/**
 * Copyright 2010 Neuroph Project http://neuroph.sourceforge.net
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.neuroph.contrib.matrixmlp;

import org.neuroph.core.data.DataSet;
import org.neuroph.core.data.DataSetRow;
import org.neuroph.nnet.MultiLayerPerceptron;
import org.neuroph.util.TransferFunctionType;

/**
 * Test class for matrix based MLP implementation.
 * @author Zoran Sevarac
 */
public class TestMatrixMLP {

    /**
     * Create and run MLP with XOR training set
     */
    public static void main(String[] args) {
        // create training set (logical XOR function)
        DataSet trainingSet = new DataSet(2, 1);
        trainingSet.addRow(new DataSetRow(new double[]{0, 0}, new double[]{0}));
        trainingSet.addRow(new DataSetRow(new double[]{0, 1}, new double[]{1}));
        trainingSet.addRow(new DataSetRow(new double[]{1, 0}, new double[]{1}));
        trainingSet.addRow(new DataSetRow(new double[]{1, 1}, new double[]{0}));

        MultiLayerPerceptron nnet = new MultiLayerPerceptron( TransferFunctionType.TANH ,2, 3, 1);
        MatrixMultiLayerPerceptron mnet = new MatrixMultiLayerPerceptron(nnet);

        System.out.println("Training network...");

        mnet.learn(trainingSet);

        System.out.println("Done training network.");
    }


}