<s> import sys <EOL> import theano <EOL> import theano . tensor as T <EOL> import numpy as np <EOL> from neupy . layers import Softmax , Output <EOL> from . base import BaseEnsemble <EOL> __all__ = ( '<STR_LIT>' , ) <EOL> class MixtureOfExperts ( BaseEnsemble ) : <EOL> """<STR_LIT>""" <EOL> def __init__ ( self , networks , gating_network = None ) : <EOL> super ( MixtureOfExperts , self ) . __init__ ( networks ) <EOL> algorithms = sys . modules [ '<STR_LIT>' ] <EOL> if not isinstance ( gating_network , algorithms . GradientDescent ) : <EOL> raise ValueError ( "<STR_LIT>" <EOL> "<STR_LIT>" ) <EOL> for network in self . networks : <EOL> if not isinstance ( network , algorithms . GradientDescent ) : <EOL> raise ValueError ( <EOL> "<STR_LIT>" <EOL> "<STR_LIT>" . format ( network . __class__ . __name__ ) <EOL> ) <EOL> if network . output_layer . size != <NUM_LIT:1> : <EOL> raise ValueError ( "<STR_LIT>" <EOL> "<STR_LIT>" . format ( network . output_layer . size ) ) <EOL> if network . error . __name__ != '<STR_LIT>' : <EOL> raise ValueError ( <EOL> "<STR_LIT>" <EOL> "<STR_LIT>" . format ( network . error . __name__ ) <EOL> ) <EOL> network . verbose = False <EOL> if gating_network . input_layer . __class__ != Softmax : <EOL> raise ValueError ( <EOL> "<STR_LIT>" . format ( <EOL> gating_network . input_layer . __class__ . __name__ <EOL> ) <EOL> ) <EOL> if gating_network . output_layer . __class__ != Output : <EOL> raise ValueError ( <EOL> "<STR_LIT>" . format ( <EOL> gating_network . output_layer . __class__ . __name__ <EOL> ) <EOL> ) <EOL> gating_network . verbose = False <EOL> gating_network_output_size = gating_network . output_layer . size <EOL> n_networks = len ( self . networks ) <EOL> if gating_network_output_size != n_networks : <EOL> raise ValueError ( "<STR_LIT>" <EOL> "<STR_LIT>" . format ( n_networks , <EOL> gating_network_output_size ) ) <EOL> if gating_network . error . __name__ != '<STR_LIT>' : <EOL> raise ValueError ( <EOL> "<STR_LIT>" <EOL> "<STR_LIT>" . format ( gating_network . error . __name__ ) <EOL> ) <EOL> self . gating_network = gating_network <EOL> x = T . matrix ( '<STR_LIT:x>' ) <EOL> y = T . matrix ( '<STR_LIT:y>' ) <EOL> gating_network . variables . network_input = x <EOL> gating_network . init_variables ( ) <EOL> gating_network . init_methods ( ) <EOL> probs = gating_network . variables . prediction_func <EOL> train_outputs , outputs = [ ] , [ ] <EOL> for i , network in enumerate ( self . networks ) : <EOL> network . variables . network_input = x <EOL> network . variables . network_output = y <EOL> network . init_variables ( ) <EOL> network . init_methods ( ) <EOL> output = network . variables . prediction_func <EOL> outputs . append ( output * probs [ : , i : i + <NUM_LIT:1> ] ) <EOL> train_output = network . variables . train_prediction_func <EOL> train_outputs . append ( train_output * probs [ : , i : i + <NUM_LIT:1> ] ) <EOL> outputs_concat = T . concatenate ( outputs , axis = <NUM_LIT:1> ) <EOL> self . prediction_func = sum ( outputs ) <EOL> self . train_prediction_func = sum ( train_outputs ) <EOL> for net in self . networks : <EOL> net . methods . error_func = net . error ( y , self . train_prediction_func ) <EOL> gating_network . methods . error_func = gating_network . error ( <EOL> gating_network . variables . network_output , <EOL> outputs_concat <EOL> ) <EOL> self . _predict = theano . function ( [ x ] , self . prediction_func ) <EOL> def train ( self , input_data , target_data , epochs = <NUM_LIT:100> ) : <EOL> if target_data . ndim == <NUM_LIT:1> : <EOL> target_data = target_data . reshape ( ( target_data . size , <NUM_LIT:1> ) ) <EOL> output_size = target_data . shape [ <NUM_LIT:1> ] <EOL> if output_size != <NUM_LIT:1> : <EOL> raise ValueError ( "<STR_LIT>" <EOL> "<STR_LIT>" . format ( output_size ) ) <EOL> input_size = input_data . shape [ <NUM_LIT:1> ] <EOL> gating_network = self . gating_network <EOL> gating_network_input_size = gating_network . input_layer . size <EOL> if gating_network_input_size != input_size : <EOL> raise ValueError ( <EOL> "<STR_LIT>" <EOL> "<STR_LIT>" . format ( gating_network_input_size , input_size ) <EOL> ) <EOL> networks = self . networks <EOL> for epoch in range ( epochs ) : <EOL> predictions = [ ] <EOL> for i , network in enumerate ( networks ) : <EOL> predictions . append ( network . predict ( input_data ) ) <EOL> network . train_epoch ( input_data , target_data ) <EOL> predictions = np . concatenate ( predictions , axis = <NUM_LIT:1> ) <EOL> gating_network . train_epoch ( input_data , predictions ) <EOL> def predict ( self , input_data ) : <EOL> return self . _predict ( input_data ) <EOL> def __repr__ ( self ) : <EOL> indent = '<STR_LIT:U+0020>' * <NUM_LIT:4> <EOL> return ( <EOL> "<STR_LIT>" <EOL> "<STR_LIT>" <EOL> "<STR_LIT>" <EOL> "<STR_LIT>" <EOL> "<STR_LIT:)>" <EOL> ) . format ( <EOL> classname = self . __class__ . __name__ , <EOL> networks = '<STR_LIT>' . join ( map ( repr , self . networks ) ) , <EOL> gating_network = repr ( self . gating_network ) , <EOL> indent = indent , <EOL> double_indent = ( <NUM_LIT:2> * indent ) <EOL> ) </s>
