diff --git a/src/ErrorFunction/ErrorFunctions.cpp b/src/ErrorFunction/ErrorFunctions.cpp
index 01d9bc34adc84a988348bfc20b67368757c8436a..0d490a06482ef2d35a796d556a6d09eb4ba39859 100644
--- a/src/ErrorFunction/ErrorFunctions.cpp
+++ b/src/ErrorFunction/ErrorFunctions.cpp
@@ -75,20 +75,19 @@ namespace lib4neuro {
return this->ds_test;
}
- std::vector<double>* ErrorFunction::get_parameters() {
- std::vector<double>* output;
- output = new std::vector<double>(this->net->get_n_weights() + this->net->get_n_biases());
+ std::vector<double> ErrorFunction::get_parameters() {
+ std::vector<double> output(this->net->get_n_weights() + this->net->get_n_biases());
// std::vector<double>* output = new std::vector<double>(this->net->get_n_weights() + this->net->get_n_biases());
size_t i = 0;
for (auto el: *this->net->get_parameter_ptr_weights()) {
- output->at(i) = el;
+ output[i] = el;
++i;
}
for (auto el: *this->net->get_parameter_ptr_biases()) {
- output->at(i) = el;
+ output[i] = el;
++i;
}
@@ -380,7 +379,7 @@ namespace lib4neuro {
//TODO check input vector sizes - they HAVE TO be allocated before calling this function
size_t n_parameters = this->get_dimension();
- std::vector<double>* parameters = this->get_parameters();
+ std::vector<double> parameters = this->get_parameters();
double delta; // Complete step size
double former_parameter_value;
@@ -388,23 +387,23 @@ namespace lib4neuro {
double f_val2; // f(x - delta)
for (size_t i = 0; i < n_parameters; i++) {
- delta = h * (1 + std::abs(parameters->at(i)));
- former_parameter_value = parameters->at(i);
+ delta = h * (1 + std::abs(parameters[i]));
+ former_parameter_value = parameters[i];
if(delta != 0) {
/* Computation of f_val1 = f(x + delta) */
- parameters->at(i) = former_parameter_value + delta;
- f_val1 = this->calculate_single_residual(input, output, parameters);
+ parameters[i] = former_parameter_value + delta;
+ f_val1 = this->calculate_single_residual(input, output, ¶meters);
/* Computation of f_val2 = f(x - delta) */
- parameters->at(i) = former_parameter_value - delta;
- f_val2 = this->calculate_single_residual(input, output, parameters);
+ parameters[i] = former_parameter_value - delta;
+ f_val2 = this->calculate_single_residual(input, output, ¶meters);
gradient->at(i) = (f_val1 - f_val2) / (2*delta);
}
/* Restore parameter to the former value */
- parameters->at(i) = former_parameter_value;
+ parameters[i] = former_parameter_value;
}
}
@@ -679,7 +678,7 @@ namespace lib4neuro {
return this->dimension;
}
- std::vector<double>* ErrorSum::get_parameters() {
+ std::vector<double> ErrorSum::get_parameters() {
return this->summand->at(0)->get_parameters();
}
diff --git a/src/ErrorFunction/ErrorFunctions.h b/src/ErrorFunction/ErrorFunctions.h
index fb1f78814e7f170b415a643266b6403aa18a8a88..edc8d6bc99af3231b594c94cb0139bb7693bbb72 100644
--- a/src/ErrorFunction/ErrorFunctions.h
+++ b/src/ErrorFunction/ErrorFunctions.h
@@ -68,7 +68,7 @@ namespace lib4neuro {
*
* @return
*/
- virtual std::vector<double>* get_parameters();
+ virtual std::vector<double> get_parameters();
/**
* @return
@@ -538,7 +538,7 @@ namespace lib4neuro {
*
* @return
*/
- LIB4NEURO_API std::vector<double>* get_parameters() override;
+ LIB4NEURO_API std::vector<double> get_parameters() override;
/**
*
diff --git a/src/LearningMethods/GradientDescent.cpp b/src/LearningMethods/GradientDescent.cpp
index d74410235856521cb8b5ecbaf1989c95548dbc8a..c82db8d6a016a53c578be5f290c6358ddacda928 100644
