手动编写KMeans聚类算法
算法步骤：
随机选取K个样本作为类中心；
计算各样本与各类中心的距离；
将各样本归于最近的类中心点；
求各类的样本的均值，作为新的类中心；
判定：若类中心不再发生变动或达到迭代次数，算法结束，否则回到第2步。
获取数据
1
| from sklearn.datasets import load_iris
|
1
2
| featrue = load_iris()['data']
print(featrue.shape)
|
(150, 4)
编写算法
1
2
3
|
random_index = np.random.choice(len(featrue), 3)
first_class_center = featrue[random_index]
|
1
2
3
4
5
6
7
8
9
10
|
def dist(a, b):
return np.sqrt(np.sum((a - b) ** 2))
dis_matrix = np.zeros((len(featrue), K))
for i in range(len(featrue)):
for j in range(K):
dis_matrix[i, j] = dist(featrue[i], first_class_center[j])
|
1
2
|
labels = np.argmin(dis_matrix, axis=1)
|
1
| np.mean(featrue[labels == 2], axis=0)
|
array([7.10454545, 3.15454545, 6.02272727, 2.15909091])
1
2
3
4
|
class_center = np.zeros_like(first_class_center)
for i in range(K):
class_center[i] = np.mean(featrue[labels == i], axis=0)
|
1
2
3
4
5
6
|
if np.all(class_center == first_class_center):
print('聚类结束')
else:
first_class_center = class_center
print('聚类未结束，继续迭代！')
|
聚类未结束，继续迭代！
循环迭代聚类过程
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
|
K = 3
epochs = 50
first_class_center = featrue[np.random.choice(len(featrue), K)]
def dist(a, b):
return np.sqrt(np.sum((a - b) ** 2))
dis_matrix = np.zeros((len(featrue), K))
for epoch in range(epochs):
for i in range(len(featrue)):
for j in range(K):
dis_matrix[i, j] = dist(featrue[i], first_class_center[j])
labels = np.argmin(dis_matrix, axis=1)
class_center = np.zeros_like(first_class_center)
for i in range(K):
class_center[i] = np.mean(featrue[labels == i], axis=0)
if np.all(class_center == first_class_center):
print('聚类结束')
break
else:
first_class_center = class_center
print('-'*50)
print(f'epoch: {epoch+1}\nclass_center:\n{first_class_center}')
|
--------------------------------------------------
epoch: 1
class_center:
[[5.006 3.428 1.462 0.246 ]
[6.59846154 2.98615385 5.35230769 1.88923077]
[5.63714286 2.66 4.07714286 1.28 ]]
--------------------------------------------------
epoch: 2
class_center:
[[5.006 3.428 1.462 0.246 ]
[6.62786885 3.00163934 5.39836066 1.92131148]
[5.68974359 2.66923077 4.13589744 1.29230769]]
--------------------------------------------------
epoch: 3
class_center:
[[5.006 3.428 1.462 0.246 ]
[6.64827586 3.00689655 5.43793103 1.9362069 ]
[5.72857143 2.68571429 4.17142857 1.31666667]]
--------------------------------------------------
epoch: 4
class_center:
[[5.006 3.428 1.462 0.246 ]
[6.67735849 3.00754717 5.51320755 1.97169811]
[5.79361702 2.71914894 4.2212766 1.34255319]]
--------------------------------------------------
epoch: 5
class_center:
[[5.006 3.428 1.462 0.246]
[6.702 3.016 5.556 1.992]
[5.822 2.728 4.256 1.36 ]]
--------------------------------------------------
epoch: 6
class_center:
[[5.006 3.428 1.462 0.246 ]
[6.76956522 3.03695652 5.6 2.00869565]
[5.82962963 2.73148148 4.31481481 1.39259259]]
--------------------------------------------------
epoch: 7
class_center:
[[5.006 3.428 1.462 0.246 ]
[6.80232558 3.04418605 5.64883721 2.03023256]
[5.85438596 2.74210526 4.34561404 1.40877193]]
--------------------------------------------------
epoch: 8
class_center:
[[5.006 3.428 1.462 0.246 ]
[6.8275 3.07 5.7 2.0625 ]
[5.885 2.74 4.37666667 1.41833333]]
--------------------------------------------------
