SuanShu, a Java numerical and statistical library
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

I

i - Variable in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.Coordinates
the row index
I - Static variable in class com.numericalmethod.suanshu.number.complex.Complex
the square root of -1; a number representing 0.0 + 1.0i
I - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Expectation
the integrator to compute the integral for each filtration/path
id - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde.RandomWalk.MultiVariateRealization
the ID of this particular realization
id - Variable in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.RandomWalk.Realization
the ID of this particular realization
idempotent(Matrix) - Static method in class com.numericalmethod.suanshu.matrix.doubles.IsMatrix
Check if a matrix is idempotent.
identity(Matrix, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.IsMatrix
Check if a diagonal matrix is an identity matrix.
Identity - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link
This class represents the link function: g(x) = x
Identity() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Identity
 
IdentityPreconditioner - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner
This identity preconditioner is used when no preconditioning is applied.
IdentityPreconditioner() - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner.IdentityPreconditioner
 
ifelse(double[], R.ifelse) - Static method in class com.numericalmethod.suanshu.misc.R
Get an array with the same length as arr which is filled with either "yes" or "no" values according to the boolean/binary classification in R.ifelse.
IID - Class in com.numericalmethod.suanshu.stats.random.multivariate
Generate a random vector of which the elements are i.i.d. drawn from the same distribution.
IID(RandomNumberGenerator, int) - Constructor for class com.numericalmethod.suanshu.stats.random.multivariate.IID
 
imaginary - Variable in class com.numericalmethod.suanshu.number.complex.Complex
the imaginary part of this complex number
ImmutableKroneckerProduct - Class in com.numericalmethod.suanshu.matrix.doubles.operation
This immutable version of KroneckerProduct is fast and efficient for construction and read-only operations.
ImmutableKroneckerProduct(Matrix, Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.ImmutableKroneckerProduct
Construct a read-only Kronecker product representation.
ImmutableMatrix - Class in com.numericalmethod.suanshu.matrix.doubles
A read-only view of a Matrix instance.
ImmutableMatrix(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.ImmutableMatrix
Construct a read-only version of matrix.
ImmutableVector - Class in com.numericalmethod.suanshu.vector.doubles
A read-only view of a Vector instance.
ImmutableVector(Vector) - Constructor for class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
Construct a read-only version of vector.
index - Variable in class com.numericalmethod.suanshu.analysis.function.rn2r1.RealScalarFunctionFixedVariables.Value
an index to a variable, counting from 0
index - Variable in class com.numericalmethod.suanshu.datastructure.MathTable.Row
the row index, a double
index() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector.Entry
Get the index of this entry in the sparse vector.
index - Variable in class com.numericalmethod.suanshu.optimization.constrained.integer.IntegerConstrainedProblem.IntegralConstraint
the index to an integral variable
index - Variable in class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.Tableau.Label
 
inequalities - Variable in class com.numericalmethod.suanshu.optimization.constrained.general.ConstrainedProblem
the inequality constraints gi(x) ≤ 0
InequalityConstraints - Class in com.numericalmethod.suanshu.optimization.constrained.general
This class represents the inequality constraints for an optimization problem.
InequalityConstraints(RealScalarFunction...) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.general.InequalityConstraints
Construct a set of inequality constraints for a constrained optimization problem.
InformationCriteria - Class in com.numericalmethod.suanshu.stats.regression.linear.ols
The information criteria measure the goodness of fit of an estimated statistical model.
informationCriteria - Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.OlsRegression
the model selection criteria
initialGuess(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.IterativeSolver.Problem
Override the initial guess of the solution.
initialGuess() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.IterativeSolver.Problem
Get the initial guess of the solution for the problem.
innerProduct(H) - Method in interface com.numericalmethod.suanshu.mathstructure.HilbertSpace
<·,·> : H × H → F Inner products formalizes the geometrical notions such as the length of a vector and the angle between two vectors.
innerProduct(Vector) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
 
innerProduct(Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.DenseVector
 
innerProduct(Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.ImmutableVector
 
