- C45 - Class in com.imsl.datamining.decisionTree
-
Generates a decision tree using the C4.5 algorithm for a categorical response
variable and categorical or quantitative predictor variables.
- C45(double[][], int, PredictiveModel.VariableType[]) - Constructor for class com.imsl.datamining.decisionTree.C45
-
Constructs a C45 object for a single response variable and
multiple predictor variables.
- C45(C45) - Constructor for class com.imsl.datamining.decisionTree.C45
-
Constructs a copy of the input C45 decision tree.
- cancelRowUpdates() - Method in class com.imsl.io.AbstractFlatFile
-
Cancels the updates made to the current row in this
ResultSet object.
- canonicalCorrelation(double[][]) - Method in class com.imsl.stat.Random
-
Method canonicalCorrelation generates a canonical
correlation matrix from an arbitrarily distributed multivariate deviate
sequence with nvar deviate variables, nseq
steps in the sequence, and a Gaussian Copula dependence structure.
- canonicalCorrelationSTC(double, double[][], double[][]) - Static method in class com.imsl.stat.Random
-
Deprecated.
- CaseStatistics(double[], double) - Constructor for class com.imsl.stat.LinearRegression.CaseStatistics
-
- CaseStatistics(double[], double, double) - Constructor for class com.imsl.stat.LinearRegression.CaseStatistics
-
- CaseStatistics(double[], double, double, int) - Constructor for class com.imsl.stat.LinearRegression.CaseStatistics
-
- CaseStatistics(double[], double, int) - Constructor for class com.imsl.stat.LinearRegression.CaseStatistics
-
- CategoricalGenLinModel - Class in com.imsl.stat
-
Analyzes categorical data using logistic, probit, Poisson, and other linear
models.
- CategoricalGenLinModel(double[][], int) - Constructor for class com.imsl.stat.CategoricalGenLinModel
-
Constructs a new CategoricalGenLinModel.
- CategoricalGenLinModel.ClassificationVariableException - Exception in com.imsl.stat
-
The ClassificationVariable vector has not been initialized.
- CategoricalGenLinModel.ClassificationVariableLimitException - Exception in com.imsl.stat
-
The Classification Variable limit set by the user through
setUpperBound has been exceeded.
- CategoricalGenLinModel.ClassificationVariableValueException - Exception in com.imsl.stat
-
The number of distinct values for each Classification Variable must be
greater than 1.
- CategoricalGenLinModel.DeleteObservationsException - Exception in com.imsl.stat
-
The number of observations to be deleted (set by
setObservationMax) has grown too large.
- CategoricalGenLinModel.RankDeficientException - Exception in com.imsl.stat
-
The model has been determined to be rank deficient.
- CategoricalGenLinModelEx1 - Class in com.imsl.test.example.stat
-
Fits a logit and probit categorical model to beetle mortality
data.
- CategoricalGenLinModelEx1() - Constructor for class com.imsl.test.example.stat.CategoricalGenLinModelEx1
-
- CategoricalGenLinModelEx2 - Class in com.imsl.test.example.stat
-
Analyzes interval type data with the Poisson model.
- CategoricalGenLinModelEx2() - Constructor for class com.imsl.test.example.stat.CategoricalGenLinModelEx2
-
- Cdf - Class in com.imsl.stat
-
Cumulative probability distribution functions.
- cdf(double) - Method in interface com.imsl.stat.CdfFunction
-
Public interface for the user-supplied cumulative distribution function
to be used by InverseCdf.
- cdf(double) - Method in class com.imsl.test.example.stat.InverseCdfEx1
-
- cdf(double) - Method in class com.imsl.test.example.stat.RandomEx1
-
- CdfEx1 - Class in com.imsl.test.example.stat
-
Evaluates various cumulative distribution functions.
- CdfEx1() - Constructor for class com.imsl.test.example.stat.CdfEx1
-
- CdfFunction - Interface in com.imsl.stat
-
Public interface for the user-supplied cumulative distribution function
to be used by InverseCdf and ChiSquaredTest.
- ceil(double) - Static method in class com.imsl.math.JMath
-
Returns the value of a double rounded toward
positive infinity to an integral value.
- CENTER_MEAN - Static variable in class com.imsl.stat.ARSeasonalFit
-
Indicates the transformed series should be centered using the
average of the differenced series.
- CENTER_MEDIAN - Static variable in class com.imsl.stat.ARSeasonalFit
-
Indicates the transformed series should be centered using the
median of the differenced series.
- CENTRAL - Static variable in class com.imsl.math.NumericalDerivatives
-
Indicates central differences.
- CHAID - Class in com.imsl.datamining.decisionTree
-
Generates a decision tree using CHAID for categorical or discrete ordered
predictor variables.
- CHAID(double[][], int, PredictiveModel.VariableType[]) - Constructor for class com.imsl.datamining.decisionTree.CHAID
-
Constructs a CHAID object for a single response variable and
multiple predictor variables.
- CHAID(CHAID) - Constructor for class com.imsl.datamining.decisionTree.CHAID
-
Constructs a copy of the input CHAID decision tree.
- checkCompatibility(Physical, Physical) - Static method in class com.imsl.math.Physical
-
Checks the compatibility of two Physical objects.
- checkMatrix(Complex[][]) - Static method in class com.imsl.math.ComplexMatrix
-
Check that all of the rows in the Complex matrix have the
same length.
- CheckMatrix(Complex[][]) - Static method in class com.imsl.math.ComplexMatrix
-
- checkMatrix(double[][]) - Static method in class com.imsl.math.Matrix
-
Check that all of the rows in the matrix have the same length.
- CheckMatrix(double[][]) - Static method in class com.imsl.math.Matrix
-
- checkSquareMatrix(Complex[][]) - Static method in class com.imsl.math.ComplexMatrix
-
Check that the Complex matrix is square.
- CheckSquareMatrix(Complex[][]) - Static method in class com.imsl.math.ComplexMatrix
-
- checkSquareMatrix() - Method in class com.imsl.math.ComplexSparseMatrix
-
Check that the matrix is square.
- checkSquareMatrix(double[][]) - Static method in class com.imsl.math.Matrix
-
Check that the matrix is square.
- CheckSquareMatrix(double[][]) - Static method in class com.imsl.math.Matrix
-
- checkSquareMatrix() - Method in class com.imsl.math.SparseMatrix
-
Check that the matrix is square.
- chi(double, double) - Static method in class com.imsl.stat.Cdf
-
Evaluates the chi-squared cumulative distribution function.
- chi(double, double) - Static method in class com.imsl.stat.InvCdf
-
Returns the inverse of the chi-squared cumulative probability distribution function.
- chi(double, double) - Static method in class com.imsl.stat.Pdf
-
Evaluates the chi-squared probability density function
- chiMean(double) - Static method in class com.imsl.stat.Cdf
-
Evaluates the mean of the chi-squared cumulative probability distribution function
- chiProb(double, double) - Static method in class com.imsl.stat.Cdf
-
- ChiSquaredTest - Class in com.imsl.stat
-
Chi-squared goodness-of-fit test.
- ChiSquaredTest(CdfFunction, double[], int) - Constructor for class com.imsl.stat.ChiSquaredTest
-
Constructor for the Chi-squared goodness-of-fit test.
- ChiSquaredTest(CdfFunction, int, int) - Constructor for class com.imsl.stat.ChiSquaredTest
-
Constructor for the Chi-squared goodness-of-fit test
- ChiSquaredTest(int) - Method in class com.imsl.stat.NormalityTest
-
Performs the chi-squared goodness-of-fit test.
- ChiSquaredTest.DidNotConvergeException - Exception in com.imsl.stat
-
The iteration did not converge
- ChiSquaredTest.NoObservationsException - Exception in com.imsl.stat
-
There are no observations.
- ChiSquaredTest.NotCDFException - Exception in com.imsl.stat
-
The function is not a Cumulative Distribution Function (CDF).
- ChiSquaredTestEx1 - Class in com.imsl.test.example.stat
-
Performs a chi-squared test on simulated data.
- ChiSquaredTestEx1() - Constructor for class com.imsl.test.example.stat.ChiSquaredTestEx1
-
- chiVariance(double) - Static method in class com.imsl.stat.Cdf
-
Evaluates the variance of the chi-squared cumulative probability distribution function
- Cholesky - Class in com.imsl.math
-
Cholesky factorization of a matrix of type double.
- Cholesky(double[][]) - Constructor for class com.imsl.math.Cholesky
-
Create the Cholesky factorization of a symmetric positive definite
matrix of type double.
- Cholesky.NotSPDException - Exception in com.imsl.math
-
The matrix is not symmetric, positive definite.
- CholeskyEx1 - Class in com.imsl.test.example.math
-
Solves a system using Cholesky factorization.
- CholeskyEx1() - Constructor for class com.imsl.test.example.math.CholeskyEx1
-
- CholeskyFactorizationAccuracyException(String) - Constructor for exception com.imsl.math.SparseLP.CholeskyFactorizationAccuracyException
-
The Cholesky factorization failed because of accuracy problems.
- CholeskyFactorizationAccuracyException(String, Object[]) - Constructor for exception com.imsl.math.SparseLP.CholeskyFactorizationAccuracyException
-
The Cholesky factorization failed because of accuracy problems.
- classError(double[], int[], int) - Method in class com.imsl.datamining.NaiveBayesClassifier
-
Returns the classification probability error for the input pattern and
known target classification.
- ClassificationVariableException() - Constructor for exception com.imsl.stat.CategoricalGenLinModel.ClassificationVariableException
-
Constructs a ClassificationVariableException.
- ClassificationVariableLimitException(int) - Constructor for exception com.imsl.stat.CategoricalGenLinModel.ClassificationVariableLimitException
-
Constructs a ClassificationVariableLimitException.
- ClassificationVariableLimitException(String) - Constructor for exception com.imsl.stat.ProportionalHazards.ClassificationVariableLimitException
-
Constructs a ClassificationVariableLimitException.
- ClassificationVariableLimitException(String, Object[]) - Constructor for exception com.imsl.stat.ProportionalHazards.ClassificationVariableLimitException
-
The Classification Variable limit set by the user through
setUpperBound has been exceeded.
- ClassificationVariableValueException(int, int) - Constructor for exception com.imsl.stat.CategoricalGenLinModel.ClassificationVariableValueException
-
Constructs a ClassificationVariableValueException.
- classify(double[], int) - Method in class com.imsl.stat.ClusterKNN
-
Classify an observation using k nearest neighbors.
- classify(double[][], int) - Method in class com.imsl.stat.ClusterKNN
-
Classify a set of observations using k nearest neighbors.
- classify(double[][]) - Method in class com.imsl.stat.DiscriminantAnalysis
-
Classify a set of observations using the linear or quadratic
discriminant functions generated during the training process.
- classify(double[][], int[]) - Method in class com.imsl.stat.DiscriminantAnalysis
-
Classify a set of observations using the linear or quadratic
discriminant functions generated during the training process.
- classify(double[][], int[], double[]) - Method in class com.imsl.stat.DiscriminantAnalysis
-
Classify a set of observations and associated frequencies and weights
using the linear or quadratic discriminant functions generated
during the training process.
- classify(double[][], int[], int[], double[]) - Method in class com.imsl.stat.DiscriminantAnalysis
-
Classify a set of observations and associated frequencies and weights
using the linear or quadratic discriminant functions generated
during the training process.
- classify(double[][], int[], int[]) - Method in class com.imsl.stat.DiscriminantAnalysis
-
Classify a set of observations and compare against known groups using
the linear or quadratic discriminant functions generated during the
training process.
- classify(double[][], int[], int[], int[], double[]) - Method in class com.imsl.stat.DiscriminantAnalysis
-
Classify a set of observations, associated frequencies and weights, and
compare against known groups using the linear or quadratic discriminant
functions generated during the training process.
- clearWarnings() - Method in class com.imsl.io.AbstractFlatFile
-
Clears all warnings reported on this ResultSet object.
- clone() - Method in class com.imsl.datamining.decisionTree.ALACART
-
Clones an ALACART decision tree.
- clone() - Method in class com.imsl.datamining.decisionTree.C45
-
Clones a C45 decision tree.
- clone() - Method in class com.imsl.datamining.decisionTree.CHAID
-
Clones a CHAID decision tree.
- clone() - Method in class com.imsl.datamining.decisionTree.QUEST
-
Clones a QUEST decision tree.
- clone() - Method in class com.imsl.datamining.decisionTree.RandomTrees
-
Clones a RandomTrees predictive model.
- clone() - Method in class com.imsl.datamining.decisionTree.Tree
-
Returns a clone of this object.
- clone() - Method in class com.imsl.datamining.decisionTree.TreeNode
-
Returns a clone of a TreeNode.
- clone() - Method in class com.imsl.datamining.GradientBoosting
-
Clones a GradientBoosting predictive model.
- clone() - Method in class com.imsl.datamining.neural.LeastSquaresTrainer
-
Clones a copy of the trainer.
- clone() - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer
-
Clones a copy of the trainer.
- clone() - Method in class com.imsl.datamining.PredictiveModel
-
Abstract clone method.
- clone() - Method in class com.imsl.datamining.supportvectormachine.Kernel
-
Returns a clone of this object.
- clone() - Method in class com.imsl.datamining.supportvectormachine.LinearKernel
-
Clones a LinearKernel kernel.
- clone() - Method in class com.imsl.datamining.supportvectormachine.PolynomialKernel
-
Clones a PolynomialKernel kernel.
- clone() - Method in class com.imsl.datamining.supportvectormachine.RadialBasisKernel
-
Clones a RadialBasisKernel kernel.
- clone() - Method in class com.imsl.datamining.supportvectormachine.SigmoidKernel
-
Clones a SigmoidKernel kernel.
- clone() - Method in class com.imsl.datamining.supportvectormachine.SVClassification
-
Clones an SVClassification predictive model.
- clone() - Method in class com.imsl.datamining.supportvectormachine.SVOneClass
-
Clones an SVOneClass predictive model.
- clone() - Method in class com.imsl.datamining.supportvectormachine.SVRegression
-
Clones an SVRegression predictive model.
- clone() - Method in class com.imsl.math.DenseLP
-
Creates and returns a copy of this object.
- clone() - Method in class com.imsl.math.LinearProgramming
-
Deprecated.
Creates and returns a copy of this object.
- clone() - Method in class com.imsl.stat.FaureSequence
-
Returns a copy of this object.
- clone() - Method in class com.imsl.stat.MersenneTwister
-
Returns a clone of this object.
- clone() - Method in class com.imsl.stat.MersenneTwister64
-
Returns a clone of this object.
- CloneNotSupportedException(String) - Constructor for exception com.imsl.datamining.PredictiveModel.CloneNotSupportedException
-
Constructs a CloneNotSupportedException and issues the
specified message.
- CloneNotSupportedException(String, Object[]) - Constructor for exception com.imsl.datamining.PredictiveModel.CloneNotSupportedException
-
Constructs a CloneNotSupportedException with the
specified detail message.
- close() - Method in class com.imsl.io.AbstractFlatFile
-
Releases this ResultSet object's database and JDBC resources
immediately instead of waiting for this to happen when it is
automatically closed.
- ClosedFormMaximumLikelihoodInterface - Interface in com.imsl.stat.distributions
-
A public interface for probability distributions that provide a method for a
closed form solution of the maximum likelihood function
- ClusterHierarchical - Class in com.imsl.stat
-
Performs a hierarchical cluster analysis from a distance matrix.
- ClusterHierarchical(double[][], int, int) - Constructor for class com.imsl.stat.ClusterHierarchical
-
- ClusterHierarchical(double[][]) - Constructor for class com.imsl.stat.ClusterHierarchical
-
Constructor for ClusterHierarchical.
- ClusterHierarchicalEx1 - Class in com.imsl.test.example.stat
-
Performs hierarchical clustering on Fisher's
iris data.
- ClusterHierarchicalEx1() - Constructor for class com.imsl.test.example.stat.ClusterHierarchicalEx1
-
- ClusterKMeans - Class in com.imsl.stat
-
Perform a K-means (centroid) cluster analysis.
- ClusterKMeans(double[][], double[][]) - Constructor for class com.imsl.stat.ClusterKMeans
-
Constructor for ClusterKMeans.
- ClusterKMeans(double[][], int) - Constructor for class com.imsl.stat.ClusterKMeans
-
Constructor for ClusterKMeans using the K-means++ algorithm
to select the initial seeds.
- ClusterKMeans(double[][], int, Random) - Constructor for class com.imsl.stat.ClusterKMeans
-
Constructor for ClusterKMeans using the K-means++ algorithm
to set the initial seeds.
- ClusterKMeans.ClusterNoPointsException - Exception in com.imsl.stat
-
There is a cluster with no points
- ClusterKMeans.NoConvergenceException - Exception in com.imsl.stat
-
Convergence did not occur within the maximum number of iterations.
- ClusterKMeans.NonnegativeFreqException - Exception in com.imsl.stat
-
- ClusterKMeans.NonnegativeWeightException - Exception in com.imsl.stat
-
- ClusterKMeansEx1 - Class in com.imsl.test.example.stat
-
Performs K-Means clustering on Fisher's iris data.
- ClusterKMeansEx1() - Constructor for class com.imsl.test.example.stat.ClusterKMeansEx1
-
- ClusterKMeansEx2 - Class in com.imsl.test.example.stat
-
Performs K-Means++ clustering on Fisher's iris data.
- ClusterKMeansEx2() - Constructor for class com.imsl.test.example.stat.ClusterKMeansEx2
-
- ClusterKNN - Class in com.imsl.stat
-
Perform a k-Nearest Neighbor classification.
- ClusterKNN(double[][], int[]) - Constructor for class com.imsl.stat.ClusterKNN
-
Constructor for ClusterKNN.
- ClusterKNNEx1 - Class in com.imsl.test.example.stat
-
Performs K-Nearest Neighbor clustering on Fisher's iris data.
- ClusterKNNEx1() - Constructor for class com.imsl.test.example.stat.ClusterKNNEx1
-
- ClusterNoPointsException(String) - Constructor for exception com.imsl.stat.ClusterKMeans.ClusterNoPointsException
-
Constructs a ClusterNoPointsException object.
- ClusterNoPointsException(String, Object[]) - Constructor for exception com.imsl.stat.ClusterKMeans.ClusterNoPointsException
-
Constructs a ClusterNoPointsException object.
- coef - Variable in class com.imsl.math.BSpline
-
The B-spline coefficient array.
- coef - Variable in class com.imsl.math.Spline
-
Coefficients of the piecewise polynomials.
- COLUMN_APPROXIMATE_MINIMUM_DEGREE - Static variable in class com.imsl.math.ComplexSuperLU
-
For column ordering, use column approximate minimum degree ordering.
- COLUMN_APPROXIMATE_MINIMUM_DEGREE - Static variable in class com.imsl.math.SuperLU
-
For column ordering, use column approximate minimum degree ordering.
- COLUMN_LABEL - Static variable in class com.imsl.math.PrintMatrixFormat
-
This flag as the type argument to format, indicates that the
formatted string for a given column label is to be returned.
- COLUMN_SCALING - Static variable in class com.imsl.math.ComplexSuperLU
-
Indicates that input matrix A was column scaled before
factorization.
- COLUMN_SCALING - Static variable in class com.imsl.math.SuperLU
-
Indicates that input matrix A was column scaled before
factorization.
- com.imsl - package com.imsl
-
Utilities used by the library.
- com.imsl.datamining - package com.imsl.datamining
-
Data mining and machine learning.
- com.imsl.datamining.decisionTree - package com.imsl.datamining.decisionTree
-
Decision trees.
- com.imsl.datamining.neural - package com.imsl.datamining.neural
-
Neural networks.
- com.imsl.datamining.supportvectormachine - package com.imsl.datamining.supportvectormachine
-
Support vector machines.
- com.imsl.finance - package com.imsl.finance
-
Financial computations.
- com.imsl.io - package com.imsl.io
-
Methods for reading files.
- com.imsl.math - package com.imsl.math
-
Mathematical functions and algorithms.
- com.imsl.stat - package com.imsl.stat
-
Statistical methods.
- com.imsl.stat.distributions - package com.imsl.stat.distributions
-
Probability distributions and parameter estimation.
- com.imsl.test.example - package com.imsl.test.example
-
Warning message example.
- com.imsl.test.example.datamining - package com.imsl.test.example.datamining
-
Data mining examples.
- com.imsl.test.example.datamining.decisionTree - package com.imsl.test.example.datamining.decisionTree
-
Decision tree examples.
- com.imsl.test.example.datamining.neural - package com.imsl.test.example.datamining.neural
-
Neural network examples.
- com.imsl.test.example.datamining.supportvectormachine - package com.imsl.test.example.datamining.supportvectormachine
-
Support vector machine examples.
- com.imsl.test.example.finance - package com.imsl.test.example.finance
-
Financial computations examples.
- com.imsl.test.example.io - package com.imsl.test.example.io
-
Input/output examples.
- com.imsl.test.example.math - package com.imsl.test.example.math
-
Math examples.
- com.imsl.test.example.stat - package com.imsl.test.example.stat
-
Statistics examples.
- com.imsl.test.example.stat.distributions - package com.imsl.test.example.stat.distributions
-
Statistical distributions examples.
- compareTo(Object) - Method in class com.imsl.math.Complex
-
Compares this Complex to another Object.
- compareTo(Complex) - Method in class com.imsl.math.Complex
-
Compares two Complex objects.
- complementaryChi(double, double) - Static method in class com.imsl.stat.Cdf
-
Calculates the complement of the chi-squared cumulative distribution function.
- complementaryF(double, double, double) - Static method in class com.imsl.stat.Cdf
-
Calculates the complement of the F distribution function.
- complementaryF2(double, double, double) - Static method in class com.imsl.stat.Cdf
-
- complementaryNoncentralF(double, double, double, double) - Static method in class com.imsl.stat.Cdf
-
Calculates the complement of the noncentral F cumulative
distribution function.
- complementaryStudentsT(double, double) - Static method in class com.imsl.stat.Cdf
-
Calculates the complement of the Student's t distribution.
- Complex - Class in com.imsl.math
-
Set of mathematical functions for complex numbers.
- Complex(Complex) - Constructor for class com.imsl.math.Complex
-
Constructs a Complex equal to the argument.
- Complex(double, double) - Constructor for class com.imsl.math.Complex
-
Constructs a Complex with real and imaginary parts given
by the input arguments.
- Complex(double) - Constructor for class com.imsl.math.Complex
-
Constructs a Complex with a zero imaginary part.
- Complex() - Constructor for class com.imsl.math.Complex
-
Constructs a Complex equal to zero.
- ComplexEigen - Class in com.imsl.math
-
Collection of complex Eigen System functions.
- ComplexEigen(Complex[][]) - Constructor for class com.imsl.math.ComplexEigen
-
Constructor for the computation of the eigenvalues and eigenvectors
of a complex square matrix.
- ComplexEigen(ComplexEigen) - Constructor for class com.imsl.math.ComplexEigen
-
Copy constructor for the computation of the eigenvalues and eigenvectors
of a complex square matrix.
- ComplexEigen.DidNotConvergeException - Exception in com.imsl.math
-
The iteration did not converge.
- ComplexEigenEx1 - Class in com.imsl.test.example.math
-
Computes the eigenvalues and eigenvectors of a complex matrix.
- ComplexEigenEx1() - Constructor for class com.imsl.test.example.math.ComplexEigenEx1
-
- ComplexEx1 - Class in com.imsl.test.example.math
-
Converts a real matrix to a complex matrix.
- ComplexEx1() - Constructor for class com.imsl.test.example.math.ComplexEx1
-
- ComplexFFT - Class in com.imsl.math
-
Complex FFT.
- ComplexFFT(int) - Constructor for class com.imsl.math.ComplexFFT
-
Constructs a complex FFT object.
- ComplexFFTEx1 - Class in com.imsl.test.example.math
-
Finds the Fourier coefficients of a complex
sequence.
- ComplexFFTEx1() - Constructor for class com.imsl.test.example.math.ComplexFFTEx1
-
- ComplexLU - Class in com.imsl.math
-
LU factorization of a matrix of type Complex.
- ComplexLU(Complex[][]) - Constructor for class com.imsl.math.ComplexLU
-
Creates the LU factorization of a square matrix of type Complex.
- ComplexLUEx1 - Class in com.imsl.test.example.math
-
Computes the LU factorization of a complex matrix.
- ComplexLUEx1() - Constructor for class com.imsl.test.example.math.ComplexLUEx1
-
- ComplexMatrix - Class in com.imsl.math
-
Complex matrix manipulation functions.
- ComplexMatrix.MatrixType - Enum in com.imsl.math
-
Indicates which matrix type is used.
- ComplexMatrixEx1 - Class in com.imsl.test.example.math
-
Initializes and prints a complex matrix.
- ComplexMatrixEx1() - Constructor for class com.imsl.test.example.math.ComplexMatrixEx1
-
- ComplexSparseCholesky - Class in com.imsl.math
-
Sparse Cholesky factorization of a matrix of type ComplexSparseMatrix.
- ComplexSparseCholesky(ComplexSparseMatrix) - Constructor for class com.imsl.math.ComplexSparseCholesky
-
Constructs the matrix structure for the Cholesky factorization of
a sparse Hermitian positive definite matrix of type ComplexSparseMatrix.
- ComplexSparseCholesky.NotSPDException - Exception in com.imsl.math
-
The matrix is not Hermitian, positive definite.
- ComplexSparseCholesky.NumericFactor - Class in com.imsl.math
-
Data structures and functions for the numeric Cholesky factor.
- ComplexSparseCholesky.SymbolicFactor - Class in com.imsl.math
-
Data structures and functions for the symbolic Cholesky factor.
- ComplexSparseCholeskyEx1 - Class in com.imsl.test.example.math
-
Uses the Cholesky factorization of a complex
sparse matrix to solve a linear system.
- ComplexSparseCholeskyEx1() - Constructor for class com.imsl.test.example.math.ComplexSparseCholeskyEx1
-
- ComplexSparseMatrix - Class in com.imsl.math
-
Sparse matrix of type Complex.
- ComplexSparseMatrix(int, int) - Constructor for class com.imsl.math.ComplexSparseMatrix
-
Creates a new instance of ComplexSparseMatrix.
- ComplexSparseMatrix(ComplexSparseMatrix) - Constructor for class com.imsl.math.ComplexSparseMatrix
-
Creates a new instance of ComplexSparseMatrix which is a copy of another
ComplexSparseMatrix.
- ComplexSparseMatrix(ComplexSparseMatrix.SparseArray) - Constructor for class com.imsl.math.ComplexSparseMatrix
-
Constructs a complex sparse matrix from a SparseArray object.
- ComplexSparseMatrix(int, int, int[][], Complex[][]) - Constructor for class com.imsl.math.ComplexSparseMatrix
-
Constructs a sparse matrix from SparseArray (Java Sparse Array) data.
- ComplexSparseMatrix.SparseArray - Class in com.imsl.math
-
The SparseArray class uses public fields to hold
the data for a sparse matrix in the Java Sparse Array format.
- ComplexSparseMatrixEx1 - Class in com.imsl.test.example.math
-
Performs operations on a sparse complex matrix.
- ComplexSparseMatrixEx1() - Constructor for class com.imsl.test.example.math.ComplexSparseMatrixEx1
-
- ComplexSuperLU - Class in com.imsl.math
-
Computes the LU factorization of a general sparse matrix of type
ComplexSparseMatrix by a column method and solves a sparse
linear system of equations \(Ax=b\).
- ComplexSuperLU(ComplexSparseMatrix) - Constructor for class com.imsl.math.ComplexSuperLU
-
Constructor for ComplexSuperLU.
- ComplexSuperLUEx1 - Class in com.imsl.test.example.math
-
Computes the LU factorization of a sparse complex matrix.
- ComplexSuperLUEx1() - Constructor for class com.imsl.test.example.math.ComplexSuperLUEx1
-
- ComplexSVD - Class in com.imsl.math
-
Singular Value Decomposition (SVD) of a rectangular matrix of type
Complex.
- ComplexSVD(Complex[][], double) - Constructor for class com.imsl.math.ComplexSVD
-
Construct the singular value decomposition of a rectangular matrix with
a given tolerance.
- ComplexSVD(Complex[][]) - Constructor for class com.imsl.math.ComplexSVD
-
Construct the singular value decomposition of a rectangular matrix with
default tolerance.
- ComplexSVD(ComplexSVD) - Constructor for class com.imsl.math.ComplexSVD
-
Copy constructor for the computation of the singular value decomposition
of a complex matrix.
- ComplexSVD.DidNotConvergeException - Exception in com.imsl.math
-
The iteration did not converge
- ComplexSVDEx1 - Class in com.imsl.test.example.math
-
Computes the SVD factorization of a complex matrix.
- ComplexSVDEx1() - Constructor for class com.imsl.test.example.math.ComplexSVDEx1
-
- compute(double[], double[]) - Method in interface com.imsl.math.BoundedLeastSquares.Function
-
Public interface for the user-supplied function to evaluate the
function that defines the least-squares problem.
