Evaluate.java

/*
 * Encog(tm) Examples v2.4
 * http://www.heatonresearch.com/encog/
 * http://code.google.com/p/encog-java/
 * 
 * Copyright 2008-2010 by Heaton Research Inc.
 * 
 * Released under the LGPL.
 *
 * This is free software; you can redistribute it and/or modify it
 * under the terms of the GNU Lesser General Public License as
 * published by the Free Software Foundation; either version 2.1 of
 * the License, or (at your option) any later version.
 *
 * This software 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
 * Lesser General Public License for more details.
 *
 * You should have received a copy of the GNU Lesser General Public
 * License along with this software; if not, write to the Free
 * Software Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
 * 02110-1301 USA, or see the FSF site: http://www.fsf.org.
 * 
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 * For information on Heaton Research trademarks, visit:
 * 
 * http://www.heatonresearch.com/copyright.html
 */

package org.encog.examples.neural.forest.feedforward;

import java.io.File;

import org.encog.engine.util.Format;
import org.encog.neural.data.NeuralData;
import org.encog.neural.networks.BasicNetwork;
import org.encog.normalize.DataNormalization;
import org.encog.normalize.output.nominal.OutputEquilateral;
import org.encog.persist.EncogPersistedCollection;
import org.encog.util.csv.ReadCSV;

public class Evaluate {

	private int[] treeCount = new int[10];
	private int[] treeCorrect = new int[10];

	public void keepScore(int actual, int ideal) {
		treeCount[ideal]++;
		if (actual == ideal)
			treeCorrect[ideal]++;
	}

	public BasicNetwork loadNetwork() {
		File file = Constant.TRAINED_NETWORK_FILE;

		if (!file.exists()) {
			System.out.println("Can't read file: " + file.getAbsolutePath());
			return null;
		}

		EncogPersistedCollection encog = new EncogPersistedCollection(file);
		BasicNetwork network = (BasicNetwork) encog
				.find(Constant.TRAINED_NETWORK_NAME);

		if (network == null) {
			System.out.println("Can't find network resource: "
					+ Constant.TRAINED_NETWORK_NAME);
			return null;
		}

		return network;
	}

	public DataNormalization loadNormalization() {
		File file = Constant.TRAINED_NETWORK_FILE;

		EncogPersistedCollection encog = new EncogPersistedCollection(file);

		DataNormalization norm = (DataNormalization) encog
				.find(Constant.NORMALIZATION_NAME);
		if (norm == null) {
			System.out.println("Can't find normalization resource: "
					+ Constant.NORMALIZATION_NAME);
			return null;
		}

		return norm;
	}

	public int determineTreeType(OutputEquilateral eqField, NeuralData output) {
		int result = 0;

		if (eqField != null) {
			result = eqField.getEquilateral().decode(output.getData());
		} else {
			double maxOutput = Double.NEGATIVE_INFINITY;
			result = -1;

			for (int i = 0; i < output.size(); i++) {
				if (output.getData(i) > maxOutput) {
					maxOutput = output.getData(i);
					result = i;
				}
			}
		}

		return result;
	}

	public void evaluate() {
		BasicNetwork network = loadNetwork();
		DataNormalization norm = loadNormalization();

		ReadCSV csv = new ReadCSV(Constant.EVALUATE_FILE.toString(), false, ',');
		double[] input = new double[norm.getInputFields().size()];
		OutputEquilateral eqField = (OutputEquilateral) norm.findOutputField(
				OutputEquilateral.class, 0);

		int correct = 0;
		int total = 0;
		while (csv.next()) {
			total++;
			for (int i = 0; i < input.length; i++) {
				input[i] = csv.getDouble(i);
			}
			NeuralData inputData = norm.buildForNetworkInput(input);
			NeuralData output = network.compute(inputData);
			int coverTypeActual = determineTreeType(eqField, output);
			int coverTypeIdeal = (int) csv.getDouble(54) - 1;

			keepScore(coverTypeActual, coverTypeIdeal);

			if (coverTypeActual == coverTypeIdeal) {
				correct++;
			}
		}

		System.out.println("Total cases:" + total);
		System.out.println("Correct cases:" + correct);
		double percent = (double) correct / (double) total;
		System.out.println("Correct percent:"
				+ Format.formatPercentWhole(percent));
		for (int i = 0; i < 7; i++) {
			double p = ((double) this.treeCorrect[i] / (double) this.treeCount[i]);
			System.out.println("Tree Type #" + i + " - Correct/total: "
					+ this.treeCorrect[i] + "/" + treeCount[i] + "("
					+ Format.formatPercentWhole(p) + ")");
		}
	}
}