Example usage for java.lang Math exp

List of usage examples for java.lang Math exp

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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:mastodon.algorithms.SABisectionAlgorithm.java

protected void setNewBest() {
    if (currScore[0] > maxScore[0]) { //set new optimum
        maxScore = currScore; //might need a clone here
        maxScorePruning.clear();/*ww w.ja  v a2s .co  m*/
        maxScorePruning.put((BitSet) currPruning.clone(), currScore.clone());
    } else if (currScore[0] == maxScore[0] && currScore[1] != 1) { //save variations with same score, but no need to if it produces no results
        maxScorePruning.put((BitSet) currPruning.clone(), currScore.clone());
    }

    //set next step
    if (Random.nextDouble() < Math.exp((currScore[0] - prevScore[0]) / currTemp)) {
        prevPruning = (BitSet) currPruning.clone();
        prevScore = currScore.clone();

        for (int a = currPruning.nextSetBit(0); a >= 0; a = currPruning.nextSetBit(a + 1)) {
            pruningFreq.put(a, pruningFreq.get(a) + 1);
        }
        totalPruningFreq++;

    } //try different pruning otherwise
}

From source file:com.opengamma.analytics.financial.forex.method.ForexOptionSingleBarrierBlackMethod.java

/**
 * Computes the currency exposure for single barrier Forex option in Black model (log-normal spot rate). The sensitivity of the volatility on the spot
 * is not taken into account. It is the currency exposure in the Black model where the volatility is suppose to be constant for curve and forward changes.
 * @param optionForex The Forex option.//ww  w.  j  a v a2 s  .c  o  m
 * @param smile The volatility and curves description.
 * @return The currency exposure.
 */
public MultipleCurrencyAmount currencyExposure(final ForexOptionSingleBarrier optionForex,
        final SmileDeltaTermStructureDataBundle smile) {
    Validate.notNull(optionForex, "Forex option");
    Validate.notNull(smile, "Smile");
    final double payTime = optionForex.getUnderlyingOption().getUnderlyingForex().getPaymentTime();
    final double rateDomestic = smile.getCurve(
            optionForex.getUnderlyingOption().getUnderlyingForex().getPaymentCurrency2().getFundingCurveName())
            .getInterestRate(payTime);
    final double rateForeign = smile.getCurve(
            optionForex.getUnderlyingOption().getUnderlyingForex().getPaymentCurrency1().getFundingCurveName())
            .getInterestRate(payTime);
    final double spot = smile.getFxRates().getFxRate(optionForex.getCurrency1(), optionForex.getCurrency2());
    final double forward = spot * Math.exp(-rateForeign * payTime) / Math.exp(-rateDomestic * payTime);
    final double foreignAmount = optionForex.getUnderlyingOption().getUnderlyingForex().getPaymentCurrency1()
            .getAmount();
    final double rebateByForeignUnit = optionForex.getRebate() / Math.abs(foreignAmount);
    final double sign = (optionForex.getUnderlyingOption().isLong() ? 1.0 : -1.0);
    final double volatility = FXVolatilityUtils.getVolatility(smile, optionForex.getCurrency1(),
            optionForex.getCurrency2(), optionForex.getUnderlyingOption().getTimeToExpiry(),
            optionForex.getUnderlyingOption().getStrike(), forward);
    final double[] priceDerivatives = new double[5];
    double price = BLACK_FUNCTION.getPriceAdjoint(optionForex.getUnderlyingOption(), optionForex.getBarrier(),
            rebateByForeignUnit, spot, rateForeign, rateDomestic, volatility, priceDerivatives);
    price *= Math.abs(foreignAmount) * sign;
    final double deltaSpot = priceDerivatives[0];
    final CurrencyAmount[] currencyExposure = new CurrencyAmount[2];
    // Implementation note: foreign currency (currency 1) exposure = Delta_spot * amount1.
    currencyExposure[0] = CurrencyAmount.of(
            optionForex.getUnderlyingOption().getUnderlyingForex().getCurrency1(),
            deltaSpot * Math.abs(foreignAmount) * sign);
    // Implementation note: domestic currency (currency 2) exposure = -Delta_spot * amount1 * spot+PV
    currencyExposure[1] = CurrencyAmount.of(
            optionForex.getUnderlyingOption().getUnderlyingForex().getCurrency2(),
            -deltaSpot * Math.abs(
                    optionForex.getUnderlyingOption().getUnderlyingForex().getPaymentCurrency1().getAmount())
                    * spot * sign + price);
    return MultipleCurrencyAmount.of(currencyExposure);
}