- compute(double[], double[]) - Method in interface com.imsl.math.BoundedLeastSquares.Jacobian
-
Public interface for the user-supplied function to compute the
Jacobian.
- compute() - Method in class com.imsl.math.CsTCB
-
Computes the tension-continuity-bias (TCB) cubic spline interpolant.
- compute() - Method in class com.imsl.math.Spline2DLeastSquares
-
Computes a two-dimensional, tensor-product spline approximant using
least squares.
- compute() - Method in class com.imsl.stat.ANCOVA
-
Performs one-way analysis of covariance assuming parallelism and returns
an array containing the parallelism tests for the one-way analysis of
covariance.
- compute() - Method in class com.imsl.stat.ANOVAFactorial
-
Analyzes a balanced factorial design with fixed effects.
- compute() - Method in class com.imsl.stat.ARAutoUnivariate
-
Determines the autoregressive model with the minimum AIC by fitting
autoregressive models from 0 to maxlag lags using the method
of moments or an estimation method specified by the user through
setEstimationMethod.
- compute() - Method in class com.imsl.stat.ARMA
-
Computes least-square estimates of parameters for an ARMA model.
- compute() - Method in class com.imsl.stat.ARMAMaxLikelihood
-
Computes the exact maximum likelihood estimates for the autoregressive
and moving average parameters of an ARMA time series
- compute(int[]) - Method in class com.imsl.stat.ARMAOutlierIdentification
-
Detects and determines outliers and simultaneously estimates the model
parameters for the given time series.
- compute() - Method in class com.imsl.stat.ARSeasonalFit
-
Computes the minimum AIC and optimum values for s and d
based upon the candidates provided in sInitial and
dInitial, and computes the values for the transformed series,
\(W_t(s,d)\).
- compute(int) - Method in class com.imsl.stat.AutoARIMA
-
Estimates potential missing values, detects and determines outliers and
simultaneously fits an optimum model from a set of different \(
\text{ARIMA}(p,0,0)\times(0,d,0)_s\) models to the outlier free
time series.
- compute(int[], int[]) - Method in class com.imsl.stat.AutoARIMA
-
Estimates potential missing values, detects and determines outliers and
simultaneously fits an optimum model from a set of different \(
\text{ARIMA}(p,0,q)\times(0,d,0)_s\) models to the outlier free
time series.
- compute(int, int, int, int) - Method in class com.imsl.stat.AutoARIMA
-
Estimates potential missing values, detects and determines outliers and
simultaneously fits an \(\text{ARIMA}(p,0,q)\times(0,d,0)_s
\) model to the outlier free time series.
- compute() - Method in class com.imsl.stat.ClusterHierarchical
-
Performs a hierarchical cluster analysis.
- compute() - Method in class com.imsl.stat.ClusterKMeans
-
Computes the cluster means.
- compute(int) - Method in class com.imsl.stat.Covariances
-
Computes the matrix.
- compute(double[], int[]) - Method in class com.imsl.stat.Difference
-
Computes a Difference series.
- compute() - Method in class com.imsl.stat.Dissimilarities
-
Computes a matrix of dissimilarities (or similarities) between the columns
(or rows) of a matrix.
- compute() - Method in class com.imsl.stat.distributions.MaximumLikelihoodEstimation
-
Computes the maximum likelihood estimates.
- compute(MaximumLikelihoodEstimation.OptimizationMethod) - Method in class com.imsl.stat.distributions.MaximumLikelihoodEstimation
-
Computes the maximum likelihood estimates with the specified optimization
method.
- compute() - Method in class com.imsl.stat.GARCH
-
Computes estimates of the parameters of a GARCH(p,q) model.
- compute() - Method in class com.imsl.stat.HoltWintersExponentialSmoothing
-
Computes the values of the smoothing parameters.
- compute(int, double[], int, int) - Static method in class com.imsl.stat.LackOfFit
-
Performs lack-of-fit test for a univariate time series or transfer
function given the appropriate correlation function using a minimum lag
of 1.
- compute(int, double[], int, int, int) - Static method in class com.imsl.stat.LackOfFit
-
Performs lack-of-fit test for a univariate time series or transfer
function given the appropriate correlation function.
- compute() - Method in class com.imsl.stat.MultipleComparisons
-
Performs Student-Newman-Keuls multiple comparisons test.
- compute(double[][], double[]) - Method in class com.imsl.stat.SelectionRegression
-
Computes the best multiple linear regression models.
- compute(double[][], double[], double[]) - Method in class com.imsl.stat.SelectionRegression
-
Computes the best weighted multiple linear regression models.
- compute(double[][], double[], double[], double[]) - Method in class com.imsl.stat.SelectionRegression
-
Computes the best weighted multiple linear regression models using
frequencies for each observation.
- compute(double[][], int) - Method in class com.imsl.stat.SelectionRegression
-
Computes the best multiple linear regression models using a
user-supplied covariance matrix.
- compute() - Method in class com.imsl.stat.SignTest
-
Performs a sign test.
- compute() - Method in class com.imsl.stat.StepwiseRegression
-
Builds the multiple linear regression models using forward selection,
backward selection, or stepwise selection.
- compute(double, double) - Method in interface com.imsl.stat.TimeSeriesOperations.Function
-
Public interface for the user-supplied function to combine two time
series values that occur at the same date and time.
- compute() - Method in class com.imsl.stat.WilcoxonRankSum
-
Performs a Wilcoxon rank sum test using an approximate p-value calculation.
- computeCoefficients(int, int, FeynmanKac.Boundaries, double[], double[]) - Method in class com.imsl.math.FeynmanKac
-
Determines the coefficients of the Hermite quintic splines that represent
an approximate solution for the Feynman-Kac PDE.
- computeExactPValues() - Method in class com.imsl.stat.WilcoxonRankSum
-
Performs a Wilcoxon rank sum test using exact p-value calculations.
- computeForecasts(int) - Method in class com.imsl.stat.ARMAOutlierIdentification
-
Computes forecasts, associated probability limits and \(\psi\)
weights for an outlier contaminated time series whose underlying outlier free
series obeys a general seasonal or non-seasonal ARMA model.
- computeLags(int[], int[], double[]) - Method in class com.imsl.datamining.neural.TimeSeriesClassFilter
-
Computes lags of an array sorted first by class designations and then
descending chronological order.
- computeLags(int, double[][]) - Method in class com.imsl.datamining.neural.TimeSeriesFilter
-
Lags time series data to a format used for input to a neural network.
- computeMin(MinUncon.Function) - Method in class com.imsl.math.MinUncon
-
Return the minimum of a smooth function of a single variable of type double
using function values only or using function values and derivatives.
- computeMin(MinUnconMultiVar.Function) - Method in class com.imsl.math.MinUnconMultiVar
-
Return the minimum point of a function of n variables of type double
using a finite-difference gradient or using a user-supplied gradient.
- computeRoots(double[]) - Method in class com.imsl.math.ZeroPolynomial
-
Computes the roots of the polynomial with real coefficients.
- computeRoots(Complex[]) - Method in class com.imsl.math.ZeroPolynomial
-
Computes the roots of the polynomial with Complex coefficients.
- computeStatistics(double[][], int[]) - Method in class com.imsl.datamining.neural.BinaryClassification
-
Computes the classification error statistics for the supplied network patterns
and their associated classifications.
- computeStatistics(double[][], int[]) - Method in class com.imsl.datamining.neural.MultiClassification
-
Computes classification statistics for the supplied
network patterns and their associated classifications.
- computeStatistics(double[][], double[][]) - Method in class com.imsl.datamining.neural.Network
-
Computes error statistics.
- computeZeros(ZeroFunction.Function, double[]) - Method in class com.imsl.math.ZeroFunction
-
Deprecated.
Returns the zeros of a univariate function.
- computeZeros(ZerosFunction.Function) - Method in class com.imsl.math.ZerosFunction
-
Returns the zeros of a univariate function.
- condition(Complex[][]) - Method in class com.imsl.math.ComplexLU
-
Return an estimate of the reciprocal of the \(L_1\) condition number.
- condition(double[][]) - Method in class com.imsl.math.LU
-
Return an estimate of the reciprocal of the \(L_1\) condition number
of a matrix.
- conditionalVarianceFunction(double) - Method in class com.imsl.stat.EGARCH
-
Implements the function \(h =\log(\sigma^2)\) for the EGARCH model.
- conditionalVarianceFunction(double) - Method in class com.imsl.stat.ExtendedGARCH
-
Abstract specification for the conditional variance \(\sigma^2_t\)
function.
- conditionalVarianceInverseFunction(double) - Method in class com.imsl.stat.EGARCH
-
Implements the inverse function \(h^{-1}(f)= \exp(h) = \sigma^2\) for
the EGARCH model.
- conditionalVarianceInverseFunction(double) - Method in class com.imsl.stat.ExtendedGARCH
-
Abstract specification for the conditional variance \(\sigma^2_t\)
inverse function.
- confidenceMean(double) - Method in class com.imsl.stat.Summary
-
Returns the confidence interval for the mean (assuming normality).
- confidenceVariance(double) - Method in class com.imsl.stat.Summary
-
Returns the confidence interval for the variance (assuming normality).
- conjugate(Complex) - Static method in class com.imsl.math.Complex
-
Returns the complex conjugate of a Complex object.
- ConjugateGradient - Class in com.imsl.math
-
Solves a real symmetric definite linear system using the conjugate gradient
method with optional preconditioning.
- ConjugateGradient(int, ConjugateGradient.Function) - Constructor for class com.imsl.math.ConjugateGradient
-
Conjugate gradient constructor.
- ConjugateGradient.Function - Interface in com.imsl.math
-
Public interface for the user supplied function to
ConjugateGradient.
- ConjugateGradient.NoConvergenceException - Exception in com.imsl.math
-
The conjugate gradient method did not converge within the allowed maximum
number of iterations.
- ConjugateGradient.NotDefiniteAMatrixException - Exception in com.imsl.math
-
The input matrix A is indefinite, that is the matrix is not positive or
negative definite.
- ConjugateGradient.NotDefiniteJacobiPreconditionerException - Exception in com.imsl.math
-
The Jacobi preconditioner is not strictly positive or negative definite.
- ConjugateGradient.NotDefinitePreconditionMatrixException - Exception in com.imsl.math
-
The Precondition matrix is indefinite.
- ConjugateGradient.Preconditioner - Interface in com.imsl.math
-
Public interface for the user supplied function to
ConjugateGradient used for preconditioning.
- ConjugateGradient.SingularPreconditionMatrixException - Exception in com.imsl.math
-
The Precondition matrix is singular.
- ConjugateGradientEx1 - Class in com.imsl.test.example.math
-
Solves a positive definite linear system using the conjugate gradient
method.
- ConjugateGradientEx1() - Constructor for class com.imsl.test.example.math.ConjugateGradientEx1
-
- ConjugateGradientEx2 - Class in com.imsl.test.example.math
-
Solves a sparse linear system using the conjugate gradient method with
preconditioning.
- ConjugateGradientEx2(int, int) - Constructor for class com.imsl.test.example.math.ConjugateGradientEx2
-
- conjugateTranspose() - Method in class com.imsl.math.ComplexSparseMatrix
-
Returns the conjugate transpose of the matrix.
- constant(String) - Static method in class com.imsl.math.Physical
-
Returns the value of a constant, given its name.
- constant(String, String) - Static method in class com.imsl.math.Physical
-
Returns the value of a constant, given its name, in the specified
units.
- ConstraintEvaluationException(String) - Constructor for exception com.imsl.math.MinConNLP.ConstraintEvaluationException
-
Constructs a ConstraintEvaluationException object.
- ConstraintEvaluationException(String, Object[]) - Constructor for exception com.imsl.math.MinConNLP.ConstraintEvaluationException
-
Constructs a ConstraintEvaluationException object.
- ConstraintsInconsistentException(String, Object[]) - Constructor for exception com.imsl.math.FeynmanKac.ConstraintsInconsistentException
-
The constraints are inconsistent.
- ConstraintsInconsistentException(String) - Constructor for exception com.imsl.math.MinConGenLin.ConstraintsInconsistentException
-
Constructs a ConstraintsInconsistentException object.
- ConstraintsInconsistentException(String, Object[]) - Constructor for exception com.imsl.math.MinConGenLin.ConstraintsInconsistentException
-
Constructs a ConstraintsInconsistentException object.
- ConstraintsNotSatisfiedException(String) - Constructor for exception com.imsl.math.MinConGenLin.ConstraintsNotSatisfiedException
-
Constructs a ConstraintsNotSatisfiedException object.
- ConstraintsNotSatisfiedException(String, Object[]) - Constructor for exception com.imsl.math.MinConGenLin.ConstraintsNotSatisfiedException
-
Constructs a ConstraintsNotSatisfiedException object.
- ConstrInconsistentException(String) - Constructor for exception com.imsl.stat.GARCH.ConstrInconsistentException
-
Constructs a ConstrInconsistentException object.
- ConstrInconsistentException(String, Object[]) - Constructor for exception com.imsl.stat.GARCH.ConstrInconsistentException
-
Constructs a ConstrInconsistentException object.
- ContingencyTable - Class in com.imsl.stat
-
Performs a chi-squared analysis of a two-way contingency table.
- ContingencyTable(double[][]) - Constructor for class com.imsl.stat.ContingencyTable
-
Constructs and performs a chi-squared analysis of a two-way contingency table.
- ContingencyTableEx1 - Class in com.imsl.test.example.stat
-
Performs a chi-squared test for independence.
- ContingencyTableEx1() - Constructor for class com.imsl.test.example.stat.ContingencyTableEx1
-
- ContingencyTableEx2 - Class in com.imsl.test.example.stat
-
Calculates a number of statistics associated with a contingency table.
- ContingencyTableEx2() - Constructor for class com.imsl.test.example.stat.ContingencyTableEx2
-
- CONTINUOUS_VARIABLE - Static variable in class com.imsl.io.MPSReader
-
Variable is a real number.
- continuousUniform(double, double, double) - Static method in class com.imsl.stat.Cdf
-
Evaluates the continuous uniform cumulative distribution function.
- continuousUniform(double, double, double) - Static method in class com.imsl.stat.InvCdf
-
Returns the inverse of the continuous uniform cumulative distribution function.
- continuousUniform(double, double, double) - Static method in class com.imsl.stat.Pdf
-
Evaluates the continuous uniform probability density function.
- ContinuousUniformPD - Class in com.imsl.stat.distributions
-
The continuous uniform probability distribution.
- ContinuousUniformPD() - Constructor for class com.imsl.stat.distributions.ContinuousUniformPD
-
Constructs a continuous uniform probability distribution.
- ContinuousUniformPDEx1 - Class in com.imsl.test.example.stat.distributions
-
Evaluates the continuous uniform probability distribution.
- ContinuousUniformPDEx1() - Constructor for class com.imsl.test.example.stat.distributions.ContinuousUniformPDEx1
-
- convert(Physical, String) - Static method in class com.imsl.math.Physical
-
Converts a value to a different set of units.
- convexity(GregorianCalendar, GregorianCalendar, double, double, int, DayCountBasis) - Static method in class com.imsl.finance.Bond
-
Returns the convexity for a security.
- copyAndSortData(double[], double[]) - Method in class com.imsl.math.Spline
-
Copy and sort xData into breakPoint and yData into the first
column of coef.
- copyAndSortData(double[], double[], double[]) - Method in class com.imsl.math.Spline
-
Copy and sort xData into breakPoint and yData into the first
column of coef.
- copysign(double, double) - Static method in class com.imsl.math.IEEE
-
Returns a value with the magnitude of x and with the sign bit of y.
- CORRECTED_SSCP_MATRIX - Static variable in class com.imsl.stat.Covariances
-
Indicates corrected sums of squares and crossproducts matrix.
- CorrectorConvergenceException(String, Object[]) - Constructor for exception com.imsl.math.FeynmanKac.CorrectorConvergenceException
-
Corrector failed to converge.
- CORRELATION_COEFFICIENT - Static variable in class com.imsl.stat.Dissimilarities
-
Indicates the correlation coefficient distance method.
- CORRELATION_MATRIX - Static variable in class com.imsl.stat.Covariances
-
Indicates correlation matrix.
- CORRELATION_MATRIX - Static variable in class com.imsl.stat.FactorAnalysis
-
Indicates correlation matrix.
- cos(Complex) - Static method in class com.imsl.math.Complex
-
Returns the cosine of a Complex.
- cos(double) - Static method in class com.imsl.math.JMath
-
Returns the cosine of a double.
- cosh(Complex) - Static method in class com.imsl.math.Complex
-
Returns the hyperbolic cosh of a Complex.
- cosh(double) - Static method in class com.imsl.math.Hyperbolic
-
Returns the hyperbolic cosine of its argument.
- cot(double) - Static method in class com.imsl.math.Sfun
-
Returns the cotangent of a double.
- countFrequency(Itemsets, int[][]) - Static method in class com.imsl.datamining.Apriori
-
Returns the frequency of each itemset in the transaction data set,
x.
- countFrequency(Itemsets, int[][], int[]) - Static method in class com.imsl.datamining.Apriori
-
Returns the frequency of each itemset in the transaction data set,
x, added to the previous frequencies.
- countTokens() - Method in class com.imsl.io.Tokenizer
-
Returns the number of times that the nextToken method
can be called without generating an exception.
- coupdaybs(GregorianCalendar, GregorianCalendar, int, DayCountBasis) - Static method in class com.imsl.finance.Bond
-
Returns the number of days starting with the beginning of the coupon
period and ending with the settlement date.
- coupdays(GregorianCalendar, GregorianCalendar, int, DayCountBasis) - Static method in class com.imsl.finance.Bond
-
Returns the number of days in the coupon period containing the settlement
date.
- coupdaysnc(GregorianCalendar, GregorianCalendar, int, DayCountBasis) - Static method in class com.imsl.finance.Bond
-
Returns the number of days starting with the settlement date and ending
with the next coupon date.
- coupncd(GregorianCalendar, GregorianCalendar, int, DayCountBasis) - Static method in class com.imsl.finance.Bond
-
Returns the first coupon date which follows the settlement date.
- coupnum(GregorianCalendar, GregorianCalendar, int, DayCountBasis) - Static method in class com.imsl.finance.Bond
-
Returns the number of coupons payable between the settlement date and the
maturity date.
- couppcd(GregorianCalendar, GregorianCalendar, int, DayCountBasis) - Static method in class com.imsl.finance.Bond
-
Returns the coupon date which immediately precedes the settlement date.
- Covariances - Class in com.imsl.stat
-
Computes the sample variance-covariance or correlation matrix.
- Covariances(double[][]) - Constructor for class com.imsl.stat.Covariances
-
Constructor for Covariances.
- Covariances.DiffObsDeletedException - Exception in com.imsl.stat
-
Deprecated.
- Covariances.MoreObsDelThanEnteredException - Exception in com.imsl.stat
-
Deprecated.
- Covariances.NonnegativeFreqException - Exception in com.imsl.stat
-
Frequencies must be nonnegative.
- Covariances.NonnegativeWeightException - Exception in com.imsl.stat
-
Weights must be nonnegative.
- Covariances.TooManyObsDeletedException - Exception in com.imsl.stat
-
Deprecated.
- CovariancesEx1 - Class in com.imsl.test.example.stat
-
Calculates a variance-covariance matrix.
- CovariancesEx1() - Constructor for class com.imsl.test.example.stat.CovariancesEx1
-
- CovarianceSingularException(String) - Constructor for exception com.imsl.stat.DiscriminantAnalysis.CovarianceSingularException
-
The variance-covariance matrix is singular.
- CovarianceSingularException(String, Object[]) - Constructor for exception com.imsl.stat.DiscriminantAnalysis.CovarianceSingularException
-
The variance-covariance matrix is singular.
- createContinuousAttribute(ProbabilityDistribution) - Method in class com.imsl.datamining.NaiveBayesClassifier
-
Create a continuous variable and the associated distribution function.
- createContinuousAttribute(ProbabilityDistribution[]) - Method in class com.imsl.datamining.NaiveBayesClassifier
-
Create a continuous variable and the associated distribution functions
for each target classification.
- createHiddenLayer() - Method in class com.imsl.datamining.neural.FeedForwardNetwork
-
Creates a HiddenLayer.
- createHiddenLayer() - Method in class com.imsl.datamining.neural.Network
-
Creates the next HiddenLayer in the Network.
- createInput() - Method in class com.imsl.datamining.neural.InputLayer
-
Creates an InputNode in the InputLayer of the
neural network.
- createInputs(int) - Method in class com.imsl.datamining.neural.InputLayer
-
Creates a number of InputNodes in this Layer
of the neural network.
- createNominalAttribute(int) - Method in class com.imsl.datamining.NaiveBayesClassifier
-
Create a nominal attribute and the number of categories
- createPerceptron(Activation, double) - Method in class com.imsl.datamining.neural.HiddenLayer
-
Creates a Perceptron in this Layer with a
specified activation function and bias.
- createPerceptron() - Method in class com.imsl.datamining.neural.HiddenLayer
-
Creates a Perceptron in this Layer of the
neural network.
- createPerceptron(Activation, double) - Method in class com.imsl.datamining.neural.OutputLayer
-
Creates a Perceptron in this Layer with a
specified Activation and bias.
- createPerceptron() - Method in class com.imsl.datamining.neural.OutputLayer
-
Creates a Perceptron in this Layer of the
neural network.
- createPerceptrons(int) - Method in class com.imsl.datamining.neural.HiddenLayer
-
Creates a number of Perceptrons in this Layer
of the neural network.
- createPerceptrons(int, Activation, double) - Method in class com.imsl.datamining.neural.HiddenLayer
-
Creates a number of Perceptrons in this Layer
with the specified bias.
- createPerceptrons(int) - Method in class com.imsl.datamining.neural.OutputLayer
-
Creates a number of Perceptrons in this Layer
of the neural network.
- createPerceptrons(int, Activation, double) - Method in class com.imsl.datamining.neural.OutputLayer
-
Creates a number of Perceptrons in this Layer
with specified activation and bias.
- CrossCorrelation - Class in com.imsl.stat
-
Computes the sample cross-correlation function of two stationary time
series.
- CrossCorrelation(double[], double[], int) - Constructor for class com.imsl.stat.CrossCorrelation
-
Constructor to compute the sample cross-correlation
function of two stationary time series.
- CrossCorrelation.NonPosVariancesException - Exception in com.imsl.stat
-
The problem is ill-conditioned.
- CrossCorrelationEx1 - Class in com.imsl.test.example.stat
-
Computes the cross-covariances and cross-correlations for the gas furnace
data.
- CrossCorrelationEx1() - Constructor for class com.imsl.test.example.stat.CrossCorrelationEx1
-
- crossValidate() - Method in class com.imsl.datamining.CrossValidation
-
Performs V-Fold cross-validation.
- CrossValidation - Class in com.imsl.datamining
-
Performs V-Fold cross-validation for predictive models.
- CrossValidation(PredictiveModel) - Constructor for class com.imsl.datamining.CrossValidation
-
Creates a CrossValidation object.
- CrossValidationEx1 - Class in com.imsl.test.example.datamining
-
Uses cross-validation to determine the
optimally pruned decision tree.
- CrossValidationEx1() - Constructor for class com.imsl.test.example.datamining.CrossValidationEx1
-
- CsAkima - Class in com.imsl.math
-
Extension of the Spline class to handle the Akima cubic spline.
- CsAkima(double[], double[]) - Constructor for class com.imsl.math.CsAkima
-
Constructs the Akima cubic spline interpolant to the given
data points.
- CsAkimaEx1 - Class in com.imsl.test.example.math
-
Computes the Akima cubic spline.
- CsAkimaEx1() - Constructor for class com.imsl.test.example.math.CsAkimaEx1
-
- CsInterpolate - Class in com.imsl.math
-
Extension of the Spline class to interpolate data points.
- CsInterpolate(double[], double[]) - Constructor for class com.imsl.math.CsInterpolate
-
Constructs a cubic spline that interpolates the given
data points.
- CsInterpolate(double[], double[], int, double, int, double) - Constructor for class com.imsl.math.CsInterpolate
-
Constructs a cubic spline that interpolates the given
data points with specified derivative endpoint conditions.
- CsInterpolateEx1 - Class in com.imsl.test.example.math
-
Computes a cubic spline.
- CsInterpolateEx1() - Constructor for class com.imsl.test.example.math.CsInterpolateEx1
-
- CsPeriodic - Class in com.imsl.math
-
Extension of the Spline class to interpolate data points with
periodic boundary conditions.
- CsPeriodic(double[], double[]) - Constructor for class com.imsl.math.CsPeriodic
-
Constructs a cubic spline that interpolates the given
data points with periodic boundary conditions.
- CsPeriodicEx1 - Class in com.imsl.test.example.math
-
Computes a cubic spline interpolant with
periodic boundary conditions.
- CsPeriodicEx1() - Constructor for class com.imsl.test.example.math.CsPeriodicEx1
-
- CsShape - Class in com.imsl.math
-
Extension of the Spline class to interpolate data points consistent
with the concavity of the data.
- CsShape(double[], double[]) - Constructor for class com.imsl.math.CsShape
-
Construct a cubic spline interpolant which is consistent
with the concavity of the data.
- CsShape.TooManyIterationsException - Exception in com.imsl.math
-
Too many iterations.
- CsShapeEx1 - Class in com.imsl.test.example.math
-
Computes a shape preserving cubic spline.
- CsShapeEx1() - Constructor for class com.imsl.test.example.math.CsShapeEx1
-
- CsSmooth - Class in com.imsl.math
-
Extension of the Spline class to construct a smooth cubic spline
from noisy data points.
- CsSmooth(double[], double[]) - Constructor for class com.imsl.math.CsSmooth
-
Constructs a smooth cubic spline from noisy data using
cross-validation to estimate the smoothing parameter.
- CsSmooth(double[], double[], double[]) - Constructor for class com.imsl.math.CsSmooth
-
Constructs a smooth cubic spline from noisy data using
cross-validation to estimate the smoothing parameter.
- CsSmoothC2 - Class in com.imsl.math
-
Extension of the Spline class used to construct a spline for noisy data
points using an alternate method.
- CsSmoothC2(double[], double[], double) - Constructor for class com.imsl.math.CsSmoothC2
-
Constructs a smooth cubic spline from noisy data using
an algorithm based on Reinsch (1967).
- CsSmoothC2(double[], double[], double[], double) - Constructor for class com.imsl.math.CsSmoothC2
-
Constructs a smooth cubic spline from noisy data using
an algorithm based on Reinsch (1967) with weights supplied
by the user.
- CsSmoothC2Ex1 - Class in com.imsl.test.example.math
-
Computes a smooth cubic spline on noisy data.
- CsSmoothC2Ex1() - Constructor for class com.imsl.test.example.math.CsSmoothC2Ex1
-
- CsSmoothEx1 - Class in com.imsl.test.example.math
-
Computes a smooth cubic spline on noisy data using an "optimized" smoothing
parameter value.
- CsSmoothEx1() - Constructor for class com.imsl.test.example.math.CsSmoothEx1
-
- CsTCB - Class in com.imsl.math
-
Extension of the Spline class to handle a tension-continuity-bias (TCB) cubic spline,
also known as a Kochanek-Bartels spline and is a generalization of the Catmull-Rom spline.
- CsTCB(double[], double[]) - Constructor for class com.imsl.math.CsTCB
-
Constructs the tension-continuity-bias (TCB) cubic spline interpolant to the given
data points.
- CsTCBEx1 - Class in com.imsl.test.example.math
-
Computes the Kochanek-Bartels cubic spline.
- CsTCBEx1() - Constructor for class com.imsl.test.example.math.CsTCBEx1
-
- CUBIC_SPLINE - Static variable in class com.imsl.stat.ARMAEstimateMissing
-
Indicates that missing values should be estimated using cublic spline
interpolation.
- cumipmt(double, int, double, int, int, int) - Static method in class com.imsl.finance.Finance
-
Returns the cumulative interest paid between two periods.
- cumprinc(double, int, double, int, int, int) - Static method in class com.imsl.finance.Finance
-
Returns the cumulative principal paid between two periods.
- CURRENT - Static variable in class com.imsl.math.Physical
-
- CyclingIsOccurringException(int) - Constructor for exception com.imsl.stat.StepwiseRegression.CyclingIsOccurringException
-
Constructs a CyclingIsOccurringException.
- CyclingOccurringException() - Constructor for exception com.imsl.math.DenseLP.CyclingOccurringException
-
The algorithm appears to be cycling.
- CyclingOccurringException(String) - Constructor for exception com.imsl.math.DenseLP.CyclingOccurringException
-
The algorithm appears to be cycling.
- CyclingOccurringException(String, Object[]) - Constructor for exception com.imsl.math.DenseLP.CyclingOccurringException
-
The algorithm appears to be cycling.
- g(double) - Method in interface com.imsl.datamining.neural.Activation
-
Returns the value of the activation function.
- g(double) - Method in interface com.imsl.math.MinUncon.Derivative
-
Public interface for the smooth function of a single
variable to be minimized.