--- a/src/LearningMethods/GradientDescent.cpp
+++ b/src/LearningMethods/GradientDescent.cpp
@@ -113,7 +113,7 @@ namespace lib4neuro {
std::vector<double>* gradient_current(new std::vector<double>(n_parameters));
std::vector<double>* gradient_prev(new std::vector<double>(n_parameters));
- std::vector<double>* params_current = ef.get_parameters();
+ std::vector<double>* params_current = new std::vector<double>(ef.get_parameters());
std::vector<double>* params_prev(new std::vector<double>(n_parameters));
std::vector<double>* ptr_mem;
@@ -306,9 +306,9 @@ namespace lib4neuro {
// delete params_prev;
}
- std::vector<double>* GradientDescent::get_parameters() {
+ std::vector<double> GradientDescent::get_parameters() {
// std::shared_ptr<std::vector<double>> ret;
// ret.reset(&this->optimal_parameters);
- return this->optimal_parameters;
+ return std::vector<double>(*this->optimal_parameters);
}
}
diff --git a/src/LearningMethods/GradientDescent.h b/src/LearningMethods/GradientDescent.h
index 3560c5123c21471d0c6d63ab1f3dbb3d137402fb..e8db77af5fe75927fe1ca77c987bd326626d8d9a 100644
--- a/src/LearningMethods/GradientDescent.h
+++ b/src/LearningMethods/GradientDescent.h
@@ -116,7 +116,7 @@ namespace lib4neuro {
*
* @return
*/
- LIB4NEURO_API std::vector<double>* get_parameters() override;
+ LIB4NEURO_API std::vector<double> get_parameters() override;
};
}
diff --git a/src/LearningMethods/GradientDescentBB.cpp b/src/LearningMethods/GradientDescentBB.cpp
index 3d8626d0d0c1b85064cd69a9db84eaa2827f8ab9..55d5043c64079bb30cb335c254be4c245444d9e8 100644
--- a/src/LearningMethods/GradientDescentBB.cpp
+++ b/src/LearningMethods/GradientDescentBB.cpp
@@ -55,7 +55,7 @@ namespace lib4neuro {
std::vector<double>* gradient_current(new std::vector<double>(n_parameters));
std::vector<double>* gradient_prev(new std::vector<double>(n_parameters));
- std::vector<double>* params_current = ef.get_parameters();
+ std::vector<double>* params_current = new std::vector<double>(ef.get_parameters());
std::vector<double>* params_prev(new std::vector<double>(n_parameters));
std::vector<double>* params_best(new std::vector<double>(*params_current));
@@ -223,10 +223,10 @@ namespace lib4neuro {
// delete params_best;
}
- std::vector<double>* GradientDescentBB::get_parameters() {
+ std::vector<double> GradientDescentBB::get_parameters() {
// std::vector<double>* ret;
// ret.reset(&this->optimal_parameters);
- return this->optimal_parameters;
+ return std::vector<double>(*this->optimal_parameters);
}
}
diff --git a/src/LearningMethods/GradientDescentBB.h b/src/LearningMethods/GradientDescentBB.h
index 5b376f0728e4343123efb0ce5c6cbf3c3698712e..db5008ad86ca3ebda1a2a7c25f967e32edab0687 100644
--- a/src/LearningMethods/GradientDescentBB.h
+++ b/src/LearningMethods/GradientDescentBB.h
@@ -82,7 +82,7 @@ namespace lib4neuro {
*
* @return
*/
- LIB4NEURO_API std::vector<double>* get_parameters() override;
+ LIB4NEURO_API std::vector<double> get_parameters() override;
};
}
diff --git a/src/LearningMethods/GradientDescentSingleItem.cpp b/src/LearningMethods/GradientDescentSingleItem.cpp
index 1340f33e4a9fda0977fd111e8e2af9174217099a..6d33cbdd51c5789c876df067bfac4055d1ecf948 100644
--- a/src/LearningMethods/GradientDescentSingleItem.cpp
+++ b/src/LearningMethods/GradientDescentSingleItem.cpp
@@ -61,7 +61,7 @@ namespace lib4neuro {
size_t iter_idx = this->maximum_niters;
