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

Example 51 with Parameter

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

the class MultipleRandomWalkIntegerOperatorParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    double weight = xo.getDoubleAttribute(MCMCOperator.WEIGHT);
    double w = xo.getDoubleAttribute(RandomWalkIntegerOperatorParser.WINDOW_SIZE);
    if (w != Math.floor(w)) {
        throw new XMLParseException("The window size of a randomWalkIntegerOperator should be an integer");
    }
    double s = xo.getDoubleAttribute(SAMPLE_SIZE);
    if (s != Math.floor(s)) {
        throw new XMLParseException("The window size of a randomWalkIntegerOperator should be an integer");
    }
    int windowSize = (int) w;
    int sampleSize = (int) s;
    Parameter parameter = (Parameter) xo.getChild(Parameter.class);
    return new MultipleRandomWalkIntegerOperator(parameter, windowSize, sampleSize, weight);
}

Example 52 with Parameter

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

the class SelectorOperatorParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    final Parameter parameter = (Parameter) xo.getChild(Parameter.class);
    final double weight = xo.getDoubleAttribute(MCMCOperator.WEIGHT);
    final SelectorOperator op = new SelectorOperator(parameter);
    op.setWeight(weight);
    return op;
}

Example 53 with Parameter

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

the class SetOperatorParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    double[] values = xo.getDoubleArrayAttribute(SET);
    double weight = xo.getDoubleAttribute(MCMCOperator.WEIGHT);
    Parameter parameter = (Parameter) xo.getChild(Parameter.class);
    System.out.println("Creating set operator for parameter " + parameter.getParameterName());
    System.out.print("  set = {" + values[0]);
    for (int i = 1; i < values.length; i++) {
        System.out.print(", " + values[i]);
    }
    System.out.println("}");
    SetOperator operator = new SetOperator(parameter, values);
    operator.setWeight(weight);
    return operator;
}

Example 54 with Parameter

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

the class ClusterWalkOperatorParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    CoercionMode mode = CoercionMode.parseMode(xo);
    double weight = xo.getDoubleAttribute(MCMCOperator.WEIGHT);
    double windowSize = xo.getDoubleAttribute(WINDOW_SIZE);
    Parameter parameter = (Parameter) xo.getChild(Parameter.class);
    Double lower = null;
    Double upper = null;
    if (xo.hasAttribute(LOWER)) {
        lower = xo.getDoubleAttribute(LOWER);
    }
    if (xo.hasAttribute(UPPER)) {
        upper = xo.getDoubleAttribute(UPPER);
    }
    ClusterWalkOperator.BoundaryCondition condition = ClusterWalkOperator.BoundaryCondition.valueOf(xo.getAttribute(BOUNDARY_CONDITION, ClusterWalkOperator.BoundaryCondition.reflecting.name()));
    if (xo.hasChildNamed(UPDATE_INDEX)) {
        XMLObject cxo = xo.getChild(UPDATE_INDEX);
        Parameter updateIndex = (Parameter) cxo.getChild(Parameter.class);
        if (updateIndex.getDimension() != parameter.getDimension())
            throw new RuntimeException("Parameter to update and missing indices must have the same dimension");
        return new ClusterWalkOperator(parameter, updateIndex, windowSize, condition, weight, mode, lower, upper);
    }
    return new ClusterWalkOperator(parameter, null, windowSize, condition, weight, mode, lower, upper);
}

Example 55 with Parameter

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

the class TreeClusterAlgorithmOperator method test2.

private void test2() {
    System.out.print("  [");
    for (int i = 0; i < numNodes; i++) {
        if ((int) indicators.getParameterValue(i) == 1) {
            System.out.print(i + " ");
        }
    }
    System.out.println("]");
    //indicators.setParameterValue( 436, 0);
    //indicators.setParameterValue( 549, 0);
    //   	indicators.setParameterValue( 615, 0);   
    //indicators.setParameterValue(648,0);
    //indicators.setParameterValue(673,0);
    //indicators.setParameterValue(794,0);
    //indicators.setParameterValue(785,0);
    //indicators.setParameterValue(690,0);
    //Note: if 615 is not turned on, 604 would be much superior to 605.
    //since I changed indicators.. should do this before calculating the original likelihood
    //1. Update the cluster labels, after the breakpoints and status parameters may have changed.
    //  		setClusterLabelsUsingIndicators();
    //setClusterLabelsArray(newClusterLabelArray);	
    //relabelClusterLabelsArray(newClusterLabelArray, oldClusterLabelArray);
    //convertClusterLabelsArrayToParameter( newClusterLabelArray);
    //oldClusterLabelArray = newClusterLabelArray; //the oldClusterLabelArray gets the current labels, so next time this is updated.
    //2. Update the virus locations (and offsets), given ...
    Tree_Clustering_Shared_Routines.updateUndriftedVirusLocations(numNodes, numdata, treeModel, virusLocationsTreeNode, indicators, mu, virusLocations, correspondingTreeIndexForVirus);
    //setVirusLocationAutoCorrelatedModel(); //set virus locations, given the breakpoints,status, and mu parameters
    double originalLikelihood = clusterLikelihood.getLogLikelihood();
    System.out.println("originalLikelihood = " + originalLikelihood);
    int originalNode1 = 605;
    int[] distance = determineTreeNeighborhood(originalNode1, 5);
    for (int g = 0; g < distance.length; g++) {
        if (distance[g] < 5 && distance[g] > 0) {
            int newNode = g;
            System.out.print(g + " distance=" + distance[g] + "\t");
            if ((int) indicators.getParameterValue(newNode) == 1) {
                System.out.print("Node already on!!!\n");
            } else {
                indicators.setParameterValue(originalNode1, 0);
                indicators.setParameterValue(newNode, 1);
                //Flip mu - so the neighbor that replaces the original node now also inherits the existing node's mu
                //Parameter originalNodeMu = mu.getParameter(originalNode1+1); //offset of 1
                Parameter originalNodeMu = mu.getParameter(originalNode1);
                double[] tmp = originalNodeMu.getParameterValues();
                //Parameter newMu = mu.getParameter(newNode+1); //offset of 1
                Parameter newMu = mu.getParameter(newNode);
                double[] tmpNew = newMu.getParameterValues();
                originalNodeMu.setParameterValue(0, tmpNew[0]);
                originalNodeMu.setParameterValue(1, tmpNew[1]);
                newMu.setParameterValue(0, tmp[0]);
                newMu.setParameterValue(1, tmp[1]);
                //1. Update the cluster labels, after the breakpoints and status parameters may have changed.
                //		setClusterLabelsUsingIndicators();
                //setClusterLabelsArray(newClusterLabelArray);	
                //relabelClusterLabelsArray(newClusterLabelArray, oldClusterLabelArray);
                //convertClusterLabelsArrayToParameter( newClusterLabelArray);
                //oldClusterLabelArray = newClusterLabelArray; //the oldClusterLabelArray gets the current labels, so next time this is updated.
                //2. Update the virus locations (and offsets), given ...
                Tree_Clustering_Shared_Routines.updateUndriftedVirusLocations(numNodes, numdata, treeModel, virusLocationsTreeNode, indicators, mu, virusLocations, correspondingTreeIndexForVirus);
                //setVirusLocationAutoCorrelatedModel(); //set virus locations, given the breakpoints,status, and mu parameters
                double testLikelihood = clusterLikelihood.getLogLikelihood();
                double diff = testLikelihood - originalLikelihood;
                if (diff > -10) {
                    System.out.print("***");
                }
                System.out.println("logL= " + testLikelihood + " and diff = " + diff);
                // 	System.out.print("  [");
                //	for(int i=0; i < numNodes; i++){
                //		if( (int)indicators.getParameterValue(i) == 1){
                //			System.out.print(i + " ");
                //		}
                //}
                //System.out.println("]");
                //revert back
                indicators.setParameterValue(originalNode1, 1);
                indicators.setParameterValue(newNode, 0);
                originalNodeMu.setParameterValue(0, tmp[0]);
                originalNodeMu.setParameterValue(1, tmp[1]);
                newMu.setParameterValue(0, tmpNew[0]);
                newMu.setParameterValue(1, tmpNew[1]);
            }
        }
    }
    System.exit(0);
}