Commit c46dd8d4 authored by Michal Kravcenko's avatar Michal Kravcenko

Finished the first 2D partial differential equation example

parent 57f9a4ed
......@@ -40,7 +40,7 @@ DataSet::DataSet(double lower_bound, double upper_bound, unsigned int size, doub
this->add_isotropic_data(lower_bound, upper_bound, size, output);
}
DataSet::DataSet(std::vector<double> bounds, unsigned int no_elems_in_one_dim, std::vector<double> (*output_func)(std::vector<double>), unsigned int output_dim) {
DataSet::DataSet(std::vector<double> &bounds, unsigned int no_elems_in_one_dim, std::vector<double> (*output_func)(std::vector<double>&), unsigned int output_dim) {
std::vector<std::pair<std::vector<double>, std::vector<double>>> new_data_vec;
this->data = new_data_vec;
this->input_dim = bounds.size()/2;
......@@ -51,7 +51,7 @@ DataSet::DataSet(std::vector<double> bounds, unsigned int no_elems_in_one_dim, s
}
void DataSet::add_data_pair(std::vector<double> inputs, std::vector<double> outputs) {
void DataSet::add_data_pair(std::vector<double> &inputs, std::vector<double> &outputs) {
if(inputs.size() != this->input_dim) {
throw InvalidDimension("Bad input dimension.");
} else if(outputs.size() != this->output_dim) {
......@@ -81,7 +81,7 @@ void DataSet::add_isotropic_data(double lower_bound, double upper_bound, unsigne
this->n_elements += size;
}
void DataSet::add_isotropic_data(std::vector<double> bounds, unsigned int no_elems_in_one_dim, std::vector<double> (*output_func)(std::vector<double>)) {
void DataSet::add_isotropic_data(std::vector<double> &bounds, unsigned int no_elems_in_one_dim, std::vector<double> (*output_func)(std::vector<double>&)) {
// TODO add check of dataset dimensions
std::vector<std::vector<double>> grid;
......@@ -100,7 +100,7 @@ void DataSet::add_isotropic_data(std::vector<double> bounds, unsigned int no_ele
grid = this->cartesian_product(&grid);
for(const auto& vec : grid) {
for(auto vec : grid) {
this->n_elements++;
this->data.emplace_back(std::make_pair(vec, output_func(vec)));
}
......@@ -114,11 +114,11 @@ size_t DataSet::get_n_elements() {
return this->n_elements;
}
unsigned int DataSet::get_input_dim() {
size_t DataSet::get_input_dim() {
return this->input_dim;
}
unsigned int DataSet::get_output_dim() {
size_t DataSet::get_output_dim() {
return this->output_dim;
}
......@@ -142,7 +142,7 @@ void DataSet::print_data() {
}
}
void DataSet::store_text(std::string file_path) {
void DataSet::store_text(std::string &file_path) {
//TODO check if stream was successfully opened
std::ofstream ofs(file_path);
boost::archive::text_oarchive oa(ofs);
......
......@@ -49,12 +49,12 @@ private:
/**
* Dimension of the input
*/
unsigned int input_dim = 0;
size_t input_dim = 0;
/**
* Dimension of the output
*/
unsigned int output_dim = 0;
size_t output_dim = 0;
/**
* Stored data in the format of pairs of corresponding
......@@ -169,7 +169,14 @@ public:
*/
DataSet(double lower_bound, double upper_bound, unsigned int size, double output);
DataSet(std::vector<double> bounds, unsigned int no_elems_in_one_dim, std::vector<double> (*output_func)(std::vector<double>), unsigned int output_dim);
/**
*
* @param bounds
* @param no_elems_in_one_dim
* @param output_func
* @param output_dim
*/
DataSet(std::vector<double> &bounds, unsigned int no_elems_in_one_dim, std::vector<double> (*output_func)(std::vector<double>&), unsigned int output_dim);
/**
* Getter for number of elements
......@@ -181,14 +188,14 @@ public:
* Returns the input dimension
* @return Input dimension
*/
unsigned int get_input_dim();
size_t get_input_dim();
/**
* Return the output dimension
* @return Output dimension
*/
unsigned int get_output_dim();
size_t get_output_dim();
/**
* Getter for the data structure
......@@ -201,7 +208,7 @@ public:
* @param inputs Vector of input data
* @param outputs Vector of output data corresponding to the input data
*/
void add_data_pair(std::vector<double> inputs, std::vector<double> outputs);
void add_data_pair(std::vector<double> &inputs, std::vector<double> &outputs);
//TODO expand method to generate multiple data types - chebyshev etc.
