List of derivative() Examples

Examples of derivative()

cc.redberry.core.tensor.AbstractScalarFunction.derivative()
com.neuralnetwork.shared.functions.IActivationFunction.derivative()
Returns the output of the derivative of the activation function. @param v the input value to the activation function @return the output of the derivative of this activation function
com.neuralnetwork.shared.functions.LinearFunction.derivative()
com.neuralnetwork.shared.functions.SigmoidFunction.derivative()
com.opengamma.analytics.math.function.DoubleFunction1D.derivative()
Returns a function that calculates the first derivative. The method used is central finite difference, with $\epsilon = 10^{-5}$. Implementing classes can override this method to return a function that is the exact functional representation of the first derivative. @return A function that calculates the first derivative of this function
org.apache.commons.math.analysis.DifferentiableUnivariateRealFunction.derivative()
Returns the derivative of the function @return the derivative function
org.apache.commons.math3.analysis.function.Power.derivative()
{@inheritDoc} @deprecated as of 3.1, replaced by {@link #value(DerivativeStructure)}
org.apache.commons.math3.analysis.function.Sin.derivative()
{@inheritDoc} @deprecated as of 3.1, replaced by {@link #value(DerivativeStructure)}
org.apache.commons.math3.analysis.polynomials.PolynomialFunction.derivative()
Returns the derivative as a {@link UnivariateFunction}. @return the derivative function.
org.jquantlib.math.distributions.CumulativeNormalDistribution.derivative()
Computes the derivative. @param x @return gaussian.evaluate(xn) / sigma
org.jquantlib.math.distributions.NormalDistribution.derivative()
{@inheritDoc}
Calculates the first derivative of a Normal distribution at point {@latex$ x } @param x @return the first derivative of a Normal distribution at point {@latex$ x }
org.opengis.referencing.operation.MathTransform1D.derivative()
Gets the derivative of this function at a value. The derivative is the 1×1 matrix of the non-translating portion of the approximate affine map at the value. @param value The value where to evaluate the derivative. @return The derivative at the specified point. @throws TransformException if the derivative can't be evaluated at thespecified point.

Examples of org.jquantlib.math.distributions.CumulativeNormalDistribution.derivative()

            D2 = D1 - 2.0 * lambda * stdDev;
            final CumulativeNormalDistribution f = new CumulativeNormalDistribution();
            cum_d1 = f.op(D1);
            cum_d2 = f.op(D2);
            n_d1 = f.derivative(D1);
            n_d2 = f.derivative(D2);
        } else {
            // TODO: not tested yet
            mu = Math.log(dividendDiscount / discount) / variance - 0.5;
            lambda = Math.sqrt( mu * mu - 2.0 * Math.log(discount) / variance);
            if (log_H_S > 0) {

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Examples of org.jquantlib.math.distributions.CumulativeNormalDistribution.derivative()

        strike = strike + displacement;
        final double d1 = Math.log(forward/strike)/stddev + .5*stddev;


        // TODO: code review
        final CumulativeNormalDistribution cdf = new CumulativeNormalDistribution();
        return discount * forward * cdf.derivative(d1);
    }


    // ---
    // ---
    // ---

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Examples of org.jquantlib.math.distributions.CumulativeNormalDistribution.derivative()

        if (stddev == 0.0) return discount * Math.max(d, 0.0);


        // TODO: code review
        final CumulativeNormalDistribution phi = new CumulativeNormalDistribution();
        @NonNegative
        final double result = discount * stddev * phi.derivative(h) + d * phi.op(h);
        if (result >= 0.0) return result;
        throw new ArithmeticException("negative value");
    }


    // ---

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Examples of org.jquantlib.math.distributions.CumulativeNormalDistribution.derivative()

            D1 = log_H_S / stdDev + mu * stdDev;
            D2 = D1 - 2.0 * mu * stdDev;
            final CumulativeNormalDistribution f = new CumulativeNormalDistribution();
            cum_d1 = f.op(D1);
            cum_d2 = f.op(D2);
            n_d1 = f.derivative(D1);
            n_d2 = f.derivative(D2);
        } else {
            if (log_H_S > 0) {
                cum_d1 = 1.0;
                cum_d2 = 1.0;

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Examples of org.jquantlib.math.distributions.CumulativeNormalDistribution.derivative()