epoch: 9
class_center:
[[5.006 3.428 1.462 0.246 ]
[6.85384615 3.07692308 5.71538462 2.05384615]
[5.88360656 2.74098361 4.38852459 1.43442623]]
聚类结束
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1,
1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 2, 2, 1, 1, 1, 1,
1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2], dtype=int64)
对代码进行封装
封装为函数
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
| def KMeans_fun(x, K=3, epochs=50):
first_class_center = x[np.random.choice(len(x), K)]
def dist(a, b):
return np.sqrt(np.sum((a - b) ** 2))
dis_matrix = np.zeros((len(x), K))
for epoch in range(epochs):
for i in range(len(x)):
for j in range(K):
dis_matrix[i, j] = dist(x[i], first_class_center[j])
labels = np.argmin(dis_matrix, axis=1)
class_center = np.zeros_like(first_class_center)
for i in range(K):
class_center[i] = np.mean(x[labels == i], axis=0)
if np.all(class_center == first_class_center):
print('聚类结束')
break
else:
first_class_center = class_center
print('-'*50)
print(f'epoch: {epoch+1}\nclass_center:\n{first_class_center}')
|
--------------------------------------------------
epoch: 1
class_center:
[[5.84333333 3.05733333 3.758 1.19933333]]
聚类结束
--------------------------------------------------
epoch: 1
class_center:
[[6.81 3.0525 5.7075 2.075 ]
[5.49181818 3.05909091 3.04909091 0.88090909]]
--------------------------------------------------
epoch: 2
class_center:
[[6.55285714 2.96571429 5.30142857 1.86571429]
[5.2225 3.1375 2.4075 0.61625 ]]
--------------------------------------------------
epoch: 3
class_center:
[[6.36777778 2.91666667 5.05666667 1.74 ]
[5.05666667 3.26833333 1.81 0.38833333]]
--------------------------------------------------
epoch: 4
class_center:
[[6.31458333 2.89583333 4.97395833 1.703125 ]
[5.00555556 3.34444444 1.5962963 0.3037037 ]]
--------------------------------------------------
epoch: 5
class_center:
[[6.30103093 2.88659794 4.95876289 1.69587629]
[5.00566038 3.36981132 1.56037736 0.29056604]]
聚类结束
--------------------------------------------------
epoch: 1
class_center:
[[5.52212389 3.0539823 3.1 0.90265487]
[7.41538462 3.07692308 6.26153846 2.03076923]
[6.50416667 3.0625 5.5 2.14583333]]
--------------------------------------------------
epoch: 2
class_center:
[[5.2025641 3.14230769 2.35641026 0.59615385]
[7.475 3.125 6.3 2.05 ]
[6.35 2.93333333 5.07166667 1.81333333]]
--------------------------------------------------
epoch: 3
class_center:
[[5.04482759 3.29137931 1.73793103 0.35862069]
[7.38571429 3.13571429 6.22857143 2.08571429]
[6.16025641 2.86923077 4.81666667 1.66538462]]
--------------------------------------------------
epoch: 4
class_center:
[[5.00566038 3.36981132 1.56037736 0.29056604]
[7.12272727 3.11363636 6.03181818 2.13181818]
[6.06 2.82 4.644 1.568 ]]
--------------------------------------------------
epoch: 5
class_center:
[[5.00784314 3.40980392 1.49215686 0.2627451 ]
[6.9125 3.1 5.846875 2.13125 ]
[5.96865672 2.76865672 4.48507463 1.46716418]]
--------------------------------------------------
epoch: 6
class_center:
[[5.006 3.428 1.462 0.246 ]
[6.87428571 3.08857143 5.79142857 2.11714286]
[5.93230769 2.75538462 4.42923077 1.43846154]]
--------------------------------------------------
epoch: 7
