10.25
Posted zhangjij
tags:
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import
numpy as np
x
=
np.random.randint(
1
,
50
,[
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,
93
)
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)
2鸢尾花花瓣长度数据做聚类并用散点图显示
#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[:,2] y = np.zeros(150) def initcenter(x,k): #初始聚类中心数组 return x[: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) if len(m) == 1: n = x[c] else: 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="Paired"); 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.
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[:,2:3] 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,np.linspace(1,150,150),c=y_means,marker=‘x‘) plt.show()
4.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=‘x‘) plt.show()
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