SuanShu, a Java numerical and statistical library
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E

Eigen - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.eigen
This class implements the eigenvalue decomposition of a matrix.
Eigen(Matrix, Eigen.Method, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.Eigen
Compute the eigenvalue and eigenvectors for a matrix.
Eigen(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.Eigen
Compute the eigenvalue and eigenvectors for a matrix.
Eigen.Method - Enum in com.numericalmethod.suanshu.matrix.doubles.factorization.eigen
the methods available to compute eigenvalue and eigenvectors
Eigen.Property - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.eigen
Property contains the eigen information about a particular eigen value, such as its multiplicity and the associated eigen vectors.
eigenbasis() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.Eigen.Property
Get a copy of the eigen vectors for the eigen value.
EigenDecomposition - Interface in com.numericalmethod.suanshu.matrix.doubles.factorization.eigen
Let A be a square (N×N) matrix with N linearly independent eigenvectors.
eigenvalue(int) - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.Eigen
Get the i-th eigenvalue.
eigenvalue() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.Eigen.Property
Get the eigenvalue of this Property.
eigenvalues() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.CharacteristicPolynomial
 
eigenvalues() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.Eigen
 
eigenvalues() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.QRAlgorithm
Get all the eigenvalues.
eigenvalues() - Method in class com.numericalmethod.suanshu.stats.cointegration.CointegrationMle
Get the set of real eigenvalues.
eigenVector() - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.Eigen.Property
Get an eigenvector for this eigen value.
ElementaryFunction - Class in com.numericalmethod.suanshu.number.complex
This class contains some elementary functions for complex number Complex.
ElementaryFunction() - Constructor for class com.numericalmethod.suanshu.number.complex.ElementaryFunction
 
ElementaryOperation - Class in com.numericalmethod.suanshu.matrix.doubles.operation
There are three elementary row operations which are equivalent to left multiplying an elementary matrix.
ElementaryOperation(int, int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.ElementaryOperation
Construct an instance of ElementaryOperation of dimension nRows x nCols.
ElementaryOperation(int) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.ElementaryOperation
Construct an instance of ElementaryOperation of dimension dim.
ElementaryOperation(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.ElementaryOperation
Get a copy of a transformation matrix.
EmpiricalDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
An empirical cumulative probability distribution function is a cumulative probability distribution function that assigns probability 1/n at each of the n numbers in a sample.
EmpiricalDistribution(double[], Quantile.Type) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
Construct an empirical distribution from a sample.
EmpiricalDistribution(double[]) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
Construct an empirical distribution from a sample.
end - Variable in class com.numericalmethod.suanshu.interval.Interval
the end of this interval
ENDING_OF_TIME - Static variable in class com.numericalmethod.suanshu.time.JodaTimeUtils
This represents a time after all (representable) times.
ENDING_OF_TIME_LONG - Static variable in class com.numericalmethod.suanshu.time.JodaTimeUtils
This represents a time after all (representable) times, in long representation.
entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution
 
entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ChiSquareDistribution
 
entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.EmpiricalDistribution
Deprecated. Not supported yet.
entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.ExponentialDistribution
 
entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.FDistribution
Deprecated. Not supported yet.
entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.GammaDistribution
Deprecated. Not supported yet.
entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.NormalDistribution
 
entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.RayleighDistribution
 
entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.TDistribution
 
entropy() - Method in interface com.numericalmethod.suanshu.stats.distribution.univariate.UnivariateDistribution
Get the entropy of this distribution.
entropy() - Method in class com.numericalmethod.suanshu.stats.distribution.univariate.WeibullDistribution
 
