Example usage for org.apache.commons.math3.stat.descriptive.moment StandardDeviation StandardDeviation

Introduction

In this page you can find the example usage for org.apache.commons.math3.stat.descriptive.moment StandardDeviation StandardDeviation.

Prototype

public StandardDeviation() 

Source Link

Document

Constructs a StandardDeviation.

Usage

From source file:com.itemanalysis.psychometrics.polycor.PolyserialPlugin.java

public PolyserialPlugin() {
    r = new PearsonCorrelation();
    sdX = new StandardDeviation();
    sdY = new StandardDeviation();
    freqY = new Frequency();
    norm = new NormalDistribution();
}

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From source file:com.itemanalysis.psychometrics.polycor.AbstractPolyserialCorrelation.java

public void summarize(double[] x, int[] y) {
    if (x.length != y.length)
        throw new IllegalArgumentException("X and Y are of different lengths.");
    N = (double) x.length;
    Mean meanX = new Mean();
    StandardDeviation sdX = new StandardDeviation();
    PearsonCorrelation rxy = new PearsonCorrelation();
    Frequency table = new Frequency();

    for (int i = 0; i < N; i++) {
        meanX.increment(x[i]);/*from  w  w  w  .ja  v a  2  s  . co  m*/
        sdX.increment(x[i]);
        rxy.increment(x[i], (double) y[i]);
        table.addValue(y[i]);
    }

    //compute thresholds
    int nrow = table.getUniqueCount();
    double[] freqDataY = new double[nrow];
    double ntotal = table.getSumFreq();
    for (int i = 0; i < (nrow - 1); i++) {
        freqDataY[i] = table.getCumFreq(i + 1);
        thresholds[i] = norm.inverseCumulativeProbability(freqDataY[i] / ntotal);
    }
    thresholds[nrow - 1] = 10;//set last threshold to a large number less than infinity

}

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From source file:com.itemanalysis.psychometrics.kernel.LikelihoodCrossValidation.java

private void computeBounds() {
    StandardDeviation sd = new StandardDeviation();
    this.max = sd.evaluate(x);
}

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From source file:com.facebook.presto.operator.aggregation.TestDoubleStdDevAggregation.java

@Override
public Number getExpectedValue(int start, int length) {
    if (length < 2) {
        return null;
    }//from  w w  w.j  ava2  s.  c  om

    double[] values = new double[length];
    for (int i = 0; i < length; i++) {
        values[i] = start + i;
    }

    StandardDeviation stdDev = new StandardDeviation();
    return stdDev.evaluate(values);
}

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From source file:com.itemanalysis.psychometrics.kernel.LeastSquaresCrossValidation.java

private void computeBounds() throws Exception {
    StandardDeviation stdev = new StandardDeviation();
    this.sd = stdev.evaluate(x);
    Min min = new Min();
    double from = min.evaluate(x);
    Max max = new Max();
    double to = max.evaluate(x);

}

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From source file:com.itemanalysis.psychometrics.polycor.PolyserialLogLikelihoodTwoStep.java

public void summarize() throws DimensionMismatchException {
    if (dataX.length != dataY.length)
        throw new DimensionMismatchException(dataX.length, dataY.length);
    Frequency table = new Frequency();
    meanX = new Mean();
    sdX = new StandardDeviation();
    rxy = new PearsonCorrelation();
    for (int i = 0; i < nrow; i++) {
        meanX.increment(dataX[i]);// w  w w.  jav a  2 s  .c  om
        sdX.increment(dataX[i]);
        rxy.increment(dataX[i], (double) dataY[i]);
        table.addValue(dataY[i]);
    }

    //compute thresholds
    nrow = table.getUniqueCount();
    freqDataY = new double[nrow];
    double ntotal = table.getSumFreq();
    for (int i = 0; i < (nrow - 1); i++) {
        freqDataY[i] = table.getCumFreq(i + 1);
        alpha[i] = normal.inverseCumulativeProbability(freqDataY[i] / ntotal);
    }
    alpha[nrow - 1] = 10;//set last threshold to a large number less than infinity
}

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From source file:de.biomedical_imaging.traJ.features.StandardDeviationDirectionFeature.java

