From 857513738829a044f4f736fc65b9000b0e9f473c Mon Sep 17 00:00:00 2001
From: Martin Beseda <martin.beseda@vsb.cz>
Date: Thu, 21 Mar 2019 02:55:56 +0100
Subject: [PATCH] WIP: Trying to solve 'core dumped' error...
---
src/ErrorFunction/ErrorFunctions.cpp | 103 +++++++++---------
src/ErrorFunction/ErrorFunctions.h | 92 ++++++++--------
src/LearningMethods/GradientDescent.cpp | 13 ++-
src/LearningMethods/GradientDescent.h | 6 +-
src/LearningMethods/GradientDescentBB.cpp | 7 +-
.../GradientDescentSingleItem.cpp | 11 +-
src/LearningMethods/LearningSequence.cpp | 14 ++-
src/LearningMethods/LevenbergMarquardt.cpp | 5 +-
src/LearningMethods/ParticleSwarm.cpp | 15 ++-
src/LearningMethods/RandomSolution.cpp | 2 +-
src/Network/NeuralNetwork.cpp | 6 +-
src/Network/NeuralNetwork.h | 6 +-
src/Network/NeuralNetworkSum.cpp | 4 +-
src/Network/NeuralNetworkSum.h | 16 +--
src/Solvers/DESolver.cpp | 4 +-
src/Solvers/DESolver.h | 2 +-
src/examples/net_test_1.cpp | 4 +-
src/examples/net_test_2.cpp | 4 +-
src/examples/network_serialization.cpp | 4 +-
src/examples/seminar.cpp | 2 +-
20 files changed, 166 insertions(+), 154 deletions(-)
diff --git a/src/ErrorFunction/ErrorFunctions.cpp b/src/ErrorFunction/ErrorFunctions.cpp
index 02744c67..3d53690b 100644
--- a/src/ErrorFunction/ErrorFunctions.cpp
+++ b/src/ErrorFunction/ErrorFunctions.cpp
@@ -78,7 +78,7 @@ namespace lib4neuro {
std::shared_ptr<std::vector<double>> ErrorFunction::get_parameters() {
std::shared_ptr<std::vector<double>> output;
output.reset(new std::vector<double>(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());
+// std::shared_ptr<std::vector<double>>output = new std::vector<double>(this->net->get_n_weights() + this->net->get_n_biases());
size_t i = 0;
@@ -101,9 +101,9 @@ namespace lib4neuro {
this->dimension = net->get_n_weights() + net->get_n_biases();
}
- double MSE::eval_on_single_input(std::vector<double>* input,
- std::vector<double>* output,
- std::vector<double>* weights) {
+ double MSE::eval_on_single_input(std::shared_ptr<std::vector<double>>input,
+ std::shared_ptr<std::vector<double>>output,
+ std::shared_ptr<std::vector<double>> weights) {
std::vector<double> predicted_output(this->get_network_instance()->get_n_outputs());
this->net->eval_single(*input, predicted_output, weights);
double result = 0;
@@ -119,7 +119,7 @@ namespace lib4neuro {
double MSE::eval_on_data_set(lib4neuro::DataSet* data_set,
std::ofstream* results_file_path,
- std::vector<double>* weights,
+ std::shared_ptr<std::vector<double>> weights,
bool denormalize_data,
bool verbose) {
size_t dim_in = data_set->get_input_dim();
@@ -268,7 +268,7 @@ namespace lib4neuro {
double MSE::eval_on_data_set(DataSet* data_set,
std::string results_file_path,
- std::vector<double>* weights,
+ std::shared_ptr<std::vector<double>> weights,
bool verbose) {
std::ofstream ofs(results_file_path);
if (ofs.is_open()) {
@@ -284,7 +284,7 @@ namespace lib4neuro {
}
double MSE::eval_on_data_set(DataSet* data_set,
- std::vector<double>* weights,
+ std::shared_ptr<std::vector<double>> weights,
bool verbose) {
return this->eval_on_data_set(data_set,
nullptr,
@@ -293,7 +293,7 @@ namespace lib4neuro {
verbose);
}
- double MSE::eval(std::vector<double>* weights,
+ double MSE::eval(std::shared_ptr<std::vector<double>> weights,
bool denormalize_data,
bool verbose) {
return this->eval_on_data_set(this->ds,
@@ -303,7 +303,7 @@ namespace lib4neuro {
verbose);
}
- double MSE::eval_on_test_data(std::vector<double>* weights,
+ double MSE::eval_on_test_data(std::shared_ptr<std::vector<double>> weights,
bool verbose) {
return this->eval_on_data_set(this->ds_test,
weights,
@@ -311,7 +311,7 @@ namespace lib4neuro {
}
double MSE::eval_on_test_data(std::string results_file_path,
- std::vector<double>* weights,
+ std::shared_ptr<std::vector<double>> weights,
bool verbose) {
return this->eval_on_data_set(this->ds_test,
results_file_path,
@@ -320,7 +320,7 @@ namespace lib4neuro {
}
double MSE::eval_on_test_data(std::ofstream* results_file_path,
- std::vector<double>* weights,
+ std::shared_ptr<std::vector<double>> weights,
bool verbose) {
return this->eval_on_data_set(this->ds_test,
results_file_path,
@@ -345,11 +345,11 @@ namespace lib4neuro {
}
std::vector<double> error_derivative(dim_out);
-
+ std::shared_ptr<std::vector<double>> params_tmp = std::make_shared<std::vector<double>>(params);
for (auto el: *data) { // Iterate through every element in the test set
this->net->eval_single(el.first, error_derivative,
- ¶ms); // Compute the net output and store it into 'output' variable
+ params_tmp); // Compute the net output and store it into 'output' variable
for (size_t j = 0; j < dim_out; ++j) {
error_derivative[j] = 2.0 * (error_derivative[j] - el.second[j]); //real - expected result
@@ -362,9 +362,9 @@ namespace lib4neuro {
}
}
- double MSE::calculate_single_residual(std::vector<double>* input,
- std::vector<double>* output,
- std::vector<double>* parameters) {
+ double MSE::calculate_single_residual(std::shared_ptr<std::vector<double>>input,
+ std::shared_ptr<std::vector<double>>output,
+ std::shared_ptr<std::vector<double>>parameters) {
//TODO maybe move to the general ErrorFunction
//TODO check input vector sizes - they HAVE TO be allocated before calling this function
@@ -372,9 +372,9 @@ namespace lib4neuro {
return -this->eval_on_single_input(input, output, parameters);
}
- void MSE::calculate_residual_gradient(std::vector<double>* input,
- std::vector<double>* output,
- std::vector<double>* gradient,
+ void MSE::calculate_residual_gradient(std::shared_ptr<std::vector<double>>input,
