included kernel ridge regression module

2024-02-14 15:33:25 +01:00
parent 7172e26a50
commit c0ce70fbc0
8 changed files with 3111 additions and 3 deletions
--- a/test_reg.cc
+++ b/test_reg.cc
@@ -0,0 +1,131 @@
+/*
+    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 "reg.h"
+
+using namespace std;
+using namespace LA;
+
+
+int main(int argc, char *argv[])
+{
+// unsigned int i;
+
+double x;
+double y;
+bool end = false;
+string user_input;
+string model_x = "reg_test_x.csv";
+string model_y = "reg_test_y.csv";
+string model_p = "reg_test_p.csv";
+REG<double> reg(model_x, model_y, model_p);
+/*
+double* reference_value;
+double* score;
+double RMSE;
+double Loss;
+double* params;
+double* weights;
+string kernel;
+
+reference_value = new double[1];
+score = new double[1];
+params = new double[4];
+weights = new double[451];
+
+reference_value[0] = 1.00;
+*/
+// creating model
+// fitting model
+reg.REG_Fit(0.0, 20);
+//reg.REG_Fit_grad(0.1, 50, 0.2);
+// get predictions
+
+cout << "Program for testing regression model \n" << endl;
+/*
+reg.REG_Get_weight_coeff(weights);
+cout << "The weight coefficients are: " << kernel << endl;
+for (i = 0; i < 451; i++)
+    cout << weights[i] << endl;
+*/
+while (not end)
+    {
+    cout << "Please enter the x value or y to end \n" << endl;
+    cin >> user_input;
+    if (user_input == "y")
+        {
+        end = true;
+        break;
+        }
+    x = stod(user_input);
+    
+    reg.REG_Get_predictions(&x, &y, 1);
+    cout << "The model prediction is: " << y << endl;
+    /*
+    reg.REG_Get_Score(&x, &y, 1, reference_value, score);
+    cout << "The model prediction is: " << y << endl;
+    cout << "The reference value is: " << reference_value[0] << endl;
+    cout << "The score is: " << score[0] << endl;
+    
+    RMSE = reg.REG_RMSE();
+    Loss = reg.Loss_Function();
+    cout << "The RMSE is: " << RMSE << endl;
+    cout << "The Loss is: " << Loss << endl;
+    
+    reg.REG_Get_params(params, &kernel);
+    cout << "The parameters of model are: " << endl;
+    for (i = 0; i < 4; i++)
+        cout << "param: " << params[i] << endl;
+        
+    cout << "The type of kernel is: " << kernel << endl;
+    cout << endl;
+    
+    params[0] = 0;
+    kernel = "Gaussian";
+    reg.REG_Set_params(params, kernel);
+    
+    reg.REG_Get_params(params, &kernel);
+    cout << "The new parameters o model are: " << RMSE << endl;
+    for (i = 0; i < 4; i++)
+        cout << "param: " << params[i] << endl;
+        
+    cout << "The new type of kernel is: " << kernel << endl;
+    cout << endl;
+    */
+    }
+/*reg.REG_Save_fitted("reg_test_m.csv");
+reg.REG_Load_fitted("reg_test_m.csv");
+end = false;
+while (not end)
+    {
+    cout << "Please enter the x value or y to end \n" << endl;
+    cin >> user_input;
+    if (user_input == "y")
+        {
+        end = true;
+        break;
+        }
+    x = stod(user_input);
+    reg.REG_Get_predictions(&x, &y, 1);
+    cout << "The model prediction is: " << y << endl;
+    }*/
+return(0);
+};