From 5d897b89daa71f204f60402adc586fd6297c1d07 Mon Sep 17 00:00:00 2001
From: Martin Beseda <martin.beseda@vsb.cz>
Date: Fri, 22 Mar 2019 11:40:30 +0100
Subject: [PATCH] [ENH] [RUNTIME ERROR]: moved 'optimal_parameters' variable
and its getter to the LearningMethod class.
---
src/LearningMethods/GradientDescent.cpp | 10 +----
src/LearningMethods/GradientDescent.h | 10 -----
src/LearningMethods/GradientDescentBB.cpp | 10 +----
src/LearningMethods/GradientDescentBB.h | 10 -----
.../GradientDescentSingleItem.cpp | 6 ---
.../GradientDescentSingleItem.h | 6 ---
src/LearningMethods/LearningMethod.h | 9 ++++-
src/LearningMethods/LearningMethods.cpp | 4 ++
src/LearningMethods/LearningSequence.cpp | 17 ++++-----
src/LearningMethods/LearningSequence.h | 11 ------
src/LearningMethods/LevenbergMarquardt.cpp | 15 +-------
src/LearningMethods/LevenbergMarquardt.h | 2 -
src/LearningMethods/ParticleSwarm.cpp | 38 ++++++++-----------
src/LearningMethods/ParticleSwarm.h | 10 -----
src/LearningMethods/RandomSolution.cpp | 8 ----
src/LearningMethods/RandomSolution.h | 6 ---
src/Solvers/DESolver.cpp | 2 +-
17 files changed, 42 insertions(+), 132 deletions(-)
diff --git a/src/LearningMethods/GradientDescent.cpp b/src/LearningMethods/GradientDescent.cpp
index c82db8d6..15cd6c30 100644
--- a/src/LearningMethods/GradientDescent.cpp
+++ b/src/LearningMethods/GradientDescent.cpp
@@ -295,20 +295,14 @@ namespace lib4neuro {
#endif
}
- this->optimal_parameters = params_current;
+ this->optimal_parameters = *params_current;
// std::shared_ptr<std::vector<double>> params;
// params.reset(this->optimal_parameters);
- ef.get_network_instance()->copy_parameter_space( this->optimal_parameters );
+ ef.get_network_instance()->copy_parameter_space( &this->optimal_parameters );
// delete gradient_current;
// delete gradient_prev;
// delete params_current;
// delete params_prev;
}
-
- std::vector<double> GradientDescent::get_parameters() {
-// std::shared_ptr<std::vector<double>> ret;
-// ret.reset(&this->optimal_parameters);
- return std::vector<double>(*this->optimal_parameters);
- }
}
diff --git a/src/LearningMethods/GradientDescent.h b/src/LearningMethods/GradientDescent.h
index e8db77af..ff1a7b3f 100644
--- a/src/LearningMethods/GradientDescent.h
+++ b/src/LearningMethods/GradientDescent.h
@@ -41,11 +41,6 @@ namespace lib4neuro {
*/
long long int maximum_niters;
- /**
- * Vector of minima coordinates
- */
- std::vector<double>* optimal_parameters = new std::vector<double>(5);
-
/**
* Adaptive calculation of the step-size based on several historical characteristics.
