Example usage for java.lang Double NEGATIVE_INFINITY

Introduction

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

Prototype

double NEGATIVE_INFINITY

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Document

A constant holding the negative infinity of type double .

Usage

From source file:de.cebitec.readXplorer.plotting.CreatePlots.java

public synchronized static ChartPanel createInfPlot(Map<PersistentFeature, Pair<Double, Double>> data,
        String xName, String yName, XYToolTipGenerator toolTip) {
    XYSeriesCollection normal = new XYSeriesCollection();
    XYSeriesCollection posInf = new XYSeriesCollection();
    XYSeriesCollection negInf = new XYSeriesCollection();
    XYSeries nor = new XYSeries("Normal");
    XYSeries pos = new XYSeries("Positive Infinite");
    XYSeries neg = new XYSeries("Negative Infinite");
    for (Iterator<PersistentFeature> it = data.keySet().iterator(); it.hasNext();) {
        PersistentFeature key = it.next();
        Pair<Double, Double> pair = data.get(key);
        Double X = pair.getFirst();
        Double Y = pair.getSecond();

        if (Y == Double.POSITIVE_INFINITY) {
            Y = 0d;// w  w  w .ja v a2  s .  com
            pos.add(new PlotDataItem(key, X, Y));
        }
        if (Y == Double.NEGATIVE_INFINITY) {
            Y = 0d;
            neg.add(new PlotDataItem(key, X, Y));
        }
        if (!Y.isInfinite() && !X.isInfinite()) {
            nor.add(new PlotDataItem(key, X, Y));
        }
    }
    normal.addSeries(nor);
    posInf.addSeries(pos);
    negInf.addSeries(neg);
    JFreeChart chart = createCombinedChart(normal, posInf, negInf, xName, yName, toolTip);
    chart.removeLegend();
    ChartPanel panel = new ChartPanel(chart, true, false, true, true, true);
    panel.setInitialDelay(0);
    panel.setMaximumDrawHeight(1080);
    panel.setMaximumDrawWidth(1920);
    panel.setMouseWheelEnabled(true);
    panel.setMouseZoomable(true);
    MouseActions mouseAction = new MouseActions();
    panel.addChartMouseListener(mouseAction);
    ChartPanelOverlay overlay = new ChartPanelOverlay(mouseAction);
    panel.addOverlay(overlay);
    return panel;
}

---

From source file:nl.gridline.zieook.inx.movielens.hbase.RecommendationsImportMap.java

@Override
public void map(IntWritable key, VectorWritable value, Context context)
        throws IOException, InterruptedException {

    Vector similarityMatrixRow = value.get();

    // Remove self similarity
    similarityMatrixRow.set(key.get(), Double.NEGATIVE_INFINITY); // from the equation

    // determine max non-zero element: (==item index)
    List<RecommendationElement> recommendations = new ArrayList<RecommendationElement>();

    // collect non-zero items:
    Iterator<Element> it = similarityMatrixRow.iterateNonZero();
    while (it.hasNext()) {
        Element e = it.next();//from w  ww.  j a  v a  2s  .  c o  m
        recommendations.add(new RecommendationElement(e.index(), e.get()));
        // LOG.info("created new recommendation for " + e.index());
    }

    // sorted list of recommendations: now we have an item id, and similarity value:
    Collections.sort(recommendations, new SimilarityComparator());
    LOG.info("sorted: " + recommendations.size());

    int rank = 1;
    Put put = new Put(RowKeys.getRecommendationKey(collection, recommender, key.get()));
    for (RecommendationElement el : recommendations) {
        // if (el.getSimilarityValue() > 0)
        // {
        byte[] data = Recommend.getRecommendation(el.getItemIndex(), rank, el.getSimilarityValue());
        put.add(RECOMMENDATION_COLUMN, Bytes.toBytes(rank), data);
        rank++;
        // }
    }
    context.write(new LongWritable(key.get()), put);

}

---

From source file:com.opengamma.strata.math.impl.statistics.distribution.NonCentralChiSquaredDistribution.java

/**
 * {@inheritDoc}/* ww  w  .ja v a 2s.  c  om*/
 */
@Override
public double getCDF(Double x) {
    ArgChecker.notNull(x, "x");
    if (x < 0) {
        return 0.0;
    }

    if ((_dofOverTwo + _lambdaOverTwo) > 1000) {
        return getFraserApproxCDF(x);
    }

    double regGammaStart = 0;
    double halfX = x / 2.0;
    double logX = Math.log(halfX);
    try {
        regGammaStart = Gamma.regularizedGammaP(_dofOverTwo + _k, halfX);
    } catch (MaxCountExceededException ex) {
        throw new MathException(ex);
    }

    double sum = _pStart * regGammaStart;
    double oldSum = Double.NEGATIVE_INFINITY;
    double p = _pStart;
    double regGamma = regGammaStart;
    double temp;
    int i = _k;

