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:edu.snu.leader.spatial.calculator.CompactSigmoidSueurDecisionProbabilityCalculator.java

/**
 * Calculates k coefficient for the collective movement equations
 *
 * @param value/*from w  ww  . j a  v a 2s .  c  om*/
 * @return The k coefficient
 * @see edu.snu.leader.spatial.calculator.AbstractSueurDecisionProbabilityCalculator#calculateK(float)
 */
@Override
protected float calculateK(float value, DecisionType type) {
    float k = 0.0f;

    // If it is initiation or following, use a range of [0.1,1.9]
    if (DecisionType.INITIATE.equals(type) || DecisionType.FOLLOW.equals(type)) {
        k = 0.1f + (1.8f * (1.0f / (1.0f + (float) Math.exp((0.5f - value) * _sigmoidSlopeValue))));
    }
    // Otherwise, if it is a cancel, use a more compact range
    else if (DecisionType.CANCEL.equals(type)) {
        //            k = 0.6f + ( 0.8f * ( 1.0f / (1.0f +
        //                    (float) Math.exp( (0.5f-value) * _sigmoidSlopeValue) ) ) );
        k = 0.59623023f
                + (0.80753954f * (1.0f / (1.0f + (float) Math.exp((0.5f - value) * _sigmoidSlopeValue))));
    }

    return k;
}

---

From source file:es.udc.gii.common.eaf.benchmark.multiobjective.dtlz2.S_Dtlz2_Objective.java

@Override
public double evaluate(double[] x) {
    int nx = x.length;

    int i = 0;/*from  w  w  w .  j  a  va 2s  .  c o  m*/
    int j = 0;
    int k = nx - numberOfObjectives + 1;
    double g = 0;
    double[] z = new double[nx];
    double[] zz = new double[nx];
    double[] p = new double[nx];
    double[] psum = new double[numberOfObjectives];

    // denormalize vector:
    for (i = 0; i < nx; i++) {
        x[i] = (bounds[1][i] - bounds[0][i]) / 2 * x[i] + (bounds[1][i] + bounds[0][i]) / 2;
    }

    for (i = 0; i < nx; i++) {
        z[i] = x[i] - o[i];

        if (z[i] < 0) {
            zz[i] = -lambda[i] * z[i];
            p[i] = -z[i] / d[i];
        } else {
            zz[i] = z[i];
            p[i] = 0;
        }
    }

    for (j = 0; j < numberOfObjectives; j++) {
        psum[j] = 0;
    }

    for (i = nx - k + 1; i <= nx; i++) {
        g += Math.pow(zz[i - 1] - 0.5, 2);
        for (j = 0; j < numberOfObjectives; j++) {
            psum[j] = Math.sqrt(Math.pow(psum[j], 2) + Math.pow(p[i - 1], 2));
        }
    }

    double ff = (1 + g);

    for (j = numberOfObjectives - objNumber; j >= 1; j--) {
        ff *= Math.cos(zz[j - 1] * Math.PI / 2.0);
        psum[objNumber - 1] = Math.sqrt(Math.pow(psum[objNumber - 1], 2) + Math.pow(p[j - 1], 2));
    }

    if (objNumber > 1) {
        ff *= Math.sin(zz[(numberOfObjectives - objNumber + 1) - 1] * Math.PI / 2.0);
        psum[objNumber - 1] = Math.sqrt(
                Math.pow(psum[objNumber - 1], 2) + Math.pow(p[(numberOfObjectives - objNumber + 1) - 1], 2));
    }

    return 2.0 / (1 + Math.exp(-psum[objNumber - 1])) * (ff + 1);
}

---

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

@Override
public Function1D<HullWhiteStochasticVolatilityModelDataBundle, Double> getPricingFunction(
        final OptionDefinition definition) {
    Validate.notNull(definition);/*from  ww  w.j  ava 2  s. c  o m*/
    final Function1D<HullWhiteStochasticVolatilityModelDataBundle, Double> pricingFunction = new Function1D<HullWhiteStochasticVolatilityModelDataBundle, Double>() {