From source file:dr.evomodel.speciation.BirthDeathGernhard08Model.java

public double logNodeProbability(Tree tree, NodeRef node) {
    final double height = tree.getNodeHeight(node);
    final double r = getR();
    final double mrh = -r * height;
    final double a = getA();

    if (!conditionalOnRoot) {
        final double rho = getRho();
        final double z = Math.log(rho + ((1 - rho) - a) * Math.exp(mrh));
        double l = -2 * z + mrh;

        if (tree.getRoot() == node) {
            l += mrh - z;//from ww  w. jav a2 s.c  o  m
        }
        return l;
    } else {
        double l;
        if (tree.getRoot() != node) {
            final double z = Math.log(1 - a * Math.exp(mrh));
            l = -2 * z + mrh;
        } else {
            // Root dependent coefficient from each internal node
            final double ca = 1 - a;
            final double emrh = Math.exp(-mrh);
            if (emrh != 1.0) {
                l = (tree.getTaxonCount() - 2) * Math.log(r * ca * (1 + ca / (emrh - 1)));
            } else { // use exp(x)-1 = x for x near 0
                l = (tree.getTaxonCount() - 2) * Math.log(ca * (r + ca / height));
            }
        }
        return l;
    }
}

From source file:de.tudarmstadt.ukp.dkpro.core.jazzy.CorrectionsContextualizer.java

protected double getSentenceProbability(List<String> words) throws AnalysisEngineProcessException {
    double sentenceProbability = 0.0;

    if (words.size() < 1) {
        return 0.0;
    }//from   w  ww . j a v  a2 s .c o m

    long nrOfUnigrams;
    try {
        nrOfUnigrams = provider.getNrOfTokens();
    } catch (Exception e) {
        throw new AnalysisEngineProcessException(e);
    }

    List<String> trigrams = new ArrayList<String>();

    // in the google n-grams this is not represented (only single BOS markers)
    // but I leave it in place in case we add another n-gram provider
    trigrams.add(getTrigram(BOS, BOS, words.get(0)));

    if (words.size() > 1) {
        trigrams.add(getTrigram(BOS, words.get(0), words.get(1)));
    }

    for (String trigram : new NGramStringIterable(words, 3, 3)) {
        trigrams.add(trigram);
    }

    // FIXME - implement backoff or linear interpolation

    for (String trigram : trigrams) {
        long trigramFreq = getNGramCount(trigram);

        String[] parts = StringUtils.split(trigram, " ");

        String bigram = StringUtils.join(Arrays.copyOfRange(parts, 0, 2), " ");
        long bigramFreq = getNGramCount(bigram);

        String unigram = StringUtils.join(Arrays.copyOfRange(parts, 0, 1), " ");
        long unigramFreq = getNGramCount(unigram);

        if (trigramFreq < 1) {
            trigramFreq = 1;
        }
        if (bigramFreq < 1) {
            bigramFreq = 1;
        }
        if (unigramFreq < 1) {
            unigramFreq = 1;
        }

        double trigramProb = Math.log((double) trigramFreq / bigramFreq);
        double bigramProb = Math.log((double) bigramFreq / unigramFreq);
        double unigramProb = Math.log((double) unigramFreq / nrOfUnigrams);

        double interpolated = (trigramProb + bigramProb + unigramProb) / 3.0;

        sentenceProbability += interpolated;
    }

    return Math.exp(sentenceProbability);
}

From source file:ch.epfl.leb.sass.models.illuminations.internal.SquareUniformElectricField.java

/**
* Returns the y-component of the time-independent electric field at the position (x, y, z).
* 
* @param x The x-position within the sample.
* @param y The y-position within the sample.
* @param z The z-position within the sample.
* @return The y-component of the electric field at the position (x, y, z).
*///w w w  . ja  v a  2s . c om
@Override
public Complex getEy(double x, double y, double z) {
    if (x < 0 || x > width)
        return new Complex(0);
    if (y < 0 || y > height)
        return new Complex(0);

    Complex arg = getArgument(x, y, z);
    double magnitude = orientation.getY() * Math.exp(-arg.getImaginary());
    return ComplexUtils.polar2Complex(magnitude, arg.getReal());
}

