def test_structTrainDL():
img_file = 'lena.png'
try:
img = Image.open(img_file)
except Exception as e:
print "Cannot load image %s (%s) : skipping test" %(img_file,e)
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])),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)),dtype = myfloat)
param = { 'K' : 64, # learns a dictionary with 100 elements
'lambda1' : 0.05, 'tol' : 1e-3,
'numThreads' : 4, 'batchsize' : 400,
'iter' : 20}
paramL = {'lambda1' : 0.05, 'numThreads' : 4}
param['regul'] = 'l1'
print "with Fista Regression %s" %param['regul']
tic = time.time()
D = spams.structTrainDL(X,**param)
tac = time.time()
t = tac - tic
print 'time of computation for Dictionary Learning: %f' %t
_objective(X,D,param)
#
param['regul'] = 'l2'
print "with Fista Regression %s" %param['regul']
tic = time.time()
D = spams.structTrainDL(X,**param)
tac = time.time()
t = tac - tic
print 'time of computation for Dictionary Learning: %f' %t
_objective(X,D,param)
#
param['regul'] = 'elastic-net'
print "with Fista %s" %param['regul']
param['lambda2'] = 0.1
tic = time.time()
D = spams.structTrainDL(X,**param)
tac = time.time()
t = tac - tic
print 'time of computation for Dictionary Learning: %f' %t
_objective(X,D,param)
## if we want a pause :
## s = raw_input("graph> ")
########### GRAPH
param['lambda1'] = 0.1
param['tol'] = 1e-5
param['K'] = 10
eta_g = np.array([1, 1, 1, 1, 1],dtype=myfloat)
groups = ssp.csc_matrix(np.array([[0, 0, 0, 1, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 1, 0, 0]],dtype=np.bool),dtype=np.bool)
groups_var = ssp.csc_matrix(np.array([[1, 0, 0, 0, 0],
[1, 0, 0, 0, 0],
[1, 0, 0, 0, 0],
[1, 1, 0, 0, 0],
[0, 1, 0, 1, 0],
[0, 1, 0, 1, 0],
[0, 1, 0, 0, 1],
[0, 0, 0, 0, 1],
[0, 0, 0, 0, 1],
[0, 0, 1, 0, 0]],dtype=np.bool),dtype=np.bool)
graph = {'eta_g': eta_g,'groups' : groups,'groups_var' : groups_var}
param['graph'] = graph
param['tree'] = None
param['regul'] = 'graph'
print "with Fista %s" %param['regul']
tic = time.time()
D = spams.structTrainDL(X,**param)
tac = time.time()
t = tac - tic
print 'time of computation for Dictionary Learning: %f' %t
_objective(X,D,param)
param['regul'] = 'graph-ridge'
print "with Fista %s" %param['regul']
tic = time.time()
D = spams.structTrainDL(X,**param)
tac = time.time()
t = tac - tic
print 'time of computation for Dictionary Learning: %f' %t
_objective(X,D,param)
## if we want a pause :
## s = raw_input("tree> ")
##### TREE
tree_data = """0 1. [] -> 1 4
1 1. [0 1 2] -> 2 3
4 2. [] -> 5 6
2 1. [3 4]
3 2. [5]
5 2. [6 7]
6 2.5 [8] -> 7
7 2.5 [9]
"""
param['lambda1'] = 0.001
param['tol'] = 1e-5
own_variables = np.array([0,0,3,5,6,6,8,9],dtype=np.int32)
N_own_variables = np.array([0,3,2,1,0,2,1,1],dtype=np.int32)
eta_g = np.array([1,1,1,2,2,2,2.5,2.5],dtype=myfloat)
groups = np.asfortranarray([[0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0]],dtype = np.bool)
groups = ssp.csc_matrix(groups,dtype=np.bool)
tree = {'eta_g': eta_g,'groups' : groups,'own_variables' : own_variables,
'N_own_variables' : N_own_variables}
param['tree'] = tree
param['graph'] = None
param['regul'] = 'tree-l0'
print "with Fista %s" %param['regul']
tic = time.time()
D = spams.structTrainDL(X,**param)
tac = time.time()
t = tac - tic
print 'time of computation for Dictionary Learning: %f' %t
_objective(X,D,param)
gstruct = spams.groupStructOfString(tree_data)
(perm,tree,nbvars) = spams.treeOfGroupStruct(gstruct)
param['tree'] = tree
param['regul'] = 'tree-l2'
print "with Fista %s" %param['regul']
tic = time.time()
D = spams.structTrainDL(X,**param)
tac = time.time()
t = tac - tic
print 'time of computation for Dictionary Learning: %f' %t
_objective(X,D,param)
param['regul'] = 'tree-linf'
print "with Fista %s" %param['regul']
tic = time.time()
D = spams.structTrainDL(X,**param)
tac = time.time()
t = tac - tic
print 'time of computation for Dictionary Learning: %f' %t
_objective(X,D,param)
def test_structTrainDL():