@Override
public double[] evaluate() {
    StandardDeviation sd = new StandardDeviation();
    double[] values = new double[t.size() - timelag - 1];
    double subx = 0;
    double suby = 0;
    double subz = 0;

    for (int i = timelag + 1; i < t.size(); i++) {

        subx = t.get(i - timelag - 1).x;
        suby = t.get(i - timelag - 1).y;
        subz = t.get(i - timelag - 1).z;

        Vector3d v1 = new Vector3d(t.get(i - timelag).x - subx, t.get(i - timelag).y - suby,
                t.get(i - timelag).z - subz);

        subx = t.get(i - 1).x;//from   www  . jav a 2s .c  o m
        suby = t.get(i - 1).y;
        subz = t.get(i - 1).z;
        Vector3d v2 = new Vector3d(t.get(i).x - subx, t.get(i).y - suby, t.get(i).z - subz);

        double v = v1.angle(v2);

        boolean v1IsZero = TrajectoryUtil.isZero(v1.x) && TrajectoryUtil.isZero(v1.y)
                && TrajectoryUtil.isZero(v1.z);
        boolean v2IsZero = TrajectoryUtil.isZero(v2.x) && TrajectoryUtil.isZero(v2.y)
                && TrajectoryUtil.isZero(v2.z);
        if (v1IsZero || v2IsZero) {
            v = 0;
        }
        values[i - timelag - 1] = v;
        //System.out.println("da " + v1.angle(v2));
    }

    sd.setData(values);
    result = new double[] { sd.evaluate() };

    return result;
}

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From source file:com.itemanalysis.psychometrics.measurement.ClassicalItemStatistics.java

public ClassicalItemStatistics(Object id, boolean biasCorrection, boolean pearson, boolean dIndex) {
    this.biasCorrection = biasCorrection;
    this.pearson = pearson;
    this.dIndex = dIndex;
    mean = new Mean();
    sd = new StandardDeviation();

    if (dIndex) {
        upper = new Mean();
        lower = new Mean();
    }//from   w w  w .  j  ava 2 s .com

    if (this.pearson) {
        pointBiserial = new PearsonCorrelation();
    } else {
        polyserial = new PolyserialPlugin();
    }
}

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From source file:com.cloudera.oryx.rdf.common.information.NumericInformationTest.java

@Test
public void testInformationCategoricalFeature() {
    ExampleSet exampleSet = examplesForValuesForCategories(new float[][] { new float[] { 1.0f, 1.5f },
            new float[] { 5.5f, 7.0f }, new float[] { 2.0f, 5.0f }, });
    List<Decision> decisions = Decision.decisionsFromExamples(exampleSet, 0, 100);
    assertEquals(2, decisions.size());/*from w w w  .ja  v  a  2  s.com*/
    BitSet categories0 = ((CategoricalDecision) decisions.get(0)).getCategoryIDs();
    BitSet categories1 = ((CategoricalDecision) decisions.get(1)).getCategoryIDs();
    assertEquals(1, categories0.cardinality());
    assertTrue(categories0.get(0));
    assertEquals(2, categories1.cardinality());
    assertTrue(categories1.get(0));
    assertTrue(categories1.get(2));

    Pair<Decision, Double> best = NumericalInformation.bestGain(decisions, exampleSet);
    assertEquals(categories0, ((CategoricalDecision) best.getFirst()).getCategoryIDs());

    StandardDeviation all = new StandardDeviation();
    all.incrementAll(new double[] { 1.0, 1.5, 5.5, 7.0, 2.0, 5.0 });
    StandardDeviation positive = new StandardDeviation();
    positive.incrementAll(new double[] { 1.0, 1.5 });
    StandardDeviation negative = new StandardDeviation();
    negative.incrementAll(new double[] { 5.5, 7.0, 2.0, 5.0 });

    assertEquals(differentialEntropy(all) - (2.0 / 6.0) * differentialEntropy(positive)
            - (4.0 / 6.0) * differentialEntropy(negative), best.getValue().doubleValue());
}

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From source file:net.sf.sessionAnalysis.SessionVisitorSessionLengthNanosStatistics.java

public double computeSessionLengthStdDev() {
    double[] lengths = computeLengthVector();
    StandardDeviation stdDevObj = new StandardDeviation();
    return stdDevObj.evaluate(lengths);
}

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