    // first add terms below _k
    while (i > 0 && Math.abs(sum - oldSum) / sum > _eps) {
        i--;
        p *= (i + 1) / _lambdaOverTwo;
        temp = (_dofOverTwo + i) * logX - halfX - Gamma.logGamma(_dofOverTwo + i + 1);
        regGamma += Math.exp(temp);
        oldSum = sum;
        sum += p * regGamma;
    }

    p = _pStart;
    regGamma = regGammaStart;
    oldSum = Double.NEGATIVE_INFINITY;
    i = _k;
    while (Math.abs(sum - oldSum) / sum > _eps) {
        i++;
        p *= _lambdaOverTwo / i;
        temp = (_dofOverTwo + i - 1) * logX - halfX - Gamma.logGamma(_dofOverTwo + i);
        regGamma -= Math.exp(temp);
        oldSum = sum;
        sum += p * regGamma;
    }

    return sum;
}

---

From source file:edu.jhuapl.bsp.detector.OpenMath.java

public static double min(double[] in) {
    if (in != null) {
        double minvalue = Double.POSITIVE_INFINITY;
        for (int i = 0; i < in.length; i++) {
            minvalue = Math.min(in[i], minvalue);
        }/*  w  w  w . j a v a 2  s. co m*/
        return minvalue;
    }
    return Double.NEGATIVE_INFINITY;
}

---

From source file:bacter.model.ACGCoalescent.java

@Override
public double calculateLogP() {

    // Check whether conversion count exceeds bounds.
    if (acg.getTotalConvCount() < lowerCCBoundInput.get() || acg.getTotalConvCount() > upperCCBoundInput.get())
        return Double.NEGATIVE_INFINITY;

    logP = calculateClonalFrameLogP();/*from   w w  w  . ja  v a2s.c o  m*/
    double poissonMean = rhoInput.get().getValue() * acg.getClonalFrameLength()
            * (acg.getTotalConvertibleSequenceLength()
                    + acg.getConvertibleLoci().size() * (deltaInput.get().getValue() - 1.0));

    // Probability of conversion count:
    if (poissonMean > 0.0) {
        logP += -poissonMean + acg.getTotalConvCount() * Math.log(poissonMean);
        //      - GammaFunction.lnGamma(acg.getConvCount()+1);
    } else {
        if (acg.getTotalConvCount() > 0)
            logP = Double.NEGATIVE_INFINITY;
    }

    for (Locus locus : acg.getConvertibleLoci())
        for (Conversion conv : acg.getConversions(locus))
            logP += calculateConversionLogP(conv);

    // This N! takes into account the permutation invariance of
    // the individual conversions, and cancels with the N! in the
    // denominator of the Poissonian above.
    // logP += GammaFunction.lnGamma(acg.getConvCount() + 1);

    if (lowerCCBoundInput.get() > 0 || upperCCBoundInput.get() < Integer.MAX_VALUE) {
        try {
            logP -= new PoissonDistributionImpl(poissonMean).cumulativeProbability(lowerCCBoundInput.get(),
                    upperCCBoundInput.get());
        } catch (MathException e) {
            throw new RuntimeException("Error computing modification to ARG "
                    + "prior density required by conversion number constraint.");
        }
    }

    return logP;
}

---

From source file:com.opengamma.analytics.math.statistics.distribution.NonCentralChiSquaredDistribution.java

/**
 * {@inheritDoc}//from w w  w.  ja  v  a  2s  .c o  m
 */
@Override
public double getCDF(final Double x) {
    Validate.notNull(x, "x");
    if (x < 0) {
        return 0.0;
    }

    if ((_dofOverTwo + _lambdaOverTwo) > 1000) {
        return getFraserApproxCDF(x);
    }

    double regGammaStart = 0;
    final double halfX = x / 2.0;
    final double logX = Math.log(halfX);
    try {
        regGammaStart = Gamma.regularizedGammaP(_dofOverTwo + _k, halfX);
    } catch (final org.apache.commons.math.MathException ex) {
        throw new MathException(ex);
    }

    double sum = _pStart * regGammaStart;
    double oldSum = Double.NEGATIVE_INFINITY;
    double p = _pStart;
    double regGamma = regGammaStart;
    double temp;
    int i = _k;

