diff --git a/src/LearningMethods/LevenbergMarquardt.cpp b/src/LearningMethods/LevenbergMarquardt.cpp
index 778ee70ba39c97bfc320069153ff1d108bd94691..6637ba1c7657168390ca16e28217a8b8ab4fbf92 100644
--- a/src/LearningMethods/LevenbergMarquardt.cpp
+++ b/src/LearningMethods/LevenbergMarquardt.cpp
@@ -52,9 +52,18 @@ void lib4neuro::LevenbergMarquardt::LevenbergMarquardtImpl::get_jacobian_and_rhs
std::vector<std::vector<double>> jacobian;
std::vector<double> rhs_vec;
- ef.divide_data_train_test((double)data_subset_size / (double)ef.get_n_data_set());
+ if(data_subset_size <= 0){
+ data_subset_size = ef.get_n_data_set();
+ }
+
+ if(data_subset_size < ef.get_n_data_set()){
+ ef.divide_data_train_test((double)data_subset_size / (double)ef.get_n_data_set());
+ }
ef.get_jacobian_and_rhs(jacobian, rhs_vec);
- ef.return_full_data_set_for_training();
+
+ if(data_subset_size < ef.get_n_data_set()){
+ ef.return_full_data_set_for_training();
+ }
size_t dim_out = jacobian.size();
size_t n_parameters = rhs_vec.size();
diff --git a/src/Simulator/Simulator.cpp.orig b/src/Simulator/Simulator.cpp.orig
deleted file mode 100644
index 744d0223ab9b0dba93367b36e61fca630a6a6a2e..0000000000000000000000000000000000000000
--- a/src/Simulator/Simulator.cpp.orig
+++ /dev/null
@@ -1,348 +0,0 @@
-/**
- * DESCRIPTION OF THE FILE
- *
- * @author Michal KravÄŤenko
- * @date 11.3.19 -
- */
-
-<<<<<<< HEAD
-
-#include "Neuron/NeuronBiased.h"
-#include "Simulator.h"
-#include "../exceptions.h"
-=======
-
-#include <Neuron/NeuronBiased.h>
-#include "../message.h"
-#include "../exceptions.h"
-#include "../settings.h"
-
-#include "../General/ExprtkWrapper.h"
-#include "../Network/NeuralNetwork.h"
-
-#include "Simulator.h"
-
->>>>>>> 79990d25651f9a1588ed1649fc19659503371fd0
-namespace lib4neuro {
-
- Simulator::Simulator(
- size_t n_outputs,
- std::vector<size_t>& hidden_net_structure,
- std::vector<std::vector<double>>& t,
- std::vector<std::vector<double>>& xi
- ) {
-
- this->neurons = new ::std::vector<Neuron*>(0);
- this->neuron_biases = new ::std::vector<double>(0);
- this->neuron_potentials = new ::std::vector<double>(0);
- this->neuron_bias_indices = new ::std::vector<int>(0);
-
- this->connection_weights = new ::std::vector<double>(0);
- this->connection_list = new ::std::vector<ConnectionFunctionGeneral*>(0);
- this->inward_adjacency = new ::std::vector<std::vector<std::pair<size_t, size_t>>*>(0);
- this->outward_adjacency = new ::std::vector<std::vector<std::pair<size_t, size_t>>*>(0);
-
- this->neuron_layers_feedforward = new ::std::vector<std::vector<size_t>*>(0);
- this->neuron_layers_feedbackward = new ::std::vector<std::vector<size_t>*>(0);
-
- this->input_neuron_indices = new ::std::vector<size_t>(0);
- this->output_neuron_indices = new ::std::vector<size_t>(0);
-
- this->delete_weights = true;
- this->delete_biases = true;
- this->layers_analyzed = false;
-
- size_t inp_dim = 1; //!< Network input dimension
- size_t out_dim = n_outputs; //!< Network output dimension
- size_t transfer_dim = xi.size();
- this->n_valves = transfer_dim;
-
- COUT_DEBUG("Simulator is being constructed:" << std::endl);
- COUT_DEBUG("# of inputs: " << inp_dim << std::endl);
- COUT_DEBUG("# of outputs: " << out_dim << std::endl);
- COUT_DEBUG("# transfer functions: " << transfer_dim << std::endl);
-
- std::vector<size_t> input_layer_neuron_indices;
- std::vector<size_t> output_layer_neuron_indices;
- std::vector<size_t> previous_layer_neuron_indices;
- std::vector<size_t> current_layer_neuron_indices;
-
- /* Creation of INPUT layer neurons */
- current_layer_neuron_indices.reserve(inp_dim);
- input_layer_neuron_indices.reserve(inp_dim);
- output_layer_neuron_indices.reserve(out_dim);