size_t dim = ef.get_network_instance()->get_n_biases() + ef.get_network_instance()->get_n_weights();
- std::vector<double> parameter_vector = *ef.get_parameters();
+ std::vector<double> parameter_vector = ef.get_parameters();
std::vector<double> gradient_vector(dim);
std::vector<double> search_direction(dim);
std::vector<double> error_vector(ef.get_network_instance()->get_n_outputs());
@@ -73,7 +73,7 @@ namespace lib4neuro {
updated_elements = 0;
std::fill(search_direction.begin(), search_direction.end(), 0);
for( size_t i = 0; i < ef.get_dataset()->get_n_elements(); ++i){
- error = ef.eval_single_item_by_idx( i, ef.get_parameters(), error_vector );
+ error = ef.eval_single_item_by_idx( i, ¶meter_vector, error_vector );
if( error > max_error ){
max_error = error;
@@ -105,10 +105,10 @@ namespace lib4neuro {
}
- std::vector<double>* GradientDescentSingleItem::get_parameters() {
+ std::vector<double> GradientDescentSingleItem::get_parameters() {
// std::shared_ptr<std::vector<double>> ret;
// ret.reset(&this->optimal_parameters);
- return this->optimal_parameters;
+ return std::vector<double>(*this->optimal_parameters);
}
}
diff --git a/src/LearningMethods/GradientDescentSingleItem.h b/src/LearningMethods/GradientDescentSingleItem.h
index 49e8d20d8707f34f6be1b204d92b403632e6743e..61084edcd1cdfb3b8feb83c3f336dc0602b7cad0 100644
--- a/src/LearningMethods/GradientDescentSingleItem.h
+++ b/src/LearningMethods/GradientDescentSingleItem.h
@@ -97,7 +97,7 @@ namespace lib4neuro {
*
* @return
*/
- LIB4NEURO_API std::vector<double>* get_parameters() override;
+ LIB4NEURO_API std::vector<double> get_parameters() override;
};
}
diff --git a/src/LearningMethods/LearningMethod.h b/src/LearningMethods/LearningMethod.h
index 7173ce75870c1df8b58dd4dd2d0a31ec96de8507..29b36233447bb4d2765e1cc1ff451845a83f1132 100644
--- a/src/LearningMethods/LearningMethod.h
+++ b/src/LearningMethods/LearningMethod.h
@@ -24,7 +24,7 @@ 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() = 0;
};
class GradientLearningMethod : public LearningMethod {
diff --git a/src/LearningMethods/LearningSequence.cpp b/src/LearningMethods/LearningSequence.cpp
index 39502e6f983e398e8addcdc49b0358243eeff2c9..33e943cd915bb9a8a6664ab2c70e446990d87bda 100644
--- a/src/LearningMethods/LearningSequence.cpp
+++ b/src/LearningMethods/LearningSequence.cpp
@@ -18,14 +18,8 @@ namespace lib4neuro {
LearningSequence::~LearningSequence() = default;
- std::vector<double>* LearningSequence::get_parameters() {
- if( this->learning_sequence.size() > 0 ){
- return this->learning_sequence.at(0).get()->get_parameters( );
- }
-
-// std::shared_ptr<std::vector<double>> ret;
-// ret.reset();
- return nullptr;
+ std::vector<double> LearningSequence::get_parameters() {
+ return this->best_parameters;
}
void LearningSequence::add_learning_method(std::shared_ptr<LearningMethod> method) {
@@ -44,6 +38,7 @@ namespace lib4neuro {
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::vector<double> params;
while( error > this->tol && mcycles != 0){
mcycles--;
cycle_idx++;
@@ -57,18 +52,19 @@ namespace lib4neuro {
m->optimize( ef, ofs );
puts("*********************** 9");
- error = ef.eval(m->get_parameters());
+ params = m->get_parameters();
+ error = ef.eval(¶ms);
puts("*********************** 10");
// std::shared_ptr<std::vector<double>> params;
// params.reset(m->get_parameters());
- ef.get_network_instance()->copy_parameter_space(m->get_parameters());
