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test_linalg.py
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test_linalg.py
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import sys
import numpy as np
import scipy
import scipy.sparse as ssp
import spams
import time
from test_utils import *
if not ('rand' in ssp.__dict__):
import myscipy_rand
ssprand = myscipy_rand.rand
else:
ssprand = ssp.rand
def test_sort():
n = 2000000
X = np.asfortranarray(np.random.normal(size = (n,)),dtype=myfloat)
return Xtest('np.sort(X)','spams.sort(X,True)',locals())
def test_calcAAt():
"""
test A * A'
"""
m=200; n = 200000; d= 0.05
A = ssprand(m,n,density=d,format='csc',dtype=myfloat)
return Xtest('A * A.T','spams.calcAAt(A)',locals())
def test_calcXAt():
m=200; n = 200000; d= 0.05
A = ssprand(m,n,density=d,format='csc',dtype=myfloat)
X = np.asfortranarray(np.random.normal(size = (64,n)),dtype=myfloat)
#* dot is very very slow betewwen a full and a sparse matrix
return Xtest('np.dot(X,A.T.todense())','spams.calcXAt(X,A)',locals())
def test_calcXY():
X = np.asfortranarray(np.random.normal(size = (64,200)),dtype=myfloat)
Y = np.asfortranarray(np.random.normal(size = (200,20000)),dtype=myfloat)
return Xtest('np.dot(X,Y)','spams.calcXY(X,Y)',locals())
def test_calcXYt():
X = np.asfortranarray(np.random.normal(size = (64,200)),dtype=myfloat)
Y = np.asfortranarray(np.random.normal(size = (20000,200)),dtype=myfloat)
return Xtest('np.dot(X,Y.T)','spams.calcXYt(X,Y)',locals())
def test_calcXtY():
X = np.asfortranarray(np.random.normal(size = (200,64)),dtype=myfloat)
Y = np.asfortranarray(np.random.normal(size = (200,20000)),dtype=myfloat)
return Xtest('np.dot(X.T,Y)','spams.calcXtY(X,Y)',locals())
def test_bayer():
n = 2000000
X = np.asfortranarray(np.random.normal(size = (n,)),dtype=myfloat)
Z = Xtest1('spams','spams.bayer(X,0)',locals())
return None
def test_conjGrad():
A = np.asfortranarray(np.random.normal(size = (5000,500)))
#* np.random.seed(0)
#* A = np.asfortranarray(np.random.normal(size = (10,5)))
A = np.asfortranarray(np.dot(A.T,A),dtype=myfloat)
b = np.ones((A.shape[1],),dtype=myfloat,order="FORTRAN")
x0 = b
tol = 1e-4
itermax = int(0.5 * len(b))
tic = time.time()
for i in xrange(0,20):
y1 = np.linalg.solve(A,b)
tac = time.time()
print " Time (numpy): ", tac - tic
x1 = np.abs(b - np.dot(A,y1))
print "Mean error on b : %f" %(x1.sum() / b.shape[0])
tic = time.time()
for i in xrange(0,20):
y2 = spams.conjGrad(A,b,x0,tol,itermax)
#* y2 = spams.conjGrad(A,b)
tac = time.time()
print " Time (spams): ", tac - tic
x1 = np.dot(A,y2)
x2 = np.abs(b - x1)
print "Mean error on b : %f" %(x2.sum() / b.shape[0])
err = abs(y1 - y2)
return err.max()
def test_invSym():
A = np.asfortranarray(np.random.random(size = (1000,1000)))
A =np.asfortranarray( np.dot(A.T,A),dtype=myfloat)
return Xtest('np.linalg.inv(A)','spams.invSym(A)',locals())
def test_normalize():
A = np.asfortranarray(np.random.random(size = (100,1000)),dtype=myfloat)
res2 = Xtest1('spams','spams.normalize(A)',locals())
return None
tests = [
'sort' , test_sort,
'calcAAt' , test_calcAAt,
'calcXAt' , test_calcXAt,
'calcXY' , test_calcXY,
'calcXYt' , test_calcXYt,
'calcXtY' , test_calcXtY,
'bayer' , test_bayer,
'conjGrad' , test_conjGrad,
'invSym' , test_invSym,
'normalize' , test_normalize,
]