Examples with ScaleOperator dr.inference.operators.ScaleOperator used on opensource projects

Example 1 with ScaleOperator

use of dr.inference.operators.ScaleOperator in project beast-mcmc by beast-dev.

the class ARGAddRemoveOperatorTest method getSchedule.

public static OperatorSchedule getSchedule(ARGModel arg) {
    CompoundParameter rootHeight = (CompoundParameter) arg.createNodeHeightsParameter(true, false, false);
    CompoundParameter internalHeights = (CompoundParameter) arg.createNodeHeightsParameter(false, true, false);
    //CompoundParameter allInternalNodeHeights = (CompoundParameter) arg.createNodeHeightsParameter(true, true, false);
    //        CompoundParameter rates = (CompoundParameter) arg.createNodeRatesParameter(false, true, true);
    //        ARGAddRemoveEventOperator operator1 = new ARGAddRemoveEventOperator(arg, 5, 0.5,
    //                CoercionMode.COERCION_ON, internalHeights, allInternalNodeHeights, rates, 0.9, null,-1);
    ScaleOperator operator2 = new ScaleOperator(rootHeight, 0.75, CoercionMode.COERCION_ON, 5);
    ScaleOperator operator3 = new ScaleOperator(internalHeights, 0.75, CoercionMode.COERCION_ON, 10);
    OperatorSchedule schedule = new SimpleOperatorSchedule();
    //        schedule.addOperator(operator1);
    schedule.addOperator(operator2);
    schedule.addOperator(operator3);
    return schedule;
//		<scaleOperator id="rootOperator" scaleFactor="0.5"
//			weight="10">
//			<parameter idref="argModel.rootHeight" />
//		</scaleOperator>
//
//		<scaleOperator scaleFactor="0.95" weight="10">
//			<parameter idref="argModel.internalNodeHeights" />
//		</scaleOperator>
//            <ARGEventOperator weight="5" addProbability="0.5"
//			autoOptimize="false">
//			<argTreeModel idref="argModel" />
//			<internalNodes>
//				<parameter idref="argModel.internalNodeHeights" />
//			</internalNodes>
//			<internalNodesPlusRoot>
//				<parameter idref="argModel.allInternalNodeHeights" />
//			</internalNodesPlusRoot>
//			<nodeRates>
//				<parameter idref="argModel.rates" />
//			</nodeRates>
//		</ARGEventOperator>
}

Example 2 with ScaleOperator

use of dr.inference.operators.ScaleOperator in project beast-mcmc by beast-dev.

the class GibbsSubtreeSwapTestProblem method getOperatorSchedule.

public OperatorSchedule getOperatorSchedule(TreeModel treeModel) {
    Parameter rootParameter = treeModel.createNodeHeightsParameter(true, false, false);
    Parameter internalHeights = treeModel.createNodeHeightsParameter(false, true, false);
    GibbsSubtreeSwap operator = new GibbsSubtreeSwap(treeModel, false, 1.0);
    ScaleOperator scaleOperator = new ScaleOperator(rootParameter, 0.75, CoercionMode.COERCION_ON, 1.0);
    UniformOperator uniformOperator = new UniformOperator(internalHeights, 1.0);
    OperatorSchedule schedule = new SimpleOperatorSchedule();
    schedule.addOperator(operator);
    schedule.addOperator(scaleOperator);
    schedule.addOperator(uniformOperator);
    return schedule;
}

Example 3 with ScaleOperator

use of dr.inference.operators.ScaleOperator in project beast-mcmc by beast-dev.

the class Tutorial1 method main.

public static void main(String[] arg) throws IOException, TraceException {
    // constructing random variable representing mean of normal distribution
    Variable.D mean = new Variable.D("mean", 1.0);
    // give mean a uniform prior [-1000, 1000]
    mean.addBounds(new Parameter.DefaultBounds(1000, -1000, 1));
    // constructing random variable representing stdev of normal distribution
    Variable.D stdev = new Variable.D("stdev", 1.0);
    // give stdev a uniform prior [0, 1000]
    stdev.addBounds(new Parameter.DefaultBounds(1000, 0, 1));
    // construct normal distribution model
    NormalDistributionModel normal = new NormalDistributionModel(mean, stdev);
    // construct a likelihood for normal distribution
    DistributionLikelihood likelihood = new DistributionLikelihood(normal);
    // construct data
    Attribute.Default<double[]> d = new Attribute.Default<double[]>("x", new double[] { 1, 2, 3, 4, 5, 6, 7, 8, 9 });
    // add data (representing 9 independent observations) to likelihood
    likelihood.addData(d);
    // construct two "operators" to be used as the proposal distribution
    MCMCOperator meanMove = new ScaleOperator(mean, 0.75);
    MCMCOperator stdevMove = new ScaleOperator(stdev, 0.75);
    // construct a logger to log progress of MCMC run to stdout (screen)
    MCLogger logger1 = new MCLogger(100);
    logger1.add(mean);
    logger1.add(stdev);
    // construct a logger to log to a log file for later analysis
    MCLogger logger2 = new MCLogger("tutorial1.log", 100, false, 0);
    logger2.add(mean);
    logger2.add(stdev);
    // construct MCMC object
    MCMC mcmc = new MCMC("tutorial1:normal");
    // initialize MCMC with chain length, likelihood, operators and loggers
    mcmc.init(100000, likelihood, new MCMCOperator[] { meanMove, stdevMove }, new Logger[] { logger1, logger2 });
    // run the mcmc
    mcmc.chain();
    // perform post-analysis
    TraceAnalysis.report("tutorial1.log");
}

