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test_decomp.py
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test_decomp.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 *
def test_sparseProject():
np.random.seed(0)
X = np.asfortranarray(np.random.normal(size = (20000,100)),dtype=myfloat)
#* matlab : X=X./repmat(sqrt(sum(X.^2)),[size(X,1) 1]);
X = np.asfortranarray(X / np.tile(np.sqrt((X*X).sum(axis=0)),(X.shape[0],1)),dtype=myfloat)
param = {'numThreads' : -1, # number of processors/cores to use (-1 => all cores)
'pos' : False,
'mode': 1, # projection on the l1 ball
'thrs' : 2}
print "\n Projection on the l1 ball"
tic = time.time()
X1 = spams.sparseProject(X,**param)
tac = time.time()
t = tac - tic
print " Time : ", t
if (t != 0):
print "%f signals of size %d projected per second" %((X.shape[1] / t),X.shape[0])
s = np.abs(X1).sum(axis=0)
print "Checking constraint: %f, %f" %(min(s),max(s))
print "\n Projection on the Elastic-Net"
param['mode'] = 2 # projection on the Elastic-Net
param['lambda1'] = 0.15
tic = time.time()
X1 = spams.sparseProject(X,**param)
tac = time.time()
t = tac - tic
print " Time : ", t
if (t != 0):
print "%f signals of size %d projected per second" %((X.shape[1] / t),X.shape[0])
constraints = (X1*X1).sum(axis=0) + param['lambda1'] * np.abs(X1).sum(axis=0)
print 'Checking constraint: %f, %f (Projection is approximate : stops at a kink)' %(min(constraints),max(constraints))
print "\n Projection on the FLSA"
param['mode'] = 6 # projection on the FLSA
param['lambda1'] = 0.7
param['lambda2'] = 0.7
param['lambda3'] = 1.0
X = np.asfortranarray(np.random.random(size = (2000,100)))
#* matlab : X=X./repmat(sqrt(sum(X.^2)),[size(X,1) 1]);
X = np.asfortranarray(X / np.tile(np.sqrt((X*X).sum(axis=0)),(X.shape[0],1)),dtype=myfloat)
tic = time.time()
X1 = spams.sparseProject(X,**param)
tac = time.time()
t = tac - tic
print " Time : ", t
if (t != 0):
print "%f signals of size %d projected per second" %((X.shape[1] / t),X.shape[0])
constraints = 0.5 * param['lambda3'] * (X1*X1).sum(axis=0) + param['lambda1'] * np.abs(X1).sum(axis=0) + \
param['lambda2'] * np.abs(X1[2:,] - X1[1:-1,]).sum(axis=0)
print 'Checking constraint: %f, %f (Projection is approximate : stops at a kink)' %(min(constraints),max(constraints))
return None
def test_cd():
np.random.seed(0)
X = np.asfortranarray(np.random.normal(size = (64,100)))
X = np.asfortranarray(X / np.tile(np.sqrt((X*X).sum(axis=0)),(X.shape[0],1)),dtype=myfloat)
D = np.asfortranarray(np.random.normal(size = (64,100)))
D = np.asfortranarray(D / np.tile(np.sqrt((D*D).sum(axis=0)),(D.shape[0],1)),dtype=myfloat)
# parameter of the optimization procedure are chosen
lambda1 = 0.015
mode = spams.PENALTY
tic = time.time()
alpha = spams.lasso(X,D,lambda1 = lambda1,mode = mode,numThreads = 4)
tac = time.time()
t = tac - tic
xd = X - D * alpha
E = np.mean(0.5 * (xd * xd).sum(axis=0) + lambda1 * np.abs(alpha).sum(axis=0))
print "%f signals processed per second for LARS" %(X.shape[1] / t)
print 'Objective function for LARS: %g' %E
tol = 0.001
itermax = 1000
tic = time.time()
# A0 = ssp.csc_matrix(np.empty((alpha.shape[0],alpha.shape[1])))
A0 = ssp.csc_matrix((alpha.shape[0],alpha.shape[1]),dtype=myfloat)
alpha2 = spams.cd(X,D,A0,lambda1 = lambda1,mode = mode,tol = tol, itermax = itermax,numThreads = 4)
tac = time.time()
t = tac - tic
print "%f signals processed per second for CD" %(X.shape[1] / t)
xd = X - D * alpha2
E = np.mean(0.5 * (xd * xd).sum(axis=0) + lambda1 * np.abs(alpha).sum(axis=0))