            D2 = D1 - 2.0 * mu * stdDev;
            final CumulativeNormalDistribution f = new CumulativeNormalDistribution();
            cum_d1 = f.op(D1);
            cum_d2 = f.op(D2);
            n_d1 = f.derivative(D1);
            n_d2 = f.derivative(D2);
        } else {
            if (log_H_S > 0) {
                cum_d1 = 1.0;
                cum_d2 = 1.0;
            }

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Examples of org.jquantlib.math.distributions.CumulativeNormalDistribution.derivative()

                D1 = Math.log(forward / strike) / stdDev + 0.5 * stdDev;
                D2 = D1 - stdDev;
                final CumulativeNormalDistribution f = new CumulativeNormalDistribution();
                cum_d1 = f.op(D1);
                cum_d2 = f.op(D2);
                n_d1 = f.derivative(D1);
                n_d2 = f.derivative(D2);
            }
        } else {
            if (forward > strike) {
                cum_d1 = 1.0;

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Examples of org.jquantlib.math.distributions.CumulativeNormalDistribution.derivative()

                D2 = D1 - stdDev;
                final CumulativeNormalDistribution f = new CumulativeNormalDistribution();
                cum_d1 = f.op(D1);
                cum_d2 = f.op(D2);
                n_d1 = f.derivative(D1);
                n_d2 = f.derivative(D2);
            }
        } else {
            if (forward > strike) {
                cum_d1 = 1.0;
                cum_d2 = 1.0;

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Examples of org.jquantlib.math.distributions.CumulativeNormalDistribution.derivative()

        case Call:
            Q = (-(n-1.0) + Math.sqrt(((n-1.0)*(n-1.0)) + 4 * K)) / 2;
            LHS = Si - payoff.strike();
            RHS = temp + (1 - dividendDiscount * cumNormalDist.op(d1)) * Si / Q;
            bi =  dividendDiscount * cumNormalDist.op(d1) * (1 - 1/Q)
            + (1 - dividendDiscount * cumNormalDist.derivative(d1) / Math.sqrt(variance)) / Q;
            while (Math.abs(LHS - RHS)/payoff.strike() > tolerance) {
                Si = (payoff.strike() + RHS - bi * Si) / (1 - bi);
                forwardSi = Si * dividendDiscount / riskFreeDiscount;
                d1 = (Math.log(forwardSi/payoff.strike())+0.5*variance)/Math.sqrt(variance);
                LHS = Si - payoff.strike();

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Examples of org.jquantlib.math.distributions.CumulativeNormalDistribution.derivative()

                d1 = (Math.log(forwardSi/payoff.strike())+0.5*variance)/Math.sqrt(variance);
                LHS = Si - payoff.strike();
                final double /*@Real*/ temp2 = BlackFormula.blackFormula(payoff.optionType(), payoff.strike(), forwardSi, Math.sqrt(variance))*riskFreeDiscount;
                RHS = temp2 + (1 - dividendDiscount * cumNormalDist.op(d1)) * Si / Q;
                bi = dividendDiscount * cumNormalDist.op(d1) * (1 - 1 / Q)
                + (1 - dividendDiscount * cumNormalDist.derivative(d1) / Math.sqrt(variance)) / Q;
            }
            break;
        case Put:
            Q = (-(n-1.0) - Math.sqrt(((n-1.0)*(n-1.0)) + 4 * K)) / 2;
            LHS = payoff.strike() - Si;

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Examples of org.jquantlib.math.distributions.CumulativeNormalDistribution.derivative()

        case Put:
            Q = (-(n-1.0) - Math.sqrt(((n-1.0)*(n-1.0)) + 4 * K)) / 2;
            LHS = payoff.strike() - Si;
            RHS = temp - (1 - dividendDiscount * cumNormalDist.op(-d1)) * Si / Q;
            bi = -dividendDiscount * cumNormalDist.op(-d1) * (1 - 1/Q)
            - (1 + dividendDiscount * cumNormalDist.derivative(-d1) / Math.sqrt(variance)) / Q;
            while (Math.abs(LHS - RHS)/payoff.strike() > tolerance) {
                Si = (payoff.strike() - RHS + bi * Si) / (1 + bi);
                forwardSi = Si * dividendDiscount / riskFreeDiscount;
                d1 = (Math.log(forwardSi/payoff.strike())+0.5*variance)/Math.sqrt(variance);
                LHS = payoff.strike() - Si;

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