def __init__(self,
device,
name,
tolerance,
t_max,
local_search_iterations=0,
epsilon=0.05):
super(SurveyPropagatorSolver, self).__init__(
device=device,
name=name,
propagator=pdp_propagate.SurveyPropagator(device,
decimator_dimension=1,
include_adaptors=False),
decimator=pdp_decimate.SequentialDecimator(
device,
message_dimension=(3, 1),
scorer=pdp_predict.SurveyScorer(device,
message_dimension=1,
include_adaptors=False),
tolerance=tolerance,
t_max=t_max),
predictor=pdp_predict.IdentityPredictor(device=device,
random_fill=True),
local_search_iterations=local_search_iterations,
epsilon=epsilon)
def __init__(
self,
device,
name,
pi=0.1,
decimation_probability=0.5,
local_search_iterations=0,
epsilon=0.05,
):
super(ReinforceSurveyPropagatorSolver, self).__init__(
device=device,
name=name,
propagator=pdp_propagate.SurveyPropagator(
device, decimator_dimension=1, include_adaptors=False, pi=pi
),
decimator=pdp_decimate.ReinforceDecimator(
device,
scorer=pdp_predict.SurveyScorer(
device, message_dimension=1, include_adaptors=False, pi=pi
),
decimation_probability=decimation_probability,
),
predictor=pdp_predict.ReinforcePredictor(device=device),
local_search_iterations=local_search_iterations,
epsilon=epsilon,
)
def __init__(self,
device,
name,
edge_dimension,
meta_data_dimension,
decimator_dimension,
mem_hidden_dimension,
agg_hidden_dimension,
mem_agg_hidden_dimension,
prediction_dimension,
variable_classifier=None,
function_classifier=None,
dropout=0,
local_search_iterations=0,
epsilon=0.05):
super(NeuralSurveyPropagatorSolver, self).__init__(
device=device,
name=name,
propagator=pdp_propagate.SurveyPropagator(device,
decimator_dimension,
include_adaptors=True),
decimator=pdp_decimate.NeuralDecimator(
device, (3, 2), meta_data_dimension, decimator_dimension,
mem_hidden_dimension, mem_agg_hidden_dimension,
agg_hidden_dimension, edge_dimension, dropout),
predictor=pdp_predict.NeuralPredictor(
device, decimator_dimension, prediction_dimension,
edge_dimension, meta_data_dimension, mem_hidden_dimension,
agg_hidden_dimension, mem_agg_hidden_dimension,
variable_classifier, function_classifier),
local_search_iterations=local_search_iterations,
epsilon=epsilon)