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122 lines
3.5 KiB
122 lines
3.5 KiB
//==============================================================================
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//
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// Copyright (c) 2002-
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// Authors:
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// * Vincent Nimal <vincent.nimal@comlab.ox.ac.uk> (University of Oxford)
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// * Dave Parker <david.parker@comlab.ox.ac.uk> (University of Oxford)
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//
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//------------------------------------------------------------------------------
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//
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// This file is part of PRISM.
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//
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// PRISM is free software; you can redistribute it and/or modify
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// it under the terms of the GNU General Public License as published by
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// the Free Software Foundation; either version 2 of the License, or
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// (at your option) any later version.
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//
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// PRISM is distributed in the hope that it will be useful,
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// but WITHOUT ANY WARRANTY; without even the implied warranty of
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// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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// GNU General Public License for more details.
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//
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// You should have received a copy of the GNU General Public License
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// along with PRISM; if not, write to the Free Software Foundation,
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// Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
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//
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//==============================================================================
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package simulator.method;
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import prism.PrismException;
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import simulator.sampler.Sampler;
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import cern.jet.stat.Probability;
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/**
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* SimulationMethod class for the CI ("confidence interval") approach.
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* Case where 'confidence' is unknown parameter.
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*/
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public final class CIconfidence extends CIMethod
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{
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// Estimate of variance (from sampling)
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private double varEstimator;
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public CIconfidence(double width, int iterations)
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{
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this.width = width;
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this.numSamples = iterations;
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varEstimator = 0.0;
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}
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@Override
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public void computeMissingParameterAfterSim()
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{
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// Only compute for numSamples > 1
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// (Student's t-distribution only defined for v > 0)
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if (numSamples > 1) {
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double prob, x;
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x = width * Math.sqrt(numSamples / varEstimator);
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// Note: need to hard-code case x=Inf
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if (Double.isInfinite(x)) {
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prob = 1.0;
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}
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// (Note: Colt's studentT seems to break for v=1 so do manually)
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else if (numSamples - 1 > 1) {
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prob = Probability.studentT(x, numSamples - 1);
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} else {
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// PDF for v=1 is 1/2 + arctan(x)/pi
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prob = 0.5 + Math.atan(x) / Math.PI;
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}
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confidence = 2 * (1.0 - prob);
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missingParameterComputed = true;
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}
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}
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@Override
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public Object getMissingParameter() throws PrismException
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{
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if (!missingParameterComputed)
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throw new PrismException("Missing parameter not computed yet");
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return confidence;
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}
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@Override
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public String getParametersString()
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{
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if (!missingParameterComputed)
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return "width=" + width + ", confidence=" + "unknown" + ", number of samples=" + numSamples;
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else
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return "width=" + width + ", confidence=" + confidence + ", number of samples=" + numSamples;
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}
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@Override
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public boolean shouldStopNow(int iters, Sampler sampler)
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{
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if (iters >= numSamples) {
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// Store final variance for confidence computation later
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varEstimator = sampler.getVariance();
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return true;
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}
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return false;
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}
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@Override
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public int getProgress(int iters, Sampler sampler)
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{
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// Easy: percentage of iters done so far
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return ((10 * iters) / numSamples) * 10;
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}
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@Override
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public SimulationMethod clone()
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{
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CIconfidence m = new CIconfidence(width, numSamples);
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// Remaining CIMethod stuff
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m.confidence = confidence;
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m.missingParameterComputed = missingParameterComputed;
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m.prOp = prOp;
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m.theta = theta;
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// Local stuff
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m.varEstimator = varEstimator;
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return m;
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}
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}
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