Example usage for org.apache.commons.math3.distribution NormalDistribution NormalDistribution

Introduction

In this page you can find the example usage for org.apache.commons.math3.distribution NormalDistribution NormalDistribution.

Prototype

public NormalDistribution() 

Source Link

Document

Create a normal distribution with mean equal to zero and standard deviation equal to one.

Usage

From source file:net.demilich.metastone.game.behaviour.decicionTreeBheavior.DecisionDataBase.java

public synchronized void learn(Example[] examples) {
    double totalGood = Arrays.asList(examples).stream().filter(e -> e.classification == 2).count();
    NormalDistribution norm = new NormalDistribution();
    this.percentGood = totalGood / examples.length;
    for (int i = 0; i < examples[0].values.length; i++) {
        final int feature = i; //i feel like smeting should be wrong with this.... doesn't compile without the final variable
        //System.err.println("feature: " + feature);

        List<Example> featureTrue = Arrays.asList(examples).stream().filter(e -> e.values[feature] > .5)
                .collect(Collectors.toList());

        makeNewStat(featureTrue, feature, totalGood, norm, (double) examples.length, 1);

        List<Example> featureFalse = Arrays.asList(examples).stream().filter(e -> e.values[feature] < .5)
                .collect(Collectors.toList());

        makeNewStat(featureFalse, feature, totalGood, norm, (double) examples.length, 0);
    }/*w w w  .j  a v  a  2  s .c  o  m*/

    Collections.sort(stats);
    for (int i = 0; i < usedFeatures.length; i++) {
        usedFeatures[i] = stats.get(i).feature;
        if (stats.get(i).pValue < MaxBiasedProbability.threshold) {
            stats.get(i).percentGood = this.percentGood;
        }
    }
}

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From source file:edu.cmu.tetrad.data.DataUtils.java

public static DataSet getNonparanormalTransformed(DataSet dataSet) {
    final TetradMatrix data = dataSet.getDoubleData();
    final TetradMatrix X = data.like();
    final double n = dataSet.getNumRows();
    final double delta = 1.0 / (4.0 * Math.pow(n, 0.25) * Math.sqrt(Math.PI * Math.log(n)));

    final NormalDistribution normalDistribution = new NormalDistribution();

    double std = Double.NaN;

    for (int j = 0; j < data.columns(); j++) {
        final double[] x1 = data.getColumn(j).toArray();
        double std1 = StatUtils.sd(x1);
        double mu1 = StatUtils.mean(x1);
        double[] x = ranks(data, x1);

        for (int i = 0; i < x.length; i++) {
            x[i] /= n;/*w  w  w .j a  va2 s .c o  m*/
            if (x[i] < delta)
                x[i] = delta;
            if (x[i] > (1. - delta))
                x[i] = 1. - delta;
            x[i] = normalDistribution.inverseCumulativeProbability(x[i]);
        }

        if (Double.isNaN(std)) {
            std = StatUtils.sd(x);
        }

        for (int i = 0; i < x.length; i++) {
            x[i] /= std;
            x[i] *= std1;
            x[i] += mu1;
        }

        X.assignColumn(j, new TetradVector(x));
    }
    return ColtDataSet.makeContinuousData(dataSet.getVariables(), X);
}

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From source file:Option2017Interface.DistFunctions.java

public static double PDF(double z) {

    NormalDistribution nD = new NormalDistribution();
    double pdf;/* ww  w .  java  2  s .  c o  m*/
    pdf = nD.density(z);
    return pdf;

}

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From source file:org.alfresco.bm.event.DelayingSampleEventProcessor.java

/**
 * @param outputEventName       the output event name
 * @param minTime               the minimum apparent execution time
 * @param maxTime               the maximum apparent execution time
 * @param failurePercent        the percentage of events that must fail
 *///ww w  .  j  a  va  2 s. c o  m
public DelayingSampleEventProcessor(String outputEventName, long minTime, long maxTime, int failurePercent) {
    this.outputEventName = outputEventName;
    this.normalDistribution = new NormalDistribution();
    this.minTime = minTime;
    this.maxTime = maxTime;
    this.failurePercent = failurePercent;
}