From source file:com.opengamma.analytics.financial.interestrate.inflation.method.InflationMarketModelConvexityAdjustmentForCapFloor.java

/**
 * Computes the convexity adjustment for year on year inflation swap with an interpolated index.
 * @param cap The year on year coupon.//from w ww  . j a v  a2 s. co  m
 * @param inflationConvexity The inflation provider.
 * @return The convexity adjustment.
 */
public double getYearOnYearConvexityAdjustment(final CapFloorInflationYearOnYearInterpolation cap,
        final BlackSmileCapInflationYearOnYearWithConvexityProviderInterface inflationConvexity) {
    Validate.notNull(cap, "Coupon");
    Validate.notNull(inflationConvexity, "Inflation");

    final double firstFixingTime = cap.getWeightStart() * cap.getReferenceStartTime()[0]
            + (1 - cap.getWeightStart()) * cap.getReferenceStartTime()[1];
    final double secondFixingTime = cap.getWeightEnd() * cap.getReferenceEndTime()[0]
            + (1 - cap.getWeightEnd()) * cap.getReferenceEndTime()[1];
    final double firstNaturalPaymentTime = cap.getNaturalPaymentStartTime();
    final double secondNaturalPaymentTime = cap.getNaturalPaymentEndTime();
    final double paymentTime = cap.getPaymentTime();
    final double volatilityStart = inflationConvexity.getInflationConvexityAdjustmentParameters()
            .getPriceIndexAtmVolatility()[0];
    final double volatilityEnd = inflationConvexity.getInflationConvexityAdjustmentParameters()
            .getPriceIndexAtmVolatility()[1];
    final double correlationInflation = inflationConvexity.getInflationConvexityAdjustmentParameters()
            .getPriceIndexCorrelation().getZValue(firstFixingTime, secondFixingTime);
    final double correlationInflationRateStart = inflationConvexity.getInflationConvexityAdjustmentParameters()
            .getPriceIndexRateCorrelation().getYValue(firstFixingTime);
    final double correlationInflationRateEnd = inflationConvexity.getInflationConvexityAdjustmentParameters()
            .getPriceIndexRateCorrelation().getYValue(secondFixingTime);
    final double volBondForwardStart = getVolBondForward(firstNaturalPaymentTime, paymentTime,
            inflationConvexity);
    final double volBondForwardEnd = getVolBondForward(secondNaturalPaymentTime, paymentTime,
            inflationConvexity);
    final double adjustment = volatilityStart
            * (volatilityStart - volatilityEnd * correlationInflation
                    - volBondForwardStart * correlationInflationRateStart)
            * firstNaturalPaymentTime
            + volatilityEnd * volBondForwardEnd * correlationInflationRateEnd * secondNaturalPaymentTime;
    return Math.exp(adjustment);

}

From source file:com.opengamma.analytics.financial.credit.creditdefaultswap.pricing.vanilla.isdanew.AnalyticCDSPricer.java

/**
 * Compute the present value of the protection leg with a notional of 1, which is given by the integral
 * $\frac{1-R}{P(T_{v})} \int_{T_a} ^{T_b} P(t) \frac{dQ(t)}{dt} dt$ where $P(t)$ and $Q(t)$ are the discount and survival curves
 * respectively, $T_a$ and $T_b$ are the start and end of the protection respectively, $T_v$ is the valuation time (all measured
 * from $t = 0$, 'today') and $R$ is the recovery rate.
 * @param cds analytic description of a CDS traded at a certain time
 * @param yieldCurve The yield (or discount) curve
 * @param creditCurve the credit (or survival) curve
 * @return The value of the protection leg (on a unit notional)
 *//*from   w  w w .  j av a2  s.  c  o  m*/
public double protectionLeg(final CDSAnalytic cds, final ISDACompliantYieldCurve yieldCurve,
        final ISDACompliantCreditCurve creditCurve) {
    ArgumentChecker.notNull(cds, "null cds");
    ArgumentChecker.notNull(yieldCurve, "null yieldCurve");
    ArgumentChecker.notNull(creditCurve, "null creditCurve");
    if (cds.getProtectionEnd() <= 0.0) { //short cut already expired CDSs
        return 0.0;
    }

    final double[] integrationSchedule = getIntegrationsPoints(cds.getProtectionStart(), cds.getProtectionEnd(),
            yieldCurve, creditCurve);

