List of op() Examples

Examples of op()

com.google.dexmaker.Code.op()
Executes {@code op} and sets {@code target} to the result.
kodkod.ast.BinaryExpression.op()
Returns this.op. @return this.op
kodkod.ast.BinaryFormula.op()
Returns the operator of this. @return this.op
kodkod.ast.ComparisonFormula.op()
Returns the operator of this. @return this.op
kodkod.ast.NaryFormula.op()
Returns the operator of this. @return this.op
kodkod.engine.bool.BooleanAccumulator.op()
Returns the operator for this accumulator. @return this.op
kodkod.engine.bool.BooleanValue.op()
Returns the operator representing the function computed by this gate. @return this.op
org.jquantlib.math.distributions.BinomialDistribution.op()
Computes the probability of k successful trials. @param k Number of successful trials @return Math.exp(binomialCoefficientLn(nExp, k) + k * logP + (nExp-k) * logOneMinusP);
org.jquantlib.math.distributions.BivariateNormalDistribution.op()
Computes the Bivariate Normal Distribution of the two variables x and y which can be correlated in a particular manner. @param x First variable @param y Second variable @return BVN
org.jquantlib.math.distributions.CumulativeBinomialDistribution.op()
{@inheritDoc}
Computes the Cumulative Binomial Distribution. @param k @return 1.0 - Beta.incompleteBetaFunction(k+1, n_-k, p_, accuracy, maxIteration)
org.jquantlib.math.distributions.CumulativeNormalDistribution.op()
Computes the cumulative normal distribution.
Asymptotic expansion for very negative z as references on M. Abramowitz book. @see cite: "Monte Carlo Methods in Finance", ISBN-13: 978-0471497417 @see cite: M. Abramowitz and A. Stegun, Pocketbook of Mathematical Functions, ISBN 3-87144818-4, p.408, item 26.2.12 @param z @return result
org.jquantlib.math.distributions.CumulativePoissonDistribution.op()
{@inheritDoc}
Computes the cumulative Poisson distribution by using the incomplete gamma function . @param k is the number of occurrences of an event @return the cumulative Poisson distribution by using the incomplete gamma function
org.jquantlib.math.distributions.GammaDistribution.op()
Computes the Gamma distribution. @param x random variable @return Gamma distribution of x
org.jquantlib.math.distributions.InverseCumulativeNormal.op()
Computes the inverse cumulative normal distribution. @param x @returns average + z * sigma
org.jquantlib.math.distributions.InverseCumulativePoisson.op()
Computes the inverse cumulative poisson distribution. @param x @returns the inverse of the cumulative poisson distribution of input x
org.jquantlib.math.distributions.MoroInverseCumulativeNormal.op()
org.jquantlib.math.distributions.NonCentralChiSquaredDistribution.op()
org.jquantlib.math.distributions.NormalDistribution.op()
{@inheritDoc}
Computes the Normal distribution at point {@latex$ x } @param x @return the Normal distribution at point {@latex$ x }
org.jquantlib.math.distributions.PoissonDistribution.op()
{@inheritDoc}
PoissonDistribution evaluation
Compute the Poisson Distribution with input {@latex$ \mu} and {@latex$ k}. @param k @return Math.exp(k*Math.log(mu_) - logFactorial - mu_)
org.jquantlib.math.integrals.GaussKronrodAdaptive.op()
org.jquantlib.math.integrals.GaussKronrodPatterson.op()
org.jquantlib.math.integrals.Integrator.op()
org.jquantlib.math.integrals.SegmentIntegral.op()
org.jquantlib.math.integrals.SimpsonIntegral.op()
org.jquantlib.math.interpolations.BackwardFlatInterpolation.op()
org.jquantlib.math.interpolations.CubicInterpolation.op()
org.jquantlib.math.interpolations.ForwardFlatInterpolation.op()
org.jquantlib.math.interpolations.Interpolation.op()
org.jquantlib.math.interpolations.NaturalCubicInterpolation.op()
org.jquantlib.math.matrixutilities.internal.Address.MatrixAddress.MatrixOffset.op()
org.locationtech.udig.validation.ValidateGeometry.op()
org.locationtech.udig.validation.ValidateLineMustBeASinglePart.op()
org.locationtech.udig.validation.ValidateLineNoSelfIntersect.op()
org.locationtech.udig.validation.ValidateLineNoSelfOverlapping.op()
org.locationtech.udig.validation.ValidateOverlaps.op()
org.mvel2.util.ExecutionStack.op()
ro.redeul.google.go.lang.psi.expressions.binary.GoRelationalExpression.op()

Examples of org.jquantlib.math.distributions.NonCentralChiSquaredDistribution.op()

        final NonCentralChiSquaredDistribution chis = new NonCentralChiSquaredDistribution(df, ncps);
        final NonCentralChiSquaredDistribution chit = new NonCentralChiSquaredDistribution(df, ncpt);


        final double z = Math.log(super.A(t, s) / strike) / b;
        final double call = discountS * chis.op(2.0 * z * (rho + psi + b)) - strike * discountT
        * chit.op(2.0 * z * (rho + psi));
        if (type.equals(Option.Type.Call))
            return call;
        else
            return call - discountS + strike * discountT;

