def __init__(self,
class_dict_coder=None,
n_folds=None,
sparse_coder=None,
param_grid=None,
n_class_samples=None,
n_test_samples=None,
n_tests=1,
method="global",
mmap=False,
n_jobs=1):
classifier.__init__(self,
n_folds=n_folds,
param_grid=param_grid,
n_class_samples=n_class_samples,
n_test_samples=n_test_samples,
n_tests=n_tests,
name=method + 'src_feature_classifier')
# a class that will do class dictionary learning
# of the data
self.class_dict_coder = class_dict_coder
self.sparse_coder = sparse_coder
# if method='global' then we extract global SRC features
# if method='local' then we extract local SRC features
self.method = method
self.mmap = mmap
self.n_jobs = n_jobs
self.sparse_coder.mmap = self.mmap
self.sparse_coder.n_jobs = self.n_jobs
self.D = None
self.features_extracted = False
self.Z_train = None
self.Z_test = None
def __init__(self,
class_dict_coder=None,
n_folds=None,
sparse_coder=None,
n_class_samples=None,
n_test_samples=None,
n_tests=1,
method="global",
mmap=False,
n_jobs=1):
classifier.__init__(self,
n_folds=n_folds,
n_class_samples=n_class_samples,
n_test_samples=n_test_samples,
n_tests=n_tests,
name=method + '_src_classifier')
self.class_dict_coder = class_dict_coder
self.sparse_coder = sparse_coder
# if method='global' then we apply the global SRC classifier
# if method='local' then we apply the local SRC classifier
self.method = method
self.mmap = mmap
self.n_jobs = n_jobs
def __init__(self, class_dict_coder=None, param_grid=None,
sparse_coder=None, max_iter=2, approx=True, eta=0,
n_class_samples=None, n_test_samples=None, n_tests=1, n_folds=None,
alpha=1, beta=1, mmap=False, verbose=False, n_jobs=1):
classifier.__init__(self, n_folds=n_folds, param_grid=param_grid,
n_class_samples=n_class_samples, n_test_samples=n_test_samples,
n_tests=n_tests, name='lc_ksvd_classifier')
# n_class_atoms,n_nonzero_coefs are arrays
# that specify the params of each class dict
self.class_dict_coder = class_dict_coder
self.n_class_atoms = None
self.sparse_coder = sparse_coder
self.max_iter = max_iter
self.approx = approx
# the parameters of the LC-KSVD algorithm
self.alpha = alpha
self.beta = beta
self.mmap = mmap
self.verbose = verbose
self.n_jobs = n_jobs
self.sparse_coder.n_jobs = n_jobs
def __init__(self,
class_dict_coder=None,
param_grid=None,
sparse_coder=None,
max_iter=2,
approx=True,
eta=0,
n_class_samples=None,
n_test_samples=None,
n_tests=1,
n_folds=None,
alpha=1,
beta=1,
mmap=False,
verbose=False,
n_jobs=1):
classifier.__init__(self,
n_folds=n_folds,
param_grid=param_grid,
n_class_samples=n_class_samples,
n_test_samples=n_test_samples,
n_tests=n_tests,
name='lc_ksvd_classifier')
# n_class_atoms,n_nonzero_coefs are arrays
# that specify the params of each class dict
self.class_dict_coder = class_dict_coder
self.n_class_atoms = None
self.sparse_coder = sparse_coder
self.max_iter = max_iter
self.approx = approx
# the parameters of the lc_ksvd algorithm
self.alpha = alpha
self.beta = beta
self.mmap = mmap
self.verbose = verbose
self.n_jobs = n_jobs
self.sparse_coder.n_jobs = n_jobs