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_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 _transform_lasso(self, X, mask):
"""
Performs LASSO transformation
:param X: Input data. See `.fit` for more information.
:param mask: Mask on input data. See `.fit` for more information.
:return: Reconstruction parameters
"""
if mask is None:
return spams.lasso(**self.lasso_params(X)).T
return spams.lassoMask(B=np.asfortranarray(mask.T),
**self.lasso_params(X)).T