Example usage for java.lang Math sqrt

Introduction

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

Prototype

@HotSpotIntrinsicCandidate
public static double sqrt(double a) 

Source Link

Document

Returns the correctly rounded positive square root of a double value.

Usage

From source file:com.opengamma.analytics.financial.model.option.pricing.analytic.formula.NormalPriceFunction.java

@Override
public Function1D<NormalFunctionData, Double> getPriceFunction(final EuropeanVanillaOption option) {
    Validate.notNull(option, "option");
    final double strike = option.getStrike();
    final double t = option.getTimeToExpiry();
    return new Function1D<NormalFunctionData, Double>() {

        @SuppressWarnings("synthetic-access")
        @Override//from  w  ww. j  a  va 2s.c o  m
        public final Double evaluate(final NormalFunctionData data) {
            Validate.notNull(data, "data");
            final double forward = data.getForward();
            final double numeraire = data.getNumeraire();
            final double sigma = data.getNormalVolatility();
            final double sigmaRootT = sigma * Math.sqrt(t);
            final int sign = option.isCall() ? 1 : -1;
            if (sigmaRootT < 1e-16) {
                final double x = sign * (forward - strike);
                return (x > 0 ? numeraire * x : 0.0);
            }
            final double arg = sign * (forward - strike) / sigmaRootT;
            return numeraire
                    * (sign * (forward - strike) * NORMAL.getCDF(arg) + sigmaRootT * NORMAL.getPDF(arg));
        }
    };
}

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From source file:Estadistica.java

/**
 * Metodo  para calcular r//from www .j  a  v a  2 s  .  c  o m
 * param  dos listas de valores (xArrayList,ArrayList)
 * @return el valor de r
 */
public static double calR(ArrayList<Double> xArrayList, ArrayList<Double> yArrayList) {
    double cal = 0;
    cal = xArrayList.size() * multiple(xArrayList, yArrayList) - multiple(xArrayList) * multiple(yArrayList);
    double minus = 0;
    minus = xArrayList.size() * multiple(xArrayList, xArrayList) - multiple(xArrayList) * multiple(xArrayList);
    minus *= yArrayList.size() * multiple(yArrayList, yArrayList) - multiple(yArrayList) * multiple(yArrayList);
    cal = cal / Math.sqrt(minus);
    return cal;
}

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From source file:com.test.zmisc.App.java

public static double standardDeviation(double[] a, double avg) {
    double aStdDev = 0;
    for (int i = 0; i < a.length; i++) {
        double tmp = (a[i] - avg);
        aStdDev += tmp * tmp;//from ww w  .j a v  a  2  s . c  o  m
    }
    aStdDev = Math.sqrt(aStdDev / (a.length));
    return aStdDev;
}

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From source file:de.tud.kom.p2psim.impl.overlay.dht.kademlia2.measurement.file.ConfidenceCalculator.java

/**
 * @return 0: mean, 1: standard deviation, 2: delta (half interval width),
 *         3: lower bound, 4: upper bound of confidence interval.
 *///  w  w w.  j a v  a  2 s .c om
protected static double[] calc(double[] sample, double alpha) {
    double mean, standardDeviation, delta, ivLow, ivHigh;

    mean = StatUtils.mean(sample);
    standardDeviation = Math.sqrt(StatUtils.variance(sample, mean));
    delta = ConfidenceInterval.getDeltaBound(standardDeviation, sample.length, alpha);
    ivLow = mean - delta;
    ivHigh = mean + delta;

    return new double[] { mean, standardDeviation, delta, ivLow, ivHigh };
}

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From source file:Main.java

public static double transformlat(double lng, double lat) {
    double ret = -100.0 + 2.0 * lng + 3.0 * lat + 0.2 * lat * lat + 0.1 * lng * lat
            + 0.2 * Math.sqrt(Math.abs(lng));
    ret += (20.0 * Math.sin(6.0 * lng * pi) + 20.0 * Math.sin(2.0 * lng * pi)) * 2.0 / 3.0;
    ret += (20.0 * Math.sin(lat * pi) + 40.0 * Math.sin(lat / 3.0 * pi)) * 2.0 / 3.0;
    ret += (160.0 * Math.sin(lat / 12.0 * pi) + 320 * Math.sin(lat * pi / 30.0)) * 2.0 / 3.0;
    return ret;/*from  w  ww .  j av a  2  s  .  com*/
}

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From source file:com.itemanalysis.psychometrics.factoranalysis.PrincipalComponentsMethod.java

public double estimateParameters() {

    EigenDecomposition eigen = new EigenDecomposition(R);
    RealMatrix eigenVectors = eigen.getV().getSubMatrix(0, nVariables - 1, 0, nFactors - 1);

    double[] ev = new double[nFactors];
    for (int i = 0; i < nFactors; i++) {
        ev[i] = Math.sqrt(eigen.getRealEigenvalue(i));
    }/*from w w w .  j a  va 2s .  co m*/
    DiagonalMatrix evMatrix = new DiagonalMatrix(ev);//USE Apache version of Diagonal matrix when upgrade to version 3.2
    RealMatrix LOAD = eigenVectors.multiply(evMatrix);

    //rotate factor loadings
    if (rotationMethod != RotationMethod.NONE) {
        GPArotation gpa = new GPArotation();
        RotationResults results = gpa.rotate(LOAD, rotationMethod);
        LOAD = results.getFactorLoadings();
    }

    Sum[] colSums = new Sum[nFactors];
    Sum[] colSumsSquares = new Sum[nFactors];

    for (int j = 0; j < nFactors; j++) {
        colSums[j] = new Sum();
        colSumsSquares[j] = new Sum();
    }

    factorLoading = new double[nVariables][nFactors];
    communality = new double[nVariables];
    uniqueness = new double[nVariables];

    for (int i = 0; i < nVariables; i++) {
        for (int j = 0; j < nFactors; j++) {
            factorLoading[i][j] = LOAD.getEntry(i, j);
            colSums[j].increment(factorLoading[i][j]);
            colSumsSquares[j].increment(Math.pow(factorLoading[i][j], 2));
            communality[i] += Math.pow(factorLoading[i][j], 2);
        }
    }

    //check sign of factor
    double sign = 1.0;
    for (int i = 0; i < nVariables; i++) {
        for (int j = 0; j < nFactors; j++) {
            if (colSums[j].getResult() < 0) {
                sign = -1.0;
            } else {
                sign = 1.0;
            }
            factorLoading[i][j] = factorLoading[i][j] * sign;
        }
        uniqueness[i] = 1.0 - communality[i];
    }

    double totSumOfSquares = 0.0;
    sumsOfSquares = new double[nFactors];
    proportionOfExplainedVariance = new double[nFactors];
    proportionOfVariance = new double[nFactors];
    for (int j = 0; j < nFactors; j++) {
        sumsOfSquares[j] = colSumsSquares[j].getResult();
        totSumOfSquares += sumsOfSquares[j];
    }
    for (int j = 0; j < nFactors; j++) {
        proportionOfExplainedVariance[j] = sumsOfSquares[j] / totSumOfSquares;
        proportionOfVariance[j] = sumsOfSquares[j] / nVariables;
    }

    return 0.0;

}

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From source file:com.cloudera.oryx.app.batch.mllib.als.Evaluation.java

/**
 * Computes root mean squared error of {@link Rating#rating()} versus predicted value.
 *//*from   w  w w . jav a2s  .  c  o  m*/
static double rmse(MatrixFactorizationModel mfModel, JavaRDD<Rating> testData) {
    JavaPairRDD<Tuple2<Integer, Integer>, Double> testUserProductValues = testData
            .mapToPair(rating -> new Tuple2<>(new Tuple2<>(rating.user(), rating.product()), rating.rating()));
    @SuppressWarnings("unchecked")
    RDD<Tuple2<Object, Object>> testUserProducts = (RDD<Tuple2<Object, Object>>) (RDD<?>) testUserProductValues
            .keys().rdd();
    JavaRDD<Rating> predictions = testData.wrapRDD(mfModel.predict(testUserProducts));
    double mse = predictions
            .mapToPair(rating -> new Tuple2<>(new Tuple2<>(rating.user(), rating.product()), rating.rating()))
            .join(testUserProductValues).values().mapToDouble(valuePrediction -> {
                double diff = valuePrediction._1() - valuePrediction._2();
                return diff * diff;
            }).mean();
    return Math.sqrt(mse);
}

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From source file:de.termininistic.serein.examples.benchmarks.functions.multimodal.SchwefelFunction.java

@Override
public double map(RealVector v) {
    double[] x = v.toArray();
    int n = x.length;
    double fx = 418.9829 * n;
    for (int i = 0; i < n; i++) {
        fx += -x[i] * Math.sin((Math.sqrt(Math.abs(x[i]))));
    }/*w  w w . j  a v  a2s . c o  m*/
    return fx;
}

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

protected double equation(LowLevelHeuristic op, HashMap<String, Double> frr, HashMap<String, Integer> nt,
        double sumNt) {
    /*  KE LI code//from w  w w.j  av  a 2 s .  c  o m
     http://www.cs.cityu.edu.hk/~51888309/code/bandits.zip
     temp1 = 2 * Math.log(total_usage);
     temp2 = temp1 / strategy_usage[i];
     temp3 = Math.sqrt(temp2);
     quality[i] = rewards[i] + scale_ * temp3;
     */

    double numerator = 2 * Math.log(((int) sumNt));
    double denominator = nt.get(op.getName());
    double fraction = numerator / denominator;
    double sqrt = Math.sqrt(fraction);
    double frr_value = frr.get(op.getName());
    return frr_value + this.C * sqrt;
}

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From source file:ComplexNumber.java

/** Compute the magnitude of a complex number */
public double magnitude() {
    return Math.sqrt(x * x + y * y);
}

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