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文章目录

前言一、K-Means二、pass总结

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前言

锻炼自己实现算法的能力，

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一、K-Means

K-Means算法

''' K-means算法是一种常用的无监督聚类算法，可以视作同时优化质心和每个样本的标签，使得损失函数最小，算法执行过程提现了EM的思想 ''' import numpy as np import random class KMeans(): def __init__(self, K=2): self.k = K def __dist(self, a, b): return np.sqrt(sum(np.power((a-b), 2))) #输入ndarray类型X，输出每个样本所对应的簇 def fit(self, X): #1.初始化质心 clusters = [[X[random.randint(0, len(X)-1)], 1] for i in range(self.k)] labels = [0 for i in range(len(X))] #2.每次更新节点所属簇 changed = True while changed: changed = False old_labels = labels.copy() for i in range(len(X)): c0 = clusters[0][0] / clusters[0][1] cur_dist = self.__dist(c0, X[i]) for j in range(1, len(clusters)): cj = clusters[j][0] / clusters[j][1] if cur_dist > self.__dist(cj, X[i]): labels[i] = j cur_dist = self.__dist(cj, X[i]) if old_labels != labels:changed = True #3.更新质点 for k in range(len(clusters)): clusters[k] = [np.zeros((X.shape[1])), 0] for i in range(len(X)): clusters[labels[i]][0] += X[i] clusters[labels[i]][1] += 1 return labels if __name__ == '__main__': dataSet = [ [0.697, 0.460], [0.774, 0.376], [0.634, 0.264], [0.608, 0.318], [0.556, 0.215], [0.403, 0.237], [0.481, 0.149], [0.437, 0.211], [0.666, 0.091], [0.243, 0.267], [0.245, 0.057], [0.343, 0.099], [0.639, 0.161], [0.657, 0.198], [0.360, 0.370], [0.593, 0.042], [0.719, 0.103], [0.359, 0.188], [0.339, 0.241], [0.282, 0.257], [0.748, 0.232], [0.714, 0.346], [0.483, 0.312], [0.478, 0.437], [0.525, 0.369], [0.751, 0.489], [0.532, 0.472], [0.473, 0.376], [0.725, 0.445], [0.446, 0.459] ] k = 3 X = np.array(dataSet) result = KMeans(K=k).fit(X) import matplotlib.pyplot as plt colors = ['b', 'g', 'r'] for i in range(k): ys = [] for j in range(len(result)): if result[j] == i:ys.append(j) plt.scatter(X[ys,0], X[ys,1], c=colors[i], alpha=0.5) plt.show() print(result)

二、pass

总结

pass