/*

* mlp-java, Copyright (C) 2012 Davide Gessa

*

* 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 2 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/>.

*/

package multilayersperceptronlib.test;

import multilayersperceptronlib.MultiLayerPerceptron;

import multilayersperceptronlib.transferfunctions.SigmoidalTransfer;

public class OrTest

{

/**

* @param args

*/

public static void main(String[] args)

{

int[] layers = new int[]{ 2, 5, 1 };

MultiLayerPerceptron net = new MultiLayerPerceptron(layers, 0.6, new SigmoidalTransfer());

/* Learning */

for(int i = 0; i < 10000; i++)

{

double[] inputs = new double[]{Math.round(Math.random()), Math.round(Math.random())};

double[] output = new double[1];

double error;

if((inputs[0] == 1.0) || (inputs[1] == 1.0))

output[0] = 1.0;

else

output[0] = 0.0;

System.out.println(inputs[0]+" or "+inputs[1]+" = "+output[0]);

error = net.backPropagate(inputs, output);

System.out.println("Error at step "+i+" is "+error);

}

System.out.println("Learning completed!");

/* Test */

double[] inputs = new double[]{0.0, 1.0};

double[] output = net.execute(inputs);

System.out.println(inputs[0]+" or "+inputs[1]+" = "+Math.round(output[0])+" ("+output[0]+")");

}

}