/**
 * Copyright 2013 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.samples.uci;

import java.util.Arrays;
import org.neuroph.core.NeuralNetwork;
import org.neuroph.core.data.DataSet;
import org.neuroph.core.data.DataSetRow;
import org.neuroph.core.events.LearningEvent;
import org.neuroph.core.events.LearningEventListener;
import org.neuroph.nnet.MultiLayerPerceptron;
import org.neuroph.nnet.learning.BackPropagation;
import org.neuroph.nnet.learning.MomentumBackpropagation;

/**
 *
 * @author Ivana Bajovic
 */

/*
 DATA SET INFORMATION:
 * Car Evaluation Database was derived from a simple hierarchical decision model originally developed for the demonstration of DEX, M. Bohanec, V. Rajkovic: Expert system for decision making.
 * The model evaluates cars according to the following concept structure: 

 CAR car acceptability 
 . PRICE overall price 
 . . buying buying price 
 . . maint price of the maintenance 
 . TECH technical characteristics 
 . . COMFORT comfort 
 . . . doors number of doors 
 . . . persons capacity in terms of persons to carry 
 . . . lug_boot the size of luggage boot 
 . . safety estimated safety of the car 

 ATTRIBUTE INFORMATION:
 1. buying: vhigh, high, med, low
 1,0,0,0 instead of vhigh, 0,1,0,0 instead of high, 0,0,1,0 instead of med, 0,0,0,1 instead of low
 2. maint: vhigh, high, med, low
 1,0,0,0 instead of vhigh, 0,1,0,0 instead of high, 0,0,1,0 instead of med, 0,0,0,1 instead of low
 3. doors: 2, 3, 4, more
 0,0,0,1 instead of 2, 0,0,1,0 instead of 3, 0,1,0,0 instead of 4, 1,0,0,0 instead of more
 4. persons: 2, 4, more
 0,0,1 instead of 2, 0,1,0 instead of 4, 1,0,0 instead of more
 5. lug_boot: small, med, big
 0,0,1 instead of small, 0,1,0 instead of med, 1,0,0 instead of big
 6. safety: low, med, high
 0,0,1 instead of low, 0,1,0 instead of med, 1,0,0 instead of high
 7. class values: unacc, acc, good, vgood 
 0,0,0,1 instead of unacc, 0,0,1,0 instead of acc, 0,1,0,0 instead of good, 1,0,0,0 instead of vgood. 
 
 1.,2.,3.,4.,5.,6. - inputs
 7. - output
 
 The original data set that will be used in this experiment can be found at link http://archive.ics.uci.edu/ml/datasets/Car+Evaluation
 */
public class CarEvaluationSample implements LearningEventListener {

    /**
     * @param args the command line arguments
     */
    public static void main(String[] args) {
        (new CarEvaluationSample()).run();
    }

    public void run() {

        System.out.println("Creating training set...");
        String trainingSetFileName = "data_sets/car_evaluation_data.txt";
        int inputsCount = 21;
        int outputsCount = 4;

        // create training set from file
        DataSet dataSet = DataSet.createFromFile(trainingSetFileName, inputsCount, outputsCount, "\t", false);
       
        
        System.out.println("Creating neural network...");
        // create MultiLayerPerceptron neural network
        MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 22, outputsCount);
       
        
        // attach listener to learning rule
        MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
        learningRule.addListener(this);

        // set learning rate and max error
        learningRule.setLearningRate(0.2);
        learningRule.setMaxError(0.01);

        System.out.println("Training network...");
        // train the network with training set
        neuralNet.learn(dataSet);

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

        testNeuralNetwork(neuralNet, dataSet);

        System.out.println("Saving network");
        // save neural network to file
        neuralNet.save("MyNeuralNetCarEvaluation.nnet");

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

    public void testNeuralNetwork(NeuralNetwork neuralNet, DataSet testSet) {

        for (DataSetRow testSetRow : testSet.getRows()) {
            neuralNet.setInput(testSetRow.getInput());
            neuralNet.calculate();
            double[] networkOutput = neuralNet.getOutput();

            System.out.print("Input: " + Arrays.toString(testSetRow.getInput()));
            System.out.println(" Output: " + Arrays.toString(networkOutput));
        }
    }

    @Override
    public void handleLearningEvent(LearningEvent event) {
        BackPropagation bp = (BackPropagation) event.getSource();
        System.out.println(bp.getCurrentIteration() + ". iteration | Total network error: " + bp.getTotalNetworkError());
    }
}