print 'Objective function for CD: %g' %E
print 'With Random Design, CD can be much faster than LARS'
return None
def test_l1L2BCD():
np.random.seed(0)
X = np.asfortranarray(np.random.normal(size = (64,100)),dtype=myfloat)
D = np.asfortranarray(np.random.normal(size = (64,200)))
D = np.asfortranarray(D / np.tile(np.sqrt((D*D).sum(axis=0)),(D.shape[0],1)),dtype=myfloat)
ind_groups = np.array(xrange(0,X.shape[1],10),dtype=np.int32) #indices of the first signals in each group
# parameters of the optimization procedure are chosen
itermax = 100
tol = 1e-3
mode = spams.PENALTY
lambda1 = 0.15 # squared norm of the residual should be less than 0.1
numThreads = -1 # number of processors/cores to use the default choice is -1
# and uses all the cores of the machine
alpha0 = np.zeros((D.shape[1],X.shape[1]),dtype= myfloat,order="FORTRAN")
tic = time.time()
alpha = spams.l1L2BCD(X,D,alpha0,ind_groups,lambda1 = lambda1,mode = mode,itermax = itermax,tol = tol,numThreads = numThreads)
tac = time.time()
t = tac - tic
print "%f signals processed per second" %(X.shape[1] / t)
return None
def test_lasso():
np.random.seed(0)
print "test lasso"
##############################################
# Decomposition of a large number of signals
##############################################
# data generation
X = np.asfortranarray(np.random.normal(size=(100,100000)))
#* X=X./repmat(sqrt(sum(X.^2)),[size(X,1) 1]);
X = np.asfortranarray(X / np.tile(np.sqrt((X*X).sum(axis=0)),(X.shape[0],1)),dtype= myfloat)
D = np.asfortranarray(np.random.normal(size=(100,200)))
D = np.asfortranarray(D / np.tile(np.sqrt((D*D).sum(axis=0)),(D.shape[0],1)),dtype= myfloat)
# parameter of the optimization procedure are chosen
#param.L=20; # not more than 20 non-zeros coefficients (default: min(size(D,1),size(D,2)))
param = {
'lambda1' : 0.15, # not more than 20 non-zeros coefficients
'numThreads' : -1, # number of processors/cores to use; the default choice is -1
# and uses all the cores of the machine
'mode' : spams.PENALTY} # penalized formulation
tic = time.time()
alpha = spams.lasso(X,D = D,return_reg_path = False,**param)
tac = time.time()
t = tac - tic
print "%f signals processed per second\n" %(float(X.shape[1]) / t)
########################################
# Regularization path of a single signal
########################################
X = np.asfortranarray(np.random.normal(size=(64,1)),dtype= myfloat)
D = np.asfortranarray(np.random.normal(size=(64,10)))
D = np.asfortranarray(D / np.tile(np.sqrt((D*D).sum(axis=0)),(D.shape[0],1)),dtype= myfloat)
(alpha,path) = spams.lasso(X,D = D,return_reg_path = True,**param)
return None
def test_lassoMask():
np.random.seed(0)
print "test lassoMask"
##############################################
# Decomposition of a large number of signals
##############################################
# data generation
X = np.asfortranarray(np.random.normal(size=(300,300)))
# X=X./repmat(sqrt(sum(X.^2)),[size(X,1) 1]);
X = np.asfortranarray(X / np.tile(np.sqrt((X*X).sum(axis=0)),(X.shape[0],1)),dtype= myfloat)
D = np.asfortranarray(np.random.normal(size=(300,50)))
D = np.asfortranarray(D / np.tile(np.sqrt((D*D).sum(axis=0)),(D.shape[0],1)),dtype= myfloat)
mask = np.asfortranarray((X > 0)) # generating a binary mask
param = {
'lambda1' : 0.15, # not more than 20 non-zeros coefficients
'numThreads' : -1, # number of processors/cores to use; the default choice is -1
# and uses all the cores of the machine
'mode' : spams.PENALTY} # penalized formulation
tic = time.time()