    // first add terms below _k
    while (i > 0 && Math.abs(sum - oldSum) / sum > _eps) {
        i--;
        p *= (i + 1) / _lambdaOverTwo;
        temp = (_dofOverTwo + i) * logX - halfX - Gamma.logGamma(_dofOverTwo + i + 1);
        regGamma += Math.exp(temp);
        oldSum = sum;
        sum += p * regGamma;
    }

    p = _pStart;
    regGamma = regGammaStart;
    oldSum = Double.NEGATIVE_INFINITY;
    i = _k;
    while (Math.abs(sum - oldSum) / sum > _eps) {
        i++;
        p *= _lambdaOverTwo / i;
        temp = (_dofOverTwo + i - 1) * logX - halfX - Gamma.logGamma(_dofOverTwo + i);
        regGamma -= Math.exp(temp);
        oldSum = sum;
        sum += p * regGamma;
    }

    return sum;
}

---

From source file:ffx.numerics.ModifiedBessel.java

/**
 * Returns Double.MAX_VALUE in place of Double.POSITIVE_INFINITY; Returns
 * Double.MIN_VALUE in place of Double.NEGATIVE_INFINITY
 *
 * @param x input parameter//from  w  w  w.  j a  va  2 s. c  o  m
 * @return exp(x)
 */
private static double eToThe(double x) {
    double res = exp(x);
    if (res == Double.POSITIVE_INFINITY) {
        return Double.MAX_VALUE;
    } else if (res == Double.NEGATIVE_INFINITY) {
        return Double.MIN_VALUE;
    }
    return res;
}

---

From source file:dr.evomodel.epidemiology.casetocase.periodpriors.NormalPeriodPriorDistribution.java

public double calculateLogPosteriorProbability(double newValue, double minValue) {
    double out = calculateLogPosteriorPredictiveProbability(newValue);
    if (minValue != Double.NEGATIVE_INFINITY) {
        out -= calculateLogPosteriorPredictiveCDF(minValue, true);
    }/*from w w w.ja  v  a  2 s  .  c om*/
    logL += out;
    update(newValue);
    return out;
}

---

From source file:org.jfree.experimental.chart.renderer.xy.VectorRenderer.java

/**
 * Returns the lower and upper bounds (range) of the x-values in the 
 * specified dataset.//from   ww w  . j a  va  2s .  c  om
 * 
 * @param dataset  the dataset (<code>null</code> permitted).
 * 
 * @return The range (<code>null</code> if the dataset is <code>null</code>
 *         or empty).
 */
public Range findDomainBounds(XYDataset dataset) {
    if (dataset == null) {
        throw new IllegalArgumentException("Null 'dataset' argument.");
    }
    double minimum = Double.POSITIVE_INFINITY;
    double maximum = Double.NEGATIVE_INFINITY;
    int seriesCount = dataset.getSeriesCount();
    double lvalue;
    double uvalue;
    if (dataset instanceof VectorXYDataset) {
        VectorXYDataset vdataset = (VectorXYDataset) dataset;
        for (int series = 0; series < seriesCount; series++) {
            int itemCount = dataset.getItemCount(series);
            for (int item = 0; item < itemCount; item++) {
                double delta = vdataset.getDeltaXValue(series, item);
                if (delta < 0.0) {
                    uvalue = vdataset.getXValue(series, item);
                    lvalue = uvalue + delta;
                } else {
                    lvalue = vdataset.getXValue(series, item);
                    uvalue = lvalue + delta;
                }
                minimum = Math.min(minimum, lvalue);
                maximum = Math.max(maximum, uvalue);
            }
        }
    } else {
        for (int series = 0; series < seriesCount; series++) {
            int itemCount = dataset.getItemCount(series);
            for (int item = 0; item < itemCount; item++) {
                lvalue = dataset.getXValue(series, item);
                uvalue = lvalue;
                minimum = Math.min(minimum, lvalue);
                maximum = Math.max(maximum, uvalue);
            }
        }
    }
    if (minimum > maximum) {
        return null;
    } else {
        return new Range(minimum, maximum);
    }
}

---

From source file:bb.mcmc.analysis.ESSConvergeStat.java

@Override
protected double calculateEachStat(String key) {

    List<Double> listDouble = Arrays.asList(ArrayUtils.toObject(traceValues.get(key)));

    TraceCorrelation<Double> traceCorrelation = new TraceCorrelation<Double>(listDouble, TRACETYPE, stepSize);
    double stat = traceCorrelation.getESS();

    if (Double.isNaN(stat)) { //Use two separate if to handle other NaN cases later
        if (traceCorrelation.getVariance() == 0) {
            stat = Double.NEGATIVE_INFINITY;
            System.err.println(STATISTIC_NAME + " could not be calculated for variable with id " + key
                    + ". This is due to logged values being unchanged during the run");//. Check log file for details. ");
        }//from  ww w. j ava 2  s . com
    }
    return stat;

}

---

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