+ std::shared_ptr<std::vector<double>>output,
+ std::shared_ptr<std::vector<double>>gradient,
double h) {
//TODO check input vector sizes - they HAVE TO be allocated before calling this function
@@ -394,11 +394,11 @@ namespace lib4neuro {
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.get());
+ f_val1 = this->calculate_single_residual(input, output, parameters);
/* Computation of f_val2 = f(x - delta) */
parameters->at(i) = former_parameter_value - delta;
- f_val2 = this->calculate_single_residual(input, output, parameters.get());
+ f_val2 = this->calculate_single_residual(input, output, parameters);
gradient->at(i) = (f_val1 - f_val2) / (2*delta);
}
@@ -432,10 +432,11 @@ namespace lib4neuro {
std::fill(grad_sum.begin(), grad_sum.end(), 0.0);
this->net->write_weights();
this->net->write_biases();
+ std::shared_ptr<std::vector<double>> params_tmp = std::make_shared<std::vector<double>>(params);
for (auto el: *data) { // Iterate through every element in the test set
this->net->eval_single_debug(el.first, error_derivative,
- ¶ms); // Compute the net output and store it into 'output' variable
+ params_tmp); // Compute the net output and store it into 'output' variable
std::cout << "Input[";
for( auto v: el.first){
std::cout << v << ", ";
@@ -484,7 +485,7 @@ namespace lib4neuro {
std::cout << "]" << std::endl << std::endl;
}
- double MSE::eval_single_item_by_idx(size_t i, std::vector<double> *parameter_vector,
+ double MSE::eval_single_item_by_idx(size_t i, std::shared_ptr<std::vector<double>> parameter_vector,
std::vector<double> &error_vector) {
double output = 0, val;
@@ -504,7 +505,7 @@ namespace lib4neuro {
ErrorSum::ErrorSum() {
this->summand = nullptr;
- this->summand_coefficient = nullptr;
+// this->summand_coefficient = nullptr;
this->dimension = 0;
}
@@ -512,12 +513,12 @@ namespace lib4neuro {
if (this->summand) {
delete this->summand;
}
- if (this->summand_coefficient) {
- delete this->summand_coefficient;
- }
+// if (this->summand_coefficient) {
+// delete this->summand_coefficient;
+// }
}
- double ErrorSum::eval_on_test_data(std::vector<double>* weights,
+ double ErrorSum::eval_on_test_data(std::shared_ptr<std::vector<double>> weights,
bool verbose) {
//TODO take care of the case, when there are no test data
@@ -528,7 +529,7 @@ namespace lib4neuro {
ef = this->summand->at(i);
if (ef) {
- output += ef->eval_on_test_data(weights) * this->summand_coefficient->at(i);
+ output += ef->eval_on_test_data(weights) * this->summand_coefficient.at(i);
}
}
@@ -536,7 +537,7 @@ namespace lib4neuro {
}
double ErrorSum::eval_on_test_data(std::string results_file_path,
- std::vector<double>* weights,
+ std::shared_ptr<std::vector<double>> weights,
bool verbose) {
THROW_NOT_IMPLEMENTED_ERROR();
@@ -544,14 +545,14 @@ namespace lib4neuro {
}
double ErrorSum::eval_on_test_data(std::ofstream* results_file_path,
- std::vector<double>* weights,
+ std::shared_ptr<std::vector<double>> weights,
bool verbose) {
THROW_NOT_IMPLEMENTED_ERROR();
return -1;
}
double ErrorSum::eval_on_data_set(lib4neuro::DataSet* data_set,
- std::vector<double>* weights,
+ std::shared_ptr<std::vector<double>> weights,
bool verbose) {
THROW_NOT_IMPLEMENTED_ERROR();
@@ -560,7 +561,7 @@ namespace lib4neuro {
double ErrorSum::eval_on_data_set(lib4neuro::DataSet* data_set,
std::string results_file_path,
- std::vector<double>* weights,
+ std::shared_ptr<std::vector<double>> weights,
bool verbose) {
THROW_NOT_IMPLEMENTED_ERROR();
@@ -569,14 +570,14 @@ namespace lib4neuro {
double ErrorSum::eval_on_data_set(lib4neuro::DataSet* data_set,
std::ofstream* results_file_path,
- std::vector<double>* weights,
+ std::shared_ptr<std::vector<double>> weights,
bool denormalize_data,
bool verbose) {
THROW_NOT_IMPLEMENTED_ERROR();
return -1;
}
- double ErrorSum::eval(std::vector<double>* weights,
+ double ErrorSum::eval(std::shared_ptr<std::vector<double>> weights,
bool denormalize_data,
bool verbose) {
double output = 0.0;
@@ -586,14 +587,14 @@ namespace lib4neuro {
ef = this->summand->at(i);
if (ef) {
- output += ef->eval(weights) * this->summand_coefficient->at(i);
+ output += ef->eval(weights) * this->summand_coefficient.at(i);
}
}
return output;
}
- double ErrorSum::eval_single_item_by_idx(size_t i, std::vector<double> *parameter_vector,
+ double ErrorSum::eval_single_item_by_idx(size_t i, std::shared_ptr<std::vector<double>>parameter_vector,
std::vector<double> &error_vector) {
double output = 0.0;
ErrorFunction* ef = nullptr;
@@ -604,10 +605,10 @@ namespace lib4neuro {
ef = this->summand->at(i);
if (ef) {
- output += ef->eval_single_item_by_idx(i, parameter_vector, error_vector_mem) * this->summand_coefficient->at(j);
+ output += ef->eval_single_item_by_idx(i, parameter_vector, error_vector_mem) * this->summand_coefficient.at(j);
for( size_t k = 0; k < error_vector_mem.size(); ++k){
- error_vector[k] += error_vector_mem[k] * this->summand_coefficient->at(j);
+ error_vector[k] += error_vector_mem[k] * this->summand_coefficient.at(j);
}
}
}
@@ -623,7 +624,7 @@ namespace lib4neuro {
ef = this->summand->at(i);
if (ef) {
- ef->calculate_error_gradient(params, grad, this->summand_coefficient->at(i) * alpha, batch);
+ ef->calculate_error_gradient(params, grad, this->summand_coefficient.at(i) * alpha, batch);
}
}
}
@@ -641,7 +642,7 @@ namespace lib4neuro {
ef = this->summand->at(i);
if (ef) {
- ef->calculate_error_gradient(params, grad, this->summand_coefficient->at(i) * alpha, batch);
+ ef->calculate_error_gradient(params, grad, this->summand_coefficient.at(i) * alpha, batch);
}
}
}
@@ -652,10 +653,10 @@ namespace lib4neuro {
}
this->summand->push_back(F);
- if (!this->summand_coefficient) {
- this->summand_coefficient = new std::vector<double>(0);
- }
- this->summand_coefficient->push_back(alpha);
+// if (!this->summand_coefficient) {
+// this->summand_coefficient = new std::vector<double>(0);
+// }
+ this->summand_coefficient.push_back(alpha);
if (F) {
if (F->get_dimension() > this->dimension) {
@@ -687,16 +688,16 @@ namespace lib4neuro {
};
- void ErrorSum::calculate_residual_gradient(std::vector<double>* input,
- std::vector<double>* output,
- std::vector<double>* gradient,
+ void ErrorSum::calculate_residual_gradient(std::shared_ptr<std::vector<double>>input,
+ std::shared_ptr<std::vector<double>>output,
+ std::shared_ptr<std::vector<double>>gradient,
double h) {
THROW_NOT_IMPLEMENTED_ERROR();
}
- double ErrorSum::calculate_single_residual(std::vector<double>* input,
- std::vector<double>* output,
- std::vector<double>* parameters) {
+ double ErrorSum::calculate_single_residual(std::shared_ptr<std::vector<double>>input,
+ std::shared_ptr<std::vector<double>>output,
+ std::shared_ptr<std::vector<double>>parameters) {
THROW_NOT_IMPLEMENTED_ERROR();
return 0;
diff --git a/src/ErrorFunction/ErrorFunctions.h b/src/ErrorFunction/ErrorFunctions.h
index dc7c6a49..23c146c4 100644
--- a/src/ErrorFunction/ErrorFunctions.h
+++ b/src/ErrorFunction/ErrorFunctions.h
@@ -27,7 +27,7 @@ namespace lib4neuro {
* @param weights
* @return
*/
- virtual double eval(std::vector<double>* weights = nullptr, bool denormalize_data=false,
+ virtual double eval(std::shared_ptr<std::vector<double>> weights = nullptr, bool denormalize_data=false,
bool verbose = false) = 0;
/**
@@ -100,7 +100,7 @@ namespace lib4neuro {
/**
*
*/
- virtual double eval_on_test_data(std::vector<double>* weights = nullptr, bool verbose = false) = 0;
+ virtual double eval_on_test_data(std::shared_ptr<std::vector<double>> weights = nullptr, bool verbose = false) = 0;
/**
*
@@ -108,7 +108,7 @@ namespace lib4neuro {
* @param weights
* @return
*/
- virtual double eval_on_test_data(std::string results_file_path, std::vector<double>* weights = nullptr,
+ virtual double eval_on_test_data(std::string results_file_path, std::shared_ptr<std::vector<double>> weights = nullptr,
bool verbose = false) = 0;
/**
@@ -117,7 +117,7 @@ namespace lib4neuro {
* @param weights
* @return
*/
- virtual double eval_on_test_data(std::ofstream* results_file_path, std::vector<double>* weights = nullptr,
+ virtual double eval_on_test_data(std::ofstream* results_file_path, std::shared_ptr<std::vector<double>> weights = nullptr,
bool verbose = false) = 0;
/**
@@ -126,7 +126,7 @@ namespace lib4neuro {
* @param weights
* @return
*/
- virtual double eval_on_data_set(DataSet* data_set, std::vector<double>* weights = nullptr,
+ virtual double eval_on_data_set(DataSet* data_set, std::shared_ptr<std::vector<double>> weights = nullptr,
bool verbose = false) = 0;
/**
@@ -137,7 +137,7 @@ namespace lib4neuro {
* @return
*/
virtual double
- eval_on_data_set(DataSet* data_set, std::string results_file_path, std::vector<double>* weights = nullptr,
+ eval_on_data_set(DataSet* data_set, std::string results_file_path, std::shared_ptr<std::vector<double>> weights = nullptr,
bool verbose = false) = 0;
/**
@@ -149,7 +149,7 @@ namespace lib4neuro {
*/
virtual double eval_on_data_set(DataSet* data_set,
std::ofstream* results_file_path = nullptr,
- std::vector<double>* weights = nullptr,
+ std::shared_ptr<std::vector<double>> weights = nullptr,
bool denormalize_data = true,
bool verbose = false) = 0;
@@ -160,7 +160,7 @@ namespace lib4neuro {
* @param error_vector
* @return
*/
- virtual double eval_single_item_by_idx(size_t i, std::vector<double> *parameter_vector, std::vector<double> &error_vector) = 0;
+ virtual double eval_single_item_by_idx(size_t i, std::shared_ptr<std::vector<double>> parameter_vector, std::vector<double> &error_vector) = 0;
/**
*
@@ -177,9 +177,9 @@ namespace lib4neuro {
* @param h
*/
virtual void
- calculate_residual_gradient(std::vector<double>* input,
- std::vector<double>* output,
- std::vector<double>* gradient,
+ calculate_residual_gradient(std::shared_ptr<std::vector<double>> input,
+ std::shared_ptr<std::vector<double>> output,
+ std::shared_ptr<std::vector<double>> gradient,
double h = 1e-3) = 0;
/**
@@ -190,9 +190,9 @@ namespace lib4neuro {
* @return
*/
virtual double
- calculate_single_residual(std::vector<double>* input,
- std::vector<double>* output,
- std::vector<double>* parameters = nullptr) = 0;
+ calculate_single_residual(std::shared_ptr<std::vector<double>> input,
+ std::shared_ptr<std::vector<double>> output,
+ std::shared_ptr<std::vector<double>> parameters = nullptr) = 0;
protected:
@@ -237,7 +237,7 @@ namespace lib4neuro {
* @param weights
* @return
*/
- LIB4NEURO_API double eval(std::vector<double>* weights = nullptr,
+ LIB4NEURO_API double eval(std::shared_ptr<std::vector<double>> weights = nullptr,
bool denormalize_data = false,
bool verbose = false) override;
@@ -275,9 +275,9 @@ namespace lib4neuro {
* @return
*/
LIB4NEURO_API
- virtual double calculate_single_residual(std::vector<double>* input,
- std::vector<double>* output,
- std::vector<double>* parameters) override;
+ virtual double calculate_single_residual(std::shared_ptr<std::vector<double>> input,
+ std::shared_ptr<std::vector<double>> output,
+ std::shared_ptr<std::vector<double>> parameters) override;
/**
* Compute gradient of the residual function f(x) = 0 - MSE(x) for a specific input x.
