第八次作业

1.用python实现K均值算法

K-means是一个反复迭代的过程，算法分为四个步骤：

  （x,k,y)

1） 选取数据空间中的K个对象作为初始中心，每个对象代表一个聚类中心；

  def initcenter(x, k): kc

2） 对于样本中的数据对象，根据它们与这些聚类中心的欧氏距离，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；

  def nearest(kc, x[i]): j

  def xclassify(x, y, kc):y[i]=j

3） 更新聚类中心：将每个类别中所有对象所对应的均值作为该类别的聚类中心，计算目标函数的值；

  def kcmean(x, y, kc, k):

4） 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)

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].reshape(k)

def nearest(kc,i):
    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]):
        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 m[0].shape != (0,):
            n = np.mean(x[m])
        if l[c] != n:
            l[c] = n
            flag = True     
        return (np.array(l),flag)

k = 3
kc = initcenter(x,k)
flag = True 
print(x,y,kc,flag)
while flag:
    y = xclassify(x,y,kc)
    kc,flag = kcmean(x, y, kc, k)
print(y,kc)
print(x,y)

2. 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):
    return x[0:k].reshape(k)

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

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  
    return (np.array(l), flag)

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

k = 3
kc = initcenter(x, k)
flag = True
print(x, y, kc, flag)
while flag:
    y = xclassify(x, y, kc)
    kc, flag = kcmean(x, y, kc, k)
print(y, kc, type(kc))

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

3. 用sklearn.cluster.KMeans，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.cluster import KMeans
from sklearn.datasets import load_iris
import matplotlib.pyplot as plt

iris= load_iris()
x=iris.data

petal_length = x[:, 2:3]
 
print(petal_length)
est = KMeans(n_clusters=3)
est.fit(petal_length)
kc = est.cluster_centers_
y_kmeans = est.predict(petal_length)

print(y_kmeans,kc)
print(kc.shape,y_kmeans.shape,np.shape)
plt.scatter(petal_length,np.linspace(1,150,150),c=y_kmeans,marker='o',cmap='rainbow')
plt.show()

4. 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
from sklearn.cluster import KMeans

iris=load_iris()
x=iris.data

est = KMeans(n_clusters = 3)
est.fit(x)
kc = est.cluster_centers_
y_kmeans = est.predict(x)  

print(y_kmeans,kc)
print(kc.shape,y_kmeans.shape,np.shape)

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