/**
......@@ -230,7 +237,7 @@ public:
* @param size Number of input-output pairs generated
* @param output_func Function determining output value
*/
void add_isotropic_data(std::vector<double> bounds, unsigned int no_elems_in_one_dim, std::vector<double> (*output_func)(std::vector<double>));
void add_isotropic_data(std::vector<double> &bounds, unsigned int no_elems_in_one_dim, std::vector<double> (*output_func)(std::vector<double>&));
//TODO Chebyshev - ch. interpolation points, i-th point = cos(i*alpha) from 0 to pi
......@@ -242,7 +249,7 @@ public:
/**
* Stores the DataSet object to the binary file
*/
void store_text(std::string file_path);
void store_text(std::string &file_path);
};
#endif //INC_4NEURO_DATASET_H
......@@ -131,8 +131,8 @@ double Particle::change_coordinate(double w, double c1, double c2, std::vector<d
for(unsigned int i = 0; i < this->coordinate_dim; ++i) {
vel_mem = w * (*this->velocity)[i]
+ c1 * this->r1 * ((*this->optimal_coordinate)[i] - (*this->coordinate)[i])
+ c2 * this->r2 * (glob_min_coord[i] - (*this->coordinate)[i]);
// + (c1+c2)/2 * this->r3 * ((*random_global_best)[i] - (*this->coordinate)[i]);
+ c2 * this->r2 * (glob_min_coord[i] - (*this->coordinate)[i])
+ (c1+c2)/2 * this->r3 * ((*random_global_best)[i] - (*this->coordinate)[i]);
if ((*this->coordinate)[i] + vel_mem > this->domain_bounds[2 * i + 1]) {
this->randomize_velocity();
......
......@@ -361,10 +361,10 @@ NeuralNetwork* DESolver::get_solution() {
return this->solution;
}
double DESolver::eval_equation( size_t equation_idx, std::vector<double> &weight_and_biases, std::vector<double> &input ) {
double DESolver::eval_equation( size_t equation_idx, 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 );
this->differential_equations->at( equation_idx )->eval_single( input, output, weight_and_biases );
// printf("Input: ");
// for( auto e: input ){
......
......@@ -165,7 +165,7 @@ public:
/**
* For testing purposes only
*/
double eval_equation( size_t equation_idx, std::vector<double> &weights_and_biases, std::vector<double> &input );
double eval_equation( size_t equation_idx, std::vector<double> *weights_and_biases, std::vector<double> &input );
/**
* For testing purposes only
......
......@@ -541,7 +541,7 @@ void test_analytical_gradient_y(std::vector<double> &guess, double accuracy, siz
delete conjugate_direction_prev;
}
void test_odr(double accuracy, size_t n_inner_neurons, size_t train_size, double ds, double de, size_t n_test_points, double ts, double te, size_t max_iters, size_t n_particles){
void test_ode(double accuracy, size_t n_inner_neurons, size_t train_size, double ds, double de, size_t n_test_points, double ts, double te, size_t max_iters, size_t n_particles){
/* SOLVER SETUP */
size_t n_inputs = 1;
......@@ -664,8 +664,6 @@ void test_odr(double accuracy, size_t n_inner_neurons, size_t train_size, double
printf("\tRepresentation test %6d, error of eq1: %10.8f, error of eq2: %10.8f, error of eq3: %10.8f, total error: %10.8f\n", (int)testi, std::sqrt(test_error_eq1), std::sqrt(test_error_eq2), std::sqrt(test_error_eq3), (total_error_analytical - total_error_de_solver) * (total_error_analytical - total_error_de_solver));
}
return;
/* PARTICLE SWARM TRAINING METHOD SETUP */
//must encapsulate each of the partial error functions
......@@ -675,11 +673,11 @@ return;
domain_bounds[2 * i + 1] = 10.0;
}
double c1 = 0.5, c2 = 0.8, w = 0.7;
double c1 = 1.7, c2 = 1.7, w = 0.7;
double gamma = 0.5, epsilon = 0.02, delta = 0.9;
double gamma = 0.5, epsilon = 0.02, delta = 1.0;
solver_01.solve_via_particle_swarm( domain_bounds, c1, c2, w, n_particles, max_iters, gamma, epsilon, delta );
......@@ -712,7 +710,7 @@ return;
int main() {
unsigned int n_inner_neurons = 2;
unsigned int train_size = 150;
unsigned int train_size = 10;
double accuracy = 1e-4;
double ds = 0.0;
double de = 4.0;
......@@ -722,8 +720,8 @@ int main() {
double te = de + 2;
size_t particle_swarm_max_iters = 1000;
size_t n_particles = 10;
test_odr(accuracy, n_inner_neurons, train_size, ds, de, test_size, ts, te, particle_swarm_max_iters, n_particles);
size_t n_particles = 100;
test_ode(accuracy, n_inner_neurons, train_size, ds, de, test_size, ts, te, particle_swarm_max_iters, n_particles);
// bool optimize_weights = true;
// bool optimize_biases = true;
......
