Skip to content

Instantly share code, notes, and snippets.

@omitevski
Created December 1, 2010 16:29
Show Gist options
  • Save omitevski/723736 to your computer and use it in GitHub Desktop.
Save omitevski/723736 to your computer and use it in GitHub Desktop.
binary PCA
"""
Author: Oliver Mitevski
References:
A Generalized Linear Model for Principal Component Analysis of Binary Data,
Andrew I. Schein; Lawrence K. Saul; Lyle H. Ungar
The code was translated and adapted from Jakob Verbeek's
"Hidden Markov models and mixtures for Binary PCA" implementation in MATLAB
"""
import sys
from scipy import *
from numpy import *
from scipy.linalg import diagsvd, svd
from scipy.sparse import linalg
import cPickle, gzip, numpy, time
import pylab as p
from save_load import save_file, load_file
def bpca(X, L=2, max_iters = 30):
N, D = X.shape
x = X
X = 2*X - 1
delta = random.permutation(N); Delta = X[delta[0],:]/100
U = 1e-4*random.random( (N,L) )
V = 1e-4*random.random( (L,D) )
#for c=1:C; Th(:,:,c) = U(:,:,c)*V(:,:,c) + ones(N,1)*Delta(c,:); end;
Th = zeros((N,D)); Th = dot( U, V) + outer(ones((N,1)),Delta)
for iter in range(max_iters):
print iter
# Update U
T= tanh(Th/2)/Th
pp = outer(ones((N,1)),Delta[:])
B = dot(V, (X - T*pp).T)
for n in range(N):
cc = outer(ones((L, 1)), T[n,:])
A = dot(V*cc, V.T)
U[n,:] = numpy.linalg.solve(A, B[:,n]).T
Th = dot( U, V) + outer(ones((N,1)),Delta)
Q = random.random(N)
#normalize it
Q = sqrt(dot(Q,Q.conj()))
# Update V
T= tanh(Th/2)/Th
U2 = c_[U, ones((N,1))];
U2 = U2*tile(Q,(L+1,1)).T;
B = dot(U2.T, X)
for d in range(D):
ff = outer(ones((L+1,1)),T[:,d].T)
A = dot((U2.T * ff), c_[U, ones((N,1))])
V2 = numpy.linalg.solve(A, B[:,d])
Delta[d] = V2[-1]
if L>0:
V[:,d] = V2[0:-1]
Th = dot( U, V) + outer(ones((N,1)),Delta)
print U.shape
#plotM1(U[0:10000:1,0:2], labels[0:10000:10])
U1, S, V = svd(U); Vh = V.T
U1, Vh = mat(U1[:,0:L]), mat(Vh[0:L,:])
codes = array(U*Vh.T)
return codes
def main():
# Load the dataset
try:
inputData = load_file(sys.argv[1])
sparse = False
except:
print 'loading sparse'
sparse = True
from scipy.io import mmio as mm
inputData = mm.mmread(sys.argv[1])
inputData = array(inputData.todense())
N, D = inputData.shape
print N, D
# make data binary
# for i in range(N):
# for j in range(D):
# if inputData[i,j] > 0.0:
# inputData[i,j] = 1.0
# save_file('news-10-stemmed.index2200/binDict', inputData)
# print 'saved binary'
X = inputData
labels = load_file(sys.argv[2])
start = time.clock()
codes = bpca(X, L=2, max_iters = 20)
print "Time for computing binary PCA took", time.clock()-start
save_file(sys.argv[3], codes)
if __name__ == "__main__":
main()
@ogrisel
Copy link

ogrisel commented Dec 4, 2010

I agree with @agramfort comments. You can use the pep8 tool to check for python code style conventions: http://pypi.python.org/pypi/pep8

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment