Examples with PoissonDistribution dr.math.distributions.PoissonDistribution used on opensource projects

Example 1 with PoissonDistribution

use of dr.math.distributions.PoissonDistribution in project beast-mcmc by beast-dev.

the class ApproximatePoissonTreeLikelihoodTest method setUp.

public void setUp() throws Exception {
    super.setUp();
    branchRateModel = new StrictClockBranchRates(new Parameter.Default(1.0));
    NewickImporter importer = new NewickImporter("((1:1.0,2:1.0,3:1.0):1.0,4:1.0);");
    NewickImporter importer2 = new NewickImporter("(((1:1.0,2:1.0):0.1,3:1.1):1.0,4:1.0);");
    tree = importer.importTree(null);
    treeModel = new BigFastTreeModel(importer2.importTree(null));
    cladeModel = new CladeNodeModel(tree, treeModel);
    BranchLengthProvider constrainedBranchLengthProvider = new ConstrainedTreeBranchLengthProvider(cladeModel);
    approximatePoissonTreeLikelihood = new ApproximatePoissonTreeLikelihood("approximateTreeLikelihood", 1, treeModel, branchRateModel, constrainedBranchLengthProvider);
    expectedLL = 0;
    double[] expectations = { 1d, 1d, 1.1, 2d, 0.1 };
    // time
    double[] mutations = { 1d, 1d, 1.0, 2d, 0 };
    for (int i = 0; i < expectations.length; i++) {
        PoissonDistribution p = new PoissonDistribution(expectations[i]);
        expectedLL += p.logPdf(mutations[i]);
    }
    approximatePoissonTreeLikelihood.getLogLikelihood();
}

Example 2 with PoissonDistribution

use of dr.math.distributions.PoissonDistribution in project beast-mcmc by beast-dev.

the class ApproximatePoissonTreeLikelihoodTest method testRootPolytomyHeightChange.

public void testRootPolytomyHeightChange() throws IOException, Importer.ImportException, TreeUtils.MissingTaxonException {
    branchRateModel = new StrictClockBranchRates(new Parameter.Default(1.0));
    NewickImporter importer = new NewickImporter("((1:1.0,2:1.0):1.0,3:1.0,4:1.0,5:1.0);");
    NewickImporter importer2 = new NewickImporter("(((1:1.0,2:1.0):1.0,3:1.1):0.1,(4:1.0,5:1.0):0.1));");
    tree = importer.importTree(null);
    treeModel = new BigFastTreeModel(importer2.importTree(null));
    CladeNodeModel cladeModel = new CladeNodeModel(tree, treeModel);
    BranchLengthProvider constrainedBranchLengthProvider = new ConstrainedTreeBranchLengthProvider(cladeModel);
    approximatePoissonTreeLikelihood = new ApproximatePoissonTreeLikelihood("approximateTreeLikelihood", 1, treeModel, branchRateModel, constrainedBranchLengthProvider);
    approximatePoissonTreeLikelihood.getLogLikelihood();
    expectedLL = 0;
    double[] expectations = { 1d, 1d, 1d, 1.1, 1.1 };
    // time
    double[] mutations = { 1d, 1d, 1d, 1.0, 1 };
    for (int i = 0; i < expectations.length; i++) {
        PoissonDistribution p = new PoissonDistribution(expectations[i]);
        expectedLL += p.logPdf(mutations[i]);
    }
    NodeRef rootNode = treeModel.getRoot();
    treeModel.setNodeHeight(rootNode, 2.05);
    double LL = approximatePoissonTreeLikelihood.getLogLikelihood();
    approximatePoissonTreeLikelihood.makeDirty();
    double newLL = approximatePoissonTreeLikelihood.getLogLikelihood();
    assertEquals(LL, newLL);
}

Example 3 with PoissonDistribution

use of dr.math.distributions.PoissonDistribution in project beast-mcmc by beast-dev.

the class RandomLocalClockTestProblem method testRandomLocalClock.