innerProduct(Vector) - Method in interface com.numericalmethod.suanshu.vector.doubles.Vector
Inner product in the Euclidean space is the dot product.
InnovationAlgorithm - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma
This is an implementation, adapted for an ARMA process, of the innovation algorithm, which is an efficient way of obtaining a one step least square linear predictor.
InnovationAlgorithm(MultiVariateTimeSeries, ArmaModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.InnovationAlgorithm
Construct an instance of InnovationAlgorithm for a multivariate ARMA time series.
InnovationAlgorithm - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess
The innovation algorithm is an efficient way of obtaining a one step least square linear predictor for a linear time series {Xt} with known covariance structure.
InnovationAlgorithm(MultiVariateTimeSeries, AutoCovarianceFunction) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.InnovationAlgorithm
Construct an instance of InnovationAlgorithm for a multivariate time series with known auto-covariance structure.
InnovationAlgorithm - Class in com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess
The innovation algorithm is an efficient way of obtaining a one step least square linear predictor for a linear time series {Xt} with known auto-covariance.
InnovationAlgorithm(TimeSeries, AutoCovarianceFunction) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.InnovationAlgorithm
Construct an instance of InnovationAlgorithm for a univariate time series with known auto-covariance structure.
InnovationAlgorithmImpl - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess
This class implements the part of the innovation algorithm that computes the prediction coefficients, V and Θ.
InnovationAlgorithmImpl() - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.InnovationAlgorithmImpl
 
Innovations - Class in com.numericalmethod.suanshu.signalprocessing
An innovation is the difference between the observed value of a variable at time t and the optimal forecast of that value based on information available prior to time t.
Innovations(double[]) - Constructor for class com.numericalmethod.suanshu.signalprocessing.Innovations
Construct an Innovations from an array of innovations.
Innovations(Innovations) - Constructor for class com.numericalmethod.suanshu.signalprocessing.Innovations
Construct a new Innovations from another one.
Innovations(int, UnivariateDistribution, double, double, long) - Constructor for class com.numericalmethod.suanshu.signalprocessing.Innovations
Construct an Innovations from a distribution.
Innovations(int, UnivariateDistribution, double, double) - Constructor for class com.numericalmethod.suanshu.signalprocessing.Innovations
Construct an Innovations from a distribution.
Innovations(int, UnivariateDistribution) - Constructor for class com.numericalmethod.suanshu.signalprocessing.Innovations
Construct an Innovations from a distribution.
Innovations(int, RandomNumberGenerator, int) - Constructor for class com.numericalmethod.suanshu.signalprocessing.Innovations
Construct an Innovations from a random number generator.
Innovations(int, RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.signalprocessing.Innovations
Construct an Innovations from a random number generator.
Innovations(int, double, double, int) - Constructor for class com.numericalmethod.suanshu.signalprocessing.Innovations
Construct an innovation series from a Normal distribution.
Innovations(int, double, double) - Constructor for class com.numericalmethod.suanshu.signalprocessing.Innovations
Construct an innovation series from a Normal distribution.
Innovations(int) - Constructor for class com.numericalmethod.suanshu.signalprocessing.Innovations
Construct an innovation series from the standard Normal distribution.
intArray2ArrayList(int[]) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
Convert an int[] to ArrayList.
intArray2doubleArray(int...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
Convert an int[] array to a double[] array.
IntegerConstrainedMinimizer - Interface in com.numericalmethod.suanshu.optimization.constrained.integer
 
IntegerConstrainedProblem - Class in com.numericalmethod.suanshu.optimization.constrained.integer
 
IntegerConstrainedProblem(RealScalarFunction, EqualityConstraints, InequalityConstraints, IntegerConstrainedProblem.IntegralConstraint...) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.integer.IntegerConstrainedProblem
Construct a constrained optimization problem with index constraints.
IntegerConstrainedProblem.IntegralConstraint - Class in com.numericalmethod.suanshu.optimization.constrained.integer
 
