+SRrze6OxQhhKiQJIB6ZDLL4llCCM/ltgRw4MABpk6dSp8+fRgwYAAvvvgier2+wn337NnDuHHjCAsLY/To0ezatcvF0QohROPjlgSQnZ3N3LlzueOOOzh8+DDr16/n4MGDfPDBB+X2TUxMJCYmhnnz5nH48GFiYmKYP38+6enpbohcCCEaD7ckgMDAQPbv38/kyZNRKBTk5ORQUlJCYGBguX3Xr19PZGQkw4cPR61WM2bMGPr27cuaNWvcELkQQjQebpsF5OfnB8DgwYNJT08nMjKSyZMnl9svISGBHj16lNkWEhJCfHy8S+IUQojGyu3TQLdt20Zubi6PPfYYDz/8MKtWrSrzfmFhId7e3mW26XQ6ioqKanQes9lc51hrwmqxYrFYsVqsNTq3fV9Xx+sq8vkaLrPZjEqlcncYwoncngB0Oh06nY7HH3+cqVOnkpubi7+/v+N9b2/vcoPDer0eX1/fGp0nLi7OKfFWh0qlwuTVnLT0bNrpSshKzqvxBcGV8bqDfL6GKSIiwt0hCCdySwI4cuQICxcuZMOGDWi1WgAMBgMajabc3X6PHj04ceJEmW0JCQn07t27RucMDQ116d1Lao6etnovWrcKoF1A52r/nNlsJi4uzuXxuop8voarMbZqmjq3JICePXui1+t5/fXXefTRR8nIyGDZsmVMmTLFkRDsxo8fz8cff8yWLVsYOXIk27Zt4+DBgyxatKhG51SpVC79B6lQKlAqFSiUilqd19Xxupp8PiHczy2zgHx9fVm1ahWnT59mwIABzJgxg5tvvpmFCxcCEB4ezoYNGwDo1q0b77zzDitXrqRv3768++67rFixgmuuucYdoQshRKPhtjGAkJAQPvroowrfO3r0aJnvBw0axKBBg1wRlhBCNBmyFIQQQjRRkgCEEKKJkgQghBBNlCQAIYRooiQBCCFEEyUJQAghmihJAEII0URJAhBCiCZKEoAQQjRRkgCEEKKJkgQghBBNlCQAIYRooiQBCCFEEyUJQAghmihJAEII0URJAhBCiCZKEoAQQjRRkgCEEKKJcltJyMYot9hIvt6IUgElRou7wxFCiKuSBOBE+XojR87loFUp6Rzk4+5whBDiqiQBOJnRbEHh7iCEEKIaZAxACCGaKEkAQgjRREkCEEKIJkoSgBBCNFGSAIQQoolyyyyg+Ph4li1bxokTJ9BoNAwYMICnnnqKwMDAcvvOmTOH2NhY1OrLob711lvccsstrgxZCCEaHZe3APR6PXPmzCE8PJx9+/axadMmcnJyWLhwYYX7Hz9+nNWrV3P06FHHSy7+QghRdy5PAKmpqfTq1YsHH3wQrVZLixYtmD59OocOHSq3b3JyMrm5uVx33XWuDlMIIRo9l3cBde3alVWrVpXZ9v3333P99deX2zcuLg5fX18WLFhAXFwcQUFBzJo1iylTptT4vGazudYxV5fVYsVisWBRAlYrFosVq8Vao3Pb93VFvO4gn6/hMpvNqFQqd4chnMitTwJbrVbefPNNdu3axRdffFHufYPBQFhYGAsWLKB79+7ExsYSExODr68vo0ePrtG54uLinBV2hVQqFSav5qSkpqPTqmnnpyT9YjrpuhKykvNqfEGo73jdTT5fwxQREeHuEIQTKaxWq9UdJy4oKODpp5/mxIkTvPfee/Ts2bNaP7d48WKysrJYvnx5tfY3m80cO3aM0NDQer97Sc3R89MfWWjVSnq0bsbp9ALCOwXQLkBX7WOYzWbi4uJcEq87yOdruMxmM1qt1t1hCCdySwsgKSmJe++9l3bt2rF27doKZ/8ArF27ttzdvsFgwMvLq8bnVKlU9f4PUqFUoFQqUSqUoFCgVCpQKBW1Oq8r4nUn+XxCuJ/LB4Fzc3OZOXMmffr0YfXq1ZVe/MHWSnjxxRc5efIkFouF3bt3s2nTJqZPn+7CiIUQonFyeQtg3bp1pKamsnXrVr777rsy7x09epTw8HAWL17M+PHjmTlzJkVFRTz00ENkZWXRsWNHli1bRmRkpKvDFkKIRsflCWD27NnMnj270vePHj3q+FqhUBAdHU10dLQrQhNCiCZF6gHUg4v5eopNZnw00gcshPBckgCczGi28OaO0+TrTdwz8BrCOgW4OyQhhKiQLAbnZKfTC8jXmwCIO5/j3mCEEOIqJAE42YU8vePr5EvFboxECCGuThKAk2UWlDi+zsgvwU3P2QkhRJUkATjZlQnAZLGSU2R0YzRCCFE5SQBOlllgKPN9en5JJXsKIYR7SQJwIqvVSr7edsffwkcDQKYkACGEh5IE4ET5JSYspV3+nVv6ApBTLF1AQgjPJAnAiXIKbRd7b42Klr62VRNziw1X+xEhhHAbSQBOdKnIdrH306lp7m