@@ -288,9 +288,9 @@ namespace lib4neuro {
* @param[in] h Step used in the central difference
*/
LIB4NEURO_API void
- calculate_residual_gradient(std::vector<double>* input,
- std::vector<double>* output,
- std::vector<double>* gradient,
+ calculate_residual_gradient(std::shared_ptr<std::vector<double>> input,
+ std::shared_ptr<std::vector<double>> output,
+ std::shared_ptr<std::vector<double>> gradient,
double h=1e-3) override;
/**
@@ -298,16 +298,16 @@ namespace lib4neuro {
* @param input
* @return
*/
- LIB4NEURO_API double eval_on_single_input(std::vector<double>* input,
- std::vector<double>* output,
- std::vector<double>* weights = nullptr);
+ LIB4NEURO_API double eval_on_single_input(std::shared_ptr<std::vector<double>> input,
+ std::shared_ptr<std::vector<double>> output,
+ std::shared_ptr<std::vector<double>> weights = nullptr);
/**
*
* @param weights
* @return
*/
- LIB4NEURO_API double eval_on_test_data(std::vector<double>* weights = nullptr, bool verbose = false) override;
+ LIB4NEURO_API double eval_on_test_data(std::shared_ptr<std::vector<double>> weights = nullptr, bool verbose = false) override;
/**
*
@@ -316,7 +316,7 @@ namespace lib4neuro {
* @return
*/
LIB4NEURO_API double eval_on_test_data(std::string results_file_path = nullptr,
- std::vector<double>* weights = nullptr,
+ std::shared_ptr<std::vector<double>> weights = nullptr,
bool verbose = false);
/**
@@ -326,7 +326,7 @@ namespace lib4neuro {
* @return
*/
LIB4NEURO_API double eval_on_test_data(std::ofstream* results_file_path,
- std::vector<double>* weights = nullptr,
+ std::shared_ptr<std::vector<double>> weights = nullptr,
bool verbose = false) override;
/**
@@ -338,7 +338,7 @@ namespace lib4neuro {
*/
LIB4NEURO_API double eval_on_data_set(DataSet* data_set,
std::ofstream* results_file_path,
- std::vector<double>* weights = nullptr,
+ std::shared_ptr<std::vector<double>> weights = nullptr,
bool denormalize_data = false,
bool verbose = false) override;
@@ -349,7 +349,7 @@ namespace lib4neuro {
* @return
*/
LIB4NEURO_API double eval_on_data_set(DataSet* data_set,
- std::vector<double>* weights = nullptr,
+ std::shared_ptr<std::vector<double>> weights = nullptr,
bool verbose = false) override;
/**
@@ -361,7 +361,7 @@ namespace lib4neuro {
*/
LIB4NEURO_API double eval_on_data_set(DataSet* data_set,
std::string results_file_path,
- std::vector<double>* weights = nullptr,
+ std::shared_ptr<std::vector<double>> weights = nullptr,
bool verbose = false) override;
/**
@@ -371,7 +371,7 @@ namespace lib4neuro {
* @param error_vector
* @return
*/
- LIB4NEURO_API virtual double eval_single_item_by_idx(size_t i, std::vector<double> *parameter_vector, std::vector<double> &error_vector) override;
+ LIB4NEURO_API virtual double eval_single_item_by_idx(size_t i, std::shared_ptr<std::vector<double>> parameter_vector, std::vector<double> &error_vector) override;
/**
*
@@ -398,7 +398,7 @@ namespace lib4neuro {
* @param weights
* @return
*/
- LIB4NEURO_API double eval(std::vector<double>* weights = nullptr,
+ LIB4NEURO_API double eval(std::shared_ptr<std::vector<double>> weights = nullptr,
bool denormalize_data = false,
bool verbose = false);
@@ -407,7 +407,7 @@ namespace lib4neuro {
* @param weights
* @return
*/
- LIB4NEURO_API double eval_on_test_data(std::vector<double>* weights = nullptr, bool verbose = false) override;
+ LIB4NEURO_API double eval_on_test_data(std::shared_ptr<std::vector<double>> weights = nullptr, bool verbose = false) override;
/**
*
@@ -416,7 +416,7 @@ namespace lib4neuro {
* @return
*/
LIB4NEURO_API double eval_on_test_data(std::string results_file_path,
- std::vector<double>* weights = nullptr,
+ std::shared_ptr<std::vector<double>> weights = nullptr,
bool verbose = false) override;
/**
@@ -426,7 +426,7 @@ namespace lib4neuro {
* @return
*/
LIB4NEURO_API double eval_on_test_data(std::ofstream* results_file_path,
- std::vector<double>* weights = nullptr,
+ std::shared_ptr<std::vector<double>> weights = nullptr,
bool verbose = false) override;
/**
@@ -436,7 +436,7 @@ namespace lib4neuro {
* @return
*/
LIB4NEURO_API double eval_on_data_set(DataSet* data_set,
- std::vector<double>* weights = nullptr,
+ std::shared_ptr<std::vector<double>> weights = nullptr,
bool verbose = false) override;
/**
@@ -448,7 +448,7 @@ namespace lib4neuro {
*/
LIB4NEURO_API double eval_on_data_set(DataSet* data_set,
std::string results_file_path,
- std::vector<double>* weights = nullptr,
+ std::shared_ptr<std::vector<double>> weights = nullptr,
bool verbose = false) override;
/**
@@ -460,7 +460,7 @@ namespace lib4neuro {
*/
LIB4NEURO_API double eval_on_data_set(DataSet* data_set,
std::ofstream* results_file_path,
- std::vector<double>* weights = nullptr,
+ std::shared_ptr<std::vector<double>> weights = nullptr,
bool denormalize_data = true,
bool verbose = false) override;
@@ -471,7 +471,7 @@ namespace lib4neuro {
* @param error_vector
* @return
*/
- LIB4NEURO_API virtual double eval_single_item_by_idx(size_t i, std::vector<double> *parameter_vector, std::vector<double> &error_vector) override;
+ LIB4NEURO_API virtual double eval_single_item_by_idx(size_t i, std::shared_ptr<std::vector<double>> parameter_vector, std::vector<double> &error_vector) override;