    double ht0 = creditCurve.getRT(integrationSchedule[0]);
    double rt0 = yieldCurve.getRT(integrationSchedule[0]);
    double b0 = Math.exp(-ht0 - rt0); // risky discount factor

    double pv = 0.0;
    final int n = integrationSchedule.length;
    for (int i = 1; i < n; ++i) {

        final double ht1 = creditCurve.getRT(integrationSchedule[i]);
        final double rt1 = yieldCurve.getRT(integrationSchedule[i]);
        final double b1 = Math.exp(-ht1 - rt1);

        final double dht = ht1 - ht0;
        final double drt = rt1 - rt0;
        final double dhrt = dht + drt;

        // The formula has been modified from ISDA (but is equivalent) to avoid log(exp(x)) and explicitly calculating the time
        // step - it also handles the limit
        double dPV;
        if (Math.abs(dhrt) < 1e-5) {
            dPV = dht * b0 * epsilon(-dhrt);
        } else {
            dPV = (b0 - b1) * dht / dhrt;
        }

        pv += dPV;
        ht0 = ht1;
        rt0 = rt1;
        b0 = b1;
    }
    pv *= cds.getLGD();

    // Compute the discount factor discounting the upfront payment made on the cash settlement date back to the valuation date
    final double df = yieldCurve.getDiscountFactor(cds.getValuationTime());
    pv /= df;

    return pv;
}

From source file:com.itemanalysis.psychometrics.irt.model.IrmGPCM.java

/**
 * Computes the numerator of the item response model. This method is used internally for the computation
 * of the probability of an item response. It uses item parameter values stored in the object.
 *
 * @param theta person ability value.//from  w w w .  j  av a  2  s . c om
 * @param category response category.
 * @return
 */
private double numer(double theta, int category) {
    double Zk = 0;
    for (int k = 0; k <= category; k++) {
        Zk += D * discrimination * (theta - step[k]);
    }
    return Math.exp(Zk);
}

From source file:ch.unil.genescore.vegas.Farebrother.java

/**
 * ruben.cpp://ww  w . j  a  v  a 2 s .c  o m
 * Algorithm AS 204 Appl. Statist. (1984) Vol. 33, No.3
 * ruben evaluates the probability that a positive definite quadratic form in Normal variates is less than a given value
 */
private void ruben() {

    ifault_ = -41; // I added this initialization --daniel

    //void ruben(double *lambda, int *mult, double *delta, int *n, double *c, double *mode, int *maxit, double *eps, double *dnsty, int *ifault, double *res) {
    // Initialized at 0 in the R code
    double dnsty = 0.0;

    int i, k, m, j;
    //double pnorm(double q, double mean, double sd, int lower_tail, int log_p);
    double ao, aoinv, z, bbeta, eps2, hold, hold2, sum, sum1, dans, lans, pans, prbty, tol;
    double[] gamma = new double[lambda_.length];
    double[] theta = new double[lambda_.length];
    double[] a = new double[maxit_];
    double[] b = new double[maxit_];

    if ((lambda_.length < 1) || (q_ <= 0) || (maxit_ < 1) || (eps_ <= 0.0)) {
        res_ = -2.0;
        ifault_ = 2;
        return;
    }

    tol = -200.0;

    // Preliminaries
    sum = lambda_[0];
    bbeta = sum;

    for (i = 1; i <= lambda_.length; i++) {
        hold = lambda_[i - 1];
        if ((hold <= 0.0) || (h_[i - 1] < 1) || (delta_[i - 1] < 0.0)) {
            res_ = -7.0;
            ifault_ = -i;
            return;
        }
        if (bbeta > hold)
            bbeta = hold; // calcul du max des lambdas
        if (sum < hold)
            sum = hold; // calcul du min des lambdas
    }

    if (mode_ > 0.0) {
        // if ((2.0/(1.0/bbeta+1.0/sum))>1.8*sum) bbeta = sum; // comme dans NAG : methode avec betaA
        bbeta = mode_ * bbeta;
    } else {
        bbeta = 2.0 / (1.0 / bbeta + 1.0 / sum); // methode avec betaB
    }

