Example usage for org.apache.commons.math3.stat.descriptive DescriptiveStatistics addValue

Introduction

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

Prototype

public void addValue(double v) 

Source Link

Document

Adds the value to the dataset.

Usage

From source file:com.clust4j.algo.preprocess.RobustScaler.java

@Override
public RobustScaler fit(RealMatrix X) {
    synchronized (fitLock) {
        this.centerer = new MedianCenterer().fit(X);

        // Get percentile
        final int n = X.getColumnDimension();
        double[][] transpose = X.transpose().getData();

        // top row will be 25th, bottom 75
        double[][] quantiles_25_75 = new double[2][n];

        // Quantile engine
        DescriptiveStatistics stats;
        for (int j = 0; j < n; j++) {
            stats = new DescriptiveStatistics();

            for (int i = 0; i < transpose[j].length; i++) {
                stats.addValue(transpose[j][i]);
            }/* w  w w  .j  a v  a  2 s .com*/

            quantiles_25_75[0][j] = stats.getPercentile(25);
            quantiles_25_75[0][j] = stats.getPercentile(75);
        }

        // set the scale
        this.scale = VecUtils.subtract(quantiles_25_75[1], quantiles_25_75[0]);

        // If we have a constant value, we might get zeroes in the scale:
        for (int i = 0; i < scale.length; i++) {
            if (scale[i] == 0) {
                scale[i] = 1.0;
            }
        }

        return this;
    }
}

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From source file:com.kircherelectronics.accelerationexplorer.data.SampleAxisState.java

/**
 * Stop recording sample measurements./* w ww .jav  a  2 s . c  om*/
 */
@Override
public void stopSample() {
    sampler.setSampling(false);

    DescriptiveStatistics stats = new DescriptiveStatistics();

    // Add the data from the array
    for (int i = 0; i < acceleration.length; i++) {
        stats.addValue(acceleration[i]);
    }

    sampleRMS = stats.getStandardDeviation();

    sampleMean = StatUtils.mean(acceleration);

    sampleMax = StatUtils.max(acceleration);
    sampleMin = StatUtils.min(acceleration);

}

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From source file:algorithms.quality.AttentionQuality.java

@Override
public double getQuality(Colormap2D colormap) {
    // max L + max c (which is the same as a or b)
    double normFac = Math.sqrt(100 * 100 + 150 * 150);

    DescriptiveStatistics stats = new DescriptiveStatistics();

    for (Point2D pt : sampling.getPoints()) {
        Color color = colormap.getColor(pt.getX(), pt.getY());
        double[] lch = new CIELABLch().fromColor(color);
        double attention = Math.sqrt(lch[0] * lch[0] + lch[1] * lch[1]) / normFac;

        stats.addValue(attention);
    }/*  w  ww  .  ja v  a 2 s  . c  o m*/

    return stats.getVariance();
}

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From source file:es.csic.iiia.planes.util.InverseWishartDistributionTest.java

private double testScale(double scale, double df) {
    RealMatrix scaleMatrix = new Array2DRowRealMatrix(new double[][] { { 1 / scale, 0 }, { 0, 1 / scale }, });

    DescriptiveStatistics stats = new DescriptiveStatistics(WISHART_SAMPLES * NORMAL_SAMPLES);
    for (int i = 0; i < WISHART_SAMPLES; i++) {
        InverseWishartDistribution instance = new InverseWishartDistribution(scaleMatrix, df);
        stats.addValue(computeRadius(instance));
    }//w  w  w.ja  v a2  s  .  co  m

    return stats.getPercentile(50);
}

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From source file:es.csic.iiia.planes.util.InverseWishartDistributionTest.java

private double testGaussian(double scale) {
    double[] means = new double[] { 0, 0 };
    double[][] covariances = new double[][] { { scale, 0 }, { 0, scale } };
    MultivariateNormalDistribution uniform = new MultivariateNormalDistribution(means, covariances);
    DescriptiveStatistics stats = new DescriptiveStatistics(NORMAL_SAMPLES);
    for (int i = 0; i < NORMAL_SAMPLES; i++) {
        double[] xy = uniform.sample();
        final double x = xy[0];
        final double y = xy[1];
        stats.addValue(Math.sqrt(x * x + y * y));
    }//w  w w. j  av a 2s . c o  m

    return stats.getPercentile(90);
}

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From source file:es.csic.iiia.planes.util.InverseWishartDistributionTest.java

private double computeRadius(InverseWishartDistribution instance) {
    double[] means = new double[] { 0, 0 };
    MultivariateNormalDistribution uniform = null;

    while (uniform == null) {
        try {/* w w  w  . ja v a2 s  .co m*/
            double[][] covariances = instance.sample().getData();
            uniform = new MultivariateNormalDistribution(means, covariances);
        } catch (MathUnsupportedOperationException ex) {
        }
    }

