Example usage for java.lang Double NEGATIVE

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:edu.oregonstate.eecs.mcplan.bandit.HeuristicBandit.java

@Override
protected final int selectArm(final RandomGenerator rng) {
    int istar = 0;
    double hstar = Double.NEGATIVE_INFINITY;
    for (int i = 0; i < Narms(); ++i) {
        final double h = heuristic(i);
        if (h > hstar) {
            hstar = h;//from   w ww.  j  a  v  a  2  s  .c om
            istar = i;
        }
        if (h == Double.POSITIVE_INFINITY) {
            // Since POSITIVE_INFINITY > POSITIVE_INFINITY is false, no
            // other arm can replace istar in the future.
            break;
        }
    }
    return istar;
}

From source file:jasima.core.random.continuous.DblNormal.java

@Override
public Pair<Double, Double> getValueRange() {
    return new Pair<>(Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY);
}

From source file:com.golemgame.functional.functions.KnottedFunctionView.java

@Override
public UnivariateRealFunction getFunction() {
    CollectionType xKnots = interpreter.getXKnots();
    CollectionType yKnots = interpreter.getYKnots();
    int size = Math.min(xKnots.getValues().size(), yKnots.getValues().size());
    double[] xs = new double[size];
    double[] ys = new double[size];

    for (int i = 0; i < size; i++) {
        DataType x = xKnots.getElement(i);
        DataType y = yKnots.getElement(i);

        if (y.getType() == DataType.Type.DOUBLE && x.getType() == DataType.Type.DOUBLE) {
            xs[i] = ((DoubleType) x).getValue();
            ys[i] = ((DoubleType) y).getValue();

        } else/*  w w w. j  av  a 2s.  c  om*/
            xs[i] = Double.NEGATIVE_INFINITY;
    }
    return LinearInterpolator.getInstance().interpolate(xs, ys);
}

From source file:com.clust4j.kernel.CircularKernel.java

@Override
public double getPartialSimilarity(double[] a, double[] b) {
    final double lp = toHilbertPSpace(a, b);

    // Per corner case condition
    if (lp >= getSigma())
        return 0.0;

    final double twoOverPi = (2d / FastMath.PI);
    final double lpOverSig = lp / getSigma();

    /* Front segment */
    final double front = twoOverPi * FastMath.acos(-lpOverSig);

    /* Back segment */
    final double first = twoOverPi * lpOverSig;
    final double second = FastMath.sqrt(1.0 - FastMath.pow(lpOverSig, 2));
    final double back = first * second;
    final double answer = front - back;

    return Double.isNaN(answer) ? Double.NEGATIVE_INFINITY : answer;
}

From source file:jeans.math.SingleStat.java

public void reset() {
    m_Sum = 0.0;/*from  www  . j a v  a 2 s  . c  om*/
    m_SumSQ = 0.0;
    m_Count = 0.0;
    m_Min = Double.POSITIVE_INFINITY;
    m_Max = Double.NEGATIVE_INFINITY;
    //      m_Distribution = null;
}

From source file:hyperheuristics.algorithm.moeadfrrmab.UCBSelector.java

public LowLevelHeuristic selectOperator(HashMap<String, Double> frr, HashMap<String, Integer> nt) {
    if (!this.alltried) {
        for (int i = 0; i < this.operators.size(); i++) {
            LowLevelHeuristic op = this.operators.get(i);
            if (nt.get(op.getName()) == 0) {
                //never tried
                return op;
            }//from  w  ww  .  j a  va2s  .co  m
        }
        this.alltried = true;
    }

    ArrayList<Integer> selected = new ArrayList<>();
    double biggervalue = Double.NEGATIVE_INFINITY;
    double sumNt = this.sumNt(nt);

    for (int i = 0; i < this.operators.size(); i++) {
        LowLevelHeuristic op = this.operators.get(i);
        double value = this.equation(op, frr, nt, sumNt);
        if (value > biggervalue) {
            biggervalue = value;
            selected = new ArrayList<>();
            selected.add(i);
        } else if (value == biggervalue) {
            selected.add(i);
        }
    }
    Random rand = new Random();
    int p = rand.nextInt(selected.size());
    int select = selected.get(p);
    LowLevelHeuristic llh = this.operators.get(select);
    return llh;
}

