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.integrals.SegmentIntegral.op()

        final SegmentIntegral integrator = new SegmentIntegral(5000);


        final double infinity = 10.0*Math.sqrt(variance);
        r.value =
            process.riskFreeRate().currentLink().discount(a.exercise.lastDate()) /
            Math.sqrt(2.0*Math.PI*variance) * integrator.op(f, drift-infinity, drift+infinity);
    }




    //
    // private inner classes

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

                    CubicInterpolation.DerivativeApprox.Spline, false,
                    CubicInterpolation.BoundaryCondition.NotAKnot, Constants.NULL_REAL,
                    CubicInterpolation.BoundaryCondition.NotAKnot, Constants.NULL_REAL);
          f.update();


          result = Math.sqrt(integral.op(makeErrorFunction(f), -1.7, 1.9));
          result /= scaleFactor;
            assertFalse("Not-a-knot spline interpolation "
                    +"\n    sample points:      "+n
                    +"\n    norm of difference: "+result
                    +"\n    it should be:       "+tabulatedErrors[i],

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

                    CubicInterpolation.DerivativeApprox.Spline, true,
                    CubicInterpolation.BoundaryCondition.NotAKnot, Constants.NULL_REAL,
                    CubicInterpolation.BoundaryCondition.NotAKnot, Constants.NULL_REAL);
          f.update();


            result = Math.sqrt(integral.op(makeErrorFunction(f), -1.7, 1.9));
          result /= scaleFactor;
            assertFalse ("MC Not-a-knot spline interpolation "
                    + "\n    sample points:      "
                    + "\n    norm of difference: " + result
                    + "\n    it should be:       " + tabulatedErrors[i],

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Examples of org.jquantlib.math.interpolations.BackwardFlatInterpolation.op()

        final double tolerance = 1.0e-12;


        // at original points
        for (int i=0; i<N; i++) {
            final double p = x.get(i);
            final double calculated = f.op(p);
            final double expected = y.get(i);
            assertFalse("failed to reproduce expected datum"
                    +"\n    expected value:   "+expected
                    +"\n    calculated value: "+calculated
                    +"\n    error:              "+Math.abs(calculated-expected),

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Examples of org.jquantlib.math.interpolations.BackwardFlatInterpolation.op()

        }


        // at middle points
        for (int i=0; i<N-1; i++) {
            final double p = (x.get(i)+x.get(i+1))/2;
            final double calculated = f.op(p);
            final double expected = y.get(i+1);
            assertFalse("failed to reproduce expected datum"
                    +"\n    expected value:   "+expected
                    +"\n    calculated value: "+calculated
                    +"\n    error:              "+Math.abs(calculated-expected),

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Examples of org.jquantlib.math.interpolations.BackwardFlatInterpolation.op()

        f.enableExtrapolation();


        {
            // this is just a block
            double p = x.get(0) - 0.5;
            double calculated = f.op(p);
            double expected = y.get(0);
            assertFalse("failed to reproduce expected datum"
                    +"\n    expected value:   "+expected
                    +"\n    calculated value: "+calculated
                    +"\n    error:              "+Math.abs(calculated-expected),

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Examples of org.jquantlib.math.interpolations.BackwardFlatInterpolation.op()

                    +"\n    calculated value: "+calculated
                    +"\n    error:              "+Math.abs(calculated-expected),
                    Math.abs(calculated - expected) > tolerance);


            p = x.get(N-1) + 0.5;
            calculated = f.op(p);
            expected = y.get(N-1);
            assertFalse("failed to reproduce expected datum"
                    +"\n    expected value:   "+expected
                    +"\n    calculated value: "+calculated
                    +"\n    error:              "+Math.abs(calculated-expected),

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Examples of org.jquantlib.math.interpolations.CubicInterpolation.op()

                  +"\n interpolated value: "+interpolated
                  +"\n expected value:     "+generic_natural_y2.get(i)
                  +"\n error:              "+error,
                  abs(error) > 3e-16);
        }
        x35[1] = f.op(3.5);




        // Clamped spline
        final double y1a = 0.0;
        final double y1b = 0.0;

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Examples of org.jquantlib.math.interpolations.CubicInterpolation.op()

        f.update();


        checkValues("Clamped spline", f, generic_x, generic_y);
        check1stDerivativeValue("Clamped spline", f, generic_x.first(), 0.0);
        check1stDerivativeValue("Clamped spline", f, generic_x.last(),  0.0);
        x35[0] = f.op(3.5);




        // Not-a-knot spline
        f = new CubicInterpolation(
                generic_x, generic_y,

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Examples of org.jquantlib.math.interpolations.CubicInterpolation.op()

                CubicInterpolation.BoundaryCondition.NotAKnot, Constants.NULL_REAL);
        f.update();


        checkValues("Not-a-knot spline", f, generic_x, generic_y);
        checkNotAKnotCondition("Not-a-knot spline", f);
        x35[2] = f.op(3.5);


        assertFalse("Spline interpolation failure"
            +"\n at x = "+3.5
            +"\n clamped spline    "+x35[0]
            +"\n natural spline    "+x35[1]

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