        @SuppressWarnings("synthetic-access")
        @Override
        public Double evaluate(final HullWhiteStochasticVolatilityModelDataBundle data) {
            Validate.notNull(data);
            final ZonedDateTime date = data.getDate();
            final double s = data.getSpot();
            final double k = definition.getStrike();
            final double t = definition.getTimeToExpiry(date);
            final double sigma = data.getVolatility(t, k);
            final double r = data.getInterestRate(t);
            final double b = data.getCostOfCarry();
            final double lambda = data.getHalfLife();
            final double sigmaLR = data.getLongRunVolatility();
            final double volOfSigma = data.getVolatilityOfVolatility();
            final double rho = data.getCorrelation();
            final StandardOptionDataBundle bsmData = new StandardOptionDataBundle(data);
            final OptionDefinition call = definition.isCall() ? definition
                    : new EuropeanVanillaOptionDefinition(k, definition.getExpiry(), true);
            final double beta = -Math.log(2) / lambda;
            final double alpha = -beta * sigmaLR * sigmaLR;
            final double delta = beta * t;
            final double eDelta = Math.exp(delta);
            final boolean betaIsZero = CompareUtils.closeEquals(beta, 0, ZERO);
            final double variance = sigma * sigma;
            final double meanVariance = getMeanVariance(betaIsZero, variance, alpha, t, beta, eDelta, delta);
            final double df = getDF(r, b, t);
            final double sDf = s * df;
            final double d1 = getD(s, k, b, meanVariance, t);
            final double d2 = d1 - Math.sqrt(meanVariance * t);
            final double nD1 = NORMAL.getPDF(d1);
            final double f0 = BSM.getPricingFunction(call).evaluate(bsmData.withVolatilitySurface(
                    new VolatilitySurface(ConstantDoublesSurface.from(Math.sqrt(meanVariance)))));
            final double f1 = getF1(betaIsZero, variance, rho, alpha, t, beta, delta, eDelta, sDf, nD1, d2,
                    meanVariance);
            final double f2 = getF2(betaIsZero, variance, rho, alpha, t, beta, delta, eDelta, sDf, nD1, d1, d2,
                    meanVariance);
            final double callPrice = f0 + f1 * volOfSigma + f2 * volOfSigma * volOfSigma;
            if (!definition.isCall()) {
                return callPrice - s * df + k * Math.exp(-r * t);
            }
            return callPrice;
        }

    };
    return pricingFunction;
}

---

From source file:com.opengamma.analytics.financial.model.interestrate.BlackDermanToyYieldOnlyInterestRateModel.java

public Function1D<StandardDiscountBondModelDataBundle, RecombiningBinomialTree<Triple<Double, Double, Double>>> getTrees(
        final ZonedDateTime time) {
    Validate.notNull(time, "time");
    return new Function1D<StandardDiscountBondModelDataBundle, RecombiningBinomialTree<Triple<Double, Double, Double>>>() {

        @SuppressWarnings({ "unchecked", "synthetic-access" })
        @Override//w w  w.j  a  va  2  s. c o m
        public RecombiningBinomialTree<Triple<Double, Double, Double>> evaluate(
                final StandardDiscountBondModelDataBundle data) {
            Validate.notNull(data, "data");
            final double[][] r = new double[_n + 1][_j];
            final double[][] q = new double[_n + 1][_j];
            final double[][] d = new double[_n + 1][_j];
            final double[] u = new double[_n + 1];
            final double[] p = new double[_n + 2];
            final double t = DateUtils.getDifferenceInYears(data.getDate(), time);
            final double dt = t / _n;
            final double dtSqrt = Math.sqrt(dt);
            final double r1 = data.getShortRate(dt);
            final double sigma = data.getShortRateVolatility(dt);
            p[0] = 1.0;
            for (int i = 1; i <= _n + 1; i++) {
                p[i] = 1. / Math.pow((1 + data.getShortRate(i) * dt), dt * i);
            }
            q[0][0] = 1.;
            u[0] = r1;
            r[0][0] = r1;
            d[0][0] = 1. / (1 + r1 * dt);
            for (int i = 1; i <= _n; i++) {
                q[i][0] = 0.5 * q[i - 1][0] * d[i - 1][0];
                q[i][i] = 0.5 * q[i - 1][i - 1] * d[i - 1][i - 1];
                for (int j = -i + 2, k = 1; j <= i - 2; j += 2, k++) {
                    q[i][k] = 0.5 * (q[i - 1][k - 1] * d[i - 1][k - 1] + q[i - 1][k] * d[i - 1][k]);
                }
                u[i] = _rootFinder.getRoot(getMedian(sigma, i, dt, q, p[i + 1]), 0., 1.);
                for (int j = -i, k = 0; j <= i; j += 2, k++) {
                    r[i][k] = u[i] * Math.exp(sigma * j * dtSqrt);
                    d[i][k] = 1. / (1 + r[i][k] * dt);
                }
            }
            final Triple<Double, Double, Double>[][] result = new Triple[_n + 1][_j];
            for (int i = 0; i <= _n; i++) {
                for (int j = 0; j < _j; j++) {
                    result[i][j] = new Triple<>(r[i][j], d[i][j], q[i][j]);
                }
            }
            return new RecombiningBinomialTree<>(result);
        }

