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

4

用python实现K均值算法

x=np.random.randint(1,100,[20,1])
y=np.zeros(20)
k=3

def initcenter(x,k):
    return x[:k]

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,14)

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

运行结果为:

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

运行结果为:

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

运行结果为:

def initcenter(x,k):
    return x[:k]

def nearest(kc, i):
    d = (abs(kc - 1))
    w= np.where(d == np.min(d))
    return w[0][0]

def xclassify(x,y,kc):
    for i in range(x.shape[0]):
        y[i] = nearest(kc,x[i])
        return y

kc = initcenter(x,k)
nearest(kc,93)

m  = np.where(y == 0)

np.mean(x[m])

kc[0]=24
flag = True

import numpy as np
from sklearn.datasets import load_iris
iris = load_iris()
x = iris.data[:,1]
y = np.zeros(150)

def nearest(kc,i):  #初始聚类中心数组
  return x[0: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 kcmean(x, y, kc, k):  #计算各聚类新均值
    l =list(kc)
    flag = False
    for c in range(k):
        m = np.where(y == c)
        if m[0].shape != (0,):
            n = np.mean(x[m])
            if l[c] != n:
                l[c] = n
                flag = True #聚类中心发生改变
                return (np.array(1),flag)

def xclassify(x,y,kc):
    for i in range(x.shape[0]): #对数组的每个值分类
        y[i] = nearest(kc,x[i])
    return y

k = 3
kc = initcenter(x,k)

falg = True
print(x, y, kc, flag)
while flag:
    y = xclassify(x, y, kc)
    xc, flag = kcmean(x, y, kc, k)

print(y,kc)

运行结果为：

import matplotlib.pyplot as plt
plt.scatter(x, x, c=y, s=50, cmap=‘rainbow‘,marker=‘p‘,alpha=0.5);
plt.show()

运行结果为：

鸢尾花花瓣长度数据做聚类并用散点图显示。

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()

运行结果为：

import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import load_iris

iris = load_iris()
X=iris.data
X

用sklearn.cluster.KMeans，鸢尾花花瓣长度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()
X=iris.data
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,X.shape)

plt.scatter(X[:,0],X[:,1],c=y_kmeans, s=50, cmap=‘rainbow‘);
plt.show()

运行结果为

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()

运行结果为：

原文地址：https://www.cnblogs.com/fanfanfan/p/9862233.html