* ----------------------------------------------------------------------------------
@@ -112,11 +107,6 @@ namespace lib4neuro {
*/
LIB4NEURO_API void optimize(lib4neuro::ErrorFunction &ef, std::ofstream* ofs = nullptr) override;
- /**
- *
- * @return
- */
- LIB4NEURO_API std::vector<double> get_parameters() override;
};
}
diff --git a/src/LearningMethods/GradientDescentBB.cpp b/src/LearningMethods/GradientDescentBB.cpp
index 55d5043c..823edc1f 100644
--- a/src/LearningMethods/GradientDescentBB.cpp
+++ b/src/LearningMethods/GradientDescentBB.cpp
@@ -209,12 +209,12 @@ namespace lib4neuro {
#endif
}
- this->optimal_parameters = params_best;
+ this->optimal_parameters = *params_best;
// ef.analyze_error_gradient(*params_current, *gradient_current, 1.0, this->batch);
// std::vector<double>* params;
// params.reset(this->optimal_parameters);
- ef.get_network_instance()->copy_parameter_space(this->optimal_parameters);
+ ef.get_network_instance()->copy_parameter_space(&this->optimal_parameters);
//
// delete gradient_current;
// delete gradient_prev;
@@ -223,10 +223,4 @@ namespace lib4neuro {
// delete params_best;
}
- std::vector<double> GradientDescentBB::get_parameters() {
-// std::vector<double>* ret;
-// ret.reset(&this->optimal_parameters);
- return std::vector<double>(*this->optimal_parameters);
- }
-
}
diff --git a/src/LearningMethods/GradientDescentBB.h b/src/LearningMethods/GradientDescentBB.h
index db5008ad..5284c281 100644
--- a/src/LearningMethods/GradientDescentBB.h
+++ b/src/LearningMethods/GradientDescentBB.h
@@ -52,11 +52,6 @@ namespace lib4neuro {
*/
long long int maximum_niters;
- /**
- * Vector of minima coordinates
- */
- std::vector<double>* optimal_parameters = new std::vector<double>(5);
-
public:
/**
@@ -78,11 +73,6 @@ namespace lib4neuro {
*/
LIB4NEURO_API void optimize(lib4neuro::ErrorFunction &ef, std::ofstream* ofs = nullptr) override;
- /**
- *
- * @return
- */
- LIB4NEURO_API std::vector<double> get_parameters() override;
};
}
diff --git a/src/LearningMethods/GradientDescentSingleItem.cpp b/src/LearningMethods/GradientDescentSingleItem.cpp
index 6d33cbdd..58d473e6 100644
--- a/src/LearningMethods/GradientDescentSingleItem.cpp
+++ b/src/LearningMethods/GradientDescentSingleItem.cpp
@@ -105,10 +105,4 @@ namespace lib4neuro {
}
- std::vector<double> GradientDescentSingleItem::get_parameters() {
-// std::shared_ptr<std::vector<double>> ret;
-// ret.reset(&this->optimal_parameters);
- return std::vector<double>(*this->optimal_parameters);
- }
-
}
diff --git a/src/LearningMethods/GradientDescentSingleItem.h b/src/LearningMethods/GradientDescentSingleItem.h
index 61084edc..fccca9f4 100644
--- a/src/LearningMethods/GradientDescentSingleItem.h
+++ b/src/LearningMethods/GradientDescentSingleItem.h
@@ -92,12 +92,6 @@ namespace lib4neuro {
* @param ef
*/
LIB4NEURO_API void optimize(lib4neuro::ErrorFunction &ef, std::ofstream* ofs = nullptr) override;
-
- /**
- *
- * @return
- */
- LIB4NEURO_API std::vector<double> get_parameters() override;
};
}
diff --git a/src/LearningMethods/LearningMethod.h b/src/LearningMethods/LearningMethod.h
index 29b36233..aadc75a3 100644
--- a/src/LearningMethods/LearningMethod.h
+++ b/src/LearningMethods/LearningMethod.h
@@ -14,6 +14,13 @@
namespace lib4neuro {
class LearningMethod {
+ protected:
+
+ /**
+ * Vector of minima coordinates
+ */
+ std::vector<double> optimal_parameters;
+
public:
/**
* Runs the method specific learning algorithm minimizing the given error function
@@ -24,7 +31,7 @@ namespace lib4neuro {
/**
* Updates the optimal weight&bias settings in the passed vector
*/
- virtual std::vector<double> get_parameters() = 0;
+ virtual std::vector<double>* get_parameters();
};
class GradientLearningMethod : public LearningMethod {
diff --git a/src/LearningMethods/LearningMethods.cpp b/src/LearningMethods/LearningMethods.cpp
index 8c4b3b48..f1aba80e 100644
--- a/src/LearningMethods/LearningMethods.cpp