3rAsotNrkzJCGEqJQkACfKLrQlgGZeaprpbA9Z50kXkBDCQ0kCcKJLpVM+/XRq/B0tAEkAQgjPJAnAiez9/X7ayy0ASQBCCE8lCcCJCkrXAPLRqi63AORBMCGEh5IE4EQFJbbC795aFX5ethZAfokMAgshPJMkACcqKL3Y6zQqfEsTQKEkACGEh5IE4EQFJbbuHh+NCh+trRhMicmC0WxxZ1hCCFEhSQBOZO8C0mlVeF9RDUxaAUIITyQJwIkcg8AaFWqVEo1KAeAoECOEEJ5EEoAT2e/0vUu7f3SlrYACaQEIITyQJAAnsVqtjhk/9gu/JAAhhCeTBOAkxUYz5tKlQH0cCcD26y2QLiAhhAeSBOAk9n5+pQK0atuv1VttSwTyLIAQwhNJAnAS+6JvOo0KhcI2+KsrHQuQFoAQwhO5JQHEx8cze/Zs+vXrx4ABA3jiiSfIzs6ucN89e/Ywbtw4wsLCGD16NLt27XJxtNWTpy/b/w+gU9vHAGQ5CCGE53F5AtDr9cyZM4fw8HD27dvHpk2byMnJYeHCheX2TUxMJCYmhnnz5nH48GFiYmKYP38+6enprg67Snn6y8Vg7GQMQAjhyVyeAFJTU+nVqxcPPvggWq2WFi1aMH36dA4dOlRu3/Xr1xMZGcnw4cNRq9WMGTOGvn37smbNGleHXSV7F1DZBGD7Ok8SgBDCA6ldfcKuXbuyatWqMtu+//57rr/++nL7JiQk0KNHjzLbQkJCiI+Pr/F5zWZzjX+mJvJKq4F5aZRYrBawWvEqHQzOKzZU+/z2/eo7XneRz9dwmc1mVCpV1TuKBsPlCeBKVquVN998k127dvHFF1+Ue7+wsBBvb+8y23Q6HUVFRTU+V1xcXK3jrI7T52wxWYx60i6k0c5PiUFfCEBaRjbHjh2r0fHqO153k8/XMEVERLg7BOFEbksABQUFPP3005w4cYIvvviCnj17ltvH29sbvV5fZpter8fX17fG5wsNDa3Xu5ddmaeBPFo086Ntm7b4+foSHBAA5KHx9iMsLKxaxzGbzcTFxdV7vO4in6/haoytmqbOLQkgKSmJe++9l3bt2rF
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 409.5x300 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 409.5x300 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEiCAYAAADwEwVaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAABmoUlEQVR4nO3dd3xUVfr48c+dOzOZSSa9JwQCCQlIDQSyKkpVFBXUFXT1Z1tRlFUEy+LKqoDKYlldRbGBddWviqKAIiAqsoqEFgktkEB6SO9tyr2/PyaJxBRSJpmEnLeveb3IzC1PrpP73HvuOc+RVFVVEQRBEPocjbMDEARBEJxDJABBEIQ+SiQAQRCEPkokAEEQhD5KJABBEIQ+SiQAQRCEPkokAEEQhD5KJABBEIQ+6pxPAKqqYrPZEOPdBEEQGjvnE4CiKCQkJKAoSqe2cfDgwU5to68Rx6xjxHETutM5nwAcQVVVLBaLuItoB3HMOkYcN6E7iQQgCILQR4kEIAiC0EeJBCAIgtBHiQQgCILQR4kEIAiC0EeJBCAIgtBHiQQgCILQR4kEIAiC0EeJBCAIgtBHaZ0dgNAxpdUWymssrS7jbtDhadR1U0SCIPQ2IgH0UuU1FvanlWCxNV8zRidrGDPASyQAQRBaJBJAL2axKdRaRdEwQRA6RjwDEARB6KNEAhAEQeijRAIQBEHoo0QCEARB6KNEAhAEQeijRAIQBEHoo5yeAIqKirjkkkvYvXv3WZc9fvw4o0aNatOygiAIQuucmgD27dvH9ddfT3p6+lmXra6u5sEHH6SmpqYbIhMEQTj3OS0BrF+/noceeohFixa1aflly5Yxbdq0Lo5KEASh73BaApgwYQLbtm1jxowZZ132yy+/JC0tjXvvvbcbIhMEQegbnFYKwt/fv03LpaSk8OKLL/Lxxx8jy3KH92ez2Tq9bme24WiqoqIoCorSfCkIRbEv46yYe+Ix6w16+nHrzN+g0PP06FpAtbW1LFq0iEcffZSQkJBObSsxMbHT8ThiG44gyzJWFw+ysnOpNlubXcao15JrslKYUebUk0lPOWa9TU89bmPHjnV2CIID9egEkJiYSGpqKkuWLGHJkiUN7999993MmjWLpUuXtnlbI0aM6PDVi81mIzExsVPbcLTskhpCK7QtFoNz0WoIDPAlxGtAN0dm1xOPWW8gjpvQnXp0AoiNjeXgwYON3ouOjub1118nLi6uXduSZbnTf1CO2IajSBoJjUaDpoWnOBqNBkkjOT3ennTMehNx3ITu4PRxAM2JiYlhw4YNzg5DEAThnNYj7gCSkpIa/XzgwIE2LysIgiB0TI+8AxAEQRC6nkgAgiAIfZRIAIIgCH2USACCIAh9lEgAgiAIfZRIAIIgCH2USACCIAh9lEgAgiAIfZRIAIIgCH2USACCIAh9lEgAgiAIfZRIAIIgCH2USACCIAh9lEgAgiAIfZRIAIIgCH2USACCIAh9lEgAgiAIfZRIAIIgCH2USACCIAh9lEgAgiAIfZTTE0BRURGXXHIJu3fvbnGZjz/+mOnTpxMTE8P06dP58MMPuzFCQRCEc5PWmTvft28fjzzyCOnp6S0u89133/HCCy/w1ltvMWrUKBISErjrrrvw8/Nj+vTp3RitIAjCucVpdwDr16/noYceYtGiRa0ul5uby5133sno0aORJImYmBji4uLYs2dPN0UqCIJwbnLaHcCECRO46qqr0Gq1rSaBm266qdHPhYWF7Nmzh3/84x/t2p/NZutQnGeu25ltOJqqqCiKgqIozX6uKPZlnBVzTzxmvUFPP26yLDs7BMGBnJYA/P39271Ofn4+8+bNY/jw4Vx55ZXtWjcxMbHd++uKbTiCLMtYXTzIys6l2mxtdhmjXkuuyUphRplTTyY95Zj1Nj31uI0dO9bZIQgO5NRnAO2RkJDA/fffT2xsLP/617/QatsX+ogRIzp89WKz2UhMTOzUNhwtu6SG0Aottdbm7wBctBoCA3wJ8RrQzZHZ9cRj1huI4yZ0p16RANatW8dTTz3FggUL+Otf/9qhbciy3Ok/KEdsw1EkjYRGo0HTwlMcjUaDpJGcHm9POma9iThuQnfo8Qlgy5YtLF26lNdee42LLrrI2eEIgiCcM5w+DqA5MTExbNiwAYBXXnkFm83GggULiImJaXg9/vjjTo5SEAShd+sRdwBJSUmNfj5w4EDDvzdu3Njd4QiCIPQJPfIOQBAEQeh6IgEIgiD0USIBCIIg9FEiAQiCIPRRIgH0coqqYlNUZ4chCEIv1CN6AQkdczSnjI/i01FV+POYUM4L8XR2SIIg9CLiDqCXKq4089/daVSZbVRbbHy6L5OyaouzwxIEoRcRCaCX+nx/JjUWhUAPF4I9DZitCr+eLHR2WIIg9CIiAfRCqqqy9UguAJOiA5gUHQDAwaxSVFU8DxAEoW3EM4BeKCm3nOySGrQaiaFBHqioyBqJokoz+RW1BLgbnB2iIAi9gLgD6IW2HbZf/UcFuqPXanDRygz0cwMg6XS5M0MTBKEXEQmgF9p5ogCAYSEeDe9FB7oDkJxX4ZSYBEHofUQC6GXMVoXfMksAGOTv1vD+AF9XADKLq8VzAEEQ2kQkgF7mUHYptVYFL6MOf5NLw/tBngZkjUS1xUZRpdmJEQqC0FuIBNDL7EstBmB4qCeSJDW8r9VoCPG0P/zNLK52SmyCIPQuIgH0MgkZJQAMD/Vo8lmod30zUFV3hiQIQi8lEkAvcySnDPj9oe+ZQr2MAOSU1nRrTIIg9E4iAfQiVWYrqYWVAEQGmJp8HuhhfyaQW17brXEJgtA7iQTQixw7XY6qgr+7C95u+iaf1w8Aq6y1UlFr7e7wBEHoZZyeAIqKirjkkkvYvXt3i8vs2LGDq666itGjR3P55Zfzww8/dGOEPcfRuuafocFN2/8B9FoN3q46AE6LZiBBEM7CqQlg3759XH/99aSnp7e4TGpqKvfddx/3338/e/fu5b777mPhwoXk5uZ2Y6Q9w5Hs+gTQtP2/XqCH/S7gdJlIAIIgtM5pCWD9+vU89NBDLFq06KzLxcbGMm3aNLRaLTNmzGDcuHF88skn3RRpz1F/B3BeC3cA8HsCyBUJQBCEs3BaMbgJEyZw1VVXodVqW00CycnJREVFNXovMjKSY8eOtWt/NputQ3GeuW5nttFZiqJyrK7OT3SgCVVRURQFRVEaLefrZm8CyiurQVVUp8XcE45Zb9TTj5ssy84OQXAgpyUAf3//Ni1XWVmJ0Whs9J7BYKCqqn193RMTE9u1fFdto6NyK61UmW1oNVCZc5IKoydZ2blUmxs/7LVV2X/OLa0mNy+Xwowyp55MnHnMerOeetzGjh3r7BAEB+rx5aCNRiM1NY2bM2pqanBzc2thjeaNGDGiw1cvNpuNxMTETm2js35MygcKGORvYvSokWSX1BBaoaXW2vgOwLvWyldJSVSYFbx8/BjoN8Ap8faEY9YbieMmdKcenwCioqI4fPhwo/eSk5MZPnx4u7Yjy3Kn/6AcsY2OOlVov+OJDDAhyzKSRkKj0aD5w1Mcd4MOF62GWqtCdmktkYEtPy/