IntegerConstrainedProblem.IntegralConstraint(int, int[]) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.integer.IntegerConstrainedProblem.IntegralConstraint
 
IntegerConstrainedProblem.IntegralConstraint(int, int, int) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.integer.IntegerConstrainedProblem.IntegralConstraint
 
IntegerConstrainedProblem.IntegralConstraint(int, int, int, int) - Constructor for class com.numericalmethod.suanshu.optimization.constrained.integer.IntegerConstrainedProblem.IntegralConstraint
 
integerIndices() - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.IntegerConstrainedProblem
 
integral(Filtration) - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Integrator
Integrate the function for a given filtration.
integralConstraint(int) - Method in class com.numericalmethod.suanshu.optimization.constrained.integer.IntegerConstrainedProblem
 
IntegralDB - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration
This class implements the following class of integrals.
IntegralDB(FiltrationFunction) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.IntegralDB
Construct an integral for f with respect to dB.
IntegralDt - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration
This class implements the following class of integrals.
IntegralDt(FiltrationFunction) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.IntegralDt
Construct an integral for f with respect to dt.
integrate(UnivariateRealFunction, double, double) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.ChangeOfVariable
 
integrate(UnivariateRealFunction, double, double) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.EulerMaclaurin
 
integrate(UnivariateRealFunction, double, double) - Method in interface com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Integrator
Integrate function f from a to b.
integrate(UnivariateRealFunction, double, double) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Riemann
 
integrate(UnivariateRealFunction, double, double, Substitution) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Riemann
Riemann function f from a to b, using a Substitution rule.
integrate(UnivariateRealFunction, double, double) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Romberg
 
integrate(UnivariateRealFunction, double, double) - Method in class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.Simpson
 
Integrator - Interface in com.numericalmethod.suanshu.analysis.integration.univariate.riemann
This is the interface for numerical integration of univariate functions.
Integrator - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration
The class represents an integral for a function, in the Lebesgue sense.
Integrator(FiltrationFunction) - Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Integrator
Construct an integral from an integrand.
intercept - Variable in class com.numericalmethod.suanshu.stats.cointegration.CointegrationMle
indicate whether an intercept is included in the computation
Interval<T extends java.lang.Comparable<T>> - Class in com.numericalmethod.suanshu.interval
This class represents an interval.
Interval(T, T) - Constructor for class com.numericalmethod.suanshu.interval.Interval
Construct an interval [begin, end].
Intervals<T extends java.lang.Comparable<T>> - Class in com.numericalmethod.suanshu.interval
This class represents a disjoint set of Intervals.
Intervals() - Constructor for class com.numericalmethod.suanshu.interval.Intervals
Construct an empty set of Intervals.
Intervals(Interval<T>) - Constructor for class com.numericalmethod.suanshu.interval.Intervals
Construct a set that contains only one Interval.
Intervals(T, T) - Constructor for class com.numericalmethod.suanshu.interval.Intervals
Construct a set that contains only one interval [begin, end].
Intervals(Interval<T>...) - Constructor for class com.numericalmethod.suanshu.interval.Intervals
Construct a set of Intervals from the given intervals.
Intervals(Intervals<T>) - Constructor for class com.numericalmethod.suanshu.interval.Intervals
Copy constructor.
intValue() - Method in class com.numericalmethod.suanshu.number.complex.Complex
Deprecated. Not supported yet.
intValue() - Method in class com.numericalmethod.suanshu.number.Real
 
intValue() - Method in class com.numericalmethod.suanshu.number.ScientificNotation
 