Example 4 with ScaleOperator

use of dr.inference.operators.ScaleOperator in project beast-mcmc by beast-dev.

the class LognormalPriorTest method testLognormalPrior.

public void testLognormalPrior() {
    //        ConstantPopulation constant = new ConstantPopulation(Units.Type.YEARS);
    //        constant.setN0(popSize); // popSize
    Parameter popSize = new Parameter.Default(6.0);
    popSize.setId(ConstantPopulationModelParser.POPULATION_SIZE);
    ConstantPopulationModel demo = new ConstantPopulationModel(popSize, Units.Type.YEARS);
    //Likelihood
    Likelihood dummyLikelihood = new DummyLikelihood(demo);
    // Operators
    OperatorSchedule schedule = new SimpleOperatorSchedule();
    MCMCOperator operator = new ScaleOperator(popSize, 0.75);
    operator.setWeight(1.0);
    schedule.addOperator(operator);
    // Log
    ArrayLogFormatter formatter = new ArrayLogFormatter(false);
    MCLogger[] loggers = new MCLogger[2];
    loggers[0] = new MCLogger(formatter, 1000, false);
    //        loggers[0].add(treeLikelihood);
    loggers[0].add(popSize);
    loggers[1] = new MCLogger(new TabDelimitedFormatter(System.out), 100000, false);
    //        loggers[1].add(treeLikelihood);
    loggers[1].add(popSize);
    // MCMC
    MCMC mcmc = new MCMC("mcmc1");
    MCMCOptions options = new MCMCOptions(1000000);
    // meanInRealSpace="false"
    DistributionLikelihood logNormalLikelihood = new DistributionLikelihood(new LogNormalDistribution(1.0, 1.0), 0);
    logNormalLikelihood.addData(popSize);
    List<Likelihood> likelihoods = new ArrayList<Likelihood>();
    likelihoods.add(logNormalLikelihood);
    Likelihood prior = new CompoundLikelihood(0, likelihoods);
    likelihoods.clear();
    likelihoods.add(dummyLikelihood);
    Likelihood likelihood = new CompoundLikelihood(-1, likelihoods);
    likelihoods.clear();
    likelihoods.add(prior);
    likelihoods.add(likelihood);
    Likelihood posterior = new CompoundLikelihood(0, likelihoods);
    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("LognormalPriorTest", traces, 0);
    for (int i = 1; i < traces.size(); i++) {
        traceList.analyseTrace(i);
    }
    //      <expectation name="param" value="4.48168907"/>
    TraceCorrelation popSizeStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(ConstantPopulationModelParser.POPULATION_SIZE));
    System.out.println("Expectation of Log-Normal(1,1) is e^(M+S^2/2) = e^(1.5) = " + Math.exp(1.5));
    assertExpectation(ConstantPopulationModelParser.POPULATION_SIZE, popSizeStats, Math.exp(1.5));
}

Example 5 with ScaleOperator

use of dr.inference.operators.ScaleOperator in project beast-mcmc by beast-dev.

the class ScaleOperatorParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    final boolean scaleAll = xo.getAttribute(SCALE_ALL, false);
    final boolean scaleAllInd = xo.getAttribute(SCALE_ALL_IND, false);
    final int degreesOfFreedom = xo.getAttribute(DEGREES_OF_FREEDOM, 0);
    final CoercionMode mode = CoercionMode.parseMode(xo);
    final double weight = xo.getDoubleAttribute(MCMCOperator.WEIGHT);
    final double scaleFactor = xo.getDoubleAttribute(SCALE_FACTOR);
    if (scaleFactor <= 0.0 || scaleFactor >= 1.0) {
        throw new XMLParseException("scaleFactor must be between 0.0 and 1.0");
    }
    final Parameter parameter = (Parameter) xo.getChild(Parameter.class);
    Bounds<Double> bounds = parameter.getBounds();
    for (int dim = 0; dim < parameter.getDimension(); dim++) {
        if (bounds.getLowerLimit(dim) < 0.0) {
            throw new XMLParseException("Scale operator can only be used on parameters with a lower bound of zero (" + parameter.getId() + ")");
        }
        if (!Double.isInfinite(bounds.getUpperLimit(dim))) {
            throw new XMLParseException("Scale operator can't be used on parameters with a finite upper bound (use a RandomWalk) (" + parameter.getId() + ")");
        }
    }
    Parameter indicator = null;
    double indicatorOnProb = 1.0;
    final XMLObject inds = xo.getChild(INDICATORS);
    if (inds != null) {
        indicator = (Parameter) inds.getChild(Parameter.class);
        if (inds.hasAttribute(PICKONEPROB)) {
            indicatorOnProb = inds.getDoubleAttribute(PICKONEPROB);
            if (!(0 <= indicatorOnProb && indicatorOnProb <= 1)) {
                throw new XMLParseException("pickoneprob must be between 0.0 and 1.0");
            }
        }
    }
    ScaleOperator operator = new ScaleOperator(parameter, scaleAll, degreesOfFreedom, scaleFactor, mode, indicator, indicatorOnProb, scaleAllInd);
    operator.setWeight(weight);
    return operator;
}