From 8ba85123908e78623dc42a4cbdfb6bda4ab2a003 Mon Sep 17 00:00:00 2001
From: kra568 <kra568@login1.head.smc.salomon.it4i.cz>
Date: Fri, 20 Sep 2019 15:17:25 +0200
Subject: [PATCH] [WIP] commit before merge
---
.gitlab-ci.yml | 36 +-
data/HE21+T1.xyz | 2002 ------------------
run_devsandbox_queue.sh | 17 +
run_test.sh | 2 +
src/Coordinates/coordinates.cpp | 2 +
src/DataSet/DataSet.cpp | 28 +-
src/DataSet/DataSet.h | 28 +
src/ErrorFunction/ErrorFunctions.cpp | 117 +-
src/ErrorFunction/ErrorFunctions.h | 55 +-
src/LearningMethods/GradientDescentBB.cpp | 8 +-
src/LearningMethods/LazyLearning.cpp | 93 +
src/LearningMethods/LazyLearning.h | 60 +
src/LearningMethods/LevenbergMarquardt.cpp | 23 +-
src/LearningMethods/ParticleSwarm.cpp | 2 -
src/Network/ACSFNeuralNetwork.cpp | 68 +-
src/Network/ACSFNeuralNetwork.h | 12 +
src/Reader/Reader.cpp | 4 +-
src/Reader/XYZReader.cpp | 35 +-
src/Reader/XYZReader.h | 1 +
src/examples/CMakeLists.txt | 82 +-
src/examples/acsf.cpp | 310 ++-
src/examples/dev_sandbox.cpp | 3 +-
src/examples/net_test_1.cpp | 2 +-
src/examples/net_test_2.cpp | 2 +-
src/examples/net_test_3.cpp | 2 +-
src/examples/net_test_harmonic_oscilator.cpp | 2 +-
src/examples/net_test_ode_1.cpp | 2 +-
src/examples/net_test_pde_1.cpp | 2 +-
src/examples/network_serialization.cpp | 2 +-
src/examples/seminar.cpp | 3 +-
src/examples/x2_fitting.cpp | 2 +-
31 files changed, 756 insertions(+), 2251 deletions(-)
delete mode 100644 data/HE21+T1.xyz
create mode 100755 run_devsandbox_queue.sh
create mode 100755 run_test.sh
create mode 100644 src/LearningMethods/LazyLearning.cpp
create mode 100644 src/LearningMethods/LazyLearning.h
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index ecbcae4e..207f13ad 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -27,24 +27,24 @@ win_visual_studio_static_local_deps:
# Windows 10 with dependencies
# downloaded (and compiled) locally
# compiled by VisualStudio 2015
-#win_visual_studio_2015_static_local_deps:
-# tags:
-# - Win
-#
-# image: windows:latest
-#
-# stage: build
-#
-# before_script:
-# - rmdir /s /q build external_dependencies/*
-# - set DEPENDENCIES_LINK_TYPE=static
-# - set clean_after=yes
-# - set BUILD_LIB=yes
-# - set BUILD_EXAMPLES=yes
-# - set BUILD_TESTS=yes
-#
-# script:
-# - call build_scripts\windows\win_VS2015_build_x64_release.bat
+win_visual_studio_2015_static_local_deps:
+ tags:
+ - Win
+
+ image: windows:latest
+
+ stage: build
+
+ before_script:
+ - rmdir /s /q build external_dependencies/*
+ - set DEPENDENCIES_LINK_TYPE=static
+ - set clean_after=yes
+ - set BUILD_LIB=yes
+ - set BUILD_EXAMPLES=yes
+ - set BUILD_TESTS=yes
+
+ script:
+ - call build_scripts\windows\win_VS2015_build_x64_release.bat
# Latest Ubuntu with dependencies
# in system directories, Boost
diff --git a/data/HE21+T1.xyz b/data/HE21+T1.xyz
deleted file mode 100644
index 35b655f9..00000000
--- a/data/HE21+T1.xyz
+++ /dev/null
@@ -1,2002 +0,0 @@
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- He 0.81896019435208400 1.4578449458967699 3.7236308080559000
- He 1.4356058808723999 1.1298999570181301 -1.1471334150994299
- He 1.2654913465730799 -3.8829488930113700 -0.86102158259004302
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- He 6.9026709104060302 1.0190507989369899 2.5929640793295698
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- He -2.8871824532075401 2.9703972749583598 -3.9849411907642902
- He 3.7203569213073600 -1.7399006972005899 -5.5215860554567602
- He -2.7885198181766500 -0.57975834410391103 7.8139615371945101
- He 4.4855685508904797 5.6593257357309197 1.1499136325694601
- He -0.77996128504670603 6.0461080498250599 0.11375264572681600
- He -6.0310806480563199 6.4576582568612801 -1.4425437838363500
diff --git a/run_devsandbox_queue.sh b/run_devsandbox_queue.sh
new file mode 100755
index 00000000..636e56f0
--- /dev/null
+++ b/run_devsandbox_queue.sh
@@ -0,0 +1,17 @@
+#!/bin/sh
+#PBS -N STDIN
+#PBS -l select=1:ncpus=24:mpiprocs=1:ompthreads=24
+#PBS -l walltime=08:00:00
+#PBS -q qprod
+#PBS -A OPEN-15-10
+
+
+cd $PBS_O_WORKDIR/
+source ./build_scripts/load_salomon_modules.inc
+cd ./build/examples
+./dev_sandbox > out.txt
+cd ../..
+
+
+
+
diff --git a/run_test.sh b/run_test.sh
new file mode 100755
index 00000000..89b560fb
--- /dev/null
+++ b/run_test.sh
@@ -0,0 +1,2 @@
+#!/bin/sh
+./build_scripts/compile_salomon.sh && ( source ./build_scripts/load_salomon_modules.inc ; cd ./build/examples; ./dev_sandbox; cd ../.. )
diff --git a/src/Coordinates/coordinates.cpp b/src/Coordinates/coordinates.cpp
index 2f0e59cd..180dce61 100644
--- a/src/Coordinates/coordinates.cpp
+++ b/src/Coordinates/coordinates.cpp
@@ -2,6 +2,8 @@
// Created by martin on 30.08.19.
//
+#define _USE_MATH_DEFINES
+
#include <vector>
#include <cmath>
#include "coordinates.h"
diff --git a/src/DataSet/DataSet.cpp b/src/DataSet/DataSet.cpp
index e65a67e0..8bf39621 100644
--- a/src/DataSet/DataSet.cpp
+++ b/src/DataSet/DataSet.cpp
@@ -1,9 +1,13 @@
#include <algorithm>
+
+#include <armadillo>
#include <boost/serialization/export.hpp>
#include "DataSetSerialization.h"
#include "exceptions.h"
+#include "DataSet.h"
+
BOOST_CLASS_EXPORT_IMPLEMENT(lib4neuro::DataSet);
@@ -34,7 +38,6 @@ namespace lib4neuro {
}
this->normalization_strategy = std::make_shared<DoubleUnitStrategy>(DoubleUnitStrategy());
-
}
DataSet::DataSet(std::vector<std::pair<std::vector<double>, std::vector<double>>>* data_ptr,
@@ -497,4 +500,27 @@ namespace lib4neuro {
}
this->output_dim += n_columns;
}
+
+ arma::Mat<double>* DataSet::get_inputs_matrix() {
+ this->inputs_matrix = new arma::Mat<double>(this->data.size(), this->data.at(0).first.size());
+// arma::Mat<double> m(this->data.size(), this->data.at(0).first.size());
+
+ for (size_t i = 0; i < this->data.size(); i++) {
+ this->inputs_matrix->row(i) = arma::Row<double>(this->data.at(i).first);
+ }
+
+// this->inputs_matrix = &m;
+ return this->inputs_matrix;
+ }
+
+ arma::Mat<double>* DataSet::get_outputs_matrix() {
+ this->outputs_matrix = new arma::Mat<double>(this->data.size(), this->data.at(0).second.size());
+
+ for(size_t i = 0; i < this->data.size(); i++) {
+ this->outputs_matrix->row(i) = arma::Row<double>(this->data.at(i).second);
+ }
+
+// this->outputs_matrix = &m;
+ return this->outputs_matrix;
+ }
}
diff --git a/src/DataSet/DataSet.h b/src/DataSet/DataSet.h
index f253d9da..0989bddc 100644
--- a/src/DataSet/DataSet.h
+++ b/src/DataSet/DataSet.h
@@ -14,6 +14,11 @@
#include "../settings.h"
#include "../NormalizationStrategy/NormalizationStrategy.h"
+/* Forward declaration or arma::Mat<> type */
+namespace arma {
+ template<class T>
+ class Mat;
+}
namespace lib4neuro {
/**
@@ -68,6 +73,11 @@ namespace lib4neuro {
//TODO let user choose in the constructor!
std::shared_ptr<NormalizationStrategy> normalization_strategy;
+ arma::Mat<double>* inputs_matrix;
+
+ arma::Mat<double>* outputs_matrix;
+
+ unsigned int last_batch_vector_ind = 0;
public:
@@ -306,14 +316,32 @@ namespace lib4neuro {
* @param max
* @return
*/
+ [[deprecated("get_next_random_data_batch() should be used instead")]]
LIB4NEURO_API std::vector<std::pair<std::vector<double>, std::vector<double>>>
get_random_data_batch(size_t max);
+ /**
+ * Function, that returns being&end iterators of the next random disjunct batch of DataSet.
