Examples with Parameter dr.inference.model.Parameter used on opensource projects

Example 41 with Parameter

use of dr.inference.model.Parameter in project beast-mcmc by beast-dev.

the class TreeClusterSequentialSampling method setVirusLocationAndOffsets.

private void setVirusLocationAndOffsets() {
    //change the mu in the toBin and fromBIn
    //borrow from getLogLikelihood:
    double[] meanYear = new double[binSize];
    double[] groupCount = new double[binSize];
    for (int i = 0; i < numdata; i++) {
        int label = (int) clusterLabels.getParameterValue(i);
        double year = 0;
        if (virusOffsetsParameter != null) {
            //	System.out.print("virus Offeset Parameter present"+ ": ");
            //	System.out.print( virusOffsetsParameter.getParameterValue(i) + " ");
            //	System.out.print(" drift= " + drift + " ");
            //just want year[i]
            year = virusOffsetsParameter.getParameterValue(i);
        //make sure that it is equivalent to double offset  = year[virusIndex] - firstYear;
        } else {
            System.out.println("virus Offeset Parameter NOT present. We expect one though. Something is wrong.");
        }
        meanYear[label] = meanYear[label] + year;
        groupCount[label] = groupCount[label] + 1;
    }
    for (int i = 0; i < binSize; i++) {
        if (groupCount[i] > 0) {
            meanYear[i] = meanYear[i] / groupCount[i];
        }
    //System.out.println(meanYear[i]);
    }
    mu0_offset = new double[binSize];
    //now, change the mu..
    for (int i = 0; i < binSize; i++) {
        //System.out.println(meanYear[i]*beta);
        mu0_offset[i] = meanYear[i];
    //System.out.println("group " + i + "\t" + mu0_offset[i]);
    }
    //virus in the same cluster has the same position
    for (int i = 0; i < numdata; i++) {
        int label = (int) clusterLabels.getParameterValue(i);
        Parameter vLoc = virusLocations.getParameter(i);
        //setting the virus locs to be equal to the corresponding mu
        double muValue = mu.getParameter(label).getParameterValue(0);
        vLoc.setParameterValue(0, muValue);
        double muValue2 = mu.getParameter(label).getParameterValue(1);
        vLoc.setParameterValue(1, muValue2);
    //System.out.println("vloc="+ muValue + "," + muValue2);
    }
    for (int i = 0; i < numdata; i++) {
        int label = (int) clusterLabels.getParameterValue(i);
        //if we want to apply the mean year virus cluster offset to the cluster
        if (clusterOffsetsParameter != null) {
            //setting the clusterOffsets to be equal to the mean year of the virus cluster
            // by doing this, the virus changes cluster AND updates the offset simultaneously
            clusterOffsetsParameter.setParameterValue(i, mu0_offset[label]);
        }
    //		System.out.println("mu0_offset[label]=" + mu0_offset[label]);
    //		System.out.println("clusterOffsets " +  i +" now becomes =" + clusterOffsetsParameter.getParameterValue(i) );   			
    }
//    	System.out.println("===The on nodes===");
//    	for(int i=0; i < binSize; i++){	    
//    		if((int) excisionPoints.getParameterValue(i) == 1){
//    			System.out.println("Cluster node " + i + " = " + (int) indicators.getParameterValue(i) + "\tstatus=" + (int) excisionPoints.getParameterValue(i));
//    		}
//    	}
}

Example 42 with Parameter

use of dr.inference.model.Parameter in project beast-mcmc by beast-dev.

the class BeagleTreeLikelihood method main.