public void testRandomLocalClock() throws Exception {
    Parameter popSize = new Parameter.Default(ConstantPopulationModelParser.POPULATION_SIZE, 0.077, 0, Double.POSITIVE_INFINITY);
    ConstantPopulationModel constantModel = createRandomInitialTree(popSize);
    TreeIntervals intervalList = new TreeIntervals(treeModel, null, null);
    CoalescentLikelihood coalescent = new CoalescentLikelihood(intervalList, constantModel);
    coalescent.setId("coalescent");
    // clock model
    Parameter ratesParameter = new Parameter.Default(RandomLocalClockModelParser.RATES, 10, 1);
    Parameter rateIndicatorParameter = new Parameter.Default(RandomLocalClockModelParser.RATE_INDICATORS, 10, 1);
    Parameter meanRateParameter = new Parameter.Default(RandomLocalClockModelParser.CLOCK_RATE, 1, 1.0);
    RandomLocalClockModel branchRateModel = new RandomLocalClockModel(treeModel, meanRateParameter, rateIndicatorParameter, ratesParameter, false, 0.5);
    SumStatistic rateChanges = new SumStatistic("rateChangeCount", true, null);
    rateChanges.addStatistic(rateIndicatorParameter);
    RateStatistic meanRate = new RateStatistic("meanRate", treeModel, branchRateModel, true, true, RateStatisticParser.MEAN);
    RateStatistic coefficientOfVariation = new RateStatistic(RateStatisticParser.COEFFICIENT_OF_VARIATION, treeModel, branchRateModel, true, true, RateStatisticParser.COEFFICIENT_OF_VARIATION);
    RateCovarianceStatistic covariance = new RateCovarianceStatistic("covariance", treeModel, branchRateModel);
    // Sub model
    Parameter freqs = new Parameter.Default(alignment.getStateFrequencies());
    Parameter kappa = new Parameter.Default(HKYParser.KAPPA, 1.0, 0, Double.POSITIVE_INFINITY);
    FrequencyModel f = new FrequencyModel(Nucleotides.INSTANCE, freqs);
    HKY hky = new HKY(kappa, f);
    // siteModel
    GammaSiteModel siteModel = new GammaSiteModel(hky);
    Parameter mu = new Parameter.Default(GammaSiteModelParser.MUTATION_RATE, 1.0, 0, Double.POSITIVE_INFINITY);
    siteModel.setMutationRateParameter(mu);
    // treeLikelihood
    SitePatterns patterns = new SitePatterns(alignment, null, 0, -1, 1, true);
    TreeLikelihood treeLikelihood = new TreeLikelihood(patterns, treeModel, siteModel, branchRateModel, null, false, false, true, false, false);
    treeLikelihood.setId(TreeLikelihoodParser.TREE_LIKELIHOOD);
    // Operators
    OperatorSchedule schedule = new SimpleOperatorSchedule();
    MCMCOperator operator = new ScaleOperator(kappa, 0.75);
    operator.setWeight(1.0);
    schedule.addOperator(operator);
    operator = new ScaleOperator(ratesParameter, 0.75);
    operator.setWeight(10.0);
    schedule.addOperator(operator);
    operator = new BitFlipOperator(rateIndicatorParameter, 15.0, true);
    schedule.addOperator(operator);
    operator = new ScaleOperator(popSize, 0.75);
    operator.setWeight(3.0);
    schedule.addOperator(operator);
    Parameter rootHeight = ((DefaultTreeModel) treeModel).getRootHeightParameter();
    rootHeight.setId(TREE_HEIGHT);
    operator = new ScaleOperator(rootHeight, 0.75);
    operator.setWeight(3.0);
    schedule.addOperator(operator);
    Parameter internalHeights = ((DefaultTreeModel) treeModel).createNodeHeightsParameter(false, true, false);
    operator = new UniformOperator(internalHeights, 30.0);
    schedule.addOperator(operator);
    operator = new SubtreeSlideOperator(((DefaultTreeModel) treeModel), 15.0, 0.0077, true, false, false, false, AdaptationMode.ADAPTATION_ON, AdaptableMCMCOperator.DEFAULT_ADAPTATION_TARGET);
    schedule.addOperator(operator);
    operator = new ExchangeOperator(ExchangeOperator.NARROW, treeModel, 15.0);
    // operator.doOperation();
    schedule.addOperator(operator);
    operator = new ExchangeOperator(ExchangeOperator.WIDE, treeModel, 3.0);
    // operator.doOperation();
    schedule.addOperator(operator);
    operator = new WilsonBalding(treeModel, 3.0);
    // operator.doOperation();
    schedule.addOperator(operator);
    // CompoundLikelihood
    OneOnXPrior likelihood1 = new OneOnXPrior();
    likelihood1.addData(popSize);
    OneOnXPrior likelihood2 = new OneOnXPrior();
    likelihood2.addData(kappa);
    DistributionLikelihood likelihood3 = new DistributionLikelihood(new GammaDistribution(0.5, 2.0), 0.0);
    likelihood3.addData(ratesParameter);
    DistributionLikelihood likelihood4 = new DistributionLikelihood(new PoissonDistribution(1.0), 0.0);
    likelihood4.addData(rateChanges);