    };
}

---

From source file:FILA.Consumidor.java

private double Exponential(double y, double x) {
    return y * Math.exp(-y * x);
    //return 1 - Math.pow(Math.E, -y*x);
}

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From source file:beast.math.distributions.GammaDistribution.java

/**
 * probability density function of the Gamma distribution
 *
 * @param x     argument//from www  .j a  v  a 2  s.  com
 * @param shape shape parameter
 * @param scale scale parameter
 * @return pdf value
 */
public static double pdf(double x, double shape, double scale) {
    // return Math.pow(scale,-shape)*Math.pow(x, shape-1.0)/
    // Math.exp(x/scale + GammaFunction.lnGamma(shape));
    if (x < 0)
        return 0; // to make BEAUti plot continue
    //            throw new IllegalArgumentException();
    if (x == 0) {
        if (shape == 1.0)
            return 1.0 / scale;
        else
            return 0.0;
    }

    final double xs = x / scale;

    if (shape == 1.0) {
        return Math.exp(-xs) / scale;
    }

    final double a = Math.exp((shape - 1.0) * Math.log(xs) - xs - GammaFunction.lnGamma(shape));

    return a / scale;
}

---

From source file:es.udc.gii.common.eaf.benchmark.multiobjective.dtlz3.S_Dtlz3_Objective.java

@Override
public double evaluate(double[] x) {

    int nx = x.length;

    int i = 0;// w  w w.ja  v a 2 s.c om
    int j = 0;
    int k = nx - numberOfObjectives + 1;
    double g = 0;

    double[] z = new double[nx];
    double[] zz = new double[nx];
    double[] p = new double[nx];
    double[] psum = new double[numberOfObjectives];

    // denormalize vector:
    for (i = 0; i < nx; i++) {
        x[i] = (bounds[1][i] - bounds[0][i]) / 2 * x[i] + (bounds[1][i] + bounds[0][i]) / 2;
    }

    for (i = 0; i < nx; i++) {
        z[i] = x[i] - o[i];

        if (z[i] < 0) {
            zz[i] = -lambda[i] * z[i];
            p[i] = -z[i] / d[i];
        } else {
            zz[i] = z[i];
            p[i] = 0;
        }
    }

    for (j = 0; j < numberOfObjectives; j++) {
        psum[j] = 0;
    }

    for (i = nx - k + 1; i <= nx; i++) {
        g += Math.pow(zz[i - 1] - 0.5, 2) - Math.cos(20 * Math.PI * (zz[i - 1] - 0.5));
        for (j = 0; j < numberOfObjectives; j++) {
            psum[j] = Math.sqrt(Math.pow(psum[j], 2) + Math.pow(p[i - 1], 2));
        }
    }

    g = 100 * (k + g);

    double ff = (1 + g);

    for (j = numberOfObjectives - objNumber; j >= 1; j--) {
        ff *= Math.cos(zz[j - 1] * Math.PI / 2.0);
        psum[objNumber - 1] = Math.sqrt(Math.pow(psum[objNumber - 1], 2) + Math.pow(p[j - 1], 2));
    }

    if (objNumber > 1) {
        ff *= Math.sin(zz[(numberOfObjectives - objNumber + 1) - 1] * Math.PI / 2.0);
        psum[objNumber - 1] = Math.sqrt(
                Math.pow(psum[objNumber - 1], 2) + Math.pow(p[(numberOfObjectives - objNumber + 1) - 1], 2));
    }

    return 2.0 / (1 + Math.exp(-psum[objNumber - 1])) * (ff + 1);

}

---

From source file:edu.illinois.cs.cogcomp.utils.SparseDoubleVector.java

/**
 * Takes the exponential of each element in the vector.
 * @return/*from ww w  . jav  a  2s.  c om*/
 */
public SparseDoubleVector<TKey> Exp() {
    SparseDoubleVector<TKey> ret = new SparseDoubleVector<>();

    for (TKey k : this.keySet()) {
        ret.put(k, Math.exp(this.get(k)));
    }
    return ret;
}

