use of dr.oldevomodel.substmodel.TwoPhaseModel 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);
}
use of dr.oldevomodel.substmodel.TwoPhaseModel in project beast-mcmc by beast-dev.
the class TwoPhaseModelParser method parseXMLObject.
//AbstractXMLObjectParser implementation
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
OnePhaseModel subModel = (OnePhaseModel) xo.getElementFirstChild(SUBMODEL);
Microsatellite dataType = (Microsatellite) xo.getChild(Microsatellite.class);
Parameter.Default geoParam = (Parameter.Default) xo.getElementFirstChild(GEO_PARAM);
Parameter paramP = (Parameter) xo.getElementFirstChild(ONEPHASEPR_PARAM);
Parameter limitE = null;
if (xo.hasChildNamed(TRANS_PARAM)) {
limitE = (Parameter) xo.getElementFirstChild(TRANS_PARAM);
}
boolean estimateSubmodelParams = xo.getAttribute(ESTIMATE_SUBMODEL_PARAMS, false);
FrequencyModel freqModel = null;
if (xo.hasChildNamed(FrequencyModelParser.FREQUENCIES)) {
freqModel = (FrequencyModel) xo.getElementFirstChild(FrequencyModelParser.FREQUENCIES);
}
return new TwoPhaseModel(dataType, freqModel, subModel, paramP, geoParam, limitE, estimateSubmodelParams);
}
use of dr.oldevomodel.substmodel.TwoPhaseModel in project beast-mcmc by beast-dev.
the class TwoPhaseModelTest method testTwoPhaseModel.
public void testTwoPhaseModel() {
for (Instance test : all) {
OnePhaseModel subModel = test.getSubModel();
Microsatellite microsat = (Microsatellite) subModel.getDataType();
Parameter pParam = new Parameter.Default(test.getPParam());
Parameter mParam = new Parameter.Default(test.getMParam());
TwoPhaseModel tpm = new TwoPhaseModel(microsat, null, subModel, pParam, mParam, null, false);
int k;
tpm.computeStationaryDistribution();
double[] statDist = tpm.getStationaryDistribution();
final double[] expectedStatDist = test.getPi();
for (k = 0; k < statDist.length; ++k) {
assertEquals(statDist[k], expectedStatDist[k], 1e-10);
}
int stateCount = microsat.getStateCount();
double[] mat = new double[stateCount * stateCount];
tpm.getTransitionProbabilities(test.getDistance(), mat);
final double[] result = test.getExpectedResult();
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++], tpm.getOneTransitionProbabilityEntry(test.getDistance(), i, j), 1e-10);
}
}
for (int j = 0; j < microsat.getStateCount(); j++) {
double[] colTransitionProb = tpm.getColTransitionProbabilities(test.getDistance(), j);
for (int i = 0; i < microsat.getStateCount(); i++) {
assertEquals(result[i * microsat.getStateCount() + j], colTransitionProb[i], 1e-10);
}
}
for (int i = 0; i < microsat.getStateCount(); i++) {
double[] rowTransitionProb = tpm.getRowTransitionProbabilities(test.getDistance(), i);
for (int j = 0; j < microsat.getStateCount(); j++) {
assertEquals(result[i * microsat.getStateCount() + j], rowTransitionProb[j], 1e-10);
}
}
}
}
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