DisplayBoxesPanel.java

/*
 * Artificial Intelligence for Humans
 * Volume 3: Deep Learning and Neural Networks
 * Java Version
 * http://www.aifh.org
 * http://www.jeffheaton.com
 *
 * Code repository:
 * https://github.com/jeffheaton/aifh
 *
 * Copyright 2014-2015 by Jeff Heaton
 *
 * 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.
 *
 * For more information on Heaton Research copyrights, licenses
 * and trademarks visit:
 * http://www.heatonresearch.com/copyright
 */
package com.heatonresearch.aifh.examples.neat.hyperneat;

import org.encog.mathutil.IntPair;
import org.encog.neural.hyperneat.HyperNEATCODEC;
import org.encog.neural.hyperneat.substrate.Substrate;
import org.encog.neural.hyperneat.substrate.SubstrateFactory;
import org.encog.neural.neat.NEATNetwork;
import org.encog.neural.neat.NEATPopulation;
import org.encog.neural.neat.training.NEATGenome;

import javax.swing.*;
import java.awt.*;
import java.util.Random;

/**
 * Created by jeff on 11/21/15.
 */
public class DisplayBoxesPanel extends JPanel {
    /**
     * The serial.
     */
    private static final long serialVersionUID = 1L;
    private final BoxTrialCase testCase = new BoxTrialCase(new Random());
    private final NEATPopulation pop;
    private int resolution = BoxTrialCase.BASE_RESOLUTION;

    public DisplayBoxesPanel(NEATPopulation thePopulation) {
        this.testCase.initTestCase(0);
        this.pop = thePopulation;
    }

    @Override
    public void paint(Graphics g) {

        NEATGenome genome = (NEATGenome) this.pop.getBestGenome();
        Substrate substrate = SubstrateFactory.factorSandwichSubstrate(this.resolution, this.resolution);
        HyperNEATCODEC codec = new HyperNEATCODEC();
        NEATNetwork phenotype = (NEATNetwork) codec.decode(this.pop, substrate, genome);

        TrialEvaluation trial = new TrialEvaluation(phenotype, this.testCase);
        IntPair actualPos = trial.query(this.resolution);

        // clear what was there before
        g.setColor(Color.white);
        g.fillRect(0, 0, getWidth(), getHeight());

        //
        int boxWidth = this.getWidth()/ this.resolution;
        int boxHeight = this.getHeight()/ this.resolution;
        double delta = 2.0 / this.resolution;
        int index = 0;

        for(int row = 0; row < this.resolution; row++ ) {
            double y = -1 + (row*delta);
            int boxY = row * boxHeight;
            for(int col = 0; col< this.resolution; col++ ) {
                double x = -1 + (col*delta);
                int boxX = col*boxWidth;

                if( this.testCase.getPixel(x, y)>0 ) {
                    g.setColor(Color.blue);
                    g.fillRect(boxX, boxY, boxWidth, boxHeight);
                } else {
                    double d = trial.getOutput().getData(index);
                    int c = trial.normalize(d,255);
                    g.setColor(new Color(255,c,255));
                    g.fillRect(boxX, boxY, boxWidth, boxHeight);
                    g.setColor(Color.black);
                    g.drawRect(boxX, boxY, boxWidth, boxHeight);
                    g.drawRect(boxX+1, boxY+1, boxWidth-2, boxHeight-2);
                }
                index++;
            }
        }

        g.setColor(Color.red);
        g.fillRect(actualPos.getX()*boxWidth, actualPos.getY()*boxHeight, boxWidth, boxHeight);
    }

    public void createNewCase(int theResolution) {
        Random r = new Random();
        this.resolution = theResolution;
        this.testCase.initTestCase(r.nextInt(3));
        this.repaint();
    }
}