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 _transform_omp(self, X, mask):
"""
Performs the OMP 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.omp(**self.omp_params(X)).T
return spams.ompMask(
X=np.asfortranarray(X.T),
D=np.asfortranarray(self.dictionary),
B=np.asfortranarray(mask.T),
L=self.l0_max,
lambda1=self.l1_reconstruct,
numThreads=2,
).T
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