Examples with HKY dr.oldevomodel.substmodel.HKY used on opensource projects

Example 6 with HKY

use of dr.oldevomodel.substmodel.HKY in project beast-mcmc by beast-dev.

the class AlignmentScore method main.

public static void main(String[] args) throws java.io.IOException, Importer.ImportException {
    NexusImporter importer = new NexusImporter(new FileReader(args[0]));
    Alignment alignment = importer.importAlignment();
    ExtractPairs pairs = new ExtractPairs(alignment);
    Parameter muParam = new Parameter.Default(1.0);
    Parameter kappaParam = new Parameter.Default(1.0);
    kappaParam.addBounds(new Parameter.DefaultBounds(100.0, 0.0, 1));
    muParam.addBounds(new Parameter.DefaultBounds(1.0, 1.0, 1));
    Parameter freqParam = new Parameter.Default(alignment.getStateFrequencies());
    FrequencyModel freqModel = new FrequencyModel(Nucleotides.INSTANCE, freqParam);
    SubstitutionModel substModel = new HKY(kappaParam, freqModel);
    SiteModel siteModel = new GammaSiteModel(substModel, muParam, null, 1, null);
    ScoreMatrix scoreMatrix = new ScoreMatrix(siteModel, 0.1);
    double threshold = 0.1;
    List<PairDistance> pairDistances = new ArrayList<PairDistance>();
    Set<Integer> sequencesUsed = new HashSet<Integer>();
    List<Integer> allGaps = new ArrayList<Integer>();
    for (int i = 0; i < alignment.getSequenceCount(); i++) {
        for (int j = i + 1; j < alignment.getSequenceCount(); j++) {
            Alignment pairAlignment = pairs.getPairAlignment(i, j);
            if (pairAlignment != null) {
                SitePatterns patterns = new SitePatterns(pairAlignment);
                double distance = getGeneticDistance(scoreMatrix, patterns);
                if (distance < threshold) {
                    List gaps = new ArrayList();
                    GapUtils.getGapSizes(pairAlignment, gaps);
                    pairDistances.add(new PairDistance(i, j, distance, gaps, pairAlignment.getSiteCount()));
                    System.out.print(".");
                } else {
                    System.out.print("*");
                }
            } else {
                System.out.print("x");
            }
        }
        System.out.println();
    }
    Collections.sort(pairDistances);
    int totalLength = 0;
    for (PairDistance pairDistance : pairDistances) {
        Integer x = pairDistance.x;
        Integer y = pairDistance.y;
        if (!sequencesUsed.contains(x) && !sequencesUsed.contains(y)) {
            allGaps.addAll(pairDistance.gaps);
            sequencesUsed.add(x);
            sequencesUsed.add(y);
            System.out.println("Added pair (" + x + "," + y + ") d=" + pairDistance.distance + " L=" + pairDistance.alignmentLength);
            totalLength += pairDistance.alignmentLength;
        }
    }
    printFrequencyTable(allGaps);
    System.out.println("total length=" + totalLength);
}

Example 7 with HKY

use of dr.oldevomodel.substmodel.HKY 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);
    CoalescentLikelihood coalescent = new CoalescentLikelihood(treeModel, null, new ArrayList<TaxonList>(), 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);
    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 = treeModel.getRootHeightParameter();
    rootHeight.setId(TREE_HEIGHT);
    operator = new ScaleOperator(rootHeight, 0.75);
    operator.setWeight(3.0);
    schedule.addOperator(operator);
    Parameter internalHeights = treeModel.createNodeHeightsParameter(false, true, false);
    operator = new UniformOperator(internalHeights, 30.0);
    schedule.addOperator(operator);
    operator = new SubtreeSlideOperator(treeModel, 15.0, 0.0077, true, false, false, false, CoercionMode.COERCION_ON);
    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 8 with HKY

use of dr.oldevomodel.substmodel.HKY in project beast-mcmc by beast-dev.

the class SeqGen method main.