entropy() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution
Deprecated. Not supported yet.
entropy() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
Deprecated. Not supported yet.
entropy() - Method in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
Deprecated. Not supported yet.
entropy() - Method in class com.numericalmethod.suanshu.stats.test.distribution.normality.ShapiroWilkDistribution
Deprecated. Not supported yet.
entropy() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonRankSumDistribution
Deprecated. Not supported yet.
entropy() - Method in class com.numericalmethod.suanshu.stats.test.rank.wilcoxon.WilcoxonSignedRankDistribution
Deprecated. Not supported yet.
EPSILON - Static variable in class com.numericalmethod.suanshu.Constant
the default epsilon used in this library
epsilon - Variable in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.Eigen
a precision parameter: when a number |x| ≤ ε, it is considered 0
epsilon - Variable in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.QRAlgorithm
a precision parameter: when a number |x| ≤ ε, it is considered 0
epsilon - Variable in class com.numericalmethod.suanshu.matrix.doubles.factorization.gaussianelimination.GaussianElimination
a precision parameter: when a number |x| ≤ ε, it is considered 0
epsilon - Variable in class com.numericalmethod.suanshu.matrix.doubles.factorization.gaussianelimination.GaussJordanElimination
a precision parameter: when a number |x| ≤ ε, it is considered 0
epsilon - Variable in class com.numericalmethod.suanshu.matrix.doubles.factorization.qr.GramSchmidt
a precision parameter: when a number |x| ≤ ε, it is considered 0
epsilon - Variable in class com.numericalmethod.suanshu.matrix.doubles.factorization.qr.HouseholderReflection
a precision parameter: when a number |x| ≤ ε, it is considered 0
epsilon - Variable in class com.numericalmethod.suanshu.matrix.doubles.factorization.qr.QR
a precision parameter: when a number |x| ≤ ε, it is considered 0
epsilon - Variable in class com.numericalmethod.suanshu.matrix.doubles.factorization.svd.GloubKahanSVD
a precision parameter: when a number |x| ≤ ε, it is considered 0
epsilon - Variable in class com.numericalmethod.suanshu.matrix.doubles.factorization.svd.SVD
a precision parameter: when a number |x| ≤ ε, it is considered 0
epsilon - Variable in class com.numericalmethod.suanshu.matrix.doubles.factorization.triangle.Doolittle
a precision parameter: when a number |x| ≤ ε, it is considered 0
epsilon - Variable in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.Kernel
a precision parameter: when a number |x| ≤ ε, it is considered 0
epsilon - Variable in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.OLSSolver
a precision parameter: when a number |x| ≤ ε, it is considered 0
epsilon - Variable in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.Solver
a precision parameter: when a number |x| ≤ ε, it is considered 0
epsilon - Variable in class com.numericalmethod.suanshu.matrix.doubles.operation.Inverse
a precision parameter: when a number |x| ≤ ε, it is considered 0
epsilon - Variable in class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.Tableau
a precision parameter: when a number |x| ≤ ε, it is considered 0
epsilon - Variable in class com.numericalmethod.suanshu.vector.doubles.dense.operation.VectorSpace
a precision parameter: when a number |x| ≤ ε, it is considered 0
equal(Matrix, Matrix, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.AreMatrices
Check the equality of two matrices up to a precision.
equal(Vector, Vector, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.AreMatrices
Check if two vectors are equal up to a precision.
equal(double, double, double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
Check if two doubles are close enough, hence equal.
equal(double[], double[], double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
Check if two double arrays are close enough, hence equal, entry-by-entry.
equal(double[][], double[][], double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
Check if two 2D double[][] arrays are close enough, hence equal, entry-by-entry.
equal(int[], int[]) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
Check if two int[] arrays are equal, entry-by-entry.
equal(Number, Number, double) - Static method in class com.numericalmethod.suanshu.number.NumberUtils
Check the equality of two Numbers, up to a precision.
equalities - Variable in class com.numericalmethod.suanshu.optimization.constrained.general.ConstrainedProblem
the equality constraints hi(x) = 0
EqualityConstraints - Class in com.numericalmethod.suanshu.optimization.constrained.general
This class represents the equality constraints for an optimization problem.
EqualityConstraints(RealScalarFunction...) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.EqualityConstraints
Construct a set of equality constraints for a constrained optimization problem.
equals(Object) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
 
equals(Object) - Method in class com.numericalmethod.suanshu.interval.Interval
 
equals(Object) - Method in class com.numericalmethod.suanshu.interval.Intervals
 
equals(Object) - Method in class com.numericalmethod.suanshu.matrix.doubles.ImmutableMatrix
 