/**
*
@@ -518,15 +518,15 @@ namespace lib4neuro {
size_t batch = 0) override;
LIB4NEURO_API void
- calculate_residual_gradient(std::vector<double>* input,
- std::vector<double>* output,
- std::vector<double>* gradient,
+ calculate_residual_gradient(std::shared_ptr<std::vector<double>> input,
+ std::shared_ptr<std::vector<double>> output,
+ std::shared_ptr<std::vector<double>> gradient,
double h = 1e-3) override;
LIB4NEURO_API double
- calculate_single_residual(std::vector<double>* input,
- std::vector<double>* output,
- std::vector<double>* parameters = nullptr) override;
+ calculate_single_residual(std::shared_ptr<std::vector<double>> input,
+ std::shared_ptr<std::vector<double>> output,
+ std::shared_ptr<std::vector<double>> parameters = nullptr) override;
/**
@@ -543,7 +543,7 @@ namespace lib4neuro {
protected:
std::vector<ErrorFunction*>* summand;
- std::vector<double>* summand_coefficient;
+ std::vector<double> summand_coefficient;
};
}
diff --git a/src/LearningMethods/GradientDescent.cpp b/src/LearningMethods/GradientDescent.cpp
index e041bcd3..390ca504 100644
--- a/src/LearningMethods/GradientDescent.cpp
+++ b/src/LearningMethods/GradientDescent.cpp
@@ -47,9 +47,9 @@ namespace lib4neuro {
lib4neuro::ErrorFunction &ef,
double error_previous,
double step_coefficient,
- std::vector<double> *direction,
- std::vector<double> *parameters_before,
- std::vector<double> *parameters_after
+ std::shared_ptr<std::vector<double>> direction,
+ std::shared_ptr<std::vector<double>> parameters_before,
+ std::shared_ptr<std::vector<double>> parameters_after
) {
size_t i;
@@ -124,7 +124,7 @@ namespace lib4neuro {
std::fill(gradient_current->begin(), gradient_current->end(), 0.0);
std::fill(gradient_prev->begin(), gradient_prev->end(), 0.0);
- val = ef.eval(params_current.get());
+ val = ef.eval(params_current);
double coeff = 1;
bool it_analyzed = false;
size_t counter_good_guesses = 0, counter_bad_guesses = 0, counter_simplified_direction_good = 0, counter_simplified_direction_bad = 0;
@@ -181,7 +181,7 @@ namespace lib4neuro {
for (i = 0; i < gradient_current->size(); ++i) {
(*params_prev)[i] = (*params_current)[i] - cooling * gamma * (*gradient_current)[i];
}
- val = ef.eval(params_prev.get());
+ val = ef.eval(params_prev);
// val = prev_val + 1.0;
// coeff = 1;
@@ -296,7 +296,8 @@ namespace lib4neuro {
}
this->optimal_parameters = *params_current;
- ef.get_network_instance()->copy_parameter_space( &this->optimal_parameters );
+ std::shared_ptr<std::vector<double>> params = std::make_shared<std::vector<double>>(this->optimal_parameters);
+ ef.get_network_instance()->copy_parameter_space( params );
// delete gradient_current;
// delete gradient_prev;
diff --git a/src/LearningMethods/GradientDescent.h b/src/LearningMethods/GradientDescent.h
index 96b6429b..ab507466 100644
--- a/src/LearningMethods/GradientDescent.h
+++ b/src/LearningMethods/GradientDescent.h
@@ -86,9 +86,9 @@ namespace lib4neuro {
lib4neuro::ErrorFunction &ef,
double error_previous,
double step_coefficient,
- std::vector<double> * direction,
- std::vector<double> *parameters_before,
- std::vector<double> *parameters_after
+ std::shared_ptr<std::vector<double>> direction,
+ std::shared_ptr<std::vector<double>> parameters_before,
+ std::shared_ptr<std::vector<double>> parameters_after
);
public:
diff --git a/src/LearningMethods/GradientDescentBB.cpp b/src/LearningMethods/GradientDescentBB.cpp
index 86d67b6d..5577777f 100644
--- a/src/LearningMethods/GradientDescentBB.cpp
+++ b/src/LearningMethods/GradientDescentBB.cpp
@@ -68,7 +68,7 @@ namespace lib4neuro {
std::fill(gradient_current->begin(), gradient_current->end(), 0.0);
std::fill(gradient_prev->begin(), gradient_prev->end(), 0.0);
- val = ef.eval(params_current.get());
+ val = ef.eval(params_current);
val_best = val;
// this-> batch = 0;
@@ -143,7 +143,7 @@ namespace lib4neuro {
params_prev = params_current;
params_current = ptr_mem;
- val = ef.eval(params_current.get());
+ val = ef.eval(params_current);
if( val < val_best ){
val_best = val;
@@ -212,7 +212,8 @@ namespace lib4neuro {
this->optimal_parameters = *params_best;
// ef.analyze_error_gradient(*params_current, *gradient_current, 1.0, this->batch);
- ef.get_network_instance()->copy_parameter_space(&this->optimal_parameters);
+ std::shared_ptr<std::vector<double>> params = std::make_shared<std::vector<double>>(this->optimal_parameters);
+ ef.get_network_instance()->copy_parameter_space(params);
//
// delete gradient_current;
// delete gradient_prev;
diff --git a/src/LearningMethods/GradientDescentSingleItem.cpp b/src/LearningMethods/GradientDescentSingleItem.cpp
index ea6b41a4..953396e6 100644
--- a/src/LearningMethods/GradientDescentSingleItem.cpp
+++ b/src/LearningMethods/GradientDescentSingleItem.cpp
@@ -36,15 +36,15 @@ namespace lib4neuro {
double value_shifted = value + 1.0;
- std::vector<double> shifted_x(x);
+ std::shared_ptr<std::vector<double>> shifted_x = std::make_shared<std::vector<double>>(std::vector<double>(x));
while( value_shifted > value ){
alpha *= 0.5;
for( size_t i = 0; i < x.size(); ++i ){
- shifted_x[ i ] = x [ i ] - alpha * d[ i ];