-
- size_t neuron_id = this->add_neuron(new NeuronLinear,
- BIAS_TYPE::NO_BIAS);
- input_layer_neuron_indices.emplace_back(neuron_id);
- /* Creation of OUTPUT layer neurons */
- for (unsigned int i = 0; i < out_dim; i++) {
- size_t neuron_id = this->add_neuron(new NeuronLinear,
- BIAS_TYPE::NO_BIAS);
- output_layer_neuron_indices.emplace_back(neuron_id);
- }
-
-
- std::vector<size_t> bias_layer_neuron_indices;
- for (size_t hi = 0; hi < transfer_dim; ++hi) {
- COUT_DEBUG(" CONSTRUCTING TRANSFER FUNCTION BETWEEN VALVE #" << (hi + 1)
- << " AND THE OUTPUTS, # OF COPIES PER OUTPUT: "
- << xi[hi].size() << std::endl);
-
- this->index_start_output_connections_inclusive.push_back(this->connection_list->size());
-
- //connection towards the bias neurons
- bias_layer_neuron_indices.resize(0);
- for (size_t nn = 0; nn < xi[hi].size(); ++nn) {
- size_t new_n_idx = this->add_neuron(new NeuronBiased(xi[hi][nn]),
- BIAS_TYPE::NO_BIAS);
- bias_layer_neuron_indices.emplace_back(new_n_idx);
-
- this->add_connection_constant(input_layer_neuron_indices[0],
- new_n_idx,
- 1.0);
- }
-
- for (size_t oi = 0; oi < out_dim; ++oi) {
- COUT_DEBUG(" CONSTRUCTING TRANSFER FUNCTION TOWARDS OUTPUT #" << (oi + 1) << std::endl);
- size_t first_connection_idx = this->connection_weights->size();
- size_t first_bias_idx = this->neuron_biases->size();
-
- COUT_DEBUG(" CONSTRUCTING TRANSFER FUNCTION #" << (1) << std::endl);
- //for each timestep, we add a 'copy' of one neural network
- std::vector<size_t> current_layer_neuron_indices_local, input_layer_neuron_indices_local, previous_layer_neuron_indices_local;
- std::vector<size_t> bias_indices, connection_weights_indices;
- /* Creation of INPUT layer neurons */
- current_layer_neuron_indices_local.reserve(inp_dim);
- input_layer_neuron_indices_local.reserve(inp_dim);
- size_t first_neuron_id = bias_layer_neuron_indices[0];
- input_layer_neuron_indices_local.emplace_back(first_neuron_id);
- current_layer_neuron_indices_local = input_layer_neuron_indices_local;
-
- /* Creation of HIDDEN layers */
- for (unsigned int i = 1; i <= hidden_net_structure.size() - 2; i++) {
- previous_layer_neuron_indices_local.reserve(hidden_net_structure.at(i - 1));
- previous_layer_neuron_indices_local = current_layer_neuron_indices_local;
- current_layer_neuron_indices_local.clear();
- current_layer_neuron_indices_local.reserve(hidden_net_structure.at(i));
-
- /* Creation of one single hidden layer */
- for (unsigned int j = 0; j < hidden_net_structure.at(i); j++) {
-
- /* Create a new hidden neuron */
- size_t neuron_id = this->add_neuron(new NeuronLogistic,
- BIAS_TYPE::NEXT_BIAS);
-
- current_layer_neuron_indices_local.emplace_back(neuron_id);
-
- /* Connect the new neuron with all neurons from the previous layer */
- for (auto ind : previous_layer_neuron_indices_local) {
- this->add_connection_simple(ind,
- neuron_id,
- l4n::SIMPLE_CONNECTION_TYPE::NEXT_WEIGHT);
- }
- }
- }
-
- previous_layer_neuron_indices_local.reserve(hidden_net_structure.back() - 1);
- previous_layer_neuron_indices_local = current_layer_neuron_indices_local;
- current_layer_neuron_indices_local.clear();
- current_layer_neuron_indices_local.reserve(1);
-
- /* Creation of OUTPUT layer neurons */
- size_t last_neuron_id = this->add_neuron(new NeuronLinear,
- BIAS_TYPE::NO_BIAS);
- current_layer_neuron_indices_local.emplace_back(last_neuron_id);
- /* Connect new neuron with all neurons from the previous layer */
- for (auto ind : previous_layer_neuron_indices_local) {
- this->add_connection_simple(ind,
- last_neuron_id,
- l4n::SIMPLE_CONNECTION_TYPE::NEXT_WEIGHT);
- }
- /* connection towards the output */
- this->add_connection_constant(last_neuron_id,