+ * Randomness is given by copying the current DataSet, shuffling it once by Fisher-Yates algorithm and then
+ * selecting first 'max' elements, which weren't selected before.
+ *
+ * If the shuffled vector is depleted, the process repeats with copying the DataSet again and another random
+ * shuffle...
+ *
+ * @param max
+ * @return
+ */
+ LIB4NEURO_API std::vector<std::pair<std::vector<double>, std::vector<double>>> get_next_data_batch(size_t max);
+
/**
* Adds a new output column filled with zeros
* @param n_columns Number of columns to be inserted
*/
LIB4NEURO_API void add_zero_output_columns(size_t n_columns);
+
+ [[nodiscard]] LIB4NEURO_API arma::Mat<double>* get_inputs_matrix();
+
+ [[nodiscard]] LIB4NEURO_API arma::Mat<double>* get_outputs_matrix();
};
}
#endif //INC_4NEURO_DATASET_H
diff --git a/src/ErrorFunction/ErrorFunctions.cpp b/src/ErrorFunction/ErrorFunctions.cpp
index 45139360..8b659ffe 100644
--- a/src/ErrorFunction/ErrorFunctions.cpp
+++ b/src/ErrorFunction/ErrorFunctions.cpp
@@ -1,5 +1,6 @@
#include <vector>
+#include <utility>
#include <cmath>
#include <sstream>
#include <boost/random/mersenne_twister.hpp>
@@ -15,7 +16,19 @@ namespace lib4neuro {
return this->dimension;
}
- void MSE::divide_data_train_test(double percent_test) {
+ std::vector<NeuralNetwork*>& ErrorFunction::get_nets() {
+ return nets;
+ }
+
+ DataSet* ErrorFunction::get_dataset() const {
+ return ds;
+ }
+
+ void ErrorFunction::set_dataset(DataSet* ds) {
+ this->ds = ds;
+ }
+
+ void ErrorFunction::divide_data_train_test(double percent_test) {
size_t ds_size = this->ds->get_n_elements();
/* Store the full data set */
@@ -30,7 +43,7 @@ namespace lib4neuro {
std::vector<unsigned int> test_indices;
test_indices.reserve(test_set_size);
- for (unsigned int i = 0; i < test_set_size; i++) {
+ for (size_t i = 0; i < test_set_size; i++) {
test_indices.emplace_back(dist(gen));
}
std::sort(test_indices.begin(),
@@ -47,6 +60,8 @@ namespace lib4neuro {
/* Move the testing data from train_data to test_data */
for (auto ind : test_indices) {
test_data.emplace_back(train_data.at(ind));
+ }
+ for(auto ind : test_indices) {
train_data.erase(train_data.begin() + ind);
}
@@ -59,9 +74,67 @@ namespace lib4neuro {
this->ds_full->get_normalization_strategy());
}
- void MSE::return_full_data_set_for_training() {
- if (this->ds_test) {
+ size_t ErrorFunction::divide_data_worst_subset(
+ std::vector<size_t> &subset_indices,
+ std::vector<bool> &active_subset,
+ std::vector<float> &entry_errors
+ ) {
+ if( this->ds_full == nullptr ){
+ this->ds_full = this->ds;
+ }
+ size_t ds_size = this->ds_full->get_n_elements();
+
+ if( entry_errors.size() != ds_size ){
+ entry_errors.resize( ds_size );
+ }
+
+ if( active_subset.size() != ds_size ){
+ active_subset.resize( ds_size );
+ std::fill(active_subset.begin(), active_subset.end(), false);
+ }
+
+ std::vector<double> error_vector( this->get_n_outputs());
+ for( size_t i = 0; i < ds_size; ++i ) {
+ entry_errors[ i ] = this->eval_single_item_by_idx( i, nullptr, error_vector );
+ }
+
+ std::vector<std::pair<std::vector<double>, std::vector<double>>> train_set;
+ double max_error = -1.0;
+ size_t max_error_entry_idx = 0;
+ for( size_t i = 0; i < ds_size; ++i ){
+ if( active_subset[ i ] ){
+ continue;
+ }
+
+ if( entry_errors[ i ] > max_error ){
+ max_error = entry_errors[ i ];
+ max_error_entry_idx = i;
+ }
+ }
+ if( max_error >= 0.0 ){
+ subset_indices.push_back( max_error_entry_idx );
+ active_subset[max_error_entry_idx] = true;
+ }
+
+ for( auto el: subset_indices ){
+ train_set.emplace_back( this->ds_full->get_data( )->at( el ) );
+ }
+
+ if( this->ds != this->ds_full ){
+ delete this->ds;
+ }
+ this->ds = new DataSet(&train_set,
+ this->ds_full->get_normalization_strategy());
+
+ return train_set.size( );
+
+ }
+
+ void ErrorFunction::return_full_data_set_for_training() {
+ if (this->ds_test || this->ds != this->ds_full) {
+// delete this->ds;
this->ds = this->ds_full;
+ this->ds_full = nullptr;
}
}
@@ -95,10 +168,13 @@ namespace lib4neuro {
}
MSE::MSE(NeuralNetwork* net,
- DataSet* ds) {
+ DataSet* ds,
+ bool rescale_error
+ ) {
this->nets.push_back(net);
this->ds = ds;
this->dimension = net->get_n_weights() + net->get_n_biases();
+ this->rescale_error = rescale_error;
}
double MSE::eval_on_single_input(std::vector<double>* input,
@@ -116,7 +192,7 @@ namespace lib4neuro {
result += val * val;
}
- return std::sqrt(result);
+ return result;
}
double MSE::eval_on_data_set(lib4neuro::DataSet* data_set,
@@ -161,7 +237,6 @@ namespace lib4neuro {
this->nets[0]->eval_single(data->at(i).first,
output,
weights);
-
outputs.at(i) = output;
}
@@ -200,6 +275,8 @@ namespace lib4neuro {
output_norm += denormalized_output * denormalized_output;
}
+// std::cout << " entry #" << i+1 << ", error: " << loc_error << std::endl;
+
std::stringstream ss_ind;
ss_ind << "[" << i << "]";
@@ -210,9 +287,9 @@ namespace lib4neuro {
<< R_ALIGN << ss_input.str() << " "
<< R_ALIGN << ss_real_output.str() << " "
<< R_ALIGN << ss_predicted_output.str() << " "
- << R_ALIGN << std::sqrt(loc_error) << " "
+ << R_ALIGN << loc_error << " "
<< R_ALIGN
- << 200.0 * std::sqrt(loc_error) / (std::sqrt(loc_error) + std::sqrt(output_norm))
+ << 200.0 * loc_error / (loc_error + output_norm)
<< std::endl);
}
@@ -223,14 +300,14 @@ namespace lib4neuro {
<< R_ALIGN << ss_input.str() << " "
<< R_ALIGN << ss_real_output.str() << " "
<< R_ALIGN << ss_predicted_output.str() << " "
- << R_ALIGN << std::sqrt(loc_error) << " "
+ << R_ALIGN << loc_error << " "
<< R_ALIGN
- << 200.0 * std::sqrt(loc_error) / (std::sqrt(loc_error) + std::sqrt(output_norm))
+ << 200.0 * loc_error / (loc_error + output_norm)
<< std::endl;
}
}
- double result = std::sqrt(error) / n_elements;
+ double result = error / (this->rescale_error?n_elements:1.0);
if (verbose) {
COUT_DEBUG("MSE = " << result << std::endl);
@@ -335,7 +412,7 @@ namespace lib4neuro {
this->nets[0]->add_to_gradient_single(el.first,
error_derivative,
- alpha / n_elements,
+ alpha / (this->rescale_error?n_elements:1.0),
grad);
}
}
@@ -503,7 +580,7 @@ namespace lib4neuro {