......@@ -17,21 +17,15 @@
#include <random>
#include <iostream>
#include <fstream>
#include "../../include/4neuro.h"
#include "../Solvers/DESolver.h"
void test_odr(size_t n_inner_neurons){
void test_pde(double accuracy, size_t n_inner_neurons, size_t train_size, double ds, double de, size_t n_test_points, double ts, double te, size_t max_iters, size_t n_particles){
/* solution properties */
size_t train_size = 10;
double d1_s = 0.0, d1_e = 1.0;
/* swarm optimizer properties */
unsigned int max_iters = 100;
unsigned int n_particles = 10;
/* do not change below */
size_t n_inputs = 2;
......@@ -42,9 +36,13 @@ void test_odr(size_t n_inner_neurons){
MultiIndex alpha_00( n_inputs );
MultiIndex alpha_01( n_inputs );
MultiIndex alpha_20( n_inputs );
alpha_00.set_partial_derivative(0, 0);
alpha_01.set_partial_derivative(0, 1);
alpha_20.set_partial_derivative(2, 0);
alpha_00.set_partial_derivative(1, 0);
alpha_01.set_partial_derivative(1, 1);
alpha_20.set_partial_derivative(0, 2);
/* the governing differential equation */
solver_01.add_to_differential_equation( 0, alpha_20, 1.0 );
......@@ -61,44 +59,36 @@ void test_odr(size_t n_inner_neurons){
double frac, x, t;
/* TRAIN DATA FOR THE GOVERNING DE */
std::vector<std::pair<std::vector<double>, std::vector<double>>> data_vec_g;
std::vector<double> test_bounds_2d = {ds, de, ds, de};
/* GOVERNING EQUATION RHS */
auto f1 = [](std::vector<double>&input) -> std::vector<double> {
std::vector<double> output(1);
output[0] = 0.0;
return output;
};
DataSet ds_00(test_bounds_2d, train_size, f1, 1);
/* ISOTROPIC TRAIN SET */
frac = (d1_e - d1_s) / (train_size - 1);
for(size_t i = 0; i < train_size; ++i){
inp = {0.0, 0.0};
out = {0.0};
data_vec_g.emplace_back(std::make_pair(inp, out));
}
DataSet ds_00(&data_vec_g);
/* ISOTROPIC TRAIN DATA FOR THE FIRST DIRICHLET BC */
std::vector<std::pair<std::vector<double>, std::vector<double>>> data_vec_t;
frac = (d1_e - d1_s) / (train_size - 1);
for(size_t i = 0; i < train_size; ++i){
t = i * frac;
inp = { 0.0, t };
out = { std::sin( t ) };
data_vec_t.emplace_back(std::make_pair(inp, out));
}
DataSet ds_t(&data_vec_t);
/* ISOTROPIC TRAIN DATA FOR THE SECOND DIRICHLET BC */
std::vector<std::pair<std::vector<double>, std::vector<double>>> data_vec_x;
frac = (d1_e - d1_s) / (train_size - 1);
for(size_t i = 0; i < train_size; ++i){
x = i * frac;
inp = { x, 0.0 };
out = { std::pow(E, -0.707106781 * x) * std::sin( -0.707106781 * x )};
/* ISOTROPIC TRAIN SET */
frac = (de - ds) / (train_size - 1);
for(unsigned int i = 0; i < train_size; ++i){
inp = {0.0, frac * i};
out = {std::sin(inp[1])};
data_vec_t.emplace_back(std::make_pair(inp, out));
inp = {frac * i, 0.0};
out = {std::pow(E, -0.707106781 * inp[0]) * std::sin( -0.707106781 * inp[0] )};
data_vec_x.emplace_back(std::make_pair(inp, out));
}
DataSet ds_t(&data_vec_t);
DataSet ds_x(&data_vec_x);
/* Placing the conditions into the solver */
solver_01.set_error_function( 0, ErrorFunctionType::ErrorFuncMSE, &ds_00 );
solver_01.set_error_function( 1, ErrorFunctionType::ErrorFuncMSE, &ds_t );
......@@ -112,40 +102,74 @@ void test_odr(size_t n_inner_neurons){
//must encapsulate each of the partial error functions
double *domain_bounds = new double[ 6 * n_inner_neurons ];
for(unsigned int i = 0; i < 3 * n_inner_neurons; ++i){
domain_bounds[2 * i] = -800.0;
domain_bounds[2 * i + 1] = 800.0;
domain_bounds[2 * i] = -20.0;
domain_bounds[2 * i + 1] = 20.0;
}
double c1 = 0.5, c2 = 0.75, w = 0.8;
double c1 = 1.7, c2 = 1.7, w = 0.7;
double gamma = 0.5, epsilon = 0.02, delta = 0.9;
solver_01.solve_via_particle_swarm( domain_bounds, c1, c2, w, n_particles, max_iters, gamma, epsilon, delta );
/* PRACTICAL END OF THE EXAMPLE */