- g(double) - Method in interface com.imsl.math.RadialBasis.Function
-
The derivative of the radial basis function used to calculate the
gradient of the radial basis approximation.
- g(double) - Method in class com.imsl.math.RadialBasis.Gaussian
-
The derivative of the Gaussian basis function used to calculate the
gradient of the radial basis approximation.
- g(double) - Method in class com.imsl.math.RadialBasis.HardyMultiquadric
-
The derivative of the Hardy multiquadric basis function used to
calculate the gradient of the radial basis
approximation.
- g(double) - Method in class com.imsl.test.example.math.MinUnconEx2
-
- g(double) - Method in class com.imsl.test.example.math.RadialBasisEx2.PolyHarmonicSpline
-
- gamma(double) - Static method in class com.imsl.math.Sfun
-
Returns the Gamma function of a double.
- gamma(double, double) - Static method in class com.imsl.stat.Cdf
-
Evaluates the gamma cumulative probability distribution function.
- gamma(double, double) - Static method in class com.imsl.stat.InvCdf
-
Returns the inverse of the gamma cumulative probability distribution function.
- gamma(double, double, double) - Static method in class com.imsl.stat.Pdf
-
Evaluates the gamma probability density function.
- GammaDistribution - Class in com.imsl.stat
-
Evaluates a gamma probability density for a given set of data.
- GammaDistribution() - Constructor for class com.imsl.stat.GammaDistribution
-
- gammaIncomplete(double, double) - Static method in class com.imsl.math.Sfun
-
Evaluates the incomplete gamma function.
- GammaPD - Class in com.imsl.stat.distributions
-
The gamma probability distribution.
- GammaPD() - Constructor for class com.imsl.stat.distributions.GammaPD
-
Constructor for the gamma probability distribution.
- GammaPDEx1 - Class in com.imsl.test.example.stat.distributions
-
Evaluates the gamma probability distribution.
- GammaPDEx1() - Constructor for class com.imsl.test.example.stat.distributions.GammaPDEx1
-
- gammaProb(double, double, double) - Static method in class com.imsl.stat.Cdf
-
- GARCH - Class in com.imsl.stat
-
Computes estimates of the parameters of a GARCH(p,q) model.
- GARCH(int, int, double[], double[]) - Constructor for class com.imsl.stat.GARCH
-
Constructor for GARCH.
- GARCH.ConstrInconsistentException - Exception in com.imsl.stat
-
The equality constraints are inconsistent.
- GARCH.EqConstrInconsistentException - Exception in com.imsl.stat
-
The equality constraints and the bounds on the variables are
found to be inconsistent.
- GARCH.NoVectorXException - Exception in com.imsl.stat
-
No vector X satisfies all of the constraints.
- GARCH.TooManyIterationsException - Exception in com.imsl.stat
-
Number of function evaluations exceeded 1000.
- GARCH.VarsDeterminedException - Exception in com.imsl.stat
-
The variables are determined by the equality constraints.
- GARCHEx1 - Class in com.imsl.test.example.stat
-
Estimates a \(\text{GARCH}(p,q)\) model from simulated data.
- GARCHEx1() - Constructor for class com.imsl.test.example.stat.GARCHEx1
-
- Gaussian(double) - Constructor for class com.imsl.math.RadialBasis.Gaussian
-
Creates a Gaussian basis function \(e^{-ax^2}\).
- GENERALIZED_LEAST_SQUARES - Static variable in class com.imsl.stat.FactorAnalysis
-
Indicates generalized least squares method.
- generalizedExtremeValue(double, double, double, double) - Static method in class com.imsl.stat.Cdf
-
Evaluates the generalized extreme value cumulative distribution function.
- generalizedExtremeValue(double, double, double, double) - Static method in class com.imsl.stat.InvCdf
-
Returns the inverse of the generalized extreme value cumulative distribution function.
- generalizedExtremeValue(double, double, double, double) - Static method in class com.imsl.stat.Pdf
-
Evaluates the generalized extreme value probability density function.
- generalizedGaussian(double, double, double, double) - Static method in class com.imsl.stat.Cdf
-
Evaluates the generalized Gaussian (normal) cumulative distribution function.
- generalizedGaussian(double, double, double, double) - Static method in class com.imsl.stat.InvCdf
-
Returns the inverse of the generalized Gaussian (normal) cumulative
distribution function.
- generalizedGaussian(double, double, double, double) - Static method in class com.imsl.stat.Pdf
-
Evaluates the generalized Gaussian (normal) probability density function.
- GeneralizedGaussianPD - Class in com.imsl.stat.distributions
-
The generalized Gaussian probability distribution.
- GeneralizedGaussianPD() - Constructor for class com.imsl.stat.distributions.GeneralizedGaussianPD
-
Constructs a generalized Gaussian probability distribution.
- generalizedPareto(double, double, double, double) - Static method in class com.imsl.stat.Cdf
-
Evaluates the generalized Pareto cumulative distribution function.
- generalizedPareto(double, double, double, double) - Static method in class com.imsl.stat.InvCdf
-
Returns the inverse of the generalized Pareto cumulative distribution function.
- generalizedPareto(double, double, double, double) - Static method in class com.imsl.stat.Pdf
-
Evaluates the generalized Pareto probability density function.
- GenMinRes - Class in com.imsl.math
-
Linear system solver using the restarted Generalized Minimum Residual (GMRES)
method.
- GenMinRes(int, GenMinRes.Function) - Constructor for class com.imsl.math.GenMinRes
-
GMRES linear system solver constructor.
- GenMinRes.Formatter - Class in com.imsl.math
-
- GenMinRes.Function - Interface in com.imsl.math
-
Public interface for the user supplied function to
GenMinRes.
- GenMinRes.Norm - Interface in com.imsl.math
-
Public interface for the user supplied function to the
GenMinRes object used for the norm \( \Vert X \Vert \)
when the Gram-Schmidt implementation is used.
- GenMinRes.Preconditioner - Interface in com.imsl.math
-
Public interface for the user supplied function to
GenMinRes used for preconditioning.
- GenMinRes.TooManyIterationsException - Exception in com.imsl.math
-
Maximum number of iterations exceeded.
- GenMinRes.VectorProducts - Interface in com.imsl.math
-
Public interface for the user supplied function to the
GenMinRes object used for the inner
product when the Gram-Schmidt implementation is used.
- GenMinResEx1 - Class in com.imsl.test.example.math
-
Solves a small linear system with the Generalized Minimum Residual (GMRES)
method.
- GenMinResEx1() - Constructor for class com.imsl.test.example.math.GenMinResEx1
-
- GenMinResEx2 - Class in com.imsl.test.example.math
-
Solves a small linear system with user supplied inner
product.
- GenMinResEx2() - Constructor for class com.imsl.test.example.math.GenMinResEx2
-
- GenMinResEx3 - Class in com.imsl.test.example.math
-
Solves a small linear system stored in sparse form.
- GenMinResEx3() - Constructor for class com.imsl.test.example.math.GenMinResEx3
-
- GenMinResEx4 - Class in com.imsl.test.example.math
-
Solves a small linear system stored in sparse form with
preconditioning.
- GenMinResEx4() - Constructor for class com.imsl.test.example.math.GenMinResEx4
-
- GenMinResEx5 - Class in com.imsl.test.example.math
-
Solves the Poisson equation using the second Householder
implementation.
- GenMinResEx5() - Constructor for class com.imsl.test.example.math.GenMinResEx5
-
- GenMinResEx6 - Class in com.imsl.test.example.math
-
Solves the Poisson equation using the second Householder
implementation and preconditioning.
- GenMinResEx6(int) - Constructor for class com.imsl.test.example.math.GenMinResEx6
-
- GenMinResEx7 - Class in com.imsl.test.example.math
-
Solves a small linear system with logging.
- GenMinResEx7() - Constructor for class com.imsl.test.example.math.GenMinResEx7
-
- geometric(int, double) - Static method in class com.imsl.stat.Cdf
-
Evaluates the discrete geometric cumulative probability distribution function.
- geometric(double, double) - Static method in class com.imsl.stat.InvCdf
-
Returns the inverse of the discrete geometric cumulative probability distribution function.
- geometric(int, double) - Static method in class com.imsl.stat.Pdf
-
Evaluates the geometric probability density (or mass) function.
- GeometricPD - Class in com.imsl.stat.distributions
-
The geometric probability distribution.
- GeometricPD() - Constructor for class com.imsl.stat.distributions.GeometricPD
-
Constructor for the geometric probability distribution.
- GeometricPDEx1 - Class in com.imsl.test.example.stat.distributions
-
Evaluates the geometric probability distribution.
- GeometricPDEx1() - Constructor for class com.imsl.test.example.stat.distributions.GeometricPDEx1
-
- geometricProb(int, double) - Static method in class com.imsl.stat.Cdf
-
- get(int, int) - Method in class com.imsl.math.ComplexSparseMatrix
-
Returns the value of an element in the matrix.
- get(int, int) - Method in class com.imsl.math.SparseMatrix
-
Returns the value of an element in the matrix.
- getAbsoluteErrorTolerances() - Method in class com.imsl.math.FeynmanKac
-
Returns absolute error tolerances.
- getActivation() - Method in class com.imsl.datamining.neural.Perceptron
-
Returns the activation function.
- getAdjustedANOVA() - Method in class com.imsl.stat.ANCOVA
-
Returns the partial sum of squares for the one-way analysis of
covariance.
- getAdjustedRSquared() - Method in class com.imsl.stat.ANOVA
-
Returns the adjusted R-squared (in percent).
- getAIC() - Method in class com.imsl.stat.ARAutoUnivariate
-
Returns the final estimate for Akaike's Information Criterion (AIC) at
the optimum.
- getAIC() - Method in class com.imsl.stat.ARMAOutlierIdentification
-
Returns Akaike's information criterion (AIC).
- getAIC() - Method in class com.imsl.stat.ARSeasonalFit
-
Returns the final estimate for Akaike's Information Criterion (AIC) at
the optimum.
- getAIC() - Method in class com.imsl.stat.AutoARIMA
-
Returns Akaike's information criterion (AIC) for the optimum model.
- getAICC() - Method in class com.imsl.stat.ARMAOutlierIdentification
-
Returns Akaike's Corrected Information Criterion (AICC).
- getAICC() - Method in class com.imsl.stat.AutoARIMA
-
Returns Akaike's Corrected Information Criterion (AICC) for the optimum
model.
- getAkaike() - Method in class com.imsl.stat.GARCH
-
Returns the value of Akaike Information Criterion evaluated at the
estimated parameter array.
- getANCOVA() - Method in class com.imsl.stat.ANCOVA
-
Returns an array containing the one-way analysis of covariance assuming
parallelism.
- getANOVA() - Method in class com.imsl.math.RadialBasis
-
Returns the ANOVA statistics from the linear regression.
- getANOVA() - Method in class com.imsl.stat.LinearRegression
-
Get an analysis of variance table and related statistics.
- getANOVA() - Method in class com.imsl.stat.StepwiseRegression
-
Gets an analysis of variance table and related statistics.
- getANOVA() - Method in class com.imsl.stat.UserBasisRegression
-
Get an analysis of variance table and related statistics.
- getANOVATable() - Method in class com.imsl.stat.ANOVAFactorial
-
Returns the analysis of variance table.
- getANOVATables() - Method in class com.imsl.stat.ANCOVA
-
Returns a matrix of size ngroup by 15 containing the analysis of
variance tables for each linear regression model fitted separately to
each treatment group.
- getAR() - Method in class com.imsl.stat.ARAutoUnivariate
-
Returns the final auto regressive parameter estimates at the optimum AIC
using the estimation method specified in setEstimationMethod
.
- getAR() - Method in class com.imsl.stat.ARMA
-
Returns the final autoregressive parameter estimates.
- getAR() - Method in class com.imsl.stat.ARMAMaxLikelihood
-
Returns the final autoregressive parameter estimates.
- getAR() - Method in class com.imsl.stat.ARMAOutlierIdentification
-
Returns the final autoregressive parameter estimates.
- getAR() - Method in class com.imsl.stat.ARSeasonalFit
-
Returns the final autoregressive parameter estimates at the optimum in
the transformed series \(W_t\).
- getAR() - Method in class com.imsl.stat.AutoARIMA
-
Returns the final autoregressive parameter estimates of the optimum
model.
- getAR() - Method in class com.imsl.stat.GARCH
-
- getARCH() - Method in class com.imsl.stat.ExtendedGARCH
-
Returns the value of the ARCH lag parameter for the given instance.
- getARCH() - Method in class com.imsl.stat.GARCH
-
Returns the estimated values of the ARCH coefficients.
- getARConstants() - Method in class com.imsl.stat.VectorAutoregression
-
Returns the current settings of the constants used in the autoregression
model.
- getARModel() - Method in class com.imsl.stat.VectorAutoregression
-
Returns the autoregressive model configuration.
- getAROrder() - Method in class com.imsl.stat.ARSeasonalFit
-
Returns optimum number of lags, p, for the optimum autoregressive
AR(p) model.
- getArray(int) - Method in class com.imsl.io.AbstractFlatFile
-
Deprecated.
- getArray(String) - Method in class com.imsl.io.AbstractFlatFile
-
Returns the value of the designated column in the current row of this
ResultSet object as an Array object in the Java
programming language.
- getArray() - Method in class com.imsl.stat.ANOVA
-
Returns the ANOVA values as an array.
- getAsciiStream(int) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as a stream of ASCII characters.
- getAsciiStream(String) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as a stream of ASCII characters.
- getAssociationRules(Itemsets, double, double) - Static method in class com.imsl.datamining.Apriori
-
Returns strong association rules among the itemsets in
itemsets.
- getAutoCorrelations() - Method in class com.imsl.stat.AutoCorrelation
-
Returns the autocorrelations of the time series x.
- getAutoCorrelationX() - Method in class com.imsl.stat.CrossCorrelation
-
Returns the autocorrelations of the time series x.
- getAutoCorrelationY() - Method in class com.imsl.stat.CrossCorrelation
-
Returns the autocorrelations of the time series y.
- getAutoCovariance() - Method in class com.imsl.stat.ARMA
-
Returns the autocovariances of the time series z.
- getAutoCovariances() - Method in class com.imsl.stat.AutoCorrelation
-
Returns the variance and autocovariances of the time series
x.
- getAutoCovarianceX() - Method in class com.imsl.stat.CrossCorrelation
-
Returns the autocovariances of the time series x.
- getAutoCovarianceY() - Method in class com.imsl.stat.CrossCorrelation
-
Returns the autocovariances of the time series y.
- getBackwardOrigin() - Method in class com.imsl.stat.ARAutoUnivariate
-
Returns the maximum backward origin.
- getBackwardOrigin() - Method in class com.imsl.stat.ARMA
-
Returns the user-specified backward origin
- getBackwardOrigin() - Method in class com.imsl.stat.ARMAMaxLikelihood
-
Returns the current value for forecasting backward origin.
- getBalancedTable() - Method in class com.imsl.stat.TableMultiWay
-
Returns an object containing the balanced table.
- getBase() - Method in class com.imsl.stat.FaureSequence
-
Returns the base.
- getBias() - Method in class com.imsl.datamining.neural.Perceptron
-
Returns the bias for this Perceptron.
- getBIC() - Method in class com.imsl.stat.ARMAOutlierIdentification
-
Returns the Bayesian Information Criterion (BIC).
- getBIC() - Method in class com.imsl.stat.AutoARIMA
-
Returns the Bayesian Information Criterion (BIC) for the optimum model.
- getBigDecimal(int, int) - Method in class com.imsl.io.AbstractFlatFile
-
Deprecated.
- getBigDecimal(String, int) - Method in class com.imsl.io.AbstractFlatFile
-
Deprecated.
- getBigDecimal(int) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as a java.math.BigDecimal with
full precision.
- getBigDecimal(String) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as a java.math.BigDecimal with
full precision.
- getBinaryStream(int) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as a binary stream of uninterpreted bytes.
- getBinaryStream(String) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as a stream of uninterpreted
bytes.
- getBlob(int) - Method in class com.imsl.io.AbstractFlatFile
-
Returns the value of the designated column in the current row of this
ResultSet object as a Blob object in the Java
programming language.
- getBlob(String) - Method in class com.imsl.io.AbstractFlatFile
-
Returns the value of the designated column in the current row of this
ResultSet object as a Blob object in the Java
programming language.
- getBlomScores(double[]) - Method in class com.imsl.stat.Ranks
-
Gets the Blom version of normal scores for each observation.
- getBoolean(int) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as a boolean in the Java
programming language.
- getBoolean(String) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as a boolean in the Java
programming language.
- getBounds() - Method in class com.imsl.datamining.neural.ScaleFilter
-
Retrieves bounds used during bounded scaling.
- getBreakpoints() - Method in class com.imsl.math.Spline
-
Returns a copy of the breakpoints.
- getByte(int) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as a byte in the Java
programming language.
- getByte(String) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as a byte in the Java
programming language.
- getBytes(int) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as a byte array in the Java
programming language.
- getBytes(String) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as a byte array in the Java
programming language.
- getCaseAnalysis() - Method in class com.imsl.stat.CategoricalGenLinModel
-
Returns the case analysis.
- getCaseStatistics() - Method in class com.imsl.stat.LinearRegression.CaseStatistics
-
- getCaseStatistics(double[], double) - Method in class com.imsl.stat.LinearRegression
-
Returns the case statistics for an observation.
- getCaseStatistics(double[], double, double) - Method in class com.imsl.stat.LinearRegression
-
Returns the case statistics for an observation and a weight.
- getCaseStatistics(double[], double, int) - Method in class com.imsl.stat.LinearRegression
-
Returns the case statistics for an observation and future response count
for the desired prediction interval.
- getCaseStatistics(double[], double, double, int) - Method in class com.imsl.stat.LinearRegression
-
Returns the case statistics for an observation, weight, and future
response count for the desired prediction interval.
- getCaseStatistics() - Method in class com.imsl.stat.ProportionalHazards
-
Returns the case statistics for each observation.
- getCellCounts() - Method in class com.imsl.stat.ChiSquaredTest
-
Returns the cell counts.
- getCensorColumn() - Method in class com.imsl.stat.KaplanMeierEstimates
-
Returns the column index of x containing the
optional censoring code for each observation.
- getCensorColumn() - Method in class com.imsl.stat.ProportionalHazards
-
Returns the column index of x containing the
optional censoring code for each observation.
- getCenter() - Method in class com.imsl.datamining.neural.ScaleFilter
-
Retrieves the measure of center to be used during z-score scaling.
- getCenter() - Method in class com.imsl.stat.ARSeasonalFit
-
Returns the current setting for centering the input time series.
- getCentroidDistance() - Method in class com.imsl.math.NelderMead
-
Returns the average distance from the final complex vertices to their
centroid.
- getCharacterStream(int) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as a java.io.Reader object.
- getCharacterStream(String) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as a java.io.Reader object.
- getChildId(int) - Method in class com.imsl.datamining.decisionTree.TreeNode
-
Returns the id of a child node.
- getChildrenIds() - Method in class com.imsl.datamining.decisionTree.TreeNode
-
Returns the array of child node id's.
- getChiSquared() - Method in class com.imsl.stat.ChiSquaredTest
-
Returns the chi-squared statistic.
- getChiSquared() - Method in class com.imsl.stat.ContingencyTable
-
Returns the Pearson chi-squared test statistic.
- getChiSquared() - Method in class com.imsl.stat.NormalityTest
-
Returns the chi-square statistic for the chi-squared goodness-of-fit test.
- getChiSquaredTest() - Method in class com.imsl.stat.NormOneSample
-
Returns the test statistic associated with the chi-squared test
for variances.
- getChiSquaredTest() - Method in class com.imsl.stat.NormTwoSample
-
Returns the test statistic associated with the chi-squared test
for the (assumed) common variance.
- getChiSquaredTestDF() - Method in class com.imsl.stat.NormOneSample
-
Returns the degrees of freedom associated with the chi-squared
test for variances.
- getChiSquaredTestDF() - Method in class com.imsl.stat.NormTwoSample
-
Returns the degrees of freedom associated with the chi-squared
test for the common variance.
- getChiSquaredTestP() - Method in class com.imsl.stat.NormOneSample
-
Returns the probability of a larger chi-squared associated
with the chi-squared test for variances.
- getChiSquaredTestP() - Method in class com.imsl.stat.NormTwoSample
-
Returns the probability of a larger value than the
chi-squared statistic associated with the test for
the common variance, assuming the null hypothesis is true (i.e.,
the p-value for the test).
- getClassCounts(int[]) - Method in class com.imsl.datamining.NaiveBayesClassifier
-
Returns the number of patterns for each target classification.
- getClassCounts() - Method in class com.imsl.datamining.PredictiveModel
-
Returns the counts of each class (level) of the categorical response
variable.
- getClassErrors(double[], double[]) - Method in class com.imsl.datamining.PredictiveModel
-
Returns classification error information.
- getClassFittedValues() - Method in class com.imsl.datamining.GradientBoosting
-
Returns the fitted values \({f(x_i)}\) for a categorical
response variable with two or more levels.
- getClassificationErrors() - Method in class com.imsl.datamining.NaiveBayesClassifier
-
Returns the classification probability errors for each pattern in the
training data.
- getClassificationVariableCounts() - Method in class com.imsl.stat.CategoricalGenLinModel
-
Returns the number of values taken by each classification variable.
- getClassificationVariableValues() - Method in class com.imsl.stat.CategoricalGenLinModel
-
Returns the distinct values of the classification variables in ascending
order.
- getClassLabels() - Method in class com.imsl.datamining.PredictiveModel
-
Returns the current class labels for a categorical response variable.
- getClassLabels() - Method in class com.imsl.datamining.supportvectormachine.SVModel
-
Returns the class labels.
- getClassMembership() - Method in class com.imsl.stat.DiscriminantAnalysis
-
Returns the group number to which the observation was classified.
- getClassPenaltyWeights() - Method in class com.imsl.datamining.supportvectormachine.SVClassification
-
Returns the class weights.
- getClassProbabilities() - Method in class com.imsl.datamining.GradientBoosting
-
Returns the predicted probabilities on the training data for a
categorical response variable.
- getClassProbabilities() - Method in class com.imsl.datamining.PredictiveModel
-
Returns a matrix containing the predicted class probabilities for each
observation in the training data
- getClassTable() - Method in class com.imsl.stat.DiscriminantAnalysis
-
Returns the classification table.
- getClassValueCounts() - Method in class com.imsl.stat.ProportionalHazards
-
Returns the number of values taken by each classification variable.
- getClassValues() - Method in class com.imsl.stat.ProportionalHazards
-
Returns the class values taken by each classification variable.
- getClassWeightLabels() - Method in class com.imsl.datamining.supportvectormachine.SVClassification
-
Returns the weight labels array.
- getClob(int) - Method in class com.imsl.io.AbstractFlatFile
-
Returns the value of the designated column in the current row of this
ResultSet object as a Clob object in the Java
programming language.
- getClob(String) - Method in class com.imsl.io.AbstractFlatFile
-
Returns the value of the designated column in the current row of this
ResultSet object as a Clob object in the Java
programming language.
- getClosedFormMLE(double[]) - Method in interface com.imsl.stat.distributions.ClosedFormMaximumLikelihoodInterface
-
Returns the maximum likelihood estimates (MLEs).
- getClosedFormMLE(double[]) - Method in class com.imsl.stat.distributions.ContinuousUniformPD
-
Returns the closed form maximum likelihood estimates.
- getClosedFormMLE(double[]) - Method in class com.imsl.stat.distributions.ExponentialPD
-
Returns the closed form maximum likelihood estimates.
- getClosedFormMLE(double[]) - Method in class com.imsl.stat.distributions.GeometricPD
-
Returns the maximum likelihood estimate for the parameter.
- getClosedFormMLE(double[]) - Method in class com.imsl.stat.distributions.LogNormalPD
-
Returns the closed form maximum likelihood estimates.
- getClosedFormMLE(double[]) - Method in class com.imsl.stat.distributions.NormalPD
-
Returns the closed form maximum likelihood estimates.
- getClosedFormMLE(double[]) - Method in class com.imsl.stat.distributions.PoissonPD
-
Returns the closed form maximum likelihood estimate.
- getClosedFormMLE(double[]) - Method in class com.imsl.stat.distributions.RayleighPD
-
Returns the closed form maximum likelihood estimate.
- getClosedFormMlStandardError(double[]) - Method in interface com.imsl.stat.distributions.ClosedFormMaximumLikelihoodInterface
-
Returns the standard error based on the closed form solution of the
maximum liklihood for the sample data.
- getClosedFormMlStandardError(double[]) - Method in class com.imsl.stat.distributions.ContinuousUniformPD
-
Returns the standard error based on the closed form maximum likelihood
estimates.
- getClosedFormMlStandardError(double[]) - Method in class com.imsl.stat.distributions.ExponentialPD
-
Returns the standard error based on the closed form maximum likelihood estimates.
- getClosedFormMlStandardError(double[]) - Method in class com.imsl.stat.distributions.GeometricPD
-
Returns the standard error of the maximum likelihood estimate.
- getClosedFormMlStandardError(double[]) - Method in class com.imsl.stat.distributions.LogNormalPD
-
Returns the standard errors of the closed form maximum likelihood estimates.
- getClosedFormMlStandardError(double[]) - Method in class com.imsl.stat.distributions.NormalPD
-
Returns the standard errors of the closed form maximum likelihood estimates.
- getClosedFormMlStandardError(double[]) - Method in class com.imsl.stat.distributions.PoissonPD
-
Returns the standard error based on the closed form maximum likelihood estimate.
- getClosedFormMlStandardError(double[]) - Method in class com.imsl.stat.distributions.RayleighPD
-
Returns the standard error based on the closed form maximum likelihood
estimate.
- getClusterCounts() - Method in class com.imsl.stat.ClusterKMeans
-
Returns the number of observations in each cluster.
- getClusterLeftSons() - Method in class com.imsl.stat.ClusterHierarchical
-
Returns the left sons of each merged cluster.
- getClusterLevel() - Method in class com.imsl.stat.ClusterHierarchical
-
Returns the level at which the clusters are joined.
- getClusterMembership(int) - Method in class com.imsl.stat.ClusterHierarchical
-
Returns the cluster membership of each observation.
- getClusterMembership() - Method in class com.imsl.stat.ClusterKMeans
-
Returns the cluster membership for each observation.
- getClusterRightSons() - Method in class com.imsl.stat.ClusterHierarchical
-
Returns the right sons of each merged cluster.
- getClusterSSQ() - Method in class com.imsl.stat.ClusterKMeans
-
Returns the within sum of squares for each cluster.
- getCoefficient(int) - Method in class com.imsl.stat.LinearRegression.CoefficientTTests
-
Returns the estimate for a coefficient.
- getCoefficient(int) - Method in class com.imsl.stat.NonlinearRegression
-
Returns the estimate for a coefficient.
- getCoefficient(int) - Method in class com.imsl.stat.StepwiseRegression.CoefficientTTests
-
Returns the estimate for a coefficient of the independent variable.
- getCoefficientOfVariation() - Method in class com.imsl.stat.ANOVA
-
Returns the coefficient of variation (in percent).
- getCoefficients() - Method in class com.imsl.io.MPSReader.Row
-
Returns the coeffients of this row as a dense array.
- getCoefficients() - Method in class com.imsl.math.Spline2D
-
Returns the coefficients for the tensor-product spline.
- getCoefficients() - Method in class com.imsl.stat.DiscriminantAnalysis
-
Returns the linear discriminant function coefficients.
- getCoefficients() - Method in class com.imsl.stat.LinearRegression
-
Returns the regression coefficients.
- getCoefficients() - Method in class com.imsl.stat.NonlinearRegression
-
Returns the regression coefficients.
- getCoefficients() - Method in class com.imsl.stat.UserBasisRegression
-
Returns the regression coefficients.
- getCoefficientStatistics(int) - Method in class com.imsl.stat.SelectionRegression.Statistics
-
Returns the coefficients statistics for each of the best regressions
found for each subset considered.
- getCoefficientTable(int) - Method in class com.imsl.stat.ANCOVA
-
Returns a matrix of size ncov + 1 by 4 containing statistics for a
linear regression model fitted separately for each of the ngroup
treatment groups.
- getCoefficientTables() - Method in class com.imsl.stat.ANCOVA
-
Returns an array containing statistics for a linear regression model
fitted separately for all ngroup treatments.
- getCoefficientTTests() - Method in class com.imsl.stat.LinearRegression
-
Returns statistics relating to the regression coefficients.
- getCoefficientTTests() - Method in class com.imsl.stat.StepwiseRegression
-
Returns the student-t test statistics for the regression
coefficients.
- getCoefficientVIF() - Method in class com.imsl.stat.StepwiseRegression
-
Returns the variance inflation factors for the final model in this
invocation.
- getColumn() - Method in class com.imsl.io.MPSReader.Element
-
Returns the column index.
- getColumnClass(int) - Method in class com.imsl.io.AbstractFlatFile
-
Returns the class of the items in the specified column.