2.0 * (error_vector.at(j) - this->ds->get_data()->at(i).second.at(j)); //real - expected result
}
- return sqrt(output);
+ return output;
}
@@ -797,6 +874,18 @@ namespace lib4neuro {
}
}
+ size_t ErrorSum::divide_data_worst_subset(
+ std::vector<size_t> &subset_indices,
+ std::vector<bool> &active_subset,
+ std::vector<float> &entry_errors
+ ) {
+ size_t output = 0;
+ assert( false );
+ return output;
+ }
+
+
+
void ErrorSum::return_full_data_set_for_training() {
for (auto n: *this->summand) {
n->return_full_data_set_for_training();
diff --git a/src/ErrorFunction/ErrorFunctions.h b/src/ErrorFunction/ErrorFunctions.h
index f15e10d2..efbd2cc3 100644
--- a/src/ErrorFunction/ErrorFunctions.h
+++ b/src/ErrorFunction/ErrorFunctions.h
@@ -106,12 +106,23 @@ namespace lib4neuro {
* @param percent_train
* @return
*/
- virtual void divide_data_train_test(double percent_test) = 0;
+ LIB4NEURO_API virtual void divide_data_train_test(double percent_test);
+
+ /**
+ *
+ * @param percent_train
+ * @return
+ */
+ LIB4NEURO_API virtual size_t divide_data_worst_subset(
+ std::vector<size_t> &subset_indices,
+ std::vector<bool> &active_subset,
+ std::vector<float> &entry_errors
+ );
/**
*
*/
- virtual void return_full_data_set_for_training() = 0;
+ LIB4NEURO_API virtual void return_full_data_set_for_training();
/**
*
@@ -232,6 +243,12 @@ namespace lib4neuro {
*/
virtual void randomize_parameters(double scaling) = 0;
+ [[nodiscard]] std::vector<NeuralNetwork*>& get_nets();
+
+ [[nodiscard]] DataSet* get_dataset() const;
+
+ void set_dataset(DataSet* ds);
+
protected:
/**
@@ -262,27 +279,21 @@ namespace lib4neuro {
class MSE : public ErrorFunction {
+ private:
+ bool rescale_error;
public:
/**
* Constructor for single neural network
* @param net
* @param ds
+ * @param rescale_error if true, the means square error is used, if false, the absolute value of the error is used
*/
LIB4NEURO_API MSE(NeuralNetwork* net,
- DataSet* ds);
+ DataSet* ds,
+ bool rescale_error = true
+ );
- /**
- *
- * @param percent_train
- * @return
- */
- LIB4NEURO_API virtual void divide_data_train_test(double percent_test) override;
-
- /**
- *
- */
- LIB4NEURO_API virtual void return_full_data_set_for_training() override;
/**
*
@@ -667,9 +678,21 @@ namespace lib4neuro {
/**
*
- * @param percent
+ * @param percent_train
+ * @return
+ */
+ LIB4NEURO_API virtual void divide_data_train_test(double percent_test) override;
+
+ /**
+ *
+ * @param percent_train
+ * @return
*/
- LIB4NEURO_API virtual void divide_data_train_test(double percent) override;
+ LIB4NEURO_API virtual size_t divide_data_worst_subset(
+ std::vector<size_t> &subset_indices,
+ std::vector<bool> &active_subset,
+ std::vector<float> &entry_errors
+ ) override;
/**
*
diff --git a/src/LearningMethods/GradientDescentBB.cpp b/src/LearningMethods/GradientDescentBB.cpp
index 971d15ef..09fda2e1 100644
--- a/src/LearningMethods/GradientDescentBB.cpp
+++ b/src/LearningMethods/GradientDescentBB.cpp
@@ -19,9 +19,7 @@ namespace lib4neuro {
this->batch = batch;
}
- GradientDescentBB::~GradientDescentBB() {
- }
-
+ GradientDescentBB::~GradientDescentBB() {}
void GradientDescentBB::optimize(lib4neuro::ErrorFunction& ef,
std::ofstream* ofs) {
@@ -176,7 +174,7 @@ namespace lib4neuro {
if (iter_idx == 0) {
- COUT_INFO(std::endl << "Maximum number of iterations (" << this->maximum_niters
+ COUT_INFO("Maximum number of iterations (" << this->maximum_niters
<< ") was reached! Final error: " << val_best << std::endl);
if (ofs && ofs->is_open()) {
@@ -186,7 +184,7 @@ namespace lib4neuro {
}
} else {
- COUT_INFO(std::endl << "Gradient Descent method converged after "
+ COUT_INFO("Gradient Descent method converged after "
<< this->maximum_niters - iter_idx
<< " iterations. Final error:" << val_best
<< std::endl);
diff --git a/src/LearningMethods/LazyLearning.cpp b/src/LearningMethods/LazyLearning.cpp
new file mode 100644
index 00000000..b04d666d
--- /dev/null
+++ b/src/LearningMethods/LazyLearning.cpp
@@ -0,0 +1,93 @@
+/**
+ * DESCRIPTION OF THE FILE
+ *
+ * @author Michal KravÄenko
+ * @date 30.7.18 -
+ */
+
+#include <random.hpp>
+#include <limits>
+
+#include "message.h"
+
+#include "LazyLearning.h"
+
+namespace lib4neuro {
+ LazyLearning::LazyLearning(
+ LearningMethod &inner_trainer,
+ double tol
+ ){
+ this->inner_method = &inner_trainer;
+ this->tolerance = tol;
+ }
+
+ LazyLearning::~LazyLearning( ) {}
+
+ void LazyLearning::optimize(
+ lib4neuro::ErrorFunction& ef,
+ std::ofstream* ofs
+ ) {
+
+ std::vector<size_t> subset_indices;
+ std::vector<bool> active_subset;
+ std::vector<float> entry_errors;
+ while( true ){
+
+ ef.divide_data_worst_subset( subset_indices, active_subset, entry_errors );
+ /* errors of the active subset */
+ float subset_error_min = std::numeric_limits<float>::max();
+ float subset_error_max = 0.0;
+ float subset_error_total = 0.0;
+ size_t new_subset_index = subset_indices[subset_indices.size() - 1];
+ float new_subset_entry_error = entry_errors[new_subset_index];
+
+
+ /* errors of the elements not considered */
+ float shelved_error_min = subset_error_min;
+ float shelved_error_max = 0.0;
+ float shelved_error_total = 0.0;
+
+ /* processing of the errors */
+ for( size_t i = 0; i < entry_errors.size(); ++i){
+ if( active_subset[ i ] ){
+ if( i == new_subset_index ){
+
+ }
+ else{
+ subset_error_total += entry_errors[ i ];
+ subset_error_max = std::max(subset_error_max, entry_errors[ i ] );
+ subset_error_min = std::min(subset_error_min, entry_errors[ i ] );
+ }
+ }
+ else{
+ shelved_error_total += entry_errors[ i ];
+ shelved_error_max = std::max(shelved_error_max, entry_errors[ i ] );
+ shelved_error_min = std::min(shelved_error_min, entry_errors[ i ] );
+ }
+ }
+
+ if( subset_indices.size() > 1 ){
+ COUT_INFO( "[" << subset_indices.size() << "] subset error: " << subset_error_total << ", in range: " << subset_error_min << " - " << subset_error_max << ", new entry error: " << new_subset_entry_error );
+ }
+ else{
+ COUT_INFO( "[" << subset_indices.size() << "] new entry error: " << new_subset_entry_error );
+ }