+ ef.get_network_instance()->copy_parameter_space(¶ms);
if( error < the_best_error ){
the_best_error = error;
- this->best_parameters = *ef.get_parameters();
+ this->best_parameters = ef.get_parameters();
// best_params = ef.get_parameters();
// best_params.reset(ef.get_parameters().get());
}
diff --git a/src/LearningMethods/LearningSequence.h b/src/LearningMethods/LearningSequence.h
index 7643a3449ecca15fd00a9169216c8b76bd9b726e..1399081a83bff035b8a7316c1e41effbf991cbe3 100644
--- a/src/LearningMethods/LearningSequence.h
+++ b/src/LearningMethods/LearningSequence.h
@@ -65,7 +65,7 @@ namespace lib4neuro {
*
* @return
*/
- LIB4NEURO_API std::vector<double>* get_parameters() override;
+ LIB4NEURO_API std::vector<double> get_parameters() override;
/**
*
diff --git a/src/LearningMethods/LevenbergMarquardt.cpp b/src/LearningMethods/LevenbergMarquardt.cpp
index 98d248af1f7366f09d9a7cecda95d4607d2f070c..8320487a2ee892274655da98cdafd5853d9163a0 100644
--- a/src/LearningMethods/LevenbergMarquardt.cpp
+++ b/src/LearningMethods/LevenbergMarquardt.cpp
@@ -122,7 +122,7 @@ namespace lib4neuro {
if( this->p_impl->batch_size > 0 ){
n_data_points = this->p_impl->batch_size;
}
- std::vector<double>* params_current = ef.get_parameters();
+ std::vector<double>* params_current = new std::vector<double>(ef.get_parameters());
std::shared_ptr<std::vector<double>> params_tmp;
params_tmp.reset(new std::vector<double>(n_parameters));
@@ -247,10 +247,10 @@ namespace lib4neuro {
}
- std::vector<double>* LevenbergMarquardt::get_parameters() {
+ 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;
+ return this->p_impl->optimal_parameters;
}
LevenbergMarquardt::~LevenbergMarquardt() = default;
diff --git a/src/LearningMethods/LevenbergMarquardt.h b/src/LearningMethods/LevenbergMarquardt.h
index b831b62a178f53e3d3e1626e53ba6d08b504fc88..0ae18154a334d1f25e35a7c7eaf353434d296fa6 100644
--- a/src/LearningMethods/LevenbergMarquardt.h
+++ b/src/LearningMethods/LevenbergMarquardt.h
@@ -47,7 +47,7 @@ namespace lib4neuro {
LM_UPDATE_TYPE update_type,
std::ofstream* ofs = nullptr);
- std::vector<double>* get_parameters() override;
+ std::vector<double> get_parameters() override;
~LevenbergMarquardt();
};
diff --git a/src/LearningMethods/ParticleSwarm.cpp b/src/LearningMethods/ParticleSwarm.cpp
index 23dca22026dfa4996ab6e11d62146a16f95a7c90..eadc193f284512fcf0cae3a8573bdebf24addeaa 100644
--- a/src/LearningMethods/ParticleSwarm.cpp
+++ b/src/LearningMethods/ParticleSwarm.cpp
@@ -306,14 +306,14 @@ namespace lib4neuro {
if (this->particle_swarm.at(pi)) {
delete this->particle_swarm.at(pi);
}
- this->particle_swarm.at(pi) = new Particle(&ef, ef.get_parameters(), this->radius_factor);
+ this->particle_swarm.at(pi) = new Particle(&ef, new std::vector<double>(ef.get_parameters()), this->radius_factor);
}
this->radius_factor *= 1.25;
// if (!this->p_min_glob) {
// this->p_min_glob = new std::vector<double>(this->func_dim);
// } else {
- this->p_min_glob->resize(this->func_dim);
+ this->p_min_glob.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->p_min_glob, 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->p_min_glob, 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->p_min_glob) == global_best_vec.end()) {
+ global_best_vec.emplace_back(this->p_min_glob); // 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->p_min_glob,
global_best_vec);
// particle->print_coordinate();
@@ -414,7 +414,7 @@ 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);