- getColumnCount() - Method in class com.imsl.io.AbstractFlatFile
-
Returns the number of columns in this ResultSet object.
- getColumnCount() - Method in class com.imsl.io.FlatFile
-
Returns the number of columns in this ResultSet object.
- getColumnPermutationMethod() - Method in class com.imsl.math.ComplexSuperLU
-
Returns the method that will be used to permute the columns of the input matrix.
- getColumnPermutationMethod() - Method in class com.imsl.math.SuperLU
-
Returns the method that will be used to permute the columns of the
input matrix.
- getCompleteTimes() - Method in class com.imsl.stat.ARMAEstimateMissing
-
Returns an int array of all time points, including values
for times with missing values in z.
- getCompleteTimes() - Method in class com.imsl.stat.AutoARIMA
-
Returns all time points at which the original series was observed,
including values for times with missing values in x.
- getCompleteTimeSeries() - Method in class com.imsl.stat.ARMAEstimateMissing
-
Returns a double precision vector of length
tpoints[tpoints.length-1]-tpoints[0]+1 containing the
observed values in the time series z plus estimates for
missing values in gaps identified in tpoints.
- getCompleteTimeSeries() - Method in class com.imsl.stat.AutoARIMA
-
Returns the original series with potentially missing values replaced by
estimates.
- getConcurrency() - Method in class com.imsl.io.AbstractFlatFile
-
Returns the concurrency mode of this ResultSet object.
- getConditionalVariance() - Method in class com.imsl.stat.ExtendedGARCH
-
Returns the current conditional variance array.
- getConditionNumber() - Method in class com.imsl.math.ComplexSuperLU
-
Returns the estimate of the reciprocal condition number of the matrix
A.
- getConditionNumber() - Method in class com.imsl.math.SuperLU
-
Returns the estimate of the reciprocal condition number of the matrix
A.
- getConfidence() - Method in class com.imsl.datamining.AssociationRule
-
The confidence measure of the association rule.
- getConfidence() - Method in class com.imsl.stat.ARAutoUnivariate
-
Returns the confidence level for calculating confidence limit deviations
returned from getDeviations.
- getConfidence() - Method in class com.imsl.stat.ARMAMaxLikelihood
-
Returns the confidence level used for calculating deviations in
getDeviations.
- getConfidenceInterval(double, int, int, int) - Method in class com.imsl.stat.ANOVA
-
Computes the confidence interval associated with the difference of means
between two groups using a specified method.
- getConfidenceInterval() - Method in class com.imsl.stat.LinearRegression.CaseStatistics
-
Returns the Confidence Interval of the population mean for an
observation.
- getConstant() - Method in class com.imsl.math.SparseLP
-
Returns the value of the constant term in the objective function.
- getConstant() - Method in class com.imsl.stat.ARAutoUnivariate
-
Returns the estimate for the constant parameter in the ARMA series.
- getConstant() - Method in class com.imsl.stat.ARMA
-
Returns the constant parameter estimate.
- getConstant() - Method in class com.imsl.stat.ARMAMaxLikelihood
-
Returns the estimate for the constant parameter in the ARMA series.
- getConstant() - Method in class com.imsl.stat.ARMAOutlierIdentification
-
Returns the constant parameter estimate.
- getConstant() - Method in class com.imsl.stat.AutoARIMA
-
Returns the constant parameter estimate for the optimum model.
- getConstantColumn() - Method in class com.imsl.stat.ProportionalHazards
-
Returns the column index of x containing the constant to be added
to the linear response.
- getConstraintResiduals() - Method in class com.imsl.math.MinConNLP
-
Returns the constraint residuals.
- getConstraintType() - Method in class com.imsl.math.SparseLP
-
Returns the types of general constraints in the matrix A.
- getContingencyCoef() - Method in class com.imsl.stat.ContingencyTable
-
Returns contingency coefficient.
- getContributions() - Method in class com.imsl.stat.ContingencyTable
-
Returns the contributions to chi-squared for each cell in the table.
- getConvergenceTol() - Method in class com.imsl.stat.ProportionalHazards
-
Returns the convergence tolerance used.
- getConvergenceTolerance() - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine
-
Returns the convergence tolerance.
- getConvergenceTolerance() - Method in class com.imsl.stat.ARAutoUnivariate
-
Returns the tolerance level used to determine convergence of the
nonlinearleast-squares and maximum likelihood algorithms.
- getConvergenceTolerance() - Method in class com.imsl.stat.ARMAEstimateMissing
-
Returns the current value of convergence tolerance used by the
AR_1 and AR_P estimation methods.
- getCooksDistance() - Method in class com.imsl.stat.LinearRegression.CaseStatistics
-
Returns Cook's Distance for an observation.
- getCorrelations() - Method in class com.imsl.stat.FactorAnalysis
-
Returns the correlations of the principal components.
- getCost() - Method in class com.imsl.datamining.decisionTree.TreeNode
-
Returns the misclassification cost of a node (in-sample cost measure at
the current node).
- getCostComplexityValues() - Method in class com.imsl.datamining.decisionTree.DecisionTree
-
Returns an array containing cost-complexity values.
- getCostMatrix() - Method in class com.imsl.datamining.PredictiveModel
-
Returns the cost matrix for a categorical response variable.
- getCount() - Method in class com.imsl.stat.FaureSequence
-
- getCountXY(double[][], int, int, int, int, int[], int[], double[]) - Method in class com.imsl.datamining.decisionTree.DecisionTreeInfoGain
-
Calculates a two-way frequency table with input frequencies.
- getCovariance() - Method in class com.imsl.stat.DiscriminantAnalysis
-
Returns the array of covariances.
- getCovarianceMatrix() - Method in class com.imsl.stat.CategoricalGenLinModel
-
Returns the estimated asymptotic covariance matrix of the coefficients.
- getCovariancesSwept() - Method in class com.imsl.stat.StepwiseRegression
-
Returns the results after cov has been swept for the columns
corresponding to the variables in the model.
- getCovB() - Method in class com.imsl.stat.KalmanFilter
-
Returns the mean squared error matrix for b divided by sigma squared.
- getCovV() - Method in class com.imsl.stat.KalmanFilter
-
Returns the variance-covariance matrix of v divided by sigma squared.
- getCramersV() - Method in class com.imsl.stat.ContingencyTable
-
Returns Cramer's V.
- getCriteriaValueCategorical(double[][], double[], int, int) - Method in class com.imsl.datamining.decisionTree.DecisionTreeInfoGain
-
Calculates and returns the value of the criterion on the node represented
by the data set S = xy.
- getCriterionOption() - Method in class com.imsl.stat.SelectionRegression
-
Returns the criterion option used to calculate the regression estimates.
- getCriterionValues(int) - Method in class com.imsl.stat.SelectionRegression.Statistics
-
Returns an array containing the values of the best criterion for
the number of variables considered.
- getCrossCorrelation() - Method in class com.imsl.stat.CrossCorrelation
-
Returns the cross-correlations between the time series x
and y.
- getCrossCorrelation() - Method in class com.imsl.stat.MultiCrossCorrelation
-
Returns the cross-correlations between the channels of x
and y.
- getCrossCovariance() - Method in class com.imsl.stat.CrossCorrelation
-
Returns the cross-covariances between the time series x
and y.
- getCrossCovariance() - Method in class com.imsl.stat.MultiCrossCorrelation
-
Returns the cross-covariances between the channels of x
and y.
- getCrossValidatedError() - Method in class com.imsl.datamining.CrossValidation
-
Returns the cross-validated error.
- getCursorName() - Method in class com.imsl.io.AbstractFlatFile
-
Gets the name of the SQL cursor used by this ResultSet
object.
- getCutpoints() - Method in class com.imsl.stat.ChiSquaredTest
-
Returns the cutpoints.
- getDataSeries() - Method in class com.imsl.stat.ExtendedGARCH
-
Returns the data series, usually the time series of asset returns.
- getDate(int) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as a java.sql.Date object in
the Java programming language.
- getDate(String) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as a java.sql.Date object in
the Java programming language.
- getDate(int, Calendar) - Method in class com.imsl.io.AbstractFlatFile
-
Returns the value of the designated column in the current row of this
ResultSet object as a java.sql.Date object in
the Java programming language.
- getDate(String, Calendar) - Method in class com.imsl.io.AbstractFlatFile
-
Returns the value of the designated column in the current row of this
ResultSet object as a java.sql.Date object in
the Java programming language.
- getDateIncrement() - Method in class com.imsl.stat.TimeSeries
-
Returns the date increment for this TimeSeries object.
- getDates() - Method in class com.imsl.stat.TimeSeries
-
Returns the date array associated with the time series.
- getDaysInYear(GregorianCalendar) - Method in interface com.imsl.finance.BasisPart
-
Returns the number of days in the year.
- getDaysInYear(GregorianCalendar, GregorianCalendar) - Method in interface com.imsl.finance.BasisPart
-
- getDecisionTree() - Method in class com.imsl.datamining.decisionTree.DecisionTree
-
Returns a Tree object.
- getDegreesOfFreedom() - Method in class com.imsl.stat.ChiSquaredTest
-
Returns the degrees of freedom in chi-squared.
- getDegreesOfFreedom() - Method in class com.imsl.stat.ContingencyTable
-
Returns the degrees of freedom for the chi-squared tests associated with the table.
- getDegreesOfFreedom() - Method in class com.imsl.stat.NormalityTest
-
Returns the degrees of freedom for the chi-squared goodness-of-fit test.
- getDegreesOfFreedomForError() - Method in class com.imsl.stat.ANOVA
-
Returns the degrees of freedom for error.
- getDegreesOfFreedomForModel() - Method in class com.imsl.stat.ANOVA
-
Returns the degrees of freedom for model.
- getDesignVariableMeans() - Method in class com.imsl.stat.CategoricalGenLinModel
-
Returns the means of the design variables.
- getDeviations() - Method in class com.imsl.stat.ARAutoUnivariate
-
Returns the deviations used for calculating the forecast confidence
limits.
- getDeviations() - Method in class com.imsl.stat.ARMA
-
Returns the deviations used for calculating the forecast confidence
limits.
- getDeviations() - Method in class com.imsl.stat.ARMAMaxLikelihood
-
Returns the deviations from each forecast used for calculating the
forecast confidence limits.
- getDeviations() - Method in class com.imsl.stat.ARMAOutlierIdentification
-
Returns the deviations used for calculating the
forecast confidence limits.
- getDeviations() - Method in class com.imsl.stat.AutoARIMA
-
Returns the deviations used for calculating the forecast confidence
limits.
- getDFError() - Method in class com.imsl.stat.NonlinearRegression
-
Returns the degrees of freedom for error.
- getDFFITS() - Method in class com.imsl.stat.LinearRegression.CaseStatistics
-
Returns DFFITS for an observation.
- getDiagonalPivotThreshold() - Method in class com.imsl.math.ComplexSuperLU
-
Returns the threshold used for a diagonal entry to be an acceptable pivot.
- getDiagonalPivotThreshold() - Method in class com.imsl.math.SuperLU
-
Returns the threshold used for a diagonal entry to be an acceptable pivot.
- getDiffMean() - Method in class com.imsl.stat.NormTwoSample
-
Returns the difference in sample means.
- getDiffMean() - Method in class com.imsl.stat.WelchsTTest
-
Returns the difference in sample means.
- getDimension() - Method in class com.imsl.datamining.KohonenSOM
-
Returns the number of weights for each node.
- getDimension() - Method in class com.imsl.stat.FaureSequence
-
Returns the dimension of the sequence.
- getDimension() - Method in interface com.imsl.stat.RandomSequence
-
Returns the dimension of the sequence.
- getDInitial() - Method in class com.imsl.stat.ARSeasonalFit
-
Returns the candidate values for d to evaluate.
- getDistanceMatrix() - Method in class com.imsl.stat.Dissimilarities
-
Returns the distance matrix.
- getDistanceMethod() - Method in class com.imsl.stat.Dissimilarities
-
Returns the method used in computing the dissimilarities or similarities.
- getDouble(int) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as a double in the Java
programming language.
- getDouble(String) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as a double in the Java
programming language.
- getDual() - Method in class com.imsl.math.QuadraticProgramming
-
Returns the dual (Lagrange multipliers).
- getDualInfeasibility() - Method in class com.imsl.math.SparseLP
-
Returns the dual infeasibility of the solution.
- getDualInfeasibilityTolerance() - Method in class com.imsl.math.SparseLP
-
Returns the dual infeasibility tolerance.
- getDualSolution() - Method in class com.imsl.math.BoundedVariableLeastSquares
-
Returns the dual solution vector, w.
- getDualSolution() - Method in class com.imsl.math.DenseLP
-
Returns the dual solution.
- getDualSolution() - Method in class com.imsl.math.LinearProgramming
-
Deprecated.
Returns the dual solution.
- getDualSolution() - Method in class com.imsl.math.NonNegativeLeastSquares
-
Returns the dual solution vector,
w.
- getDualSolution() - Method in class com.imsl.math.SparseLP
-
Returns the dual solution.
- getDummyMethod() - Method in class com.imsl.stat.RegressorsForGLM
-
Returns the dummy method.
- getDunnSidak(int, int) - Method in class com.imsl.stat.ANOVA
-
- getEffects() - Method in class com.imsl.stat.RegressorsForGLM
-
Returns the effects.
- getEffectsColumns() - Method in class com.imsl.stat.RegressorsForGLM
-
Returns a mapping of effects to regressor columns.
- getEpochSize() - Method in class com.imsl.datamining.neural.EpochTrainer
-
Returns the number of sample training patterns in each stage 1 epoch.
- getEquilibrate() - Method in class com.imsl.math.ComplexSuperLU
-
Returns the equilibration flag.
- getEquilibrate() - Method in class com.imsl.math.SuperLU
-
Returns the equilibration flag.
- getEquilibrationMethod() - Method in class com.imsl.math.ComplexSuperLU
-
Returns information on the type of equilibration used
before matrix factorization.
- getEquilibrationMethod() - Method in class com.imsl.math.SuperLU
-
Returns information on the type of equilibration used
before matrix factorization.
- getError() - Method in class com.imsl.datamining.neural.BinaryClassification
-
Returns the error function for use by QuasiNewtonTrainer
for training a binary classification network.
- getError() - Method in class com.imsl.datamining.neural.MultiClassification
-
Returns the error function for use by QuasiNewtonTrainer
for training a classification network.
- getError() - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer
-
Returns the function used to compute the error to be minimized.
- getErrorEstimate() - Method in class com.imsl.math.EpsilonAlgorithm
-
Returns the current error estimate.
- getErrorEstimate() - Method in class com.imsl.math.HyperRectangleQuadrature
-
Returns an estimate of the relative error in the computed result.
- getErrorEstimate() - Method in class com.imsl.math.Quadrature
-
Returns an estimate of the relative error in the computed result.
- getErrorGradient() - Method in class com.imsl.datamining.neural.EpochTrainer
-
Returns the value of the gradient of the error function with
respect to the weights.
- getErrorGradient() - Method in class com.imsl.datamining.neural.LeastSquaresTrainer
-
Returns the value of the gradient of the error function with
respect to the weights.
- getErrorGradient() - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer
-
Returns the value of the gradient of the
error function with respect to the weights.
- getErrorGradient() - Method in interface com.imsl.datamining.neural.Trainer
-
Returns the value of the gradient of the
error function with respect to the weights.
- getErrorMeanSquare() - Method in class com.imsl.stat.ANOVA
-
Returns the error mean square.
- getErrorNumber() - Method in exception com.imsl.LicenseException
-
Returns the error number for this exception.
- getErrorStatus() - Method in class com.imsl.datamining.neural.EpochTrainer
-
Returns the training error status.
- getErrorStatus() - Method in class com.imsl.datamining.neural.LeastSquaresTrainer
-
Returns the error status from the trainer.
- getErrorStatus() - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer
-
Returns the error status from the trainer.
- getErrorStatus() - Method in interface com.imsl.datamining.neural.Trainer
-
Returns the error status.
- getErrorStatus() - Method in class com.imsl.math.MinUnconMultiVar
-
Returns the non-fatal error status.
- getErrorStatus() - Method in class com.imsl.math.NonlinLeastSquares
-
Get information about the performance of NonlinLeastSquares.
- getErrorStatus() - Method in class com.imsl.math.Quadrature
-
Returns the non-fatal error status.
- getErrorStatus() - Method in class com.imsl.stat.NonlinearRegression
-
Gets information about the performance of
NonlinearRegression.
- getErrorSumOfSquares() - Method in class com.imsl.math.Spline2DLeastSquares
-
Returns the weighted error sum of squares.
- getErrorValue() - Method in class com.imsl.datamining.neural.EpochTrainer
-
Returns the value of the error function.
- getErrorValue() - Method in class com.imsl.datamining.neural.LeastSquaresTrainer
-
Returns the final value of the error function.
- getErrorValue() - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer
-
Returns the final value of the error function.
- getErrorValue() - Method in interface com.imsl.datamining.neural.Trainer
-
Returns the value of the error function minimized by the trainer.
- getEstimates() - Method in class com.imsl.stat.distributions.MaximumLikelihoodEstimation
-
Returns the parameter estimates.
- getEstimates() - Method in class com.imsl.stat.VectorAutoregression
-
Returns the parameter estimates (coefficients) of the vector
autoregression model.
- getEstimationMethod() - Method in class com.imsl.stat.ARAutoUnivariate
-
Returns the estimation method used for estimating the autoregressive
coefficients.
- getEstimationMethod() - Method in class com.imsl.stat.ARMAEstimateMissing
-
Returns the method used for estimating the final autoregressive
coefficients for missing value estimation methods AR_1 and
AR_P.
- getExactMean() - Method in class com.imsl.stat.ContingencyTable
-
Returns exact mean.
- getExactStdev() - Method in class com.imsl.stat.ContingencyTable
-
Returns exact standard deviation.
- getExclude() - Method in class com.imsl.stat.ARSeasonalFit
-
Returns the current setting for excluding or replacing the inital values
in the transformed series.
- getExpectedCounts() - Method in class com.imsl.stat.ChiSquaredTest
-
Returns the expected counts.
- getExpectedValues() - Method in class com.imsl.stat.ContingencyTable
-
Returns the expected values of each cell in the table.
- getExtendedLikelihoodObservations() - Method in class com.imsl.stat.CategoricalGenLinModel
-
Returns a vector indicating which observations are included in the
extended likelihood.
- getF() - Method in class com.imsl.stat.ANOVA
-
Returns the F statistic.
- getFactorLoadings() - Method in class com.imsl.stat.FactorAnalysis
-
Returns the unrotated factor loadings.
- getFactorScoreCoefficients(FactorAnalysis.ScoreMethod) - Method in class com.imsl.stat.FactorAnalysis
-
Computes the matrix of factor score coefficients.
- getFactorScores(double[][], double[][]) - Method in class com.imsl.stat.FactorAnalysis
-
Computes factor scores.
- getFeature() - Method in exception com.imsl.LicenseException
-
Returns the name of the feature that could not be licensed.
- getFetchDirection() - Method in class com.imsl.io.AbstractFlatFile
-
Returns the fetch direction for this ResultSet object.
- getFetchSize() - Method in class com.imsl.io.AbstractFlatFile
-
Returns the fetch size for this ResultSet object.
- getFinalActiveConstraints() - Method in class com.imsl.math.MinConGenLin
-
Returns the indices of the final active constraints.
- getFinalActiveConstraintsNum() - Method in class com.imsl.math.MinConGenLin
-
Returns the final number of active constraints.
- getFinalLogLikelihood() - Method in class com.imsl.stat.ExtendedGARCH
-
Returns the value of the log-likelihood at the final estimates of the
parameters.
- getFittedMeanSquaredError() - Method in class com.imsl.datamining.decisionTree.DecisionTree
-
Returns the mean squared error on the training data.
- getFittedValues() - Method in class com.imsl.datamining.GradientBoosting
-
Returns the fitted values \({f(x_i)}\) for a continuous
response variable after gradient boosting.
- getFloat(int) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as a float in the Java
programming language.
- getFloat(String) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as a float in the Java
programming language.
- getFloor() - Method in class com.imsl.math.ODE
-
Returns the value used in the norm computation.
- getForecast(int) - Method in class com.imsl.stat.ARAutoUnivariate
-
Returns forecasts
- getForecast(int) - Method in class com.imsl.stat.ARMA
-
Returns forecasts
- getForecast(int) - Method in class com.imsl.stat.ARMAMaxLikelihood
-
Returns forecasts
- getForecast() - Method in class com.imsl.stat.ARMAOutlierIdentification
-
Returns forecasts for the original outlier contaminated series.
- getForecast() - Method in class com.imsl.stat.AutoARIMA
-
Returns forecasts for the original outlier contaminated series.
- getForecastGradient(double[]) - Method in class com.imsl.datamining.neural.FeedForwardNetwork
-
Returns the derivatives of the outputs with respect to the weights.
- getForecastGradient(double[]) - Method in class com.imsl.datamining.neural.Network
-
Returns the derivatives of the outputs with respect to the weights.
- getForecasts() - Method in class com.imsl.stat.HoltWintersExponentialSmoothing
-
Returns the forecasts past the series data.
- getForecasts() - Method in class com.imsl.stat.VectorAutoregression
-
Returns the h-step ahead forecast at times
t=nT, nT+1, ..., T, where
h=1,2, ..., maxStepsAhead.
- getFormatter() - Static method in class com.imsl.datamining.neural.QuasiNewtonTrainer
-
Returns the logging formatter object.
- getFormatter() - Static method in class com.imsl.datamining.neural.Trace
-
Returns the formatter.
- getForwardErrorBound() - Method in class com.imsl.math.ComplexSuperLU
-
Returns the estimated forward error bound for each solution vector.
- getForwardErrorBound() - Method in class com.imsl.math.SuperLU
-
Returns the estimated forward error bound for the solution vector.
- getFrequencyColumn() - Method in class com.imsl.stat.KaplanMeierEstimates
-
Returns the column index of x containing the frequency of
response for each observation.
- getFrequencyColumn() - Method in class com.imsl.stat.ProportionalHazards
-
Returns the column index of x containing the frequency of
response for each observation.
- getFrequencyTable() - Method in class com.imsl.stat.TableOneWay
-
Returns the one-way frequency table.
- getFrequencyTable(double, double) - Method in class com.imsl.stat.TableOneWay
-
Returns a one-way frequency table using known bounds.
- getFrequencyTable() - Method in class com.imsl.stat.TableTwoWay
-
Returns the two-way frequency table.
- getFrequencyTable(double, double, double, double) - Method in class com.imsl.stat.TableTwoWay
-
Compute a two-way frequency table using intervals of equal
length and user supplied upper and lower bounds, xLowerBound,
xUpperBound, yLowerBound, yUpperBound.
- getFrequencyTableUsingClassmarks(double[]) - Method in class com.imsl.stat.TableOneWay
-
Returns the one-way frequency table using class marks.
- getFrequencyTableUsingClassmarks(double[], double[]) - Method in class com.imsl.stat.TableTwoWay
-
Returns the two-way frequency table using
class marks.
- getFrequencyTableUsingCutpoints(double[]) - Method in class com.imsl.stat.TableOneWay
-
Returns the one-way frequency table using cutpoints.
- getFrequencyTableUsingCutpoints(double[], double[]) - Method in class com.imsl.stat.TableTwoWay
-
Returns the two-way frequency table using cutpoints.
- getFrequentItemsets(int[][], int, int, double) - Static method in class com.imsl.datamining.Apriori
-
Computes the frequent itemsets in the transaction set x.
- getFrom() - Method in class com.imsl.datamining.neural.Link
-
Returns the origination Node for this Link.
- getFTest() - Method in class com.imsl.stat.NormTwoSample
-
Returns the F statistic value calculated in an
F-test for equality of variances.
- getFTestDFdenominator() - Method in class com.imsl.stat.NormTwoSample
-
Returns the denominator degrees of freedom of the F
test for equality of variances.
- getFTestDFnumerator() - Method in class com.imsl.stat.NormTwoSample
-
Returns the numerator degrees of freedom in the \(F\)-test for
equality of variances.
- getFTestP() - Method in class com.imsl.stat.NormTwoSample
-
Returns the probability of a larger (in absolute value)
F statistic value, assuming equal variances (i.e.,
the p-value for the test).
- getGARCH() - Method in class com.imsl.stat.ExtendedGARCH
-
Returns the value of the GARCH lag parameter for the given instance.
- getGARCH() - Method in class com.imsl.stat.GARCH
-
Returns the estimated values of the GARCH coefficients.
- getGaussLegendreDegree() - Method in class com.imsl.math.FeynmanKac
-
Returns the number of quadrature points used in the
Gauss-Legendre quadrature formula.
- getGradient() - Method in class com.imsl.stat.ProportionalHazards
-
Returns the inverse of the Hessian times the gradient vector,
computed at the initial estimates.
- getGradients() - Method in class com.imsl.stat.ARMAMaxLikelihood
-
Returns the gradients for the final parameter estimates.
- getGradientTolerance() - Method in class com.imsl.stat.ARMAMaxLikelihood
-
Returns the gradient tolerance for the convergence algorithm.
- getGridType() - Method in class com.imsl.datamining.KohonenSOM
-
Returns the grid type.
- getGroupCounts() - Method in class com.imsl.stat.DiscriminantAnalysis
-
Returns the group counts.
- getGroupCounts() - Method in class com.imsl.stat.PooledCovariances
-
Returns the number of observations in each group.
- getGroupInformation() - Method in class com.imsl.stat.ANOVA
-
Returns information concerning the groups.
- getGroups() - Method in class com.imsl.stat.TableMultiWay
-
Returns the number of observations (rows) in each group.
- getGroupTotal(double) - Method in class com.imsl.stat.KaplanMeierEstimates
-
Returns the total number in the group for the specified group value.
- getGSquared() - Method in class com.imsl.stat.ContingencyTable
-
Returns the likelihood ratio G2 (chi-squared).
- getGSquaredP() - Method in class com.imsl.stat.ContingencyTable
-
Returns the probability of a larger G2 (chi-squared).
- getGuess() - Method in class com.imsl.math.GenMinRes
-
Returns the initial guess of the solution.
- getHessian() - Method in class com.imsl.stat.CategoricalGenLinModel
-
Returns the Hessian computed at the initial parameter estimates.
- getHessian() - Method in class com.imsl.stat.distributions.MaximumLikelihoodEstimation
-
Returns the Hessian of the log-likelihood function evaluated at the
current parameter estimates.
- getHessian() - Method in class com.imsl.stat.ProportionalHazards
-
Returns the inverse of the Hessian of the negative of the log-likelihood,
computed at the initial estimates.
- getHessianOption() - Method in class com.imsl.stat.ProportionalHazards
-
Returns the boolean used to indicate whether or not to compute the Hessian
and gradient at the initial estimates.
- getHiddenLayers() - Method in class com.imsl.datamining.neural.FeedForwardNetwork
-
Returns the HiddenLayers in this network.
- getHistory() - Method in class com.imsl.stat.StepwiseRegression
-
Returns the stepwise regression history for the independent variables.
- getHoldability() - Method in class com.imsl.io.FlatFile
-
Retrieves the holdability of this ResultSet object.
- getIncidenceMatrix() - Method in class com.imsl.stat.Covariances
-
Returns the incidence matrix.
- getIndependentVariables(int) - Method in class com.imsl.stat.SelectionRegression.Statistics
-
- getIndex() - Method in class com.imsl.datamining.neural.Layer
-
Returns the index of this Layer.
- getIndex() - Method in class com.imsl.datamining.supportvectormachine.DataNode
-
Returns the index of the node.
- getIndex() - Method in class com.imsl.stat.Dissimilarities
-
Returns the indices of the rows (columns) used in computing the distance measure.
- getIndices() - Method in class com.imsl.stat.RandomSamples
-
Returns the indices computed from a call to getSamples().
- getInfo() - Method in class com.imsl.math.ComplexSVD
-
Returns convergence information about the singular values.
- getInfo() - Method in class com.imsl.math.SVD
-
Returns convergence information about S, U, and V.
- getInitialCenters() - Method in class com.imsl.stat.ClusterKMeans
-
Returns the initial cluster centers.
- getInitialComplex() - Method in class com.imsl.math.NelderMead
-
Returns the initial complex.
- getInitialEstimates() - Method in class com.imsl.stat.ProportionalHazards
-
Gets the initial parameter estimates.
- getInitialStepsize() - Method in class com.imsl.math.FeynmanKac
-
Returns the starting step size for the integration.
- getInitialStepsize() - Method in class com.imsl.math.ODE
-
Returns the initial internal step size.
- getInnovationVariance() - Method in class com.imsl.stat.ARAutoUnivariate
-
Returns the final estimate for the innovation variance.
- getInnovationVariance() - Method in class com.imsl.stat.ARMA
-
Returns the variance of the random shock.
- getInnovationVariance() - Method in class com.imsl.stat.ARMAMaxLikelihood
-
Returns the estimated innovation variance of this series.
