机器学习篇 - K-Measn 聚类 - 找出原数据集的正态分布中心点

#!/usr/bin/env Python3
# -*- coding: utf-8 -*-
# @Software: PyCharm
# @virtualenv：ai
# @contact: 1040691703@qq.com
# @Desc：对K-means.py 文件的解析
__author__ = '未昔/AngelFate'
__date__ = '2019/8/15 20:10'
import numpy as np


def kmeans_():
    """
    聚类，找出原始数据集的中心点
    :return: 
    """
    from sklearn.cluster import KMeans
    X = np.loadtxt('test.txt', dtype=float, delimiter=',')
    kmeans = KMeans(n_clusters=2, random_state=0).fit(X)
    print(kmeans.labels_)

    print('kmeans.predict: ',kmeans.predict([[0, 0], [12, 3]]))

    #这个聚类中心点的值,和生成时用的正态分布的mean很接近.
    print(kmeans.cluster_centers_)

if __name__ == '__main__':
    kmeans_()