+ COUT_INFO( "[" << active_subset.size() - subset_indices.size() << "] remaining error: " << shelved_error_total << ", in range: " << shelved_error_min << " - " << shelved_error_max << std::endl );
+
+ if( shelved_error_max < this->tolerance && subset_error_max < this->tolerance && new_subset_entry_error < this->tolerance ){
+ break;
+ }
+
+ this->inner_method->optimize( ef, ofs );
+
+ double sub_error_after = ef.eval( );
+ while( sub_error_after > this->tolerance ){
+ this->inner_method->optimize( ef, ofs );
+ sub_error_after = ef.eval( );
+ }
+ ef.return_full_data_set_for_training( );
+ COUT_INFO( "------------------------" );
+ }
+ }
+
+}//end of namespace lib4neuro
diff --git a/src/LearningMethods/LazyLearning.h b/src/LearningMethods/LazyLearning.h
new file mode 100644
index 00000000..9d5dec49
--- /dev/null
+++ b/src/LearningMethods/LazyLearning.h
@@ -0,0 +1,60 @@
+/**
+ * DESCRIPTION OF THE FILE
+ *
+ * @author Michal KravÄenko
+ * @date 18.09.19 -
+ */
+
+#ifndef INC_4NEURO_LAZYLEARNING_H
+#define INC_4NEURO_LAZYLEARNING_H
+
+#include <memory>
+#include "../settings.h"
+#include "../constants.h"
+#include "LearningMethod.h"
+#include "../ErrorFunction/ErrorFunctions.h"
+
+namespace lib4neuro {
+ /**
+ *
+ */
+ class LazyLearning : public LearningMethod {
+
+ private:
+ size_t subset_size;
+
+ LearningMethod *inner_method;
+
+ double tolerance;
+
+ public:
+
+ /**
+ * Creates an instance of Lazy Learning Optimizer
+ * it trains the network iteratively using chunks of the train data which cause
+ * the highest error
+ * @param inner_trainer method used for individual optimizations
+ * @param subset_size number of data entries to be propagated in one batch
+ * @param tolerance desired accuracy
+ */
+ LIB4NEURO_API explicit LazyLearning(
+ LearningMethod &inner_trainer,
+ double tol = 1e-6
+ );
+
+ /**
+ * Deallocates the instance
+ */
+ LIB4NEURO_API ~LazyLearning();
+
+ /**
+ *
+ * @param ef
+ */
+ LIB4NEURO_API void optimize(lib4neuro::ErrorFunction& ef,
+ std::ofstream* ofs = nullptr) override;
+
+ };
+}
+
+#endif //INC_4NEURO_GRADIENTDESCENT_H
diff --git a/src/LearningMethods/LevenbergMarquardt.cpp b/src/LearningMethods/LevenbergMarquardt.cpp
index 394dddd1..7a617a07 100644
--- a/src/LearningMethods/LevenbergMarquardt.cpp
+++ b/src/LearningMethods/LevenbergMarquardt.cpp
@@ -1,3 +1,4 @@
+#define ARMA_ALLOW_FAKE_GCC
#include <armadillo>
#include "LevenbergMarquardt.h"
@@ -121,7 +122,7 @@ namespace lib4neuro {
double current_err = ef.eval();
COUT_INFO(
- "Finding a solution via a Levenberg-Marquardt method... Starting error: " << current_err << std::endl);
+ "Finding a solution via the Levenberg-Marquardt method... Starting error: " << current_err << std::endl);
if (ofs && ofs->is_open()) {
*ofs << "Finding a solution via a Levenberg-Marquardt method... Starting error: " << current_err
<< std::endl;
@@ -159,8 +160,8 @@ namespace lib4neuro {
//-------------------//
size_t iter_counter = 0;
do {
- COUT_INFO("Iteration: " << iter_counter << " Current error: " << current_err << ", Current gradient norm: "
- << gradient_norm << ", Direction norm: " << update_norm << "\r");
+// COUT_INFO("Iteration: " << iter_counter << " Current error: " << current_err << ", Current gradient norm: "
+// << gradient_norm << ", Direction norm: " << update_norm << "\r");
if (update_J) {
/* Get Jacobian matrix */
@@ -218,11 +219,6 @@ namespace lib4neuro {
/* Check, if the parameter update improved the function */
if (current_err < prev_err) {
- /* If the convergence threshold is achieved, finish the computation */
- if (current_err < this->p_impl->tolerance) {
- break;
- }
-
/* If the error is lowered after parameter update, accept the new parameters and lower the damping
* factor lambda */
@@ -236,6 +232,10 @@ namespace lib4neuro {
prev_err = current_err;
update_J = true;
+ /* If the convergence threshold is achieved, finish the computation */
+ if (current_err < this->p_impl->tolerance) {
+ break;
+ }
} else {
/* If the error after parameters update is not lower, increase the damping factor lambda */
@@ -244,13 +244,13 @@ namespace lib4neuro {
}
// COUT_DEBUG("Iteration: " << iter_counter << " Current error: " << current_err << ", Current gradient norm: "
// << gradient_norm << ", Direction norm: " << update_norm << "\r");
-
+
if (ofs && ofs->is_open()) {
*ofs << "Iteration: " << iter_counter << " Current error: " << current_err << ", Current gradient norm: "
<< gradient_norm << ", Direction norm: " << update_norm << std::endl;
}
- } while (iter_counter++ < this->p_impl->maximum_niters && (update_norm > this->p_impl->tolerance));
+ } while (iter_counter++ < this->p_impl->maximum_niters && (update_norm > this->p_impl->tolerance_parameters));
COUT_DEBUG("Iteration: " << iter_counter << " Current error: " << current_err << ", Current gradient norm: "
<< gradient_norm << ", Direction norm: " << update_norm << std::endl);
if (ofs && ofs->is_open()) {
@@ -269,8 +269,7 @@ namespace lib4neuro {
ef.set_parameters(this->optimal_parameters);
- COUT_INFO("Finished in " << iter_counter << " iterations with error: " << current_err << " and gradient norm: " << gradient_norm << std::endl);
-
+ COUT_INFO("Finished in " << iter_counter << " iterations with error: " << current_err << " and gradient norm: " << gradient_norm << " and update norm: " << update_norm << std::endl);
}
LevenbergMarquardt::~LevenbergMarquardt() = default;
diff --git a/src/LearningMethods/ParticleSwarm.cpp b/src/LearningMethods/ParticleSwarm.cpp
index c8528554..7ba249b2 100644
--- a/src/LearningMethods/ParticleSwarm.cpp
+++ b/src/LearningMethods/ParticleSwarm.cpp
@@ -292,8 +292,6 @@ namespace lib4neuro {
*/
void ParticleSwarm::optimize(lib4neuro::ErrorFunction& ef,
std::ofstream* ofs) {
- //TODO add output to the 'ofs'
-
COUT_INFO("Finding optima via Globalized Particle Swarm method..." << std::endl);
if (ofs && ofs->is_open()) {
*ofs << "Finding optima via Globalized Particle Swarm method..." << std::endl;
diff --git a/src/Network/ACSFNeuralNetwork.cpp b/src/Network/ACSFNeuralNetwork.cpp
index 08d8b42f..9ab44255 100644
--- a/src/Network/ACSFNeuralNetwork.cpp
+++ b/src/Network/ACSFNeuralNetwork.cpp
@@ -2,14 +2,33 @@
// Created by martin on 19.08.19.