- getInputLayer() - Method in class com.imsl.datamining.neural.FeedForwardNetwork
-
Returns the InputLayer.
- getInputLayer() - Method in class com.imsl.datamining.neural.Network
-
Returns the InputLayer object.
- getInsensitivityBand() - Method in class com.imsl.datamining.supportvectormachine.SVRegression
-
Returns the insensitivity band parameter,
\(\epsilon\), in the standard formulation of the SVM
regression problem.
- getInt(int) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as an int in the Java
programming language.
- getInt(String) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as an int in the Java
programming language.
- getIntegrationMethod() - Method in class com.imsl.math.OdeAdamsGear
-
Returns the integration method used.
- getIntercept() - Method in class com.imsl.stat.StepwiseRegression
-
Returns the intercept.
- getInverse() - Method in class com.imsl.math.ComplexSVD
-
Compute the Moore-Penrose generalized inverse.
- getInverse() - Method in class com.imsl.math.SVD
-
Compute the Moore-Penrose generalized inverse.
- getItemset(int) - Method in class com.imsl.datamining.Itemsets
-
Returns a particular itemset.
- getItemsetsMatrix() - Method in class com.imsl.datamining.Itemsets
-
Returns the set of Itemsets as an integer matrix.
- getIterationCount() - Method in class com.imsl.math.DenseLP
-
Returns the iteration count.
- getIterationCount() - Method in class com.imsl.math.SparseLP
-
Returns the number of iterations used by the primal-dual solver.
- getIterations() - Method in class com.imsl.datamining.KohonenSOMTrainer
-
Returns the number of iterations used for training.
- getIterations() - Method in class com.imsl.math.BoundedVariableLeastSquares
-
Returns the number of iterations used to find the solution.
- getIterations() - Method in class com.imsl.math.ConjugateGradient
-
Returns the number of iterations needed by the conjugate gradient algorithm.
- getIterations() - Method in class com.imsl.math.GenMinRes
-
Returns the actual number of GMRES iterations used.
- getIterations() - Method in class com.imsl.math.MinConNLP
-
Returns the actual number of iterations used.
- getIterations() - Method in class com.imsl.math.MinUnconMultiVar
-
Returns the number of iterations used to compute a minimum.
- getIterations() - Method in class com.imsl.math.NonNegativeLeastSquares
-
Returns the number of iterations used to find the solution.
- getIterations(int) - Method in class com.imsl.math.ZeroFunction
-
Deprecated.
Returns the number of iterations used to compute a root.
- getIterationsArray() - Method in class com.imsl.datamining.GradientBoosting
-
Returns the array of different values for the number of iterations.
- getIterativeRefinement() - Method in class com.imsl.math.ComplexSuperLU
-
Returns a value specifying whether iterative refinement is to be performed or not.
- getIterativeRefinement() - Method in class com.imsl.math.SuperLU
-
Returns a value specifying whether iterative refinement is to be performed or not.
- getJackknifeResidual() - Method in class com.imsl.stat.LinearRegression.CaseStatistics
-
Returns the Jackknife Residual for an observation.
- getJacobi() - Method in class com.imsl.math.ConjugateGradient
-
Returns the Jacobi preconditioning matrix.
- getJacobian() - Method in class com.imsl.math.BoundedLeastSquares
-
Returns the Jacobian at the approximate solution.
- getKernel() - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine
-
Returns the kernel object being used in the optimization.
- getKernelParameters() - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine
-
Returns the kernel parameters.
- getKnots() - Method in class com.imsl.math.BSpline
-
Returns a copy of the knot sequence.
- getKurtosis() - Method in class com.imsl.stat.Summary
-
Returns the kurtosis.
- getL() - Method in class com.imsl.math.ComplexLU
-
Returns the lower triangular portion of the LU
factorization of A.
- getL() - Method in class com.imsl.math.LU
-
Returns the lower triangular portion of the LU
factorization of A.
- getLagrangeMultiplerEst() - Method in class com.imsl.math.MinConGenLin
-
- getLagrangeMultiplierEst() - Method in class com.imsl.math.MinConGenLin
-
Returns the Lagrange multiplier estimates of the final active constraints.
- getLagrangeMultiplierEst() - Method in class com.imsl.math.MinConNLP
-
Returns the Lagrange multiplier estimates of the constraints.
- getLargestCPRatio() - Method in class com.imsl.math.SparseLP
-
Returns the ratio of the largest complementarity product to the average.
- getLargestDiagonalElement() - Method in class com.imsl.math.ComplexSparseCholesky
-
Returns the largest diagonal element of the Cholesky factor.
- getLargestDiagonalElement() - Method in class com.imsl.math.SparseCholesky
-
Returns the largest diagonal element of the Cholesky factor.
- getLastParameterUpdates() - Method in class com.imsl.stat.CategoricalGenLinModel
-
Returns the last parameter updates (excluding step halvings).
- getLastUpdates() - Method in class com.imsl.stat.ProportionalHazards
-
Gets the last parameter updates.
- getLayer() - Method in class com.imsl.datamining.neural.Node
-
Returns the Layer in which this Node exists.
- getLearningCoefficient(int) - Method in class com.imsl.datamining.KohonenSOMTrainer
-
Returns the learning coefficient.
- getLearningCoefficient(int) - Method in class com.imsl.test.example.datamining.KohonenSOMEx1
-
- getLeftEndTangent() - Method in class com.imsl.math.CsTCB
-
Returns the value of the tangent at the leftmost endpoint.
- getLength() - Method in class com.imsl.stat.TimeSeries
-
Returns the length of the time series.
- getLeverage() - Method in class com.imsl.stat.LinearRegression.CaseStatistics
-
Returns the Leverage for an observation.
- getLicensePath() - Method in exception com.imsl.LicenseException
-
Returns the license file path for this exception.
- getLifeTable() - Method in class com.imsl.stat.LifeTables
-
Compute a cohort table.
- getLift() - Method in class com.imsl.datamining.AssociationRule
-
The lift measure of the association rule.
- getLikelihood() - Method in class com.imsl.stat.ARAutoUnivariate
-
Returns the final estimate for \(L=e^{-(\mbox{AIC} - 2p)/2}
\), where p is the AR order, AIC is Akaike's Information
Criterion, and L is the likelihood function evaluated for the
optimum autoregressive model.
- getLikelihood() - Method in class com.imsl.stat.ARMAMaxLikelihood
-
Returns the final estimate for \(-2\ln(L)\), where
\(L\) is equal to the likelihood function evaluated
using the final parameter estimates.
- getLinks() - Method in class com.imsl.datamining.neural.FeedForwardNetwork
-
Return all of the Links in this Network.
- getLinks() - Method in class com.imsl.datamining.neural.Network
-
Returns an array containing the Link objects in the
Network.
- getListCells() - Method in class com.imsl.stat.TableMultiWay.UnbalancedTable
-
Returns for each row, a list of the levels of nKeys
corresponding classification variables that describe a cell.
- getLocalizedMessage() - Method in exception com.imsl.LicenseException
-
Returns the localized error message for this exception.
- getLogDeterminant() - Method in class com.imsl.stat.KalmanFilter
-
Returns the natural log of the product of the nonzero eigenvalues of P
where \( P * \sigma^2 \) is the variance-covariance matrix
of the observations.
- getLogger() - Static method in class com.imsl.datamining.neural.QuasiNewtonTrainer
-
Returns the Logger object.
- getLogger(String) - Static method in class com.imsl.datamining.neural.Trace
-
Returns a logger.
- getLogger() - Method in class com.imsl.math.GenMinRes
-
Returns the logger object.
- getLogger() - Method in class com.imsl.math.MinConNLP
-
Returns the logger object.
- getLogger() - Method in class com.imsl.math.ZeroSystem
-
Returns the logger object.
- getLogger() - Method in class com.imsl.stat.ARAutoUnivariate
-
Returns the logger object.
- getLogger() - Method in class com.imsl.stat.ProportionalHazards
-
Returns the logger object and enables logging.
- getLogLikelihood(double[], double...) - Method in class com.imsl.stat.distributions.MaximumLikelihoodEstimation
-
Returns the log-likelihood.
- getLogLikelihood() - Method in class com.imsl.stat.GARCH
-
Returns the value of Log-likelihood function evaluated at the estimated
parameter array.
- getLogLikelihood(double) - Method in class com.imsl.stat.KaplanMeierEstimates
-
Returns the Kaplan-Meier log-likelihood of the group with the specified group value.
- getLong(int) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as a long in the Java
programming language.
- getLong(String) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as a long in the Java
programming language.
- getLossType() - Method in class com.imsl.datamining.GradientBoosting
-
Returns the current loss function type.
- getLossValue() - Method in class com.imsl.datamining.GradientBoosting
-
Returns the loss function value.
- getLowerBound(int) - Method in class com.imsl.io.MPSReader
-
Returns the lower bound for a variable.
- getLowerBound() - Method in class com.imsl.math.SparseLP
-
Returns the lower bound on the variables.
- getLowerCICommonVariance() - Method in class com.imsl.stat.NormTwoSample
-
Returns the lower confidenceVariance \(*100%\) confidence
limit for the common variance.
- getLowerCIDiff() - Method in class com.imsl.stat.NormTwoSample
-
Returns the lower confidence limit for the difference,
\(\mu_x - \mu_y\) for
equal or unequal variances depending on the value
set by setUnequalVariances.
- getLowerCIDiff() - Method in class com.imsl.stat.WelchsTTest
-
Returns the (approximate) lower confidenceMean*100%
confidence limit for the difference in population means, \(\mu_x -
\mu_y\).
- getLowerCIMean() - Method in class com.imsl.stat.NormOneSample
-
Returns the lower confidence limit for the mean.
- getLowerCIRatioVariance() - Method in class com.imsl.stat.NormTwoSample
-
Returns the approximate lower confidence limit in an
interval estimate for the ratio of variances,
\(\sigma_1^2/\sigma_2^2\).
- getLowerCIVariance() - Method in class com.imsl.stat.NormOneSample
-
Returns the lower confidence limits for the variance.
- getLowerRange(int) - Method in class com.imsl.io.MPSReader
-
Returns the lower range value for a constraint equation.
- getMA() - Method in class com.imsl.stat.ARMA
-
Returns the final moving average parameter estimates.
- getMA() - Method in class com.imsl.stat.ARMAMaxLikelihood
-
Returns the final moving average parameter estimates.
- getMA() - Method in class com.imsl.stat.ARMAOutlierIdentification
-
Returns the final moving average parameter estimates.
- getMA() - Method in class com.imsl.stat.AutoARIMA
-
Returns the final moving average parameter estimates of the optimum
model.
- getMA() - Method in class com.imsl.stat.GARCH
-
- getMahalanobis() - Method in class com.imsl.stat.DiscriminantAnalysis
-
Returns the Mahalanobis distances between the group means.
- getMannWhitney() - Method in class com.imsl.stat.WilcoxonRankSum
-
Returns the Mann-Whitney test statistic.
- getMaxClass() - Method in class com.imsl.stat.ProportionalHazards
-
Returns the upper bound used on the sum of the number of distinct values found among
the classification variables in x.
- getMaxDepth() - Method in class com.imsl.datamining.decisionTree.DecisionTree
-
Returns the maximum depth a tree is allowed to have.
- getMaxDifference() - Method in class com.imsl.stat.NormalityTest
-
Returns the maximum absolute difference between the empirical and the
theoretical distributions for the Lilliefors test.
- getMaxEvaluations() - Method in class com.imsl.math.ZerosFunction
-
Returns the maximum number of function evaluations allowed.
- getMaxFunctionEvaluations() - Method in class com.imsl.stat.ARMA
-
Returns the maximum number of function evaluations.
- getMaximum() - Method in class com.imsl.stat.Summary
-
Returns the maximum.
- getMaximum() - Method in class com.imsl.stat.TableOneWay
-
Returns maximum value of x.
- getMaximumBDFOrder() - Method in class com.imsl.math.FeynmanKac
-
Returns the maximum order of the BDF formulas.
- getMaximumDifference() - Method in class com.imsl.stat.KolmogorovOneSample
-
Returns \(D^{+}\),
the maximum difference between the theoretical and empirical CDF's.
- getMaximumDifference() - Method in class com.imsl.stat.KolmogorovTwoSample
-
Returns \(D^{+}\),
the maximum difference between the theoretical and empirical CDF's.
- getMaximumFunctionEvaluations() - Method in class com.imsl.math.OdeAdamsGear
-
Returns the maximum number of function evaluations of \(y'\) allowed.
- getMaximumLikelihood() - Method in class com.imsl.stat.ProportionalHazards
-
Returns the maximized log-likelihood.
- getMaximumStepsize() - Method in class com.imsl.math.FeynmanKac
-
Returns the maximum internal step size used by the integrator.
- getMaximumStepsize() - Method in class com.imsl.math.ODE
-
Returns the maximum internal step size.
- getMaximumTime() - Method in class com.imsl.math.MinConNLP
-
Returns the maximum time allowed for the solve step.
- getMaximumX() - Method in class com.imsl.stat.TableTwoWay
-
Returns the maximum value of x.
- getMaximumY() - Method in class com.imsl.stat.TableTwoWay
-
Returns the maximum value of y.
- getMaxIterations() - Method in class com.imsl.math.ComplexEigen
-
Returns the maximum number of iterations.
- getMaxIterations() - Method in class com.imsl.math.ConjugateGradient
-
Returns the maximum number of iterations allowed.
- getMaxIterations() - Method in class com.imsl.math.Eigen
-
Returns the maximum number of iterations.
- getMaxIterations() - Method in class com.imsl.math.GenMinRes
-
Returns the maximum number of iterations allowed.
- getMaxIterations() - Method in class com.imsl.math.SparseLP
-
Returns the maximum number of iterations allowed for the primal-dual
solver.
- getMaxIterations() - Method in class com.imsl.stat.ARAutoUnivariate
-
Returns the value currently being used as the maximum number of
iterations allowed in the nonlinear equation solver used in both the
method of moments and least-squares algorithms.
- getMaxIterations() - Method in class com.imsl.stat.ARMA
-
Returns the maximum number of iterations.
- getMaxIterations() - Method in class com.imsl.stat.ARMAEstimateMissing
-
Returns the maximum number of estimation iterations used by missing value
estimation methods AR_1 and AR_P.
- getMaxIterations() - Method in class com.imsl.stat.ARMAMaxLikelihood
-
Returns the maximum number of iterations.
- getMaxIterations() - Method in class com.imsl.stat.ProportionalHazards
-
Return the maximum number of iterations allowed.
- getMaxKrylovDim() - Method in class com.imsl.math.GenMinRes
-
Returns the maximum Krylov subspace dimension, i.e., the maximum allowable
number of GMRES iterations allowed before restarting.
- getMaxlag() - Method in class com.imsl.stat.ARAutoUnivariate
-
Returns the current value used to represent the maximum number of
autoregressive lags to achieve the minimum AIC.
- getMaxlag() - Method in class com.imsl.stat.ARMAEstimateMissing
-
Returns the current value of autoregressive lags used in the
AR_P estimation method.
- getMaxlag() - Method in class com.imsl.stat.ARSeasonalFit
-
Returns the maximum lag used to fit the AR(p) model.
- getMaxNodes() - Method in class com.imsl.datamining.decisionTree.DecisionTree
-
Returns the maximum number of
TreeNode instances allowed in a
tree.
- getMaxNumberOfCategories() - Method in class com.imsl.datamining.PredictiveModel
-
Returns the maximum number of categories allowed.
- getMaxNumberOfIterations() - Method in class com.imsl.datamining.PredictiveModel
-
Returns the maximum number of iterations allowed for the fitting
procedure or training algorithm.
- getMaxOrder() - Method in class com.imsl.math.OdeAdamsGear
-
Returns the highest order formula to use of implicit METHOD_ADAMS type or METHOD_BDF type.
- getMaxSteps() - Method in class com.imsl.math.FeynmanKac
-
Returns the maximum number of internal steps allowed.
- getMaxSteps() - Method in class com.imsl.math.ODE
-
Returns the maximum number of internal steps allowed.
- getMean() - Method in class com.imsl.stat.ARAutoUnivariate
-
Returns the mean used to center the time series z.
- getMean() - Method in class com.imsl.stat.ARMA
-
Returns an update of the mean of the time series z.
- getMean() - Method in class com.imsl.stat.ARMAEstimateMissing
-
Returns the mean value used to center the series.
- getMean() - Method in class com.imsl.stat.ARMAMaxLikelihood
-
Returns the mean used to center the time series.
- getMean() - Method in class com.imsl.stat.AutoCorrelation
-
Returns the mean of the time series x.
- getMean() - Method in class com.imsl.stat.LogNormalDistribution
-
Returns the lognormal probability distribution mean parameter.
- getMean() - Method in class com.imsl.stat.NormalDistribution
-
Returns the population mean of xData.
- getMean() - Method in class com.imsl.stat.NormOneSample
-
Returns the mean of the sample.
- getMean() - Method in class com.imsl.stat.Summary
-
Returns the population mean.
- getMeanCenteredSeries(TimeSeries) - Method in class com.imsl.stat.TimeSeriesOperations
-
Returns the mean-centered values of all the series in a
TimeSeries object.
- getMeanCenteredSeries(TimeSeries, int) - Method in class com.imsl.stat.TimeSeriesOperations
-
Returns the mean-centered values of the k-th series in a
TimeSeries object.
- getMeanEstimate() - Method in class com.imsl.stat.ARMA
-
- getMeanModel() - Method in class com.imsl.stat.ExtendedGARCH
-
Returns the mean ARMA model specification as an integer array,
\(\{p,d,q\}\).
- getMeanModelParameters() - Method in class com.imsl.stat.ExtendedGARCH
-
Returns the current estimates of the mean model parameters.
- getMeanOfY() - Method in class com.imsl.stat.ANOVA
-
Returns the mean of the response (dependent variable).
- getMeans(double[][], int[]) - Method in class com.imsl.datamining.NaiveBayesClassifier
-
Returns a table of means for each continuous attribute in
continuousData segmented by the target classes in
classificationData.
- getMeans() - Method in class com.imsl.stat.ANCOVA
-
Returns a matrix containing the unadjusted means for the covariates and
the response variate and the means for the response variate adjusted for
the covariates.
- getMeans() - Method in class com.imsl.stat.ANOVAFactorial
-
Returns the subgroup means.
- getMeans() - Method in class com.imsl.stat.Covariances
-
Returns the means of the variables in x.
- getMeans() - Method in class com.imsl.stat.DiscriminantAnalysis
-
Returns the variable means.
- getMeans() - Method in class com.imsl.stat.PooledCovariances
-
Returns the means of each group.
- getMeans() - Method in class com.imsl.stat.ProportionalHazards
-
Returns the means of the design variables.
- getMeanSquaredPredictionError() - Method in class com.imsl.datamining.BootstrapAggregation
-
- getMeanSquaredPredictionError() - Method in class com.imsl.datamining.decisionTree.DecisionTree
-
Returns the mean squared error.
- getMeanX() - Method in class com.imsl.stat.CrossCorrelation
-
Returns the mean of the time series x.
- getMeanX() - Method in class com.imsl.stat.MultiCrossCorrelation
-
Returns the mean of each channel of x.
- getMeanX() - Method in class com.imsl.stat.NormTwoSample
-
Returns the mean of the first sample.
- getMeanX() - Method in class com.imsl.stat.WelchsTTest
-
Returns the mean of the first sample.
- getMeanY() - Method in class com.imsl.stat.CrossCorrelation
-
Returns the mean of the time series y.
- getMeanY() - Method in class com.imsl.stat.MultiCrossCorrelation
-
Returns the mean of each channel of y.
- getMeanY() - Method in class com.imsl.stat.NormTwoSample
-
Returns the mean of the second sample.
- getMeanY() - Method in class com.imsl.stat.WelchsTTest
-
Returns the mean of the second sample.
- getMergeCategoriesSigLevel() - Method in class com.imsl.datamining.decisionTree.CHAID
-
Returns the significance level for merging categories.
- getMetaData() - Method in class com.imsl.io.AbstractFlatFile
-
Retrieves the number, types and properties of this ResultSet
object's columns.
- getMethod() - Method in class com.imsl.math.GenMinRes
-
Returns the implementation method to be used.
- getMethod() - Method in class com.imsl.stat.ClusterHierarchical
-
Returns the clustering method used.
- getMethodOfMomentsEstimates(double[]) - Method in class com.imsl.stat.distributions.GeometricPD
-
Returns the method-of-moments estimates given the sample data.
- getMethodOfMomentsEstimates(double[]) - Method in class com.imsl.stat.distributions.LogisticPD
-
Returns the method-of-moments estimates given the sample data.
- getMethodOfMomentsEstimates(double[]) - Method in class com.imsl.stat.distributions.LogNormalPD
-
Returns the method-of-moments estimates given the sample data.
- getMethodOfMomentsEstimates(double[]) - Method in class com.imsl.stat.distributions.ParetoPD
-
Returns the method-of-moments estimates given the sample data.
- getMethodOfMomentsEstimates(double[]) - Method in class com.imsl.stat.distributions.PoissonPD
-
Returns the method-of-moments estimate given the sample data.
- getMethodOfMomentsEstimates(double[]) - Method in class com.imsl.stat.distributions.RayleighPD
-
Returns the method-of-moments estimate given the sample data.
- getMethodOfMomentsEstimates(double[]) - Method in class com.imsl.stat.distributions.WeibullPD
-
Returns the method-of-moments estimates given the sample data.
- getMinCostComplexityValue() - Method in class com.imsl.datamining.decisionTree.DecisionTree
-
- getMinimum() - Method in class com.imsl.stat.Summary
-
Returns the minimum.
- getMinimum() - Method in class com.imsl.stat.TableOneWay
-
Returns the minimum value of x.
- getMinimumDifference() - Method in class com.imsl.stat.KolmogorovOneSample
-
Returns \(D^{-}\),
the minimum difference between the theoretical and empirical CDF's.
- getMinimumDifference() - Method in class com.imsl.stat.KolmogorovTwoSample
-
Returns \(D^{-}\),
the minimum difference between the theoretical and empirical CDF's.
- getMinimumStepsize() - Method in class com.imsl.math.ODE
-
Returns the minimum internal step size.
- getMinimumX() - Method in class com.imsl.stat.TableTwoWay
-
Returns the minimum value of x.
- getMinimumY() - Method in class com.imsl.stat.TableTwoWay
-
Returns the minimum value of y.
- getMinObsPerChildNode() - Method in class com.imsl.datamining.decisionTree.DecisionTree
-
Returns the minimum number of observations that are required for any
child node before performing a split.
- getMinObsPerNode() - Method in class com.imsl.datamining.decisionTree.DecisionTree
-
Returns the minimum number of observations that are required in a node
before performing a split.
- getMinusLogLikelihood() - Method in class com.imsl.stat.distributions.MaximumLikelihoodEstimation
-
Returns minus the log-likelihood evaluated at the parameter estimates.
- getMissingIndicator() - Method in class com.imsl.stat.TimeSeries
-
Returns an array of missing value indicators.
- getMissingTestYFlag() - Method in class com.imsl.datamining.GradientBoosting
-
Returns the flag indicating whether the test data is missing the response
variable data.
- getMissingTimes() - Method in class com.imsl.stat.ARMAEstimateMissing
-
Returns an int array of the times with missing values.
- getMissingValueMethod() - Method in class com.imsl.stat.ARMAEstimateMissing
-
Returns the current missing value estimation method.
- getMLEs(double[]) - Method in class com.imsl.stat.distributions.NormalPD
-
- getModel() - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine
-
Returns the model object.
- getModelCoefficients() - Method in class com.imsl.stat.ANCOVA
-
Returns a matrix containing statistics for the regression coefficients
for the model assuming parallelism.
- getModelErrorStdev() - Method in class com.imsl.stat.ANOVA
-
Returns the estimated standard deviation of the model error.
- getModelMeanSquare() - Method in class com.imsl.stat.ANOVA
-
Returns the model mean square.
- getMonthBasis() - Method in class com.imsl.finance.DayCountBasis
-
Returns the (days in month) portion of the Day Count Basis.
- getMultinomialResponse() - Method in class com.imsl.datamining.GradientBoosting
-
Returns the multinomial representation of the response variable.
- getName() - Method in class com.imsl.io.MPSReader
-
Returns the name of the MPS problem.
- getName() - Method in class com.imsl.io.MPSReader.Row
-
Returns the name of this row.
- getNameBounds() - Method in class com.imsl.io.MPSReader
-
Returns the name of the BOUNDS set.
- getNameColumn(int) - Method in class com.imsl.io.MPSReader
-
Returns the name of a constraint column.
- getNameObjective() - Method in class com.imsl.io.MPSReader
-
Returns the name of the free row containing the objective.
- getNameRanges() - Method in class com.imsl.io.MPSReader
-
Returns the name of the RANGES set.
- getNameRHS() - Method in class com.imsl.io.MPSReader
-
Returns the name of the RHS section.
- getNameRow(int) - Method in class com.imsl.io.MPSReader
-
Returns the name of a contraint row.
- getNCells() - Method in class com.imsl.stat.TableMultiWay.UnbalancedTable
-
Returns the number of non-empty cells.
- getNCharacterStream(int) - Method in class com.imsl.io.FlatFile
-
Retrieves the value of the designated column in the current row of this
ResultSet object as a java.io.Reader object.
- getNCharacterStream(String) - Method in class com.imsl.io.FlatFile
-
Retrieves the value of the designated column in the current row of this
ResultSet object as a java.io.Reader object.
- getNClob(int) - Method in class com.imsl.io.FlatFile
-
Retrieves the value of the designated column in the current row of this
ResultSet object as a NClob object in the Java
programming language.
- getNClob(String) - Method in class com.imsl.io.FlatFile
-
Retrieves the value of the designated column in the current row of this
ResultSet object as a NClob object in the Java
programming language.
- getNeighborhoodType() - Method in class com.imsl.datamining.KohonenSOM
-
Returns the neighborhood type for the rectangular grid.
- getNeighborhoodValue(int, double) - Method in class com.imsl.datamining.KohonenSOMTrainer
-
Returns the neighborhood function value.
- getNeighborhoodValue(int, double) - Method in class com.imsl.test.example.datamining.KohonenSOMEx1
-
- getNetwork() - Method in class com.imsl.datamining.neural.BinaryClassification
-
Returns the network being used for classification.
- getNetwork() - Method in class com.imsl.datamining.neural.MultiClassification
-
Returns the network being used for classification.
- getNLost() - Method in class com.imsl.stat.ARSeasonalFit
-
Returns the number of values in the initial part of the series lost to
differencing.
- getNode(int) - Method in class com.imsl.datamining.decisionTree.Tree
-
Returns a copy of the specified node of the decision tree.
- getNodeAssigments(double[][]) - Method in class com.imsl.datamining.decisionTree.DecisionTree
-
Returns the terminal node assignments for each row of the test data.
- getNodeId() - Method in class com.imsl.datamining.decisionTree.TreeNode
-
Returns the id of the current node.
- getNodes() - Method in class com.imsl.datamining.decisionTree.Tree
-
Returns nodes within a decision tree.
- getNodes() - Method in class com.imsl.datamining.neural.InputLayer
-
Return the Perceptrons in the InputLayer.
- getNodes() - Method in class com.imsl.datamining.neural.Layer
-
Return a list of the Perceptrons in this
Layer.
- getNodes() - Method in class com.imsl.datamining.neural.OutputLayer
-
Return the Perceptrons in the OutputLayer.
- getNodeSplitCriteriaValue() - Method in class com.imsl.datamining.decisionTree.TreeNode
-
Returns the value of the split criteria at the node.
- getNodeSplitValue() - Method in class com.imsl.datamining.decisionTree.TreeNode
-
Returns the value around which the node may be split, if the split
variable is of a continuous type.
- getNodeValueIndicator(int) - Method in class com.imsl.datamining.decisionTree.TreeNode
-
Returns the indicator value for the i-th value of the split
variable in the current node, if the split variable is categorical
- getNodeValuesIndicator() - Method in class com.imsl.datamining.decisionTree.TreeNode
-
Returns the array indicating which values of the split variable apply in
the current node, if the split variable is of discrete type.
- getNodeVariableId() - Method in class com.imsl.datamining.decisionTree.TreeNode
-
Returns the id of the variable that defines the split in the current
node.
- getNorm() - Method in class com.imsl.math.ODE
-
Returns the switch for determining the error norm.
- getNormalScores(double[]) - Method in class com.imsl.stat.Ranks
-
Gets the expected value of normal order statistics
(for tied observations, the average of the expected normal scores).
- getNRowsMissing() - Method in class com.imsl.stat.CategoricalGenLinModel
-
Returns the number of rows of data in x that contain
missing values in one or more specific columns of x.
- getNRowsMissing() - Method in class com.imsl.stat.DiscriminantAnalysis
-
- getNString(int) - Method in class com.imsl.io.FlatFile
-
Retrieves the value of the designated column in the current row of this
ResultSet object as a String in the Java
programming language.
