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#!/usr/bin/env python
import numpy as np
import scipy
from scipy.sparse.linalg import svds
m = 512
n = 1024
M = np.random.rand(m,n)
print "original "+str(m*n)
# svds
if False:
print "svds"
U, sigma, V = svds(M, k = 42)
S = np.diag(sigma)
M_ = np.dot(U, np.dot(S, V))
shapes = U.shape, S.shape, V.shape
sum = 0
for sh in shapes:
sum = sum + sh[0]*sh[1]
print "params "+str(sum)
print np.absolute(np.sum(M - M_))
print
# qr
if False:
print "qr"
q, r = np.linalg.qr(M,mode='reduced')
sum = 0
for sh in (q.shape, r.shape):
sum = sum + sh[0]*sh[1]
print "params "+str(sum)
# eig
#print "eig (just square, also: Cholesky)"
#la, v = scipy.linalg.eig(M)
#print "M"
#print M
#print "U"
#print U
#print "V"
#print V
#print "shapes"
#print "U*diag(S)*V^T"
#print(M_)
#print "M_ - M"
#print M.shape, M_.shape
#print M - M_
#print "sum"
#print "shapes"
#print U.shape, S.shape, V.shape
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