Example usage for java.lang Math pow

List of usage examples for java.lang Math pow

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Introduction

In this page you can find the example usage for java.lang Math pow.

Prototype

@HotSpotIntrinsicCandidate
public static double pow(double a, double b)

Source Link

Document

Returns the value of the first argument raised to the power of the second argument.

Usage

From source file:org.wallerlab.yoink.density.service.densityProperties.DensityOverlapRegionsIndicatorComputer.java

/**
 * calculate dori value of a grid point//w  w  w.  j  a v a 2s  .c  o  m
 * 
 * @param densityPoint
 *            -{@link org.wallerlab.yoink.api.model.density.DensityPoint}
 * @param doriValue
 *            , pre-calculated
 * @return doriValue, final-calculated
 * 
 */
protected double getSilvaValue(DensityPoint densityPoint, double doriValue) {
    double gradient = densityPoint.getGradient();
    doriValue *= (4.0 / Math.pow(gradient, 3));
    doriValue /= (1.0 + doriValue);
    return doriValue;
}

From source file:ldbc.snb.datagen.generator.distribution.utils.Bucket.java

public static ArrayList<Bucket> bucketizeHistogram(ArrayList<Pair<Integer, Integer>> histogram,
        int num_buckets) {

    ArrayList<Bucket> buckets = new ArrayList<Bucket>();
    int population = 0;
    int num_edges = 0;
    for (Pair<Integer, Integer> i : histogram) {
        population += i.getValue();/*from w  ww.  j  av  a2  s  .com*/
        num_edges += i.getValue() * i.getKey();
    }
    num_edges /= 2;

    int avgDegreeAt1B = 200;
    int avgDegree = num_edges / population;
    double aCoeff = Math.log(avgDegreeAt1B) / Math.log(1000000000);
    double bCoeff = (aCoeff - (Math.log(avgDegree) / Math.log(population))) / Math.log10(population);

    int target_mean = (int) Math.round(
            Math.pow(DatagenParams.numPersons, (aCoeff - bCoeff * Math.log10(DatagenParams.numPersons))));
    System.out.println("Distribution mean degree: " + avgDegree + " Distribution target mean " + target_mean);
    int bucket_size = (int) (Math.ceil(population / (double) (num_buckets)));
    int current_histogram_index = 0;
    int current_histogram_left = histogram.get(current_histogram_index).getValue();
    for (int i = 0; i < num_buckets && (current_histogram_index < histogram.size()); ++i) {
        int current_bucket_count = 0;
        int min = population;
        int max = 0;
        while (current_bucket_count < bucket_size && current_histogram_index < histogram.size()) {
            int degree = histogram.get(current_histogram_index).getKey();
            min = degree < min ? degree : min;
            max = degree > max ? degree : max;
            if ((bucket_size - current_bucket_count) > current_histogram_left) {
                current_bucket_count += current_histogram_left;
                current_histogram_index++;
                if (current_histogram_index < histogram.size()) {
                    current_histogram_left = histogram.get(current_histogram_index).getValue();
                }
            } else {
                current_histogram_left -= (bucket_size - current_bucket_count);
                current_bucket_count = bucket_size;
            }
        }
        min = (int) (min * target_mean / (double) avgDegree);
        max = (int) (max * target_mean / (double) avgDegree);
        buckets.add(new Bucket(min, max));
    }
    return buckets;
}

From source file:com.xerox.amazonws.ec2.EC2Utils.java

/**
 * This method makes a best effort to fetch all instance metadata.
 *
 * @return map of metadata/*from www .  j a  v  a 2 s .  c  o  m*/
 */
public static Map<String, String> getInstanceMetadata() {
    HashMap<String, String> result = new HashMap<String, String>();
    int retries = 0;
    while (true) {
        try {
            URL url = new URL("http://169.254.169.254/latest/meta-data/");
            BufferedReader rdr = new BufferedReader(new InputStreamReader(url.openStream()));
            String line = rdr.readLine();
            while (line != null) {
                try {
                    String val = getInstanceMetadata(line);
                    result.put(line, val);
                } catch (IOException ex) {
                    logger.error("Problem fetching piece of instance metadata!", ex);
                }
                line = rdr.readLine();
            }
            return result;
        } catch (IOException ex) {
            if (retries == 5) {
                logger.debug("Problem getting instance data, retries exhausted...");
                return result;
            } else {
                logger.debug("Problem getting instance data, retrying...");
                try {
                    Thread.sleep((int) Math.pow(2.0, retries) * 1000);
                } catch (InterruptedException e) {
                }
            }
        }
    }
}

From source file:change_point_detection.BetaDistributionChangePoint.java

public int/*estimated change point*/ detectChange() throws Exception {
    int estimatedChangePoint = -1;
    int N = this.dynamicWindow.size();
    this.cushion = Math.max(100, (int) Math.floor(Math.pow(N, gamma)));
    //mean conf. should not fall below 0.3
    if (N > (2 * this.cushion) && calculateMean(0, N - 1) <= 0.3)
        return N - 1;
    double threshold = -Math.log(this.sensitivity);

    double w = 0;
    int kAtMaxW = -1;
    for (int k = this.cushion; k <= N - this.cushion; k++) {
        if (calculateMean(k, N - 1) <= 0.95 * calculateMean(0, k - 1)) {
            double skn = 0;
            /* estimate pre and post change parameters */
            double alphaPreChange = calcBetaDistAlpha(0, k - 1);
            double betaPreChange = calculateBetaDistBeta(alphaPreChange, 0, k - 1);
            double alphaPostChange = calcBetaDistAlpha(k, N - 1);
            double betaPostChange = calculateBetaDistBeta(alphaPostChange, k, N - 1);