- getNString(String) - Method in class com.imsl.io.FlatFile
-
Retrieves the value of the designated column in the current row of this
ResultSet object as a String in the Java
programming language.
- getNumberAtRisk() - Method in class com.imsl.stat.KaplanMeierEstimates
-
Returns the number of individuals at risk at each failure point.
- getNumberFormat() - Method in class com.imsl.math.PrintMatrixFormat
-
Returns the NumberFormat to be used in formatting double
and Complex entries.
- getNumberMissing() - Method in class com.imsl.stat.ARMAEstimateMissing
-
Returns the number of missing values in the original series
- getNumberMissing() - Method in class com.imsl.stat.KolmogorovOneSample
-
Returns the number of missing values in the data.
- getNumberMissingX() - Method in class com.imsl.stat.KolmogorovTwoSample
-
Returns the number of missing values in the x sample.
- getNumberMissingX() - Method in class com.imsl.stat.WilcoxonRankSum
-
Returns the number of missing observations detected in x.
- getNumberMissingY() - Method in class com.imsl.stat.KolmogorovTwoSample
-
Returns the number of missing values in the y sample.
- getNumberMissingY() - Method in class com.imsl.stat.WilcoxonRankSum
-
Returns the number of missing observations detected in y.
- getNumberOfBackcasts() - Method in class com.imsl.stat.ARMA
-
Returns the number of backcasts used to calculate the AR coefficients for
the time series z.
- getNumberOfBinaryConstraints() - Method in class com.imsl.io.MPSReader
-
Returns the number of binary constraints.
- getNumberOfCases() - Method in class com.imsl.datamining.decisionTree.TreeNode
-
Returns the number of cases in the training data that fall into the
current node.
- getNumberOfChildren() - Method in class com.imsl.datamining.decisionTree.TreeNode
-
Returns the number of child nodes associated with the current node.
- getNumberOfClasses() - Method in class com.imsl.datamining.decisionTree.Tree
-
Returns the number of classes assumed by the response variable, if the
response variable is categorical.
- getNumberOfClasses() - Method in class com.imsl.datamining.neural.UnsupervisedNominalFilter
-
Retrieves the number of classes in the nominal variable.
- getNumberOfClasses() - Method in class com.imsl.datamining.neural.UnsupervisedOrdinalFilter
-
Retrieves the number of categories associated with this ordinal variable.
- getNumberOfClasses() - Method in class com.imsl.datamining.PredictiveModel
-
Returns the number of unique classes found in the categorical response
data.
- getNumberOfCoefficients() - Method in class com.imsl.stat.ProportionalHazards
-
Returns the number of estimated coefficients in the model.
- getNumberOfColumns() - Method in class com.imsl.datamining.KohonenSOM
-
Returns the number of columns of the node grid.
- getNumberOfColumns() - Method in class com.imsl.datamining.PredictiveModel
-
Returns the number of columns in the training data xy.
- getNumberOfColumns() - Method in class com.imsl.io.MPSReader
-
Returns the number of columns in the constraint matrix.
- getNumberOfColumns() - Method in class com.imsl.math.ComplexSparseMatrix
-
Returns the number of columns in the matrix.
- getNumberOfColumns() - Method in class com.imsl.math.SparseMatrix
-
Returns the number of columns in the matrix.
- getNumberOfComplexityValues() - Method in class com.imsl.datamining.decisionTree.DecisionTree
-
Returns the number of cost complexity values determined by the pruning
algorithm.
- getNumberOfEpochs() - Method in class com.imsl.datamining.neural.EpochTrainer
-
Returns the number of epochs used during stage I training.
- getNumberOfEvaluations() - Method in class com.imsl.math.ZerosFunction
-
Returns the actual number of function evaluations performed.
- getNumberOfFailures() - Method in class com.imsl.stat.KaplanMeierEstimates
-
Returns the number of failures which occurred at each failure point.
- getNumberOfFcnEvals() - Method in class com.imsl.math.OdeAdamsGear
-
Returns the number of function evaluations of \(y'\) made.
- getNumberOfFunctionEvaluations() - Method in class com.imsl.math.NelderMead
-
The number of function evaluations needed.
- getNumberOfGroups() - Method in class com.imsl.stat.PooledCovariances
-
Returns the number of groups used in the analysis.
- getNumberOfInputs() - Method in class com.imsl.datamining.neural.FeedForwardNetwork
-
Returns the number of inputs to the Network.
- getNumberOfInputs() - Method in class com.imsl.datamining.neural.Network
-
Returns the number of Network inputs.
- getNumberOfIntegerConstraints() - Method in class com.imsl.io.MPSReader
-
Returns the number of integer constraints.
- getNumberOfItemsets() - Method in class com.imsl.datamining.Itemsets
-
Returns the number of itemsets in this Itemsets.
- getNumberOfIterations() - Method in class com.imsl.datamining.GradientBoosting
-
Returns the current setting for the number of iterations to use in the
gradient boosting algorithm.
- getNumberOfJacobianEvals() - Method in class com.imsl.math.OdeAdamsGear
-
Returns the number of Jacobian matrix evaluations used.
- getNumberOfLevels() - Method in class com.imsl.datamining.decisionTree.Tree
-
Returns the number of levels or depth of a tree.
- getNumberOfLinks() - Method in class com.imsl.datamining.neural.FeedForwardNetwork
-
Returns the number of Links in the Network.
- getNumberOfLinks() - Method in class com.imsl.datamining.neural.Network
-
Returns the number of Network Links among the
nodes.
- getNumberOfMissing() - Method in class com.imsl.datamining.PredictiveModel
-
Returns the number of missing values of the response variable found in
the data xy.
- getNumberOfMissing() - Method in class com.imsl.stat.ANCOVA
-
Returns the number of cases with missing values in covariates
or responses.
- getNumberOfMissingRows() - Method in class com.imsl.stat.PooledCovariances
-
Returns the total number of observations that contain missing values
(Double.NaN or group[i] == 0).
- getNumberOfMissingRows() - Method in class com.imsl.stat.RegressorsForGLM
-
Returns the number of rows in the regressors matrix containing NaN (not a number).
- getNumberOfNodes() - Method in class com.imsl.datamining.decisionTree.Tree
-
Returns the number of nodes (size of a tree).
- getNumberOfNonZeros() - Method in class com.imsl.io.MPSReader
-
Returns the number of nonzeros in the constraint matrix.
- getNumberOfNonZeros() - Method in class com.imsl.io.MPSReader.Row
-
Returns the number of nonzero elements in this row.
- getNumberOfNonzeros() - Method in class com.imsl.math.ComplexSparseCholesky
-
Returns the number of nonzeros in the Cholesky factor.
- getNumberOfNonZeros() - Method in class com.imsl.math.ComplexSparseMatrix
-
Returns the number of nonzeros in the matrix.
- getNumberOfNonzeros() - Method in class com.imsl.math.SparseCholesky
-
Returns the number of nonzeros in the Cholesky factor.
- getNumberOfNonZeros() - Method in class com.imsl.math.SparseMatrix
-
Returns the number of nonzeros in the matrix.
- getNumberOfObservations() - Method in class com.imsl.stat.Summary
-
Returns the number of non-missing observations.
- getNumberOfOutliers() - Method in class com.imsl.stat.ARMAOutlierIdentification
-
Returns the number of outliers detected.
- getNumberOfOutliers() - Method in class com.imsl.stat.AutoARIMA
-
Returns the number of outliers detected.
- getNumberOfOutputs() - Method in class com.imsl.datamining.neural.FeedForwardNetwork
-
Returns the number of outputs from the Network.
- getNumberOfOutputs() - Method in class com.imsl.datamining.neural.Network
-
Returns the number of Network output Perceptrons.
- getNumberOfParameters() - Method in class com.imsl.stat.distributions.ProbabilityDistribution
-
Returns the number of parameters of the probability distribution.
- getNumberOfParameters() - Method in class com.imsl.stat.ExtendedGARCH
-
Returns the number of parameters in the Extended GARCH model.
- getNumberOfPoints() - Method in class com.imsl.stat.KaplanMeierECDF
-
Retrieves the number of points in the empirical CDF
- getNumberOfPredictors() - Method in class com.imsl.datamining.decisionTree.Tree
-
Returns the number of predictors used in the model.
- getNumberOfPredictors() - Method in class com.imsl.datamining.PredictiveModel
-
Returns the number of predictors.
- getNumberOfRandomFeatures() - Method in class com.imsl.datamining.decisionTree.DecisionTree
-
Returns the number of random features used in the splitting rules when
randomFeatureSelection=true.
- getNumberOfRandomFeatures() - Method in class com.imsl.datamining.decisionTree.RandomTrees
-
Returns the number of random features used in the splitting rules.
- getNumberOfRegressors() - Method in class com.imsl.stat.RegressorsForGLM
-
Returns the number regressors.
- getNumberOfRoots() - Method in class com.imsl.math.ZerosFunction
-
Returns the requested number of roots to be found.
- getNumberOfRootsFound() - Method in class com.imsl.math.ZerosFunction
-
Returns the number of zeros found.
- getNumberOfRows() - Method in class com.imsl.datamining.KohonenSOM
-
Returns the number of rows of the node grid.
- getNumberOfRows() - Method in class com.imsl.datamining.PredictiveModel
-
Returns the number of rows in xy (observations).
- getNumberOfRows() - Method in class com.imsl.io.MPSReader
-
Returns the number of rows in the constraint matrix.
- getNumberOfRows() - Method in class com.imsl.math.ComplexSparseMatrix
-
Returns the number of rows in the matrix.
- getNumberOfRows() - Method in class com.imsl.math.SparseMatrix
-
Returns the number of rows in the matrix.
- getNumberOfRowsMissing() - Method in class com.imsl.stat.DiscriminantAnalysis
-
Returns the number of rows of data encountered containing missing
values (Double.NaN).
- getNumberOfRowsMissing() - Method in class com.imsl.stat.KaplanMeierEstimates
-
Returns the number of rows of data in x that contain
missing values in one or more specific columns of x.
- getNumberOfSampleFolds() - Method in class com.imsl.datamining.CrossValidation
-
Returns the number of folds set for the cross-validation.
- getNumberOfSamples() - Method in class com.imsl.datamining.BootstrapAggregation
-
Returns the number of bootstrap samples.
- getNumberOfSeries() - Method in class com.imsl.stat.TimeSeries
-
Returns the number of series stored in this TimeSeries object.
- getNumberOfSteps() - Method in class com.imsl.math.OdeAdamsGear
-
Returns the number of internal steps taken.
- getNumberOfSupportVectors() - Method in class com.imsl.datamining.supportvectormachine.SVModel
-
Returns the number of support vectors.
- getNumberOfSupportVectorsPerClass() - Method in class com.imsl.datamining.supportvectormachine.SVModel
-
Returns the number of support vectors per class.
- getNumberOfSurrogateSplits() - Method in class com.imsl.datamining.decisionTree.ALACART
-
Returns the number of surrogate splits.
- getNumberOfSurrogateSplits() - Method in interface com.imsl.datamining.decisionTree.DecisionTreeSurrogateMethod
-
Indicates the number of surrogate splits.
- getNumberOfSurrogateSplits() - Method in class com.imsl.datamining.decisionTree.Tree
-
Returns the number of surrogate splits searched for at each tree node.
- getNumberOfThreads() - Method in class com.imsl.datamining.BootstrapAggregation
-
Returns the maximum number of java.lang.Thread instances
that may be used for parallel processing.
- getNumberOfThreads() - Method in class com.imsl.datamining.CrossValidation
-
Returns the maximum number of java.lang.Thread instances
that may be used for parallel processing.
- getNumberOfThreads() - Method in class com.imsl.datamining.KohonenSOMTrainer
-
Returns the number of java.lang.Thread instances used for
parallel processing.
- getNumberOfThreads() - Method in class com.imsl.datamining.neural.EpochTrainer
-
Gets the number of java.lang.Thread instances to use during
stage I training.
- getNumberOfThreads() - Method in class com.imsl.math.BoundedLeastSquares
-
Returns the number of java.lang.Thread instances used for
parallel processing.
- getNumberOfThreads() - Method in class com.imsl.math.MinConGenLin
-
Returns the number of java.lang.Thread instances used for
parallel processing.
- getNumberOfThreads() - Method in class com.imsl.math.MinConNLP
-
Returns the number of java.lang.Thread instances used for
parallel processing.
- getNumberOfThreads() - Method in class com.imsl.math.MinUnconMultiVar
-
Returns the number of java.lang.Thread instances used for
parallel processing.
- getNumberOfThreads() - Method in class com.imsl.math.NelderMead
-
Returns the number of java.lang.Thread instances used for
parallel processing.
- getNumberOfThreads() - Method in class com.imsl.math.NonlinLeastSquares
-
Returns the number of java.lang.Thread instances used for
parallel processing.
- getNumberOfThreads() - Method in class com.imsl.stat.AutoCorrelation
-
Returns the number of java.lang.Thread instances used for
parallel processing.
- getNumberOfTies() - Method in class com.imsl.stat.KolmogorovOneSample
-
Returns the number of ties in the data.
- getNumberOfTransactions() - Method in class com.imsl.datamining.Itemsets
-
Returns an int indicating the number of transactions used to
construct the itemsets.
- getNumberOfTrees() - Method in class com.imsl.datamining.decisionTree.RandomTrees
-
Returns the number of trees.
- getNumberOfUniquePredictorValues() - Method in class com.imsl.datamining.PredictiveModel
-
Returns an array containing the number of distinct values of each
predictor found in the input data.
- getNumberOfVariables() - Method in class com.imsl.stat.PooledCovariances
-
Returns the number of variables used in the analysis.
- getNumberOfWeights() - Method in class com.imsl.datamining.neural.FeedForwardNetwork
-
Returns the number of weights in the Network.
- getNumberOfWeights() - Method in class com.imsl.datamining.neural.Network
-
Returns the number of weights in the Network.
- getNumberRowsMissing() - Method in class com.imsl.stat.ProportionalHazards
-
Returns the number of rows of data in x that contain
missing values in one or more specific columns of x.
- getNumericFactor() - Method in class com.imsl.math.ComplexSparseCholesky
-
Returns the numeric Cholesky factor.
- getNumericFactor() - Method in class com.imsl.math.SparseCholesky
-
Returns the numeric Cholesky factor.
- getNumericFactorizationMethod() - Method in class com.imsl.math.ComplexSparseCholesky
-
Returns the method used in the numerical factorization of the permuted
input matrix.
- getNumericFactorizationMethod() - Method in class com.imsl.math.SparseCholesky
-
Returns the method used in the numerical factorization of the permuted
input matrix.
- getNumMissing() - Method in class com.imsl.stat.TimeSeries
-
Returns the number of missing values.
- getNumPositiveDev() - Method in class com.imsl.stat.SignTest
-
Returns the number of positive differences.
- getNumRowMissing() - Method in class com.imsl.stat.Covariances
-
Returns the total number of observations that contain any missing values
(Double.NaN).
- getNumZeroDev() - Method in class com.imsl.stat.SignTest
-
Returns the number of zero differences.
- getNuParameter() - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine
-
Returns the value of the \(\nu\) parameter.
- getNvalues() - Method in class com.imsl.stat.TableMultiWay.BalancedTable
-
Returns an array of length nKeys containing in its
i-th element (i=0,1,...nKeys-1), the number of levels
or categories of the i-th classification variable (column).
- getObject(int) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as an Object in the Java
programming language.
- getObject(String) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as an Object in the Java
programming language.
- getObject(int, Map) - Method in class com.imsl.io.AbstractFlatFile
-
Returns the value of the designated column in the current row of this
ResultSet object as an Object in the Java
programming language.
- getObject(String, Class<T>) - Method in class com.imsl.io.AbstractFlatFile
-
Returns the value of the designated column in the current row of this
ResultSet object converted to the requested data type in the
Java programming language.
- getObject(int, Class<T>) - Method in class com.imsl.io.AbstractFlatFile
-
Returns the value of the designated column in the current row of this
ResultSet object converted to the requested data type in the
Java programming language.
- getObject(String, Map) - Method in class com.imsl.io.AbstractFlatFile
-
Returns the value of the designated column in the current row of this
ResultSet object as an Object in the Java
programming language.
- getObject(int) - Method in class com.imsl.io.FlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as an Object in the Java
programming language.
- getObjective() - Method in class com.imsl.io.MPSReader
-
Returns the objective as a Row.
- getObjectiveCoefficients() - Method in class com.imsl.io.MPSReader
-
Returns the coefficents of the objective row.
- getObjectiveValue() - Method in class com.imsl.math.MinConGenLin
-
Returns the value of the objective function.
- getObjectiveValue() - Method in class com.imsl.math.NelderMead
-
Returns the value of the objective function at the computed solution.
- getObservations() - Method in class com.imsl.stat.Covariances
-
Returns the sum of the frequencies.
- getObservationsLost() - Method in class com.imsl.stat.Difference
-
Returns the number of observations lost because of differencing the time
series.
- getObservedResponse() - Method in class com.imsl.stat.LinearRegression.CaseStatistics
-
Returns the observed response for an observation.
- getObsPerCluster(int) - Method in class com.imsl.stat.ClusterHierarchical
-
Returns the number of observations in each cluster.
- getOmegaWeights() - Method in class com.imsl.stat.ARMAOutlierIdentification
-
Returns the \(\omega\) weights for the
detected outliers.
- getOneSidedPValue() - Method in class com.imsl.stat.KolmogorovOneSample
-
Probability of the statistic exceeding D under
the null hypothesis of equality and against the
one-sided alternative.
- getOneSidedPValue() - Method in class com.imsl.stat.KolmogorovTwoSample
-
Probability of the statistic exceeding D under
the null hypothesis of equality and against the
one-sided alternative.
- getOptimalValue() - Method in class com.imsl.math.DenseLP
-
Returns the optimal value of the objective function.
- getOptimalValue() - Method in class com.imsl.math.LinearProgramming
-
Deprecated.
Returns the optimal value of the objective function.
- getOptimalValue() - Method in class com.imsl.math.MinConNLP
-
Returns the value of the objective function.
- getOptimalValue() - Method in class com.imsl.math.QuadraticProgramming
-
Returns the optimal value.
- getOptimalValue() - Method in class com.imsl.math.SparseLP
-
Returns the optimal value of the objective function.
- getOptimizedCriterion() - Method in class com.imsl.stat.CategoricalGenLinModel
-
Returns the optimized criterion.
- getOptimumD() - Method in class com.imsl.stat.ARSeasonalFit
-
Returns the optimum values for d selected among the candidates in
dInitial.
- getOptimumModelOrder() - Method in class com.imsl.stat.AutoARIMA
-
Returns the order \((p,0,q)\times(0,d,0)_s\) of the
optimum model.
- getOptimumS() - Method in class com.imsl.stat.ARSeasonalFit
-
Returns the optimum values for s selected among the candidates in
sInitial.
- getOrder() - Method in class com.imsl.stat.ARAutoUnivariate
-
Returns the order of the AR model selected with the minimum AIC.
- getOrder() - Method in class com.imsl.test.example.math.RadialBasisEx2.PolyHarmonicSpline
-
- getOutlierFreeForecast() - Method in class com.imsl.stat.ARMAOutlierIdentification
-
Returns forecasts for the outlier free series.
- getOutlierFreeForecast() - Method in class com.imsl.stat.AutoARIMA
-
Returns forecasts for the outlier free series.
- getOutlierFreeSeries() - Method in class com.imsl.stat.ARMAOutlierIdentification
-
Returns the outlier free series.
- getOutlierFreeSeries() - Method in class com.imsl.stat.AutoARIMA
-
Returns the outlier free series.
- getOutlierStatistics() - Method in class com.imsl.stat.ARMAOutlierIdentification
-
Returns the outlier statistics.
- getOutlierStatistics() - Method in class com.imsl.stat.AutoARIMA
-
Returns the outlier statistics.
- getOutOfBagPredictionError() - Method in class com.imsl.datamining.BootstrapAggregation
-
Returns the out-of-bag mean squared prediction error for regression
problems, or the out-of-bag classification percentage error for
classification problems.
- getOutOfBagPredictionError() - Method in class com.imsl.datamining.decisionTree.RandomTrees
-
Returns the out-of-bag prediction error.
- getOutOfBagPredictions() - Method in class com.imsl.datamining.BootstrapAggregation
-
Returns the out-of-bag predicted values.
- getOutOfBagPredictions() - Method in class com.imsl.datamining.decisionTree.RandomTrees
-
Returns the out-of-bag predicted values for the examples in the
training data.
- getOutputLayer() - Method in class com.imsl.datamining.neural.FeedForwardNetwork
-
Returns the OutputLayer.
- getOutputLayer() - Method in class com.imsl.datamining.neural.Network
-
Returns the OutputLayer.
- getP() - Method in class com.imsl.stat.ANOVA
-
Returns the p-value.
- getP() - Method in class com.imsl.stat.ARMAMaxLikelihood
-
Returns the number of autoregressive terms in the ARMA model
- getP() - Method in class com.imsl.stat.ChiSquaredTest
-
Returns the p-value for the chi-squared statistic.
- getP() - Method in class com.imsl.stat.ContingencyTable
-
Returns the Pearson chi-squared p-value for independence of rows and columns.
- getParamEstimatesCovariance() - Method in class com.imsl.stat.ARMA
-
Returns the covariances of parameter estimates.
- getParameterLowerBounds() - Method in class com.imsl.stat.distributions.BetaPD
-
Returns the lower bounds of the parameters.
- getParameterLowerBounds() - Method in class com.imsl.stat.distributions.ContinuousUniformPD
-
Returns the lower bounds of the parameters.
- getParameterLowerBounds() - Method in class com.imsl.stat.distributions.ExponentialPD
-
Returns the lower bound for the parameter.
- getParameterLowerBounds() - Method in class com.imsl.stat.distributions.ExtremeValuePD
-
Returns the lower bounds of the parameters.
- getParameterLowerBounds() - Method in class com.imsl.stat.distributions.GammaPD
-
Returns the lower bounds of the parameters.
- getParameterLowerBounds() - Method in class com.imsl.stat.distributions.GeneralizedGaussianPD
-
Returns the lower bounds of the parameters.
- getParameterLowerBounds() - Method in class com.imsl.stat.distributions.GeometricPD
-
Returns the lower bound of the parameter.
- getParameterLowerBounds() - Method in class com.imsl.stat.distributions.LogisticPD
-
Returns the lower bounds of the parameters.
- getParameterLowerBounds() - Method in class com.imsl.stat.distributions.LogNormalPD
-
Returns the lower bounds of the parameters.
- getParameterLowerBounds() - Method in class com.imsl.stat.distributions.NormalPD
-
Returns the lower bounds of the parameters.
- getParameterLowerBounds() - Method in class com.imsl.stat.distributions.ParetoPD
-
Returns the lower bounds of the parameters.
- getParameterLowerBounds() - Method in class com.imsl.stat.distributions.PoissonPD
-
Returns the lower bound of the parameter.
- getParameterLowerBounds() - Method in class com.imsl.stat.distributions.ProbabilityDistribution
-
Returns the lower bounds of the parameters.
- getParameterLowerBounds() - Method in class com.imsl.stat.distributions.RayleighPD
-
Returns the lower bound for the parameter.
- getParameterLowerBounds() - Method in class com.imsl.stat.distributions.WeibullPD
-
Returns the lower bounds of the parameters.
- getParameterLowerBounds() - Method in class com.imsl.stat.ExtendedGARCH
-
Returns the parameter lower bounds.
- getParameters() - Method in class com.imsl.datamining.supportvectormachine.Kernel
-
Returns the kernel parameters.
- getParameters() - Method in class com.imsl.stat.CategoricalGenLinModel
-
Returns the parameter estimates and associated statistics.
- getParameters() - Method in class com.imsl.stat.ExtendedGARCH
-
Returns the (current) parameter estimates of the extended GARCH object.
- getParameters() - Method in class com.imsl.stat.GammaDistribution
-
Returns the current parameters of the gamma probability
density function.
- getParameters() - Method in class com.imsl.stat.LogNormalDistribution
-
Returns the current parameters of the lognormal probability
density function
- getParameters() - Method in class com.imsl.stat.NormalDistribution
-
Returns the current parameters of the normal probability
density function.
- getParameters() - Method in class com.imsl.stat.PoissonDistribution
-
Returns the current parameters of the Poisson probability
density function.
- getParameters() - Method in interface com.imsl.stat.ProbabilityDistribution
-
Returns the current parameters of the probability density function.
- getParameters() - Method in class com.imsl.test.example.datamining.NaiveBayesClassifierEx3.TestGaussFcn1
-
- getParameterStatistics() - Method in class com.imsl.stat.ProportionalHazards
-
Returns the parameter estimates and associated statistics.
- getParameterUpdates() - Method in class com.imsl.stat.FactorAnalysis
-
Returns the parameter updates.
- getParameterUpperBounds() - Method in class com.imsl.stat.distributions.BetaPD
-
Returns the upper bounds of the parameters.
- getParameterUpperBounds() - Method in class com.imsl.stat.distributions.ContinuousUniformPD
-
Returns the upper bounds of the parameters.
- getParameterUpperBounds() - Method in class com.imsl.stat.distributions.ExponentialPD
-
Returns the upper bound for the parameter.
- getParameterUpperBounds() - Method in class com.imsl.stat.distributions.ExtremeValuePD
-
Returns the upper bounds of the parameters.
- getParameterUpperBounds() - Method in class com.imsl.stat.distributions.GammaPD
-
Returns the upper bounds of the parameters.
- getParameterUpperBounds() - Method in class com.imsl.stat.distributions.GeneralizedGaussianPD
-
Returns the upper bounds of the parameters.
- getParameterUpperBounds() - Method in class com.imsl.stat.distributions.GeometricPD
-
Returns the upper bound of the parameter.
- getParameterUpperBounds() - Method in class com.imsl.stat.distributions.LogisticPD
-
Returns the upper bounds of the parameters.
- getParameterUpperBounds() - Method in class com.imsl.stat.distributions.LogNormalPD
-
Returns the upper bounds of the parameters.
- getParameterUpperBounds() - Method in class com.imsl.stat.distributions.NormalPD
-
Returns the upper bounds of the parameters.
- getParameterUpperBounds() - Method in class com.imsl.stat.distributions.ParetoPD
-
Returns the upper bounds of the parameters.
- getParameterUpperBounds() - Method in class com.imsl.stat.distributions.PoissonPD
-
Returns the upper bound of the parameter.
- getParameterUpperBounds() - Method in class com.imsl.stat.distributions.ProbabilityDistribution
-
Returns the upper bounds of the parameters.
- getParameterUpperBounds() - Method in class com.imsl.stat.distributions.RayleighPD
-
Returns the upper bound for the parameter.
- getParameterUpperBounds() - Method in class com.imsl.stat.distributions.WeibullPD
-
Returns the upper bounds of the parameters.
- getParameterUpperBounds() - Method in class com.imsl.stat.ExtendedGARCH
-
Returns the parameter upper bounds.
- getParentId() - Method in class com.imsl.datamining.decisionTree.TreeNode
-
Returns the id of the parent node of the current node.
- getPartialAutoCorrelations() - Method in class com.imsl.stat.AutoCorrelation
-
Returns the sample partial autocorrelation function of the stationary
time series x.
- getPartialCorrelationMatrix() - Method in class com.imsl.stat.PartialCovariances
-
Returns the partial correlation matrix.
- getPartialCovarianceMatrix() - Method in class com.imsl.stat.PartialCovariances
-
Returns the partial covariance matrix.
- getPartialDegreesOfFreedom() - Method in class com.imsl.stat.PartialCovariances
-
Returns the degrees of freedom in the test that the
partial correlation (covariance) is zero.
- getPDFGradient(double, double...) - Method in class com.imsl.stat.distributions.ContinuousUniformPD
-
Returns the analytic gradient of the pdf.
- getPDFGradient(double, double...) - Method in class com.imsl.stat.distributions.ExponentialPD
-
Returns the analytic gradient of the pdf.
- getPDFGradient(double, double...) - Method in class com.imsl.stat.distributions.ExtremeValuePD
-
Returns the analytic gradient of the pdf.
- getPDFGradient(double, double...) - Method in class com.imsl.stat.distributions.GeometricPD
-
Returns the analytic gradient of the pdf evaluated at x.
- getPDFGradient(double, double...) - Method in class com.imsl.stat.distributions.LogisticPD
-
Returns the analytic gradient of the pdf.
- getPDFGradient(double, double...) - Method in class com.imsl.stat.distributions.LogNormalPD
-
Returns the analytic gradient of the pdf evaluated at x.
- getPDFGradient(double, double...) - Method in class com.imsl.stat.distributions.NormalPD
-
Returns the analytic gradient of the pdf.
- getPDFGradient(double, double...) - Method in class com.imsl.stat.distributions.ParetoPD
-
Returns the analytic gradient of the pdf.
- getPDFGradient(double, double...) - Method in interface com.imsl.stat.distributions.PDFGradientInterface
-
Returns the gradient of the probability density function.