public static void main(String[] args) {
    try {
        MathUtils.setSeed(666);
        System.out.println("Test case 1: simulateOnePartition");
        int sequenceLength = 1000;
        ArrayList<Partition> partitionsList = new ArrayList<Partition>();
        // create tree
        NewickImporter importer = new NewickImporter("(SimSeq1:73.7468,(SimSeq2:25.256989999999995,SimSeq3:45.256989999999995):18.48981);");
        Tree tree = importer.importTree(null);
        TreeModel treeModel = new TreeModel(tree);
        // create Frequency Model
        Parameter freqs = new Parameter.Default(new double[] { 0.25, 0.25, 0.25, 0.25 });
        FrequencyModel freqModel = new FrequencyModel(Nucleotides.INSTANCE, freqs);
        // create branch model
        Parameter kappa1 = new Parameter.Default(1, 1);
        Parameter kappa2 = new Parameter.Default(1, 1);
        HKY hky1 = new HKY(kappa1, freqModel);
        HKY hky2 = new HKY(kappa2, freqModel);
        HomogeneousBranchModel homogenousBranchSubstitutionModel = new HomogeneousBranchModel(hky1);
        List<SubstitutionModel> substitutionModels = new ArrayList<SubstitutionModel>();
        substitutionModels.add(hky1);
        substitutionModels.add(hky2);
        List<FrequencyModel> freqModels = new ArrayList<FrequencyModel>();
        freqModels.add(freqModel);
        Parameter epochTimes = new Parameter.Default(1, 20);
        // create branch rate model
        Parameter rate = new Parameter.Default(1, 0.001);
        BranchRateModel branchRateModel = new StrictClockBranchRates(rate);
        // create site model
        GammaSiteRateModel siteRateModel = new GammaSiteRateModel("siteModel");
        BranchModel homogeneousBranchModel = new HomogeneousBranchModel(hky1);
        BranchModel epochBranchModel = new EpochBranchModel(treeModel, substitutionModels, epochTimes);
        // create partition
        Partition partition1 = new //
        Partition(//
        treeModel, //
        homogenousBranchSubstitutionModel, //
        siteRateModel, //
        branchRateModel, //
        freqModel, // from
        0, // to
        sequenceLength - 1, // every
        1);
        partitionsList.add(partition1);
        // feed to sequence simulator and generate data
        BeagleSequenceSimulator simulator = new BeagleSequenceSimulator(partitionsList);
        Alignment alignment = simulator.simulate(false, false);
        BeagleTreeLikelihood nbtl = new BeagleTreeLikelihood(alignment, treeModel, homogeneousBranchModel, siteRateModel, branchRateModel, null, false, PartialsRescalingScheme.DEFAULT, false);
        System.out.println("nBTL(homogeneous) = " + nbtl.getLogLikelihood());
        nbtl = new BeagleTreeLikelihood(alignment, treeModel, epochBranchModel, siteRateModel, branchRateModel, null, false, PartialsRescalingScheme.DEFAULT, false);
        System.out.println("nBTL(epoch) = " + nbtl.getLogLikelihood());
    } catch (Exception e) {
        e.printStackTrace();
        System.exit(-1);
    }
// END: try-catch block
}

Example 43 with Parameter

use of dr.inference.model.Parameter in project beast-mcmc by beast-dev.

the class ConjugateRootTraitPrior method parseConjugateRootTraitPrior.

public static ConjugateRootTraitPrior parseConjugateRootTraitPrior(XMLObject xo, final int dim) throws XMLParseException {
    XMLObject cxo = xo.getChild(CONJUGATE_ROOT_PRIOR);
    Parameter meanParameter = (Parameter) cxo.getChild(MultivariateDistributionLikelihood.MVN_MEAN).getChild(Parameter.class);
    if (meanParameter.getDimension() != dim) {
        throw new XMLParseException("Root prior mean dimension does not match trait diffusion dimension");
    }
    Parameter sampleSizeParameter = (Parameter) cxo.getChild(PRIOR_SAMPLE_SIZE).getChild(Parameter.class);
    return new ConjugateRootTraitPrior(meanParameter.getParameterValues(), sampleSizeParameter.getParameterValue(0));
}

Example 44 with Parameter

use of dr.inference.model.Parameter in project beast-mcmc by beast-dev.

