Java tutorial
/* * * Copyright 2013-2014 University Of Southern California * * 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. * */ package org.workflowsim.utils; import java.util.Arrays; import org.apache.commons.math3.distribution.GammaDistribution; import org.apache.commons.math3.distribution.LogNormalDistribution; import org.apache.commons.math3.distribution.NormalDistribution; import org.apache.commons.math3.distribution.RealDistribution; import org.apache.commons.math3.distribution.WeibullDistribution; /** * This is a OverheadDistributionGenrator for one typic overhead per level. * * @author Weiwei Chen * @since WorkflowSim Toolkit 1.0 * @date Mar 11, 2014 */ public class DistributionGenerator { protected DistributionFamily dist; protected double scale; protected double shape; protected double scale_prior; protected double shape_prior; protected double likelihood_prior; protected double[] samples; protected double[] cumulativeSamples; protected int cursor; protected final int SAMPLE_SIZE = 1500; //DistributionGenerator will automatically increase the size public enum DistributionFamily { LOGNORMAL, GAMMA, WEIBULL, NORMAL } /** * * @param dist * @param scale * @param shape */ public DistributionGenerator(DistributionFamily dist, double scale, double shape) { this.dist = dist; this.scale = scale; this.shape = shape; this.scale_prior = scale; this.shape_prior = shape; RealDistribution distribution = getDistribution(scale, shape); samples = distribution.sample(SAMPLE_SIZE); updateCumulativeSamples(); cursor = 0; } public DistributionGenerator(DistributionFamily dist, double scale, double shape, double a, double b, double c) { this(dist, scale, shape); this.scale_prior = b; this.shape_prior = a; this.likelihood_prior = c; } /** * Gets the sample data * * @return samples */ public double[] getSamples() { return samples; } /** * Gets the cumulative Samples * * @return cumulativeSamples */ public double[] getCumulativeSamples() { return cumulativeSamples; } /** * Extends the sample size */ public void extendSamples() { double[] new_samples = getDistribution(scale, shape).sample(SAMPLE_SIZE); samples = concat(samples, new_samples); updateCumulativeSamples(); } /** * Update cumulativeSamples from samples */ public void updateCumulativeSamples() { cumulativeSamples = new double[samples.length]; cumulativeSamples[0] = samples[0]; for (int i = 1; i < samples.length; i++) { cumulativeSamples[i] = cumulativeSamples[i - 1] + samples[i]; } } /** * Gets the Prior Knowledge based Estimation of samples * * @return delay */ public double getPKEMean() { return this.shape_prior / this.scale_prior; } /** * Gets the average of samples * * @return average */ public double getMean() { double sum = 0.0; for (int i = 0; i < cursor; i++) { sum += samples[i]; } return sum / cursor; } /** * Gets the likelihood prior * * @return likelihood_prior */ public double getLikelihoodPrior() { return this.likelihood_prior; } /** * Gets the Maximum Likelihood Estimation of samples based on the ftc paper * * @return the delay */ public double getMLEMean() { double a = shape_prior, b = scale_prior; double sum = 0.0; for (int i = 0; i < cursor; i++) { switch (dist) { case GAMMA: sum += samples[i]; break; case WEIBULL: sum += Math.pow(samples[i], likelihood_prior); break; } } double result = 0.0; switch (dist) { case GAMMA: result = (b + sum) / (a + cursor * likelihood_prior - 1); break; case WEIBULL: result = (b + sum) / (a + cursor + 1); break; default: break; } return result; } /** * Vary the distribution parameters but not the prior knowledge * * @param scale the first param * @param shape the second param */ public void varyDistribution(double scale, double shape) { this.scale = scale; this.shape = shape; RealDistribution distribution = getDistribution(scale, shape); samples = distribution.sample(SAMPLE_SIZE); updateCumulativeSamples(); //cursor = 0; } /** * Merge two arrays * * @param <T> array type * @param first first array * @param second second array * @return new array */ public double[] concat(double[] first, double[] second) { double[] result = Arrays.copyOf(first, first.length + second.length); System.arraycopy(second, 0, result, first.length, second.length); return result; } /** * Gets the next sample from samples * * @return delay */ public double getNextSample() { while (cursor >= samples.length) { double[] new_samples = getDistribution(scale, shape).sample(SAMPLE_SIZE); samples = concat(samples, new_samples); updateCumulativeSamples(); } double delay = samples[cursor]; cursor++; return delay; } /** * Gets the RealDistribution with two parameters * * @param scale the first param scale * @param shape the second param shape * @return the RealDistribution Object */ public RealDistribution getDistribution(double scale, double shape) { RealDistribution distribution = null; switch (this.dist) { case LOGNORMAL: distribution = new LogNormalDistribution(scale, shape); break; case WEIBULL: distribution = new WeibullDistribution(shape, scale); break; case GAMMA: distribution = new GammaDistribution(shape, scale); break; case NORMAL: //shape is the std, scale is the mean distribution = new NormalDistribution(scale, shape); break; default: break; } return distribution; } /** * Gets the scale parameter * @return scale */ public double getScale() { return this.scale; } /** * Gets the shape parameter * @return shape */ public double getShape() { return this.shape; } }