Kmeans_K均值算法-----机器学习（非监督学习）

K means教程

 

0.引入依赖

import numpy as np
import matplotlib.pyplot as plt

# 引入数据
from sklearn.datasets import make_blobs

1.数据加载

x, y = make_blobs( n_samples = 100, centers = 6, random_state=1234, cluster_std=0.6)

plt.figure(figsize=(6,6)) #定义画布大小
plt.scatter(x[:,0], x[:,1], c=y)
plt.show()

 

 

2.算法实现

# 距离函数，默认计算欧式距离
from scipy.spatial.distance import cdist

class K_Means(object):
    # 初始化， 参数 n_clusters(K)、迭代次数：max_iter、初始质心：centroids
    def __init__(self, n_clusters=6, max_iter=300, centroids=[]):
        self.n_clusters = n_clusters
        self.max_iter = max_iter
        self.centroids = np.array( centroids, dtype=np.float )
        
    #训练模型方法，k_means聚类过程，传入原始数据
    def fit(self, data):
        #加入没有指定初始质心，就随机选取data中的点作为初始质心
        if (self.centroids.shape == (0,) ):
        # 从data中随机生成0到data行数的6个整数，作为索引值
            self.centroids = data[ np.random.randint( 0, data.shape[0], slef.n_clusters ) ,: ]
        
        # 开始迭代
        for i in range(self.max_iter):
            # 1. 计算距离矩阵， 得到一个100*6的矩阵
            distances = cdist(data, self.centroids)

            # 2. 对距离按由远到近排序，选取最近的质心点的类别(下标)，作为当前点的分类
            c_ind = np.argmin( distances, axis=1 )

            # 3. 对每一类数据进行均值计算，更新质心点坐标
            for i in range(self.n_clusters):
                # 排除掉没有在c_ind里的类别
                if i in c_ind:
                    # 选出所有类别是i的点，取data里面坐标的均值，更新第i个质心
                    self.centroids[i] = np.mean( data[c_ind==i], axis=0 )

    # 实现预测方法
    def predict(self, samples):
        # 跟上面一样，先计算距离矩阵，然后选取距离最近的那个质心的类别
        distances = cdist(samples, self.centroids)
        c_ind = np.argmin( distances, axis=1 )

        return c_ind

dist = np.array([[121,221,32,43],
                [121,1,12,23],
                [65,21,2,43],
                [1,221,32,43],
                [21,11,22,3],])
c_ind = np.argmin( dist, axis=1 )
print(c_ind)
x_new = x[0:5]

print(x_new)
print(c_ind==2)
print(x_new[c_ind == 2])

np.mean(x_new[c_ind==2], axis=0)

 

3.测试

 

def plotKMeans(x, y, centroids, subplot, title):
    # 分配子图
    plt.subplot(subplot)
    plt.scatter(x[:,0],x[:,1],c='r')
    # 画出质心点
    plt.scatter(centroids[:,0], centroids[:,1], c=np.array(range(6)), s=100)
    plt.title(title)
    
kmeans = K_Means(max_iter = 300, centroids=np.array([[2,1],[2,2],[2,3],[2,4],[2,5],[2,6]]))

plt.figure(figsize=(16, 6))
plotKMeans( x, y, kmeans.centroids, 121, 'Initial State')

# 开始聚类
kmeans.fit(x)

plotKMeans( x, y, kmeans.centroids, 122, 'Final State')

# 预测新数据点的类别
x_new = np.array([[0,0],[10,7]])
y_pred = kmeans.predict(x_new)

print(y_pred)

plt.scatter(x_new[:,0], x_new[:,1], s=100, c='black')