InvalidLicense - Error in com.numericalmethod.suanshu.license
Exception thrown when calling a class or method that is unlicensed.
invdet() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.HilbertMatrix
One over the determinant of H.
inverse() - Method in interface com.numericalmethod.suanshu.mathstructure.Field
For each a in F, there exists an element b in F such that a × b = b × a = 1 That is, it is the object such as this.multiply(this.inverse()) == this.ONE
Inverse - Class in com.numericalmethod.suanshu.matrix.doubles.operation
For a square matrix A, A-1, if the inverse exists.
Inverse(Matrix, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.Inverse
Construct a DenseMatrix that is the inverse of a DenseMatrix A.
Inverse(Matrix) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.Inverse
Construct a DenseMatrix that is the inverse of a DenseMatrix A.
Inverse(UpperTriangularMatrix, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.Inverse
Invert an upper triangular matrix.
Inverse(LowerTriangularMatrix, double) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.operation.Inverse
Invert a lower triangular matrix.
inverse() - Method in class com.numericalmethod.suanshu.number.complex.Complex
 
inverse() - Method in class com.numericalmethod.suanshu.number.Real
 
inverse(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Cloglog
 
inverse(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Identity
 
Inverse - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link
This class represents the link function: g(x) = 1/x
Inverse() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Inverse
 
inverse(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Inverse
 
inverse(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.InverseSquared
 
inverse(double) - Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.LinkFunction
Inverse of the link function, i.e., g-1(x).
inverse(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Log
 
inverse(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Logit
Inverse of the link function, i.e., g-1(x).
inverse(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Probit
 
inverse(double) - Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.Sqrt
 
InverseGaussian - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution
The Inverse Gaussian distribution for the error distribution in a GLM model.
InverseGaussian() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.InverseGaussian
Construct an instance of InverseGaussian.
InverseGaussian(LinkFunction) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.InverseGaussian
Construct an instance of InverseGaussian with an overriding link function.
InverseGaussian - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family
The quasi InverseGaussian family of GLM.
InverseGaussian() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family.InverseGaussian
Create an instance of InverseGaussian.
InverseGaussian(LinkFunction) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family.InverseGaussian
Create an instance of InverseGaussian with an overriding link function.
InverseSquared - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link
This class represents the link function: g(x) = 1/x2
InverseSquared() - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link.InverseSquared
 
