Example usage for java.lang Math log

Introduction

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

Prototype

@HotSpotIntrinsicCandidate
public static double log(double a) 

Source Link

Document

Returns the natural logarithm (base e) of a double value.

Usage

From source file:StatFunctions.java

public static double qnorm(double p, boolean upper) {
    /*//w ww  .  ja  v a 2  s  . com
     * Reference: J. D. Beasley and S. G. Springer Algorithm AS 111:
     * "The Percentage Points of the Normal Distribution" Applied Statistics
     */
    if (p < 0 || p > 1)
        throw new IllegalArgumentException("Illegal argument " + p + " for qnorm(p).");
    double split = 0.42, a0 = 2.50662823884, a1 = -18.61500062529, a2 = 41.39119773534, a3 = -25.44106049637,
            b1 = -8.47351093090, b2 = 23.08336743743, b3 = -21.06224101826, b4 = 3.13082909833,
            c0 = -2.78718931138, c1 = -2.29796479134, c2 = 4.85014127135, c3 = 2.32121276858,
            d1 = 3.54388924762, d2 = 1.63706781897, q = p - 0.5;
    double r, ppnd;
    if (Math.abs(q) <= split) {
        r = q * q;
        ppnd = q * (((a3 * r + a2) * r + a1) * r + a0) / ((((b4 * r + b3) * r + b2) * r + b1) * r + 1);
    } else {
        r = p;
        if (q > 0)
            r = 1 - p;
        if (r > 0) {
            r = Math.sqrt(-Math.log(r));
            ppnd = (((c3 * r + c2) * r + c1) * r + c0) / ((d2 * r + d1) * r + 1);
            if (q < 0)
                ppnd = -ppnd;
        } else {
            ppnd = 0;
        }
    }
    if (upper)
        ppnd = 1 - ppnd;
    return (ppnd);
}

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From source file:dr.math.distributions.PoissonDistribution.java

public static double logPdf(double x, double mean) {
    PoissonDistributionImpl dist = new PoissonDistributionImpl(mean);
    double pdf = dist.probability(x);
    if (pdf == 0 || Double.isNaN(pdf)) { // bad estimate
        return x * Math.log(mean) - Poisson.gammln(x + 1) - mean;
    }//from   w  ww . ja  v a  2s  . c o  m
    return Math.log(pdf);

}

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From source file:com.joptimizer.functions.SOCPLogarithmicBarrier.java

public double value(double[] X) {
    RealVector x = new ArrayRealVector(X);

    double ret = 0;
    for (int i = 0; i < socpConstraintParametersList.size(); i++) {
        SOCPConstraintParameters param = socpConstraintParametersList.get(i);
        double t = this.buildT(param, x);
        if (t < 0) {
            return Double.NaN;
        }//from w w w.  jav a 2 s  . c  o m
        RealVector u = this.buildU(param, x);
        double t2uu = t * t - u.dotProduct(u);
        if (t2uu <= 0) {
            return Double.NaN;
        }
        double ret_i = -Math.log(t2uu);
        ret += ret_i;
    }

    return ret;
}

---

From source file:com.davidbracewell.math.distribution.NormalDistribution.java

@Override
public double logProbability(double x) {
    return Math.log(probability(x));
}

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From source file:hivemall.utils.math.MathUtils.java

public static double logit(final double p, final double hi, final double lo) {
    return Math.log((p - lo) / (hi - p));
}

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From source file:com.analog.lyric.dimple.factorfunctions.Gamma.java

public Gamma(double alpha, double beta) {
    this();//from  ww  w .j a v a  2 s  . c  o  m
    _alpha = alpha;
    _beta = beta;
    _alphaMinusOne = _alpha - 1;
    _logBeta = Math.log(_beta);
    _logGammaAlphaMinusAlphaLogBeta = org.apache.commons.math3.special.Gamma.logGamma(_alpha)
            - _alpha * _logBeta;
    _parametersConstant = true;
    _firstDirectedToIndex = 0;
    if (_alpha <= 0)
        throw new DimpleException("Non-positive alpha parameter. This must be a positive value.");
    if (_beta <= 0)
        throw new DimpleException("Non-positive beta parameter. This must be a positive value.");
}

---

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

@Override
public Function1D<StandardOptionWithSpotTimeSeriesDataBundle, Double> getPricingFunction(
        final ExtremeSpreadOptionDefinition definition) {
    Validate.notNull(definition, "definition");
    return new Function1D<StandardOptionWithSpotTimeSeriesDataBundle, Double>() {

