119 lines
3.4 KiB
C++
119 lines
3.4 KiB
C++
/*
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LA: linear algebra C++ interface library
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Kernel ridge regression module Copyright (C) 2024
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Pavel Florian <florian43@seznam.cz> and Jiri Pittner <jiri.pittner@jh-inst.cas.cz> or <jiri@pittnerovi.com>
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This program is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <http://www.gnu.org/licenses/>.
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*/
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#include "regsurf.h"
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using namespace std;
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using namespace LA;
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int main(int argc, char *argv[])
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{
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unsigned int i, j;
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unsigned int count_levels;
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unsigned int count_kernels;
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double x;
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double y;
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double RMSE;
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double Loss;
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bool end = false;
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string user_input;
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string model_geoms = "xyz.dat";
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string model_y_values = "E_sum.dat";
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string model_params = "conf_surf.dat";
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string model_Z_file = "Z.dat";
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string model_test_geom = "nea_geoms.xyz";
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/*
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double* weight_coeff;
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double* params;
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string model_weight_file;
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string* descriptor;
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string* kernel;
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*/
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count_levels = 10;
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count_kernels = 12;/*
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descriptor = new string[1];
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kernel = new string[1];
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weight_coeff = new double[count_kernels * count_levels];
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params = new double[22];
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model_weight_file = "weights.dat";
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*/
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REGSURF<double> regsurf(model_geoms, model_y_values, model_params, model_Z_file);
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// creating model
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// fitting model
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regsurf.s_Fit(0.0, 100, 0.5);
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//reg.REG_Fit_grad(0.1, 50, 0.2);
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// get predictions
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cout << "Program for testing regression model \n" << endl;
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cout << "The root mean square errors: " << endl;
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for (i = 0; i < count_levels; i++)
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{
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RMSE = regsurf.s_RMSE(i);
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cout << RMSE << endl;
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}
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cout << "The values of Loss_function: " << endl;
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for (i = 0; i < count_levels; i++)
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{
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Loss = regsurf.Loss_Function(i);
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cout << Loss << endl;
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}
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/*
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cout << "The weights of kernels: " << endl;
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for (i = 0; i < count_levels; i++)
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{
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cout << "Level " << i << ": ";
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regsurf.s_Get_weight_coeff(i, weight_coeff + (i * count_kernels));
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for (j = 0; j < count_kernels; j++)
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cout << weight_coeff[(i * count_kernels) + j] << endl;
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}
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regsurf.s_Save_fitted(model_weight_file);
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regsurf.s_Load_fitted(model_weight_file);
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regsurf.s_Get_params(descriptor, kernel, params);
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cout << "The descriptor is: " << descriptor[0] << endl;
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cout << "The kernel is: " << kernel[0] << endl;
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cout << "The parameters of model are: ";
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for (i = 0; i < (2 * count_levels) + 1; i++)
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cout << params[i] << endl;
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descriptor[0] = "Inverse_pairs";
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kernel[0] = "Laplacian";
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params[1] = 0.2;
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regsurf.s_Set_params(descriptor[0], kernel[0], params);
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regsurf.s_Get_params(descriptor, kernel, params);
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cout << "The new descriptor is: " << descriptor[0] << endl;
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cout << "The new kernel is: " << kernel[0] << endl;
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cout << "The new parameters of model are: ";
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for (i = 0; i < (2 * count_levels) + 1; i++)
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cout << params[i] << endl;
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*/
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regsurf.s_Load_ML_NEA_geometries(50000, "nea_geoms.xyz");
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regsurf.s_Predict_ML_NEA_geometries();
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regsurf.s_Compute_ML_NEA_spectra(0.00, 0.0025, 4000, 0.2);
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regsurf.s_Save_ML_NEA_spectra("spectra.dat");
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return(0);
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};
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