- getPDFGradient(double, double...) - Method in class com.imsl.stat.distributions.PoissonPD
-
Returns the analytic gradient of the pdf.
- getPDFGradient(double, double...) - Method in class com.imsl.stat.distributions.RayleighPD
-
Returns the analytic gradient of the pdf.
- getPDFGradient(double, double...) - Method in class com.imsl.stat.distributions.WeibullPD
-
Returns the analytic gradient of the pdf.
- getPDFGradientApproximation(double, double...) - Method in class com.imsl.stat.distributions.ProbabilityDistribution
-
Returns the approximate gradient of the probability density function,
pdf.
- getPDFHessian(double, double...) - Method in class com.imsl.stat.distributions.ContinuousUniformPD
-
Returns the analytic Hessian of the pdf.
- getPDFHessian(double, double...) - Method in class com.imsl.stat.distributions.ExponentialPD
-
Returns the analytic Hessian of the pdf.
- getPDFHessian(double, double...) - Method in class com.imsl.stat.distributions.ExtremeValuePD
-
Returns the analytic Hessian of the pdf.
- getPDFHessian(double, double...) - Method in class com.imsl.stat.distributions.GeometricPD
-
Returns the analytic Hessian matrix evaluated at x.
- getPDFHessian(double, double...) - Method in class com.imsl.stat.distributions.LogisticPD
-
Returns the analytic Hessian of the pdf.
- getPDFHessian(double, double...) - Method in class com.imsl.stat.distributions.LogNormalPD
-
Returns the analytic Hessian of the pdf evaluated at
x.
- getPDFHessian(double, double...) - Method in class com.imsl.stat.distributions.NormalPD
-
Returns the analytic Hessian matrix of the pdf.
- getPDFHessian(double, double...) - Method in class com.imsl.stat.distributions.ParetoPD
-
Returns the analytic Hessian of the pdf.
- getPDFHessian(double, double...) - Method in interface com.imsl.stat.distributions.PDFHessianInterface
-
Returns the hessian of the probability density function.
- getPDFHessian(double, double...) - Method in class com.imsl.stat.distributions.PoissonPD
-
Returns the analytic Hessian matrix of the pdf.
- getPDFHessian(double, double...) - Method in class com.imsl.stat.distributions.RayleighPD
-
Returns the analytic Hessian matrix of the pdf.
- getPDFHessian(double, double...) - Method in class com.imsl.stat.distributions.WeibullPD
-
Returns the analytic Hessian of the pdf.
- getPDFHessianApproximation(double, double...) - Method in class com.imsl.stat.distributions.ProbabilityDistribution
-
Returns the approximate hessian of the probability density function,
pdf.
- getPercentageFactor() - Method in class com.imsl.math.NumericalDerivatives
-
Returns the percentage factor for differencing.
- getPercentages() - Method in class com.imsl.datamining.neural.UnsupervisedOrdinalFilter
-
Retrieves the cumulative percentages used for encoding and decoding.
- getPercents() - Method in class com.imsl.stat.FactorAnalysis
-
Returns the cumulative percent of the total variance explained by each
principal component.
- getPerceptrons() - Method in class com.imsl.datamining.neural.FeedForwardNetwork
-
Returns the Perceptrons in this Network.
- getPerceptrons() - Method in class com.imsl.datamining.neural.Network
-
Returns an array containing the Perceptrons in the
Network.
- getPerformanceIndex() - Method in class com.imsl.math.ComplexEigen
-
Returns the performance index of the complex eigensystem.
- getPerformanceTuningParameters(int) - Method in class com.imsl.math.ComplexSuperLU
-
Returns a performance tuning parameter value.
- getPerformanceTuningParameters(int) - Method in class com.imsl.math.SuperLU
-
Returns a performance tuning parameter value.
- getPermutation(int) - Method in class com.imsl.stat.RandomSamples
-
Returns a permutation array of integers.
- getPermutationMatrix() - Method in class com.imsl.math.ComplexLU
-
Returns the permutation matrix which results from the
LU factorization of A.
- getPermutationMatrix() - Method in class com.imsl.math.LU
-
Returns the permutation matrix which results from the
LU factorization of A.
- getPermute() - Method in class com.imsl.math.QR
-
Returns an integer vector containing information about the permutation
of the elements of the matrix during pivoting.
- getPermute() - Method in class com.imsl.stat.LinearRegression
-
Returns an integer vector containing information about the permutation
of the columns of the matrix of regressors during QR factorization.
- getPhi() - Method in class com.imsl.stat.ContingencyTable
-
Returns phi.
- getPivotGrowth() - Method in class com.imsl.math.ComplexSuperLU
-
Returns the reciprocal pivot growth factor flag.
- getPivotGrowth() - Method in class com.imsl.math.SuperLU
-
Returns the reciprocal pivot growth factor flag.
- getPkgProperties() - Static method in class com.imsl.Version
-
Get the version information about this library.
- getPooledCovariances() - Method in class com.imsl.stat.PooledCovariances
-
Computes and returns the pooled covariances.
- getPooledVariance() - Method in class com.imsl.stat.NormTwoSample
-
Returns the pooled variance for the two samples.
- getPopulationTable(int[]) - Method in class com.imsl.stat.LifeTables
-
Compute a population table.
- getPreconditionerSolves() - Method in class com.imsl.math.GenMinRes
-
Returns the total number of GMRES right preconditioner solves.
- getPredictedClass() - Method in class com.imsl.datamining.decisionTree.TreeNode
-
Returns the predicted class at the current node, for response variables
of categorical type.
- getPredictedClass() - Method in class com.imsl.datamining.NaiveBayesClassifier
-
Returns the predicted classification for each training pattern.
- getPredictedResponse() - Method in class com.imsl.stat.LinearRegression.CaseStatistics
-
Returns the predicted response for an observation.
- getPredictedVal() - Method in class com.imsl.datamining.decisionTree.TreeNode
-
Returns the predicted value at the current node for response variables of
continuous type.
- getPredictionError() - Method in class com.imsl.datamining.BootstrapAggregation
-
Returns the mean squared prediction error for regression problems, or the
classification percentage error for classification problems.
- getPredictionError() - Method in class com.imsl.stat.KalmanFilter
-
Returns the one-step-ahead prediction error.
- getPredictionInterval() - Method in class com.imsl.stat.LinearRegression.CaseStatistics
-
Returns the Prediction Interval of the predicted response for an
observation.
- getPredictions() - Method in class com.imsl.datamining.BootstrapAggregation
-
Returns the predicted values.
- getPredictorIndexes() - Method in class com.imsl.datamining.PredictiveModel
-
Returns the column indices of xy in which the predictor
variables reside.
- getPredictorNumberOfValues() - Method in class com.imsl.datamining.decisionTree.Tree
-
Returns the number of distinct values of each predictor variable.
- getPredictorType(int) - Method in class com.imsl.datamining.decisionTree.Tree
-
- getPredictorTypes() - Method in class com.imsl.datamining.PredictiveModel
-
Returns an array of VariableType objects that correspond to
the predictor data types in xy.
- getPreordering() - Method in class com.imsl.math.SparseLP
-
Returns the variant of the Minimum Degree Ordering (MDO) algorithm used
in the preordering of the normal equations or augmented system matrix.
- getPresolve() - Method in class com.imsl.math.SparseLP
-
Returns the presolve option.
- getPrimalInfeasibility() - Method in class com.imsl.math.SparseLP
-
Returns the primal infeasibility of the solution.
- getPrimalInfeasibilityTolerance() - Method in class com.imsl.math.SparseLP
-
Returns the primal infeasibility tolerance.
- getPrimalSolution() - Method in class com.imsl.math.DenseLP
-
Returns the solution x of the linear programming problem.
- getPrimalSolution() - Method in class com.imsl.math.LinearProgramming
-
Deprecated.
Returns the solution x of the linear programming problem.
- getPrintLevel() - Method in class com.imsl.datamining.BootstrapAggregation
-
Returns the current print level.
- getPrintLevel() - Method in class com.imsl.datamining.PredictiveModel
-
Returns the current print level.
- getPrintLevel() - Method in class com.imsl.math.SparseLP
-
Returns the print level.
- getPrior() - Method in class com.imsl.stat.DiscriminantAnalysis
-
Returns the prior probabilities.
- getPriorProbabilities() - Method in class com.imsl.datamining.PredictiveModel
-
Returns an array containing the prior probabilities.
- getProbabilities() - Method in class com.imsl.datamining.NaiveBayesClassifier
-
Returns the predicted classification probabilities for each target class.
- getProbability() - Method in class com.imsl.stat.DiscriminantAnalysis
-
Returns the posterior probabilities for each observation.
- getProduct() - Method in class com.imsl.stat.CategoricalGenLinModel
-
Returns the inverse of the Hessian times the gradient vector computed at
the input parameter estimates.
- getProducts() - Method in class com.imsl.math.GenMinRes
-
Returns the total number of GMRES matrix-vector products used.
- getPsiWeights() - Method in class com.imsl.stat.ARMA
-
Returns the psi weights of the infinite order moving average form of the
model.
- getPsiWeights() - Method in class com.imsl.stat.ARMAMaxLikelihood
-
Returns the psi weights used for calculating forecasts from the infinite
order moving average form of the ARMA model.
- getPsiWeights() - Method in class com.imsl.stat.ARMAOutlierIdentification
-
Returns the \(\psi\) weights of the infinite order moving average
form of the model.
- getPsiWeights() - Method in class com.imsl.stat.AutoARIMA
-
Returns the \(\psi\) weights of the infinite order
moving average form of the model.
- getPValue(int) - Method in class com.imsl.stat.LinearRegression.CoefficientTTests
-
Returns the p-value for the two-sided test.
- getPValue(int) - Method in class com.imsl.stat.StepwiseRegression.CoefficientTTests
-
Returns the p-value for the two-sided test \(H_0 : {
\beta} = 0\) vs.
- getPValues() - Method in class com.imsl.stat.PartialCovariances
-
Calculates the p-values for testing the null hypothesis that the
associated partial covariance/correlation is zero.
- getQ() - Method in class com.imsl.math.QR
-
Returns the orthogonal or unitary matrix Q.
- getQ() - Method in class com.imsl.stat.ARMAMaxLikelihood
-
Returns the number of moving average terms in the ARMA model
- getQ() - Method in class com.imsl.stat.EmpiricalQuantiles
-
Returns the empirical quantiles.
- getR() - Method in class com.imsl.math.Cholesky
-
Returns the R matrix that results from the Cholesky factorization.
- getR() - Method in class com.imsl.math.QR
-
Returns the upper trapezoidal matrix R.
- getR() - Method in class com.imsl.stat.ANCOVA
-
Returns the R matrix from the QR decomposition.
- getR() - Method in class com.imsl.stat.LinearRegression
-
Returns a copy of the R matrix.
- getR() - Method in class com.imsl.stat.NonlinearRegression
-
Returns a copy of the R matrix.
- getRadialFunction() - Method in class com.imsl.math.RadialBasis
-
Returns the radial function.
- getRadius(int) - Method in class com.imsl.math.ZeroPolynomial
-
Returns an a-posteriori absolute error bound on the root.
- getRandom() - Method in class com.imsl.datamining.neural.EpochTrainer
-
Returns the random number generator used to perturb the stage 1 guesses.
- getRandomObject() - Method in class com.imsl.datamining.CrossValidation
-
Returns the random object being used in the permutation of the
observations.
- getRandomObject() - Method in class com.imsl.datamining.PredictiveModel
-
Returns the random object being used in the permutation of the
observations.
- getRandomObject() - Method in class com.imsl.math.NelderMead
-
Returns the random object being used in the computation of the
vertices of the initial complex.
- getRandomSampleIndicies() - Method in class com.imsl.datamining.neural.EpochTrainer
-
Gets the random number generators used to select random training
patterns in stage 1.
- getRangeOfX() - Method in class com.imsl.stat.distributions.ProbabilityDistribution
-
Returns the proper range of the random variable having the current
probability distribution.
- getRank() - Method in class com.imsl.math.ComplexSVD
-
Returns the rank of the matrix used to construct this instance.
- getRank() - Method in class com.imsl.math.QR
-
Returns the rank of the matrix used to construct this instance.
- getRank() - Method in class com.imsl.math.SVD
-
Returns the rank of the matrix used to construct this instance.
- getRank() - Method in class com.imsl.stat.KalmanFilter
-
Returns the rank of the variance-covariance matrix for all the observations.
- getRank() - Method in class com.imsl.stat.LinearRegression
-
Returns the rank of the matrix.
- getRank() - Method in class com.imsl.stat.NonlinearRegression
-
Returns the rank of the matrix.
- getRanks(double[]) - Method in class com.imsl.stat.Ranks
-
Gets the rank for each observation.
- getReciprocalPivotGrowthFactor() - Method in class com.imsl.math.ComplexSuperLU
-
Returns the reciprocal pivot growth factor.
- getReciprocalPivotGrowthFactor() - Method in class com.imsl.math.SuperLU
-
Returns the reciprocal pivot growth factor.
- getRef(int) - Method in class com.imsl.io.AbstractFlatFile
-
Returns the value of the designated column in the current row of this
ResultSet object as a Ref object in the Java
programming language.
- getRef(String) - Method in class com.imsl.io.AbstractFlatFile
-
Returns the value of the designated column in the current row of this
ResultSet object as a Ref object in the Java
programming language.
- getRegressors() - Method in class com.imsl.stat.RegressorsForGLM
-
Returns the regressor array.
- getRegularizationParameter() - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine
-
Returns the value of the regularization parameter, C.
- getRelativeBackwardError() - Method in class com.imsl.math.ComplexSuperLU
-
Returns the componentwise relative backward error of the solution vector.
- getRelativeBackwardError() - Method in class com.imsl.math.SuperLU
-
Returns the componentwise relative backward error of the solution vector.
- getRelativeError(double) - Method in class com.imsl.math.ConjugateGradient
-
Returns the relative error used for stopping the algorithm.
- getRelativeError() - Method in class com.imsl.math.GenMinRes
-
Returns the stopping tolerance.
- getRelativeError() - Method in class com.imsl.stat.ARMAEstimateMissing
-
Returns the relative error used for the METHOD_OF_MOMENTS
and LEAST_SQARES estimation methods.
- getRelativeErrorTolerances() - Method in class com.imsl.math.FeynmanKac
-
Returns relative error tolerances.
- getRelativeOptimalityTolerance() - Method in class com.imsl.math.SparseLP
-
Returns the relative optimality tolerance.
- getResidual() - Method in class com.imsl.stat.ARMA
-
Returns the residuals.
- getResidual() - Method in class com.imsl.stat.ARMAOutlierIdentification
-
Returns the residuals.
- getResidual() - Method in class com.imsl.stat.LinearRegression.CaseStatistics
-
Returns the Residual for an observation.
- getResidualNorm() - Method in class com.imsl.math.BoundedVariableLeastSquares
-
Returns the euclidean norm of the residual vector,
\(\|Ax-b\|^2\).
- getResidualNorm() - Method in class com.imsl.math.GenMinRes
-
Returns the final residual norm, \({\Vert b-Ax \Vert}_2\) .
- getResidualNorm() - Method in class com.imsl.math.NonNegativeLeastSquares
-
Returns the euclidean norm of the residual vector,
\(\|Ax-b\|^2\).
- getResiduals() - Method in class com.imsl.math.BoundedLeastSquares
-
Returns the residuals at the approximate solution.
- getResiduals() - Method in class com.imsl.stat.ARAutoUnivariate
-
Returns the current values of the vector of residuals.
- getResiduals() - Method in class com.imsl.stat.ARMAMaxLikelihood
-
Returns the current values of the vector of residuals.
- getResiduals() - Method in class com.imsl.stat.AutoARIMA
-
Returns the residuals.
- getResidualSeries() - Method in class com.imsl.stat.ExtendedGARCH
-
Returns the epsilon series (the current residuals).
- getResidualStandardError() - Method in class com.imsl.stat.ARMAOutlierIdentification
-
Returns the residual standard error of the outlier free series.
- getResidualStandardError() - Method in class com.imsl.stat.AutoARIMA
-
Returns the residual standard error of the outlier free series.
- getResidualUpdating() - Method in class com.imsl.math.GenMinRes
-
Returns the residual updating method to be used.
- getResponseColumn() - Method in class com.imsl.stat.KaplanMeierEstimates
-
Returns the column index of x containing the
response time for each observation.
- getResponseColumn() - Method in class com.imsl.stat.ProportionalHazards
-
Returns the column index of x containing the
response time for each observation.
- getResponseColumnIndex() - Method in class com.imsl.datamining.PredictiveModel
-
Returns the column index in xy containing the response
variable.
- getResponseType() - Method in class com.imsl.datamining.decisionTree.Tree
-
- getResponseVariableAverage() - Method in class com.imsl.datamining.PredictiveModel
-
Returns the weighted average value of the response variable.
- getResponseVariableMostFrequentClass() - Method in class com.imsl.datamining.PredictiveModel
-
Returns the most frequent value of the response variable.
- getResponseVariableType() - Method in class com.imsl.datamining.PredictiveModel
-
Returns the variable type of the response variable.
- getRHS() - Method in class com.imsl.stat.LinearRegression
-
Returns the right hand side of the regression problem.
- getRightEndTangent() - Method in class com.imsl.math.CsTCB
-
Returns the value of the tangent at the rightmost endpoint.
- getRiskStandardErrors() - Method in class com.imsl.datamining.CrossValidation
-
Returns the estimated standard errors for the risk values.
- getRiskValues() - Method in class com.imsl.datamining.CrossValidation
-
Returns the vector of risk values.
- getRoot(int) - Method in class com.imsl.math.ZeroPolynomial
-
Returns a zero of the polynomial.
- getRoots() - Method in class com.imsl.math.ZeroPolynomial
-
Returns the zeros of the polynomial.
- getRow() - Method in class com.imsl.io.AbstractFlatFile
-
Retrieves the current row number.
- getRow(int) - Method in class com.imsl.io.MPSReader
-
Returns a row of the constraint matrix or a free row.
- getRow() - Method in class com.imsl.stat.Dissimilarities
-
Returns a boolean indicating whether distances are computed
between rows or columns of x.
- getRowCoefficients(int) - Method in class com.imsl.io.MPSReader
-
Returns the coefficents of a row.
- getRowId(int) - Method in class com.imsl.io.FlatFile
-
Retrieves the value of the designated column in the current row of this
ResultSet object as a java.sql.RowId object in
the Java programming language.
- getRowId(String) - Method in class com.imsl.io.FlatFile
-
Retrieves the value of the designated column in the current row of this
ResultSet object as a java.sql.RowId object in
the Java programming language.
- getRSquared() - Method in class com.imsl.stat.ANOVA
-
Returns the R-squared (in percent).
- getS() - Method in class com.imsl.math.ComplexSVD
-
Returns the singular values.
- getS() - Method in class com.imsl.math.SVD
-
Returns the singular values.
- getSampleIndices(int, int) - Method in class com.imsl.stat.RandomSamples
-
Computes and returns an array of sampled indices.
- getSamples(double[], int) - Method in class com.imsl.stat.RandomSamples
-
Generates a pseudorandom sample from a given population array, without
replacement.
- getSamples(double[][], int) - Method in class com.imsl.stat.RandomSamples
-
Generates a pseudorandom sample from a given population matrix, without
replacement.
- getSampleSizeProportion() - Method in class com.imsl.datamining.GradientBoosting
-
Returns the current setting of the sample size proportion.
- getSampleStandardDeviation() - Method in class com.imsl.stat.Summary
-
Returns the sample standard deviation.
- getSampleVariance() - Method in class com.imsl.stat.Summary
-
Returns the sample variance.
- getSavageScores(double[]) - Method in class com.imsl.stat.Ranks
-
Gets the Savage scores (the expected value of exponential order statistics).
- getScale() - Method in class com.imsl.math.ODE
-
Returns the scaling factor.
- getScaleParameter() - Method in class com.imsl.stat.GammaDistribution
-
Returns the maximum-likelihood estimate found for the gamma scale
parameter.
- getScalingFactors() - Method in class com.imsl.math.NumericalDerivatives
-
Returns the scaling factors for the y values.
- getScalingOption() - Method in class com.imsl.stat.Dissimilarities
-
Returns the scaling option.
- getSeriesValues() - Method in class com.imsl.stat.TimeSeries
-
Returns the data values of the time series.
- getSeriesValues(int) - Method in class com.imsl.stat.TimeSeries
-
Returns the data values of the k-th time series.
- getShapeParameter() - Method in class com.imsl.stat.GammaDistribution
-
Returns the maximum-likelihood estimate found for the gamma shape
parameter.
- getShapiroWilkW() - Method in class com.imsl.stat.NormalityTest
-
Returns the Shapiro-Wilk W statistic for the Shapiro-Wilk W test.
- getShort(int) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as a short in the Java
programming language.
- getShort(String) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as a short in the Java
programming language.
- getShrinkageParameter() - Method in class com.imsl.datamining.GradientBoosting
-
Returns the current shrinkage parameter.
- getSigma() - Method in class com.imsl.stat.GARCH
-
Returns the estimated value of sigma squared.
- getSInitial() - Method in class com.imsl.stat.ARSeasonalFit
-
Returns the the candidate values for s to evaluate.
- getSkewness() - Method in class com.imsl.stat.Summary
-
Returns the skewness.
- getSkip() - Method in class com.imsl.stat.FaureSequence
-
Returns the number of points skipped at the beginning of the sequence.
- getSmallestCPRatio() - Method in class com.imsl.math.SparseLP
-
Returns the ratio of the smallest complementarity product to the average.
- getSmallestDiagonalElement() - Method in class com.imsl.math.ComplexSparseCholesky
-
Returns the smallest diagonal element of the Cholesky factor.
- getSmallestDiagonalElement() - Method in class com.imsl.math.SparseCholesky
-
Returns the smallest diagonal element of the Cholesky factor.
- getSmoothedSeries() - Method in class com.imsl.stat.HoltWintersExponentialSmoothing
-
Returns the fitted series values.
- getSolution() - Method in class com.imsl.math.BoundedLeastSquares
-
Returns the solution.
- getSolution() - Method in class com.imsl.math.BoundedVariableLeastSquares
-
Returns the solution to the problem.
- getSolution() - Method in class com.imsl.math.MinConGenLin
-
Returns the computed solution.
- getSolution() - Method in class com.imsl.math.MinConNLP
-
Returns the last computed solution.
- getSolution() - Method in class com.imsl.math.NelderMead
-
Returns the solution.
- getSolution() - Method in class com.imsl.math.NonNegativeLeastSquares
-
Returns the solution to the problem, x.
- getSolution() - Method in class com.imsl.math.QuadraticProgramming
-
Returns the solution.
- getSolution() - Method in class com.imsl.math.SparseLP
-
Returns the solution x of the linear programming problem.
- getSolveMethod() - Method in class com.imsl.math.OdeAdamsGear
-
Returns the method for solving the formula equations.
- getSpline() - Method in class com.imsl.math.BSpline
-
Returns a Spline representation of the B-spline.
- getSplineCoefficients() - Method in class com.imsl.math.FeynmanKac
-
Returns the coefficients of the Hermite quintic splines that represent an
approximate solution of the Feynman-Kac PDE.
- getSplineCoefficientsPrime() - Method in class com.imsl.math.FeynmanKac
-
Returns the first derivatives of the Hermite quintic spline coefficients
that represent an approximate solution of the Feynman-Kac PDE.
- getSplineValue(double[], double[], int) - Method in class com.imsl.math.FeynmanKac
-
Evaluates for time value 0 or a time value in tGrid the derivative of the
Hermite quintic spline interpolant at evaluation points within the
range of xGrid.
- getSplitMergedCategoriesSigLevel() - Method in class com.imsl.datamining.decisionTree.CHAID
-
Returns the significance level for splitting previously merged
categories.
- getSplitVariableSelectionCriterion() - Method in class com.imsl.datamining.decisionTree.QUEST
-
Returns the significance level for split variable selection.
- getSplitVariableSignificanceLevel() - Method in class com.imsl.datamining.decisionTree.CHAID
-
Returns the significance level for split variable selection.
- getSpread() - Method in class com.imsl.datamining.neural.ScaleFilter
-
Retrieves the measure of spread to be used during scaling.
- getSQLXML(int) - Method in class com.imsl.io.FlatFile
-
Retrieves the value of the designated column in the current row of this
ResultSet as a java.sql.SQLXML object in the
Java programming language.
- getSQLXML(String) - Method in class com.imsl.io.FlatFile
-
Retrieves the value of the designated column in the current row of this
ResultSet as a java.sql.SQLXML object in the
Java programming language.
- getSSE() - Method in class com.imsl.stat.NonlinearRegression
-
Returns the sums of squares for error.
- getSSResidual() - Method in class com.imsl.stat.ARMA
-
Returns the sum of squares of the random shock.
- getStage1Trainer() - Method in class com.imsl.datamining.neural.EpochTrainer
-
Returns the stage 1 trainer.
- getStage2Trainer() - Method in class com.imsl.datamining.neural.EpochTrainer
-
Returns the stage 1 trainer.
- getStandardDeviation() - Method in class com.imsl.stat.LogNormalDistribution
-
Returns the lognormal probability distribution standard deviation parameter.
- getStandardDeviation() - Method in class com.imsl.stat.NormalDistribution
-
Returns the population standard deviation.
- getStandardDeviation() - Method in class com.imsl.stat.Summary
-
Returns the population standard deviation.
- getStandardDeviations(double[][], int[]) - Method in class com.imsl.datamining.NaiveBayesClassifier
-
Returns a table of standard deviations for each continuous attribute in
continuousData segmented by the target classes in
classificationData.
- getStandardError(int) - Method in class com.imsl.stat.LinearRegression.CoefficientTTests
-
Returns the estimated standard error for a coefficient estimate.
- getStandardError(int) - Method in class com.imsl.stat.StepwiseRegression.CoefficientTTests
-
Returns the estimated standard error for a coefficient estimate.
- getStandardErrors(int) - Method in class com.imsl.stat.AutoCorrelation
-
Returns the standard errors of the autocorrelations of the time series
x.
- getStandardErrors(int) - Method in class com.imsl.stat.CrossCorrelation
-
Returns the standard errors of the cross-correlations between the
time series x and y.
- getStandardErrors() - Method in class com.imsl.stat.distributions.MaximumLikelihoodEstimation
-
Returns the approximate standard errors of the maximum likelihood
estimates.
- getStandardErrors() - Method in class com.imsl.stat.FactorAnalysis
-
Returns the estimated asymptotic standard errors of the eigenvalues.
- getStandardErrors() - Method in class com.imsl.stat.KaplanMeierEstimates
-
Returns Greenwood's estimated standard errors.
- getStandardizedResidual() - Method in class com.imsl.stat.LinearRegression.CaseStatistics
-
Returns the Standardized Residual for an observation.
- getStartDate() - Method in class com.imsl.stat.TimeSeries
-
Returns the starting date of the time series.
- getStatement() - Method in class com.imsl.io.AbstractFlatFile
-
Returns the Statement object that produced this
ResultSet object.
- getStateVector() - Method in class com.imsl.stat.KalmanFilter
-
Returns the estimated state vector at time k + 1 given the observations
through time k.
- getStatistics() - Method in class com.imsl.stat.ContingencyTable
-
Returns the statistics associated with this table.
- getStatistics() - Method in class com.imsl.stat.DiscriminantAnalysis
-
Returns statistics.
- getStatistics() - Method in class com.imsl.stat.FactorAnalysis
-
Returns statistics.
- getStatistics() - Method in class com.imsl.stat.SelectionRegression
-
Returns a new Statistics object.
- getStatistics() - Method in class com.imsl.stat.WilcoxonRankSum
-
Returns the statistics.
- getStatus() - Method in class com.imsl.math.NumericalDerivatives
-
Returns status information.
- getStatus(int) - Method in class com.imsl.math.ZeroPolynomial
-
Returns the error status of a root.
- getStdDev() - Method in class com.imsl.stat.NormOneSample
-
Returns the standard deviation of the sample.
- getStdDevX() - Method in class com.imsl.stat.NormTwoSample
-
Returns the standard deviation of the first sample.
- getStdDevX() - Method in class com.imsl.stat.WelchsTTest
-
Returns the standard deviation of the first sample.
- getStdDevY() - Method in class com.imsl.stat.NormTwoSample
-
Returns the standard deviation of the second sample.
- getStdDevY() - Method in class com.imsl.stat.WelchsTTest
-
Returns the standard deviation of the second sample.
- getStepControlMethod() - Method in class com.imsl.math.FeynmanKac
-
Returns the step control method used in the integration of the
Feynman-Kac PDE.
- getStratumColumn() - Method in class com.imsl.stat.KaplanMeierEstimates
-
Returns the column index of x containing the stratum number
for each observation.
- getStratumColumn() - Method in class com.imsl.stat.ProportionalHazards
-
Returns the column index of x containing the stratum number
for each observation.
- getStratumNumbers() - Method in class com.imsl.stat.ProportionalHazards
-
Returns the stratum number used for each observation.