class_center:
[[5.006 3.428 1.462 0.246 ]
[6.85 3.07368421 5.74210526 2.07105263]
[5.9016129 2.7483871 4.39354839 1.43387097]]
聚类结束
--------------------------------------------------
epoch: 1
class_center:
[[4.853125 3.18125 1.509375 0.26875 ]
[6.01973684 2.80394737 4.60263158 1.54342105]
[5.26842105 3.79473684 1.46315789 0.25263158]
[7.11304348 3.11304348 5.99130435 2.13913043]]
--------------------------------------------------
epoch: 2
class_center:
[[4.7862069 3.16206897 1.49310345 0.26551724]
[5.96865672 2.76865672 4.48507463 1.46716418]
[5.3 3.73636364 1.49090909 0.25909091]
[6.9125 3.1 5.846875 2.13125 ]]
--------------------------------------------------
epoch: 3
class_center:
[[4.744 3.152 1.436 0.22 ]
[5.93230769 2.75538462 4.42923077 1.43846154]
[5.268 3.704 1.488 0.272 ]
[6.87428571 3.08857143 5.79142857 2.11714286]]
--------------------------------------------------
epoch: 4
class_center:
[[4.72916667 3.14583333 1.425 0.20833333]
[5.9016129 2.7483871 4.39354839 1.43387097]
[5.26153846 3.68846154 1.49615385 0.28076923]
[6.85 3.07368421 5.74210526 2.07105263]]
--------------------------------------------------
epoch: 5
class_center:
[[4.7173913 3.13478261 1.4173913 0.2 ]
[5.9016129 2.7483871 4.39354839 1.43387097]
[5.25185185 3.67777778 1.5 0.28518519]
[6.85 3.07368421 5.74210526 2.07105263]]
--------------------------------------------------
epoch: 6
class_center:
[[4.70454545 3.12272727 1.41363636 0.2 ]
[5.9016129 2.7483871 4.39354839 1.43387097]
[5.24285714 3.66785714 1.5 0.28214286]
[6.85 3.07368421 5.74210526 2.07105263]]
聚类结束
--------------------------------------------------
epoch: 1
class_center:
[[4.53333333 2.96666667 1.34444444 0.2 ]
[5.1097561 3.52926829 1.48780488 0.25609756]
[5.4125 2.46875 3.74375 1.16875 ]
[6.67333333 2.99111111 5.64222222 2.04444444]
[6.13589744 2.9 4.53333333 1.45897436]]
--------------------------------------------------
epoch: 2
class_center:
[[4.66470588 3.04705882 1.41176471 0.2 ]
[5.18181818 3.62424242 1.48787879 0.26969697]
[5.46666667 2.53809524 3.84761905 1.18095238]
[6.82105263 3.06578947 5.74736842 2.09473684]
[6.15121951 2.86341463 4.66829268 1.54146341]]
--------------------------------------------------
epoch: 3
class_center:
[[4.685 3.1 1.39 0.2 ]
[5.22 3.64666667 1.51 0.27666667]
[5.5125 2.58333333 3.88333333 1.19166667]
[6.87428571 3.08857143 5.79142857 2.11714286]
[6.17804878 2.85609756 4.74878049 1.58292683]]
--------------------------------------------------
epoch: 4
class_center:
[[4.70454545 3.12272727 1.41363636 0.2 ]
[5.24285714 3.66785714 1.5 0.28214286]
[5.508 2.6 3.908 1.204 ]
[6.9125 3.1 5.846875 2.13125 ]
[6.21627907 2.86046512 4.78604651 1.61162791]]
--------------------------------------------------
epoch: 5
class_center:
[[4.70454545 3.12272727 1.41363636 0.2 ]
[5.24285714 3.66785714 1.5 0.28214286]
[5.51538462 2.60769231 3.93076923 1.20769231]
[6.9125 3.1 5.846875 2.13125 ]
[6.22857143 2.86190476 4.79285714 1.61904762]]
--------------------------------------------------
epoch: 6
class_center:
[[4.70454545 3.12272727 1.41363636 0.2 ]
[5.24285714 3.66785714 1.5 0.28214286]
[5.52962963 2.62222222 3.94074074 1.21851852]
[6.9125 3.1 5.846875 2.13125 ]
[6.23658537 2.85853659 4.80731707 1.62195122]]
聚类结束
封装为类
