Example usage for java.lang Math exp

Introduction

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

Prototype

@HotSpotIntrinsicCandidate
public static double exp(double a) 

Source Link

Document

Returns Euler's number e raised to the power of a double value.

Usage

From source file:com.opengamma.analytics.financial.model.finitedifference.MarkovChainApprox.java

private double getI1(final double t) {
    final double lt = _lambda * t;
    if (lt < 1e-7) {
        return _probState1;
    }/*w  w  w .j a  v a2s  .c  om*/

    return _theta + (_probState1 - _theta) * (1 - Math.exp(-_lambda * t)) / lt;
}

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From source file:io.s4.model.GaussianMixtureModel.java

/**
 * @param obs/* w ww .j av  a  2s .  c  o m*/
 *            the observed data vector.
 * @return the probability.
 */
public double prob(D1Matrix64F obs) {

    return Math.exp(logProb(obs));
}

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From source file:org.jfree.data.statistics.Regression.java

/**
 * Returns the parameters 'a' and 'b' for an equation y = ax^b, fitted to
 * the data using a power regression equation.  The result is returned as
 * an array, where double[0] --> a, and double[1] --> b.
 *
 * @param data  the data./*ww w. java 2 s.  c om*/
 * @param series  the series to fit the regression line against.
 *
 * @return The parameters.
 */
public static double[] getPowerRegression(XYDataset data, int series) {

    int n = data.getItemCount(series);
    if (n < 2) {
        throw new IllegalArgumentException("Not enough data.");
    }

    double sumX = 0;
    double sumY = 0;
    double sumXX = 0;
    double sumXY = 0;
    for (int i = 0; i < n; i++) {
        double x = Math.log(data.getXValue(series, i));
        double y = Math.log(data.getYValue(series, i));
        sumX += x;
        sumY += y;
        double xx = x * x;
        sumXX += xx;
        double xy = x * y;
        sumXY += xy;
    }
    double sxx = sumXX - (sumX * sumX) / n;
    double sxy = sumXY - (sumX * sumY) / n;
    double xbar = sumX / n;
    double ybar = sumY / n;

    double[] result = new double[2];
    result[1] = sxy / sxx;
    result[0] = Math.pow(Math.exp(1.0), ybar - result[1] * xbar);

    return result;

}

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From source file:com.datumbox.framework.core.statistics.distributions.ContinuousDistributions.java

/**
 * Calculates the probability from 0 to X under Beta Distribution
 * /*from w w  w . j a  va 2s . c  o  m*/
 * @param x
 * @param a
 * @param b
 * @return 
 */
public static double betaCdf(double x, double a, double b) {
    if (x < 0 || a <= 0 || b <= 0) {
        throw new IllegalArgumentException("All the parameters must be positive.");
    }

    double Bcdf = 0.0;

    if (x == 0) {
        return Bcdf;
    } else if (x >= 1) {
        Bcdf = 1.0;
        return Bcdf;
    }

    double S = a + b;

    double BT = Math.exp(logGamma(S) - logGamma(b) - logGamma(a) + a * Math.log(x) + b * Math.log(1 - x));
    if (x < (a + 1.0) / (S + 2.0)) {
        Bcdf = BT * betinc(x, a, b);
    } else {
        Bcdf = 1.0 - BT * betinc(1.0 - x, b, a);
    }

    return Bcdf;
}

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From source file:com.datumbox.framework.statistics.distributions.ContinuousDistributions.java

/**
 * Calculates the probability from 0 to X under Beta Distribution
 * //from   w  ww . j ava  2 s .  c  om
 * @param x
 * @param a
 * @param b
 * @return 
 */
public static double BetaCdf(double x, double a, double b) throws IllegalArgumentException {
    if (x < 0 || a <= 0 || b <= 0) {
        throw new IllegalArgumentException();
    }

    double Bcdf = 0.0;

    if (x == 0) {
        return Bcdf;
    } else if (x >= 1) {
        Bcdf = 1.0;
        return Bcdf;
    }

    double S = a + b;

    double BT = Math.exp(LogGamma(S) - LogGamma(b) - LogGamma(a) + a * Math.log(x) + b * Math.log(1 - x));
    if (x < (a + 1.0) / (S + 2.0)) {
        Bcdf = BT * Betinc(x, a, b);
    } else {
        Bcdf = 1.0 - BT * Betinc(1.0 - x, b, a);
    }

    return Bcdf;
}

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From source file:com.bmwcarit.barefoot.matcher.MatcherTest.java

private void assertTransition(Tuple<MatcherTransition, Double> transition,
        Tuple<MatcherCandidate, MatcherSample> source, Tuple<MatcherCandidate, MatcherSample> target,
        double lambda) {
    List<Road> edges = router.route(source.one().point(), target.one().point(), cost);

    if (edges == null) {
        // fail();
    }//w  w w .j a  v a  2s. c o  m

    Route route = new Route(source.one().point(), target.one().point(), edges);

    assertEquals(route.length(), transition.one().route().length(), 10E-6);
    assertEquals(route.source().edge().id(), transition.one().route().source().edge().id());
    assertEquals(route.target().edge().id(), transition.one().route().target().edge().id());

    double beta = lambda == 0 ? (2.0 * (target.two().time() - source.two().time()) / 1000) : 1 / lambda;
    double base = 1.0 * spatial.distance(source.two().point(), target.two().point()) / 60;
    double p = (1 / beta) * Math.exp((-1.0) * Math.max(0, route.cost(new TimePriority()) - base) / beta);

    assertEquals(transition.two(), p, 10E-6);
}

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From source file:stratego.neural.net.SimulatedAnnealing.java

public double calculateProbability(double[] weights, double e) {

    double prob = Math.exp(-((e - energy) / ((double) temperature / (double) start_temperature))); // dividing the temperature by the start temperature to normalize 
    prob = Math.log(prob);/*from   ww  w  .  j a v  a2s .  co  m*/
    System.out.println("Acceptance probability: " + prob);
    return prob;
}