InverseTransformSampling - Class in com.numericalmethod.suanshu.stats.random.distribution
This class creates a random variable from a UnivariateDistribution instance.
InverseTransformSampling(UnivariateDistribution, RandomNumberGenerator) - Constructor for class com.numericalmethod.suanshu.stats.random.distribution.InverseTransformSampling
Construct a random number generator by sampling from a distribution.
InverseTransformSampling(UnivariateDistribution) - Constructor for class com.numericalmethod.suanshu.stats.random.distribution.InverseTransformSampling
Construct a random number generator by sampling from a distribution.
Invertibility - Class in com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma
This class computes the inverse representation of an Autoregressive Moving Average (ARMA) model.
Invertibility(ArmaModel, int) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.Invertibility
Construct the inverse representation of an ARMA model.
Invertibility(ArmaModel) - Constructor for class com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arima.arma.Invertibility
Construct the inverse representation of an ARMA model up to the default number of lags Invertibility.DEFAULT_NLAGS.
InvertingVariable - Class in com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution
This is the inverting-variable transformation.
InvertingVariable(double, double) - Constructor for class com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution.InvertingVariable
Construct an instance of the InvertingVariable substitution rule.
invOfwAtwA() - Method in class com.numericalmethod.suanshu.stats.regression.linear.LmProblem
(wA' %*% wA)-1
is(Algebra.Relation, Interval<T>) - Method in class com.numericalmethod.suanshu.interval.Interval
Check whether this and Y satisfies a certain Algebra.Relation.
isColumnVector(MatrixDimension) - Static method in class com.numericalmethod.suanshu.matrix.DimensionCheck
Check if a matrix is a column vector.
isFat(MatrixDimension) - Static method in class com.numericalmethod.suanshu.matrix.DimensionCheck
Check if a matrix is a fat matrix.
isFeasible() - Method in class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.Tableau
Check if this table is feasible.
isFullRank - Variable in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.Solver
true iff A has full rank
isHessenberg(Matrix, double) - Static method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.Hessenberg
Check if H is upper Hessenberg.
isInfinite(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.Complex
Check if this complex number is an infinity; i.e., the number is not a NaN and either the real or the imaginary part is infinite, c.f., Double.isInfinite().
isInKernel(Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.operation.VectorSpace
Deprecated. Not supported yet.
isIntegerIndex(int) - Method in class com.numericalmethod.suanshu.analysis.function.rn2r1.RealScalarFunctionFixedVariables
Check whether a particular index corresponds an integral variable.
IsMatrix - Class in com.numericalmethod.suanshu.matrix.doubles
This class collects the boolean operators that take a matrix or vector and check if it satisfies a certain property.
isN(double) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
Check if a double is a number, i.e., it is not or NaN.
isNaN(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.Complex
Check if this complex number is a NaN; i.e., either the real or the imaginary part is a NaN.
isNegligible(Matrix, int, int, double) - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.Hessenberg.DefaultDeflationCriterion
Check if H[row, col] is negligible by Steward's deflation criterion.
isNegligible(Matrix, int, int, double) - Method in interface com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.Hessenberg.DeflationCriterion
Check whether a sub-diagonal element is sufficiently small.
isPow2(int) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
Check if an integer is a power of 2.
isQuasiTriangular - Variable in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.Hessenberg.Deflation
true iff the matrix is a quasi-triangular matrix
isReal(Complex) - Static method in class com.numericalmethod.suanshu.number.complex.Complex
Check if this complex number is a real number; i.e., the imaginary part is 0.
isReal(Number) - Static method in class com.numericalmethod.suanshu.number.NumberUtils
Check if a Number is a real number.
isReducible(Matrix, double) - Method in class com.numericalmethod.suanshu.matrix.doubles.factorization.eigen.Hessenberg
Check if H is upper Hessenberg and is reducible.
isRelationBetween(Interval<T>, Interval<T>) - Method in enum com.numericalmethod.suanshu.interval.Algebra.Relation
Check if x and y satisfy a certain relation.
isRowVector(MatrixDimension) - Static method in class com.numericalmethod.suanshu.matrix.DimensionCheck
Check if a matrix is a row vector.
isSpanned(Vector) - Method in class com.numericalmethod.suanshu.vector.doubles.dense.operation.VectorSpace
Check whether a vector is in the span of the the basis.
isSquare(MatrixDimension) - Static method in class com.numericalmethod.suanshu.matrix.DimensionCheck
Check if a matrix is a square matrix.
isTall(MatrixDimension) - Static method in class com.numericalmethod.suanshu.matrix.DimensionCheck
Check if a matrix is a tall matrix.
isThereAnyDuplicate(double...) - Static method in class com.numericalmethod.suanshu.number.DoubleUtils
Check if a double[] array contains any duplicates.
isTrue(double, int) - Method in interface com.numericalmethod.suanshu.misc.R.which
Decide whether x is to be selected.
isUnbounded() - Method in class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.standard.Phase2ByFerrisMangasarianWright
 
isUnreduced(double) - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
A bidiagonal matrix is unreduced iff it has no 0 on both the super and main diagonals.
isVector(MatrixDimension) - Static method in class com.numericalmethod.suanshu.matrix.DimensionCheck
Check if a matrix is a row or a column vector.
IsVector - Class in com.numericalmethod.suanshu.vector.doubles
This class collects the utility functions to validate input arguments for vector operations.
IsVector.SizeMismatch - Exception in com.numericalmethod.suanshu.vector.doubles
This exception should be thrown when a vector operation is performed on two vectors with different sizes.
IsVector.SizeMismatch(int, int) - Constructor for exception com.numericalmethod.suanshu.vector.doubles.IsVector.SizeMismatch
 
IsVector.VectorAccessException - Exception in com.numericalmethod.suanshu.vector.doubles
This exception should be thrown if any invalid access to a Vector instance is detected.
IsVector.VectorAccessException(int, int) - Constructor for exception com.numericalmethod.suanshu.vector.doubles.IsVector.VectorAccessException
 
