def test_trainDL_Memory():
img_file = 'lena.png'
try:
img = Image.open(img_file)
except:
print("Cannot load image %s : skipping test" %img_file)
return None
I = np.array(img) / 255.
if I.ndim == 3:
A = np.asfortranarray(I.reshape((I.shape[0],I.shape[1] * I.shape[2])))
rgb = True
else:
A = np.asfortranarray(I)
rgb = False
m = 8
n = 8
X = spams.im2col_sliding(A,m,n,rgb)
X = X - np.tile(np.mean(X,0),(X.shape[0],1))
X = np.asfortranarray(X / np.tile(np.sqrt((X * X).sum(axis=0)),(X.shape[0],1)))
X = np.asfortranarray(X[:,np.arange(0,X.shape[1],10)],dtype = myfloat)
param = { 'K' : 200, # learns a dictionary with 100 elements
'lambda1' : 0.15, 'numThreads' : 4,
'iter' : 100}
############# FIRST EXPERIMENT ##################
tic = time.time()
D = spams.trainDL_Memory(X,**param)
tac = time.time()
t = tac - tic
print('time of computation for Dictionary Learning: %f' %t)
print('Evaluating cost function...')
lparam = _extract_lasso_param(param)
alpha = spams.lasso(X,D = D,**lparam)
xd = X - D * alpha
R = np.mean(0.5 * (xd * xd).sum(axis=0) + param['lambda1'] * np.abs(alpha).sum(axis=0))
print("objective function: %f" %R)
#* ? DISPLAY
############# SECOND EXPERIMENT ##################
tic = time.time()
D = spams.trainDL(X,**param)
tac = time.time()
t = tac - tic
print('time of computation for Dictionary Learning: %f' %t)
print('Evaluating cost function...')
alpha = spams.lasso(X,D = D,**lparam)
xd = X - D * alpha
R = np.mean(0.5 * (xd * xd).sum(axis=0) + param['lambda1'] * np.abs(alpha).sum(axis=0))
print("objective function: %f" %R)
#* ? DISPLAY
return None
def test_trainDL_Memory():
img_file = 'lena.png'
try:
img = Image.open(img_file)
except:
print("Cannot load image %s : skipping test" %img_file)
return None
I = np.array(img) / 255.
if I.ndim == 3:
A = np.asfortranarray(I.reshape((I.shape[0],I.shape[1] * I.shape[2])))
rgb = True
else:
A = np.asfortranarray(I)
rgb = False
m = 8;n = 8;
X = spams.im2col_sliding(A,m,n,rgb)
X = X - np.tile(np.mean(X,0),(X.shape[0],1))
X = np.asfortranarray(X / np.tile(np.sqrt((X * X).sum(axis=0)),(X.shape[0],1)))
X = np.asfortranarray(X[:,np.arange(0,X.shape[1],10)],dtype = myfloat)
param = { 'K' : 200, # learns a dictionary with 100 elements
'lambda1' : 0.15, 'numThreads' : 4,
'iter' : 100}
############# FIRST EXPERIMENT ##################
tic = time.time()
D = spams.trainDL_Memory(X,**param)
tac = time.time()
t = tac - tic
print('time of computation for Dictionary Learning: %f' %t)
print('Evaluating cost function...')
lparam = _extract_lasso_param(param)
alpha = spams.lasso(X,D = D,**lparam)
xd = X - D * alpha
R = np.mean(0.5 * (xd * xd).sum(axis=0) + param['lambda1'] * np.abs(alpha).sum(axis=0))
print("objective function: %f" %R)
#* ? DISPLAY
############# SECOND EXPERIMENT ##################
tic = time.time()
D = spams.trainDL(X,**param)
tac = time.time()
t = tac - tic
print('time of computation for Dictionary Learning: %f' %t)
print('Evaluating cost function...')
alpha = spams.lasso(X,D = D,**lparam)
xd = X - D * alpha
R = np.mean(0.5 * (xd * xd).sum(axis=0) + param['lambda1'] * np.abs(alpha).sum(axis=0))
print("objective function: %f" %R)
#* ? DISPLAY
return None
def sparseCoding(X):
X = np.asfortranarray(X)
param = { 'K' : NCLUSTER, # size of the dictionary
'lambda1' : 0.15,
#'posD' : True, # dictionary positive constrain
#'modeD' : 1, # L1 regulization regularization on D
'iter' : ITER} # runtime limit 15mins
D = spams.trainDL_Memory(X,**param)
lparam = _extract_lasso_param(param)
print 'genrating codes...'
alpha = spams.lasso(X,D = D,**lparam)
return D, alpha
R = np.mean(0.5 * (xd * xd).sum(axis=0) + param['lambda1'] * np.abs(alpha).sum(axis=0))
print "objective function: %f" %R
############
img_file = '../extdata/portrait.png'
try:
img = Image.open(img_file)
except:
print "Cannot load image %s : skipping test" %img_file
exit()
I = np.array(img) / 255.
if I.ndim == 3:
A = np.asfortranarray(I.reshape((I.shape[0],I.shape[1] * I.shape[2])),dtype = myfloat)
rgb = True
else:
A = np.asfortranarray(I,dtype = myfloat)
rgb = False
m = 8;n = 8;
X = spams.im2col_sliding(A,m,n,rgb)
X = X - np.tile(np.mean(X,0),(X.shape[0],1))
X = np.asfortranarray(X / np.tile(np.sqrt((X * X).sum(axis=0)),(X.shape[0],1)))
X = np.asfortranarray(X[:,np.arange(0,X.shape[1],10)],dtype = myfloat)
param = { 'K' : 200, # learns a dictionary with 100 elements
'lambda1' : 0.15, 'numThreads' : 4,
'iter' : 100}
D = spams.trainDL_Memory(X,param)
print "DTYPE %s" %str(D.dtype)