当前位置: 首页>>代码示例>>Java>>正文

Java EmpiricalBayesPoissonSmoother类代码示例

本文整理汇总了Java中dr.math.EmpiricalBayesPoissonSmoother类的典型用法代码示例。如果您正苦于以下问题：Java EmpiricalBayesPoissonSmoother类的具体用法？Java EmpiricalBayesPoissonSmoother怎么用？Java EmpiricalBayesPoissonSmoother使用的例子？那么, 这里精选的类代码示例或许可以为您提供帮助。


EmpiricalBayesPoissonSmoother类属于dr.math包，在下文中一共展示了EmpiricalBayesPoissonSmoother类的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。

示例1: doSmoothing

import dr.math.EmpiricalBayesPoissonSmoother; //导入依赖的package包/类
private void doSmoothing(double[] in) {

        double meanX = DiscreteStatistics.mean(in);
        double varX = DiscreteStatistics.variance(in);
        System.err.println("Original mean: " + meanX);
        System.err.println("Original var : " + varX + "\n");

        double[] out = EmpiricalBayesPoissonSmoother.smooth(in);

        double meanY = DiscreteStatistics.mean(out);
        double varY = DiscreteStatistics.variance(out);
        System.err.println("Smoothed mean: " + meanY);
        System.err.println("Smoothed var : " + varY);
        System.err.println("");

        assertEquals(meanX, meanY, tolerance);
        assertTrue(varY <= varX);
    }
开发者ID:beast-dev，项目名称:beast-mcmc，代码行数:19，代码来源:EmpiricalBayesPoissonSmootherTest.java

示例2: performSmoothing

import dr.math.EmpiricalBayesPoissonSmoother; //导入依赖的package包/类
private double[][][] performSmoothing(double[][][] allSamples) {
    double[][][] smoothedArray = new double[allSamples.length][][];
    double[][][] transpose = new double[allSamples.length][][];
    for (int i = 0; i < allSamples.length; i++) {
        transpose[i] = transpose(allSamples[i]); // transpose means: [allSamples][site]
        for (int sample = 0; sample < numSamples; ++sample) {
            transpose[i][sample] = EmpiricalBayesPoissonSmoother.smooth(transpose[i][sample]);
        }
        smoothedArray[i] = transpose(transpose[i]);
    }
    return smoothedArray;

}
开发者ID:beast-dev，项目名称:beast-mcmc，代码行数:14，代码来源:OldDnDsPerSiteAnalysis.java

示例3: doSmoothing

import dr.math.EmpiricalBayesPoissonSmoother; //导入依赖的package包/类
private void doSmoothing() {

        if (cachedValues == null) {
            cachedValues = new double[NUM_TRAITS][];
        }

        for (int i = 0; i < NUM_TRAITS; i++) {
            if (useSmoothing) {
                cachedValues[i] = EmpiricalBayesPoissonSmoother.smooth((double[]) traits[i].getTrait(tree, tree.getRoot()));
            } else {
                cachedValues[i] = (double[]) traits[i].getTrait(tree, tree.getRoot());
            }
        }
    }
开发者ID:beast-dev，项目名称:beast-mcmc，代码行数:15，代码来源:DnDsLogger.java

示例4: testSmooth

import dr.math.EmpiricalBayesPoissonSmoother; //导入依赖的package包/类
@Test
public void testSmooth() throws Exception {
    final double[] arr = new double[] {0, 1, 3, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 3, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 4, 1, 0, 0, 1, 0, 0, 1, 0, 0, 2, 1, 0, 0, 1, 2, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 2, 1, 1, 0, 0, 0, 0, 0, 1, 1, 2, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 2, 0, 3, 1, 0, 0, 0, 0, 2, 0, 0, 1, 3, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 3, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0, 1, 1, 1, 0, 0, 2, 2, 3, 0, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 2, 0, 0, 0, 1, 0, 0, 6, 1, 0, 3, 1, 1, 3, 0, 2, 0, 1, 0, 0, 0, 2, 1, 4, 1, 0, 0, 1, 0, 0, 0, 1, 0, 2, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 2, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 1, 0, 0, 1, 1, 5, 0, 0, 2, 0, 0, 0, 0, 1, 1, 0, 2, 1, 0, 2, 1, 0, 0, 0, 1, 0, 0, 2, 3, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 1, 0, 0, 1, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 2, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 1, 5, 3, 0, 1, 0, 0, 0, 0, 1, 3, 0, 3, 4, 0, 0, 2, 1, 0, 2, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 3, 5, 0, 0, 0, 0, 0, 1, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 1, 3, 2, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 4, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 2, 3, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 2, 0, 1, 0, 3, 1, 2, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 4, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 3, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 2, 4, 1, 2, 0, 1, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 0, 1, 1, 0, 0, 0, 3, 0, 2, 2, 1, 0, 0, 0, 1, 0, 2, 0, 0, 0, 2, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 2, 0, 0, 1, 3, 2, 2, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 4, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 2, 0, 1, 2, 1, 1, 1, 0, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 2, 1, 0, 0, 0, 0, 2, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 1, 1, 0, 0, 0, 0, 3, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 2, 1, 1, 2, 3, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 2, 2, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1};
    final double[] t = Doubles.constantArray(arr.length, 1.0);
    final double largeT = 10.0;

    final double mean = StatUtils.mean(arr);
    final double var = StatUtils.variance(arr);
    final double mom_alpha = (mean * mean) / (var - mean),
                 mom_beta = mean / (var - mean);

    final double[] result = TLambdaPoissonSmoother.estimateAlphaBeta(arr, t).getPointRef();
    final double[] resultBl = TLambdaPoissonSmoother.estimateAlphaBeta(arr, Doubles.constantArray(t.length, largeT)).getPointRef();