    k = 0;
    sum = 1.0;
    sum1 = 0.0;
    for (i = 1; i <= lambda_.length; i++) {
        hold = bbeta / lambda_[i - 1];
        gamma[i - 1] = 1.0 - hold;
        sum = sum * Math.pow(hold, h_[i - 1]); //???? pas sur ..
        sum1 = sum1 + delta_[i - 1];
        k = k + h_[i - 1];
        theta[i - 1] = 1.0;
    }

    ao = Math.exp(0.5 * (Math.log(sum) - sum1));
    if (ao <= 0.0) {
        res_ = 0.0;
        dnsty = 0.0;
        ifault_ = 1; // returns after this (the rest of the function is in the else)
    } else { // evaluate probability and density of chi-squared on k degrees of freedom. The constant 0.22579135264473 is ln(sqrt(pi/2))
        z = q_ / bbeta;

        if ((k % 2) == 0) { // k est un entier donc on regarde si k est divisible par 2: k == (k/2)*k 
            i = 2;
            lans = -0.5 * z;
            dans = Math.exp(lans);
            pans = 1.0 - dans;
        } else {
            i = 1;
            lans = -0.5 * (z + Math.log(z)) - 0.22579135264473;
            dans = Math.exp(lans);
            //pans = pnorm(Math.sqrt(z),0.0,1.0,1,0) - pnorm(-Math.sqrt(z),0.0,1.0,1,0); 
            pans = normal_.cumulativeProbability(Math.sqrt(z)) - normal_.cumulativeProbability(-Math.sqrt(z));
        }

        k = k - 2;
        for (j = i; j <= k; j = j + 2) {
            if (lans < tol) {
                lans = lans + Math.log(z / (double) j);
                dans = Math.exp(lans);
            } else {
                dans = dans * z / (double) j;
            }
            pans = pans - dans;
        }

        // evaluate successive terms of expansion

        prbty = pans;
        dnsty = dans;
        eps2 = eps_ / ao;
        aoinv = 1.0 / ao;
        sum = aoinv - 1.0;

        for (m = 1; m <= maxit_; m++) {
            sum1 = 0.0;
            for (i = 1; i <= lambda_.length; i++) {
                hold = theta[i - 1];
                hold2 = hold * gamma[i - 1];
                theta[i - 1] = hold2;
                sum1 = sum1 + hold2 * h_[i - 1] + m * delta_[i - 1] * (hold - hold2);
            }
            sum1 = 0.5 * sum1;
            b[m - 1] = sum1;
            for (i = m - 1; i >= 1; i--) {
                sum1 = sum1 + b[i - 1] * a[m - i - 1];
            }
            sum1 = sum1 / (double) m;
            a[m - 1] = sum1;
            k = k + 2;
            if (lans < tol) {
                lans = lans + Math.log(z / (double) k);
                dans = Math.exp(lans);
            } else {
                dans = dans * z / (double) k;
            }
            pans = pans - dans;
            sum = sum - sum1;
            dnsty = dnsty + dans * sum1;
            sum1 = pans * sum1;
            prbty = prbty + sum1;
            if (prbty < (-aoinv)) {
                res_ = -3.0;
                ifault_ = 3;
                return;
            }
            if (Math.abs(pans * sum) < eps2) {
                if (Math.abs(sum1) < eps2) {
                    ifault_ = 0; // this is overwritten below (ifault_=4) -- I now changed the code below
                    // I COMMENTED THIS SO WE CAN See IF THERE WAS CONVERGENCE OR NOT -daniel
                    //m = maxit_+1; // and WHY would you do that?
                    break;
                }
            }
        }

        if (m > maxit_) // I ADDED THIS IF, OTHERWISE IT MAKES NO SENSE -daniel
            ifault_ = 4;
        // Check if I understood correctly
        assert ifault_ == 0 || ifault_ == 4;

        dnsty = ao * dnsty / (bbeta + bbeta);
        prbty = ao * prbty;

        // With my edits above, this now makes a bit mores sense
        if (prbty < 0.0 || prbty > 1.0) {
            ifault_ = ifault_ + 5; // why the ... would they write it like this? I.e., ifault_ = 9
        } else {
            if (dnsty < 0.0)
                ifault_ = ifault_ + 6;
        }
        res_ = prbty;
    }

    return;
}

From source file:dr.app.bss.Utils.java

public static void exponentiate(double[] array) {
    for (int i = 0; i < array.length; i++) {
        array[i] = Math.exp(array[i]);
    }/*from  ww  w  . j  a  v a  2s .  co m*/
}