Create KMeanspp.py

Author: zhaozhiyong19890102

Chapter_10 KMeans/KMeanspp.py

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+# coding:UTF-8
+'''
+Date:20160923
+@author: zhaozhiyong
+'''
+
+import numpy as np
+from random import random
+from KMeans import load_data, kmeans, distance, save_result
+
+FLOAT_MAX = 1e100 # 设置一个较大的值作为初始化的最小的距离
+
+def nearest(point, cluster_centers):
+    '''计算point和cluster_centers之间的最小距离
+    input:  point(mat):当前的样本点
+            cluster_centers(mat):当前已经初始化的聚类中心
+    output: min_dist(float):点point和当前的聚类中心之间的最短距离
+    '''
+    min_dist = FLOAT_MAX
+    m = np.shape(cluster_centers)[0]  # 当前已经初始化的聚类中心的个数
+    for i in xrange(m):
+        # 计算point与每个聚类中心之间的距离
+        d = distance(point, cluster_centers[i, ])
+        # 选择最短距离
+        if min_dist > d:
+            min_dist = d
+    return min_dist
+
+def get_centroids(points, k):
+    '''KMeans++的初始化聚类中心的方法
+    input:  points(mat):样本
+            k(int):聚类中心的个数
+    output: cluster_centers(mat):初始化后的聚类中心
+    '''
+    m, n = np.shape(points)
+    cluster_centers = np.mat(np.zeros((k , n)))
+    # 1、随机选择一个样本点为第一个聚类中心
+    index = np.random.randint(0, m)
+    cluster_centers[0, ] = np.copy(points[index, ])
+    # 2、初始化一个距离的序列
+    d = [0.0 for _ in xrange(m)]
+ 
+    for i in xrange(1, k):
+        sum_all = 0
+        for j in xrange(m):
+            # 3、对每一个样本找到最近的聚类中心点
+            d[j] = nearest(points[j, ], cluster_centers[0:i, ])
+            # 4、将所有的最短距离相加
+            sum_all += d[j]
+        # 5、取得sum_all之间的随机值
+        sum_all *= random()
+        # 6、获得距离最远的样本点作为聚类中心点
+        for j, di in enumerate(d):
+            sum_all -= di
+            if sum_all > 0:
+                continue
+            cluster_centers[i] = np.copy(points[j, ])
+            break
+    return cluster_centers
+
+if __name__ == "__main__":
+    k = 4#聚类中心的个数
+    file_path = "data.txt"
+    # 1、导入数据
+    print "---------- 1.load data ------------"
+    data = load_data(file_path)
+    # 2、KMeans++的聚类中心初始化方法
+    print "---------- 2.K-Means++ generate centers ------------"
+    centroids = get_centroids(data, k)
+    # 3、聚类计算
+    print "---------- 3.kmeans ------------"
+    subCenter = kmeans(data, k, centroids)
+    # 4、保存所属的类别文件
+    print "---------- 4.save subCenter ------------"
+    save_result("sub_pp", subCenter)
+    # 5、保存聚类中心
+    print "---------- 5.save centroids ------------"
+    save_result("center_pp", centroids)