use of dr.oldevomodel.substmodel.FrequencyModel in project beast-mcmc by beast-dev.
the class GTRParser method parseXMLObject.
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
XMLObject cxo = xo.getChild(FREQUENCIES);
FrequencyModel freqModel = (FrequencyModel) cxo.getChild(FrequencyModel.class);
Variable rateACValue = null;
if (xo.hasChildNamed(A_TO_C)) {
rateACValue = (Variable) xo.getElementFirstChild(A_TO_C);
}
Variable rateAGValue = null;
if (xo.hasChildNamed(A_TO_G)) {
rateAGValue = (Variable) xo.getElementFirstChild(A_TO_G);
}
Variable rateATValue = null;
if (xo.hasChildNamed(A_TO_T)) {
rateATValue = (Variable) xo.getElementFirstChild(A_TO_T);
}
Variable rateCGValue = null;
if (xo.hasChildNamed(C_TO_G)) {
rateCGValue = (Variable) xo.getElementFirstChild(C_TO_G);
}
Variable rateCTValue = null;
if (xo.hasChildNamed(C_TO_T)) {
rateCTValue = (Variable) xo.getElementFirstChild(C_TO_T);
}
Variable rateGTValue = null;
if (xo.hasChildNamed(G_TO_T)) {
rateGTValue = (Variable) xo.getElementFirstChild(G_TO_T);
}
int countNull = 0;
if (rateACValue == null)
countNull++;
if (rateAGValue == null)
countNull++;
if (rateATValue == null)
countNull++;
if (rateCGValue == null)
countNull++;
if (rateCTValue == null)
countNull++;
if (rateGTValue == null)
countNull++;
if (countNull != 1)
throw new XMLParseException("Only five parameters may be specified in GTR, leave exactly one out, the others will be specifed relative to the one left out.");
return new GTR(rateACValue, rateAGValue, rateATValue, rateCGValue, rateCTValue, rateGTValue, freqModel);
}
use of dr.oldevomodel.substmodel.FrequencyModel in project beast-mcmc by beast-dev.
the class Coevolve method makePairSubstModel.
private SubstitutionModel makePairSubstModel(Alignment alignment, Alignment pairAlignment, Parameter beta) {
DataType newDataType = makePairDataType(alignment);
double[] frequencies = pairAlignment.getStateFrequencies();
Parameter frequencyParameter = new Parameter.Default(frequencies);
FrequencyModel freqModel = new FrequencyModel(newDataType, frequencyParameter);
return new SitePairSubstitutionModel(newDataType, freqModel, beta);
}
use of dr.oldevomodel.substmodel.FrequencyModel 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);
}
use of dr.oldevomodel.substmodel.FrequencyModel 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);
}
use of dr.oldevomodel.substmodel.FrequencyModel 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);
}
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