def __init__(self, input_vocab, output_vocab=None, # noqa: C901
input_keys=None, output_keys=None,
default_output_key=None, threshold=0.3,
inhibitable=False, wta_output=False,
wta_inhibit_scale=3.0, wta_synapse=0.005,
threshold_output=False, label=None, seed=None,
add_to_container=None):
super(AssociativeMemory, self).__init__(label, seed, add_to_container)
if input_keys is None:
input_keys = input_vocab.keys
input_vectors = input_vocab.vectors
else:
input_vectors = input_vocab.create_subset(input_keys).vectors
# If output vocabulary is not specified, use input vocabulary
# (i.e autoassociative memory)
if output_vocab is None:
output_vocab = input_vocab
output_vectors = input_vectors
else:
if output_keys is None:
output_keys = input_keys
output_vectors = output_vocab.create_subset(output_keys).vectors
if default_output_key is None:
default_output_vector = None
else:
default_output_vector = output_vocab.parse(default_output_key).v
# Create nengo network
with self:
self.am = AssocMem(input_vectors=input_vectors,
output_vectors=output_vectors,
threshold=threshold,
inhibitable=inhibitable,
label=label, seed=seed,
add_to_container=add_to_container)
if default_output_vector is not None:
self.am.add_default_output_vector(default_output_vector)
if wta_output:
self.am.add_wta_network(wta_inhibit_scale, wta_synapse)
if threshold_output:
self.am.add_threshold_to_outputs()
self.input = self.am.input
self.output = self.am.output
if inhibitable:
self.inhibit = self.am.inhibit
self.utilities = self.am.utilities
if threshold_output:
self.thresholded_utilities = self.am.thresholded_utilities
self.inputs = dict(default=(self.input, input_vocab))
self.outputs = dict(default=(self.output, output_vocab))
def __init__(
self,
input_vocab,
output_vocab=None, # noqa: C901
input_keys=None,
output_keys=None,
default_output_key=None,
threshold=0.3,
inhibitable=False,
wta_output=False,
wta_inhibit_scale=3.0,
wta_synapse=0.005,
cleanup_output=False,
replace_output_with_cleaned_output=True,
label=None,
**module_kwargs):
super(AssociativeMemory, self).__init__(label=label, **module_kwargs)
if input_keys is None:
input_keys = input_vocab.keys
input_vectors = input_vocab.vectors
else:
input_vectors = input_vocab.create_subset(input_keys).vectors
# If output vocabulary is not specified, use input vocabulary
# (i.e autoassociative memory)
if output_vocab is None:
output_vocab = input_vocab
output_vectors = input_vectors
else:
if output_keys is None:
output_keys = input_keys
output_vectors = output_vocab.create_subset(output_keys).vectors
if default_output_key is None:
default_output_vector = None
else:
default_output_vector = output_vocab.parse(default_output_key).v
# Create nengo network
with self:
self.am = AssocMem(input_vectors=input_vectors,
output_vectors=output_vectors,
threshold=threshold,
inhibitable=inhibitable,
label=label,
**module_kwargs)
if default_output_vector is not None:
self.am.add_default_output_vector(default_output_vector)
if wta_output:
self.am.add_wta_network(wta_inhibit_scale, wta_synapse)
if cleanup_output:
self.am.add_cleanup_output(
replace_output=replace_output_with_cleaned_output)
self.input = self.am.input
self.output = self.am.output
if cleanup_output and not replace_output_with_cleaned_output:
self.cleaned_output = self.am.cleaned_output
if inhibitable:
self.inhibit = self.am.inhibit
self.utilities = self.am.output_utilities
if cleanup_output:
self.cleaned_utilities = self.am.cleaned_output_utilities
self.inputs = dict(default=(self.input, input_vocab))
self.outputs = dict(default=(self.output, output_vocab))