img_file = 'lena.png'
try:
img = Image.open(img_file)
except Exception as e:
print("Cannot load image %s (%s) : skipping test" %(img_file,e))
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])),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)),dtype = myfloat)
param = { 'K' : 64, # learns a dictionary with 100 elements
'lambda1' : 0.05, 'tol' : 1e-3,
'numThreads' : 4, 'batchsize' : 400,
'iter' : 20}
paramL = {'lambda1' : 0.05, 'numThreads' : 4}
param['regul'] = 'l1'
print("with Fista Regression %s" %param['regul'])
tic = time.time()
D = spams.structTrainDL(X,**param)
tac = time.time()
t = tac - tic
print('time of computation for Dictionary Learning: %f' %t)
_objective(X,D,param)
#
param['regul'] = 'l2'
print("with Fista Regression %s" %param['regul'])
tic = time.time()
D = spams.structTrainDL(X,**param)
tac = time.time()
t = tac - tic
print('time of computation for Dictionary Learning: %f' %t)
_objective(X,D,param)
#
param['regul'] = 'elastic-net'
print("with Fista %s" %param['regul'])
param['lambda2'] = 0.1
tic = time.time()
D = spams.structTrainDL(X,**param)
tac = time.time()
t = tac - tic
print('time of computation for Dictionary Learning: %f' %t)
_objective(X,D,param)
## if we want a pause :
## s = raw_input("graph> ")
########### GRAPH
param['lambda1'] = 0.1
param['tol'] = 1e-5
param['K'] = 10
eta_g = np.array([1, 1, 1, 1, 1],dtype=myfloat)
groups = ssp.csc_matrix(np.array([[0, 0, 0, 1, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 1, 0, 0]],dtype=np.bool),dtype=np.bool)
groups_var = ssp.csc_matrix(np.array([[1, 0, 0, 0, 0],
[1, 0, 0, 0, 0],
[1, 0, 0, 0, 0],
[1, 1, 0, 0, 0],
[0, 1, 0, 1, 0],
[0, 1, 0, 1, 0],
[0, 1, 0, 0, 1],
[0, 0, 0, 0, 1],
[0, 0, 0, 0, 1],
[0, 0, 1, 0, 0]],dtype=np.bool),dtype=np.bool)
graph = {'eta_g': eta_g,'groups' : groups,'groups_var' : groups_var}
param['graph'] = graph
param['tree'] = None
param['regul'] = 'graph'
print("with Fista %s" %param['regul'])
tic = time.time()
D = spams.structTrainDL(X,**param)
tac = time.time()
t = tac - tic
print('time of computation for Dictionary Learning: %f' %t)
_objective(X,D,param)
param['regul'] = 'graph-ridge'
print("with Fista %s" %param['regul'])
tic = time.time()
D = spams.structTrainDL(X,**param)
tac = time.time()
t = tac - tic
print('time of computation for Dictionary Learning: %f' %t)
_objective(X,D,param)
## if we want a pause :
## s = raw_input("tree> ")
##### TREE
tree_data = """0 1. [] -> 1 4
1 1. [0 1 2] -> 2 3
4 2. [] -> 5 6
2 1. [3 4]
3 2. [5]
5 2. [6 7]
6 2.5 [8] -> 7
7 2.5 [9]
"""
param['lambda1'] = 0.001
param['tol'] = 1e-5
own_variables = np.array([0,0,3,5,6,6,8,9],dtype=np.int32)
N_own_variables = np.array([0,3,2,1,0,2,1,1],dtype=np.int32)
eta_g = np.array([1,1,1,2,2,2,2.5,2.5],dtype=myfloat)
groups = np.asfortranarray([[0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0]],dtype = np.bool)
groups = ssp.csc_matrix(groups,dtype=np.bool)
tree = {'eta_g': eta_g,'groups' : groups,'own_variables' : own_variables,
'N_own_variables' : N_own_variables}
param['tree'] = tree
param['graph'] = None
param['regul'] = 'tree-l0'
print("with Fista %s" %param['regul'])
tic = time.time()
D = spams.structTrainDL(X,**param)
tac = time.time()
t = tac - tic
print('time of computation for Dictionary Learning: %f' %t)
_objective(X,D,param)
gstruct = spams.groupStructOfString(tree_data)
(perm,tree,nbvars) = spams.treeOfGroupStruct(gstruct)
param['tree'] = tree
param['regul'] = 'tree-l2'
print("with Fista %s" %param['regul'])
tic = time.time()
D = spams.structTrainDL(X,**param)
tac = time.time()
t = tac - tic
print('time of computation for Dictionary Learning: %f' %t)
_objective(X,D,param)
param['regul'] = 'tree-linf'
print("with Fista %s" %param['regul'])
tic = time.time()
D = spams.structTrainDL(X,**param)
tac = time.time()
t = tac - tic
print('time of computation for Dictionary Learning: %f' %t)
_objective(X,D,param)
def test_structTrainDL():
