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ex7_pca.py
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ex7_pca.py
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#!/usr/local/Cellar/python/2.7.6/bin/python
# -*- coding: utf-8 -*-
'''Standard python modules'''
import sys
'''For scientific computing'''
from numpy import *
import scipy.misc, scipy.io, scipy.optimize
'''For plotting'''
from matplotlib import pyplot, cm, colors, lines
from mpl_toolkits.mplot3d import Axes3D
from util import Util
from timeit import Timer
from sklearn.decomposition import RandomizedPCA
from ex7 import *
def displayData( X ):
width = 32
rows = cols = int(sqrt( shape(X)[0] ))
out = zeros(( width * rows, width * cols ))
counter = 0
for y in range(0, rows):
for x in range(0, cols):
start_x = x * width
start_y = y * width
out[start_x:start_x+width, start_y:start_y+width] = X[counter].reshape( width, width ).T
counter += 1
img = scipy.misc.toimage( out )
axes = pyplot.gca()
figure = pyplot.gcf()
axes.imshow( img ).set_cmap( 'gray' )
def pca( X ):
covariance = X.T.dot( X ) / shape( X )[0]
U, S, V = linalg.svd( covariance )
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 part2_1():
mat = scipy.io.loadmat( "/Users/saburookita/Downloads/mlclass-ex7-004/mlclass-ex7/ex7data1.mat" )
X = mat['X']
pyplot.plot( X[:, 0], X[:, 1], 'bo' )
pyplot.axis( [0.5, 6.5, 2, 8] )
pyplot.axis( 'equal' )
pyplot.show( block=True )
def part2_2():
mat = scipy.io.loadmat( "/Users/saburookita/Downloads/mlclass-ex7-004/mlclass-ex7/ex7data1.mat" )
X = mat['X']
X_norm, mu, sigma = Util.featureNormalize( X )
U, S = pca( X_norm )
error = 1 - (sum( S[:1]) / sum( S))
print error
mu = mu.reshape( 1, 2)[0]
mu_1 = mu + 1.5 * S[0] * U[:, 0]
mu_2 = mu + 1.5 * S[1] * U[:, 1]
pyplot.plot( X[:, 0], X[:, 1], 'bo' )
pyplot.gca().add_line( lines.Line2D( xdata=[mu[0], mu_1[0]], ydata=[mu[1], mu_1[1]], c='r', lw=2 ) )
pyplot.gca().add_line( lines.Line2D( xdata=[mu[0], mu_2[0]], ydata=[mu[1], mu_2[1]], c='r', lw=2 ) )
pyplot.axis( [0.5, 6.5, 2, 8] )
pyplot.axis( 'equal' )
pyplot.show( block=True )
def part2_3():
mat = scipy.io.loadmat( "/Users/saburookita/Downloads/mlclass-ex7-004/mlclass-ex7/ex7data1.mat" )
X = mat['X']
X_norm, mu, sigma = Util.featureNormalize( X )
U, S = pca( X_norm )
K = 1
Z = projectData( X_norm, U, K )
print Z[0] # Should be 1.481
X_rec = recoverData( Z, U, K )
for i in range( 0, shape( X_rec)[0] ):
pyplot.gca().add_line( lines.Line2D( xdata=[X_norm[i,0], X_rec[i,0]], ydata=[X_norm[i,1], X_rec[i,1]], c='g', lw=1, ls='--' ) )
pyplot.plot( X_norm[:, 0], X_norm[:, 1], 'bo' )
pyplot.plot( X_rec[:, 0], X_rec[:, 1], 'ro' )
pyplot.axis( 'equal' )
pyplot.axis( [-4, 3, -4, 3] )
pyplot.show( block=True )
def part2_4():
mat = scipy.io.loadmat( "/Users/saburookita/Downloads/mlclass-ex7-004/mlclass-ex7/ex7faces.mat" )
X = mat['X']
# displayData( X[:100, :] )
X_norm, mu, sigma = Util.featureNormalize( X )
U, S = pca( X_norm )
# displayData( U[:, :36].T )
K = 100
Z = projectData( X_norm, U, K )
X_rec = recoverData( Z, U, K )
pyplot.subplot( 1, 2, 1 )
displayData( X_norm[:100, :] )
pyplot.subplot( 1, 2, 2 )
displayData( X_rec[:100, :] )
pyplot.show( block=True )
def partExtra():
A = scipy.misc.imread( "/Users/saburookita/Downloads/mlclass-ex7-004/mlclass-ex7/bird_small.png" )
A = A / 255.0
img_size = shape( A )
X = A.reshape( img_size[0] * img_size[1], 3 )
K = 16
max_iters = 10
initial_centroids = kMeansInitCentroids( X, K )
centroids, idx = runkMeans( X, initial_centroids, max_iters )
fig = pyplot.figure()
# axis = fig.add_subplot( 111, projection='3d' )
# axis.scatter( X[:1000, 0], X[:1000, 1], X[:1000, 2], c=idx[:1000], marker='o' )
# pyplot.show(block=True)
X_norm, mu, sigma = Util.featureNormalize( X )
U, S = pca( X_norm )
Z = projectData( X_norm, U, 2 )
axis.scatter( Z[:100, 0], Z[:100, 1], c='r', marker='o' )
pyplot.show(block=True)
def main():
part2_1()
part2_2()
part2_3()
part2_4()
partExtra()
if __name__ == '__main__':
main()