normalizeinput.java

VHDL制作的ann的code

/*
 * Encog Neural Network and Bot Library for Java v1.x
 * http://www.heatonresearch.com/encog/
 * http://code.google.com/p/encog-java/
 * 
 * Copyright 2008, Heaton Research Inc., and individual contributors.
 * See the copyright.txt in the distribution for a full listing of 
 * individual contributors.
 *
 * 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.
 */
package org.encog.util;

import org.encog.matrix.Matrix;
import org.encog.matrix.MatrixMath;
import org.encog.neural.data.NeuralData;
import org.encog.neural.networks.layers.SOMLayer;

/**
 * NormalizeInput: Input into a Self Organizing Map must be normalized.
 */
public class NormalizeInput {

	/**
	 * This class support two normalization types. Z-AXIS is the most commonly
	 * used normalization type. Multiplicative is used over z-axis when the
	 * values are in very close range.
	 * 
	 * @author jheaton
	 * 
	 */
	public enum NormalizationType {
		/**
		 * Use z-axis normalization.
		 */
		Z_AXIS, 
		/**
		 * Use multiplicative normalization.
		 */
		MULTIPLICATIVE
	}

	/**
	 * What type of normalization should be used.
	 */
	private final NormalizationType type;

	/**
	 * The normalization factor.
	 */
	private double normfac;

	/**
	 * The synthetic input.
	 */
	private double synth;

	/**
	 * The input expressed as a matrix.
	 */
	private final Matrix inputMatrix;

	/**
	 * Normalize an input array into a matrix. The resulting matrix will have
	 * one extra column that will be occupied by the synthetic input.
	 * 
	 * @param input
	 *            The input array to be normalized.
	 * @param type
	 *            What type of normalization to use.
	 */
	public NormalizeInput(final NeuralData input, 
			final NormalizationType type) {
		this.type = type;
		calculateFactors(input);
		this.inputMatrix = createInputMatrix(input, this.synth);
	}

	/**
	 * Determine both the normalization factor and the synthetic input for the
	 * given input.
	 * 
	 * @param input The input to normalize.
	 */
	protected void calculateFactors(final NeuralData input) {

		final Matrix inputMatrix2 = Matrix.createColumnMatrix(
				input.getData());
		double len = MatrixMath.vectorLength(inputMatrix2);
		len = Math.max(len, SOMLayer.VERYSMALL);
		final int numInputs = input.size();

		if (this.type == NormalizationType.MULTIPLICATIVE) {
			this.normfac = 1.0 / len;
			this.synth = 0.0;
		} else {
			this.normfac = 1.0 / Math.sqrt(numInputs);
			final double d = numInputs - Math.pow(len, 2);
			if (d > 0.0) {
				this.synth = Math.sqrt(d) * this.normfac;
			} else {
				this.synth = 0;
			}
		}
	}

	/**
	 * Create an input matrix that has enough space to hold the extra synthetic
	 * input.
	 * 
	 * @param pattern
	 *            The input pattern to create.
	 * @param extra
	 *            The synthetic input.
	 * @return A matrix that contains the input pattern and the synthetic input.
	 */
	protected Matrix createInputMatrix(final NeuralData pattern,
			final double extra) {
		final Matrix result = new Matrix(1, pattern.size() + 1);
		for (int i = 0; i < pattern.size(); i++) {
			result.set(0, i, pattern.getData(i));
		}

		result.set(0, pattern.size(), extra);

		return result;
	}

	/**
	 * Get the resulting input matrix.
	 * 
	 * @return The resulting input matrix.
	 */
	public Matrix getInputMatrix() {
		return this.inputMatrix;
	}

	/**
	 * The normalization factor.
	 * 
	 * @return The normalization factor.
	 */
	public double getNormfac() {
		return this.normfac;
	}

	/**
	 * The synthetic input.
	 * 
	 * @return The synthetic input.
	 */
	public double getSynth() {
		return this.synth;
	}
}