+++ b/src/LearningMethods/LearningMethods.cpp
@@ -8,6 +8,10 @@
#include "LearningMethod.h"
namespace lib4neuro {
+ std::vector<double>* LearningMethod::get_parameters() {
+ return &this->optimal_parameters;
+ }
+
void GradientLearningMethod::optimize(ErrorFunction& ef,
std::ofstream* ofs) {
this->optimize(ef, ofs);
diff --git a/src/LearningMethods/LearningSequence.cpp b/src/LearningMethods/LearningSequence.cpp
index 33e943cd..92bf350d 100644
--- a/src/LearningMethods/LearningSequence.cpp
+++ b/src/LearningMethods/LearningSequence.cpp
@@ -13,15 +13,11 @@ namespace lib4neuro {
LearningSequence::LearningSequence( double tolerance, int max_n_cycles ){
this->tol = tolerance;
this->max_number_of_cycles = max_n_cycles;
-// this->best_parameters = new std::vector<double>();
+// this->optimal_parameters = new std::vector<double>();
}
LearningSequence::~LearningSequence() = default;
- std::vector<double> LearningSequence::get_parameters() {
- return this->best_parameters;
- }
-
void LearningSequence::add_learning_method(std::shared_ptr<LearningMethod> method) {
this->learning_sequence.push_back( method );
}
@@ -37,7 +33,7 @@ namespace lib4neuro {
double the_best_error = error;
int mcycles = this->max_number_of_cycles, cycle_idx = 0;
-// std::shared_ptr<std::vector<double>> best_params = std::make_shared<std::vector<double>>(this->best_parameters);
+// std::shared_ptr<std::vector<double>> best_params = std::make_shared<std::vector<double>>(this->optimal_parameters);
std::vector<double> params;
while( error > this->tol && mcycles != 0){
mcycles--;
@@ -52,7 +48,8 @@ namespace lib4neuro {
m->optimize( ef, ofs );
puts("*********************** 9");
- params = m->get_parameters();
+ //TODO do NOT copy vectors if not needed
+ params = *m->get_parameters();
error = ef.eval(¶ms);
puts("*********************** 10");
@@ -64,18 +61,18 @@ namespace lib4neuro {
if( error < the_best_error ){
the_best_error = error;
- this->best_parameters = ef.get_parameters();
+ this->optimal_parameters = ef.get_parameters();
// best_params = ef.get_parameters();
// best_params.reset(ef.get_parameters().get());
}
if( error <= this->tol ){
- ef.get_network_instance()->copy_parameter_space( &this->best_parameters );
+ ef.get_network_instance()->copy_parameter_space( &this->optimal_parameters );
return;
}
}
COUT_DEBUG("Cycle: " << cycle_idx << ", the lowest error: " << the_best_error << std::endl );
}
- ef.get_network_instance()->copy_parameter_space( &this->best_parameters );
+ ef.get_network_instance()->copy_parameter_space( &this->optimal_parameters );
}
}
\ No newline at end of file
diff --git a/src/LearningMethods/LearningSequence.h b/src/LearningMethods/LearningSequence.h
index 1399081a..2e2b9511 100644
--- a/src/LearningMethods/LearningSequence.h
+++ b/src/LearningMethods/LearningSequence.h
@@ -31,11 +31,6 @@ namespace lib4neuro {
*/
double tol;
- /**
- *
- */
- std::vector<double> best_parameters; // = nullptr;
-
/**
*
*/
@@ -61,12 +56,6 @@ namespace lib4neuro {
*/
LIB4NEURO_API void optimize(lib4neuro::ErrorFunction &ef, std::ofstream* ofs = nullptr) override;
- /**
- *
- * @return
- */
- LIB4NEURO_API std::vector<double> get_parameters() override;
-
/**
*
* @param method
diff --git a/src/LearningMethods/LevenbergMarquardt.cpp b/src/LearningMethods/LevenbergMarquardt.cpp
index 8320487a..71bc9019 100644
--- a/src/LearningMethods/LevenbergMarquardt.cpp
+++ b/src/LearningMethods/LevenbergMarquardt.cpp
@@ -28,11 +28,6 @@ struct lib4neuro::LevenbergMarquardt::LevenbergMarquardtImpl {
unsigned long batch_size;
- /**
- * Vector of minimum coordinates
- */
- std::vector<double> optimal_parameters;
-
/**
* Returns Jacobian matrix of the residual function using the backpropagation algorithm
* Returns the right hand side of the resulting system of equations related to data errors in @data and approximating function @f
@@ -232,7 +227,7 @@ namespace lib4neuro {
