def get_input_tree(self):
"""
Return a list of lists describing the interface to the analyzer. This
is used by the GUI to generate the menus and fields necessary for defining a simulation.
"""
algorithm = fastICA()
algorithm.trait.bound = self.INTERFACE_ATTRIBUTES_ONLY
tree = algorithm.interface[self.INTERFACE_ATTRIBUTES]
for node in tree:
if node['name'] == 'time_series':
node['conditions'] = FilterChain(fields=[FilterChain.datatype + '._nr_dimensions'],
operations=["=="], values=[4])
return tree
def get_input_tree(self):
"""
Return a list of lists describing the interface to the analyzer. This
is used by the GUI to generate the menus and fields necessary for
defining a simulation.
"""
algorithm = fastICA()
algorithm.trait.bound = self.INTERFACE_ATTRIBUTES_ONLY
tree = algorithm.interface[self.INTERFACE_ATTRIBUTES]
for node in tree:
if node['name'] == 'time_series':
node['conditions'] = FilterChain(fields = [FilterChain.datatype + '._nr_dimensions'],
operations = ["=="], values = [4])
return tree
def configure(self, time_series, n_components=None):
"""
Store the input shape to be later used to estimate memory usage. Also
create the algorithm instance.
"""
self.input_shape = time_series.read_data_shape()
log_debug_array(LOG, time_series, "time_series")
##-------------------- Fill Algorithm for Analysis -------------------##
algorithm = fastICA()
if n_components is not None:
algorithm.n_components = n_components
else:
## It will only work for Simulator results.
algorithm.n_components = self.input_shape[2]
self.algorithm = algorithm
def configure(self, time_series, n_components=None):
"""
Store the input shape to be later used to estimate memory usage. Also
create the algorithm instance.
"""
self.input_shape = time_series.read_data_shape()
log_debug_array(LOG, time_series, "time_series")
##-------------------- Fill Algorithm for Analysis -------------------##
algorithm = fastICA()
if n_components is not None:
algorithm.n_components = n_components
else:
## It will only work for Simulator results.
algorithm.n_components = self.input_shape[2]
self.algorithm = algorithm
def configure(self, time_series, n_components=None):
"""
Store the input shape to be later used to estimate memory usage. Also
create the algorithm instance.
"""
self.input_shape = time_series.read_data_shape()
log_debug_array(LOG, time_series, "time_series")
##-------------------- Fill Algorithm for Analysis -------------------##
algorithm = fastICA()
if n_components is not None:
algorithm.n_components = n_components
else:
## TODO LD: Is this correct? when no number is specified, the number of nodes is taken
## It will only work for Simulator results. SK: Correct, the same thing is already done in the Analyser.
algorithm.n_components = self.input_shape[2]
self.algorithm = algorithm