double gamma = 0.5, epsilon = 0.02, delta = 0.9;
solver_01.solve_via_particle_swarm( domain_bounds, c1, c2, w, n_particles, max_iters, gamma, epsilon, delta );
/* SOLUTION EXPORT */
printf("Exporting solution & error files...");
// NeuralNetwork *solution = solver_01.get_solution();
// std::vector<double> parameters(3 * n_inner_neurons);//w1, a1, b1, w2, a2, b2, ... , wm, am, bm
// std::vector<double> *weight_params = solution->get_parameter_ptr_weights();
// std::vector<double> *biases_params = solution->get_parameter_ptr_biases();
// for(size_t i = 0; i < n_inner_neurons; ++i){
// parameters[3 * i] = weight_params->at(i);
// parameters[3 * i + 1] = weight_params->at(i + n_inner_neurons);
// parameters[3 * i + 2] = biases_params->at(i);
// }
//
// unsigned int n_test_points = 150;
// std::vector<double> input(1), output(1);
// double x;
// for(unsigned int i = 0; i < n_test_points; ++i){
//
// x = i * ((d1_e - d1_s) / (n_test_points - 1)) + d1_s;
// input[0] = x;
//
// solution->eval_single(input, output);
//
// std::cout << i + 1 << " " << x << " " << std::pow(E, -2*x) * (3*x + 1)<< " " << output[0] << " " << std::pow(E, -2*x) * (1 - 6*x)<< " " << eval_approx_df(x, n_inner_neurons, parameters) << " " << 4 * std::pow(E, -2*x) * (3*x - 2)<< " " << eval_approx_ddf(x, n_inner_neurons, parameters) << std::endl;
// }
NeuralNetwork *solution = solver_01.get_solution();
std::vector<double> *weight_params = solution->get_parameter_ptr_weights();
std::vector<double> *biases_params = solution->get_parameter_ptr_biases();
/* solution itself */
DataSet test_set_1(test_bounds_2d, n_test_points, f1, 1);
std::vector<double> input, output(1);
std::ofstream ofs("data_2d_pde1_y.txt", std::ofstream::out);
for(auto tp: *test_set_1.get_data()){
input = tp.first;
solution->eval_single(input, output);
ofs << input[0] << " " << input[1] << " " << output[0] << std::endl;
}
ofs.close();
/* governing equation error */
ofs = std::ofstream("data_2d_pde1_first_equation_error.txt", std::ofstream::out);
for(auto tp: *test_set_1.get_data()){
input = tp.first;
double eq_value = solver_01.eval_equation(0, nullptr, input);
ofs << input[0] << " " << input[1] << " " << std::fabs(eq_value) << std::endl;
}
ofs.close();
/* ISOTROPIC TEST SET FOR BOUNDARY CONDITIONS */
frac = (de - ds) / (n_test_points - 1);
/* first boundary condition & its error */
ofs = std::ofstream("data_1d_pde1_yt.txt", std::ofstream::out);
std::ofstream ofs2("data_1d_pde1_yx.txt", std::ofstream::out);
for(unsigned int i = 0; i < n_test_points; ++i){
double x = frac * i;
double t = frac * i;
double yt = std::sin(t);
double yx = std::pow(E, -0.707106781 * x) * std::sin( -0.707106781 * x );
input = {0.0, t};
solution->eval_single( input, output, nullptr );
ofs << i + 1 << " " << t << " " << yt << " " << output[0] << " " << std::fabs(output[0] - yt) << std::endl;
input = {x, 0.0};
solution->eval_single( input, output, nullptr );
ofs2 << i + 1 << " " << x << " " << yx << " " << output[0] << " " << std::fabs(output[0] - yx) << std::endl;
}
ofs2.close();
ofs.close();
printf("done!\n");
delete [] domain_bounds;
......@@ -153,9 +177,19 @@ void test_odr(size_t n_inner_neurons){
int main() {
unsigned int n_inner_neurons = 2;
unsigned int n_inner_neurons = 6;
unsigned int train_size = 20;
double accuracy = 1e-4;
double ds = 0.0;
double de = 1.0;
unsigned int test_size = 100;
double ts = ds;
double te = de;
test_odr(n_inner_neurons);
size_t particle_swarm_max_iters = 1000;
size_t n_particles = 100;
test_pde(accuracy, n_inner_neurons, train_size, ds, de, test_size, ts, te, particle_swarm_max_iters, n_particles);
return 0;
......
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