sklearn的kmeans测试

由于需要海量的进行聚类，所以将 k-means 算法自我封装成一个方便利用的库，可以直接调用得到最优的 k值 和 中心点：

#!/usr/bin/python3.4

# -*- coding: utf-8 -*-

# k-means算法

import numpy as np

from sklearn.cluster import KMeans

from sklearn import metrics

# sklearn官方文档

# http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans

def calckmean(array, karr):

    # array是一个二维数组

    # X = X = [[1, 1], [2, 3], [3, 2], [1, 2], [5, 8], [6, 6], [5, 7], [5, 6], [6, 7], [7, 1], [8, 2], [9, 1], [7, 1], [9, 3]]

    # k是待选取K值的数组

    # karr = [2, 3, 4, 5, 8,...]

    # 将原始数据由数组变成矩阵

    x = np.array(array)

    # 用来储存轮廓系数的数组

    score = []

    # 用来储存中心坐标点的数组

    point = []

    # 用来储存各个簇的坐标

    coordinates = []

    # 用来储存各个簇点的与中心的距离

    distances = []

    for k in karr:

        # n_clusters为聚类的个数

        # max_iter为迭代的次数，这里设置最大迭代次数为300

        # n_init=10使用不同质心种子运行k-means算法的次数

        kmeans_model = KMeans(n_clusters=k, max_iter=300,n_init=10).fit(x)

        # title = 'K = %s, 轮廓系数 = %.03f' % (k, metrics.silhouette_score(X, kmeans_model.labels))

        # print(title)

        # 获取中心点的坐标

        counter_point = kmeans_model.cluster_centers_

        # print("k=" + str(k) + "时的中心点为" + "\n" + str(counter_point))

        # 记录分数

        # print(metrics.silhouette_score(x, kmeans_model.labels_,metric='euclidean'))

        score.append("%.03f" % (metrics.silhouette_score(x, kmeans_model.labels_)))

        # 记录中心坐标

        point.append(counter_point)

        # 将坐标属于哪个簇的标签储存到数组

        # k = 3 : [0 0 0 0 2 2 2 2 2 1 1 1 1 1]

        # k = 4 : [1 1 1 1 0 0 0 0 0 3 2 2 3 2]

        coordinates.append(kmeans_model.labels_)

        # 每个点和中心点的距离

        distances.append(KMeans(n_clusters=k, max_iter=300).fit_transform(x))

    # 返回轮廓系数最大的k值\中心坐标\分簇坐标

    maxscore = max(score, default=0)

    for i in range(0, len(score)):

        if maxscore == score[i]:

            # 储存分簇坐标的数组

            coordinate = []

            # 储存簇点与中心点的距离数组

            distance = []

            for j in range(0, len(point[i])):

                # 这里是得到分簇坐标

                tempcoor = []

                for item in zip(coordinates[i], array):

                    if item[0] == j:

                        tempcoor.append(item[1])

                coordinate.append(tempcoor)

                # 得到的样式为k=3，每个簇点的坐标群

                # [[[7, 1], [8, 2], [9, 1], [7, 1], [9, 3]],

                # [[5, 8], [6, 6], [5, 7], [5, 6], [6, 7]],

                # [[1, 1], [2, 3], [3, 2], [1, 2]]]

                # 这里是得到分簇与中心点的距离

                tempdis = []

                for item in zip(coordinates[i], distances[i]):

                    if item[0] == j:

                        tempdis.append(min(item[1]))

                distance.append(tempdis)

                # 得到k=3的各个簇点对中心的距离

                # [[1.1661903789690597, 0.39999999999999575, 1.166190378969066, 1.1661903789690597, 1.7204650534085277],

                # [1.2649110640673495, 0.9999999999999858, 0.4472135954999452, 0.8944271909999063, 0.6324555320336579],

                # [1.25, 1.0307764064044151, 1.25, 0.75]]

            # 得到k=3的中心点

            # [[8.0, 1.6],

            # [5.4, 6.8],

            # [1.75, 2.0]]

            return karr[i], point[i], coordinate, distance

调用的时候直接可以：

from kmeans import *

测试数据：

#!/usr/bin/python3.4

# -*- coding: utf-8 -*-

from kmeans import *

x1 = np.array([1, 2, 3, 1, 5, 6, 5])

x2 = np.array([1, 3, 2, 2, 8, 6, 7])

# a = [[1, 2, 3, 1, 5, 6, 5], [1, 3, 2, 2, 8, 6, 7], [3, 5, 9, 4, 7, 6, 1], [1, 5, 3, 4, 8, 6, 7], [5, 1, 2, 3, 6, 9, 4],[8, 4, 6, 2, 1, 6, 3]]

a = [[1, 1], [2, 3], [3, 2], [1, 2], [5, 8], [6, 6], [5, 7], [5, 6], [6, 7], [7, 1], [8, 2], [9, 1], [7, 1], [9, 3]]

karr = [2, 3, 4, 5, 8]

# print(np.array(a))

# print(list(zip(x1, x2)))

K, point, coordinate, distance = calckmean(X, tests)

print("------------------------")

print("k=" + str(K) + "时的中心点为" + "\n" + str(point) + "\n" + "各个簇点为" + "\n" + str(coordinate))

print(distance)

源文件可以在我的github下载：

TTyb

巴特西

sklearn的kmeans测试

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