---

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

/**
 * Computes the price of a barrier option in the Black world.
 * @param option The underlying European vanilla option.
 * @param barrier The barrier./*ww  w. j  av  a2 s .c  o m*/
 * @param rebate The rebate.
 * @param spot The spot price.
 * @param costOfCarry The cost of carry.
 * @param rate The interest rate.
 * @param sigma The Black volatility.
 * @return The price.
 */
public double getPrice(final EuropeanVanillaOption option, final Barrier barrier, final double rebate,
        final double spot, final double costOfCarry, final double rate, final double sigma) {
    Validate.notNull(option, "option");
    Validate.notNull(barrier, "barrier");
    final boolean isKnockIn = (barrier.getKnockType() == KnockType.IN);
    final boolean isDown = (barrier.getBarrierType() == BarrierType.DOWN);
    final double h = barrier.getBarrierLevel();
    Validate.isTrue(!(barrier.getBarrierType() == BarrierType.DOWN && spot < barrier.getBarrierLevel()),
            "The Data is not consistent with an alive barrier (DOWN and spot<barrier).");
    Validate.isTrue(!(barrier.getBarrierType() == BarrierType.UP && spot > barrier.getBarrierLevel()),
            "The Data is not consistent with an alive barrier (UP and spot>barrier).");
    final boolean isCall = option.isCall();
    final double t = option.getTimeToExpiry();
    final double strike = option.getStrike();
    final int phi = isCall ? 1 : -1;
    final double eta = isDown ? 1 : -1;
    final double df1 = Math.exp(t * (costOfCarry - rate));
    final double df2 = Math.exp(-rate * t);
    if (CompareUtils.closeEquals(sigma, 0, 1e-16)) {
        return df1 * rebate;
    }
    final double sigmaSq = sigma * sigma;
    final double sigmaT = sigma * Math.sqrt(t);
    final double mu = (costOfCarry - 0.5 * sigmaSq) / sigmaSq;
    final double lambda = Math.sqrt(mu * mu + 2 * rate / sigmaSq);
    final double m1 = sigmaT * (1 + mu);
    final double x1 = Math.log(spot / strike) / sigmaT + m1;
    final double x2 = Math.log(spot / h) / sigmaT + m1;
    final double y1 = Math.log(h * h / spot / strike) / sigmaT + m1;
    final double y2 = Math.log(h / spot) / sigmaT + m1;
    final double z = Math.log(h / spot) / sigmaT + lambda * sigmaT;
    final double xA = getA(spot, strike, df1, df2, x1, sigmaT, phi);
    final double xB = getA(spot, strike, df1, df2, x2, sigmaT, phi);
    final double xC = getC(spot, strike, df1, df2, y1, sigmaT, h, mu, phi, eta);
    final double xD = getC(spot, strike, df1, df2, y2, sigmaT, h, mu, phi, eta);
    final double xE = isKnockIn ? getE(spot, rebate, df2, x2, y2, sigmaT, h, mu, eta)
            : getF(spot, rebate, z, sigmaT, h, mu, lambda, eta);
    if (isKnockIn) {
        if (isDown) {
            if (isCall) {
                return strike > h ? xC + xE : xA - xB + xD + xE;
            }
            return strike > h ? xB - xC + xD + xE : xA + xE;
        }
        if (isCall) {
            return strike > h ? xA + xE : xB - xC + xD + xE;
        }
        return strike > h ? xA - xB + xD + xE : xC + xE;
    }
    if (isDown) {
        if (isCall) {
            return strike > h ? xA - xC + xE : xB - xD + xE;
        }
        return strike > h ? xA - xB + xC - xD + xE : xE;
    }
    if (isCall) {
        return strike > h ? xE : xA - xB + xC - xD + xE;
    }
    return strike > h ? xB - xD + xE : xA - xC + xE;
}

---

From source file:com.opengamma.analytics.financial.model.option.pricing.tree.LogNormalBinomialTreeBuilder.java

@Override
protected double getNextLowerNode(double dt, double sigma, double forward, double higherNode) {
    if (forward == 0.0) {
        return 0.0;
    }//from  ww w .  j  a  v  a  2s .com
    Function1D<Double, Double> func = new LowerNodes(dt, sigma, forward, higherNode);
    double[] limits = s_bracketRoot.getBracketedPoints(func, forward * Math.exp(-sigma * Math.sqrt(dt)),
            forward);
    return s_root.getRoot(func, limits[0], limits[1]);
}

---

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