Principal component analysis in python.

pca.py

import numpy as np
import pylab as plt
'''
Performs the Principal Coponent analysis of the Matrix X
Matrix must be n * m dimensions
where n is # features
m is # examples
'''

def PCA(X, varRetained = 0.95, show = False):

	# Compute Covariance Matrix Sigma
	(n, m) = X.shape
	Sigma = 1.0 / m * X * np.transpose(X)
	# Compute eigenvectors and eigenvalues of Sigma
	U, s, V = np.linalg.svd(Sigma, full_matrices = True)

	# compute the value k: number of minumum features that 
	# retains the given variance
	sTot = np.sum(s)
	var_i = np.array([np.sum(s[: i + 1]) / \
						sTot * 100.0 for i in range(n)])
	k = len(var_i[var_i < (varRetained * 100)])
	print '%.2f %% variance retained in %d dimensions' \
						% (var_i[k], k)
	
	# plot the variance plot
	if show:
		plt.plot(var_i)
		plt.xlabel('Number of Features')
		plt.ylabel(' Percentage Variance retained')
		plt.title('PCA $\% \sigma^2 $ vs # features')
		plt.show()

	# compute the reduced dimensional features by projction
	U_reduced = U[:, : k]
	Z = np.transpose(U_reduced) * X

	return Z, U_reduced

pca_demo.py

import csv as csv 
import numpy as np
import Activation, logReg, optim, loadData

#################################################################
# reading from csv
print 'Loading Training Data'
csv_train = csv.reader(open('../data/train.csv', 'rb'))
header = csv_train.next()
data = [[map(int, row[1:]), [int(row[0])]] for row in csv_train]

train = loadData.Data()
train.loadList(data, numClasses = 10)
train.NormalizeScale(factor = 255.0)

#################################################################
# PCA of training set
print 'Performing PCA - Principal COmponent Analysis'
import npPCA
Z, U_reduced = npPCA.PCA(train.X, varRetained = 0.95, show = True)

Hey, the value for sigma which you've used is doing element-wise multiplication rather matrix multiplication

Sigma = 1.0 / m * X * np.transpose( X )
Sigma = 1.0 / m * np.dot(X , X.T)

Similarly, Z matrix in your code.
I hope it helps.

Hey, the value for sigma which you've used is doing element-wise multiplication rather matrix multiplication

Sigma = 1.0 / m * X * np.transpose( X )
Sigma = 1.0 / m * np.dot(X , X.T)

Similarly, Z matrix in your code.
I hope it helps.

Considering that X is matrix-type, The original code is correct.
X and transpose(x) are doing element-wise multiplication only if X is an array.