手写算法系列（Python实现）

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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