            BetaDistributionImpl preBetaDist = new BetaDistributionImpl(alphaPreChange, betaPreChange);
            BetaDistributionImpl postBetaDist = new BetaDistributionImpl(alphaPostChange, betaPostChange);

            for (int i = k; i < N; i++) {
                try {
                    skn += Math.log(postBetaDist.density(this.dynamicWindow.get(i).doubleValue())
                            / preBetaDist.density(this.dynamicWindow.get(i).doubleValue()));
                } catch (Exception e) {
                    e.printStackTrace();
                    System.out.println("continuing...");
                    skn = 0;//from  w w w. ja  va2 s .  c o  m
                    break;
                }
            }
            if (skn > w) {
                w = skn;
                kAtMaxW = k;
            }
        }
    }
    if (w >= threshold && kAtMaxW != -1) {
        System.out.println("\nChangePoint Found!");
        estimatedChangePoint = kAtMaxW;
        System.out.println("Estimated change point is " + estimatedChangePoint);
    }
    //force change point if confidence falls down terribly
    if (estimatedChangePoint == -1 && N >= 100 && calculateMean(0, N - 1) < 0.3)
        estimatedChangePoint = N - 1;
    return estimatedChangePoint;
}

From source file:de.termininistic.serein.examples.benchmarks.functions.unimodal.SumOfDifferentPowersFunction.java

@Override
public double map(RealVector v) {

    double[] x = v.toArray();
    int n = x.length;
    double sum = 0.0;
    for (int i = 0; i < n; i++) {
        sum += Math.pow(Math.abs(x[i]), i + 2);
    }/*from   w  w  w .j  a va 2  s.  c om*/
    return sum;
}

From source file:com.opengamma.analytics.math.interpolation.ExponentialInterpolator1D.java

@Override
public double firstDerivative(final Interpolator1DDataBundle data, final Double value) {
    Validate.notNull(value, "value");
    Validate.notNull(data, "data bundle");
    final Double x1 = data.getLowerBoundKey(value);
    final Double y1 = data.get(x1);
    if (data.getLowerBoundIndex(value) == data.size() - 1) {
        return 0.;
    }/*  w ww .j ava 2s . c om*/
    final Double x2 = data.higherKey(x1);
    final Double y2 = data.get(x2);
    final double xDiff = x2 - x1;
    return Math.pow(y1, value * (x2 - value) / xDiff / x1) * Math.pow(y2, value * (value - x1) / xDiff / x2)
            * (Math.log(y1) * (x2 - 2. * value) / x1 + Math.log(y2) * (2. * value - x1) / x2) / xDiff;
}

From source file:com.itemanalysis.psychometrics.kernel.ScottsBandwidth.java

public double value() {
    StandardDeviation sd = new StandardDeviation();
    double q3 = pcntl.evaluate(x, 75.0);
    double q1 = pcntl.evaluate(x, 25.0);
    double IQR = (q3 - q1) / 1.34;
    double s = sd.evaluate(x);
    double N = (double) x.length;
    double m = Math.min(s, IQR);
    return 1.06 * m * Math.pow(N, -1.0 / 5.0) * adjustmentFactor;
}

From source file:com.openkm.util.FormatUtil.java

/**
 * Format the document size for human readers
 *///from w  ww . ja v a  2  s.  c o  m
public static String formatSize(long bytes) {
    for (int i = 6; i > 0; i--) {
        double step = Math.pow(1024, i);
        if (bytes > step)
            return String.format(Locale.ROOT, "%3.1f %s", bytes / step, UNITS[i]);
    }

    return Long.toString(bytes) + " " + UNITS[0];
}

From source file:com.opengamma.analytics.financial.interestrate.PeriodicInterestRate.java

@Override
public InterestRate fromPeriodic(final PeriodicInterestRate periodic) {
    Validate.notNull(periodic, "periodic");
    final int n = periodic.getCompoundingPeriodsPerYear();
    final double oneYearValue = Math.pow(1 + periodic.getRate() / n, n);
    final double r = getCompoundingPeriodsPerYear()
            * (Math.pow(oneYearValue, 1. / getCompoundingPeriodsPerYear()) - 1);
    return new PeriodicInterestRate(r, getCompoundingPeriodsPerYear());
}

From source file:org.wallerlab.yoink.adaptive.smooth.smoothfunction.SCMPSmoothFunction.java

/**
 * this smooth function is used in SCMP method. for details please see:
 * "Size-Consistent Multipartitioning QM/MM: A Stable and Efficient Adaptive
 * QM/MM Method"/*from  w  ww  .  j a v a 2 s.co m*/
 * 
 * @param currentValue
 *            , currentValue(variable) in smooth function
 * @param min
 *            , minimum value in smooth function
 * @param max
 *            , maximum value in smooth function
 * @return smooth factor
 */
public double evaluate(double currentValue, double min, double max) {
    double smoothFactor;
    if (currentValue > max) {
        smoothFactor = 0;
    } else if (currentValue < min) {
        smoothFactor = 1;
    } else {
        smoothFactor = Math.pow((currentValue - max), 2);
        smoothFactor = smoothFactor * (-3 * min + max + 2 * currentValue);
        smoothFactor = smoothFactor / Math.pow((max - min), 3);
    }
    return smoothFactor;
}