oDs48Zr2ZOG5Cd3B6N9CzmTlzJvHx8XzzzTdYrVa++eYb4uPjmTVrlrND61Yp+fYBYBH+TQeAnUmSJPzqisSJkhCCILSmRyaAmJgYNmzYAEBERASvvvoqb7zxBuPGjWP16tWsWrWKgQMHOjnK7pWSb6/zf7YEAOBrsg8SE0XhBEFoTY9oAkpKSmr084EDBxr9fNFFF3HRRRd1Z0g9zsn2JAA3+x1AhrgDEAShFT3yDkBorKTKTEGFvcb/mZPAtMRP3AEIgtAGIgH0AvXt/8GeBtxczn7T1vAMoEgkAEEQWiYSQC/QnvZ/+P0ZQH5FLdXmnjmgSBAE5xMJoBf4PQG0beyDq16LUWfvQpheJJ4DCILQPJEAeoGT9V1Am5kDoCX1zwHq5w8QBEH4ow4lgIyMDEfHIbSi/g5gkF/bE4Bv3XOA9EJxByAIQvM6lAAuv/xybr75Zr766qsmZRoEx7LYlIaTeERA28tf+NZNGJNWJO4
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 409.5x300 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.FacetGrid(iris,hue=\"species\").map(sns.distplot,\"petal_length\").add_legend()\n",
|
||
|
|
"sns.FacetGrid(iris,hue=\"species\").map(sns.distplot,\"petal_width\").add_legend()\n",
|
||
|
|
"sns.FacetGrid(iris,hue=\"species\").map(sns.distplot,\"sepal_length\").add_legend()\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 62,
|
||
|
|
"id": "dc86ef38-14d9-41c6-bb02-45a3b0b00c2b",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.boxplot(x=\"species\",y=\"petal_length\",data=iris, palette=\"Set2\")\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 70,
|
||
|
|
"id": "3b3457af-8d7d-4cfd-8f78-100d719aefc8",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.violinplot(x=\"species\",y=\"petal_length\",data=iris)\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 11,
|
||
|
|
"id": "97f58987-e092-4f9a-a456-7104ceae4b5f",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1309.5x1200 with 20 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.set_style(\"whitegrid\")\n",
|
||
|
|
"sns.pairplot(iris,hue=\"species\",size=3);\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 25,
|
||
|
|
"id": "4d26607a-d8c2-45f8-9229-6747e116d284",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from sklearn.cluster import KMeans\n",
|
||
|
|
"wcss = []\n",
|
||
|
|
"\n",
|
||
|
|
"for i in range(1, 11):\n",
|
||
|
|
" kmeans = KMeans(n_clusters = i, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)\n",
|
||
|
|
" kmeans.fit(x)\n",
|
||
|
|
" wcss.append(kmeans.inertia_)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 26,
|
||
|
|
"id": "e21ea4ee-d91c-4b9c-9049-5d1554a6806a",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.plot(range(1, 11), wcss)\n",
|
||
|
|
"plt.title('The elbow method')\n",
|
||
|
|