public static void main(String[] argv) {
    String treeFileName = argv[0];
    String outputFileStem = argv[1];
    int length = 500;
    double[] frequencies = new double[] { 0.25, 0.25, 0.25, 0.25 };
    double kappa = 10.0;
    double alpha = 0.5;
    double substitutionRate = argv.length < 3 ? 1.0E-3 : Double.parseDouble(argv[2]);
    int categoryCount = argv.length < 4 ? 8 : Integer.parseInt(argv[3]);
    //1.56E-6;
    double damageRate = argv.length < 5 ? 0 : Double.parseDouble(argv[4]);
    System.out.println("substitutionRate = " + substitutionRate + "; categoryCount = " + categoryCount + "; damageRate = " + damageRate);
    FrequencyModel freqModel = new FrequencyModel(dr.evolution.datatype.Nucleotides.INSTANCE, frequencies);
    HKY hkyModel = new HKY(kappa, freqModel);
    SiteModel siteModel = null;
    if (categoryCount > 1) {
        siteModel = new GammaSiteModel(hkyModel, alpha, categoryCount);
    } else {
        // no rate heterogeneity
        siteModel = new GammaSiteModel(hkyModel);
    }
    List<Tree> trees = new ArrayList<Tree>();
    FileReader reader = null;
    try {
        reader = new FileReader(treeFileName);
        //            TreeImporter importer = new NexusImporter(reader);
        TreeImporter importer = new NewickImporter(reader);
        while (importer.hasTree()) {
            Tree tree = importer.importNextTree();
            trees.add(tree);
            System.out.println("tree height = " + tree.getNodeHeight(tree.getRoot()) + "; leave nodes = " + tree.getExternalNodeCount());
        }
    } catch (FileNotFoundException e) {
        e.printStackTrace();
        return;
    } catch (Importer.ImportException e) {
        e.printStackTrace();
        return;
    } catch (IOException e) {
        e.printStackTrace();
        return;
    }
    SeqGen seqGen = new SeqGen(length, substitutionRate, freqModel, hkyModel, siteModel, damageRate);
    int i = 1;
    for (Tree tree : trees) {
        Alignment alignment = seqGen.simulate(tree);
        FileWriter writer = null;
        try {
            //                writer = new FileWriter(outputFileStem + (i < 10 ? "00" : (i < 100 ? "0" : "")) + i + ".nex");
            //                NexusExporter exporter = new NexusExporter(writer);
            //
            //                exporter.exportAlignment(alignment);
            //
            //                writer.close();
            String outputFileName = outputFileStem + "-" + substitutionRate + ".fasta";
            writer = new FileWriter(outputFileName);
            BufferedWriter bf = new BufferedWriter(writer);
            FastaExporter exporter = new FastaExporter(bf);
            exporter.exportSequences(alignment.getSequenceList());
            bf.close();
            System.out.println("Write " + i + "th sequence file : " + outputFileName);
            i++;
        } catch (IOException e) {
            e.printStackTrace();
            return;
        }
    }
}

Example 9 with HKY

use of dr.oldevomodel.substmodel.HKY in project beast-mcmc by beast-dev.

the class HKYParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    Variable kappaParam = (Variable) xo.getElementFirstChild(KAPPA);
    FrequencyModel freqModel = (FrequencyModel) xo.getElementFirstChild(FrequencyModelParser.FREQUENCIES);
    Logger.getLogger("dr.evomodel").info("Creating HKY substitution model. Initial kappa = " + kappaParam.getValue(0));
    return new HKY(kappaParam, freqModel);
}

Example 10 with HKY

use of dr.oldevomodel.substmodel.HKY in project beast-mcmc by beast-dev.

the class HKYTest method testHKY.

public void testHKY() {
    for (Instance test : all) {
        Parameter kappa = new Parameter.Default(1, test.getKappa());
        double[] pi = test.getPi();
        Parameter freqs = new Parameter.Default(pi);
        FrequencyModel f = new FrequencyModel(Nucleotides.INSTANCE, freqs);
        HKY hky = new HKY(kappa, f);
        double distance = test.getDistance();
        double[] mat = new double[4 * 4];
        hky.getTransitionProbabilities(distance, mat);
        final double[] result = test.getExpectedResult();
        for (int k = 0; k < mat.length; ++k) {
            assertEquals(mat[k], result[k], 1e-10);
        // System.out.print(" " + (mat[k] - result[k]));
        }
    }
}