Coursera NG 机器学习 第七周 KMeans PCA 图像压缩 Python实现

KMeans

ex7.py 

import numpy as np
import matplotlib.pyplot as plt
import time
from scipy.io import loadmat
from sklearn.cluster import KMeans
from ex7modules import *

#Part 1：Check MyKMeans
X=loadmat('ex7data2.mat')['X']
K=3
max_iters=10
init_centroids=np.array([[3,3],[6,2],[8,5]])
centroids,idx=MyKMeans(X,init_centroids,max_iters,True)

#Part 2:Image Compression
fig=loadmat('bird_small')['A']/255   #Normalize
fig_size=fig.shape[0]
plt.imshow(fig)
plt.show()

fig=fig.reshape(3,-1).T  #convert(128,128,3) to (3,128*128) ,to fit the KMeans function

K=16  #reduce nmuber of colors to 16
max_iters=10

time_start=time.time()
init_centroids=InitCentroids(fig,K)

fig_centroids,_=MyKMeans(fig,init_centroids,max_iters,False) # find the most used K colors

fig_idx=findClosestCentroids(fig,fig_centroids)  # find every pixel's closest color
time_end=time.time()

print("Using my Kmeans costs time: ",time_end-time_start)

fig_recovered=np.zeros((fig_size*fig_size,3))  #assign every pixel to the closest color
for i in range(fig_size*fig_size):
    fig_recovered[i,:]=fig_centroids[fig_idx[i]-1,:]

fig_recovered=fig_recovered.T.reshape((fig_size,fig_size,3))  #need to Transpose first,otherwise
                                                              #there is a mistake in image show
plt.imshow(fig_recovered)
plt.show()

#Part 3:Using SKlearn
time_start=time.time()
clf=KMeans(n_clusters=16,init='random',max_iter=50)
clf.fit(fig)
time_end=time.time()

print("Using SKlearn Kmeans costs time: ",time_end-time_start)

cluster_centers=clf.cluster_centers_
labels=clf.labels_

fig_recovered=np.zeros((fig_size*fig_size,3))
for i in range(fig_size*fig_size):
    fig_recovered[i,:]=cluster_cente