//
+#include <string>
+
+#include "../exceptions.h"
#include "../settings.h"
#include "ACSFNeuralNetwork.h"
+#include "../ErrorFunction/ErrorFunctions.h"
lib4neuro::ACSFNeuralNetwork::ACSFNeuralNetwork(std::unordered_map<ELEMENT_SYMBOL, Element*>& elements,
std::vector<ELEMENT_SYMBOL>& elements_list,
bool with_charge,
std::unordered_map<ELEMENT_SYMBOL, std::vector<unsigned int>> n_hidden_neurons,
std::unordered_map<ELEMENT_SYMBOL, std::vector<NEURON_TYPE>> type_hidden_neurons) {
+
+ /* Check parameters */
+ for(auto symbol : elements_list) {
+ if(n_hidden_neurons[symbol].size() != type_hidden_neurons[symbol].size()) {
+ THROW_RUNTIME_ERROR("Number of hidden layers for " + elements[symbol]->getElementSymbol() + " ("
+ + std::to_string(n_hidden_neurons[symbol].size())
+ + ") doesn't correspond with a number of hidden neuron types ("
+ + std::to_string(type_hidden_neurons[symbol].size()) + ")!");
+ }
+ }
+
+ /* Save info about elements */
+ this->elements = &elements;
+ this->elements_list = &elements_list;
+
/* Construct the neural network */
std::vector<size_t> inputs;
@@ -32,7 +51,7 @@ lib4neuro::ACSFNeuralNetwork::ACSFNeuralNetwork(std::unordered_map<ELEMENT_SYMBO
/* Create input neurons for sub-net */
std::shared_ptr<NeuronLinear> inp_n;
- for(size_t j = 0; j < elements[elements_list.at(i)]->getSymmetryFunctions().size(); j++) {
+ for(size_t j = 0; j < elements[elements_list.at(i)]->getSymmetryFunctions()->size(); j++) {
inp_n = std::make_shared<NeuronLinear>();
last_neuron_idx = this->add_neuron(inp_n, BIAS_TYPE::NO_BIAS);
previous_layer.emplace_back(last_neuron_idx);
@@ -122,52 +141,21 @@ lib4neuro::ACSFNeuralNetwork::ACSFNeuralNetwork(std::unordered_map<ELEMENT_SYMBO
new_layer.clear();
}
- /* Create hidden layers in sub-net */
-// std::vector<unsigned int> n_neurons = n_hidden_neurons[elements_list.at(i)];
-// std::vector<NEURON_TYPE> types = type_hidden_neurons[elements_list.at(i)];
-// for(size_t j = 0; j < n_neurons.size(); j++) { /* Iterate over hidden layers */
-// /* Create hidden neurons */
-// for(size_t k = 0; k < n_neurons.at(j); k++) {
-// std::shared_ptr<Neuron> hid_n;
-// switch(types.at(j)) {
-// case NEURON_TYPE::LOGISTIC: {
-// hid_n = std::make_shared<NeuronLogistic>();
-// break;
-// }
-// case NEURON_TYPE::BINARY: {
-// hid_n = std::make_shared<NeuronBinary>();
-// break;
-// }
-// case NEURON_TYPE::CONSTANT: {
-// hid_n = std::make_shared<NeuronConstant>();
-// break;
-// }
-// case NEURON_TYPE::LINEAR: {
-// hid_n = std::make_shared<NeuronLinear>();
-// break;
-// }
-// }
-//
-// neuron_idx = this->add_neuron(hid_n, BIAS_TYPE::NEXT_BIAS);
-// new_layer.emplace_back(neuron_idx);
-//
-// /* Connect hidden neuron to the previous layer */
-// for(auto prev_n : previous_layer) {
-// this->add_connection_simple(prev_n, neuron_idx, SIMPLE_CONNECTION_TYPE::NEXT_WEIGHT);
-// }
-// previous_layer = new_layer;
-// new_layer.clear();
-// }
-// }
-
/* Create output neurons for sub-net */
for(auto prev_n : previous_layer) {
this->add_connection_constant(prev_n, outputs[ 0 ], 1.0);
}
-
}
/* Specify network inputs and outputs */
this->specify_input_neurons(inputs);
this->specify_output_neurons(outputs);
}
+
+std::unordered_map<lib4neuro::ELEMENT_SYMBOL, lib4neuro::Element*>* lib4neuro::ACSFNeuralNetwork::get_elements() {
+ return this->elements;
+}
+
+std::vector<lib4neuro::ELEMENT_SYMBOL>* lib4neuro::ACSFNeuralNetwork::get_elements_list() {
+ return this->elements_list;
+}
diff --git a/src/Network/ACSFNeuralNetwork.h b/src/Network/ACSFNeuralNetwork.h
index 8c9615f4..071c58fb 100644
--- a/src/Network/ACSFNeuralNetwork.h
+++ b/src/Network/ACSFNeuralNetwork.h
@@ -5,17 +5,29 @@
#ifndef LIB4NEURO_ACSFNEURALNETWORK_H
#define LIB4NEURO_ACSFNEURALNETWORK_H
+#include <unordered_map>
+
#include "NeuralNetwork.h"
#include "../DataSet/DataSet.h"
+#include "../SymmetryFunction/SymmetryFunction.h"
namespace lib4neuro {
class ACSFNeuralNetwork : public NeuralNetwork {
+ private:
+ std::unordered_map<ELEMENT_SYMBOL, Element*>* elements;
+
+ std::vector<ELEMENT_SYMBOL>* elements_list;
+
public:
LIB4NEURO_API explicit ACSFNeuralNetwork(std::unordered_map<ELEMENT_SYMBOL, Element*>& elements,
std::vector<ELEMENT_SYMBOL>& elements_list,
bool with_charge,
std::unordered_map<ELEMENT_SYMBOL, std::vector<unsigned int >> n_hidden_neurons,
std::unordered_map<ELEMENT_SYMBOL, std::vector<NEURON_TYPE>> type_hidden_neurons);
+
+ LIB4NEURO_API std::unordered_map<ELEMENT_SYMBOL, Element*>* get_elements();
+
+ LIB4NEURO_API std::vector<ELEMENT_SYMBOL>* get_elements_list();
};
}
diff --git a/src/Reader/Reader.cpp b/src/Reader/Reader.cpp
index 069ca7a1..b0aa9a34 100644
--- a/src/Reader/Reader.cpp
+++ b/src/Reader/Reader.cpp
@@ -32,12 +32,12 @@ void lib4neuro::Reader::read() {
// TODO make filepath relative to an executable directory instead of the current one!
// TODO https://www.boost.org/doc/libs/1_36_0/libs/filesystem/doc/reference.html#initial_path
std::ifstream ifs(this->file_path);
-
+
if ( !ifs.is_open() ) {
// throw std::logic_error("File could not be opened! Possibly due to the lack of access privileges or an incorrect path.\n");
THROW_LOGIC_ERROR( "File could not be opened! Possibly due to the lack of access privileges or an incorrect path.\n" );
}
-
+
std::string line;
if (this->ignore_first_line) {
diff --git a/src/Reader/XYZReader.cpp b/src/Reader/XYZReader.cpp
index aa68f95b..bc989d70 100644
--- a/src/Reader/XYZReader.cpp
+++ b/src/Reader/XYZReader.cpp
@@ -40,6 +40,8 @@ std::shared_ptr<lib4neuro::DataSet> lib4neuro::XYZReader::get_data_set() {
line = this->data.at(1);
this->remove_white_characters_from_vector(line);
n_particles = std::stoul(line.at(0));
+ std::cout << "# of particles in cluster: " << n_particles << std::endl;
+ std::cout << "# of target configurations: " << n_configurations << std::endl;
unsigned int situation;
@@ -54,6 +56,16 @@ std::shared_ptr<lib4neuro::DataSet> lib4neuro::XYZReader::get_data_set() {
if (situation == 1) {
data_set_contents.emplace_back(std::make_pair(inputs,
outputs));
+// std::cout << "[" << data_set_contents.size() << "] inputs: ";
+// for( auto el: inputs ){
+// std::cout << el << ", ";
+// }
+// std::cout << "outputs: ";
+// for( auto el: outputs ){
+// std::cout << el << ", ";
+// }
+// std::cout << std::endl;
+
inputs.clear();
outputs.clear();
} else if (situation == 2) {