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Examples of org.jquantlib.math.distributions.NonCentralChiSquaredDistribution.op()

        final NonCentralChiSquaredDistribution chis = new NonCentralChiSquaredDistribution(df, ncps);
        final NonCentralChiSquaredDistribution chit = new NonCentralChiSquaredDistribution(df, ncpt);


        final double /* @Real */z = Math.log(A(t, s) / strike) / b;
        final double /*@Real*/ call = discountS*chis.op(2.0*z*(rho+psi+b)) -
        strike*discountT*chit.op(2.0*z*(rho+psi));


        if (type == Option.Type.Call) {
            return 0.0;
        } else {
            return 1.0;

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Examples of org.jquantlib.math.distributions.NormalDistribution.op()

        final CumulativeNormalDistribution gIntegral = new CumulativeNormalDistribution(m, std);
        final NormalDistribution g = new NormalDistribution(m, std);
        final double firstTerm = variance + m * m - 2.0 * target * m + target * target;
        final double alfa = gIntegral.op(target);
        final double secondTerm = m - target;
        final double beta = variance * g.op(target);
        final double result = alfa * firstTerm - beta * secondTerm;
        return result / alfa;
    }


    /*

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Examples of org.jquantlib.math.distributions.NormalDistribution.op()

        final double m = statistics.mean();
        final double std = statistics.standardDeviation();
        final InverseCumulativeNormal gInverse = new InverseCumulativeNormal(m, std);
        final double var = gInverse.op(1.0 - percentile);
        final NormalDistribution g = new NormalDistribution(m, std);
        final double result = m - std * std * g.op(var) / (1.0 - percentile);
        // expectedShortfall must be a loss
        // this means that it has to be MIN(result, 0.0)
        // expectedShortfall must also be a positive quantity, so -MIN(*)
        return -Math.min(result, 0.0);
    }

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Examples of org.jquantlib.math.distributions.NormalDistribution.op()

    public double gaussianAverageShortfall(final double target) {
        final double m = statistics.mean();
        final double std = statistics.standardDeviation();
        final CumulativeNormalDistribution gIntegral = new CumulativeNormalDistribution(m, std);
        final NormalDistribution g = new NormalDistribution(m, std);
        return ((target - m) + std * std * g.op(target) / gIntegral.op(target));
    }


    /*
     * TODO: where do we need this one???????????????????????????????? //! Helper class for precomputed distributions class
     * StatsHolder { public: typedef Real value_type; StatsHolder(Real mean, Real standardDeviation) : mean_(mean),

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Examples of org.jquantlib.math.distributions.PoissonDistribution.op()

            volTS.linkTo(new BlackConstantVol(rateRefDate, volcal, v, voldc));


            baseArguments.validate();
            baseEngine.calculate();


            weight = p.op(i);
            R.value += weight * baseResults.value;
            greeks.delta += weight * baseResults.greeks().delta;
            greeks.gamma += weight * baseResults.greeks().gamma;
            greeks.vega += weight * (Math.sqrt(variance / t) / v) * baseResults.greeks().vega;
            // theta modified

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Examples of org.jquantlib.math.integrals.GaussKronrodAdaptive.op()

          double tmp=0.0;
          final Var_Helper helper = new Var_Helper(this, i, j);


          final GaussKronrodAdaptive integrator = new GaussKronrodAdaptive(1e-10, 10000);
          for (int k=0; k<64; ++k) {
            tmp+=integrator.op(helper, k*t/64., (k+1)*t/64.);
        }
          return tmp;
      }


}

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Examples of org.jquantlib.math.integrals.GaussKronrodAdaptive.op()

        for (int i = 0; i < size_; ++i) {
            for (int j = 0; j <= i; ++j) {
                final Var_Helper helper = new Var_Helper(this, i, j);
                final GaussKronrodAdaptive integrator = new GaussKronrodAdaptive(1e-10, 10000);
                for(int k = 0; k<64; ++k) {
                    tmp.set(i, j, tmp.get(i, j)+integrator.op(helper, k*t/64.0,(k+1)*t/64.0));
                }
                tmp.set(j,i, tmp.get(i, j));
            }
        }

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Examples of org.jquantlib.math.integrals.GaussKronrodPatterson.op()

  }


  private void checkSingleTabulated(final Ops.DoubleOp f, final String tag, final double expected, final double tolerance) {


    final Integrator quad = new GaussKronrodPatterson();
      final double realised = quad.op(f,-1,1);


        if (Math.abs(realised-expected) > tolerance)
          fail(" integrating " + tag + "\n"
                    + "    realised: " + realised + "\n"
                    + "    expected: " + expected);

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Examples of org.jquantlib.math.integrals.Integrator.op()

  }


  private void checkSingleTabulated(final Ops.DoubleOp f, final String tag, final double expected, final double tolerance) {


    final Integrator quad = new GaussKronrodPatterson();
      final double realised = quad.op(f,-1,1);


        if (Math.abs(realised-expected) > tolerance)
          fail(" integrating " + tag + "\n"
                    + "    realised: " + realised + "\n"
                    + "    expected: " + expected);

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