equals(Object) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.DenseData
Check if two DenseData are equal.
equals(Object) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.triangle.SymmetricMatrix
 
equals(Object) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.MatrixStorageImpl
Check if two matrices (of different implementations) are equal.
equals(Object) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.PermutationMatrix
Compares this matrix to another matrix.
equals(Object) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.Coordinates
 
equals(Object) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseElement
 
equals(Object) - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.ComplexMatrix
 
equals(Object) - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.GenericMatrix
 
equals(Object) - Method in class com.numericalmethod.suanshu.matrix.generic.matrixtype.RealMatrix
 
equals(BigDecimal, BigDecimal, int) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
Compare two BigDecimals up to a precision.
equals(Object) - Method in class com.numericalmethod.suanshu.number.complex.Complex
 
equals(Object) - Method in class com.numericalmethod.suanshu.number.Real
 
equals(Object) - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.GenericTimeTimeSeries
 
equals(Object) - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.SimpleMultiVariateTimeSeries
 
equals(Object) - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.GenericTimeTimeSeries
 
equals(Object) - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.SimpleTimeSeries
 
equals(Object) - Method in class com.numericalmethod.suanshu.time.ComparableDateTime
 
equals(Object) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
IsVector the equality of two vectors, i.e., whether all entries are equal.
equals(Object) - Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
 
equalSize(Vector, Vector) - Static method in class com.numericalmethod.suanshu.vector.doubles.IsVector
Check if the input vectors are of the same size.
Erf - Class in com.numericalmethod.suanshu.analysis.function.special
This class computes an approximation to the error function, erf(x).
Erf() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.Erf
 
Erfc - Class in com.numericalmethod.suanshu.analysis.function.special
This computes an approximation to the complementary error function, erfc(x).
Erfc() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.Erfc
 
ErfInverse - Class in com.numericalmethod.suanshu.analysis.function.special
This class computes an approximation to the inverse of the error function, erf-1(x).
ErfInverse() - Constructor for class com.numericalmethod.suanshu.analysis.function.special.ErfInverse
 
error - Variable in class com.numericalmethod.suanshu.optimization.minmax.MinMaxProblem
the minmax objective function e(x, ω)
EST - Static variable in class com.numericalmethod.suanshu.time.JodaTimeUtils
EST
estimate - Variable in class com.numericalmethod.suanshu.stats.test.variance.F
the estimate of the ratio of two variances
Euler - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde
The Euler method is a first-order numerical procedure for integrating stochastic differential equations (SDEs) with a given initial value.
Euler(SDE, TimeGrid) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde.Euler
Simulate an SDE using the Euler scheme at time points specified.
Euler(SDE, int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde.Euler
Simulate an SDE using the Euler scheme at even time points, [0, 1, ......, T].
Euler - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde
The Euler scheme is the first order approximation of an SDE.
Euler(SDE) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.Euler
Discretize a multivariate SDE using the Euler scheme.
Euler - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde
The Euler method is a first-order numerical procedure for integrating stochastic differential equations (SDEs) with a given initial value.
Euler(SDE, TimeGrid) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.Euler
Simulate an SDE using the Euler scheme at time points specified.
Euler(SDE, int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.Euler
Simulate an SDE using the Euler scheme at even time points, [0, 1, ......, T].
Euler - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde
The Euler scheme is the first order approximation of an SDE.
Euler(SDE) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Euler
Discretize a univariate SDE using the Euler scheme.
EULER_MASCHERONI - Static variable in class com.numericalmethod.suanshu.Constant
the Euler–Mascheroni constant
EulerMaclaurin - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann
This class implements a specialized group of the Newton-Cotes integration methods using the Euler-Maclaurin formula.
EulerMaclaurin(int, EulerMaclaurin.NewtonCotesType, double, int) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.EulerMaclaurin
Construct an instance of the Euler-Maclaurin formula.
EulerMaclaurin.NewtonCotesType - Enum in com.numericalmethod.suanshu.analysis.integration.univariate.riemann
Newton-Cotes methods can be classified into two categories: OPEN and CLOSED.
evaluate(double...) - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.FiniteDifference
Numerically evaluate the derivative of f at point x.
evaluate(double[], double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.FiniteDifference
Numerically evaluate the derivative of f at point x with step size h.
evaluate(double...) - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.GradientFunction
Compute g(x).
evaluate(double...) - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.HessianFunction
Compute H(x).
evaluate(double...) - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.JacobianFunction
Compute J(x).
evaluate(double...) - Method in class com.numericalmethod.suanshu.analysis.differentiation.Ridders
A real function, f, takes a double array double[], {x0, x1, ..., xn} and maps it to a double, f(x0, x1, ..., xn)
evaluate(double[], double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.Ridders
Numerically evaluate the derivative of f at point x with step size h.
evaluate(double, double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.univariate.DBeta
Evaluate dB(x, y)/dx.
evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.univariate.DBetaRegularized
Evaluate dBx(p, q)/dx.
evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.univariate.DErf
Evaluate the derivative of the Error function.
evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.univariate.Dfdx
Evaluate f'(x).
evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.univariate.DGamma
Evaluate the derivative of the Gamma function.
evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.univariate.DGaussian
Evaluate the derivative of the Gaussian function.
evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.univariate.FiniteDifference
Numerically evaluate the derivative of f at point x.
evaluate(double[], double) - Method in class com.numericalmethod.suanshu.analysis.differentiation.univariate.FiniteDifference
Numerically evaluate the derivative of f at point x with step size h.
evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.matrix.R1toConstantMatrix
 