@@ -76,16 +88,14 @@ std::shared_ptr<lib4neuro::DataSet> lib4neuro::XYZReader::get_data_set() {
inputs.emplace_back(std::stod(line.at(i)));
}
- if (data_ind == this->data.size() - 1) {
- data_set_contents.emplace_back(std::make_pair(inputs,
- outputs));
- inputs.clear();
- outputs.clear();
- }
}
}
+ data_set_contents.emplace_back(std::make_pair(inputs, outputs));
+ inputs.clear();
+ outputs.clear();
this->data_set = std::make_shared<DataSet>(DataSet(&data_set_contents));
+ std::cout << "# number of clusters: " << this->data_set->get_n_elements( ) << std::endl;
/* Compute number of particles per element */
for(auto e : *this->element_list) {
@@ -130,6 +140,7 @@ void lib4neuro::XYZReader::transform_input_to_acsf(std::unordered_map<ELEMENT_SY
cartesian_coords.resize(3);
unsigned int idx;
+ this->acsf_data_set = std::make_shared<DataSet>();
for(auto configuration : *data) { /* Iterate over configurations */
idx = 0;
particles.clear();
@@ -150,7 +161,7 @@ void lib4neuro::XYZReader::transform_input_to_acsf(std::unordered_map<ELEMENT_SY
double coord_val;
for(size_t i = 0; i < particles.size(); i++) { /* Iterate over all the particles */
single_coords_check.clear();
- for (auto sym_func : element_description[particles.at(i).first]->getSymmetryFunctions()) {
+ for (auto sym_func : *element_description[particles.at(i).first]->getSymmetryFunctions()) {
coord_val = sym_func->eval(i, particles);
acsf_coords.emplace_back(coord_val);
single_coords_check.emplace_back(coord_val);
@@ -180,9 +191,9 @@ void lib4neuro::XYZReader::transform_input_to_acsf(std::unordered_map<ELEMENT_SY
THROW_RUNTIME_ERROR("Not all descriptors are unique with currently specified symmetry functions!");
}
- if(this->acsf_data_set == nullptr) {
- this->acsf_data_set = std::make_shared<DataSet>();
- }
+// if(this->acsf_data_set == nullptr) {
+// this->acsf_data_set = std::make_shared<DataSet>();
+// }
this->acsf_data_set->add_data_pair(acsf_coords, configuration.second);
acsf_coords.clear();
@@ -208,9 +219,9 @@ lib4neuro::ELEMENT_SYMBOL lib4neuro::XYZReader::get_element_symbol(std::string s
std::shared_ptr<lib4neuro::DataSet>
lib4neuro::XYZReader::get_acsf_data_set(std::unordered_map<ELEMENT_SYMBOL, Element*>& element_description) {
- if(acsf_data_set == nullptr) {
+// if(acsf_data_set == nullptr) {
this->transform_input_to_acsf(element_description);
- }
+// }
return this->acsf_data_set;
}
diff --git a/src/Reader/XYZReader.h b/src/Reader/XYZReader.h
index b6eab886..c0774184 100644
--- a/src/Reader/XYZReader.h
+++ b/src/Reader/XYZReader.h
@@ -9,6 +9,7 @@
#include <vector>
#include <memory>
#include <algorithm>
+#include <unordered_map>
#include "../settings.h"
#include "../DataSet/DataSet.h"
diff --git a/src/examples/CMakeLists.txt b/src/examples/CMakeLists.txt
index 81fa34e2..8dc0822c 100644
--- a/src/examples/CMakeLists.txt
+++ b/src/examples/CMakeLists.txt
@@ -2,57 +2,53 @@
# EXAMPLES #
############
-ADD_EXECUTABLE(seminar seminar.cpp)
-TARGET_LINK_LIBRARIES(seminar PUBLIC lib4neuro)
+#ADD_EXECUTABLE(seminar seminar.cpp)
+#TARGET_LINK_LIBRARIES(seminar PUBLIC lib4neuro)
ADD_EXECUTABLE(dev_sandbox dev_sandbox.cpp)
TARGET_LINK_LIBRARIES(dev_sandbox PUBLIC lib4neuro)
-ADD_EXECUTABLE(net_test_1 net_test_1.cpp)
-TARGET_LINK_LIBRARIES(net_test_1 PUBLIC lib4neuro)
+#ADD_EXECUTABLE(net_test_1 net_test_1.cpp)
+#TARGET_LINK_LIBRARIES(net_test_1 PUBLIC lib4neuro)
-ADD_EXECUTABLE(net_test_2 net_test_2.cpp)
-TARGET_LINK_LIBRARIES(net_test_2 PUBLIC lib4neuro)
+#ADD_EXECUTABLE(net_test_2 net_test_2.cpp)
+#TARGET_LINK_LIBRARIES(net_test_2 PUBLIC lib4neuro)
-ADD_EXECUTABLE(net_test_3 net_test_3.cpp)
-TARGET_LINK_LIBRARIES(net_test_3 PUBLIC lib4neuro)
+#ADD_EXECUTABLE(net_test_3 net_test_3.cpp)
+#TARGET_LINK_LIBRARIES(net_test_3 PUBLIC lib4neuro)
-ADD_EXECUTABLE(net_test_ode_1 net_test_ode_1.cpp)
-TARGET_LINK_LIBRARIES(net_test_ode_1 PUBLIC lib4neuro)
+#ADD_EXECUTABLE(net_test_ode_1 net_test_ode_1.cpp)
+#TARGET_LINK_LIBRARIES(net_test_ode_1 PUBLIC lib4neuro)
-ADD_EXECUTABLE(net_test_pde_1 net_test_pde_1.cpp)
-TARGET_LINK_LIBRARIES(net_test_pde_1 PUBLIC lib4neuro)
+#ADD_EXECUTABLE(net_test_pde_1 net_test_pde_1.cpp)
+#TARGET_LINK_LIBRARIES(net_test_pde_1 PUBLIC lib4neuro)
-ADD_EXECUTABLE(network_serialization network_serialization.cpp)
-TARGET_LINK_LIBRARIES(network_serialization PUBLIC lib4neuro)
+#ADD_EXECUTABLE(network_serialization network_serialization.cpp)
+#TARGET_LINK_LIBRARIES(network_serialization PUBLIC lib4neuro)
-ADD_EXECUTABLE(test_harmonic_oscilator net_test_harmonic_oscilator.cpp)
-TARGET_LINK_LIBRARIES(test_harmonic_oscilator PUBLIC lib4neuro)
+#ADD_EXECUTABLE(test_harmonic_oscilator net_test_harmonic_oscilator.cpp)
+#TARGET_LINK_LIBRARIES(test_harmonic_oscilator PUBLIC lib4neuro)
-ADD_EXECUTABLE(x2_fitting x2_fitting.cpp)
-TARGET_LINK_LIBRARIES(x2_fitting PUBLIC lib4neuro)
+#ADD_EXECUTABLE(x2_fitting x2_fitting.cpp)
+#TARGET_LINK_LIBRARIES(x2_fitting PUBLIC lib4neuro)
-ADD_EXECUTABLE(acsf acsf.cpp)
-TARGET_LINK_LIBRARIES(acsf PUBLIC lib4neuro)
-
-ADD_EXECUTABLE(acsf2 acsf2.cpp)
-TARGET_LINK_LIBRARIES(acsf2 PUBLIC lib4neuro)
+#ADD_EXECUTABLE(acsf acsf.cpp)
+#TARGET_LINK_LIBRARIES(acsf PUBLIC lib4neuro)
SET(EXAMPLES_OUTPUT_DIR ${PROJECT_BINARY_DIR}/examples)
SET_TARGET_PROPERTIES(
dev_sandbox
- net_test_1
- net_test_2
- net_test_3
- net_test_ode_1
- net_test_pde_1
- network_serialization
- test_harmonic_oscilator
- seminar
- x2_fitting
- acsf
- acsf2
+ #net_test_1
+ #net_test_2
+ #net_test_3
+ #net_test_ode_1
+ #net_test_pde_1
+ #network_serialization
+ #test_harmonic_oscilator
+ #seminar
+ #x2_fitting
+ #acsf
PROPERTIES
ARCHIVE_OUTPUT_DIRECTORY $<1:${EXAMPLES_OUTPUT_DIR}>
@@ -61,16 +57,14 @@ SET_TARGET_PROPERTIES(
INCLUDE_DIRECTORIES ${ROOT_DIR}/include
)
-TARGET_INCLUDE_DIRECTORIES(
- net_test_3
- PRIVATE
- ${ROOT_DIR}/include
- ${Boost_INCLUDE_DIRS}
-)
+#TARGET_INCLUDE_DIRECTORIES(
+# net_test_3
+# PRIVATE
+# ${Boost_INCLUDE_DIRS}
+#)
TARGET_INCLUDE_DIRECTORIES(
- acsf2
+ dev_sandbox
PRIVATE
- ${ROOT_DIR}/include
- ${Boost_INCLUDE_DIRS}
-)
+ ${ARMADILLO_INCLUDE_DIR}
+)
\ No newline at end of file
diff --git a/src/examples/acsf.cpp b/src/examples/acsf.cpp
index e1f3851a..6b91dca3 100644
--- a/src/examples/acsf.cpp
+++ b/src/examples/acsf.cpp
@@ -1,79 +1,247 @@
//
-// Created by martin on 13.08.19.
+// Created by martin on 20.08.19.
