9#ifndef INPUTSSELECTIONALGORITHM_H
10#define INPUTSSELECTIONALGORITHM_H
24#include "training_strategy.h"
30struct InputsSelectionResults;
58 enum class StoppingCondition{MaximumTime, SelectionErrorGoal, MaximumInputs, MinimumInputs, MaximumEpochs,
59 MaximumSelectionFailures, CorrelationGoal};
76 const type& get_tolerance()
const;
104 Index
get_input_index(
const Tensor<DataSet::VariableUse, 1>&,
const Index&);
120 Tensor<Index, 1> original_input_columns_indices;
121 Tensor<Index, 1> original_target_columns_indices;
153 const Eigen::array<int, 1> rows_sum = {Eigen::array<int, 1>({1})};
171 set(maximum_epochs_number);
174 Index get_epochs_number()
const
179 void set(
const Index& maximum_epochs_number)
192 void resize_history(
const Index& new_size)
200 for(Index i = 0; i < new_size; i++)
211 cout <<
"Inputs Selection Results" << endl;
215 cout <<
"Inputs: " << endl;
252 Tensor<Index, 1> optimal_input_columns_indices;
254 Tensor<bool, 1> optimal_inputs;
This class represents the concept of training strategy for a neural network in OpenNN.
This structure contains the optimization algorithm results.