evaluate(double...) - Method in class com.numericalmethod.suanshu.analysis.function.matrix.R1toMatrix
 
evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.matrix.R1toMatrix
Compute f(x).
evaluate(double...) - Method in class com.numericalmethod.suanshu.analysis.function.matrix.R2toMatrix
 
evaluate(double, double) - Method in class com.numericalmethod.suanshu.analysis.function.matrix.R2toMatrix
Compute f(x1, x2).
evaluate(double...) - Method in interface com.numericalmethod.suanshu.analysis.function.matrix.RntoMatrix
A RntoMatrix function, f, takes a double array double[], {x0, x1, ..., xn} and maps it to a Matrix, M = f(x0, x1, ..., xn)
evaluate(Number) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
Evaluate this polynomial for a Number input.
evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
Evaluate this polynomial for a real number, i.e., double input.
evaluate(Complex) - Method in class com.numericalmethod.suanshu.analysis.function.polynomial.Polynomial
Evaluate this polynomial for a Complex number input.
evaluate(double...) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.BivariateRealFunction
 
evaluate(double, double) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.BivariateRealFunction
Compute f(x1, x2).
evaluate(double, double, int) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.ContinuedFraction
This implements the modified Lentz's method.
evaluate(BigDecimal, int, int) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.ContinuedFraction
This implements the modified Lentz's method using arbitrary precision arithmetics BigDecimal.
evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.ContinuedFraction
 
evaluate(double...) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.Projection
 
evaluate(double...) - Method in interface com.numericalmethod.suanshu.analysis.function.rn2r1.RealScalarFunction
A real function, f, takes a double array double[], {x0, x1, ..., xn} and maps it to a double, f(x0, x1, ..., xn)
evaluate(double...) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.RealScalarFunctionFixedVariables
 
evaluate(double...) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.UnivariateRealFunction
 
evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.UnivariateRealFunction
Compute f(x).
evaluate(double...) - Method in interface com.numericalmethod.suanshu.analysis.function.rn2rm.RealVectorFunction
A real function, f, takes a double array double[], {x0, x1, ..., xn} and maps it to a Vector, f(x0, x1, ..., xn)
evaluate(double, double) - Method in class com.numericalmethod.suanshu.analysis.function.special.Beta
Compute B(x, y).
evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.BetaRegularized
Evaluate Bx(p, q).
evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.BetaRegularizedInverse
Evaluate Bx(p, q).
evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.CumulativeNormal
 
evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.CumulativeNormalInverse
 
evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.Digamma
 
evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.Erf
 
evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.Erfc
 
evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.ErfInverse
 
evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.Gamma
Compute for the Gamma function in the Real domain.
evaluate(double, double) - Method in class com.numericalmethod.suanshu.analysis.function.special.GammaLowerIncomplete
Evaluate γ(s, x).
evaluate(double, double) - Method in class com.numericalmethod.suanshu.analysis.function.special.GammaRegularizedP
Evaluate P(s, x).
evaluate(double, double) - Method in class com.numericalmethod.suanshu.analysis.function.special.GammaRegularizedPInverse
Evaluate x = P-1(s, u).
evaluate(double, double) - Method in class com.numericalmethod.suanshu.analysis.function.special.GammaRegularizedQ
Evaluate Q(s, x).
evaluate(double, double) - Method in class com.numericalmethod.suanshu.analysis.function.special.GammaUpperIncomplete
Compute Γ(s, x).
evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.Gaussian
 
evaluate(double, double) - Method in class com.numericalmethod.suanshu.analysis.function.special.LogBeta
Compute log(B(x, y)).
evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.function.special.LogGamma
Compute for the Gamma function in the positive Real domain.
evaluate(double) - Method in class com.numericalmethod.suanshu.analysis.interpolation.NevilleTable
 
evaluate(double) - Method in interface com.numericalmethod.suanshu.analysis.interpolation.UnivariateRealInterpolator
Interpolate the estimated function to a point x to compute y^ = f(x).
evaluate(double) - Method in interface com.numericalmethod.suanshu.analysis.sequence.Summation.Term
Evaluate the term for a given index.
evaluate(double...) - Method in class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.AbsoluteError
 
evaluate(double...) - Method in class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.Courant
 
evaluate(double...) - Method in class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.Fletcher
 
evaluate(double...) - Method in class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.SumOfPenalties
 
evaluate(double...) - Method in class com.numericalmethod.suanshu.optimization.constrained.general.penaltymethod.ZERO
 
evaluate(Object) - Method in interface com.numericalmethod.suanshu.optimization.minmax.MinMaxProblem.Error
e(x, ω): the error function for a given ω
evaluate(Object) - Method in interface com.numericalmethod.suanshu.optimization.minmax.MinMaxProblem.Gradient
g(x, ω) = ∇|e(x, ω)|: the gradient function of the absolute error, |e(x, ω)|
evaluate(Realization[]) - Method in interface com.numericalmethod.suanshu.stats.cointegration.JohansenAsymptoticDistributionSimulation.F
 
evaluate(X) - Method in interface com.numericalmethod.suanshu.stats.distribution.ProbabilityMassFunction
Compute the probability mass for a discrete realization x.
evaluate(Ft) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantDrift
 
evaluate(Ft) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ConstantSigma1
 
evaluate(Ft) - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.Diffusion
σ(dt, Xt, Zt, ...)
evaluate(Ft) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.Sigma
 
evaluate(Ft) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients.ZeroDrift
 
evaluate(Ft) - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.FtAdaptedRealFunction
Evaluate this function, f, at time t.
evaluate(Ft) - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.FtAdaptedVectorFunction
Evaluate this function, f, at time t.
evaluate(int) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Bt
 
evaluate(int) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.FiltrationFunction
Compute the value at the t-th time point, f(T[t]).
evaluate(double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.FiltrationFunction
 
evaluate(Ft) - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.FtAdaptedFunction
Evaluate this function, f, at time t.
evaluate(double) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.XtAdaptedFunction
Evaluate this function, f, based on only the current value of the stochastic process.
evaluate(Ft) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.XtAdaptedFunction
 
evaluate(double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.AutoCorrelation
 
evaluate(double, double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.AutoCorrelation
 
evaluate(double, double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.AutoCovariance
 
evaluate(double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.AutoCovariance
Get the i-th auto-covariance matrix.
evaluate(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.AutoCorrelation
Compute the auto-correlation for lag k.
evaluate(double, double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.AutoCorrelation
 
evaluate(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.AutoCovariance
Compute the auto-covariance for lag k.
evaluate(double, double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.AutoCovariance
 
evaluate(double, double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.PartialAutoCorrelation
 
