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Java MCMC.run方法代碼示例

本文整理匯總了Java中dr.inference.mcmc.MCMC.run方法的典型用法代碼示例。如果您正苦於以下問題：Java MCMC.run方法的具體用法？Java MCMC.run怎麽用？Java MCMC.run使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類dr.inference.mcmc.MCMC的用法示例。


在下文中一共展示了MCMC.run方法的6個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Java代碼示例。

示例1: testLognormalPrior

import dr.inference.mcmc.MCMC; //導入方法依賴的package包/類
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();
        options.setChainLength(1000000);
        options.setUseCoercion(true); // autoOptimize = true
        options.setCoercionDelay(100);
        options.setTemperature(1.0);
        options.setFullEvaluationCount(2000);

        DistributionLikelihood logNormalLikelihood = new DistributionLikelihood(new LogNormalDistribution(1.0, 1.0), 0); // meanInRealSpace="false"
        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));
    }
開發者ID:whdc，項目名稱:ieo-beast，代碼行數:77，代碼來源:LognormalPriorTest.java

示例2: testLognormalPrior

import dr.inference.mcmc.MCMC; //導入方法依賴的package包/類
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);

        DistributionLikelihood logNormalLikelihood = new DistributionLikelihood(new LogNormalDistribution(1.0, 1.0), 0); // meanInRealSpace="false"
        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));
    }
開發者ID:beast-dev，項目名稱:beast-mcmc，代碼行數:72，代碼來源:LognormalPriorTest.java

示例3: randomLocalYuleTester

import dr.inference.mcmc.MCMC; //導入方法依賴的package包/類
private void randomLocalYuleTester(TreeModel treeModel, Parameter I, Parameter b, OperatorSchedule schedule) {

        MCMC mcmc = new MCMC("mcmc1");
        MCMCOptions options = new MCMCOptions(1000000);

        TreeLengthStatistic tls = new TreeLengthStatistic(TL, treeModel);
        TreeHeightStatistic rootHeight = new TreeHeightStatistic(TREE_HEIGHT, treeModel);

        Parameter m = new Parameter.Default("m", 1.0, 0.0, Double.MAX_VALUE);

        SpeciationModel speciationModel = new RandomLocalYuleModel(b, I, m, false, Units.Type.YEARS, 4);

        Likelihood likelihood = new SpeciationLikelihood(treeModel, speciationModel, "randomYule.like");

        ArrayLogFormatter formatter = new ArrayLogFormatter(false);

        MCLogger[] loggers = new MCLogger[2];
        loggers[0] = new MCLogger(formatter, 100, false);
        loggers[0].add(likelihood);
        loggers[0].add(rootHeight);
        loggers[0].add(tls);
        loggers[0].add(I);

        loggers[1] = new MCLogger(new TabDelimitedFormatter(System.out), 100000, false);
        loggers[1].add(likelihood);
        loggers[1].add(rootHeight);
        loggers[1].add(tls);
        loggers[1].add(I);

        mcmc.setShowOperatorAnalysis(true);

        mcmc.init(options, likelihood, schedule, loggers);

        mcmc.run();

        List<Trace> traces = formatter.getTraces();
        ArrayTraceList traceList = new ArrayTraceList("yuleModelTest", traces, 0);

        for (int i = 1; i < traces.size(); i++) {
            traceList.analyseTrace(i);
        }

        TraceCorrelation tlStats =
                traceList.getCorrelationStatistics(traceList.getTraceIndex("root." + birthRateIndicator));

        System.out.println("mean = " + tlStats.getMean());
        System.out.println("expected mean = 0.5");

        assertExpectation("root." + birthRateIndicator, tlStats, 0.5);
    }
開發者ID:beast-dev，項目名稱:beast-mcmc，代碼行數:51，代碼來源:RLYModelTest.java

示例4: yuleTester

import dr.inference.mcmc.MCMC; //導入方法依賴的package包/類
private void yuleTester(TreeModel treeModel, OperatorSchedule schedule) {

        MCMC mcmc = new MCMC("mcmc1");
        MCMCOptions options = new MCMCOptions();
        options.setChainLength(1000000);
        options.setUseCoercion(true);
        options.setCoercionDelay(100);
        options.setTemperature(1.0);
        options.setFullEvaluationCount(2000);

        TreeLengthStatistic tls = new TreeLengthStatistic(TL, treeModel);
        TreeHeightStatistic rootHeight = new TreeHeightStatistic(TREE_HEIGHT, treeModel);

        Parameter b = new Parameter.Default("b", 2.0, 0.0, Double.MAX_VALUE);
        Parameter d = new Parameter.Default("d", 0.0, 0.0, Double.MAX_VALUE);

        SpeciationModel speciationModel = new BirthDeathGernhard08Model(b, d, null, BirthDeathGernhard08Model.TreeType.TIMESONLY,
                Units.Type.YEARS);
        Likelihood likelihood = new SpeciationLikelihood(treeModel, speciationModel, "yule.like");

        ArrayLogFormatter formatter = new ArrayLogFormatter(false);

        MCLogger[] loggers = new MCLogger[2];
        loggers[0] = new MCLogger(formatter, 100, false);
        loggers[0].add(likelihood);
        loggers[0].add(rootHeight);
        loggers[0].add(tls);

        loggers[1] = new MCLogger(new TabDelimitedFormatter(System.out), 100000, false);
        loggers[1].add(likelihood);
        loggers[1].add(rootHeight);
        loggers[1].add(tls);

        mcmc.setShowOperatorAnalysis(true);

        mcmc.init(options, likelihood, schedule, loggers);

        mcmc.run();

        List<Trace> traces = formatter.getTraces();
        ArrayTraceList traceList = new ArrayTraceList("yuleModelTest", traces, 0);

        for (int i = 1; i < traces.size(); i++) {
            traceList.analyseTrace(i);
        }

