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_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 sparse_codes(X, D, lamda):
""" Map input data in X to sparse codes using dictionary D using
Coordinate Descent from SPAM package
Parameters:
X: Input data as (num_patches, input_dim) matrix
D: Distonary of features
"""
# Map X and D to fortran array format
X0 = np.asfortranarray(np.transpose(X))
D0 = np.asfortranarray(np.transpose(D))
# Get a sparse matrix
A0 = csc_matrix(np.zeros((D.shape[0], X.shape[0])))
out = spams.cd(X0, D0, A0,
lambda1 = lamda, mode = 2, itermax = 1000, tol = 0.001,
numThreads = -1)
return np.transpose(out).todense()
def sparse_codes(X, D, lamda):
""" Map input data in X to sparse codes using dictionary D using
Coordinate Descent from SPAM package
Parameters:
X: Input data as (num_patches, input_dim) matrix
D: Distonary of features
"""
# Map X and D to fortran array format
X0 = np.asfortranarray(np.transpose(X))
D0 = np.asfortranarray(np.transpose(D))
# Get a sparse matrix
A0 = csc_matrix(np.zeros((D.shape[0], X.shape[0])))
out = spams.cd(X0,
D0,
A0,
lambda1=lamda,
mode=2,
itermax=1000,
tol=0.001,
numThreads=-1)
return np.transpose(out).todense()