From source file:ch.ethz.bsse.quasirecomb.model.hmm.EM.java

private void blackbox(Read[] reads, int N, int L, int K, int n) {
    Globals.getINSTANCE().setLOG(new StringBuilder());
    Globals.getINSTANCE().setMAX_LLH(-1);
    Globals.getINSTANCE().setMIN_BIC(Double.MAX_VALUE);
    String pathOptimum = null;//from  w  w  w.j  a  v  a2 s  . com
    if (K == 1 || Globals.getINSTANCE().isFORCE_NO_RECOMB()) {
        Globals.getINSTANCE().setNO_RECOMB(true);
    } else {
        Globals.getINSTANCE().setNO_RECOMB(false);
    }
    if (Globals.getINSTANCE().getOPTIMUM() == null) {
        double maxLLH = Double.NEGATIVE_INFINITY;
        bics = new Double[Globals.getINSTANCE().getREPEATS()];
        double[] bics_local = new double[Globals.getINSTANCE().getREPEATS()];
        for (int i = 0; i < Globals.getINSTANCE().getREPEATS(); i++) {
            SingleEM sem = new SingleEM(N, K, L, n, reads, Globals.getINSTANCE().getDELTA_LLH(), i);
            bics_local[i] = sem.getOptimalResult().getBIC();
            bics[i] = sem.getOptimalResult().getBIC();
            this.maxBIC = Math.max(this.maxBIC, sem.getOptimalResult().getBIC());
            if (sem.getLoglikelihood() > maxLLH) {
                maxLLH = sem.getLoglikelihood();
                pathOptimum = sem.getOptimumPath();
            }
        }
        medianBIC = new Median().evaluate(bics_local);
        lowerBoundBIC = medianBIC
                - new StandardDeviation().evaluate(bics_local) * Math.sqrt(1 + 1d / bics_local.length);
    } else {
        pathOptimum = Globals.getINSTANCE().getOPTIMUM();
    }
    if (Globals.getINSTANCE().isMODELSELECTION()) {
        try {
            FileInputStream fis = new FileInputStream(pathOptimum);
            try (ObjectInputStream in = new ObjectInputStream(fis)) {
                or = (OptimalResult) in.readObject();
            }
        } catch (IOException | ClassNotFoundException ex) {
            System.err.println(ex);
        }
        StatusUpdate.getINSTANCE().printBIC(K, (int) or.getBIC());
    } else {
        try {
            FileInputStream fis = new FileInputStream(pathOptimum);
            try (ObjectInputStream in = new ObjectInputStream(fis)) {
                or = (OptimalResult) in.readObject();
            }
        } catch (IOException | ClassNotFoundException ex) {
            System.err.println(ex);
        }

        StatusUpdate.getINSTANCE().printBIC(K, (int) or.getBIC());
        System.out.print("\n");
        if (!Globals.getINSTANCE().isSUBSAMPLE()) {
            //            Globals.getINSTANCE().setREFINEMENT(true);
            if (!Globals.getINSTANCE().isANNEALING()) {
                SingleEM bestEM = new SingleEM(or, Globals.getINSTANCE().getDELTA_REFINE_LLH(), reads);
                this.or = bestEM.getOptimalResult();
            }
            StatusUpdate.getINSTANCE().printBIC(K, 100, (int) this.or.getBIC());
            if (Globals.getINSTANCE().isLOGGING()) {
                Utils.saveFile(Globals.getINSTANCE().getSAVEPATH() + "support" + File.separator + "log_K" + K,
                        Globals.getINSTANCE().getLOG().toString());
            }
        }
    }
}

From source file:edu.byu.nlp.stats.VectorCategoricalDistributionTest.java

@Test
public void testLogProbabilityOf() {
    double[] logProbs = new double[] { log(0.7), log(0.0), log(0.3) };
    VectorCategoricalDistribution dist = new VectorCategoricalDistribution(new ArrayRealVector(logProbs));
    assertThat(dist.logProbabilityOf(0)).isEqualTo(logProbs[0], Delta.delta(1e-14));
    assertThat(dist.logProbabilityOf(1)).isEqualTo(Double.NEGATIVE_INFINITY, Delta.delta(1e-14));
    assertThat(dist.logProbabilityOf(2)).isEqualTo(logProbs[2], Delta.delta(1e-14));

    try {//from ww  w  . j  a v  a 2  s  .  c o m
        dist.logProbabilityOf(3);
    } catch (Exception expected) {
    } // TODO(rhaertel): more specific exception
}

From source file:ml.shifu.core.di.builtin.binning.MunroPatBinning.java

/**
 * set min/max, merge same bins/*ww  w.  ja  va  2s  .c o  m*/
 *
 * @param bins
 * @return
 */
private List<Double> binMerge(List<Double> bins) {

    List<Double> newBins = new ArrayList<Double>();
    if (bins.size() == 0) {
        bins.add(Double.NaN);
        return bins;
    }

    Double cur = bins.get(0);
    newBins.add(cur);

    int i = 1;
    while (i < bins.size()) {
        if (Math.abs(cur - bins.get(i)) > 1e-10) {
            newBins.add(bins.get(i));
        }
        cur = bins.get(i);
        i++;
    }

    if (newBins.size() == 1) {
        // special case since there is only 1 candidate in the bins
        double val = newBins.get(0);
        newBins = Arrays.asList(Double.NEGATIVE_INFINITY, val);
    } else if (newBins.size() == 2) {
        newBins.set(0, Double.NEGATIVE_INFINITY);
    } else {
        newBins.set(0, Double.NEGATIVE_INFINITY);
        // remove the max, and became open interval
        newBins.remove(newBins.size() - 1);
    }
    return newBins;
}

From source file:statistic.graph.XYChart.java

private void initXAxis(String label) {
    plot.setDomainAxis(new NumberAxis(label));
    double max = Double.NEGATIVE_INFINITY;
    double min = Double.POSITIVE_INFINITY;
    for (int s = 0; s < collection.getSeriesCount(); s++) {
        for (int d = 0; d < collection.getItemCount(s); d++) {
            XYDataItem data = collection.getSeries(s).getDataItem(d);
            if (data.getX().longValue() == Integer.MAX_VALUE || data.getX().longValue() == Integer.MIN_VALUE) {
                continue;
            }/*from ww  w. ja v a 2  s.c  o m*/
            if (data.getX().doubleValue() > max) {
                max = data.getX().doubleValue();
            }
            if (data.getX().doubleValue() < min) {
                min = data.getX().doubleValue();
            }
        }
    }
    plot.getDomainAxis().setStandardTickUnits(NumberAxis.createIntegerTickUnits());
    plot.getDomainAxis().setRange(min - 0.5, max + 0.5);
}

Example usage for java.lang Double NEGATIVE_INFINITY

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