img_file = "lena.png"
try:
img = Image.open(img_file)
except Exception as e:
print "Cannot load image %s (%s) : skipping test" % (img_file, e)
return None
I = np.array(img) / 255.0
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)), dtype=myfloat)
param = {
"K": 64, # learns a dictionary with 100 elements
"lambda1": 0.05,
"tol": 1e-3,
"numThreads": 4,
"batchsize": 400,
"iter": 20,
}
paramL = {"lambda1": 0.05, "numThreads": 4}
param["regul"] = "l1"
print "with Fista Regression %s" % param["regul"]
tic = time.time()
D = spams.structTrainDL(X, **param)
tac = time.time()
t = tac - tic
print "time of computation for Dictionary Learning: %f" % t
_objective(X, D, param)
#
param["regul"] = "l2"
print "with Fista Regression %s" % param["regul"]
tic = time.time()
D = spams.structTrainDL(X, **param)
tac = time.time()
t = tac - tic
print "time of computation for Dictionary Learning: %f" % t
_objective(X, D, param)
#
param["regul"] = "elastic-net"
print "with Fista %s" % param["regul"]
param["lambda2"] = 0.1
tic = time.time()
D = spams.structTrainDL(X, **param)
tac = time.time()
t = tac - tic
print "time of computation for Dictionary Learning: %f" % t
_objective(X, D, param)
## if we want a pause :
## s = raw_input("graph> ")
########### GRAPH
param["lambda1"] = 0.1
param["tol"] = 1e-5
param["K"] = 10
eta_g = np.array([1, 1, 1, 1, 1], dtype=myfloat)
groups = ssp.csc_matrix(
np.array([[0, 0, 0, 1, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 1, 0, 0]], dtype=np.bool),
dtype=np.bool,
)
groups_var = ssp.csc_matrix(
np.array(
[
[1, 0, 0, 0, 0],
[1, 0, 0, 0, 0],
[1, 0, 0, 0, 0],
[1, 1, 0, 0, 0],
[0, 1, 0, 1, 0],
[0, 1, 0, 1, 0],
[0, 1, 0, 0, 1],
[0, 0, 0, 0, 1],
[0, 0, 0, 0, 1],
[0, 0, 1, 0, 0],
],
dtype=np.bool,
),
dtype=np.bool,
)
graph = {"eta_g": eta_g, "groups": groups, "groups_var": groups_var}
param["graph"] = graph
param["tree"] = None
param["regul"] = "graph"
print "with Fista %s" % param["regul"]
tic = time.time()
D = spams.structTrainDL(X, **param)
tac = time.time()
t = tac - tic
print "time of computation for Dictionary Learning: %f" % t
_objective(X, D, param)
param["regul"] = "graph-ridge"
print "with Fista %s" % param["regul"]
tic = time.time()
D = spams.structTrainDL(X, **param)
tac = time.time()
t = tac - tic
print "time of computation for Dictionary Learning: %f" % t
_objective(X, D, param)
## if we want a pause :
## s = raw_input("tree> ")
##### TREE
tree_data = """0 1. [] -> 1 4
1 1. [0 1 2] -> 2 3
4 2. [] -> 5 6
2 1. [3 4]
3 2. [5]
5 2. [6 7]
6 2.5 [8] -> 7
7 2.5 [9]
"""
param["lambda1"] = 0.001
param["tol"] = 1e-5
own_variables = np.array([0, 0, 3, 5, 6, 6, 8, 9], dtype=np.int32)
N_own_variables = np.array([0, 3, 2, 1, 0, 2, 1, 1], dtype=np.int32)
eta_g = np.array([1, 1, 1, 2, 2, 2, 2.5, 2.5], dtype=myfloat)
groups = np.asfortranarray(
[
[0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0],
],
dtype=np.bool,
)
groups = ssp.csc_matrix(groups, dtype=np.bool)
tree = {"eta_g": eta_g, "groups": groups, "own_variables": own_variables, "N_own_variables": N_own_variables}
param["tree"] = tree
param["graph"] = None
param["regul"] = "tree-l0"
print "with Fista %s" % param["regul"]
tic = time.time()
D = spams.structTrainDL(X, **param)
tac = time.time()
t = tac - tic
print "time of computation for Dictionary Learning: %f" % t
_objective(X, D, param)
gstruct = spams.groupStructOfString(tree_data)
(perm, tree, nbvars) = spams.treeOfGroupStruct(gstruct)
param["tree"] = tree
param["regul"] = "tree-l2"
print "with Fista %s" % param["regul"]
tic = time.time()
D = spams.structTrainDL(X, **param)
tac = time.time()
t = tac - tic
print "time of computation for Dictionary Learning: %f" % t
_objective(X, D, param)
param["regul"] = "tree-linf"
print "with Fista %s" % param["regul"]
tic = time.time()
D = spams.structTrainDL(X, **param)
tac = time.time()
t = tac - tic
print "time of computation for Dictionary Learning: %f" % t
_objective(X, D, param)