- output_layer_neuron_indices[oi],
- xi[hi][0]);
-
- for (size_t k = 1; k < xi[hi].size(); ++k) {
- COUT_DEBUG(" CONSTRUCTING TRANSFER FUNCTION #" << (k + 1) << std::endl);
- size_t first_bias_idx_local = first_bias_idx;
- size_t first_connection_idx_local = first_connection_idx;
-
- //for each timestep, we add a 'copy' of one neural network
- current_layer_neuron_indices_local.clear();
- input_layer_neuron_indices_local.clear();
- previous_layer_neuron_indices_local.clear();
-
- /* Creation of INPUT layer neurons */
- current_layer_neuron_indices_local.reserve(inp_dim);
- input_layer_neuron_indices_local.reserve(inp_dim);
- first_neuron_id = bias_layer_neuron_indices[k];
- input_layer_neuron_indices_local.emplace_back(first_neuron_id);
- current_layer_neuron_indices_local = input_layer_neuron_indices_local;
-
- /* Creation of HIDDEN layers */
- for (unsigned int i = 1; i <= hidden_net_structure.size() - 2; i++) {
- previous_layer_neuron_indices_local.reserve(hidden_net_structure.at(i - 1));
- previous_layer_neuron_indices_local = current_layer_neuron_indices_local;
- current_layer_neuron_indices_local.clear();
- current_layer_neuron_indices_local.reserve(hidden_net_structure.at(i));
-
- /* Creation of one single hidden layer */
- for (unsigned int j = 0; j < hidden_net_structure.at(i); j++) {
-
- /* Create a new hidden neuron */
- size_t neuron_id_local = this->add_neuron(new NeuronLogistic,
- BIAS_TYPE::EXISTING_BIAS,
- first_bias_idx_local);
- first_bias_idx_local++;
- current_layer_neuron_indices_local.emplace_back(neuron_id_local);
-
- /* Connect the new neuron with all neurons from the previous layer */
- for (auto ind : previous_layer_neuron_indices_local) {
- this->add_existing_connection(ind,
- neuron_id_local,
- first_connection_idx_local,
- *this);
- first_connection_idx_local++;
- }
- }
- }
-
- previous_layer_neuron_indices_local.reserve(hidden_net_structure.back() - 1);
- previous_layer_neuron_indices_local = current_layer_neuron_indices_local;
- current_layer_neuron_indices_local.clear();
- current_layer_neuron_indices_local.reserve(1);
-
- /* Creation of OUTPUT layer neurons */
- size_t last_neuron_id_local = this->add_neuron(new NeuronLinear,
- BIAS_TYPE::NO_BIAS);
- current_layer_neuron_indices_local.emplace_back(last_neuron_id_local);
- /* Connect new neuron with all neurons from the previous layer */
- for (auto ind : previous_layer_neuron_indices_local) {
- this->add_existing_connection(ind,
- last_neuron_id_local,
- first_connection_idx_local,
- *this);
- first_connection_idx_local++;
- }
- /* connection towards the output */
- this->add_connection_constant(last_neuron_id_local,
- output_layer_neuron_indices[oi],
- xi[hi][k]);
- }
- }
- this->index_end_output_connections_inclusive.push_back(this->connection_list->size() - 1);
- }
-
- /* Init variables containing indices of INPUT nad OUTPUT neurons */
- this->input_neuron_indices = new ::std::vector<size_t>(inp_dim);
- this->output_neuron_indices = new ::std::vector<size_t>(out_dim);
-
- *this->input_neuron_indices = input_layer_neuron_indices;
- *this->output_neuron_indices = output_layer_neuron_indices;
-
- this->analyze_layer_structure();
- }
-
- size_t Simulator::get_n_valves() {
- return this->n_valves;
- }
-
- double Simulator::eval_model(double t,
- std::vector<double>& result) {
- std::vector<double> input = {t};
- std::fill(result.begin(),
- result.end(),
- 0.0);
-
- this->eval_single(input,
- result);
- }
-
- double Simulator::eval_model(double t,
- size_t valve_idx) {
-
-
- }
-
- double Simulator::eval_model(double t,
- size_t valve_idx,
- std::vector<double>& result) {
- if ((this->input_neuron_indices->size() * this->output_neuron_indices->size()) <= 0) {