IsVector.VectorAccessException(String) - Constructor for exception com.numericalmethod.suanshu.vector.doubles.IsVector.VectorAccessException
 
isZero() - Method in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.Kernel
Check if the kernel is of zero-dimension.
iterates() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.IteratesMonitor
 
IteratesMonitor - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative
This IterationMonitor stores all iterates generated during iterations.
IteratesMonitor() - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.IteratesMonitor
 
IterationBody<T> - Interface in com.numericalmethod.suanshu.parallel
 
IterationMonitor - Interface in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative
An instance of this class is passed into an iterative method to monitor each iterate generated in each iteration.
IterativeIntegrator - Interface in com.numericalmethod.suanshu.analysis.integration.univariate.riemann
An iterative integrator computes an integral by a series of sums, which approximates the value of the integral.
IterativeSolver - Interface in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative
Iterative methods for solving N-by-N (or non-square) linear systems Ax = b involve a sequence of matrix-vector multiplications.
IterativeSolver.ConvergenceFailure - Exception in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative
This exception is thrown by IterativeSolver.solve(com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.IterativeSolver.Problem) when the iterative algorithm detects a breakdown or fails to converge.
IterativeSolver.ConvergenceFailure(IterativeSolver.ConvergenceFailure.Reason) - Constructor for exception com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.IterativeSolver.ConvergenceFailure
Create an exception with reason.
IterativeSolver.ConvergenceFailure(IterativeSolver.ConvergenceFailure.Reason, String) - Constructor for exception com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.IterativeSolver.ConvergenceFailure
Create an exception with reason and error message.
IterativeSolver.ConvergenceFailure.Reason - Enum in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative
The reason for the convergence failure.
IterativeSolver.Problem - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative
This class models the problem of solving a system of linear equations (Ax = b) using an iterative method.
IterativeSolver.Problem(Matrix, Vector) - Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.IterativeSolver.Problem
Create the problem of solving Ax = b.
iterator() - Method in class com.numericalmethod.suanshu.algorithm.Combination
 
iterator() - Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.SparseVector
 
iterator() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.integration.sde.RandomWalk.MultiVariateRealization
 
iterator() - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.MultiVariateRealization
 
iterator() - Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.sde.RandomWalk.Realization
 
iterator() - Method in interface com.numericalmethod.suanshu.stats.stochasticprocess.univariate.Realization
 
iterator() - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.GenericTimeTimeSeries
 
iterator() - Method in interface com.numericalmethod.suanshu.stats.timeseries.multivariate.MultiVariateTimeSeries
Get an Iterator to read this multivariate time series.
iterator() - Method in interface com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.MultiVariateRealization
Get an iterator to read this real number -indexed multivariate time series.
iterator() - Method in interface com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.MultiVariateTimeSeries
Get an Iterator to read this integer-indexed multivariate time series.
iterator() - Method in class com.numericalmethod.suanshu.stats.timeseries.multivariate.realtime.SimpleMultiVariateTimeSeries
 
iterator() - Method in interface com.numericalmethod.suanshu.stats.timeseries.TimeSeries
Get an iterator to read this time series.
iterator() - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.GenericTimeTimeSeries
 
iterator() - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.OneDimensionTimeSeries
 
iterator() - Method in interface com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.Realization
Get an Iterator to read this real number -indexed univariate time series.
iterator() - Method in class com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.SimpleTimeSeries
 
iterator() - Method in interface com.numericalmethod.suanshu.stats.timeseries.univariate.realtime.TimeSeries
Get an Iterator to read this integer-indexed univariate time series.
iterator() - Method in interface com.numericalmethod.suanshu.stats.timeseries.univariate.TimeSeries
Get an Iterator to read this univariate time series.
IWLS - Class in com.numericalmethod.suanshu.stats.regression.linear.glm
We estimate parameters ß in a GLM model using the Iteratively Re-weighted Least Squares algorithm.
IWLS(double, int) - Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.IWLS
Construct an instance to run the Iteratively Re-weighted Least Squares algorithm.

SuanShu, a Java numerical and statistical library
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