    // Larger branch length just changes Beta
    Assert.assertEquals(result[0], resultBl[0], 1e-2);
    Assert.assertEquals(result[1], resultBl[1] / largeT, 1e-2);

    // Actual results are close to MOM result
    Assert.assertEquals(mom_alpha, result[0], TOL);
    Assert.assertEquals(mom_beta, result[1], TOL);

    final double[] tlSmoothed = TLambdaPoissonSmoother.smooth(arr, t);
    final double[] tlSmoothedBl = TLambdaPoissonSmoother.smooth(arr, Doubles.constantArray(t.length, largeT));
    Assert.assertArrayEquals(tlSmoothed, Doubles.scalarMultiply(tlSmoothedBl, largeT), 1e2);
    final double[] origSmoothed = EmpiricalBayesPoissonSmoother.smooth(arr);

    // this is a pretty loose tolerance...
    Assert.assertArrayEquals(origSmoothed, tlSmoothed, 0.1);
}
开发者ID:cmccoy，项目名称:startreerenaissance，代码行数:31，代码来源:TLambdaPoissonSmootherTestCase.java

示例5: CnCsToDnDsPerSiteAnalysis

import dr.math.EmpiricalBayesPoissonSmoother; //导入依赖的package包/类
public CnCsToDnDsPerSiteAnalysis(TraceList traceListN, TraceList traceListS) {
        this.traceListN = traceListN;
        this.traceListS = traceListS;
        this.numSites = (traceListN.getTraceCount()) / 2;
        this.format = new OutputFormat();


        setUseSample(false);


        double[][] allCn = new double[numSites][this.traceListN.getStateCount()];
        double[][] allUn = new double[numSites][this.traceListN.getStateCount()];
        double[][] allCs = new double[numSites][this.traceListS.getStateCount()];
        double[][] allUs = new double[numSites][this.traceListS.getStateCount()];

        for (int o = 0; o < numSites; o++) {
            allCn[o] = listToDoubleArray(traceListN.getValues(o));
            allUn[o] = listToDoubleArray(traceListN.getValues(numSites + o));
            allCs[o] = listToDoubleArray(traceListS.getValues(o));
            allUs[o] = listToDoubleArray(traceListS.getValues(numSites + o));
        }

        allCn = transpose(allCn);
        allUn = transpose(allUn);
        allCs = transpose(allCs);
        allUs = transpose(allUs);

        double[][] tempAllSmoothedCn = new double[this.traceListN.getStateCount()][numSites];
        double[][] tempAllSmoothedUn = new double[this.traceListN.getStateCount()][numSites];
        double[][] tempAllSmoothedCs = new double[this.traceListS.getStateCount()][numSites];
        double[][] tempAllSmoothedUs = new double[this.traceListS.getStateCount()][numSites];

        boolean first = true;

        for (int p = 0; p < this.traceListN.getStateCount(); p++) {

            // Testing code:

//            if (first) {
//                tempAllSmoothedCn[p] = EmpiricalBayesPoissonSmoother.smooth(allCn[p]);
//                System.err.println("Smooth values: " + new Vector(tempAllSmoothedCn[p]));
//
//                tempAllSmoothedCn[p] = EmpiricalBayesPoissonSmoother.smoothWithSample(allCn[p]);
//                System.err.println("Sample values: " + new Vector(tempAllSmoothedCn[p]));
//                first = false;
//            }

//            System.exit(-1);


            if (format.useSample) {
                tempAllSmoothedCn[p] = EmpiricalBayesPoissonSmoother.smoothWithSample(allCn[p]);
                tempAllSmoothedUn[p] = EmpiricalBayesPoissonSmoother.smoothWithSample(allUn[p]);
                tempAllSmoothedCs[p] = EmpiricalBayesPoissonSmoother.smoothWithSample(allCs[p]);
                tempAllSmoothedUs[p] = EmpiricalBayesPoissonSmoother.smoothWithSample(allUs[p]);
            } else {
                tempAllSmoothedCn[p] = EmpiricalBayesPoissonSmoother.smooth(allCn[p]);
                tempAllSmoothedUn[p] = EmpiricalBayesPoissonSmoother.smooth(allUn[p]);
                tempAllSmoothedCs[p] = EmpiricalBayesPoissonSmoother.smooth(allCs[p]);
                tempAllSmoothedUs[p] = EmpiricalBayesPoissonSmoother.smooth(allUs[p]);
            }

        }

        allSmoothedCn = transpose(tempAllSmoothedCn);
        allSmoothedUn = transpose(tempAllSmoothedUn);
        allSmoothedCs = transpose(tempAllSmoothedCs);
        allSmoothedUs = transpose(tempAllSmoothedUs);

        fieldWidth = 14;
        firstField = 10;
        numberFormatter = new NumberFormatter(6);
        numberFormatter.setPadding(true);
        numberFormatter.setFieldWidth(fieldWidth);
    }
开发者ID:beast-dev，项目名称:beast-mcmc，代码行数:76，代码来源:CnCsToDnDsPerSiteAnalysis.java


注：本文中的dr.math.EmpiricalBayesPoissonSmoother类示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。