included kernel ridge regression module

test_regsurf.cc

@@ -0,0 +1,118 @@
+/*
+    LA: linear algebra C++ interface library
+    Kernel ridge regression module Copyright (C) 2024
+    Pavel Florian <florian43@seznam.cz> and Jiri Pittner <jiri.pittner@jh-inst.cas.cz> or <jiri@pittnerovi.com>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+*/
+
+
+#include "regsurf.h"
+
+using namespace std;
+using namespace LA;
+
+
+
+int main(int argc, char *argv[])
+{
+unsigned int i, j;
+unsigned int count_levels;
+unsigned int count_kernels;
+double x;
+double y;
+double RMSE;
+double Loss;
+bool end = false;
+string user_input;
+string model_geoms = "xyz.dat";
+string model_y_values = "E_sum.dat";
+string model_params = "conf_surf.dat";
+string model_Z_file = "Z.dat";
+string model_test_geom = "nea_geoms.xyz";
+/*
+double* weight_coeff;
+double* params;
+string model_weight_file;
+string* descriptor;
+string* kernel;
+*/
+count_levels = 10;
+count_kernels = 12;/*
+descriptor = new string[1];
+kernel = new string[1];
+
+weight_coeff = new double[count_kernels * count_levels];
+params = new double[22];
+model_weight_file = "weights.dat";
+*/
+REGSURF<double> regsurf(model_geoms, model_y_values, model_params, model_Z_file);
+// creating model
+// fitting model
+regsurf.s_Fit(0.0, 100, 0.5);
+//reg.REG_Fit_grad(0.1, 50, 0.2);
+
+// get predictions
+
+cout << "Program for testing regression model \n" << endl;
+cout << "The root mean square errors: " << endl;
+for (i = 0; i < count_levels; i++)
+    {
+    RMSE = regsurf.s_RMSE(i);
+    cout << RMSE << endl;
+    }
+cout << "The values of Loss_function: " << endl;
+for (i = 0; i < count_levels; i++)
+    {
+    Loss = regsurf.Loss_Function(i);
+    cout << Loss << endl;
+    }
+/*
+cout << "The weights of kernels: " << endl;
+for (i = 0; i < count_levels; i++)
+    {
+    cout << "Level " << i << ": ";
+    regsurf.s_Get_weight_coeff(i, weight_coeff + (i * count_kernels));
+    for (j = 0; j < count_kernels; j++)
+        cout << weight_coeff[(i * count_kernels) + j] << endl;
+    }
+
+regsurf.s_Save_fitted(model_weight_file);
+regsurf.s_Load_fitted(model_weight_file);
+regsurf.s_Get_params(descriptor, kernel, params);
+
+cout << "The descriptor is: " << descriptor[0] << endl;
+cout << "The kernel is: " << kernel[0] << endl;
+cout << "The parameters of model are: ";
+for (i = 0; i < (2 * count_levels) + 1; i++)
+    cout << params[i] << endl;
+
+descriptor[0] = "Inverse_pairs";
+kernel[0] = "Laplacian";
+params[1] = 0.2;
+regsurf.s_Set_params(descriptor[0], kernel[0], params);
+
+regsurf.s_Get_params(descriptor, kernel, params);
+cout << "The new descriptor is: " << descriptor[0] << endl;
+cout << "The new kernel is: " << kernel[0] << endl;
+cout << "The new parameters of model are: ";
+for (i = 0; i < (2 * count_levels) + 1; i++)
+    cout << params[i] << endl;
+*/
+regsurf.s_Load_ML_NEA_geometries(50000, "nea_geoms.xyz");
+regsurf.s_Predict_ML_NEA_geometries();
+regsurf.s_Compute_ML_NEA_spectra(0.00, 0.0025, 4000, 0.2);
+regsurf.s_Save_ML_NEA_spectra("spectra.dat");
+return(0);
+};