Example usage for java.lang Double NaN

Introduction

In this page you can find the example usage for java.lang Double NaN.

Prototype

double NaN

To view the source code for java.lang Double NaN.

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Document

A constant holding a Not-a-Number (NaN) value of type double .

Usage

From source file:com.itemanalysis.psychometrics.irt.model.Irm4PL.java

public double derivTheta(double theta) {
    return Double.NaN;
}

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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;
        }/*  w ww.j a  v a2s  .co m*/
        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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From source file:dr.math.distributions.BetaDistribution.java

/**
 * quantile (inverse cumulative density function) of the distribution
 *
 * @param y argument//from   w w  w .  j  a  v  a2 s .c  o  m
 * @return icdf value
 */
public double quantile(double y) {
    if (y == 0) {
        return 0;
    } else if (y == 1) {
        return 1;
    } else {
        try {
            return super.inverseCumulativeProbability(y);
        } catch (MathException e) {
            //                throw MathRuntimeException.createIllegalArgumentException(                // AR - throwing exceptions deep in numerical code causes trouble. Catching runtime
            // exceptions is bad. Better to return NaN and let the calling code deal with it.
            return Double.NaN;

            //                    "Couldn't calculate beta quantile for alpha = " + alpha + ", beta = " + beta + ": " +e.getMessage());
        }
    }
}

---

From source file:guineu.modules.dataanalysis.correlations.CorrelationTask.java

private RealMatrix getCorrelation(PeakListRow row, PeakListRow row2, List<String> sampleNames) {

    List<Double[]> data = new ArrayList<Double[]>();
    for (int i = 0; i < sampleNames.size(); i++) {
        try {//  w  w  w.j  av  a 2s .c om
            if ((Double) row.getPeak(sampleNames.get(i)) != Double.NaN
                    && (Double) row2.getPeak(sampleNames.get(i)) != Double.NaN
                    && (Double) row.getPeak(sampleNames.get(i)) != 0.0
                    && (Double) row2.getPeak(sampleNames.get(i)) != 0.0) {
                Double[] dat = new Double[2];
                dat[0] = (Double) row.getPeak(sampleNames.get(i));
                dat[1] = (Double) row2.getPeak(sampleNames.get(i));
                data.add(dat);
                // System.out.println(sampleNames.elementAt(i) + " - " + row.getPeak(sampleNames.elementAt(i)) + " - " + row2.getPeak(sampleNames.elementAt(i)));

            }
        } catch (Exception e) {
            //System.out.println(row.getPeak(sampleNames.elementAt(i)) + " - " + row2.getPeak(sampleNames.elementAt(i)));
            //e.printStackTrace();
        }
    }

    double[][] dataMatrix = new double[data.size()][2];
    int count = 0;
    for (Double[] dat : data) {
        dataMatrix[count][0] = dat[0];
        dataMatrix[count++][1] = dat[1];
    }
    RealMatrix matrix = new BlockRealMatrix(dataMatrix);
    return matrix;

}

---

From source file:com.rapidminer.gui.plotter.charts.BubbleChartPlotter.java

private void prepareNumericalData() {
    DataTable dataTable = getDataTable();
    this.nominal = false;
    xyzDataSet = new DefaultXYZDataset();

    if (axis[X_AXIS] >= 0 && axis[Y_AXIS] >= 0 && axis[BUBBLE_SIZE_AXIS] >= 0) {

        this.bubbleSizeMin = Double.POSITIVE_INFINITY;
        this.bubbleSizeMax = Double.NEGATIVE_INFINITY;
        this.xAxisMin = Double.POSITIVE_INFINITY;
        this.xAxisMax = Double.NEGATIVE_INFINITY;
        this.yAxisMin = Double.POSITIVE_INFINITY;
        this.yAxisMax = Double.NEGATIVE_INFINITY;
        this.minColor = Double.POSITIVE_INFINITY;
        this.maxColor = Double.NEGATIVE_INFINITY;

        List<double[]> dataList = new LinkedList<>();
        synchronized (dataTable) {
            Iterator<DataTableRow> i = dataTable.iterator();
            while (i.hasNext()) {
                DataTableRow row = i.next();

                double xValue = row.getValue(axis[X_AXIS]);
                double yValue = row.getValue(axis[Y_AXIS]);
                double bubbleSizeValue = row.getValue(axis[BUBBLE_SIZE_AXIS]);

                double colorValue = Double.NaN;
                if (colorColumn >= 0) {
                    colorValue = row.getValue(colorColumn);
                }//from  w  ww.ja v  a 2s. com

                if (!Double.isNaN(xValue) && !Double.isNaN(yValue) && !Double.isNaN(bubbleSizeValue)) {
                    double[] data = new double[4];
                    data[X_AXIS] = xValue;
                    data[Y_AXIS] = yValue;
                    data[BUBBLE_SIZE_AXIS] = bubbleSizeValue;
                    data[3] = colorValue;

