def independent_components_analysis(
self, number_of_components=None, algorithm='CuBICA', diff_order=1,
factors=None, comp_list = None, mask = None, on_peaks=False, on_scores=False,
pretreatment = None, **kwargs):
"""Independent components analysis.
Available algorithms: FastICA, JADE, CuBICA, and TDSEP
Parameters
----------
number_of_components : int
number of principal components to pass to the ICA algorithm
algorithm : {FastICA, JADE, CuBICA, TDSEP}
diff_order : int
factors : numpy array
externally provided components
comp_list : boolen numpy array
choose the components to use by the boolean list. It permits to
choose non contiguous components.
mask : numpy boolean array with the same dimension as the PC
If not None, only the selected channels will be used by the
algorithm.
pretreatment: dict
Any extra parameter is passed to the ICA algorithm
"""
target=self._get_target(on_peaks)
if not hasattr(target, 'factors') or target.factors==None:
self.decomposition(on_peaks=on_peaks)
else:
if factors is None:
if on_scores:
factors = target.scores
else:
factors = target.factors
bool_index = np.zeros((factors.shape[0]), dtype = 'bool')
if number_of_components is not None:
bool_index[:number_of_components] = True
else:
if self.output_dimension is not None:
number_of_components = self.output_dimension
bool_index[:number_of_components] = True
if comp_list is not None:
for ifactors in comp_list:
bool_index[ifactors] = True
number_of_components = len(comp_list)
factors = factors[:,bool_index]
if diff_order > 0 and pretreatment is None:
factors = np.diff(factors, diff_order, axis = 0)
if pretreatment is not None:
from hyperspy.signals.spectrum import Spectrum
sfactors = Spectrum({'data' : factors.T})
if pretreatment['algorithm'] == 'savitzky_golay':
sfactors.smooth_savitzky_golay(
number_of_points = pretreatment['number_of_points'],
polynomial_order = pretreatment['polynomial_order'],
differential_order = diff_order)
if pretreatment['algorithm'] == 'tv':
sfactors.smooth_tv(
pretreatment_parameter= pretreatment[
'pretreatment_parameter'],
differential_order = diff_order)
factors = sfactors.data.T
if pretreatment['algorithm'] == 'butter':
b, a = sp.signal.butter(pretreatment['order'],
pretreatment['cutoff'], pretreatment['type'])
for i in range(factors.shape[1]):
factors[:,i] = sp.signal.filtfilt(b, a, factors[:,i])
if mask is not None:
factors = factors[mask.ravel(), :]
# first centers and scales data
factors,invsqcovmat = centering_and_whitening(factors)
if algorithm == 'orthomax':
_, unmixing_matrix = utils.orthomax(factors, **kwargs)
unmixing_matrix = unmixing_matrix.T
elif algorithm == 'sklearn_fastica':
target.ica_node = sklearn.decomposition.FastICA(**kwargs)
target.ica_node.whiten = False
target.ica_node.fit(factors)
unmixing_matrix = target.ica_node.unmixing_matrix_
else:
to_exec = 'target.ica_node=mdp.nodes.%sNode(' % algorithm
for key, value in kwargs.iteritems():
to_exec += '%s=%s,' % (key, value)
to_exec += ')'
exec(to_exec)
target.ica_node.train(factors)
unmixing_matrix = target.ica_node.get_recmatrix()
target.unmixing_matrix = np.dot(unmixing_matrix, invsqcovmat)
self._unmix_factors(target)
self._unmix_scores(target)
self._auto_reverse_ic(target)
target.ica_algorithm = algorithm
def blind_source_separation(
self,
number_of_components=None,
algorithm="sklearn_fastica",
diff_order=1,
factors=None,
comp_list=None,
mask=None,
on_loadings=False,
pretreatment=None,
**kwargs
):
"""Blind source separation (BSS) on the result on the
decomposition.
