Example usage for org.apache.commons.math.stat.descriptive.moment Skewness evaluate

Introduction

In this page you can find the example usage for org.apache.commons.math.stat.descriptive.moment Skewness evaluate.

Prototype

@Override
public double evaluate(final double[] values) 

Source Link

Document

This default implementation calls #clear , then invokes #increment in a loop over the the input array, and then uses #getResult to compute the return value.

Usage

From source file:com.discursive.jccook.math.StatExample.java

public static void main(String[] args) {
    double[] values = new double[] { 2.3, 5.4, 6.2, 7.3, 23.3 };

    System.out.println("min: " + StatUtils.min(values));
    System.out.println("max: " + StatUtils.max(values));
    System.out.println("mean: " + StatUtils.mean(values));
    System.out.println("product: " + StatUtils.product(values));
    System.out.println("sum: " + StatUtils.sum(values));
    System.out.println("variance: " + StatUtils.variance(values));

    // Measures from previous example
    Min min = new Min();
    System.out.println("min: " + min.evaluate(values));
    Max max = new Max();
    System.out.println("max: " + max.evaluate(values));
    Mean mean = new Mean();
    System.out.println("mean: " + mean.evaluate(values));
    Product product = new Product();
    System.out.println("product: " + product.evaluate(values));
    Sum sum = new Sum();
    System.out.println("sum: " + sum.evaluate(values));
    Variance variance = new Variance();
    System.out.println("variance: " + variance.evaluate(values));

    // New measures
    Percentile percentile = new Percentile();
    System.out.println("80 percentile value: " + percentile.evaluate(values, 80.0));
    GeometricMean geoMean = new GeometricMean();
    System.out.println("geometric mean: " + geoMean.evaluate(values));
    StandardDeviation stdDev = new StandardDeviation();
    System.out.println("standard dev: " + stdDev.evaluate(values));
    Skewness skewness = new Skewness();
    System.out.println("skewness: " + skewness.evaluate(values));
    Kurtosis kurtosis = new Kurtosis();
    System.out.println("kurtosis: " + kurtosis.evaluate(values));

}

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From source file:controller.FeatureController.java

public static double[] computeFeatures(float[] y, FilterCoefficients filterCoefficients) {
    double[] x = Util.floatToDouble(y);

    TDoubleArrayList features = new TDoubleArrayList();

    int wlen = 100, noutput = 100;

    float[][] stft = new float[noutput][wlen];

    filtfilt(x, filterCoefficients);/*  w w w.  jav a2  s  .  c  o m*/

    features.add(FastMath.sqrt(StatUtils.variance(x)));
    Skewness skew = new Skewness();
    features.add(skew.evaluate(x));
    Kurtosis kurt = new Kurtosis();
    features.add(kurt.evaluate(x));

    x = Math2.zscore(x);

    double[] d = Math2.diff(Math2.diff(x));
    features.add(Doubles.max(Math2.abs(d)));

    List<float[]> wdec = Entropies.wavedecomposition(Util.doubleToFloat(x));

    features.add(wdec.stream().mapToDouble(e -> FastMath.log(DoubleMath.mean(Math2.abs(Util.floatToDouble(e)))))
            .toArray());

    features.add(Entropies.wpentropy(Util.doubleToFloat(x), 6, 1));
    features.add(wdec.stream().mapToDouble(e -> Entropies.wpentropy(e, 6, 1)).toArray());

    features.add(Signal.lineSpectralPairs(x, 10));
    features.add(Arrays.copyOf(
            Util.floatToDouble(tools.Signal.logAbsStft(Util.doubleToFloat(x), wlen, noutput, stft)), 10));

    double[] x_pos = new double[x.length];
    double[] x_neg = new double[x.length];
    for (int i = 0; i < x.length; i++) {
        x_pos[i] = (x[i] >= 0) ? x[i] : 0;
        x_neg[i] = (x[i] <= 0) ? x[i] : 0;
    }

    features.add(Arrays.copyOf(
            Util.floatToDouble(tools.Signal.logAbsStft(Util.doubleToFloat(x_pos), wlen, noutput, stft)), 10));

    features.add(Arrays.copyOf(
            Util.floatToDouble(tools.Signal.logAbsStft(Util.doubleToFloat(x_neg), wlen, noutput, stft)), 10));
    return features.toArray();
}

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