        @SuppressWarnings("synthetic-access")
        @Override/*w  ww .  j  a  v  a2 s  .  co  m*/
        public Double evaluate(final StandardOptionWithSpotTimeSeriesDataBundle data) {
            Validate.notNull(data, "data");
            final double s = data.getSpot();
            final double b = data.getCostOfCarry();
            final ZonedDateTime date = data.getDate();
            final double t1 = -definition.getTimeFromPeriodEnd(date);
            final double t2 = definition.getTimeToExpiry(date);
            final double r = data.getInterestRate(t2);
            final DoubleTimeSeries<?> ts = data.getSpotTimeSeries();
            final int eta = definition.isCall() ? 1 : -1;
            final int phi = definition.isReverse() ? -1 : 1;
            final double x = eta * phi == 1 ? ts.maxValue() : ts.minValue();
            final double sigma = data.getVolatility(t2, x); //REVIEW emcleod 21-7-10 works for flat vol surfaces
            final double m = Math.log(x / s);
            final double mu1 = b - sigma * sigma / 2;
            final double mu2 = b + sigma * sigma / 2;
            final double df1 = Math.exp(t2 * (b - r));
            final double df2 = Math.exp(b * (t1 - t2));
            final double df3 = Math.exp(-r * t2);
            final double sigmaT2 = sigma * Math.sqrt(t2);
            final double sigmaT1 = sigma * Math.sqrt(t1);
            final double sigmaSq = sigma * sigma;
            final double y = sigmaSq / 2 / b;
            if (definition.isReverse()) {
                final double dt = t2 - t1;
                final double sigmaDT = sigma * Math.sqrt(dt);
                final double z1 = s * df1 * (1 + y) * NORMAL.getCDF(eta * (m - mu2 * t2) / sigmaT2);
                final double z2 = df3 * x * NORMAL.getCDF(eta * (mu1 * t2 - m) / sigmaT2);
                final double z3 = -df3 * x * y * Math.exp(2 * mu1 * m / sigmaSq)
                        * NORMAL.getCDF(eta * (m + mu1 * t2) / sigmaT2);
                final double z4 = -s * df1 * (1 + y) * NORMAL.getCDF(-eta * mu2 * dt / sigmaDT);
                final double z5 = -df2 * df1 * s * (1 - y) * NORMAL.getCDF(eta * mu1 * dt / sigmaDT);
                return -eta * (z1 + z2 + z3 + z4 + z5);
            }
            final double z1 = s * df1 * (1 + y) * NORMAL.getCDF(eta * (mu2 * t2 - m) / sigmaT2);
            final double z2 = -df2 * df1 * s * (1 + y) * NORMAL.getCDF(eta * (mu2 * t1 - m) / sigmaT1);
            final double z3 = df3 * x * NORMAL.getCDF(eta * (m - mu1 * t2) / sigmaT2);
            final double z4 = -df3 * x * y * Math.exp(2 * mu1 * m / sigmaSq)
                    * NORMAL.getCDF(-eta * (m + mu1 * t2) / sigmaT2);
            final double z5 = -df3 * x * NORMAL.getCDF(eta * (m - mu1 * t1) / sigmaT1);
            final double z6 = df3 * x * y * Math.exp(2 * mu1 * m / sigmaSq)
                    * NORMAL.getCDF(-eta * (m + mu1 * t1) / sigmaT1);
            return eta * (z1 + z2 + z3 + z4 + z5 + z6);
        }

    };
}

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From source file:com.analog.lyric.dimple.factorfunctions.NegativeExpGamma.java

public NegativeExpGamma(double alpha, double beta) {
    this();//from  w w w . ja  v a  2 s . c o m
    _alpha = alpha;
    _beta = beta;
    _alphaMinusOne = _alpha - 1;
    _logGammaAlphaMinusAlphaLogBeta = org.apache.commons.math3.special.Gamma.logGamma(_alpha)
            - _alpha * Math.log(_beta);
    _parametersConstant = true;
    _firstDirectedToIndex = 0;
    if (_alpha <= 0)
        throw new DimpleException("Non-positive alpha parameter. This must be a positive value.");
    if (_beta <= 0)
        throw new DimpleException("Non-positive beta parameter. This must be a positive value.");
}

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From source file:com.opengamma.analytics.math.statistics.distribution.NonCentralChiSquaredDistribution.java

/**
 * @param degrees The number of degrees of freedom, not negative or zero
 * @param nonCentrality The non-centrality parameter, not negative
 *//*from w ww  .  ja v a2 s  .c  o m*/
public NonCentralChiSquaredDistribution(final double degrees, final double nonCentrality) {
    Validate.isTrue(degrees > 0, "degrees of freedom must be > 0, have " + degrees);
    Validate.isTrue(nonCentrality >= 0, "non-centrality must be >= 0, have " + nonCentrality);
    _dofOverTwo = degrees / 2.0;
    _lambdaOverTwo = nonCentrality / 2.0;
    _k = (int) Math.round(_lambdaOverTwo);

    if (_lambdaOverTwo == 0) {
        _pStart = 0.0;
    } else {
        final double logP = -_lambdaOverTwo + _k * Math.log(_lambdaOverTwo) - Gamma.logGamma(_k + 1);
        _pStart = Math.exp(logP);
    }
}

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From source file:de.unijena.bioinf.IsotopePatternAnalysis.scoring.MissingPeakScorer.java

@Override
public double score(Spectrum<Peak> measuredSpectrum, Spectrum<Peak> theoreticalSpectrum, Normalization norm,
        Ms2Experiment experiment, MeasurementProfile profile) {
    final Spectrum<? extends Peak> measured, theoretical;
    if (normalization.getMode() != null && !norm.equals(normalization)) {
        measured = normalization.call(measuredSpectrum);
        theoretical = normalization.call(theoreticalSpectrum);
    } else {/*from w w  w  .j  av  a  2  s  .c o m*/
        measured = measuredSpectrum;
        theoretical = theoreticalSpectrum;
    }
    double score = 0;
    final double standardDeviation = 0.03;
    for (int i = measured.size(); i < theoretical.size(); ++i) {
        final double diff = theoretical.getIntensityAt(i);
        score += Math.log(Erf.erfc(diff / (sqrt2 * standardDeviation)));
        //score -=  lambda*theoretical.getIntensityAt(i);
    }
    return score;
}

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