    List<Likelihood> likelihoods = new ArrayList<Likelihood>();
    likelihoods.add(likelihood1);
    likelihoods.add(likelihood2);
    likelihoods.add(likelihood3);
    likelihoods.add(likelihood4);
    likelihoods.add(coalescent);
    Likelihood prior = new CompoundLikelihood(0, likelihoods);
    prior.setId(CompoundLikelihoodParser.PRIOR);
    likelihoods.clear();
    likelihoods.add(treeLikelihood);
    Likelihood likelihood = new CompoundLikelihood(-1, likelihoods);
    likelihoods.clear();
    likelihoods.add(prior);
    likelihoods.add(likelihood);
    Likelihood posterior = new CompoundLikelihood(0, likelihoods);
    posterior.setId(CompoundLikelihoodParser.POSTERIOR);
    // Log
    ArrayLogFormatter formatter = new ArrayLogFormatter(false);
    MCLogger[] loggers = new MCLogger[2];
    loggers[0] = new MCLogger(formatter, 1000, false);
    loggers[0].add(posterior);
    loggers[0].add(prior);
    loggers[0].add(treeLikelihood);
    loggers[0].add(rootHeight);
    loggers[0].add(kappa);
    // loggers[0].add(meanRate);
    loggers[0].add(rateChanges);
    loggers[0].add(coefficientOfVariation);
    loggers[0].add(covariance);
    loggers[0].add(popSize);
    loggers[0].add(coalescent);
    loggers[1] = new MCLogger(new TabDelimitedFormatter(System.out), 10000, false);
    loggers[1].add(posterior);
    loggers[1].add(treeLikelihood);
    loggers[1].add(rootHeight);
    loggers[1].add(meanRate);
    loggers[1].add(rateChanges);
    // MCMC
    MCMC mcmc = new MCMC("mcmc1");
    MCMCOptions options = new MCMCOptions(1000000);
    mcmc.setShowOperatorAnalysis(true);
    mcmc.init(options, posterior, schedule, loggers);
    mcmc.run();
    // time
    System.out.println(mcmc.getTimer().toString());
    // Tracer
    List<Trace> traces = formatter.getTraces();
    ArrayTraceList traceList = new ArrayTraceList("RandomLocalClockTest", traces, 0);
    for (int i = 1; i < traces.size(); i++) {
        traceList.analyseTrace(i);
    }
    // <expectation name="posterior" value="-1818.26"/>
    // <expectation name="prior" value="-2.70143"/>
    // <expectation name="likelihood" value="-1815.56"/>
    // <expectation name="treeModel.rootHeight" value="6.363E-2"/>
    // <expectation name="constant.popSize" value="9.67405E-2"/>
    // <expectation name="hky.kappa" value="30.0394"/>
    // <expectation name="coefficientOfVariation" value="7.02408E-2"/>
    // covariance 0.47952
    // <expectation name="rateChangeCount" value="0.40786"/>
    // <expectation name="coalescent" value="7.29521"/>
    TraceCorrelation likelihoodStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(CompoundLikelihoodParser.POSTERIOR));
    assertExpectation(CompoundLikelihoodParser.POSTERIOR, likelihoodStats, -1818.26);
    likelihoodStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(CompoundLikelihoodParser.PRIOR));
    assertExpectation(CompoundLikelihoodParser.PRIOR, likelihoodStats, -2.70143);
    likelihoodStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(TreeLikelihoodParser.TREE_LIKELIHOOD));
    assertExpectation(TreeLikelihoodParser.TREE_LIKELIHOOD, likelihoodStats, -1815.56);
    TraceCorrelation treeHeightStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(TREE_HEIGHT));
    assertExpectation(TREE_HEIGHT, treeHeightStats, 6.363E-2);
    TraceCorrelation kappaStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(HKYParser.KAPPA));
    assertExpectation(HKYParser.KAPPA, kappaStats, 30.0394);
    TraceCorrelation rateChangeStats = traceList.getCorrelationStatistics(traceList.getTraceIndex("rateChangeCount"));
    assertExpectation("rateChangeCount", rateChangeStats, 0.40786);
    TraceCorrelation coefficientOfVariationStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(RateStatisticParser.COEFFICIENT_OF_VARIATION));
    assertExpectation(RateStatisticParser.COEFFICIENT_OF_VARIATION, coefficientOfVariationStats, 7.02408E-2);
    TraceCorrelation covarianceStats = traceList.getCorrelationStatistics(traceList.getTraceIndex("covariance"));
    assertExpectation("covariance", covarianceStats, 0.47952);
    TraceCorrelation popStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(ConstantPopulationModelParser.POPULATION_SIZE));
    assertExpectation(ConstantPopulationModelParser.POPULATION_SIZE, popStats, 9.67405E-2);
    TraceCorrelation coalescentStats = traceList.getCorrelationStatistics(traceList.getTraceIndex("coalescent"));
    assertExpectation("coalescent", coalescentStats, 7.29521);
}

