Examples with Microsatellite dr.evolution.datatype.Microsatellite used on opensource projects

Example 6 with Microsatellite

use of dr.evolution.datatype.Microsatellite in project beast-mcmc by beast-dev.

the class BEAUTiImporter method importMicroSatFile.

// micro-sat
private void importMicroSatFile(File file) throws IOException, ImportException {
    try {
        Reader reader = new FileReader(file);
        BufferedReader bufferedReader = new BufferedReader(reader);
        MicroSatImporter importer = new MicroSatImporter(bufferedReader);
        List<Patterns> microsatPatList = importer.importPatterns();
        Taxa unionSetTaxonList = importer.getUnionSetTaxonList();
        Microsatellite microsatellite = importer.getMicrosatellite();
        //            options.allowDifferentTaxa = importer.isHasDifferentTaxon();
        bufferedReader.close();
        PartitionSubstitutionModel substModel = new PartitionSubstitutionModel(options, microsatPatList.get(0).getId());
        substModel.setMicrosatellite(microsatellite);
        for (Patterns patterns : microsatPatList) {
            setData(file.getName(), unionSetTaxonList, patterns, substModel, null);
        }
        // has to call after data is imported
        options.microsatelliteOptions.initModelParametersAndOpererators();
    } catch (ImportException e) {
        throw new ImportException(e.getMessage());
    } catch (IOException e) {
        throw new IOException(e.getMessage());
    }
}

Example 7 with Microsatellite

use of dr.evolution.datatype.Microsatellite in project beast-mcmc by beast-dev.

the class MsatFullLikelihoodTest method setUp.

public void setUp() throws Exception {
    super.setUp();
    //taxa
    ArrayList<Taxon> taxonList1 = new ArrayList<Taxon>();
    Collections.addAll(taxonList1, new Taxon("taxon1"), new Taxon("taxon2"), new Taxon("taxon3"));
    Taxa taxa1 = new Taxa(taxonList1);
    //msat datatype
    Microsatellite msat = new Microsatellite(1, 3);
    Patterns msatPatterns = new Patterns(msat, taxa1);
    //pattern in the correct code form.
    msatPatterns.addPattern(new int[] { 0, 1, 2 });
    //create tree
    NewickImporter importer = new NewickImporter("(taxon1:7.5,(taxon2:5.3,taxon3:5.3):2.2);");
    Tree tree = importer.importTree(null);
    //treeModel
    TreeModel treeModel = new TreeModel(tree);
    //msatsubstModel
    AsymmetricQuadraticModel aqm1 = new AsymmetricQuadraticModel(msat, null);
    //siteModel
    GammaSiteModel siteModel = new GammaSiteModel(aqm1);
    //treeLikelihood
    treeLikelihood1 = new TreeLikelihood(msatPatterns, treeModel, siteModel, null, null, false, false, true, false, false);
    setUpExample2();
    setUpExample3();
}

Example 8 with Microsatellite

use of dr.evolution.datatype.Microsatellite in project beast-mcmc by beast-dev.

the class MsatFullLikelihoodTest method setUpExample2.

private void setUpExample2() throws Exception {
    //taxa
    ArrayList<Taxon> taxonList2 = new ArrayList<Taxon>();
    Collections.addAll(taxonList2, new Taxon("taxon1"), new Taxon("taxon2"), new Taxon("taxon3"), new Taxon("taxon4"), new Taxon("taxon5"));
    Taxa taxa2 = new Taxa(taxonList2);
    //msat datatype
    Microsatellite msat = new Microsatellite(1, 3);
    Patterns msatPatterns = new Patterns(msat, taxa2);
    //pattern in the correct code form.
    msatPatterns.addPattern(new int[] { 0, 1, 2, 1, 2 });
    //create tree
    NewickImporter importer = new NewickImporter("(((taxon1:1.5,taxon2:1.5):1.5,(taxon3:2.1,taxon4:2.1):0.9):0.7,taxon5:3.7);");
    Tree tree = importer.importTree(null);
    //treeModel
    TreeModel treeModel = new TreeModel(tree);
    //msatsubstModel
    AsymmetricQuadraticModel aqm2 = new AsymmetricQuadraticModel(msat, null);
    //siteModel
    GammaSiteModel siteModel = new GammaSiteModel(aqm2);
    //treeLikelihood
    treeLikelihood2 = new TreeLikelihood(msatPatterns, treeModel, siteModel, null, null, false, false, true, false, false);
}

