聚类--K均值算法：自主实现与sklearn.cluster.KMeans调用

1.用python实现K均值算法

import numpy as np
x = np.random.randint(1,100,20)#产生的20个一到一百的随机整数
y = np.zeros(20)
k = 3
print(x)
print(y)

def initcenter(x,k):#初始化聚类中心数组
    return x[0:k].reshape(k)
kc = initcenter(x,k)
print(kc)

def nearest(kc, i):#定义函数求出kc与i之差最小的数的坐标
    d = (abs(kc - i))
    w = np.where(d == np.min(d))
    return w[0][0]

# print(nearest(kc,66))

# for i in range(x.shape[0]):
#     y[i] = nearest(kc,x[i])
# print(y)

def xclassify(x, y, kc):#按距离最近的准则将它们分到距离它们最近的聚类中心（最相似）所对应的类
    for i in range(x.shape[0]):
        y[i] = nearest(kc,x[i])
    return y

y = xclassify(x,y,kc)
print(x)
print(y)

#.用sklearn.cluster.KMeans，鸢尾花完整数据做聚类并用散点图显示.
import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import load_iris
iris=load_iris()
print(iris)
X=iris.data
print(X)
from sklearn.cluster import KMeans
est = KMeans(n_clusters=3)
est.fit(X)
kc = est.cluster_centers_
y_kmeans = est.predict(X) #预测每个样本的聚类索引
print(y_kmeans,kc)
print(kc.shape,y_kmeans.shape)
plt.scatter(X[:,0],X[:,1],c=y_kmeans,s=50,cmap='rainbow')
plt.show()

# 鸢尾花完整数据做聚类并用散点图显示.
from sklearn.cluster import KMeans
import numpy as np
from sklearn.datasets import load_iris
import matplotlib.pyplot as plt
data = load_iris()
iris = data.data
petal_len = iris
print(petal_len)
k_means = KMeans(n_clusters=3) #三个聚类中心
result = k_means.fit(petal_len) #Kmeans自动分类
kc = result.cluster_centers_ #自动分类后的聚类中心
y_means = k_means.predict(petal_len) #预测Y值
plt.scatter(petal_len[:,0],petal_len[:,2],c=y_means, marker='p',cmap='rainbow')
plt.show()