+ current_err = ef.eval(&this->p_min_glob);
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->p_min_glob, optimal_value);
//TODO rewrite following output using COUT_INFO
if (outer_it < this->iter_max) {
/* Convergence reached */
@@ -459,7 +459,7 @@ 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);
+ ef.get_network_instance()->copy_parameter_space(&this->p_min_glob);
delete centroid;
}
@@ -534,7 +534,7 @@ namespace lib4neuro {
return std::sqrt(dist);
}
- std::vector<double>* ParticleSwarm::get_parameters() {
+ std::vector<double> ParticleSwarm::get_parameters() {
// std::shared_ptr<std::vector<double>> ret;
// ret.reset(&this->p_min_glob);
return this->p_min_glob;
diff --git a/src/LearningMethods/ParticleSwarm.h b/src/LearningMethods/ParticleSwarm.h
index 6cd389f6bd1b97cfb5331d0e9d1bc701021d8219..ac6d6c9d8cd921015ae8e8f12009dff1a2fa0e75 100644
--- a/src/LearningMethods/ParticleSwarm.h
+++ b/src/LearningMethods/ParticleSwarm.h
@@ -200,7 +200,7 @@ namespace lib4neuro {
/**
* Coordinates of the found global minima
*/
- std::vector<double>* p_min_glob = new std::vector<double>;
+ std::vector<double> p_min_glob;
protected:
/**
@@ -309,7 +309,7 @@ namespace lib4neuro {
*
* @return
*/
- LIB4NEURO_API std::vector<double>* get_parameters() override;
+ LIB4NEURO_API std::vector<double> get_parameters() override;
};
}
diff --git a/src/LearningMethods/RandomSolution.cpp b/src/LearningMethods/RandomSolution.cpp
index d022e8e4d62f89fe0f9138a2a5cff2544a3ec1d3..f5a187de1b6c8c73e95832671d0d8f95a53d78cc 100644
--- a/src/LearningMethods/RandomSolution.cpp
+++ b/src/LearningMethods/RandomSolution.cpp
@@ -16,25 +16,18 @@ namespace lib4neuro {
RandomSolution::~RandomSolution() {}
- std::vector<double>* RandomSolution::get_parameters() {
+ std::vector<double> RandomSolution::get_parameters() {
// std::shared_ptr<std::vector<double>> ret;
// ret.reset(&this->optimal_parameters);
// return ret;
- return &this->optimal_parameters;
+ return this->optimal_parameters;
}
void RandomSolution::optimize(lib4neuro::ErrorFunction &ef, std::ofstream *ofs) {
ef.get_network_instance()->randomize_parameters();
- auto tmp = ef.get_parameters();
-
- this->optimal_parameters = *tmp;
-
- if(tmp) {
- delete tmp;
- tmp = nullptr;
- }
+ this->optimal_parameters = ef.get_parameters();
COUT_INFO("Producing a random solution... error: " << ef.eval(&this->optimal_parameters) << std::endl);
}
diff --git a/src/LearningMethods/RandomSolution.h b/src/LearningMethods/RandomSolution.h
index 4b66a060bf54fe0dbec4c3a9e095fd7879c1d8dc..cdcda7df9edc4f1b183e9df867c255ab865f801a 100644
--- a/src/LearningMethods/RandomSolution.h
+++ b/src/LearningMethods/RandomSolution.h
@@ -29,7 +29,7 @@ namespace lib4neuro {
void optimize(lib4neuro::ErrorFunction &ef,
std::ofstream *ofs = nullptr) override;
- std::vector<double>* get_parameters() override;
+ std::vector<double> get_parameters() override;
};
diff --git a/src/Solvers/DESolver.cpp b/src/Solvers/DESolver.cpp
index b8f3caa6bfa6cf8b47420dc889a1a81dbfbb5c49..50fcb254972ef9f481da59ee9afa0d8d60c2e74c 100644
--- a/src/Solvers/DESolver.cpp
+++ b/src/Solvers/DESolver.cpp
@@ -410,7 +410,8 @@ namespace lib4neuro {
learning_method.optimize(total_error);
// std::shared_ptr<std::vector<double>> params;
// params.reset(learning_method.get_parameters());
- this->solution->copy_parameter_space(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));
}