Example 9 with Microsatellite

use of dr.evolution.datatype.Microsatellite in project beast-mcmc by beast-dev.

the class MsatSamplingTreeLikelihoodTest method setUp.

public void setUp() throws Exception {
    super.setUp();
    //taxa
    ArrayList<Taxon> taxonList3 = new ArrayList<Taxon>();
    Collections.addAll(taxonList3, new Taxon("Taxon1"), new Taxon("Taxon2"), new Taxon("Taxon3"), new Taxon("Taxon4"), new Taxon("Taxon5"), new Taxon("Taxon6"), new Taxon("Taxon7"));
    Taxa taxa3 = new Taxa(taxonList3);
    //msat datatype
    Microsatellite msat = new Microsatellite(1, 6);
    Patterns msatPatterns = new Patterns(msat, taxa3);
    //pattern in the correct code form.
    msatPatterns.addPattern(new int[] { 0, 1, 3, 2, 4, 5, 1 });
    //create tree
    NewickImporter importer = new NewickImporter("(((Taxon1:0.3,Taxon2:0.3):0.6,Taxon3:0.9):0.9,((Taxon4:0.5,Taxon5:0.5):0.3,(Taxon6:0.7,Taxon7:0.7):0.1):1.0);");
    Tree tree = importer.importTree(null);
    //treeModel
    TreeModel treeModel = new TreeModel(tree);
    //msatsubstModel
    AsymmetricQuadraticModel eu1 = new AsymmetricQuadraticModel(msat, null);
    //create msatSamplerTreeModel
    Parameter internalVal = new Parameter.Default(new double[] { 2, 3, 4, 2, 1, 5 });
    int[] externalValues = msatPatterns.getPattern(0);
    HashMap<String, Integer> taxaMap = new HashMap<String, Integer>(externalValues.length);
    boolean internalValuesProvided = true;
    for (int i = 0; i < externalValues.length; i++) {
        taxaMap.put(msatPatterns.getTaxonId(i), i);
    }
    MicrosatelliteSamplerTreeModel msatTreeModel = new MicrosatelliteSamplerTreeModel("JUnitTestEx", treeModel, internalVal, msatPatterns, externalValues, taxaMap, internalValuesProvided);
    //create msatSamplerTreeLikelihood
    BranchRateModel branchRateModel = new StrictClockBranchRates(new Parameter.Default(1.0));
    eu1Likelihood = new MicrosatelliteSamplerTreeLikelihood(msatTreeModel, eu1, branchRateModel);
    //eu2
    TwoPhaseModel eu2 = new TwoPhaseModel(msat, null, eu1, new Parameter.Default(0.0), new Parameter.Default(0.4), null, false);
    eu2Likelihood = new MicrosatelliteSamplerTreeLikelihood(msatTreeModel, eu2, branchRateModel);
    //ec1
    LinearBiasModel ec1 = new LinearBiasModel(msat, null, eu1, new Parameter.Default(0.48), new Parameter.Default(0.0), false, false, false);
    ec1Likelihood = new MicrosatelliteSamplerTreeLikelihood(msatTreeModel, ec1, branchRateModel);
    //ec2
    TwoPhaseModel ec2 = new TwoPhaseModel(msat, null, ec1, new Parameter.Default(0.0), new Parameter.Default(0.4), null, false);
    ec2Likelihood = new MicrosatelliteSamplerTreeLikelihood(msatTreeModel, ec2, branchRateModel);
    //el1
    LinearBiasModel el1 = new LinearBiasModel(msat, null, eu1, new Parameter.Default(0.2), new Parameter.Default(-0.018), true, false, false);
    el1Likelihood = new MicrosatelliteSamplerTreeLikelihood(msatTreeModel, el1, branchRateModel);
    AsymmetricQuadraticModel pu1 = new AsymmetricQuadraticModel(msat, null, new Parameter.Default(1.0), new Parameter.Default(0.015), new Parameter.Default(0.0), new Parameter.Default(1.0), new Parameter.Default(0.015), new Parameter.Default(0.0), false);
    pu1Likelihood = new MicrosatelliteSamplerTreeLikelihood(msatTreeModel, pu1, branchRateModel);
    //ec2
    TwoPhaseModel pu2 = new TwoPhaseModel(msat, null, pu1, new Parameter.Default(0.0), new Parameter.Default(0.4), null, false);
    pu2Likelihood = new MicrosatelliteSamplerTreeLikelihood(msatTreeModel, pu2, branchRateModel);
    //ec1
    LinearBiasModel pc1 = new LinearBiasModel(msat, null, pu1, new Parameter.Default(0.48), new Parameter.Default(0.0), false, false, false);
    pc1Likelihood = new MicrosatelliteSamplerTreeLikelihood(msatTreeModel, pc1, branchRateModel);
}