        // expectation of root height for 4 tips and lambda = 2
        // rootHeight = 0.541666
        // TL = 1.5

        TraceCorrelation tlStats =
                traceList.getCorrelationStatistics(traceList.getTraceIndex(TL));

        assertExpectation(TL, tlStats, 1.5);

        TraceCorrelation treeHeightStats =
                traceList.getCorrelationStatistics(traceList.getTraceIndex(TREE_HEIGHT));

        assertExpectation(TREE_HEIGHT, treeHeightStats, 0.5416666);


    }
開發者ID:whdc，項目名稱:ieo-beast，代碼行數:63，代碼來源:YuleModelTest.java

示例5: randomLocalYuleTester

import dr.inference.mcmc.MCMC; //導入方法依賴的package包/類
private void randomLocalYuleTester(TreeModel treeModel, Parameter I, Parameter b, OperatorSchedule schedule) {

        MCMC mcmc = new MCMC("mcmc1");
        MCMCOptions options = new MCMCOptions();
        options.setChainLength(1000000);
        options.setUseCoercion(true);
        options.setCoercionDelay(100);
        options.setTemperature(1.0);
        options.setFullEvaluationCount(2000);

        TreeLengthStatistic tls = new TreeLengthStatistic(TL, treeModel);
        TreeHeightStatistic rootHeight = new TreeHeightStatistic(TREE_HEIGHT, treeModel);

        Parameter m = new Parameter.Default("m", 1.0, 0.0, Double.MAX_VALUE);

        SpeciationModel speciationModel = new RandomLocalYuleModel(b, I, m, false, Units.Type.YEARS, 4);

        Likelihood likelihood = new SpeciationLikelihood(treeModel, speciationModel, "randomYule.like");

        ArrayLogFormatter formatter = new ArrayLogFormatter(false);

        MCLogger[] loggers = new MCLogger[2];
        loggers[0] = new MCLogger(formatter, 100, false);
        loggers[0].add(likelihood);
        loggers[0].add(rootHeight);
        loggers[0].add(tls);
        loggers[0].add(I);

        loggers[1] = new MCLogger(new TabDelimitedFormatter(System.out), 100000, false);
        loggers[1].add(likelihood);
        loggers[1].add(rootHeight);
        loggers[1].add(tls);
        loggers[1].add(I);

        mcmc.setShowOperatorAnalysis(true);

        mcmc.init(options, likelihood, schedule, loggers);

        mcmc.run();

        List<Trace> traces = formatter.getTraces();
        ArrayTraceList traceList = new ArrayTraceList("yuleModelTest", traces, 0);

        for (int i = 1; i < traces.size(); i++) {
            traceList.analyseTrace(i);
        }

        TraceCorrelation tlStats =
                traceList.getCorrelationStatistics(traceList.getTraceIndex("root." + birthRateIndicator));

        System.out.println("mean = " + tlStats.getMean());
        System.out.println("expected mean = 0.5");

        assertExpectation("root." + birthRateIndicator, tlStats, 0.5);
    }
開發者ID:whdc，項目名稱:ieo-beast，代碼行數:56，代碼來源:RLYModelTest.java

示例6: yuleTester

import dr.inference.mcmc.MCMC; //導入方法依賴的package包/類
private void yuleTester(TreeModel treeModel, OperatorSchedule schedule) {

        MCMC mcmc = new MCMC("mcmc1");
        MCMCOptions options = new MCMCOptions(1000000);

        TreeLengthStatistic tls = new TreeLengthStatistic(TL, treeModel);
        TreeHeightStatistic rootHeight = new TreeHeightStatistic(TREE_HEIGHT, treeModel);

        Parameter b = new Parameter.Default("b", 2.0, 0.0, Double.MAX_VALUE);
        Parameter d = new Parameter.Default("d", 0.0, 0.0, Double.MAX_VALUE);

        SpeciationModel speciationModel = new BirthDeathGernhard08Model(b, d, null, BirthDeathGernhard08Model.TreeType.TIMESONLY,
                Units.Type.YEARS);
        Likelihood likelihood = new SpeciationLikelihood(treeModel, speciationModel, "yule.like");

        ArrayLogFormatter formatter = new ArrayLogFormatter(false);

        MCLogger[] loggers = new MCLogger[2];
        loggers[0] = new MCLogger(formatter, 100, false);
        loggers[0].add(likelihood);
        loggers[0].add(rootHeight);
        loggers[0].add(tls);

        loggers[1] = new MCLogger(new TabDelimitedFormatter(System.out), 100000, false);
        loggers[1].add(likelihood);
        loggers[1].add(rootHeight);
        loggers[1].add(tls);

        mcmc.setShowOperatorAnalysis(true);

        mcmc.init(options, likelihood, schedule, loggers);

        mcmc.run();

        List<Trace> traces = formatter.getTraces();
        ArrayTraceList traceList = new ArrayTraceList("yuleModelTest", traces, 0);

        for (int i = 1; i < traces.size(); i++) {
            traceList.analyseTrace(i);
        }

        // expectation of root height for 4 tips and lambda = 2
        // rootHeight = 0.541666
        // TL = 1.5

        TraceCorrelation tlStats =
                traceList.getCorrelationStatistics(traceList.getTraceIndex(TL));

        assertExpectation(TL, tlStats, 1.5);

        TraceCorrelation treeHeightStats =
                traceList.getCorrelationStatistics(traceList.getTraceIndex(TREE_HEIGHT));

        assertExpectation(TREE_HEIGHT, treeHeightStats, 0.5416666);


    }
開發者ID:beast-dev，項目名稱:beast-mcmc，代碼行數:58，代碼來源:YuleModelTest.java


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