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_centers[labels[i],:]
fig_recovered=fig_recovered.T.reshape((fig_size,fig_size,3))
plt.imshow(fig_recovered)
plt.show()
從左至右，依次是原圖，自己的KMeans和SKlearn的KMeans。速度上，還是差很多滴~~~
怎麼感覺圖被壓胡了。。w(゜Д゜)w。。。沒有課件裏的效果好。。
課件效果
PCA
ex7pca.py
from scipy.io import loadmat
from ex7modules import *
#part 1
X=loadmat('ex7data1.mat')['X']
X_norm,mu,sigma=featureNormalize(X)
U,S=PCA(X_norm)
visualizeEigVector(mu,U,S,X)
print('Top eigenvector: ')
print(U[0,0],U[0,1])
print()
K=1
Z=projectData(X_norm,U,K)
X_rec=recoverData(Z,U,K)
visualizePCA(X_norm,X_rec)
#Part 2
X=loadmat('ex7faces.mat')['X']
displayData(X,10)
X_norm,_,_=featureNormalize(X)
U,S=PCA(X_norm)
displayData(U.T,6)
K=100
Z=projectData(X_norm,U,K)
print('The projected data Z has a size of: ',Z.shape)
X_rec=recoverData(Z,U,K)
displayData(X_rec,10)
Cpcompare(X,X_rec,10)
前100個圖像
提取的前36個特徵（鬼出沒w(゜Д゜)w）
壓縮後還原的圖像
壓縮前跟壓縮後的對比
ex7modules.py
import numpy as np
import matplotlib.pyplot as plt
def findClosestCentroids(X,centroids):
K=centroids.shape[0]
dist=np.zeros((X.shape[0],K))
for i in range(X.shape[0]):
for j in range(K):
dist[i, j]=np.linalg.norm(X[i,:]-centroids[j,:])
idx=np.argmin(dist,axis=1)
return idx+1
def computeCentroids(X,idx,K):
centroids=np.zeros((K,X.shape[1]))
for i in range(1,K+1):
X_idx = np.where(idx == i)
X_re = X[X_idx, :].reshape(X[X_idx, :].shape[1], X[X_idx, :].shape[2])
centroids[i - 1, :] = np.sum(X_re, axis=0, keepdims=True) / len(X_idx[0])
return centroids
def MyKMeans(X,initial_centroids,max_iters,plot):
K=initial_centroids.shape[0]
for i in range(max_iters):
idx=findClosestCentroids(X,initial_centroids)
initial_centroids=computeCentroids(X,idx,K)
if plot==True:
idx1 = np.where(idx == 1)[0]
idx2 = np.where(idx == 2)[0]
idx3 = np.where(idx == 3)[0]
plt.scatter(X[idx1, 0], X[idx1, 1], c='w',edgecolors='r',s=10)
plt.scatter(X[idx2, 0], X[idx2, 1], c='w',edgecolors='b',s=10)
plt.scatter(X[idx3, 0], X[idx3, 1], c='w',edgecolors='k',s=10)
plt.scatter(initial_centroids[0, 0], initial_centroids[0, 1], marker='x', c='r')
plt.scatter(initial_centroids[1, 0], initial_centroids[1, 1], marker='x', c='b')
plt.scatter(initial_centroids[2, 0], initial_centroids[2, 1], marker='x', c='k')
plt.show()
return initial_centroids,idx
def InitCentroids(X,K):
randidx=np.random.permutation(X.shape[0])
centroids=X[randidx[0:K],:]
return centroids
def featureNormalize(X):
mu=np.mean(X,axis=0)
sigma=np.std(X,axis=0,ddof=1)
X=(X-mu)/sigma
return X,mu,sigma
def PCA(X):
U,S,_=np.linalg.svd(X.T.dot(X)/X.shape[0])
return U,S
def projectData(X,U,K):
return X.dot(U[:,:K])
def recoverData(Z,U,K):
return Z.dot(U[:,:K].T)
def displayData(X,num):
fig, ax = plt.subplots(num, num)
for i in range(num):
for j in range(num):
ax[i, j].imshow(X[i * num + j, :].reshape((32, 32)).T, cmap='gray')
ax[i, j].set_xticks([])
ax[i, j].set_yticks([])
fig.tight_layout()
plt.subplots_adjust(wspace=0, hspace=0)
plt.show()
def Cpcompare(X,X_rec,num):
fig, ax = plt.subplots(num, num*2)
for i in range(num):
for j in range(0,num*2,2):
ax[i, j].imshow(X[i * num + j, :].reshape((32, 32)).T, cmap='gray')
ax[i, j].set_xticks([])
ax[i, j].set_yticks([])
ax[i, j+1].imshow(X_rec[i * num + j, :].reshape((32, 32)).T, cmap='gray')
ax[i, j+1].set_xticks([])
ax[i, j+1].set_yticks([])
fig.tight_layout()
plt.subplots_adjust(wspace=0, hspace=0)
plt.show()
def visualizeEigVector(mu,U,S,X):
mu = mu.reshape((1, 2))
point1 = mu + 1.5 * S[0] * (U[:, 0].reshape((1, 2)))
point2 = mu + 1.5 * S[1] * (U[:, 1].reshape((1, 2)))
x1 = mu[:, 0]
y1 = mu[:, 1]
x2 = point1[:, 0]
y2 = point1[:, 1]
x3 = point2[:, 0]
y3 = point2[:, 1]
ax1 = np.array([x1, x2])
ax2 = np.array([x1, x3])
ay1 = np.array([y1, y2])
ay2 = np.array([y1, y3])
plt.scatter(X[:, 0], X[:, 1], marker='o', c='w', edgecolors='b')
plt.plot(ax1, ay1, c='k')
plt.plot(ax2, ay2, c='k')
plt.show()
def visualizePCA(X_norm,X_rec):
plt.xlim((-3, 3))
plt.ylim((-3, 3))
plt.scatter(X_norm[:, 0], X_norm[:, 1], marker='o', c='w', edgecolors='b')
plt.scatter(X_rec[:, 0], X_rec[:, 1], marker='o', c='w', edgecolors='r')
for i in range(X_norm.shape[0]):
x = np.array([X_norm[i, 0], X_rec[i, 0]])
y = np.array([X_norm[i, 1], X_rec[i, 1]])
plt.plot(x, y, 'k--')
plt.show()