alpha = spams.lassoMask(X,D,mask,**param)
tac = time.time()
t = tac - tic
print "%f signals processed per second\n" %(float(X.shape[1]) / t)
return None
def test_lassoWeighted():
np.random.seed(0)
print "test lasso weighted"
##############################################
# Decomposition of a large number of signals
##############################################
# data generation
X = np.asfortranarray(np.random.normal(size=(64,10000)))
X = np.asfortranarray(X / np.tile(np.sqrt((X*X).sum(axis=0)),(X.shape[0],1)),dtype= myfloat)
D = np.asfortranarray(np.random.normal(size=(64,256)))
D = np.asfortranarray(D / np.tile(np.sqrt((D*D).sum(axis=0)),(D.shape[0],1)),dtype= myfloat)
param = { 'L' : 20,
'lambda1' : 0.15, 'numThreads' : 8, 'mode' : spams.PENALTY}
W = np.asfortranarray(np.random.random(size = (D.shape[1],X.shape[1])),dtype= myfloat)
tic = time.time()
alpha = spams.lassoWeighted(X,D,W,**param)
tac = time.time()
t = tac - tic
print "%f signals processed per second\n" %(float(X.shape[1]) / t)
return None
def test_omp():
np.random.seed(0)
print 'test omp'
X = np.asfortranarray(np.random.normal(size=(64,100000)),dtype= myfloat)
D = np.asfortranarray(np.random.normal(size=(64,200)))
D = np.asfortranarray(D / np.tile(np.sqrt((D*D).sum(axis=0)),(D.shape[0],1)),dtype= myfloat)
L = 10
eps = 1.0
numThreads = -1
tic = time.time()
alpha = spams.omp(X,D,L=L,eps= eps,return_reg_path = False,numThreads = numThreads)
tac = time.time()
t = tac - tic
print "%f signals processed per second\n" %(float(X.shape[1]) / t)
########################################
# Regularization path of a single signal
########################################
X = np.asfortranarray(np.random.normal(size=(64,1)),dtype= myfloat)
D = np.asfortranarray(np.random.normal(size=(64,10)))
D = np.asfortranarray(D / np.tile(np.sqrt((D*D).sum(axis=0)),(D.shape[0],1)),dtype= myfloat)
L = 5
(alpha,path) = spams.omp(X,D,L=L,eps= eps,return_reg_path = True,numThreads = numThreads)
return None
def test_ompMask():
np.random.seed(0)
print 'test ompMask'
########################################
# Decomposition of a large number of signals
########################################
X = np.asfortranarray(np.random.normal(size=(300,300)))
X = np.asfortranarray(X / np.tile(np.sqrt((X*X).sum(axis=0)),(X.shape[0],1)),dtype= myfloat)
D = np.asfortranarray(np.random.normal(size=(300,50)))
D = np.asfortranarray(D / np.tile(np.sqrt((D*D).sum(axis=0)),(D.shape[0],1)),dtype= myfloat)
mask = np.asfortranarray((X > 0)) # generating a binary mask
L = 20
eps = 0.1
numThreads=-1
tic = time.time()
alpha = spams.ompMask(X,D,mask,L = L,eps = eps,return_reg_path = False,numThreads = numThreads)
tac = time.time()
t = tac - tic
print "%f signals processed per second\n" %(float(X.shape[1]) / t)
return None
def test_somp():
np.random.seed(0)
X = np.asfortranarray(np.random.normal(size = (64,10000)),dtype=myfloat)
D = np.asfortranarray(np.random.normal(size = (64,200)))
D = np.asfortranarray(D / np.tile(np.sqrt((D*D).sum(axis=0)),(D.shape[0],1)),dtype=myfloat)
ind_groups = np.array(xrange(0,10000,10),dtype=np.int32)
tic = time.time()
alpha = spams.somp(X,D,ind_groups,L = 10,eps = 0.1,numThreads=-1)
tac = time.time()
t = tac - tic
print "%f signals processed per second" %(X.shape[1] / t)
return None
tests = [
'sparseProject' , test_sparseProject,
'cd' , test_cd,
'l1L2BCD' , test_l1L2BCD,
'lasso' , test_lasso,
'lassoMask' , test_lassoMask,
'lassoWeighted' , test_lassoWeighted,
'omp' , test_omp,
'ompMask' , test_ompMask,
'somp' , test_somp,
]