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:geogebra.util.MyMath.java

final public static double acosh(double a) {
    return Math.log(a + Math.sqrt(a * a - 1.0));
}

From source file:com.opengamma.analytics.financial.var.NormalVaRParameters.java

public NormalVaRParameters(final double horizon, final double periods, final double quantile) {
    Validate.isTrue(horizon > 0, "horizon");
    Validate.isTrue(periods > 0, "periods");
    if (!ArgumentChecker.isInRangeInclusive(0, 1, quantile)) {
        throw new IllegalArgumentException("Quantile must be between 0 and 1");
    }/*  ww w  .j a v  a 2 s.  c  om*/
    _horizon = horizon;
    _periods = periods;
    _quantile = quantile;
    _z = NORMAL.getInverseCDF(quantile);
    _timeScaling = Math.sqrt(horizon / periods);
}

From source file:net.gtaun.shoebill.data.Velocity.java

public float speed2d() {
    return (float) Math.sqrt(getX() * getX() + getY() * getY());
}

From source file:edu.oregonstate.eecs.mcplan.domains.voyager.Voyager.java

public static double distance(final Planet a, final Planet b) {
    return Math.sqrt(sq_distance(a, b));
}

From source file:Main.java

private static boolean unprotectedTest(long l) {
    for (int n = 2, max = (int) Math.ceil(Math.sqrt(l)); n <= max; n++) {
        if (l % n == 0) {
            return false;
        }//from  w  w w.  java2  s  . c  o m
    }
    return true;
}

From source file:com.opengamma.analytics.financial.model.volatility.surface.SkewnessKurtosisBlackScholesMertonEquivalentVolatilitySurfaceModel.java

@Override
public VolatilitySurface getSurface(final OptionDefinition option, final SkewKurtosisOptionDataBundle data) {
    Validate.notNull(option, "option");
    Validate.notNull(data, "data");
    final double s = data.getSpot();
    final double t = option.getTimeToExpiry(data.getDate());
    final double k = option.getStrike();
    final double sigma = data.getVolatility(t, k);
    final double b = data.getCostOfCarry();
    final double skew = data.getAnnualizedSkew();
    final double kurtosis = data.getAnnualizedFisherKurtosis();
    final double d1 = (Math.log(s / k) + t * (b + sigma * sigma * 0.5)) / sigma / Math.sqrt(t);
    return new VolatilitySurface(
            ConstantDoublesSurface.from(sigma * (1 - skew * d1 / 6 - kurtosis * (1 - d1 * d1) / 24)));
}

From source file:com.golemgame.util.SqrtFunction.java

public double value(double arg0) throws FunctionEvaluationException {
    if (arg0 <= 0)
        return 0;
    return Math.sqrt(arg0);
}

From source file:com.itemanalysis.psychometrics.rasch.ScaleQualityStatistics.java

public double observedStandardDeviation() {
    return Math.sqrt(var.getResult());
}

From source file:com.anhth12.lambda.app.ml.als.Evaluation.java

/**
 * Computes root mean squared error/*ww  w. j a v a 2 s .co  m*/
 *
 * @param mfModel
 * @param testData
 * @return
 */
static double rmse(MatrixFactorizationModel mfModel, JavaRDD<Rating> testData) {
    JavaPairRDD<Tuple2<Integer, Integer>, Double> testUserProductValues = testData
            .mapToPair(new RatingToTupleDouble());
    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(new RatingToTupleDouble()).join(testUserProductValues).values()
            .mapToDouble(new DoubleFunction<Tuple2<Double, Double>>() {

                @Override
                public double call(Tuple2<Double, Double> valuePrediction) throws Exception {
                    double diff = valuePrediction._1() - valuePrediction._2();
                    return diff * diff;
                }
            }).mean();

    return Math.sqrt(mse);
}

From source file:gui.Histograma.java

private static IntervalXYDataset criarDataset() {
    //guarda os dados do histograma
    HistogramDataset dados = new HistogramDataset();
    int classes;//  ww  w .  j  a v  a2s .c o m
    double valores[] = new double[amostra.size()];

    //Definindo quantidade de classes
    if (amostra.size() <= 25) {
        classes = 5;
    } else {
        classes = (int) Math.round(Math.sqrt(amostra.size()));
    }
    //Criando vetor com valores da amostra
    for (int i = 0; i < amostra.size(); i++) {
        valores[i] = amostra.get(i);
    }

    //Adicionando os dataset para o histograma
    dados.addSeries("Frequncia das Amostras", valores, classes, min, max);
    return dados;
}