//
-#include <4neuro.h>
+#define ARMA_ALLOW_FAKE_GCC
+
+#include <4neuro_public.h>
+
+void optimize_via_particle_swarm(l4n::NeuralNetwork& net,
+ l4n::ErrorFunction& ef) {
+
+ /* TRAINING METHOD SETUP */
+ std::vector<double> domain_bounds(2 * (net.get_n_weights() + net.get_n_biases()));
+
+ for (size_t i = 0; i < domain_bounds.size() / 2; ++i) {
+ domain_bounds[2 * i] = -150;
+ domain_bounds[2 * i + 1] = 150;
+ }
+
+ double c1 = 1.7;
+ double c2 = 1.7;
+ double w = 0.7;
+ size_t n_particles = 300;
+ size_t iter_max = 500;
+
+ /* if the maximal velocity from the previous step is less than 'gamma' times the current maximal velocity, then one
+ * terminating criterion is met */
+ double gamma = 0.5;
+
+ /* if 'delta' times 'n' particles are in the centroid neighborhood given by the radius 'epsilon', then the second
+ * terminating criterion is met ('n' is the total number of particles) */
+ double epsilon = 0.02;
+ double delta = 0.7;
+
+ l4n::ParticleSwarm swarm_01(
+ &domain_bounds,
+ c1,
+ c2,
+ w,
+ gamma,
+ epsilon,
+ delta,
+ n_particles,
+ iter_max
+ );
+ swarm_01.optimize(ef);
+
+ net.copy_parameter_space(swarm_01.get_parameters());
+
+ /* ERROR CALCULATION */
+ std::cout << "Run finished! Error of the network[Particle swarm]: " << ef.eval(nullptr) << std::endl;
+ std::cout
+ << "***********************************************************************************************************************"
+ << std::endl;
+}
+
+double optimize_via_gradient_descent(l4n::NeuralNetwork& net,
+ l4n::ErrorFunction& ef) {
+
+ std::cout
+ << "***********************************************************************************************************************"
+ << std::endl;
+ l4n::GradientDescentBB gd(1e-6,
+ 1000,
+ 10000);
+
+ gd.optimize(ef);
+
+ net.copy_parameter_space(gd.get_parameters());
+
+ /* ERROR CALCULATION */
+ double err = ef.eval(nullptr);
+ std::cout << "Run finished! Error of the network[Gradient descent]: " << err << std::endl;
+
+ /* Just for validation test purposes - NOT necessary for the example to work! */
+ return err;
+}
+
+double optimize_via_LBMQ(l4n::NeuralNetwork& net,
+ l4n::ErrorFunction& ef) {
+
+ size_t max_iterations = 10000;
+ size_t batch_size = 0;
+ double tolerance = 1e-4;
+ double tolerance_gradient = tolerance;
+ double tolerance_parameters = tolerance;
+
+ std::cout
+ << "***********************************************************************************************************************"
+ << std::endl;
+ l4n::LevenbergMarquardt lm(
+ max_iterations,
+ batch_size,
+ tolerance,
+ tolerance_gradient,
+ tolerance_parameters
+ );
+
+ lm.optimize(ef);
+
+ net.copy_parameter_space(lm.get_parameters());
+
+ /* ERROR CALCULATION */
+ double err = ef.eval(nullptr);
+ // std::cout << "Run finished! Error of the network[Levenberg-Marquardt]: " << err << std::endl;
+
+ /* Just for validation test purposes - NOT necessary for the example to work! */
+ return err;
+}
+
+double optimize_via_NelderMead(l4n::NeuralNetwork& net, l4n::ErrorFunction& ef) {
+ l4n::NelderMead nm(500, 150);
+
+ nm.optimize(ef);
+ net.copy_parameter_space(nm.get_parameters());
+
+ /* ERROR CALCULATION */
+ double err = ef.eval(nullptr);
+ std::cout << "Run finished! Error of the network[Nelder-Mead]: " << err << std::endl;
+
+ /* Just for validation test purposes - NOT necessary for the example to work! */
+ return err;
+
+}
+
int main() {
- /* Representation of N2He+ molecule using Atomic-centered Symmetry Functions (Behler's approach) */
-// // Cutoff functions
-// l4n::CutoffFunction1 cutoff1(1.1);
-// l4n::CutoffFunction2 cutoff2(1.5);
-// l4n::CutoffFunction2 cutoff3(1.2);
-//
-// l4n::CutoffFunction2 cutoff4(1.3);
-// l4n::CutoffFunction2 cutoff5(1.9);
-// l4n::CutoffFunction2 cutoff6(2.9);
-//
-// // Symmetry functions
-// l4n::G1 sym_f1(&cutoff1);
-// l4n::G2 sym_f2(&cutoff2, 0.15, 0.75);
-// l4n::G2 sym_f3(&cutoff3, 0.1, 0.2);
-//
-// l4n::G3 sym_f4(&cutoff4, 0.3);
-// l4n::G4 sym_f5(&cutoff5, 0.05, true, 0.05);
-// l4n::G4 sym_f6(&cutoff5, 0.05, false, 0.05);
-// l4n::G5 sym_f7(&cutoff6, 0.05, true, 0.05);
-// l4n::G5 sym_f8(&cutoff6, 0.05, false, 0.05);
-//
-// std::vector<l4n::SymmetryFunction*> nitrogen_sym_funcs = {&sym_f1, &sym_f2, &sym_f7, &sym_f8};
-// std::vector<l4n::SymmetryFunction*> helium_sym_funcs = {&sym_f2, &sym_f3, &sym_f4, &sym_f5, &sym_f6};
-//
-// // Definition of sub-nets
-// std::vector<unsigned int> nitrogen_hidden_neurons = {10};
-// std::vector<l4n::NEURON_TYPE> nitrogen_neuron_types = {l4n::NEURON_TYPE::LOGISTIC};
-// std::vector<unsigned int> helium_hidden_neurons = {10};
-// std::vector<l4n::NEURON_TYPE> helium_neuron_types = {l4n::NEURON_TYPE::LOGISTIC};
-//
-// // Definition of elements
-// l4n::Element nitrogen = l4n::Element("N",
-// 2,
-// nitrogen_sym_funcs,
-// nitrogen_hidden_neurons,
-// nitrogen_neuron_types,
-// false);
-//
-// l4n::Element helium = l4n::Element("He",
-// 1,
-// helium_sym_funcs,
-// nitrogen_hidden_neurons,
-// nitrogen_neuron_types,
-// false);
-//
-// std::unordered_map<l4n::ELEMENT_SYMBOL, l4n::Element*> elements;
-// elements[l4n::ELEMENT_SYMBOL::N] = &nitrogen;
-// elements[l4n::ELEMENT_SYMBOL::He] = &helium;
-//
-// // Definition of particles' Cartesian coordinates
-// std::vector<double> n1 = {0,0};
-// std::vector<double> n2 = {1,0};
-// std::vector<double> he = {0,1};
-// std::vector<std::pair<l4n::ELEMENT_SYMBOL, std::vector<double>>> particles;
-// particles.emplace_back(std::make_pair(l4n::ELEMENT_SYMBOL::N, n1));
-// particles.emplace_back(std::make_pair(l4n::ELEMENT_SYMBOL::N, n2));
-// particles.emplace_back(std::make_pair(l4n::ELEMENT_SYMBOL::He, he));
-//
-// // Neural network construction
-// l4n::NeuralNetwork nn = l4n::NeuralNetwork(elements, particles);
-//
-// std::cout << nn.get_n_neurons() << std::endl;
-// std::cout << nn.get_n_inputs() << std::endl;
-// std::cout << nn.get_n_outputs() << std::endl;
-// std::cout << nn.get_n_biases() << std::endl;
-//
-// // Neural network training
-// // TODO
+ try{
+
+ /* Specify cutoff functions */
+ l4n::CutoffFunction2 cutoff2(8);
+
+ /* Specify symmetry functions */
+ l4n::G2 sym_f1(&cutoff2, 0, 0.7);
+ l4n::G2 sym_f2(&cutoff2, 0.1, 0.8);
+ l4n::G2 sym_f3(&cutoff2, 0.2, 0.04);
+ l4n::G2 sym_f4(&cutoff2, 0.3, 0.04);
+ l4n::G2 sym_f5(&cutoff2, 0.4, 0.04);
+ l4n::G2 sym_f6(&cutoff2, 0.5, 0.04);
+ l4n::G2 sym_f7(&cutoff2, 0.6, 0.04);
+
+ l4n::G5 sym_f8(&cutoff2, 0.7, -1, 0.9);
+ l4n::G5 sym_f9(&cutoff2, 0.8, -1, 0.9);
+ l4n::G5 sym_f10(&cutoff2, 0.9, -1, 0.9);
+ l4n::G5 sym_f11(&cutoff2, 1, -1, 0.9);
+ l4n::G5 sym_f12(&cutoff2, 1.1, -1, 0.9);
+ l4n::G5 sym_f13(&cutoff2, 1.2, -1, 0.9);