+ (*shifted_x).at(i) = x.at(i) - alpha * d.at(i);
}
- value_shifted = f.eval( &shifted_x );
+ value_shifted = f.eval( shifted_x );
}
// std::cout << "Error reduction: " << value - value_shifted << std::endl;
return alpha;
@@ -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, ¶meter_vector, error_vector );
+ error = ef.eval_single_item_by_idx( i, ef.get_parameters(), error_vector );
if( error > max_error ){
max_error = error;
@@ -99,7 +99,8 @@ namespace lib4neuro {
COUT_DEBUG("Iteration: " << iter << ", Total elements in train set: " << total_elements << ", # of elements with high error: " << updated_elements << ", max. error: " << max_error << std::endl);
this->optimal_parameters = parameter_vector;
- ef.get_network_instance()->copy_parameter_space( &this->optimal_parameters );
+ std::shared_ptr<std::vector<double>> opt_params = std::make_shared<std::vector<double>>(this->optimal_parameters);
+ ef.get_network_instance()->copy_parameter_space( opt_params );
}
diff --git a/src/LearningMethods/LearningSequence.cpp b/src/LearningMethods/LearningSequence.cpp
index 6bd31a51..31e05b6a 100644
--- a/src/LearningMethods/LearningSequence.cpp
+++ b/src/LearningMethods/LearningSequence.cpp
@@ -43,6 +43,7 @@ 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);
while( error > this->tol && mcycles != 0){
mcycles--;
cycle_idx++;
@@ -54,26 +55,27 @@ namespace lib4neuro {
puts("*********************** 8");
m->optimize( ef, ofs );
- error = ef.eval(m->get_parameters().get());
-
puts("*********************** 9");
- ef.get_network_instance()->copy_parameter_space(m->get_parameters().get());
-
+ error = ef.eval(m->get_parameters());
puts("*********************** 10");
+ ef.get_network_instance()->copy_parameter_space(m->get_parameters());
+
+
+
if( error < the_best_error ){
the_best_error = error;
this->best_parameters = *ef.get_parameters();
}
if( error <= this->tol ){
- ef.get_network_instance()->copy_parameter_space( &this->best_parameters );
+ ef.get_network_instance()->copy_parameter_space( best_params );
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( best_params );
}
}
\ No newline at end of file
diff --git a/src/LearningMethods/LevenbergMarquardt.cpp b/src/LearningMethods/LevenbergMarquardt.cpp
index 29873b0a..295f1a99 100644
--- a/src/LearningMethods/LevenbergMarquardt.cpp
+++ b/src/LearningMethods/LevenbergMarquardt.cpp
@@ -194,7 +194,7 @@ namespace lib4neuro {
update_norm += update.at(i) * update.at(i);
}
update_norm = std::sqrt(update_norm);
- current_err = ef.eval(params_tmp.get());
+ current_err = ef.eval(params_tmp);
/* Check, if the parameter update improved the function */
if(current_err < prev_err) {
@@ -234,7 +234,8 @@ namespace lib4neuro {
/* Store the optimized parameters */
this->p_impl->optimal_parameters = *params_current;
- ef.get_network_instance()->copy_parameter_space(&this->p_impl->optimal_parameters);
+ 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(params);
// delete params_tmp;
diff --git a/src/LearningMethods/ParticleSwarm.cpp b/src/LearningMethods/ParticleSwarm.cpp
index d1306543..ed4590e2 100644
--- a/src/LearningMethods/ParticleSwarm.cpp
+++ b/src/LearningMethods/ParticleSwarm.cpp
@@ -81,7 +81,8 @@ Particle::Particle(lib4neuro::ErrorFunction *ef, std::vector<double> *domain_bou
(*this->optimal_coordinate)[i] = (*this->coordinate)[i];
}
- this->optimal_value = this->ef->eval(this->coordinate);
+ std::shared_ptr<std::vector<double>> coord(this->coordinate);
+ this->optimal_value = this->ef->eval(coord);
}
@@ -118,7 +119,8 @@ Particle::Particle(lib4neuro::ErrorFunction *ef, std::vector<double> *central_sy
(*this->optimal_coordinate)[i] = (*this->coordinate)[i];
}
- this->optimal_value = this->ef->eval(this->coordinate);
+ std::shared_ptr<std::vector<double>> coord(this->coordinate);
+ this->optimal_value = this->ef->eval(coord);
}
@@ -208,7 +210,8 @@ double Particle::change_coordinate(double w, double c1, double c2, std::vector<d
output += std::abs(vel_mem);
}
- vel_mem = this->ef->eval(this->coordinate);
+ std::shared_ptr<std::vector<double>> coord(this->coordinate);
+ vel_mem = this->ef->eval(coord);
this->current_val = vel_mem;
if(vel_mem < this->optimal_value){
@@ -410,7 +413,8 @@ namespace lib4neuro {
// }
// }
- current_err = ef.eval(&this->p_min_glob);
+ std::shared_ptr<std::vector<double>> coord = std::make_shared<std::vector<double>>(this->p_min_glob);
+ current_err = ef.eval(coord);
COUT_DEBUG(std::string("Iteration: ") << (unsigned int)(outer_it)
<< ". Total error: " << current_err
@@ -453,7 +457,8 @@ namespace lib4neuro {
COUT_INFO( std::endl << "Max number of iterations reached (" << outer_it << ")! Objective function value: " << optimal_value <<std:: endl);
}
- ef.get_network_instance()->copy_parameter_space(&this->p_min_glob);
+ std::shared_ptr<std::vector<double>> coord = std::make_shared<std::vector<double>>(this->p_min_glob);
+ ef.get_network_instance()->copy_parameter_space(coord);
delete centroid;
}
diff --git a/src/LearningMethods/RandomSolution.cpp b/src/LearningMethods/RandomSolution.cpp
index 01e7c013..f7dd5031 100644
--- a/src/LearningMethods/RandomSolution.cpp
+++ b/src/LearningMethods/RandomSolution.cpp