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From source file:org.alfresco.random.NormalDistributionHelper.java

/**
 * Use a simple normal distribution to generate random numbers
 */
public NormalDistributionHelper() {
    this.normalDistribution = new NormalDistribution();
}

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From source file:org.briljantframework.data.dataframe.MixedDataFrameTest.java

@Test
public void testNiceBuilder() throws Exception {
    NormalDistribution gaussian = new NormalDistribution();
    DataFrame df = DataFrame.of("a", Vector.of(1, 2, 3), "b", Vector.of(2, 3, 3), "c",
            Vector.fromSupplier(gaussian::sample, 3), "d", Vector.singleton("hello", 3));

    DataFrame df2 = DataFrame.builder().set("a", Vector.fromSupplier(gaussian::sample, 4))
            .set("b", Vector.fromSupplier(gaussian::sample, 4)).build();

    System.out.println(df.transpose());

}

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From source file:org.hawkular.datamining.forecast.stats.AugmentedDickeyFullerTest.java

/**
 * Returns MacKinnon's approximate p-value for the given test statistic.
 *
 * MacKinnon, J.G. 1994  "Approximate Asymptotic Distribution Functions for
 *    Unit-Root and Cointegration Tests." Journal of Business & Economics
 *    Statistics, 12.2, 167-76.//w w  w .ja va 2 s  .c  o  m
 *
 * @param testStat "T-value" from an Augmented Dickey-Fuller regression.
 * @param maxLag The number of series believed to be I(1). For (Augmented) Dickey-Fuller n = 1.
 * @return The p-value for the ADF statistic using MacKinnon 1994.
 */
private double macKinnonPValue(double testStat, int maxLag) {
    double[] maxStat = ADF_TAU_MAX.get(type);
    if (testStat > maxStat[maxLag - 1]) {
        return 1.0;
    }
    double[] minStat = ADF_TAU_MIN.get(type);
    if (testStat < minStat[maxLag - 1]) {
        return 0.0;
    }

    double[] starStat = ADF_TAU_STAR.get(type);
    double[] tauCoef = testStat <= starStat[maxLag - 1] ? ADF_TAU_SMALLP.get(type)[maxLag - 1]
            : ADF_TAU_LARGEP.get(type)[maxLag - 1];

    return new NormalDistribution().cumulativeProbability(polyval(tauCoef, testStat));
}

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From source file:org.lightjason.agentspeak.action.builtin.TestCActionMathStatistics.java

/**
 * test add random sample/*from  ww w .ja  va 2s. c  om*/
 */
@Test
public final void randomsample() {
    final List<ITerm> l_return = new ArrayList<>();

    new CRandomSample().execute(false, IContext.EMPTYPLAN,
            Stream.of(new NormalDistribution(), 3).map(CRawTerm::from).collect(Collectors.toList()), l_return);

    Assert.assertEquals(l_return.size(), 1);
    Assert.assertTrue(l_return.get(0).raw() instanceof List);
    Assert.assertEquals(l_return.get(0).<List<Number>>raw().size(), 3);
}

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From source file:org.nmdp.ngs.tools.GenerateBedTest.java

@Before
public void setUp() {
    chrom = "1";/*from  w w w.ja v a 2 s.co m*/
    n = 10;
    size = 100;
    random = new JDKRandomGenerator();
    length = new NormalDistribution();
}

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From source file:org.nmdp.ngs.tools.GeneratePairedEndReadsTest.java

@Before
public void setUp() throws Exception {
    firstReadFile = File.createTempFile("generatePairedEndReadsTest", "fq.gz");
    secondReadFile = File.createTempFile("generatePairedEndReadsTest", "fq.gz");
    random = new JDKRandomGenerator();
    length = new NormalDistribution();
    insertSize = new NormalDistribution();
    quality = new RealDistributionQualityStrategy(new NormalDistribution());
    coverage = GeneratePairedEndReads.DEFAULT_COVERAGE;
    mutationRate = 0.0d;//from w  w w  .  j ava  2 s .  c  o m
    mutation = GeneratePairedEndReads.DEFAULT_MUTATION;
}

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