机器学习---kmeans聚类的python实现

"""
Name: study_kmeans.py
Author: KX-Lau
Time: 2020/11/6 16:59
Desc: 实现kmeans聚类
"""

import math
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.cluster import KMeans

# -----------不使用sklearn实现kmeans聚类 -------------
class MyKmeans:
    def __init__(self, k, n=50):
        self.k = k  # 聚类中心数k
        self.n = n  # 迭代次数

    def fit(self, x, centers=None):
        # 1. 随机选择K个点
        if centers is None:
            index = np.random.randint(low=0, high=len(x), size=self.k)  # 随机生成数组, 每个数组元素从low到high的整数, 元素个数为size
            centers = x[index]

        inters = 0
        while inters < self.n:
            # 构造k个点的集合
            points_set = {key: [] for key in range(self.k)}

            # 2. 遍历所有点point, 将point放入最近的聚类中心的集合中
            for point in x:
                nearest_index = np.argmin(np.sum((centers - point) ** 2, axis=1) ** 0.5)
                points_set[nearest_index].append(point)

            # 3. 遍历每一个点集, 计算新的聚类中心
            for i_k in range(self.k):
                centers[i_k] = sum(points_set[i_k]) / len(points_set[i_k])

            inters += 1

        return points_set, centers

"""
iris中文名是鸢尾花卉数据集, 是一类多重变量分析的数据集.
包含150个样本, 分为3类(山鸢尾Setosa, 变色鸢尾Versicolor, 维吉尼亚鸢尾Virginica),
每个类别50个数据, 每个数据包含4个属性(花萼长度, 花萼宽度, 花瓣长度, 花瓣宽度).
"""

iris = datasets.load_iris()
data = iris['data'][:, :2]
print(type(data))
mk = MyKmeans(3)
point_sets, centers = mk.fit(data)

category1 = np.asarray(point_sets[0])
category2 = np.asarray(point_sets[1])
category3 = np.asarray(point_sets[2])

for i, p in enumerate(centers):
    plt.scatter(p[0], p[1], s=200, marker='^', color='yellow', edgecolors='black')

plt.scatter(category1[:, 0], category1[:, 1], color='g')
plt.scatter(category2[:, 0], category2[:, 1], color='r')
plt.scatter(category3[:, 0], category3[:, 1], color='b')
plt.xlim(4, 8)
plt.ylim(1, 5)
plt.title('kmeans with k=3')
plt.show()

# -----------使用sklearn实现kmeans聚类 -------------
init = np.vstack([data[5], data[109], data[121]])       # 指定初始质心
kmeans = KMeans(n_clusters=3, init=init, max_iter=100).fit(data)
labels = kmeans.labels_
cluster_centers = kmeans.cluster_centers_

c1 = data[labels == 0]
c2 = data[labels == 1]
c3 = data[labels == 2]

print('cluster_centers', cluster_centers)
print('init', init)

plt.figure()

for i, p in enumerate(cluster_centers):
    plt.scatter(p[0], p[1], color='yellow', edgecolors='black', s=200, marker='^')

plt.scatter(c1[:, 0], c1[:, 1], color='g')
plt.scatter(c2[:, 0], c2[:, 1], color='r')
plt.scatter(c3[:, 0], c3[:, 1], color='b')
plt.xlim(4, 8)
plt.ylim(1, 5)
plt.title('kmeans using sklearn with k=3')
plt.show()