    DescriptiveStatistics stats = new DescriptiveStatistics(NORMAL_SAMPLES);
    for (int i = 0; i < NORMAL_SAMPLES; i++) {
        double[] xy = uniform.sample();
        final double x = xy[0];
        final double y = xy[1];
        stats.addValue(Math.sqrt(x * x + y * y));
    }

    return stats.getPercentile(90);
}

---

From source file:gr.iti.mklab.reveal.forensics.util.Util.java

public static float[][] medianFilterSingleChannelImage(double[][] imIn, int medianFilterSize) {
    // Median filter a 2D double array
    // medianFilterSize should be odd
    int offset = (medianFilterSize - 1) / 2;
    int imWidth = imIn.length;
    int imHeight = imIn[0].length;
    DescriptiveStatistics blockValues;

    float[][] filteredImage = new float[imWidth - 2 * offset][imHeight - 2 * offset];

    for (int ii = offset; ii <= imWidth - medianFilterSize + offset; ii = ii + 1) {
        for (int jj = offset; jj <= imHeight - medianFilterSize + offset; jj = jj + 1) {
            blockValues = new DescriptiveStatistics();
            for (int B_ii = ii - offset; B_ii < ii + offset + 1; B_ii++) {
                for (int B_jj = jj - offset; B_jj < jj + offset + 1; B_jj++) {
                    blockValues.addValue(Math.abs(imIn[B_ii][B_jj]));
                }//from  w w  w .  ja  v a 2  s.c o m
            }
            filteredImage[ii - offset][jj - offset] = (float) blockValues.getPercentile(50);
        }
    }
    return filteredImage;
}

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From source file:io.yields.math.framework.data.DataProvidersTest.java

@Explore(name = "check distributional properties of random numbers", dataProvider = DataProviders.FixedMersenneTwisterDataProvider.class, nrOfRuns = 10000)
@Exploration(name = "2D uniform samples", context = FunctionExplorerWithoutProperties.class, group = "data providers")
public void testRandomDistribution(Explorer<Pair> explorer) {
    KolmogorovSmirnovTest ksTest = new KolmogorovSmirnovTest();
    DescriptiveStatistics xStats = new DescriptiveStatistics();
    DescriptiveStatistics yStats = new DescriptiveStatistics();
    explorer.all().forEach(result -> {
        Pair pair = result.getFunctionOutcome().orElse(new Pair());
        xStats.addValue(pair.getX1());
        yStats.addValue(pair.getX2());/*from   w w  w .ja  va  2  s . c  om*/
    });
    DescriptiveStatistics cross = new DescriptiveStatistics();
    for (int i = 0; i < xStats.getN(); i++) {
        cross.addValue((xStats.getValues()[i] - .5) * (yStats.getValues()[i] - .5));
    }
    /**
     * x and y should be uniformly distributed
     */
    assertThat(ksTest.kolmogorovSmirnovStatistic(new UniformRealDistribution(0, 1), xStats.getValues()))
            .isEqualTo(0, Delta.delta(.015));
    assertThat(ksTest.kolmogorovSmirnovStatistic(new UniformRealDistribution(0, 1), yStats.getValues()))
            .isEqualTo(0, Delta.delta(.015));
    /**
     * and have zero correlation
     */
    assertThat(cross.getMean()).isEqualTo(0, Delta.delta(.05));
}

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From source file:knop.psfj.BeadAverager.java

/**
 * Filter.//w w w  .j  a  v  a  2s.  c  om
 *
 * @param frameList the frame list
 * @return the array list
 */
public ArrayList<BeadFrame> filter(ArrayList<BeadFrame> frameList) {

    ArrayList<BeadFrame> filtered = new ArrayList<BeadFrame>();