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
| class KMeans_class:
def __init__(self, K=3, epochs=50):
self.K = K
self.epochs = epochs
def dist(self, a, b):
return np.sqrt(np.sum((a - b) ** 2))
def fit(self, x):
self.centers_ = x[np.random.choice(len(x), self.K)]
self.labels_ = np.zeros(len(x))
dist_matrix = np.zeros((len(x), self.K))
for epoch in range(self.epochs):
for i in range(len(x)):
for j in range(self.K):
dist_matrix[i, j] = self.dist(x[i], self.centers_[j])
self.labels_ = np.argmin(dist_matrix, axis=1)
class_center = np.zeros_like(self.centers_)
for i in range(self.K):
class_center[i] = np.mean(x[self.labels_ == i], axis=0)
if np.all(class_center == self.centers_):
print('聚类结束')
break
else:
self.centers_ = class_center
print('-'*50)
print(f'epoch: {epoch+1}\nclass_center:\n{self.centers_}')
|
1
2
| kmeans = KMeans_class(K=1)
kmeans.fit(featrue)
|
--------------------------------------------------
epoch: 1
class_center:
[[5.84333333 3.05733333 3.758 1.19933333]]
聚类结束
1
2
| kmeans = KMeans_class(K=2)
kmeans.fit(featrue)
|
--------------------------------------------------
epoch: 1
class_center:
[[5.72214286 3.05071429 3.57 1.13571429]
[7.54 3.15 6.39 2.09 ]]
--------------------------------------------------
epoch: 2
class_center:
[[5.50178571 3.05178571 3.08482143 0.90357143]
[6.85 3.07368421 5.74210526 2.07105263]]
--------------------------------------------------
epoch: 3
class_center:
[[5.23580247 3.12716049 2.43209877 0.62469136]
[6.55652174 2.97536232 5.31449275 1.87391304]]
--------------------------------------------------
epoch: 4
class_center:
[[5.07580645 3.24677419 1.87903226 0.41612903]
[6.38409091 2.92386364 5.08181818 1.75113636]]
--------------------------------------------------
epoch: 5
class_center:
[[5.00555556 3.34444444 1.5962963 0.3037037 ]
[6.31458333 2.89583333 4.97395833 1.703125 ]]
--------------------------------------------------
epoch: 6
class_center:
[[5.00566038 3.36981132 1.56037736 0.29056604]
[6.30103093 2.88659794 4.95876289 1.69587629]]
聚类结束
1
2
| kmeans = KMeans_class(K=3)
kmeans.fit(featrue)
|
--------------------------------------------------
epoch: 1
class_center:
[[6.6826087 2.98478261 5.62391304 2.03695652]
[5.0828125 3.2125 1.9546875 0.446875 ]
[6.095 2.8925 4.4975 1.44 ]]
--------------------------------------------------
epoch: 2
class_center:
[[6.82105263 3.06578947 5.74736842 2.09473684]
[5.00566038 3.36981132 1.56037736 0.29056604]
[5.96610169 2.77118644 4.45084746 1.43898305]]
--------------------------------------------------
epoch: 3
class_center:
[[6.85 3.07368421 5.74210526 2.07105263]
[5.006 3.428 1.462 0.246 ]
[5.9016129 2.7483871 4.39354839 1.43387097]]
聚类结束
1
2
| kmeans = KMeans_class(K=4)
kmeans.fit(featrue)
|
--------------------------------------------------
epoch: 1
class_center:
[[5.95 2.88518519 4.77222222 1.66111111]
[6.78333333 2.90952381 5.23333333 1.75714286]
[4.55833333 3.075 1.31666667 0.20833333]
[5.13333333 3.42142857 1.67619048 0.33095238]]
--------------------------------------------------
epoch: 2
class_center:
[[5.86724138 2.73793103 4.4 1.43965517]
[6.84878049 3.07073171 5.66829268 2.02439024]
[4.66111111 3.08888889 1.36666667 0.2 ]
[5.1969697 3.58484848 1.56060606 0.2969697 ]]
