/*
* Encog(tm) Core v3.4 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2016 Heaton Research, Inc.
*
* 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 org.encog.mathutil.randomize;
/**
* Generally, you will not want to use this randomizer as a pure neural network
* randomizer. More on this later in the description.
*
* Generate random numbers that fall within a Gaussian curve. The mean
* represents the center of the curve, and the standard deviation helps
* determine the length of the curve on each side.
*
* This randomizer is used mainly for special cases where I want to generate
* random numbers in a Gaussian range. For a pure neural network initializer, it
* leaves much to be desired. However, it can make for a decent randomizer.
* Usually, the Nguyen Widrow randomizer performs better.
*
* Uses the "Box Muller" method.
* http://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform
*
* Ported from C++ version provided by Everett F. Carter Jr., 1994
*/
public class GaussianRandomizer extends BasicRandomizer {
/**
* The y2 value.
*/
private double y2;
/**
* Should we use the last value.
*/
private boolean useLast = false;
/**
* The mean.
*/
private final double mean;
/**
* The standard deviation.
*/
private final double standardDeviation;
/**
* Construct a Gaussian randomizer. The mean, the standard deviation.
*
* @param mean
* The mean.
* @param standardDeviation
* The standard deviation.
*/
public GaussianRandomizer(final double mean,
final double standardDeviation) {
this.mean = mean;
this.standardDeviation = standardDeviation;
}
/**
* Compute a Gaussian random number.
*
* @param m
* The mean.
* @param s
* The standard deviation.
* @return The random number.
*/
public double boxMuller(final double m, final double s) {
double x1, x2, w, y1;
// use value from previous call
if (this.useLast) {
y1 = this.y2;
this.useLast = false;
} else {
do {
x1 = 2.0 * nextDouble() - 1.0;
x2 = 2.0 * nextDouble() - 1.0;
w = x1 * x1 + x2 * x2;
} while (w >= 1.0);
w = Math.sqrt((-2.0 * Math.log(w)) / w);
y1 = x1 * w;
this.y2 = x2 * w;
this.useLast = true;
}
return (m + y1 * s);
}
/**
* Generate a random number.
*
* @param d
* The input value, not used.
* @return The random number.
*/
public double randomize(final double d) {
return boxMuller(this.mean, this.standardDeviation);
}
}