the class BranchCategoriesParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    Parameter allocationParameter = (Parameter) xo.getElementFirstChild(ALLOCATION);
    CountableBranchCategoryProvider cladeModel;
    TreeModel treeModel = (TreeModel) xo.getChild(TreeModel.class);
    if (!xo.getAttribute(RANDOMIZE, true)) {
        CountableBranchCategoryProvider.CladeBranchCategoryModel cm = new CountableBranchCategoryProvider.CladeBranchCategoryModel(treeModel, allocationParameter);
        for (int i = 0; i < xo.getChildCount(); ++i) {
            if (xo.getChild(i) instanceof XMLObject) {
                XMLObject xoc = (XMLObject) xo.getChild(i);
                if (xoc.getName().equals(LocalClockModelParser.CLADE)) {
                    TaxonList taxonList = (TaxonList) xoc.getChild(TaxonList.class);
                    boolean includeStem = xoc.getAttribute(LocalClockModelParser.INCLUDE_STEM, false);
                    boolean excludeClade = xoc.getAttribute(LocalClockModelParser.EXCLUDE_CLADE, false);
                    // XML index-start = 1 not 0
                    int rateCategory = xoc.getIntegerAttribute(CATEGORY) - 1;
                    try {
                        cm.setClade(taxonList, rateCategory, includeStem, excludeClade, false);
                    } catch (TreeUtils.MissingTaxonException e) {
                        throw new XMLParseException("Unable to find taxon for clade in countable mixture model: " + e.getMessage());
                    }
                } else if (xoc.getName().equals(LocalClockModelParser.TRUNK)) {
                    TaxonList taxonList = (TaxonList) xoc.getChild(TaxonList.class);
                    boolean includeStem = xoc.getAttribute(LocalClockModelParser.INCLUDE_STEM, false);
                    boolean excludeClade = xoc.getAttribute(LocalClockModelParser.EXCLUDE_CLADE, false);
                    // XML index-start = 1 not 0
                    int rateCategory = xoc.getIntegerAttribute(CATEGORY) - 1;
                    try {
                        cm.setClade(taxonList, rateCategory, includeStem, excludeClade, true);
                    } catch (TreeUtils.MissingTaxonException e) {
                        throw new XMLParseException("Unable to find taxon for trunk in countable mixture model: " + e.getMessage());
                    }
                }
            }
        }
        cladeModel = cm;
    } else {
        CountableBranchCategoryProvider.IndependentBranchCategoryModel cm = new CountableBranchCategoryProvider.IndependentBranchCategoryModel(treeModel, allocationParameter);
        cm.randomize();
        cladeModel = cm;
    }
    return cladeModel;
}

Example 45 with Parameter

use of dr.inference.model.Parameter in project beast-mcmc by beast-dev.

the class HamiltonianMonteCarloOperatorParser method parseXMLObject.

@Override
public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    double weight = xo.getDoubleAttribute(MCMCOperator.WEIGHT);
    int nSteps = xo.getIntegerAttribute(N_STEPS);
    double stepSize = xo.getDoubleAttribute(STEP_SIZE);
    double drawVariance = xo.getDoubleAttribute(DRAW_VARIANCE);
    int mode = xo.getAttribute(MODE, 0);
    GradientWrtParameterProvider derivative = (GradientWrtParameterProvider) xo.getChild(GradientWrtParameterProvider.class);
    Parameter parameter = (Parameter) xo.getChild(Parameter.class);
    Transform transform = null;
    if (parameter == null) {
        Transform.Collection collection = (Transform.Collection) xo.getChild(Transform.Collection.class);
        parameter = collection.getParameter();
        transform = collection;
    }
    if (derivative.getDimension() != parameter.getDimension()) {
        throw new XMLParseException("Gradient (" + derivative.getDimension() + ") must be the same dimensions as the parameter (" + parameter.getDimension() + ")");
    }
    if (mode == 0) {
        return new HamiltonianMonteCarloOperator(CoercionMode.DEFAULT, weight, derivative, parameter, transform, stepSize, nSteps, drawVariance);
    } else {
        return new NoUTurnOperator(CoercionMode.DEFAULT, weight, derivative, parameter, stepSize, nSteps, drawVariance);
    }
}