--------------------------------------------------
epoch: 3
class_center:
[[5.88360656 2.74098361 4.38852459 1.43442623]
[6.85384615 3.07692308 5.71538462 2.05384615]
[4.685 3.1 1.39 0.2 ]
[5.22 3.64666667 1.51 0.27666667]]
--------------------------------------------------
epoch: 4
class_center:
[[5.88360656 2.74098361 4.38852459 1.43442623]
[6.85384615 3.07692308 5.71538462 2.05384615]
[4.70454545 3.12272727 1.41363636 0.2 ]
[5.24285714 3.66785714 1.5 0.28214286]]
聚类结束
1
2
| kmeans = KMeans_class(K=5)
kmeans.fit(featrue)
|
--------------------------------------------------
epoch: 1
class_center:
[[7.17368421 3.15263158 6.10526316 2.15263158]
[5.24285714 3.66785714 1.5 0.28214286]
[6.175 3.2 5.35 2.35 ]
[6.09863014 2.81232877 4.65890411 1.55068493]
[4.75 2.99615385 1.7 0.32692308]]
--------------------------------------------------
epoch: 2
class_center:
[[7.27647059 3.10588235 6.12352941 2.07058824]
[5.21333333 3.65333333 1.48 0.27666667]
[6.45789474 3.05789474 5.45789474 2.17368421]
[5.98032787 2.77213115 4.47868852 1.44262295]
[4.73478261 3. 1.66521739 0.30869565]]
--------------------------------------------------
epoch: 3
class_center:
[[7.42307692 3.13076923 6.26923077 2.06923077]
[5.20645161 3.63870968 1.47096774 0.27419355]
[6.51481481 3.01481481 5.43703704 2.07777778]
[5.91016949 2.7559322 4.39491525 1.41525424]
[4.7 3.055 1.525 0.245 ]]
--------------------------------------------------
epoch: 4
class_center:
[[7.475 3.125 6.3 2.05 ]
[5.24285714 3.66785714 1.5 0.28214286]
[6.50625 3.009375 5.390625 2.028125 ]
[5.8625 2.73928571 4.33035714 1.39464286]
[4.70454545 3.12272727 1.41363636 0.2 ]]
--------------------------------------------------
epoch: 5
class_center:
[[7.475 3.125 6.3 2.05 ]
[5.24285714 3.66785714 1.5 0.28214286]
[6.45789474 2.98157895 5.32105263 1.97368421]
[5.822 2.728 4.256 1.36 ]
[4.70454545 3.12272727 1.41363636 0.2 ]]
--------------------------------------------------
epoch: 6
class_center:
[[7.475 3.125 6.3 2.05 ]
[5.24285714 3.66785714 1.5 0.28214286]
[6.40454545 2.96818182 5.25454545 1.94318182]
[5.78863636 2.70681818 4.17727273 1.30681818]
[4.70454545 3.12272727 1.41363636 0.2 ]]
--------------------------------------------------
epoch: 7
class_center:
[[7.42307692 3.13076923 6.26923077 2.06923077]
[5.24285714 3.66785714 1.5 0.28214286]
[6.37173913 2.93913043 5.21304348 1.9173913 ]
[5.77073171 2.71463415 4.12926829 1.2804878 ]
[4.70454545 3.12272727 1.41363636 0.2 ]]
--------------------------------------------------
epoch: 8
class_center:
[[7.38571429 3.13571429 6.22857143 2.08571429]
[5.24285714 3.66785714 1.5 0.28214286]
[6.37291667 2.94166667 5.15833333 1.87708333]
[5.70789474 2.68684211 4.1 1.27105263]
[4.70454545 3.12272727 1.41363636 0.2 ]]
--------------------------------------------------
epoch: 9
class_center:
[[7.38571429 3.13571429 6.22857143 2.08571429]
[5.24285714 3.66785714 1.5 0.28214286]
[6.36470588 2.95098039 5.12156863 1.85294118]
[5.66285714 2.65142857 4.06285714 1.25428571]
[4.70454545 3.12272727 1.41363636 0.2 ]]
--------------------------------------------------
epoch: 10
class_center:
[[7.34 3.14666667 6.19333333 2.11333333]
[5.24285714 3.66785714 1.5 0.28214286]
[6.35294118 2.94509804 5.1 1.83137255]
[5.65 2.64117647 4.04705882 1.25 ]
