Auction.java

/*
 * Put the input in a file called input.txt
 * This program outputs to stdout
 */

import org.encog.Encog;
import org.encog.engine.network.activation.ActivationBiPolar;
import org.encog.engine.network.activation.ActivationSigmoid;
import org.encog.mathutil.randomize.BasicRandomizer;
import org.encog.mathutil.randomize.ConsistentRandomizer;
import org.encog.ml.MLMethod;
import org.encog.ml.anneal.SimulatedAnnealing;
import org.encog.ml.data.MLData;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLData;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.ml.factory.MLMethodFactory;
import org.encog.ml.factory.MLTrainFactory;
import org.encog.ml.factory.train.AnnealFactory;
import org.encog.ml.factory.train.GeneticFactory;
import org.encog.ml.genetic.BasicGeneticAlgorithm;
import org.encog.ml.genetic.GeneticAlgorithm;
import org.encog.ml.train.MLTrain;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.layers.BasicLayer;
import org.encog.neural.networks.training.TrainingSetScore;
import org.encog.neural.networks.training.genetic.NeuralGeneticAlgorithm;
import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation;

import java.io.File;
import java.io.FileNotFoundException;
import java.io.FileReader;
import java.sql.Timestamp;
import java.util.ArrayList;
import java.util.Random;
import java.util.Scanner;

public class Auction {
	private final static int INPUT_NEURONS = 9;
	private final static int HIDDEN1_NEURONS = 15;
	private final static int HIDDEN2_NEURONS = 12;
	private final static int OUTPUT_NEURONS = 2;
	
	public static void main(String[] args) {	
		double[][] input = {
			{5, 1, 4, 5, 7, 1, 0, 1, 2},
			{3, 1, 3, 3, 3, 1, 0, 1, 1},
			{8, 1, 3, 3, 3, 1, 0, 1, 2},
			{13, 5, 7, 5, 9, 1, 3, 2, 5},
			{11, 2, 3, 5, 7, 11, 13, 17, 19},
		}, ideal = {
			{.3, .3},
			{.3, .3},
			{.2, .3},
			{.2, .2},
			{.3, .1}
		};
		MLDataSet trainingSet = new BasicMLDataSet(input, ideal);		

		BasicNetwork network = new BasicNetwork();
		
		BasicLayer inputLayer = new BasicLayer(INPUT_NEURONS);
		BasicLayer hidden1Layer = new BasicLayer(HIDDEN1_NEURONS);
		BasicLayer hidden2Layer = new BasicLayer(HIDDEN2_NEURONS);
		BasicLayer outputLayer = new BasicLayer(OUTPUT_NEURONS);
		
		inputLayer.setActivation(new ActivationSigmoid());
		hidden1Layer.setActivation(new ActivationSigmoid());
		hidden2Layer.setActivation(new ActivationSigmoid());
		//outputLayer.setActivation(new ActivationSigmoid());
		
		network.addLayer(inputLayer);
		network.addLayer(hidden1Layer);
		network.addLayer(hidden2Layer);
		network.addLayer(outputLayer);
		
		network.getStructure().finalizeStructure();
		network.reset();
		
		ResilientPropagation train = new ResilientPropagation(network, trainingSet);
		
		do {
			train.iteration();
			//log("i:" + train.getIteration() + " - e:" + train.getError());
		} while(train.getError() > .000000001);
		//log("Training complete! (e:" + train.getError() + ")");
		
		Scanner in;
		int iteration = 1;
		try {
			in = new Scanner(new File("input.txt"));
			in.nextLine();
			String line = "";
			while(in.hasNext()) {
				line = in.nextLine();
				double[] inputCode = new double[9];
				for(int i = 0; i < 9; i++) inputCode[i] = Double.parseDouble(line.split(" ")[i]);
				
				MLData output = network.compute(new BasicMLData(inputCode));
				System.out.println("Case #" + iteration++ + ": " + Math.round(output.getData(0)*10) + " " + Math.round(output.getData(1)*10));
			};
		} catch (Exception e) {}
	}
	
	public static void log(String msg) {
		System.out.println("[" + new Timestamp(new java.util.Date().getTime()) + "] " + msg);
	}
	
	public static void error(String msg) {
		System.out.println("[" + new Timestamp(new java.util.Date().getTime()) + "] ERROR: " + msg);
	}
}