def __init__(
self,
default_variable=None,
size=None,
function=Linear,
# selection_function=OneHot(mode=MAX_INDICATOR), # RE-INSTATE WHEN IMPLEMENT NHot function
integrator_function=AdaptiveIntegrator,
initial_value=None,
noise: is_numeric_or_none = 0.0,
integration_rate: is_numeric_or_none = 0.5,
integrator_mode=False,
clip=None,
enable_learning=True,
learning_rate: tc.optional(tc.any(parameter_spec, bool)) = None,
learning_function: is_function_type = Kohonen(
distance_function=GAUSSIAN),
learned_projection: tc.optional(MappingProjection) = None,
additional_output_ports: tc.optional(tc.any(str, Iterable)) = None,
name=None,
prefs: is_pref_set = None,
**kwargs):
# # Default output_ports is specified in constructor as a string rather than a list
# # to avoid "gotcha" associated with mutable default arguments
# # (see: bit.ly/2uID3s3 and http://docs.python-guide.org/en/latest/writing/gotchas/)
# if output_ports is None:
# output_ports = [RESULT]
output_ports = [
RESULT, {
NAME: INPUT_PATTERN,
VARIABLE: OWNER_VARIABLE
}
]
if additional_output_ports:
if isinstance(additional_output_ports, list):
output_ports += additional_output_ports
else:
output_ports.append(additional_output_ports)
self._learning_enabled = enable_learning
self._learning_enable_deferred = False
super().__init__(default_variable=default_variable,
size=size,
function=function,
integrator_function=integrator_function,
integrator_mode=integrator_mode,
learning_rate=learning_rate,
learning_function=learning_function,
learned_projection=learned_projection,
enable_learning=enable_learning,
initial_value=initial_value,
noise=noise,
integration_rate=integration_rate,
clip=clip,
output_ports=output_ports,
params=params,
name=name,
prefs=prefs,
**kwargs)
class Parameters(TransferMechanism.Parameters):
"""
Attributes
----------
enable_learning
see `enable_learning <KohonenMechanism.enable_learning>`
:default value: True
:type: ``bool``
learning_function
see `learning_function <KohonenMechanism.learning_function>`
:default value: `Kohonen`
:type: `Function`
learning_rate
see `learning_rate <KohonenMechanism.learning_rate>`
:default value: None
:type:
matrix
see `matrix <KohonenMechanism.matrix>`
:default value: `AUTO_ASSIGN_MATRIX`
:type: ``str``
output_ports
see `output_ports <KohonenMechanism.output_ports>`
:default value: [`RESULT`, "{name: INPUT_PATTERN, variable: OWNER_VARIABLE}"]
:type: ``list``
:read only: True
"""
learning_function = Parameter(Kohonen(distance_function=GAUSSIAN),
stateful=False,
loggable=False,
reference=True)
learning_rate = Parameter(None, modulable=True)
enable_learning = True
matrix = DEFAULT_MATRIX
output_ports = Parameter(
[RESULT, {
NAME: INPUT_PATTERN,
VARIABLE: OWNER_VARIABLE
}],
stateful=False,
loggable=False,
read_only=True,
structural=True,
)
class Parameters(TransferMechanism.Parameters):
"""
Attributes
----------
enable_learning
see `enable_learning <KohonenMechanism.enable_learning>`
:default value: True
:type: bool
learning_function
see `learning_function <KohonenMechanism.learning_function>`
:default value: `Kohonen`(distance_function=gaussian, learning_rate=0.05)
:type: `Function`
learning_rate
see `learning_rate <KohonenMechanism.learning_rate>`
:default value: None
:type:
matrix
see `matrix <KohonenMechanism.matrix>`
:default value: `AUTO_ASSIGN_MATRIX`
:type: str
"""
learning_function = Parameter(Kohonen(distance_function=GAUSSIAN),
stateful=False,
loggable=False)
learning_rate = Parameter(None, modulable=True)
enable_learning = True
matrix = DEFAULT_MATRIX