- THROW_INVALID_ARGUMENT_ERROR("Input and output neurons have not been specified!");
- }
-
- if (this->input_neuron_indices->size() != 1) {
- THROW_INVALID_ARGUMENT_ERROR("Data input size != Network input size");
- }
-
- if (this->output_neuron_indices->size() != result.size()) {
- THROW_INVALID_ARGUMENT_ERROR("Data output size != Network output size");
- }
- this->analyze_layer_structure();
-
- std::vector<double> input = {t};
- std::fill(result.begin(),
- result.end(),
- 0.0);
-
- //TODO optimize... right now it just ignores the connections to be disregarded towards all the output
-
-
- double potential, bias;
- int bias_idx;
-
-
- /* reset of the output and the neuron potentials */
- ::std::fill(this->neuron_potentials->begin(),
- this->neuron_potentials->end(),
- 0.0);
-
- /* set the potentials of the input neurons */
- for (size_t i = 0; i < this->input_neuron_indices->size(); ++i) {
- this->neuron_potentials->at(this->input_neuron_indices->at(i)) = input[i];
- }
-
- /* we iterate through SOME of the connections and transfer the signals */
- for (auto layer: *this->neuron_layers_feedforward) {
- /* we iterate through all neurons in this layer and propagate the signal to the neighboring neurons */
-
- for (auto si: *layer) {
- bias = 0.0;
- bias_idx = this->neuron_bias_indices->at(si);
- if (bias_idx >= 0) {
- bias = this->neuron_biases->at(bias_idx);
- }
- potential = this->neurons->at(si)->activate(this->neuron_potentials->at(si),
- bias);
-
- for (auto c: *this->outward_adjacency->at(si)) {
- size_t ti = c.first;
- size_t ci = c.second;
-
- if (ci >= this->index_start_output_connections_inclusive[valve_idx] &&
- ci <= this->index_end_output_connections_inclusive[valve_idx]) {
- this->neuron_potentials->at(ti) +=
- this->connection_list->at(ci)->eval(*this->connection_weights) * potential;
- }
-
- }
- }
- }
-
- unsigned int i = 0;
- for (auto oi: *this->output_neuron_indices) {
- bias = 0.0;
- bias_idx = this->neuron_bias_indices->at(oi);
- if (bias_idx >= 0) {
- bias = this->neuron_biases->at(bias_idx);
- }
- result[i] = this->neurons->at(oi)->activate(this->neuron_potentials->at(oi),
- bias);
- ++i;
- }
- }
-
-
-}//end of namespace lib4neuro
diff --git a/src/Simulator/Simulator.h.orig b/src/Simulator/Simulator.h.orig
deleted file mode 100644
index c6ea73a4d5a0f29319c3521cf44ab0313ac2c2b5..0000000000000000000000000000000000000000
--- a/src/Simulator/Simulator.h.orig
+++ /dev/null
@@ -1,59 +0,0 @@
-/**
- * DESCRIPTION OF THE FILE
- *
- * @author Michal KravÄŤenko
- * @date 11.3.19 -
- */
-
-#ifndef LIB4NEURO_SIMULATOR_H
-#define LIB4NEURO_SIMULATOR_H
-
-#include <iostream>
-#include <4neuro.h>
-
-<<<<<<< HEAD
-
-#include "../message.h"
-#include "../settings.h"
-
-#include "../General/ExprtkWrapper.h"
-#include "../Network/NeuralNetwork.h"
-=======
->>>>>>> 79990d25651f9a1588ed1649fc19659503371fd0
-
-namespace lib4neuro {
- class Simulator : public NeuralNetwork {
- private:
- size_t n_valves;
-
- std::vector<size_t> index_start_output_connections_inclusive;
- std::vector<size_t> index_end_output_connections_inclusive;
-
- public:
-
- LIB4NEURO_API explicit Simulator(
- size_t n_outputs,
- std::vector<size_t>& hidden_net_structure,
- std::vector<std::vector<double>>& t,
- std::vector<std::vector<double>>& xi
- );
-
- LIB4NEURO_API size_t get_n_valves();
-
- LIB4NEURO_API double eval_model(double t,
- std::vector<double>& result);
-
- LIB4NEURO_API double eval_model(double t,
- size_t valve_idx,
- std::vector<double>& result);
-
- //TODO
- LIB4NEURO_API double eval_model(double t,
- size_t valve_idx);
-
- };
-
-};//end of namespace lib4neuro
-
-
-#endif //LIB4NEURO_SIMULATOR_H