I am trying to perform PCA on MNIST dataset, as part of the process I need to generate the eigen vectors and visualize the top features. Following is my algorithm:
- Load images
- Subtract mean
- Generate Covariance matrix
- Derive eigen vectors and eigen values
It's fairly a simple algorithm to run; my first task is to visualize the top 10 eigen vectors as images. Following is the code that I have so far:
__author__ = "Ajay Krishna Teja Kavuri"
import numpy as np
import random
from mnist import MNIST
import matplotlib.pylab as plt
class PCAMNIST:
#Initialization
def __init__(self):
#Load MNIST datset
mnistData = MNIST('./mnistData')
self.imgTrain,self.lblTrain=mnistData.load_training()
self.imgTrainSmpl=self.imgTrain[:60000]
np.seterr(all='warn')
#1. Subtract the mean because the PCA will work better
def subMean(self):
try:
self.sumImg = np.empty([784,])
#calculate the sum
for img in self.imgTrainSmpl:
imgArr = np.asarray(img)
self.sumImg = np.add(imgArr,self.sumImg)
#Calculate the mean array
self.meanImg = self.sumImg/(len(self.imgTrainSmpl))
self.meanImg = np.nan_to_num(self.meanImg)
#subtract it out
index=0
for img in self.imgTrainSmpl:
imgArr = np.asarray(img)
self.imgTrainSmpl[index] = np.subtract(imgArr,self.meanImg).tolist()
index += 1
#for img in self.imgTrainSmpl:
#print img
except:
print Exception
#2. get the covaraince matrix for each digit
def getCov(self):
self.imgCov=[]
dgtArr = np.asarray(self.imgTrainSmpl).T
dgtCov = np.cov(dgtArr)
self.imgCov.append(dgtCov)
#for img in self.imgCov:
#print img
#3. get the eigen vectors from the covariance matrix
def getEigen(self):
self.eigVec=[]
self.eigVal=[]
dgtArr = np.asarray(self.imgCov)
tmpEigVal,tmpEigVec=np.linalg.eig(dgtArr)
self.eigVal.append(tmpEigVal.tolist())
self.eigVec.append(tmpEigVec.tolist())
#print "\nEigen values:\n"
#for img in self.eigVal:
#print img
#print "\nEigen vectors:\n"
#for img in self.eigVec:
#print img
def sortEV(self):
self.eigValArr = np.asarray(self.eigVal[0][0])
self.eigVecArr = np.asarray(self.eigVec[0][0])
self.srtdInd = np.argsort(np.abs(self.eigValArr))
self.srtdEigValArr = self.eigValArr[self.srtdInd]
self.srtdEigVecArr = self.eigVecArr[self.srtdInd]
self.srtdEigVec = self.srtdEigVecArr.real.tolist()
#print self.srtdEigValArr[0]
print len(self.srtdInd.tolist())
#print self.eigVec[self.srtdInd[0]]
#print np.asarray(self.srtdEigVec).shape
#for img in self.srtdEigVecArr:
#print img
#self.drawEig()
def plotVal(self):
"""
plt.figure()
plt.scatter(np.asarray(self.eigVal).real)
plt.show()
"""
def drawEig(self):
for vec in self.srtdEigVec[:10]:
self.drawEigV(vec)
def drawEigV(self,digit):
plt.figure()
fig=plt.imshow(np.asarray(digit).reshape(28,28),origin='upper')
fig.set_cmap('gray_r')
fig.axes.get_xaxis().set_visible(False)
fig.axes.get_yaxis().set_visible(False)
plt.savefig(str(random.randint(0,10000))+".png")
#plt.show()
plt.close()
def drawChar(self,digit):
plt.figure()
fig=plt.imshow(np.asarray(digit).reshape(28,28),clim=(-1,1.0),origin='upper')
fig.set_cmap('gray_r')
fig.axes.get_xaxis().set_visible(False)
fig.axes.get_yaxis().set_visible(False)
plt.show()
plt.close()
def drawSmpl(self):
for img in self.imgTrainSmpl:
self.drawChar(img)
def singleStep(self):
self.val, self.vec = np.linalg.eig(np.cov(np.array(self.imgTrainSmpl).transpose()))
self.srtd = np.argsort(self.val)[::-1]
print self.val
#asnmnt4=PCAMNIST()
#asnmnt4.singleStep()
asnmnt4=PCAMNIST()
asnmnt4.subMean()
asnmnt4.getCov()
asnmnt4.getEigen()
asnmnt4.sortEV()
asnmnt4.drawEig()
#asnmnt4.plotVal()
"""
asnmnt4.getSorted()
asnmnt4.printTopEigenVal()
"""
Although the above code runs perfectly and all the array sizes match the given dataset, it generates the following images a eigen vectors:
Clearly the eigen vectors make no sense as they have to represent the features of the dataset which in this case should be digits. Any help is appreciated. If you are trying to run this code you might have to install the MNIST package and download data from link.