@@ -27,7 +27,7 @@ namespace lib4neuro {
void RandomSolution::optimize(lib4neuro::ErrorFunction &ef, std::ofstream *ofs) {
ef.get_network_instance()->randomize_parameters();
this->optimal_parameters = *ef.get_parameters();
- COUT_INFO("Producing a random solution... error: " << ef.eval(&this->optimal_parameters) << std::endl);
+ COUT_INFO("Producing a random solution... error: " << ef.eval(ef.get_parameters()) << std::endl);
}
}
\ No newline at end of file
diff --git a/src/Network/NeuralNetwork.cpp b/src/Network/NeuralNetwork.cpp
index e13646bb..0edfb96d 100644
--- a/src/Network/NeuralNetwork.cpp
+++ b/src/Network/NeuralNetwork.cpp
@@ -411,7 +411,7 @@ namespace lib4neuro {
}
void NeuralNetwork::eval_single_debug(::std::vector<double> &input, ::std::vector<double> &output,
- ::std::vector<double> *custom_weights_and_biases) {
+ ::std::shared_ptr<std::vector<double>> custom_weights_and_biases) {
if ((this->input_neuron_indices.size() * this->output_neuron_indices.size()) <= 0) {
THROW_INVALID_ARGUMENT_ERROR("Input and output neurons have not been specified!");
}
@@ -542,7 +542,7 @@ namespace lib4neuro {
this->layers_analyzed = false;
}
- void NeuralNetwork::copy_parameter_space(std::vector<double> *parameters) {
+ void NeuralNetwork::copy_parameter_space(std::shared_ptr<std::vector<double>> parameters) {
if (parameters != nullptr) {
for (unsigned int i = 0; i < this->connection_weights.size(); ++i) {
this->connection_weights.at(i) = (*parameters).at(i);
@@ -575,7 +575,7 @@ namespace lib4neuro {
void NeuralNetwork::eval_single(::std::vector<double>& input,
::std::vector<double>& output,
- ::std::vector<double>* custom_weights_and_biases) {
+ ::std::shared_ptr<std::vector<double>> custom_weights_and_biases) {
if ((this->input_neuron_indices.size() * this->output_neuron_indices.size()) <= 0) {
THROW_INVALID_ARGUMENT_ERROR("Input and output neurons have not been specified!");
diff --git a/src/Network/NeuralNetwork.h b/src/Network/NeuralNetwork.h
index cc18cd56..54d8b563 100644
--- a/src/Network/NeuralNetwork.h
+++ b/src/Network/NeuralNetwork.h
@@ -184,7 +184,7 @@ namespace lib4neuro {
* @param custom_weights_and_biases
*/
LIB4NEURO_API virtual void eval_single_debug(std::vector<double> &input, std::vector<double> &output,
- std::vector<double> *custom_weights_and_biases = nullptr);
+ std::shared_ptr<std::vector<double>> custom_weights_and_biases = nullptr);
/**
@@ -232,7 +232,7 @@ namespace lib4neuro {
* Replaces the values in @{this->connection_weights} and @{this->neuron_biases} by the provided values
* @param parameters
*/
- LIB4NEURO_API virtual void copy_parameter_space(std::vector<double> *parameters);
+ LIB4NEURO_API virtual void copy_parameter_space(std::shared_ptr<std::vector<double>> parameters);
/**
* Copies the pointers @{this->connection_weights} and @{this->neuron_biases} from the parental network, sets
@@ -248,7 +248,7 @@ namespace lib4neuro {
* @param custom_weights_and_biases
*/
LIB4NEURO_API virtual void eval_single(std::vector<double> &input, std::vector<double> &output,
- std::vector<double> *custom_weights_and_biases = nullptr);
+ std::shared_ptr<std::vector<double>> custom_weights_and_biases = nullptr);
/**
*
diff --git a/src/Network/NeuralNetworkSum.cpp b/src/Network/NeuralNetworkSum.cpp
index 16dafc1e..d4a66ab3 100644
--- a/src/Network/NeuralNetworkSum.cpp
+++ b/src/Network/NeuralNetworkSum.cpp
@@ -49,7 +49,7 @@ namespace lib4neuro {
}
void NeuralNetworkSum::eval_single(std::vector<double> &input, std::vector<double> &output,
- std::vector<double> *custom_weights_and_biases) {
+ std::shared_ptr<std::vector<double>> custom_weights_and_biases) {
std::vector<double> mem_output(output.size());
std::fill(output.begin(), output.end(), 0.0);
@@ -155,7 +155,7 @@ namespace lib4neuro {
}
void NeuralNetworkSum::eval_single_debug(std::vector<double> &input, std::vector<double> &output,
- std::vector<double> *custom_weights_and_biases) {
+ std::shared_ptr<std::vector<double>> custom_weights_and_biases) {
std::vector<double> mem_output(output.size());
std::fill(output.begin(), output.end(), 0.0);
diff --git a/src/Network/NeuralNetworkSum.h b/src/Network/NeuralNetworkSum.h
index c20bb888..0fcfc284 100644
--- a/src/Network/NeuralNetworkSum.h
+++ b/src/Network/NeuralNetworkSum.h
@@ -41,7 +41,7 @@ namespace lib4neuro {
* @param custom_weights_and_biases
*/
LIB4NEURO_API void eval_single(std::vector<double> &input, std::vector<double> &output,
- std::vector<double> *custom_weights_and_biases = nullptr) override;
+ std::shared_ptr<std::vector<double>> custom_weights_and_biases = nullptr) override;
/**
*
@@ -50,7 +50,7 @@ namespace lib4neuro {
* @param custom_weights_and_biases
*/
LIB4NEURO_API void eval_single_debug(std::vector<double> &input, std::vector<double> &output,
- std::vector<double> *custom_weights_and_biases = nullptr) override;
+ std::shared_ptr<std::vector<double>> custom_weights_and_biases = nullptr) override;
/**
@@ -66,37 +66,37 @@ namespace lib4neuro {
*
* @return
*/
- LIB4NEURO_API virtual size_t get_n_inputs() override;
+ LIB4NEURO_API size_t get_n_inputs() override;
/**
*
* @return
*/
- LIB4NEURO_API virtual size_t get_n_outputs() override;
