Java tutorial
/* * OneOverStDevPeriodPriorDistribution.java * * Copyright (c) 2002-2015 Alexei Drummond, Andrew Rambaut and Marc Suchard * * This file is part of BEAST. * See the NOTICE file distributed with this work for additional * information regarding copyright ownership and licensing. * * BEAST 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 * of the License, or (at your option) any later version. * * BEAST 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 BEAST; if not, write to the * Free Software Foundation, Inc., 51 Franklin St, Fifth Floor, * Boston, MA 02110-1301 USA */ package dr.evomodel.epidemiology.casetocase.periodpriors; import dr.xml.*; import org.apache.commons.math.stat.descriptive.DescriptiveStatistics; /** * This is effectively the Jeffreys prior on the set of periods assuming that they are normally (or lognormally) * distributed; the probability is proportional to the reciprocal of their standard deviation (or the standard deviation * of their logarithms). */ public class OneOverStDevPeriodPriorDistribution extends AbstractPeriodPriorDistribution { public static final String ONE_OVER_STDEV = "oneOverStDevPeriodPriorDistribution"; public static final String LOG = "log"; public static final String ID = "id"; public OneOverStDevPeriodPriorDistribution(String name, boolean log) { super(name, log); } @Override public void reset() { } @Override public double calculateLogPosteriorProbability(double newValue, double minValue) { return 0; } @Override public double calculateLogPosteriorCDF(double limit, boolean upper) { return 0; } public double calculateLogLikelihood(double[] values) { DescriptiveStatistics stats = new DescriptiveStatistics(values); logL = -Math.log(stats.getStandardDeviation()); return logL; } public static XMLObjectParser PARSER = new AbstractXMLObjectParser() { public String getParserName() { return ONE_OVER_STDEV; } public Object parseXMLObject(XMLObject xo) throws XMLParseException { String id = (String) xo.getAttribute(ID); boolean log; log = xo.hasAttribute(LOG) ? xo.getBooleanAttribute(LOG) : false; return new OneOverStDevPeriodPriorDistribution(id, log); } public XMLSyntaxRule[] getSyntaxRules() { return rules; } private final XMLSyntaxRule[] rules = { AttributeRule.newBooleanRule(LOG, true), AttributeRule.newStringRule(ID, false) }; public String getParserDescription() { return "Calculates the probability of observing a list of doubles with probability proportional to" + "1 over their standard deviation (the Jeffreys prior for normally distributed data)"; } public Class getReturnType() { return OneOverStDevPeriodPriorDistribution.class; } }; }