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From source file:Randoms.java

/** Return a random double drawn from a Gamma distribution with mean alpha*beta+lamba and variance alpha*beta^2. */
public synchronized double nextGamma(double alpha, double beta, double lambda) {
    double gamma = 0;
    if (alpha <= 0 || beta <= 0) {
        throw new IllegalArgumentException("alpha and beta must be strictly positive.");
    }/*from  www.  j  a v  a  2 s . c  o m*/
    if (alpha < 1) {
        double b, p;
        boolean flag = false;
        b = 1 + alpha * Math.exp(-1);
        while (!flag) {
            p = b * nextUniform();
            if (p > 1) {
                gamma = -Math.log((b - p) / alpha);
                if (nextUniform() <= Math.pow(gamma, alpha - 1))
                    flag = true;
            } else {
                gamma = Math.pow(p, 1 / alpha);
                if (nextUniform() <= Math.exp(-gamma))
                    flag = true;
            }
        }
    } else if (alpha == 1) {
        gamma = -Math.log(nextUniform());
    } else {
        double y = -Math.log(nextUniform());
        while (nextUniform() > Math.pow(y * Math.exp(1 - y), alpha - 1))
            y = -Math.log(nextUniform());
        gamma = alpha * y;
    }
    return beta * gamma + lambda;
}

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

public final Double computeStrikeImpliedByDeltaViaRootFinding(final double fwdDelta, final boolean forCall) {
    try {/*from www. j av  a  2s.co  m*/
        Validate.isTrue(fwdDelta >= 0.0 && fwdDelta <= 1.0, "Delta must be between 0.0 and 1.0");
        final BlackFormula black = new BlackFormula(_forward, _strike, _expiry, _lognormalVol, _fwdMtm,
                forCall);

        final Function1D<Double, Double> difference = new Function1D<Double, Double>() {

            @Override
            public Double evaluate(final Double strike) {
                black.setStrike(strike);
                final double delta = black.computeForwardDelta();
                return delta - fwdDelta * (forCall ? 1.0 : -1.0); // TODO Case : confirm this is sufficient for calls and puts
            }
        };

        final NewtonRaphsonSingleRootFinder rootFinder = new NewtonRaphsonSingleRootFinder();
        if ((forCall && fwdDelta >= 0.5) || (!forCall && fwdDelta <= 0.5)) {
            // Strike is bounded below by 0 and above by the atmDelta
            final double atmDelta = _forward * Math.exp(0.5 * _lognormalVol * _lognormalVol * _expiry);
            return rootFinder.getRoot(difference, 0.0, atmDelta);
        } // Give it a guess, and estimate finite-difference derivative
        return rootFinder.getRoot(difference, _strike);

    } catch (final com.opengamma.analytics.math.MathException e) {
        System.err.println(e);
        System.err.println("Failed to compute ImpliedVolatility");
        return null;
    }
}

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From source file:com.opengamma.analytics.financial.credit.isdayieldcurve.ISDADateCurve.java

/**
 * A ISDA model zero default curve //from w  w w  .jav a  2s  .  c  o m
 * @param name The curve name
 * @param baseDate base date to convert other (future) dates into year fractions using the day-count convention  
 * @param curveDates The dates of points on the curve 
 * @param rates The zero default rates at points on the curve. <b>Note:</b>These as annually compounded rates   
 * @param offset TODO find out what this does 
 * @param dayCount The day-count convention 
 */
public ISDADateCurve(final String name, final ZonedDateTime baseDate, final ZonedDateTime[] curveDates,
        final double[] rates, final double offset, final DayCount dayCount) {
    super(name);
    ArgumentChecker.notNull(name, "name");
    ArgumentChecker.notNull(baseDate, "base date");
    ArgumentChecker.noNulls(curveDates, "curve dates");
    ArgumentChecker.notEmpty(rates, "rates");
    ArgumentChecker.notNull(dayCount, "day count");
    _n = curveDates.length;
    ArgumentChecker.isTrue(_n != 0, "Data arrays were empty");
    // TODO why is this test commented out?
    // ArgumentChecker.isTrue(_n == rates.length, "Have {} rates for {} dates", rates.length, _n);

    _baseDates = baseDate;

    _name = name;
    _offset = offset;
    _curveDates = new ZonedDateTime[_n];
    System.arraycopy(curveDates, 0, _curveDates, 0, _n);

    final double[] times = new double[_n];
    final double[] continuousRates = new double[_n];
    _shiftedTimePoints = new double[_n];

    for (int i = 0; i < _n; i++) {
        // Convert the ZonedDateTimes to doubles
        final double dayCountFraction = dayCount.getDayCountFraction(baseDate, curveDates[i]);
        times[i] = dayCountFraction;
        // Convert the discrete rates to continuous ones
        continuousRates[i] = new PeriodicInterestRate(rates[i], 1).toContinuous().getRate();
        _shiftedTimePoints[i] = dayCountFraction + _offset;
    }

    // Choose interpolation/extrapolation to match the behavior of curves in the ISDA CDS reference code
    if (_n > 1) {
        _curve = InterpolatedDoublesCurve.fromSorted(times, continuousRates, INTERPOLATOR);
    } else {
        _curve = ConstantDoublesCurve.from(continuousRates[0]); // Unless the curve is flat, in which case use a constant curve
    }
    _zeroDiscountFactor = Math.exp(_offset * getInterestRate(0.0));
}

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