COUT_DEBUG("Iteration: " << iter_counter << " Current error: " << current_err << ", Current gradient norm: " << gradient_norm << ", Direction norm: " << update_norm << std::endl);
/* Store the optimized parameters */
- this->p_impl->optimal_parameters = *params_current;
+ this->optimal_parameters = *params_current;
/* Dealloc vector of parameters */
if(params_current) {
@@ -241,17 +236,11 @@ namespace lib4neuro {
}
// std::shared_ptr<std::vector<double>> params = std::make_shared<std::vector<double>>(this->p_impl->optimal_parameters);
- ef.get_network_instance()->copy_parameter_space(&this->p_impl->optimal_parameters);
+ ef.get_network_instance()->copy_parameter_space(&this->optimal_parameters);
// delete params_tmp;
}
- std::vector<double> LevenbergMarquardt::get_parameters() {
-// std::shared_ptr<std::vector<double>> ret;
-// ret.reset(&this->p_impl->optimal_parameters);
- return this->p_impl->optimal_parameters;
- }
-
LevenbergMarquardt::~LevenbergMarquardt() = default;
}
\ No newline at end of file
diff --git a/src/LearningMethods/LevenbergMarquardt.h b/src/LearningMethods/LevenbergMarquardt.h
index 0ae18154..1622b88f 100644
--- a/src/LearningMethods/LevenbergMarquardt.h
+++ b/src/LearningMethods/LevenbergMarquardt.h
@@ -47,8 +47,6 @@ namespace lib4neuro {
LM_UPDATE_TYPE update_type,
std::ofstream* ofs = nullptr);
- std::vector<double> get_parameters() override;
-
~LevenbergMarquardt();
};
diff --git a/src/LearningMethods/ParticleSwarm.cpp b/src/LearningMethods/ParticleSwarm.cpp
index eadc193f..af97eb13 100644
--- a/src/LearningMethods/ParticleSwarm.cpp
+++ b/src/LearningMethods/ParticleSwarm.cpp
@@ -271,9 +271,9 @@ namespace lib4neuro {
// delete this->domain_bounds;
// }
//
-// if (this->p_min_glob) {
-// delete this->p_min_glob;
-// this->p_min_glob = nullptr;
+// if (this->optimal_parameters) {
+// delete this->optimal_parameters;
+// this->optimal_parameters = nullptr;
// }
}
@@ -310,10 +310,10 @@ namespace lib4neuro {
}
this->radius_factor *= 1.25;
-// if (!this->p_min_glob) {
-// this->p_min_glob = new std::vector<double>(this->func_dim);
+// if (!this->optimal_parameters) {
+// this->optimal_parameters = new std::vector<double>(this->func_dim);
// } else {
- this->p_min_glob.resize(this->func_dim);
+ this->optimal_parameters.resize(this->func_dim);
// }
size_t outer_it = 0;
@@ -333,7 +333,7 @@ namespace lib4neuro {
double euclidean_dist;
double current_err = -1;
- this->determine_optimal_coordinate_and_value(this->p_min_glob, optimal_value);
+ this->determine_optimal_coordinate_and_value(this->optimal_parameters, optimal_value);
// for(unsigned int i = 0; i < this->n_particles; ++i){
// this->particle_swarm[i]->print_coordinate();
// }
@@ -346,10 +346,10 @@ namespace lib4neuro {
//////////////////////////////////////////////////
// Clustering algorithm - termination condition //
//////////////////////////////////////////////////
- particle = this->determine_optimal_coordinate_and_value(this->p_min_glob, optimal_value);
+ particle = this->determine_optimal_coordinate_and_value(this->optimal_parameters, optimal_value);
- if (std::find(global_best_vec.begin(), global_best_vec.end(), this->p_min_glob) == global_best_vec.end()) {
- global_best_vec.emplace_back(this->p_min_glob); // TODO rewrite as std::set
+ if (std::find(global_best_vec.begin(), global_best_vec.end(), this->optimal_parameters) == global_best_vec.end()) {
+ global_best_vec.emplace_back(this->optimal_parameters); // TODO rewrite as std::set
}
cluster.insert(particle);
@@ -373,7 +373,7 @@ namespace lib4neuro {
for (size_t pi = 0; pi < this->n_particles; pi++) {
particle = this->particle_swarm.at(pi);
- tmp_velocity = particle->change_coordinate(this->w, this->c1, this->c2, this->p_min_glob,
+ tmp_velocity = particle->change_coordinate(this->w, this->c1, this->c2, this->optimal_parameters,
global_best_vec);
// particle->print_coordinate();
@@ -413,8 +413,8 @@ namespace lib4neuro {
// }
// }