    DescriptiveStatistics centerX = new DescriptiveStatistics();
    DescriptiveStatistics centerY = new DescriptiveStatistics();
    DescriptiveStatistics centerZ = new DescriptiveStatistics();

    for (BeadFrame frame : frameList) {
        centerX.addValue(frame.getCentroidX());
        centerY.addValue(frame.getCentroidY());
        centerZ.addValue(frame.getCentroidZ());
    }

    double thresholdUp = centerZ.getPercentile(50) + centerZ.getStandardDeviation();
    double thresholdDown = thresholdUp - (centerZ.getStandardDeviation() * 2);
    for (BeadFrame frame : frameList) {
        if (frame.getCentroidZ() < thresholdUp && frame.getCentroidZ() > thresholdDown) {
            filtered.add(frame);
        } else {
            incrementFilteredOutBeadCount();
        }
    }
    return filtered;
}

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From source file:com.caseystella.analytics.outlier.batch.rpca.RPCAOutlierAlgorithm.java

public double outlierScore(List<DataPoint> dataPoints, DataPoint value) {
    double[] inputData = new double[dataPoints.size() + 1];
    int numNonZero = 0;
    if (scaling != ScalingFunctions.NONE) {
        int i = 0;
        final DescriptiveStatistics stats = new DescriptiveStatistics();
        for (DataPoint dp : dataPoints) {
            inputData[i++] = dp.getValue();

            stats.addValue(dp.getValue());
            numNonZero += dp.getValue() > EPSILON ? 1 : 0;
        }//from   www .j a va2 s  .  c  om
        inputData[i] = value.getValue();
        GlobalStatistics globalStats = new GlobalStatistics() {
            {
                setMax(stats.getMax());
                setMin(stats.getMin());
                setMax(stats.getMean());
                setStddev(stats.getStandardDeviation());
            }
        };
        for (i = 0; i < inputData.length; ++i) {
            inputData[i] = scaling.scale(inputData[i], globalStats);
        }
    } else {
        int i = 0;
        for (DataPoint dp : dataPoints) {
            inputData[i++] = dp.getValue();
            numNonZero += dp.getValue() > EPSILON ? 1 : 0;
        }
        inputData[i] = value.getValue();
    }
    int nCols = 1;
    int nRows = inputData.length;
    if (numNonZero > minRecords) {
        AugmentedDickeyFuller dickeyFullerTest = new AugmentedDickeyFuller(inputData);
        double[] inputArrayTransformed = inputData;
        if (!this.isForceDiff && dickeyFullerTest.isNeedsDiff()) {
            // Auto Diff
            inputArrayTransformed = dickeyFullerTest.getZeroPaddedDiff();
        } else if (this.isForceDiff) {
            // Force Diff
            inputArrayTransformed = dickeyFullerTest.getZeroPaddedDiff();
        }

        if (this.spenalty == null) {
            this.lpenalty = this.LPENALTY_DEFAULT;
            this.spenalty = this.SPENALTY_DEFAULT / Math.sqrt(Math.max(nCols, nRows));
        }

        // Calc Mean
        double mean = 0;
        for (int n = 0; n < inputArrayTransformed.length; n++) {
            mean += inputArrayTransformed[n];
        }
        mean /= inputArrayTransformed.length;

        // Calc STDEV
        double stdev = 0;
        for (int n = 0; n < inputArrayTransformed.length; n++) {
            stdev += Math.pow(inputArrayTransformed[n] - mean, 2);
        }
        stdev = Math.sqrt(stdev / (inputArrayTransformed.length - 1));

        // Transformation: Zero Mean, Unit Variance
        for (int n = 0; n < inputArrayTransformed.length; n++) {
            inputArrayTransformed[n] = (inputArrayTransformed[n] - mean) / stdev;
        }

        // Read Input Data into Array
        // Read Input Data into Array
        double[][] input2DArray = new double[nRows][nCols];
        input2DArray = VectorToMatrix(inputArrayTransformed, nRows, nCols);

        RPCA rSVD = new RPCA(input2DArray, this.lpenalty, this.spenalty);

        double[][] outputE = rSVD.getE().getData();
        double[][] outputS = rSVD.getS().getData();
        double[][] outputL = rSVD.getL().getData();
        return outputS[nRows - 1][0];
    } else {
        return Double.NaN;
    }
}

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