/
k-mean.py
executable file
·43 lines (37 loc) · 1.32 KB
/
k-mean.py
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
35
36
37
38
39
40
41
42
43
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Apr 5 13:17:50 2020
@author: demon__7
"""
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
import numpy as np
from sklearn.datasets.samples_generator import make_blobs
X,y_true=make_blobs(n_samples=300,centers=4,cluster_std=0.60,random_state=0)
plt.scatter(X[:,0],X[:,1],s=50)
from sklearn.cluster import KMeans
kmeans=KMeans(n_clusters=4)
kmeans.fit(X)
y_kmeans=kmeans.predict(X)
from sklearn.metrics import pairwise_distances_argmin
def find_clusters(X, n_clusters, rseed=2):
rng=np.random.RandomState(rseed)
i=rng.permutation(X.shape[0])[:n_clusters]
centers=X[i]
while True:
labels=pairwise_distances_argmin(X, centers)
new_centers=np.array([X[labels==i].mean(0)
for i in range(n_clusters)])
centers, labels=find_clusters(X,4)
plt.scatter(X[:,0], X[:,1],c=labels,
s=50,cmap='viridis')
if np.all(centers==new_centers):
break
centers=new_centers
return centers, labels
centers,labels=find_clusters(X,4)
plt.scatter(X[:,0], X[:,1],c=y_kmeans,s=50,cmap='viridis')
plt.scatter(centers[:,0],centers[:,1],c='black',s=200,alpha=0.5);
from sklearn.datasets import load_sample_image
india=load_sample_image("flower.jpg")