"plt.xlabel('Number of clusters')\n",
|
||
|
|
"plt.ylabel('WCSS') #within cluster sum of squares\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 27,
|
||
|
|
"id": "4adcad95-5b58-467c-b1bb-a5f875db06a7",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"kmeans = KMeans(n_clusters = 3, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)\n",
|
||
|
|
"y_kmeans = kmeans.fit_predict(x)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 28,
|
||
|
|
"id": "de929a44-bc98-4c7a-a28e-9099bc09efc7",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.legend.Legend at 0x189f13ee3f0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 28,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"#Visualising the clusters\n",
|
||
|
|
"plt.scatter(x[y_kmeans == 0, 0], x[y_kmeans == 0, 1], s = 100, c = 'purple', label = 'Iris-setosa')\n",
|
||
|
|
"plt.scatter(x[y_kmeans == 1, 0], x[y_kmeans == 1, 1], s = 100, c = 'orange', label = 'Iris-versicolour')\n",
|
||
|
|
"plt.scatter(x[y_kmeans == 2, 0], x[y_kmeans == 2, 1], s = 100, c = 'green', label = 'Iris-virginica')\n",
|
||
|
|
"\n",
|
||
|
|
"#Plotting the centroids of the clusters\n",
|
||
|
|
"plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:,1], s = 100, c = 'red', label = 'Centroids')\n",
|
||
|
|
"\n",
|
||
|
|
"plt.legend()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 29,
|
||
|
|
"id": "60bfa195-09d2-40d0-b8b8-aaf795eeef73",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1500x1500 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# 3d scatterplot using matplotlib\n",
|
||
|
|
"\n",
|
||
|
|
"fig = plt.figure(figsize = (15,15))\n",
|
||
|
|
"ax = fig.add_subplot(111, projection='3d')\n",
|
||
|
|
"plt.scatter(x[y_kmeans == 0, 0], x[y_kmeans == 0, 1], s = 100, c = 'purple', label = 'Iris-setosa')\n",
|
||
|
|
"plt.scatter(x[y_kmeans == 1, 0], x[y_kmeans == 1, 1], s = 100, c = 'orange', label = 'Iris-versicolour')\n",
|
||
|
|
"plt.scatter(x[y_kmeans == 2, 0], x[y_kmeans == 2, 1], s = 100, c = 'green', label = 'Iris-virginica')\n",
|
||
|
|
"\n",
|
||
|
|
"#Plotting the centroids of the clusters\n",
|
||
|
|
"plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:,1], s = 100, c = 'red', label = 'Centroids')\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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": "3.12.3"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|