                    this.bubbleSizeMin = MathFunctions.robustMin(this.bubbleSizeMin, bubbleSizeValue);
                    this.bubbleSizeMax = MathFunctions.robustMax(this.bubbleSizeMax, bubbleSizeValue);
                    this.xAxisMin = MathFunctions.robustMin(this.xAxisMin, xValue);
                    this.yAxisMin = MathFunctions.robustMin(this.yAxisMin, yValue);
                    this.xAxisMax = MathFunctions.robustMax(this.xAxisMax, xValue);
                    this.yAxisMax = MathFunctions.robustMax(this.yAxisMax, yValue);
                    this.minColor = MathFunctions.robustMin(this.minColor, colorValue);
                    this.maxColor = MathFunctions.robustMax(this.maxColor, colorValue);

                    dataList.add(data);
                }
            }
        }

        double[][] data = new double[3][dataList.size()];
        this.colors = new double[dataList.size()];

        int index = 0;
        double scaleFactor = Math.min(this.xAxisMax - this.xAxisMin, this.yAxisMax - this.yAxisMin) / 4.0d;
        for (double[] d : dataList) {
            data[X_AXIS][index] = d[X_AXIS];
            data[Y_AXIS][index] = d[Y_AXIS];
            data[BUBBLE_SIZE_AXIS][index] = ((d[BUBBLE_SIZE_AXIS] - bubbleSizeMin)
                    / (bubbleSizeMax - bubbleSizeMin) + 0.1) * scaleFactor;
            this.colors[index] = d[3];
            index++;
        }

        xyzDataSet.addSeries("All", data);
    }
}

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From source file:net.femtoparsec.jnlmin.AbstractTokenTask.java

private void dispatchError() {
    if (this.listeners.isEmpty()) {
        return;// w  w w .j  ava2s  .c  o  m
    }
    MinimizerEventImpl event = new MinimizerEventImpl();
    event.setMinimizerStatus(MinimizerStatus.ERROR);
    event.setNbEvaluations(0);
    event.setNbIterations(0);
    event.setParameters(null);
    event.setPenalty(Double.NaN);
    this.dispatchEvent(event);
}

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From source file:com.facebook.presto.AbstractTestQueries.java

@Test
void testSpecialFloatingPointValues() throws Exception {
    MaterializedResult actual = computeActual("SELECT nan(), infinity(), -infinity()");
    MaterializedTuple tuple = Iterables.getOnlyElement(actual.getMaterializedTuples());
    assertEquals(tuple.getField(0), Double.NaN);
    assertEquals(tuple.getField(1), Double.POSITIVE_INFINITY);
    assertEquals(tuple.getField(2), Double.NEGATIVE_INFINITY);
}

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

@Test
public void testDefConst() {
    final double[][] d = new double[][] { new double[] { 1, 1, 2 }, new double[] { 1, Double.NaN, 3 },
            new double[] { 8.5, 7.9, 6 }, new double[] { 9, 8, Double.NaN }, new double[] { 3.5, 2.9, 6.1 },
            new double[] { 3, Double.NaN, 1 }, new double[] { 0, 0, 0 }, new double[] { 2, 4, 9 },
            new double[] { 1.4, 5, 6 }, };

    new BootstrapImputation().transform(d);
    new MeanImputation().transform(d);
    new MedianImputation().transform(d);
    new NearestNeighborImputation().transform(d);
}

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From source file:geogebra.common.geogebra3D.kernel3D.geos.Geo3DVec.java

final public static void complexMultiply(GeoVecInterface a, GeoVecInterface b, GeoVec2D c) {

    if (!Kernel.isZero(a.getZ()) || !Kernel.isZero(b.getZ())) {
        c.setX(Double.NaN);
        c.setY(Double.NaN);/*  ww  w. j ava2s .c o m*/
        c.setMode(Kernel.COORD_COMPLEX);
        return;
    }

    Complex out = new Complex(a.getX(), a.getY());
    out = out.multiply(new Complex(b.getX(), b.getY()));
    c.setX(out.getReal());
    c.setY(out.getImaginary());

    c.setMode(Kernel.COORD_COMPLEX);
}

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From source file:fr.ens.transcriptome.aozan.util.StatisticsUtils.java

/**
 * Compute the standard deviation for values.
 * @param isBiasCorrected false per default
 * @return standard deviation NaN if no values have been added, or 0.0 for a
 *         single value set./* w  ww .j  a v  a  2s  . c  o m*/
 */
public Double getStandardDeviation(final boolean isBiasCorrected) {
    final double average = getMean();

    double result = Double.NaN;
    double sum = 0.0;
    Double val = 0.0;
    double count = 0.0;

    if (this.ds.getN() > 0) {
        if (this.ds.getN() > 1) {

            for (int i = 0; i < this.ds.getN(); i++) {

                val = this.ds.getElement(i);

                val -= average;
                sum += val * val;
                count++;

            }

            // With bias corrected, division by count values - 1
            count -= isBiasCorrected ? 1 : 0;

            result = Math.sqrt(sum / count);

        } else {
            result = 0.0;
        }
    }
    return result;
}

---

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