Example 10 with Microsatellite

use of dr.evolution.datatype.Microsatellite in project beast-mcmc by beast-dev.

the class LinearBiasTest method testLinearBiasModel.

public void testLinearBiasModel() {
    for (Instance test : all) {
        OnePhaseModel subModel = test.getSubModel();
        Microsatellite microsat = (Microsatellite) subModel.getDataType();
        Parameter biasLinear = new Parameter.Default(1, test.getBiasLinearParam());
        Parameter biasConstant = new Parameter.Default(1, test.getBiasConstantParam());
        LinearBiasModel lbm = new LinearBiasModel(microsat, null, subModel, biasConstant, biasLinear, test.isLogistics(), false, false);
        lbm.computeStationaryDistribution();
        double[] statDist = lbm.getStationaryDistribution();
        final double[] expectedStatDist = test.getExpectedPi();
        for (int k = 0; k < statDist.length; ++k) {
            assertEquals(statDist[k], expectedStatDist[k], 1e-10);
        }
        int stateCount = microsat.getStateCount();
        double[] mat = new double[stateCount * stateCount];
        lbm.getTransitionProbabilities(test.getDistance(), mat);
        final double[] result = test.getExpectedResult();
        int k;
        for (k = 0; k < mat.length; ++k) {
            assertEquals(result[k], mat[k], 5e-9);
        //System.out.print(" " + (mat[k] - result[k]));
        }
        k = 0;
        for (int i = 0; i < microsat.getStateCount(); i++) {
            for (int j = 0; j < microsat.getStateCount(); j++) {
                assertEquals(result[k++], lbm.getOneTransitionProbabilityEntry(test.getDistance(), i, j), 5e-9);
            }
        }
        for (int j = 0; j < microsat.getStateCount(); j++) {
            double[] colTransitionProb = lbm.getColTransitionProbabilities(test.getDistance(), j);
            for (int i = 0; i < microsat.getStateCount(); i++) {
                assertEquals(result[i * microsat.getStateCount() + j], colTransitionProb[i], 5e-9);
            }
        }
        for (int i = 0; i < microsat.getStateCount(); i++) {
            double[] rowTransitionProb = lbm.getRowTransitionProbabilities(test.getDistance(), i);
            for (int j = 0; j < microsat.getStateCount(); j++) {
                assertEquals(result[i * microsat.getStateCount() + j], rowTransitionProb[j], 5e-9);
            }
        }
    }
}