import numpy as np

x = np.random.randint(1,100,[20,1])

y = np.zeros(20)

k = 3

def initcenter(x,k):

    return x[:k]

kc = initcenter(x,k)

kc

def nearest(kc,i):

    d=(abs(kc-i))

    w=np.where(d==np.min(d))

    return w[0][0]

kc = initcenter(x,k)

nearest(kc,56)

def xclassify(x,y,kc):

    for i in range(x.shape[0]):

        y[i] = nearest(kc,x[i])

    return y

kc = initcenter(x,k)

y = xclassify(x,y,kc)

print(kc,y)

def kcmean(x,y,kc,k):

    l = list(kc)

    flag = False

    for c in range(k):

        m = np.where(y == c)

        n = np.mean(x[m])

        if l[c] != n:

            l[c] = n

            flag = True

            print(l,flag)

    return (np.array(l),flag)

kc = initcenter(x,k)

flag = True

k = 3

while flag:

    y = xclassify(x,y,kc)

    kc,flag = kcmean(x,y,kc,k)

import numpy as np

from sklearn.datasets import load_iris

iris = load_iris()

x = iris.data[:,1]

y = np.zeros(150)

def initcenter(x,k):    #初始聚类中心数组

    return x[0:k].reshape(k)

def nearest(kc,i):       #数组中的值，与聚类中心最小距离所在类别的索引号

    d = (abs(kc-i))

    w = np.where(d == np.min(d))

    return w[0][0]

def xclassify(x,y,kc):

    for i in range(x.shape[0]):       #对数组的每个值进行分类，shape[0]读取矩阵第一维度的长度

        y[i] = nearest(kc,x[i])

    return y

def kcmean(x,y,kc,k):     #计算各聚类新均值

    l = list(kc)

    flag = False

    for c in range(k):

        print(c)

        m = np.where(y == c)

        n=np.mean(x[m])

        if l[c] != n:

            l[c] = n

            flag = True     #聚类中心发生变化

            print(l,flag)

    return (np.array(l),flag)

k = 3

kc = initcenter(x,k)

flag = True

print(x,y,kc,flag)

#判断聚类中心和目标函数的值是否发生改变，若不变，则输出结果，若改变，则返回2

while flag:

    y = xclassify(x,y,kc)

    kc, flag = kcmean(x,y,kc,k)

    print(y,kc,type(kc))

print(x,y)

import matplotlib.pyplot as plt

plt.scatter(x,x,c=y,s=50,cmap="rainbow");

plt.show()