evaluate(int) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.PartialAutoCorrelation
Compute the partial auto-correlation for lag k.
evaluate(double, double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.AutoCorrelation
 
evaluate(double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.AutoCorrelation
Get the i-th auto-correlation.
evaluate(double, double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.AutoCovariance
 
evaluate(double) - Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arima.arma.AutoCovariance
Get the i-th auto-covariance.
evaluateByRecursion(int, double, double...) - Method in class com.numericalmethod.suanshu.analysis.differentiation.multivariate.FiniteDifference
Evaluate the n-th derivative using Recursion.
EvenlySpacedGrid - Class in com.numericalmethod.suanshu.stats.stochasticprocess.timepoints
This class represents an evenly spaced/discretized time grid for a stochastic process.
EvenlySpacedGrid(double, double, int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.timepoints.EvenlySpacedGrid
Construct an evenly spaced/discretized time grid.
exceptions() - Method in exception com.numericalmethod.suanshu.parallel.MultipleExecutionException
 
execute(Runnable) - Method in class com.numericalmethod.suanshu.parallel.Mutex
 
executeAll(List<? extends Callable<T>>) - Method in class com.numericalmethod.suanshu.parallel.ParallelExecutor
Executes a list of Callable tasks, and returns a list of results in the same order.
executeAll(Callable<T>...) - Method in class com.numericalmethod.suanshu.parallel.ParallelExecutor
Executes an arbitrary number of Callable tasks, and returns a list of results in the same order.
executeAny(List<? extends Callable<T>>) - Method in class com.numericalmethod.suanshu.parallel.ParallelExecutor
Executes a list of tasks in parallel, and returns the result from the earliest successfully completed tasks (without throwing an exception).
executeAny(Callable<T>...) - Method in class com.numericalmethod.suanshu.parallel.ParallelExecutor
Executes a list of tasks in parallel, and returns the result from the earliest successfully completed tasks (without throwing an exception).
exp(double) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
Compute ex.
exp(double, int) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
Compute ex.
exp(BigDecimal) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
Compute ex.
exp(BigDecimal, int) - Static method in class com.numericalmethod.suanshu.number.big.BigDecimalUtils
Compute ex.
exp(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.ElementaryFunction
Exponential of a complex number (a + bi).
Expectation - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration
This class computes the expectation of the following class of integrals.
Expectation(Integrator, double, double, int, int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Expectation
Compute the expectation for the integral of a stochastic process.
Expectation - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde
This class computes the expectation of a stochastic integral.
Expectation(Construction, double, int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.Expectation
Compute an expectation of a stochastic integral.
Expectation(SDE, double, double, int, double, int) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.Expectation
Compute an expectation of a stochastic integral.
expectedContingencyTable(int[], int[]) - Static method in class com.numericalmethod.suanshu.stats.test.distribution.pearson.ChiSquare4Independence
Assume the null hypothesis of independence, we compute the expected frequency of each category.
exponent - Variable in class com.numericalmethod.suanshu.number.ScientificNotation
the exponent
Exponential - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
This transformation is good for when the upper limit is infinity, and the integrand falls off exponentially.
Exponential(double, double) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.Exponential
Construct an instance of the Exponential substitution rule.
Exponential - Class in com.numericalmethod.suanshu.stats.random.distribution
Sample pseudo random numbers from the ExponentialDistribution distribution.
Exponential(double, RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.distribution.Exponential
Construct a pseudo-random number generator of an ExponentialDistribution distribution.
Exponential() - Constructor for class com.numericalmethod.suanshu.stats.random.distribution.Exponential
Construct a pseudo random number generator of the standard ExponentialDistribution distribution.
ExponentialDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
An exponential distribution describes the times between events in a Poisson process, a process in which events occur continuously and independently at a constant average rate.
ExponentialDistribution() - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.ExponentialDistribution
Construct an instance of the standard exponential distribution, where the rate/lambda is 1.
ExponentialDistribution(double) - Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.ExponentialDistribution
Construct an exponential distribution.
ExponentialDistribution - Interface in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution
This interface represents a probability distribution from the exponential family.

SuanShu, a Java numerical and statistical library
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