鍛鍊自己實現演算法的能力，
k-means演算法
'''
k-means演算法是一種常用的無監督聚類演算法，可以視作同時優化質心和每個樣本的標籤，使得損失函式最小，演算法執行過程提現了em的思想
'''import numpy as np
import random
class
kmeans()
:def
__init__
(self, k=2)
: self.k = k
def__dist
(self, a, b)
:return np.sqrt(
sum(np.power(
(a-b),2
)))#輸入ndarray型別x，輸出每個樣本所對應的簇
deffit
(self, x)
:#1.初始化質心
clusters =
[[x[random.randint(0,
len(x)-1
)],1
]for i in
range
(self.k)
] labels =[0
for i in
range
(len
(x))
]#2.每次更新節點所屬簇
changed =
true
while changed:
changed =
false
old_labels = labels.copy(
)for i in
range
(len
(x))
: c0 = clusters[0]
[0]/ clusters[0]
[1] cur_dist = self.__dist(c0, x[i]
)for j in
range(1
,len
(clusters)):
cj = clusters[j][0
]/ clusters[j][1
]if cur_dist > self.__dist(cj, x[i]):
labels[i]
= j cur_dist = self.__dist(cj, x[i]
)if old_labels != labels:changed =
true
#3.更新質點
for k in
range
(len
(clusters)):
clusters[k]
=[np.zeros(
(x.shape[1]
)),0
]for i in
range
(len
(x))
: clusters[labels[i]][
0]+= x[i]
clusters[labels[i]][
1]+=1
return labels
if __name__ ==
'__main__'
: dataset =[[
0.697
,0.460],
[0.774
,0.376],
[0.634
,0.264],
[0.608
,0.318],
[0.556
,0.215],
[0.403
,0.237],
[0.481
,0.149],
[0.437
,0.211],
[0.666
,0.091],
[0.243
,0.267],
[0.245
,0.057],
[0.343
,0.099],
[0.639
,0.161],
[0.657
,0.198],
[0.360
,0.370],
[0.593
,0.042],
[0.719
,0.103],
[0.359
,0.188],
[0.339
,0.241],
[0.282
,0.257],
[0.748
,0.232],
[0.714
,0.346],
[0.483
,0.312],
[0.478
,0.437],
[0.525
,0.369],
[0.751
,0.489],
[0.532
,0.472],
[0.473
,0.376],
[0.725
,0.445],
[0.446
,0.459]]
k =3 x = np.array(dataset)
result = kmeans(k=k)
.fit(x)
import matplotlib.pyplot as plt
colors =
['b'
,'g'
,'r'
]for i in
range
(k):
ys =
for j in
range
(len
(result)):
if result[j]
plt.scatter(x[ys,0]
, x[ys,1]
, c=colors[i]
, alpha=
0.5)
plt.show(
)print
(result)
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