Available algorithms: FastICA, JADE, CuBICA, and TDSEP
Parameters
----------
number_of_components : int
number of principal components to pass to the BSS algorithm
algorithm : {FastICA, JADE, CuBICA, TDSEP}
diff_order : int
Sometimes it is convenient to perform the BSS on the derivative
of the signal. If diff_order is 0, the signal is not differentiated.
factors : numpy.array
Factors to decompose. If None, the BSS is performed on the result
of a previous decomposition.
comp_list : boolen numpy array
choose the components to use by the boolean list. It permits
to choose non contiguous components.
mask : numpy boolean array with the same dimension as the signal
If not None, the signal locations marked as True (masked) will
not be passed to the BSS algorithm.
on_loadings : bool
If True, perform the BSS on the loadings of a previous
decomposition. If False, performs it on the factors.
pretreatment: dict
**kwargs : extra key word arguments
Any keyword arguments are passed to the BSS algorithm.
"""
target = self.learning_results
if not hasattr(target, "factors") or target.factors == None:
raise AttributeError(
"A decomposition must be performed before blind " "source seperation or factors must be provided."
)
else:
if factors is None:
if on_loadings:
factors = target.loadings
else:
factors = target.factors
bool_index = np.zeros((factors.shape[0]), dtype="bool")
if number_of_components is not None:
bool_index[:number_of_components] = True
else:
if target.output_dimension is not None:
number_of_components = target.output_dimension
bool_index[:number_of_components] = True
if comp_list is not None:
for ifactors in comp_list:
bool_index[ifactors] = True
number_of_components = len(comp_list)
factors = factors[:, bool_index]
if pretreatment is not None:
from hyperspy.signals.spectrum import Spectrum
sfactors = Spectrum({"data": factors.T})
if pretreatment["algorithm"] == "savitzky_golay":
sfactors.smooth_savitzky_golay(
number_of_points=pretreatment["number_of_points"],
polynomial_order=pretreatment["polynomial_order"],
differential_order=diff_order,
)
if pretreatment["algorithm"] == "tv":
sfactors.smooth_tv(
smoothing_parameter=pretreatment["smoothing_parameter"], differential_order=diff_order
)
factors = sfactors.data.T
if pretreatment["algorithm"] == "butter":
b, a = sp.signal.butter(pretreatment["order"], pretreatment["cutoff"], pretreatment["type"])
for i in range(factors.shape[1]):
factors[:, i] = sp.signal.filtfilt(b, a, factors[:, i])
elif diff_order > 0:
factors = np.diff(factors, diff_order, axis=0)
if mask is not None:
factors = factors[~mask]
# first center and scale the data
factors, invsqcovmat = centering_and_whitening(factors)
if algorithm == "orthomax":
_, unmixing_matrix = utils.orthomax(factors, **kwargs)
unmixing_matrix = unmixing_matrix.T
elif algorithm == "sklearn_fastica":
# if sklearn_installed is False:
# raise ImportError(
#'sklearn is not installed. Nothing done')
if "tol" not in kwargs:
kwargs["tol"] = 1e-10
target.bss_node = FastICA(**kwargs)
target.bss_node.whiten = False
target.bss_node.fit(factors)
unmixing_matrix = target.bss_node.unmixing_matrix_
else:
if mdp_installed is False:
raise ImportError("MDP is not installed. Nothing done")
to_exec = "target.bss_node=mdp.nodes.%sNode(" % algorithm
for key, value in kwargs.iteritems():
to_exec += "%s=%s," % (key, value)
to_exec += ")"
exec (to_exec)
target.bss_node.train(factors)
unmixing_matrix = target.bss_node.get_recmatrix()
target.unmixing_matrix = np.dot(unmixing_matrix, invsqcovmat)
self._unmix_factors(target)
self._unmix_loadings(target)
self._auto_reverse_bss_component(target)
target.bss_algorithm = algorithm