+ LIB4NEURO_API size_t get_n_outputs() override;
/**
*
* @return
*/
- LIB4NEURO_API virtual size_t get_n_weights() override;
+ LIB4NEURO_API size_t get_n_weights() override;
/**
*
* @return
*/
- LIB4NEURO_API virtual size_t get_n_biases() override;
+ LIB4NEURO_API size_t get_n_biases() override;
/**
*
* @return
*/
- LIB4NEURO_API virtual size_t get_n_neurons() override;
+ LIB4NEURO_API size_t get_n_neurons() override;
/**
*
* @return
*/
//TODO only works if all the networks share the same parameters
- LIB4NEURO_API virtual std::vector<double> *get_parameter_ptr_weights() override;
+ LIB4NEURO_API std::vector<double> *get_parameter_ptr_weights() override;
/**
*
diff --git a/src/Solvers/DESolver.cpp b/src/Solvers/DESolver.cpp
index 262bb667..b3aa6697 100644
--- a/src/Solvers/DESolver.cpp
+++ b/src/Solvers/DESolver.cpp
@@ -408,7 +408,7 @@ namespace lib4neuro {
printf("error before optimization: %f\n", total_error.eval(nullptr));
learning_method.optimize(total_error);
- this->solution->copy_parameter_space(learning_method.get_parameters().get());
+ this->solution->copy_parameter_space(learning_method.get_parameters());
printf("error after optimization: %f\n", total_error.eval(nullptr));
}
@@ -425,7 +425,7 @@ namespace lib4neuro {
}
double
- DESolver::eval_equation(size_t equation_idx, std::vector<double> *weight_and_biases, std::vector<double> &input) {
+ DESolver::eval_equation(size_t equation_idx, std::shared_ptr<std::vector<double>>weight_and_biases, std::vector<double> &input) {
std::vector<double> output(1);
this->differential_equations.at(equation_idx)->eval_single(input, output, weight_and_biases);
diff --git a/src/Solvers/DESolver.h b/src/Solvers/DESolver.h
index dba092c8..c0b10618 100644
--- a/src/Solvers/DESolver.h
+++ b/src/Solvers/DESolver.h
@@ -166,7 +166,7 @@ namespace lib4neuro {
* For testing purposes only
*/
LIB4NEURO_API double
- eval_equation(size_t equation_idx, std::vector<double> *weights_and_biases, std::vector<double> &input);
+ eval_equation(size_t equation_idx, std::shared_ptr<std::vector<double>>weights_and_biases, std::vector<double> &input);
/**
* For testing purposes only
diff --git a/src/examples/net_test_1.cpp b/src/examples/net_test_1.cpp
index fa98dc05..141de453 100644
--- a/src/examples/net_test_1.cpp
+++ b/src/examples/net_test_1.cpp
@@ -49,7 +49,7 @@ void optimize_via_particle_swarm( l4n::NeuralNetwork &net, l4n::ErrorFunction &e
);
swarm_01.optimize( ef );
- std::vector<double> *parameters = swarm_01.get_parameters().get();
+ std::shared_ptr<std::vector<double>> parameters = swarm_01.get_parameters();
net.copy_parameter_space(parameters);
/* ERROR CALCULATION */
@@ -64,7 +64,7 @@ void optimize_via_gradient_descent( l4n::NeuralNetwork &net, l4n::ErrorFunction
gd.optimize( ef );
- std::vector<double> *parameters = gd.get_parameters().get();
+ std::shared_ptr<std::vector<double>> parameters = gd.get_parameters();
net.copy_parameter_space(parameters);
/* ERROR CALCULATION */
diff --git a/src/examples/net_test_2.cpp b/src/examples/net_test_2.cpp
index 54c356e6..09920239 100644
--- a/src/examples/net_test_2.cpp
+++ b/src/examples/net_test_2.cpp
@@ -48,7 +48,7 @@ void optimize_via_particle_swarm( l4n::NeuralNetwork &net, l4n::ErrorFunction &e
);
swarm_01.optimize( ef );
- std::vector<double> *parameters = swarm_01.get_parameters().get();
+ std::shared_ptr<std::vector<double>> parameters = swarm_01.get_parameters();
net.copy_parameter_space(parameters);
std::cout << "Run finished! Error of the network[Particle swarm]: " << ef.eval( nullptr ) << std::endl;
@@ -61,7 +61,7 @@ void optimize_via_gradient_descent( l4n::NeuralNetwork &net, l4n::ErrorFunction
gd.optimize( ef );
- std::vector<double> *parameters = gd.get_parameters().get();
+ std::shared_ptr<std::vector<double>> parameters = gd.get_parameters();
net.copy_parameter_space(parameters);
/* ERROR CALCULATION */
diff --git a/src/examples/network_serialization.cpp b/src/examples/network_serialization.cpp
index 75890c6a..ab2acef2 100644
--- a/src/examples/network_serialization.cpp
+++ b/src/examples/network_serialization.cpp
@@ -55,7 +55,7 @@ int main() {
size_t idx2 = net.add_neuron(i2, l4n::BIAS_TYPE::NO_BIAS);
size_t idx3 = net.add_neuron(o1, l4n::BIAS_TYPE::NEXT_BIAS);
- std::vector<double> *bv = net.get_parameter_ptr_biases();
+ std::vector<double>* bv = net.get_parameter_ptr_biases();
for(size_t i = 0; i < 1; ++i){
bv->at(i) = 1.0;
}
@@ -116,7 +116,7 @@ int main() {
);
swarm_01.optimize( mse );
- std::vector<double> *parameters = swarm_01.get_parameters().get();
+ std::shared_ptr<std::vector<double>>parameters = swarm_01.get_parameters();
net.copy_parameter_space(parameters);
printf("w1 = %10.7f\n", parameters->at( 0 ));
diff --git a/src/examples/seminar.cpp b/src/examples/seminar.cpp
index ff36d496..2d68db5a 100644
--- a/src/examples/seminar.cpp
+++ b/src/examples/seminar.cpp
@@ -116,7 +116,7 @@ int main() {
);
swarm_01.optimize( mse );
- std::vector<double> *parameters = swarm_01.get_parameters( ).get();
+ std::shared_ptr<std::vector<double>> parameters = swarm_01.get_parameters( );
XOR.copy_parameter_space(parameters);
/* ERROR CALCULATION */
--
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