python中K-means算法基礎知識點
能夠學習和掌握編程,最好的學習方式,就是去掌握基本的使用技巧,再多的概念意義,總歸都是為瞭使用服務的,K-means算法又叫K-均值算法,是非監督學習中的聚類算法。主要有三個元素,其中N是元素個數,x表示元素,c(j)表示第j簇的質心,下面就使用方式給大傢簡單介紹實例使用。
K-Means算法進行聚類分析
km = KMeans(n_clusters = 3) km.fit(X) centers = km.cluster_centers_ print(centers)
三個簇的中心點坐標為:
[[5.006 3.428 ]
[6.81276596 3.07446809]
[5.77358491 2.69245283]]
比較一下K-Means聚類結果和實際樣本之間的差別:
predicted_labels = km.labels_ fig, axes = plt.subplots(1, 2, figsize=(16,8)) axes[0].scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Set1, edgecolor='k', s=150) axes[1].scatter(X[:, 0], X[:, 1], c=predicted_labels, cmap=plt.cm.Set1, edgecolor='k', s=150) axes[0].set_xlabel('Sepal length', fontsize=16) axes[0].set_ylabel('Sepal width', fontsize=16) axes[1].set_xlabel('Sepal length', fontsize=16) axes[1].set_ylabel('Sepal width', fontsize=16) axes[0].tick_params(direction='in', length=10, width=5, colors='k', labelsize=20) axes[1].tick_params(direction='in', length=10, width=5, colors='k', labelsize=20) axes[0].set_title('Actual', fontsize=18) axes[1].set_title('Predicted', fontsize=18)
k-means算法實例擴展內容:
# -*- coding: utf-8 -*- """Excercise 9.4""" import numpy as np import pandas as pd import matplotlib.pyplot as plt import sys import random data = pd.read_csv(filepath_or_buffer = '../dataset/watermelon4.0.csv', sep = ',')[["密度","含糖率"]].values ########################################## K-means ####################################### k = int(sys.argv[1]) #Randomly choose k samples from data as mean vectors mean_vectors = random.sample(data,k) def dist(p1,p2): return np.sqrt(sum((p1-p2)*(p1-p2))) while True: print mean_vectors clusters = map ((lambda x:[x]), mean_vectors) for sample in data: distances = map((lambda m: dist(sample,m)), mean_vectors) min_index = distances.index(min(distances)) clusters[min_index].append(sample) new_mean_vectors = [] for c,v in zip(clusters,mean_vectors): new_mean_vector = sum(c)/len(c) #If the difference betweenthe new mean vector and the old mean vector is less than 0.0001 #then do not updata the mean vector if all(np.divide((new_mean_vector-v),v) < np.array([0.0001,0.0001]) ): new_mean_vectors.append(v) else: new_mean_vectors.append(new_mean_vector) if np.array_equal(mean_vectors,new_mean_vectors): break else: mean_vectors = new_mean_vectors #Show the clustering result total_colors = ['r','y','g','b','c','m','k'] colors = random.sample(total_colors,k) for cluster,color in zip(clusters,colors): density = map(lambda arr:arr[0],cluster) sugar_content = map(lambda arr:arr[1],cluster) plt.scatter(density,sugar_content,c = color) plt.show()
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