Example 4 with PoissonDistribution

use of dr.math.distributions.PoissonDistribution in project beast-mcmc by beast-dev.

the class ARGAddRemoveOperatorTest method flatPriorTester.

private void flatPriorTester(ARGModel arg, int chainLength, int sampleTreeEvery, double nodeCountSetting, double rootHeightAlpha, double rootHeightBeta, int maxCount) throws IOException, Importer.ImportException {
    MCMC mcmc = new MCMC("mcmc1");
    MCMCOptions options = new MCMCOptions(chainLength);
    // double nodeCountSetting = 2.0;
    // double rootHeightAlpha = 100;
    // double rootHeightBeta = 0.5;
    OperatorSchedule schedule = getSchedule(arg);
    ARGUniformPrior uniformPrior = new ARGUniformPrior(arg, maxCount, arg.getExternalNodeCount());
    PoissonDistribution poisson = new PoissonDistribution(nodeCountSetting);
    DistributionLikelihood nodeCountPrior = new DistributionLikelihood(poisson, 0.0);
    ARGReassortmentNodeCountStatistic nodeCountStatistic = new ARGReassortmentNodeCountStatistic("nodeCount", arg);
    nodeCountPrior.addData(nodeCountStatistic);
    DistributionLikelihood rootPrior = new DistributionLikelihood(new GammaDistribution(rootHeightAlpha, rootHeightBeta), 0.0);
    CompoundParameter rootHeight = (CompoundParameter) arg.createNodeHeightsParameter(true, false, false);
    rootPrior.addData(rootHeight);
    List<Likelihood> likelihoods = new ArrayList<Likelihood>();
    likelihoods.add(uniformPrior);
    likelihoods.add(rootPrior);
    likelihoods.add(nodeCountPrior);
    CompoundLikelihood compoundLikelihood = new CompoundLikelihood(1, likelihoods);
    compoundLikelihood.setId("likelihood1");
    MCLogger[] loggers = new MCLogger[3];
    loggers[0] = new MCLogger(new TabDelimitedFormatter(System.out), 10000, false);
    loggers[0].add(compoundLikelihood);
    loggers[0].add(arg);
    File file = new File("test.args");
    file.deleteOnExit();
    FileOutputStream out = new FileOutputStream(file);
    loggers[1] = new ARGLogger(arg, new TabDelimitedFormatter(out), sampleTreeEvery, "test");
    ArrayLogFormatter formatter = new ArrayLogFormatter(false);
    loggers[2] = new MCLogger(formatter, sampleTreeEvery, false);
    loggers[2].add(arg);
    arg.getRootHeightParameter().setId("root");
    loggers[2].add(arg.getRootHeightParameter());
    mcmc.setShowOperatorAnalysis(true);
    mcmc.init(options, compoundLikelihood, schedule, loggers);
    mcmc.run();
    out.flush();
    out.close();
    List<Trace> traces = formatter.getTraces();
    // Set<String> uniqueTrees = new HashSet<String>();
    // 
    // NexusImporter importer = new NexusImporter(new FileReader(file));
    // while (importer.hasTree()) {
    // Tree t = importer.importNextTree();
    // uniqueTrees.add(Tree.Utils.uniqueNewick(t, t.getRoot()));
    // }
    // 
    // TestCase.assertEquals(numTopologies, uniqueTrees.size());            List<Trace> traces = formatter.getTraces();
    ArrayTraceList traceList = new ArrayTraceList("ARGTest", traces, 0);
    for (int i = 1; i < traces.size(); i++) {
        traceList.analyseTrace(i);
    }
    TraceCorrelation nodeCountStats = traceList.getCorrelationStatistics(1);
    TraceCorrelation rootHeightStats = traceList.getCorrelationStatistics(4);
    assertExpectation("nodeCount", nodeCountStats, poisson.truncatedMean(maxCount));
    assertExpectation(TreeModelParser.ROOT_HEIGHT, rootHeightStats, rootHeightAlpha * rootHeightBeta);
}

Example 5 with PoissonDistribution

use of dr.math.distributions.PoissonDistribution in project beast-mcmc by beast-dev.

the class ApproximatePoissonTreeLikelihoodTest method testAfterHeightChange.

public void testAfterHeightChange() {
    NodeRef insertedNode = treeModel.getParent(treeModel.getNode(0));
    treeModel.setNodeHeight(insertedNode, 0.9);
    double ll = 0;
    double[] expectations = { 0.9, 0.9, 1.1, 2d, 0.2 };
    double[] mutations = { 1d, 1d, 1d, 2d, 0 };
    for (int i = 0; i < expectations.length; i++) {
        PoissonDistribution p = new PoissonDistribution(expectations[i]);
        ll += p.logPdf(mutations[i]);
    }
    assertEquals(ll, approximatePoissonTreeLikelihood.getLogLikelihood(), 1E-13);
}