-// std::shared_ptr<std::vector<double>> coord = std::make_shared<std::vector<double>>(this->p_min_glob);
- current_err = ef.eval(&this->p_min_glob);
+// std::shared_ptr<std::vector<double>> coord = std::make_shared<std::vector<double>>(this->optimal_parameters);
+ current_err = ef.eval(&this->optimal_parameters);
COUT_DEBUG(std::string("Iteration: ") << (unsigned int)(outer_it)
<< ". Total error: " << current_err
@@ -447,7 +447,7 @@ namespace lib4neuro {
<< ". Objective function value: " << optimal_value
<< "." << std::endl );
- this->determine_optimal_coordinate_and_value(this->p_min_glob, optimal_value);
+ this->determine_optimal_coordinate_and_value(this->optimal_parameters, optimal_value);
//TODO rewrite following output using COUT_INFO
if (outer_it < this->iter_max) {
/* Convergence reached */
@@ -458,8 +458,8 @@ namespace lib4neuro {
}
// std::shared_ptr<std::vector<double>> coord;
-// coord.reset(this->p_min_glob);
- ef.get_network_instance()->copy_parameter_space(&this->p_min_glob);
+// coord.reset(this->optimal_parameters);
+ ef.get_network_instance()->copy_parameter_space(&this->optimal_parameters);
delete centroid;
}
@@ -534,12 +534,6 @@ namespace lib4neuro {
return std::sqrt(dist);
}
- std::vector<double> ParticleSwarm::get_parameters() {
-// std::shared_ptr<std::vector<double>> ret;
-// ret.reset(&this->p_min_glob);
- return this->p_min_glob;
- }
-
void ParticleSwarm::init_constructor(std::vector<double>* domain_bounds,
double c1,
double c2,
diff --git a/src/LearningMethods/ParticleSwarm.h b/src/LearningMethods/ParticleSwarm.h
index ac6d6c9d..0a80ed70 100644
--- a/src/LearningMethods/ParticleSwarm.h
+++ b/src/LearningMethods/ParticleSwarm.h
@@ -197,11 +197,6 @@ namespace lib4neuro {
*/
std::vector<double> domain_bounds; // = nullptr;
- /**
- * Coordinates of the found global minima
- */
- std::vector<double> p_min_glob;
-
protected:
/**
*
@@ -305,11 +300,6 @@ namespace lib4neuro {
*/
LIB4NEURO_API void optimize(lib4neuro::ErrorFunction &ef, std::ofstream* ofs = nullptr) override;
- /**
- *
- * @return
- */
- LIB4NEURO_API std::vector<double> get_parameters() override;
};
}
diff --git a/src/LearningMethods/RandomSolution.cpp b/src/LearningMethods/RandomSolution.cpp
index f5a187de..8372d058 100644
--- a/src/LearningMethods/RandomSolution.cpp
+++ b/src/LearningMethods/RandomSolution.cpp
@@ -16,14 +16,6 @@ namespace lib4neuro {
RandomSolution::~RandomSolution() {}
- std::vector<double> RandomSolution::get_parameters() {
-// std::shared_ptr<std::vector<double>> ret;
-// ret.reset(&this->optimal_parameters);
-// return ret;
-
- return this->optimal_parameters;
- }
-
void RandomSolution::optimize(lib4neuro::ErrorFunction &ef, std::ofstream *ofs) {
ef.get_network_instance()->randomize_parameters();
diff --git a/src/LearningMethods/RandomSolution.h b/src/LearningMethods/RandomSolution.h
index cdcda7df..cb61a566 100644
--- a/src/LearningMethods/RandomSolution.h
+++ b/src/LearningMethods/RandomSolution.h
@@ -16,9 +16,6 @@ namespace lib4neuro {
class RandomSolution : public lib4neuro::LearningMethod {
- private:
- std::vector<double> optimal_parameters;
-
protected:
public:
@@ -28,9 +25,6 @@ namespace lib4neuro {
void optimize(lib4neuro::ErrorFunction &ef,
std::ofstream *ofs = nullptr) override;
-
- std::vector<double> get_parameters() override;
-
};
}
diff --git a/src/Solvers/DESolver.cpp b/src/Solvers/DESolver.cpp
index 50fcb254..4b9df8c6 100644
--- a/src/Solvers/DESolver.cpp
+++ b/src/Solvers/DESolver.cpp
@@ -410,7 +410,7 @@ namespace lib4neuro {
learning_method.optimize(total_error);
// std::shared_ptr<std::vector<double>> params;
// params.reset(learning_method.get_parameters());
- std::vector<double> params = learning_method.get_parameters();
+ std::vector<double> params = *learning_method.get_parameters();
this->solution->copy_parameter_space(¶ms);
printf("error after optimization: %f\n", total_error.eval(nullptr));
--
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