[4.70454545 3.12272727 1.41363636 0.2 ]]
--------------------------------------------------
epoch: 11
class_center:
[[7.24117647 3.16470588 6.15294118 2.13529412]
[5.24285714 3.66785714 1.5 0.28214286]
[6.342 2.928 5.062 1.8 ]
[5.63636364 2.63636364 4.02727273 1.25151515]
[4.70454545 3.12272727 1.41363636 0.2 ]]
--------------------------------------------------
epoch: 12
class_center:
[[7.17368421 3.15263158 6.10526316 2.15263158]
[5.24285714 3.66785714 1.5 0.28214286]
[6.33125 2.92291667 5.03541667 1.77916667]
[5.63636364 2.63636364 4.02727273 1.25151515]
[4.70454545 3.12272727 1.41363636 0.2 ]]
--------------------------------------------------
epoch: 13
class_center:
[[7.12272727 3.11363636 6.03181818 2.13181818]
[5.24285714 3.66785714 1.5 0.28214286]
[6.29347826 2.92608696 4.98913043 1.75869565]
[5.625 2.628125 4.0125 1.24375 ]
[4.70454545 3.12272727 1.41363636 0.2 ]]
--------------------------------------------------
epoch: 14
class_center:
[[7.12272727 3.11363636 6.03181818 2.13181818]
[5.24285714 3.66785714 1.5 0.28214286]
[6.29574468 2.92553191 4.97446809 1.74893617]
[5.6 2.61935484 4.00322581 1.24193548]
[4.70454545 3.12272727 1.41363636 0.2 ]]
--------------------------------------------------
epoch: 15
class_center:
[[7.08695652 3.12608696 6.01304348 2.14347826]
[5.24285714 3.66785714 1.5 0.28214286]
[6.29361702 2.9 4.95106383 1.72978723]
[5.58 2.63333333 3.98666667 1.23333333]
[4.70454545 3.12272727 1.41363636 0.2 ]]
聚类结束
array([1, 4, 4, 4, 1, 1, 4, 1, 4, 4, 1, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1,
4, 1, 4, 4, 1, 1, 1, 4, 4, 1, 1, 1, 4, 4, 1, 1, 4, 1, 1, 4, 4, 1,
1, 4, 1, 4, 1, 4, 2, 2, 2, 3, 2, 3, 2, 3, 2, 3, 3, 3, 3, 2, 3, 2,
3, 3, 2, 3, 2, 3, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 2, 3, 2, 2, 3,
3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 0, 2, 0, 2, 0, 0, 3, 0, 0, 0,
2, 2, 0, 2, 2, 2, 2, 0, 0, 2, 0, 2, 0, 2, 0, 0, 2, 2, 2, 0, 0, 0,
2, 2, 2, 0, 0, 2, 2, 0, 0, 2, 2, 0, 0, 2, 2, 2, 2, 2], dtype=int64)
调用sklean内置的KMeans类
导入KMeans类
1
| from sklearn.cluster import KMeans
|
模型的搭建和训练
1
2
3
4
5
6
|
kmeans = KMeans(n_clusters=3, max_iter=50, verbose=1, n_init=2)
|
Initialization complete
Iteration 0, inertia 151.73
Iteration 1, inertia 99.88221298209365
Iteration 2, inertia 87.74180017807669
Iteration 3, inertia 84.95217943238866
Iteration 4, inertia 84.01277888865147
Iteration 5, inertia 83.04698186876973
Iteration 6, inertia 81.74960206772619
Iteration 7, inertia 80.80637600000001
Iteration 8, inertia 79.87357983461303
Iteration 9, inertia 79.34436414532674
Iteration 10, inertia 78.92130972222222
Iteration 11, inertia 78.8556658259773
Converged at iteration 11: strict convergence.
Initialization complete
Iteration 0, inertia 145.36999999999995
Iteration 1, inertia 80.74942872639375
Iteration 2, inertia 79.16851704724697
Iteration 3, inertia 78.92130972222222
Iteration 4, inertia 78.8556658259773
Converged at iteration 4: strict convergence.
KMeans(max_iter=50, n_clusters=3, n_init=2, verbose=1)
查看聚类参数
1
2
|
kmeans.cluster_centers_
|
array([[6.85384615, 3.07692308, 5.71538462, 2.05384615],
[5.88360656, 2.74098361, 4.38852459, 1.43442623],
[5.006 , 3.428 , 1.462 , 0.246 ]])
array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1])