+ l4n::G5 sym_f14(&cutoff2, 1.3, -1, 0.9);
+ l4n::G5 sym_f15(&cutoff2, 1.4, -1, 0.9);
+ l4n::G5 sym_f16(&cutoff2, 1.5, -1, 0.9);
+ l4n::G5 sym_f17(&cutoff2, 1.6, -1, 0.9);
+ l4n::G5 sym_f18(&cutoff2, 1.7, -1, 0.9);
+
+ std::vector<l4n::SymmetryFunction*> helium_sym_funcs = {&sym_f1,
+ &sym_f2,
+ &sym_f3,
+ &sym_f4,
+ &sym_f5,
+ &sym_f6,
+ &sym_f7,
+ &sym_f8,
+ &sym_f9,
+ &sym_f10,
+ &sym_f11,
+ &sym_f12,
+ &sym_f13,
+ &sym_f14,
+ &sym_f15,
+ &sym_f16,
+ &sym_f17,
+ &sym_f18};
+
+ l4n::Element helium = l4n::Element("He",
+ helium_sym_funcs);
+ std::unordered_map<l4n::ELEMENT_SYMBOL, l4n::Element*> elements;
+ elements[l4n::ELEMENT_SYMBOL::He] = &helium;
+
+ /* Read data */
+ l4n::XYZReader reader("../../data/HE4+T1.xyz", true);
+ reader.read();
+
+ std::cout << "Finished reading data" << std::endl;
+
+ std::shared_ptr<l4n::DataSet> ds = reader.get_acsf_data_set(elements);
+
+ /* Create a neural network */
+ std::unordered_map<l4n::ELEMENT_SYMBOL, std::vector<unsigned int>> n_hidden_neurons;
+ n_hidden_neurons[l4n::ELEMENT_SYMBOL::He] = {20, 20, 1};
+
+ std::unordered_map<l4n::ELEMENT_SYMBOL, std::vector<l4n::NEURON_TYPE>> type_hidden_neurons;
+ type_hidden_neurons[l4n::ELEMENT_SYMBOL::He] = {l4n::NEURON_TYPE::LOGISTIC, l4n::NEURON_TYPE::LOGISTIC, l4n::NEURON_TYPE::LINEAR};
+
+ l4n::ACSFNeuralNetwork net(elements, *reader.get_element_list(), reader.contains_charge(), n_hidden_neurons, type_hidden_neurons);
+
+ l4n::MSE mse(&net, ds.get());
+
+ net.randomize_parameters();
+
+ for(size_t i = 0; i < ds->get_data()->at(0).first.size(); i++) {
+ std::cout << ds->get_data()->at(0).first.at(i) << " ";
+ if(i % 2 == 1) {
+ std::cout << std::endl;
+ }
+ }
+ std::cout << "----" << std::endl;
+
+ l4n::ACSFParametersOptimizer param_optim(&mse, &reader);
+ std::vector<l4n::SYMMETRY_FUNCTION_PARAMETER> fitted_params = {l4n::SYMMETRY_FUNCTION_PARAMETER::EXTENSION,
+ l4n::SYMMETRY_FUNCTION_PARAMETER::SHIFT_MAX,
+ l4n::SYMMETRY_FUNCTION_PARAMETER::SHIFT,
+ l4n::SYMMETRY_FUNCTION_PARAMETER::ANGULAR_RESOLUTION};
+
+ param_optim.fit_ACSF_parameters(fitted_params, true);
+
+ for(size_t i = 0; i < mse.get_dataset()->get_data()->at(0).first.size(); i++) {
+ std::cout << mse.get_dataset()->get_data()->at(0).first.at(i) << " ";
+ if(i % 2 == 1) {
+ std::cout << std::endl;
+ }
+ }
+ std::cout << "----" << std::endl;
+
+
+// optimize_via_particle_swarm(net, mse);
+// optimize_via_NelderMead(net, mse);
+
+ double err1 = optimize_via_LBMQ(net, mse);
+ double err2 = optimize_via_gradient_descent(net, mse);
+
+ /* Print fit comparison with real data */
+ std::vector<double> output;
+ output.resize(1);
+
+ for(auto e : *mse.get_dataset()->get_data()) {
+ std::cout << "OUTS (DS, predict): " << e.second.at(0) << " ";
+ net.eval_single(e.first, output);
+ std::cout << output.at(0) << std::endl;
+ }
+
+ } catch (const std::exception& e) {
+ std::cerr << e.what() << std::endl;
+ exit(EXIT_FAILURE);
+ }
return 0;
-}
\ No newline at end of file
+}
diff --git a/src/examples/dev_sandbox.cpp b/src/examples/dev_sandbox.cpp
index 7cd5277a..a7e18cfe 100644
--- a/src/examples/dev_sandbox.cpp
+++ b/src/examples/dev_sandbox.cpp
@@ -3,8 +3,7 @@
//
#define ARMA_ALLOW_FAKE_GCC
-#include <4neuro_public.h>
-#include "../mpi_wrapper.h"
+#include <4neuro.h>
void optimize_via_particle_swarm(l4n::NeuralNetwork& net,
diff --git a/src/examples/net_test_1.cpp b/src/examples/net_test_1.cpp
index a1f3e571..a5b30049 100644
--- a/src/examples/net_test_1.cpp
+++ b/src/examples/net_test_1.cpp
@@ -5,7 +5,7 @@
#include <vector>
#include <iostream>
-#include "4neuro.h"
+#include <4neuro_public.h>
void optimize_via_particle_swarm(l4n::NeuralNetwork& net,
l4n::ErrorFunction& ef) {
diff --git a/src/examples/net_test_2.cpp b/src/examples/net_test_2.cpp
index 345bbb88..8aff983b 100644
--- a/src/examples/net_test_2.cpp
+++ b/src/examples/net_test_2.cpp
@@ -5,7 +5,7 @@
#include <vector>
-#include "4neuro.h"
+#include <4neuro_public.h>
void optimize_via_particle_swarm(l4n::NeuralNetwork& net,
l4n::ErrorFunction& ef) {
diff --git a/src/examples/net_test_3.cpp b/src/examples/net_test_3.cpp
index d9bc6a7f..bb95542d 100644
--- a/src/examples/net_test_3.cpp
+++ b/src/examples/net_test_3.cpp
@@ -11,7 +11,7 @@
#include <assert.h>
#include <ctime>
-#include <4neuro.h>
+#include <4neuro_public.h>
#include <boost/random/mersenne_twister.hpp>
#include <boost/random/uniform_int_distribution.hpp>
diff --git a/src/examples/net_test_harmonic_oscilator.cpp b/src/examples/net_test_harmonic_oscilator.cpp
index e216200c..abd94990 100644
--- a/src/examples/net_test_harmonic_oscilator.cpp
+++ b/src/examples/net_test_harmonic_oscilator.cpp
@@ -11,7 +11,7 @@
#include <iostream>
#include <fstream>
-#include "4neuro.h"
+#include <4neuro_public.h>
void export_solution(size_t n_test_points,
double te,
diff --git a/src/examples/net_test_ode_1.cpp b/src/examples/net_test_ode_1.cpp
index 28c48ec4..25d50bd2 100644
--- a/src/examples/net_test_ode_1.cpp
+++ b/src/examples/net_test_ode_1.cpp
@@ -19,7 +19,7 @@
#include <random>
#include <iostream>
#include <chrono>
-#include "4neuro.h"
+#include <4neuro_public.h>
void optimize_via_particle_swarm(l4n::DESolver& solver,
l4n::MultiIndex& alpha,
diff --git a/src/examples/net_test_pde_1.cpp b/src/examples/net_test_pde_1.cpp
index b69cd919..c2441062 100644
--- a/src/examples/net_test_pde_1.cpp
+++ b/src/examples/net_test_pde_1.cpp
@@ -22,7 +22,7 @@
#include <iostream>
#include <fstream>
-#include "4neuro.h"
+#include <4neuro_public.h>
void optimize_via_particle_swarm(l4n::DESolver& solver,
l4n::MultiIndex& alpha,
diff --git a/src/examples/network_serialization.cpp b/src/examples/network_serialization.cpp
index bae85fcc..0a22c005 100644
--- a/src/examples/network_serialization.cpp
+++ b/src/examples/network_serialization.cpp
@@ -7,7 +7,7 @@
*/
#include <vector>
-#include "4neuro.h"
+#include <4neuro_public.h>
int main() {
std::cout << "Running lib4neuro Serialization example 1" << std::endl;
diff --git a/src/examples/seminar.cpp b/src/examples/seminar.cpp
index 11926803..735f11c9 100644
--- a/src/examples/seminar.cpp
+++ b/src/examples/seminar.cpp
@@ -9,8 +9,7 @@
#include <iostream>
#include <fstream>
-#include "4neuro.h"
-#include "../Solvers/DESolver.h"
+#include <4neuro_public.h>
int main() {
diff --git a/src/examples/x2_fitting.cpp b/src/examples/x2_fitting.cpp
index 9c273391..1484ec3c 100644
--- a/src/examples/x2_fitting.cpp
+++ b/src/examples/x2_fitting.cpp
@@ -1,6 +1,6 @@
#include <iostream>
-#include "4neuro.h"
+#include <4neuro_public.h>
void optimize_via_particle_swarm(l4n::NeuralNetwork& net,
l4n::ErrorFunction& ef) {
--
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