- getStratumRatio() - Method in class com.imsl.stat.ProportionalHazards
-
Returns the ratio at which a stratum is split into two strata.
- getString(int) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as a String in the Java
programming language.
- getString(String) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as a String in the Java
programming language.
- getSumOfSquares() - Method in class com.imsl.stat.HoltWintersExponentialSmoothing
-
Returns the sum of squares of the one step ahead forecast errors.
- getSumOfSquares() - Method in class com.imsl.stat.KalmanFilter
-
Returns the generalized sum of squares.
- getSumOfSquaresForError() - Method in class com.imsl.stat.ANOVA
-
Returns the sum of squares for error.
- getSumOfSquaresForModel() - Method in class com.imsl.stat.ANOVA
-
Returns the sum of squares for model.
- getSumOfWeights() - Method in class com.imsl.stat.Covariances
-
Returns the sum of the weights of all observations.
- getSumOfWeights() - Method in class com.imsl.stat.PooledCovariances
-
Returns the sum of the weights times the frequencies in the groups.
- getSupport() - Method in class com.imsl.datamining.AssociationRule
-
Support for the Z, X, and Y components of the association rule.
- getSupport(int) - Method in class com.imsl.datamining.Itemsets
-
Returns the support determined for a particular itemset.
- getSupportVectorCoef() - Method in class com.imsl.datamining.supportvectormachine.SVModel
-
Returns the nonzero coefficients of the support vector classifier or the
approximate function, in the case of regression.
- getSurrogateInfo() - Method in class com.imsl.datamining.decisionTree.ALACART
-
Returns the surrogate split information.
- getSurrogateInfo() - Method in interface com.imsl.datamining.decisionTree.DecisionTreeSurrogateMethod
-
Returns the surrogate split information.
- getSurrogateInfo(int) - Method in class com.imsl.datamining.decisionTree.TreeNode
-
Returns a value from the surrogate split information array.
- getSurrogateInfo() - Method in class com.imsl.datamining.decisionTree.TreeNode
-
Returns the surrogate split information array.
- getSurvivalProbabilities() - Method in class com.imsl.stat.KaplanMeierEstimates
-
Returns the estimated survival probabilities.
- getSwept() - Method in class com.imsl.stat.StepwiseRegression
-
Returns an array containing information indicating whether or not a
particular variable is in the model.
- getSymbolicFactor() - Method in class com.imsl.math.ComplexSparseCholesky
-
Returns the symbolic Cholesky factor.
- getSymbolicFactor() - Method in class com.imsl.math.SparseCholesky
-
Returns the symbolic Cholesky factor.
- getSymmetricMode() - Method in class com.imsl.math.ComplexSuperLU
-
Returns the symmetric mode flag.
- getSymmetricMode() - Method in class com.imsl.math.SuperLU
-
Returns the symmetric mode flag.
- getSysProperties() - Static method in class com.imsl.Version
-
Get the operating system and Java versions.
- getT() - Method in class com.imsl.stat.ExtendedGARCH
-
Returns the length of the GARCH time series.
- getTable() - Method in class com.imsl.stat.TableMultiWay.BalancedTable
-
Returns an array containing the frequencies for each variable.
- getTable() - Method in class com.imsl.stat.TableMultiWay.UnbalancedTable
-
Returns the frequency for each cell.
- getTauStatistics() - Method in class com.imsl.stat.ARMAOutlierIdentification
-
Returns the t value for each detected outlier.
- getTerminalNodeIndicators() - Method in class com.imsl.datamining.decisionTree.Tree
-
Returns the terminal node indicator array.
- getTerminationCriterion() - Method in class com.imsl.math.MinConNLP
-
Returns the reason the solve step terminated.
- getTerminationStatus() - Method in class com.imsl.math.SparseLP
-
Returns the termination status for the problem.
- getTestClassFittedValues() - Method in class com.imsl.datamining.GradientBoosting
-
Returns the fitted values \({f(x_i)}\) for a categorical
response variable with two or more levels on the test data.
- getTestClassProbabilities() - Method in class com.imsl.datamining.GradientBoosting
-
Returns the predicted probabilities on the test data for a categorical
response variable.
- getTestEffects() - Method in class com.imsl.stat.ANOVAFactorial
-
Returns statistics relating to the sums of squares for the effects in the
model.
- getTestFittedValues() - Method in class com.imsl.datamining.GradientBoosting
-
Returns the fitted values \({f(x_i)}\) for a continuous
response variable after gradient boosting on the test data.
- getTestLossValue() - Method in class com.imsl.datamining.GradientBoosting
-
Returns the loss function value on the test data.
- getTestStatistic() - Method in class com.imsl.stat.KolmogorovOneSample
-
Returns \(D = \max(D^{+}, D^{-})\).
- getTestStatistic() - Method in class com.imsl.stat.KolmogorovTwoSample
-
Returns \(D = \max(D^{+}, D^{-})\).
- getTheta() - Method in class com.imsl.stat.PoissonDistribution
-
Returns the mean number of successes in a given time period of the
Poisson probability distribution.
- getTiesOption() - Method in class com.imsl.stat.ProportionalHazards
-
Returns the method used for handling ties.
- getTime(int) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as a java.sql.Time object in
the Java programming language.
- getTime(String) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as a java.sql.Time object in
the Java programming language.
- getTime(int, Calendar) - Method in class com.imsl.io.AbstractFlatFile
-
Returns the value of the designated column in the current row of this
ResultSet object as a java.sql.Time object in
the Java programming language.
- getTime(String, Calendar) - Method in class com.imsl.io.AbstractFlatFile
-
Returns the value of the designated column in the current row of this
ResultSet object as a java.sql.Time object in
the Java programming language.
- getTimeBarrier() - Method in class com.imsl.math.FeynmanKac
-
Returns the barrier set for integration in the time direction.
- getTimes() - Method in class com.imsl.stat.KaplanMeierECDF
-
Retrieves the time values where the step function CDF jumps to a greater
value.
- getTimeSeries() - Method in class com.imsl.stat.ARAutoUnivariate
-
Returns the time series used for estimating the minimum AIC and the
autoregressive coefficients.
- getTimeSeries() - Method in class com.imsl.stat.ARMAMaxLikelihood
-
Returns the time series used to construct ARMAMaxLikelihood.
- getTimeSeries() - Method in class com.imsl.stat.ARSeasonalFit
-
Returns the time series.
- getTimestamp(int) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as a java.sql.Timestamp object
in the Java programming language.
- getTimestamp(String) - Method in class com.imsl.io.AbstractFlatFile
-
Gets the value of the designated column in the current row of this
ResultSet object as a java.sql.Timestamp
object.
- getTimestamp(int, Calendar) - Method in class com.imsl.io.AbstractFlatFile
-
Returns the value of the designated column in the current row of this
ResultSet object as a java.sql.Timestamp object
in the Java programming language.
- getTimestamp(String, Calendar) - Method in class com.imsl.io.AbstractFlatFile
-
Returns the value of the designated column in the current row of this
ResultSet object as a java.sql.Timestamp object
in the Java programming language.
- getTimeZone() - Method in class com.imsl.stat.TimeSeries
-
Returns the time zone of the time series.
- getTimeZoneOffset() - Method in class com.imsl.stat.TimeSeries
-
Returns the number of hours (+ or -) from GMT of the time zone associated
with the time series.
- getTimsacAR() - Method in class com.imsl.stat.ARAutoUnivariate
-
Returns the final auto regressive parameter estimates at the optimum AIC
estimated by the original TIMSAC routine (UNIMAR).
- getTimsacConstant() - Method in class com.imsl.stat.ARAutoUnivariate
-
Returns the estimate for the constant parameter in the ARMA series.
- getTimsacVariance() - Method in class com.imsl.stat.ARAutoUnivariate
-
Returns the final estimate for the innovation variance calculated by the
TIMSAC automatic AR modeling routine (UNIMAR).
- getTo() - Method in class com.imsl.datamining.neural.Link
-
Returns the destination Node for this Link.
- getTolerance() - Method in class com.imsl.math.MinConNLP
-
Returns the desired precision of the solution.
- getTolerance() - Method in class com.imsl.math.ODE
-
Returns the error tolerance.
- getTolerance() - Method in class com.imsl.stat.ARMAMaxLikelihood
-
Returns the tolerance for the convergence algorithm.
- getTotalDegreesOfFreedom() - Method in class com.imsl.stat.ANOVA
-
Returns the total degrees of freedom.
- getTotalMissing() - Method in class com.imsl.stat.ANOVA
-
Returns the total number of missing values.
- getTotalMissing() - Method in class com.imsl.stat.EmpiricalQuantiles
-
Returns the total number of missing values.
- getTotalNumberOfFailures(double) - Method in class com.imsl.stat.KaplanMeierEstimates
-
Returns the total number failing in the group for the specified group value.
- getTotalNumberOfObservations() - Method in class com.imsl.stat.PooledCovariances
-
Returns the total number of observations used in the analysis.
- getTotalSumOfSquares() - Method in class com.imsl.stat.ANOVA
-
Returns the total sum of squares.
- getTotalWeight() - Method in class com.imsl.datamining.PredictiveModel
-
Returns the sum of the active case weights.
- getTrainingErrors() - Method in class com.imsl.datamining.NaiveBayesClassifier
-
Returns a table of classification errors of non-missing classifications
for each target classification plus the overall total of classification
errors.
- getTrainingIterations() - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer
-
Returns the number of iterations used during training.
- getTransform() - Method in class com.imsl.datamining.neural.UnsupervisedOrdinalFilter
-
Retrieves the transform flag used for encoding and decoding.
- getTransformedTimeSeries() - Method in class com.imsl.stat.ARSeasonalFit
-
Returns the transformed series, \(W_t(s,d)\).
- getTransformType() - Method in class com.imsl.stat.ClusterHierarchical
-
Returns the type of transformation.
- getTStatistic(int) - Method in class com.imsl.stat.LinearRegression.CoefficientTTests
-
Returns the t-statistic for the test that the i-th coefficient
is zero.
- getTStatistic(int) - Method in class com.imsl.stat.StepwiseRegression.CoefficientTTests
-
Returns the student-t test statistic for testing the
i-th coefficient equal to zero (\({\beta}_{index} = 0
\)).
- getTTest() - Method in class com.imsl.stat.NormOneSample
-
Returns the test statistic associated with the t test.
- getTTest() - Method in class com.imsl.stat.NormTwoSample
-
Returns the test statistic for the Satterthwaite's
approximation.
- getTTest() - Method in class com.imsl.stat.WelchsTTest
-
Returns the calculated test statistic for Welch's t-test.
- getTTestDF() - Method in class com.imsl.stat.NormOneSample
-
Returns the degrees of freedom associated with the t
test for the mean.
- getTTestDF() - Method in class com.imsl.stat.NormTwoSample
-
Returns the degrees of freedom for the Satterthwaite's
approximation.
- getTTestDF() - Method in class com.imsl.stat.WelchsTTest
-
Returns the degrees of freedom used in the test.
- getTTestP() - Method in class com.imsl.stat.NormOneSample
-
Returns the probability associated with the t test
of a larger t in absolute value.
- getTTestP() - Method in class com.imsl.stat.NormTwoSample
-
Returns the approximate probability of observing a larger
value of the t-statistic given the null hypothesis is true
(i.e.,the p-value for the test).
- getTTestP() - Method in class com.imsl.stat.WelchsTTest
-
Returns the approximate probability of observing a more extreme value of
the t-statistic given the null hypothesis is true (i.e, the
approximate p-value of the test).
- getTukeyScores(double[]) - Method in class com.imsl.stat.Ranks
-
Gets the Tukey version of normal scores for each observation.
- getTwoSidedPValue() - Method in class com.imsl.stat.KolmogorovOneSample
-
Probability of the statistic exceeding D under
the null hypothesis of equality and against the
two-sided alternative.
- getTwoSidedPValue() - Method in class com.imsl.stat.KolmogorovTwoSample
-
Probability of the statistic exceeding D under
the null hypothesis of equality and against the
two-sided alternative.
- getType() - Method in class com.imsl.io.AbstractFlatFile
-
Returns the type of this ResultSet object.
- getTypeCode() - Method in class com.imsl.test.example.math.SparseMatrixEx2.MTXReader
-
- getTypeVariable(int) - Method in class com.imsl.io.MPSReader
-
Returns the type of a variable.
- getU() - Method in class com.imsl.math.ComplexLU
-
Returns the unit upper triangular portion of the LU
factorization of A.
- getU() - Method in class com.imsl.math.ComplexSVD
-
Returns the left singular vectors.
- getU() - Method in class com.imsl.math.LU
-
Returns the unit upper triangular portion of the LU
factorization of A.
- getU() - Method in class com.imsl.math.SVD
-
Returns the left singular vectors.
- getU() - Method in class com.imsl.stat.PooledCovariances
-
Returns the lower matrix U, the lower triangular for the pooled
sample crossproducts matrix.
- getUnbalancedTable() - Method in class com.imsl.stat.TableMultiWay
-
Returns an object containing the unbalanced table.
- getUnicodeStream(int) - Method in class com.imsl.io.AbstractFlatFile
-
- getUnicodeStream(String) - Method in class com.imsl.io.AbstractFlatFile
-
- getUnion(Itemsets...) - Static method in class com.imsl.datamining.Apriori
-
Return the union of a sequence of sets of itemsets.
- getUpperBound(int) - Method in class com.imsl.io.MPSReader
-
Returns the upper bound for a variable.
- getUpperBound() - Method in class com.imsl.math.SparseLP
-
Returns the upper bound on the variables.
- getUpperCICommonVariance() - Method in class com.imsl.stat.NormTwoSample
-
Returns the upper confidenceVariance \(*100%\) confidence
limit for the common variance.
- getUpperCIDiff() - Method in class com.imsl.stat.NormTwoSample
-
Returns the upper confidence limit for the difference,
\(\mu_x - \mu_y\) for
equal or unequal variances depending on the value
set by setUnequalVariances.
- getUpperCIDiff() - Method in class com.imsl.stat.WelchsTTest
-
Returns the (approximate) upper confidenceMean*100%
confidence limit for the difference in population means, \(\mu_x - \mu_y\).
- getUpperCIMean() - Method in class com.imsl.stat.NormOneSample
-
Returns the upper confidence limit for the mean.
- getUpperCIRatioVariance() - Method in class com.imsl.stat.NormTwoSample
-
Returns the approximate upper confidence limit in a
confidence interval for the ratio of variances,
\(\sigma_1^2/\sigma_2^2\).
- getUpperCIVariance() - Method in class com.imsl.stat.NormOneSample
-
Returns the upper confidence limits for the variance.
- getUpperLimit() - Method in class com.imsl.math.SparseLP
-
Returns the upper limit of the constraints that have both a lower and an
upper bound.
- getUpperRange(int) - Method in class com.imsl.io.MPSReader
-
Returns the upper range value for a constraint equation.
- getURL(String) - Method in class com.imsl.io.AbstractFlatFile
-
Retrieves the value of the designated column in the current row of this
ResultSet object as a java.net.URL object.
- getURL(int) - Method in class com.imsl.io.AbstractFlatFile
-
Retrieves the value of the designated column in the current row of this
ResultSet object as a java.net.URL object.
- getUseBackPropagation() - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer
-
Returns the use back propagation setting.
- getV() - Method in class com.imsl.math.ComplexSVD
-
Returns the right singular vectors.
- getV() - Method in class com.imsl.math.SVD
-
Returns the right singular vectors.
- getValue() - Method in class com.imsl.datamining.neural.InputNode
-
Returns the value of this node.
- getValue() - Method in class com.imsl.datamining.neural.OutputPerceptron
-
Returns the value of the OutputPerceptron
determined using the current network state and inputs.
- getValue() - Method in class com.imsl.datamining.supportvectormachine.DataNode
-
Returns the value of the node.
- getValue() - Method in class com.imsl.io.MPSReader.Element
-
Returns the value of the element.
- getValues() - Method in class com.imsl.math.ComplexEigen
-
Returns the eigenvalues of a matrix of type Complex.
- getValues() - Method in class com.imsl.math.Eigen
-
Returns the eigenvalues of a matrix of type double.
- getValues() - Method in class com.imsl.math.SymEigen
-
Returns the eigenvalues.
- getValues() - Method in class com.imsl.stat.FactorAnalysis
-
Returns the eigenvalues.
- getValues() - Method in class com.imsl.stat.TableMultiWay.BalancedTable
-
Returns the values of the classification variables.
- getVanDerWaerdenScores(double[]) - Method in class com.imsl.stat.Ranks
-
Gets the Van der Waerden version of normal scores for each observation.
- getVarCov() - Method in class com.imsl.stat.distributions.MaximumLikelihoodEstimation
-
Returns the approximate variance-covariance matrix of the maximum
likelihood estimates.
- getVarCovAdjustedMeans() - Method in class com.imsl.stat.ANCOVA
-
Returns a matrix containing the estimated variances and covariances for
the adjusted means assuming parallelism.
- getVarCovarMatrix() - Method in class com.imsl.stat.GARCH
-
Returns the variance-covariance matrix.
- getVarCovarMatrix() - Method in class com.imsl.stat.HoltWintersExponentialSmoothing
-
Returns the variance-covariance matrix of the smoothing parameters
estimated by minimizing the mean squared forecast error.
- getVarCovCoefficients() - Method in class com.imsl.stat.ANCOVA
-
Returns a matrix containing the estimated variances and covariances for
the coefficients returned using getModelCoefficients.
- getVariableImportance() - Method in class com.imsl.datamining.BootstrapAggregation
-
Returns the variable importance measure based on the out-of-bag
prediction error.
- getVariableImportance() - Method in class com.imsl.datamining.decisionTree.RandomTrees
-
Returns the variable importance measure based on the out-of-bag
prediction error.
- getVariableType() - Method in class com.imsl.datamining.PredictiveModel
-
Returns an array containing the variable types in xy.
- getVariance() - Method in class com.imsl.stat.ARMA
-
Returns the variance of the time series z.
- getVariance() - Method in class com.imsl.stat.AutoCorrelation
-
Returns the variance of the time series x.
- getVariance() - Method in class com.imsl.stat.Summary
-
Returns the population variance.
- getVarianceCovarianceMatrix() - Method in class com.imsl.stat.ProportionalHazards
-
Returns the estimated asymptotic variance-covariance matrix of the parameters.
- getVariances() - Method in class com.imsl.stat.FactorAnalysis
-
Returns the unique variances.
- getVarianceX() - Method in class com.imsl.stat.CrossCorrelation
-
Returns the variance of time series x.
- getVarianceX() - Method in class com.imsl.stat.MultiCrossCorrelation
-
Returns the variances of the channels of x.
- getVarianceY() - Method in class com.imsl.stat.CrossCorrelation
-
Returns the variance of time series y.
- getVarianceY() - Method in class com.imsl.stat.MultiCrossCorrelation
-
Returns the variances of the channels of y.
- getVectorProducts() - Method in class com.imsl.math.GenMinRes
-
Returns the user-supplied functions for the inner product and,
optionally, the norm used in the Gram-Schmidt implementations.
- getVectors() - Method in class com.imsl.math.ComplexEigen
-
Returns the eigenvectors of the input matrix.
- getVectors() - Method in class com.imsl.math.Eigen
-
Returns the eigenvectors.
- getVectors() - Method in class com.imsl.math.SymEigen
-
Return the eigenvectors of a symmetric matrix of type double.
- getVectors() - Method in class com.imsl.stat.FactorAnalysis
-
Returns the eigenvectors.
- getViolation() - Method in class com.imsl.math.SparseLP
-
Returns the violation of the variable bounds.
- getWarning() - Static method in class com.imsl.Warning
-
Gets the WarningObject.
- getWarnings() - Method in class com.imsl.io.AbstractFlatFile
-
Returns the first warning reported by calls on this
ResultSet object.
- getWeight() - Method in class com.imsl.datamining.neural.Link
-
Returns the weight for this Link.
- getWeights(int, int) - Method in class com.imsl.datamining.KohonenSOM
-
Returns the weights of the node at (i, j) in the node grid.
- getWeights() - Method in class com.imsl.datamining.KohonenSOM
-
Returns the weights of the nodes.
- getWeights() - Method in class com.imsl.datamining.neural.FeedForwardNetwork
-
Returns the weights for the Links in this network.
- getWeights() - Method in class com.imsl.datamining.neural.Network
-
Returns the weights.
- getWeights() - Method in class com.imsl.datamining.PredictiveModel
-
Returns an array containing the case weights.
- getWorkingArraySize() - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine
-
Returns the setting for the work array size.
- getX() - Method in class com.imsl.datamining.AssociationRule
-
The X components of the association rule.
- getX() - Method in class com.imsl.stat.GARCH
-
Returns the estimated parameter array, x.
- getXHi() - Method in class com.imsl.stat.EmpiricalQuantiles
-
Returns the smallest element of x greater than or
equal to the desired quantile.
- getXKnots() - Method in class com.imsl.math.Spline2D
-
Returns the knot sequences in the x-direction.
- getXLo() - Method in class com.imsl.stat.EmpiricalQuantiles
-
Returns the largest element of x less than or equal to the
desired quantile.
- getXOrder() - Method in class com.imsl.math.Spline2DLeastSquares
-
Returns the order of the spline in the x-direction.
- getXWeights() - Method in class com.imsl.math.Spline2DLeastSquares
-
Returns the weights for the least-squares fit in the x-direction.
- getXY() - Method in class com.imsl.datamining.PredictiveModel
-
Returns a copy of the xy data.
- getY() - Method in class com.imsl.datamining.AssociationRule
-
The Y components of the association rule.
- getYearBasis() - Method in class com.imsl.finance.DayCountBasis
-
Returns the (days in year) portion of the Day Count Basis.
- getYKnots() - Method in class com.imsl.math.Spline2D
-
Returns the knot sequences in the y-direction.
- getYOrder() - Method in class com.imsl.math.Spline2DLeastSquares
-
Returns the order of the spline in the y-direction.
- getYProb(int) - Method in class com.imsl.datamining.decisionTree.TreeNode
-
Returns a class probability at the current node, if the response variable
is of categorical type.
- getYProbs() - Method in class com.imsl.datamining.decisionTree.TreeNode
-
Returns the class probabilities at the current node, if the response
variable is of categorical type.
- getYWeights() - Method in class com.imsl.math.Spline2DLeastSquares
-
Returns the weights for the least-squares fit in the y-direction.
- getZ() - Method in class com.imsl.stat.KolmogorovOneSample
-
Returns the normalized D statistic without the continuity correction applied.
- getZ() - Method in class com.imsl.stat.KolmogorovTwoSample
-
Returns the normalized D statistic without the continuity correction applied.
- gFunction(double, double, int, double[], double[]) - Method in class com.imsl.stat.EGARCH
-
Returns the value of \( g(\epsilon,\sigma) \) evaluated at the input
parameter values.
- gFunction(double, double, int, double[], double...) - Method in class com.imsl.stat.ExtendedGARCH
-
Abstract method for the function \(g(\epsilon_t,\sigma_t)\).
- gradient(double[], double[]) - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer.BlockGradObjective
-
- gradient(double[], double[]) - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer.GradObjective
-
- gradient(double[], double[]) - Method in interface com.imsl.math.MinConGenLin.Gradient
-
Public interface for the user-supplied function to compute the gradient at point x.
- gradient(double[], int, double[]) - Method in interface com.imsl.math.MinConNLP.Gradient
-
Computes the value of the gradient of the function at the given point.
- gradient(int, int, double[], boolean[], double, double[], double[], double[][]) - Method in interface com.imsl.math.MinConNonlin.Gradient
-
Deprecated.
Computes the value of the gradient of the function at the given point.
- gradient(double[], double[]) - Method in interface com.imsl.math.MinUnconMultiVar.Gradient
-
Public interface for the gradient of the multivariate function
to be minimized.
- gradient(double[]) - Method in class com.imsl.math.RadialBasis
-
Returns the gradient of the radial basis approximation at a point.
- gradient(double[], int, double[]) - Method in class com.imsl.test.example.math.MinConNLPEx2
-
Defines the gradients of the objective and the constraints.
- GradientBoosting - Class in com.imsl.datamining
-
Performs stochastic gradient boosting for a single response variable and
multiple predictor variables.
- GradientBoosting(double[][], int, PredictiveModel.VariableType[]) - Constructor for class com.imsl.datamining.GradientBoosting
-
Constructs a GradientBoosting object for a single response
variable and multiple predictor variables.
- GradientBoosting(PredictiveModel) - Constructor for class com.imsl.datamining.GradientBoosting
-
Constructs a gradient boosting object.
- GradientBoosting(GradientBoosting) - Constructor for class com.imsl.datamining.GradientBoosting
-
Constructs a copy of the input GradientBoosting predictive model.
- GradientBoosting.LossFunctionType - Enum in com.imsl.datamining
-
The loss function type as specified by the error measure.
- GradientBoostingEx1 - Class in com.imsl.test.example.datamining
-
Predicts a regression response
variable based on 6 predictor variables.
- GradientBoostingEx1() - Constructor for class com.imsl.test.example.datamining.GradientBoostingEx1
-
- GradientBoostingEx2 - Class in com.imsl.test.example.datamining
-
Predicts a binary response variable based on 4 predictor variables.
- GradientBoostingEx2() - Constructor for class com.imsl.test.example.datamining.GradientBoostingEx2
-
- GradientBoostingEx3 - Class in com.imsl.test.example.datamining
-
Selects the number of iterations using
cross-validation.
- GradientBoostingEx3() - Constructor for class com.imsl.test.example.datamining.GradientBoostingEx3
-
- GradientBoostingEx4 - Class in com.imsl.test.example.datamining
-
Uses an input model to set the configuration of
the base learner.
- GradientBoostingEx4() - Constructor for class com.imsl.test.example.datamining.GradientBoostingEx4
-
- GRID_HEXAGONAL - Static variable in class com.imsl.datamining.KohonenSOM
-
Indicates a hexagonal grid.
- GRID_RECTANGULAR - Static variable in class com.imsl.datamining.KohonenSOM
-
Indicates a rectangular grid.
- sampleStandardDeviation(double[]) - Static method in class com.imsl.stat.Summary
-
Returns the sample standard deviation of the given data set.
- sampleStandardDeviation(double[], double[]) - Static method in class com.imsl.stat.Summary
-
Returns the sample standard deviation of the given data set and associated weights.
- sampleVariance(double[]) - Static method in class com.imsl.stat.Summary
-
Returns the sample variance of the given data set.
- sampleVariance(double[], double[]) - Static method in class com.imsl.stat.Summary
-
Returns the sample variance of the given data set and associated weights.
- scalbn(double, int) - Static method in class com.imsl.math.IEEE
-
Returns \(x\,2^n\) computed by exponent
manipulation rather than by actually performing an
exponentiation or a multiplication.
- scaledK(double, double, int) - Static method in class com.imsl.math.Bessel
-
Evaluate a sequence of exponentially scaled modified Bessel functions of
the third kind with fractional order and real argument.
- ScaleFactorZeroException(int) - Constructor for exception com.imsl.stat.Dissimilarities.ScaleFactorZeroException
-
Constructs a ScaleFactorZeroException.
- ScaleFactorZeroException(int) - Constructor for exception com.imsl.stat.EmpiricalQuantiles.ScaleFactorZeroException
-
Constructs a ScaleFactorZeroException.
- ScaleFilter - Class in com.imsl.datamining.neural
-
Scales or unscales continuous data prior to its use in neural network
training, testing, or forecasting.
- ScaleFilter(int) - Constructor for class com.imsl.datamining.neural.ScaleFilter
-
Constructor for ScaleFilter.
- ScaleFilterEx1 - Class in com.imsl.test.example.datamining.neural
-
Applies scaling methods to three data sets.
- ScaleFilterEx1() - Constructor for class com.imsl.test.example.datamining.neural.ScaleFilterEx1
-
- SCHEFFE - Static variable in class com.imsl.stat.ANOVA
-
The Scheffe method
- SECOND_DERIVATIVE - Static variable in class com.imsl.math.CsInterpolate
-
- SECOND_GRAM_SCHMIDT - Static variable in class com.imsl.math.GenMinRes
-
Indicates the second Gram-Schmidt implementation method is to be used.
- SECOND_HOUSEHOLDER - Static variable in class com.imsl.math.GenMinRes
-
Indicates the second Householder implementation method is to be used.
- SelectionRegression - Class in com.imsl.stat
-
Selects the best multiple linear regression models.
- SelectionRegression(int) - Constructor for class com.imsl.stat.SelectionRegression
-
Constructs a new SelectionRegression object.
- SelectionRegression.NoVariablesException - Exception in com.imsl.stat
-
No Variables can enter the model.
- SelectionRegression.Statistics - Class in com.imsl.stat
-
Statistics contains statistics related to the regression
coefficients.
- SelectionRegressionEx1 - Class in com.imsl.test.example.stat
-
Finds the best regressions using the \(R^2\) criterion.
- SelectionRegressionEx1() - Constructor for class com.imsl.test.example.stat.SelectionRegressionEx1
-
- SelectionRegressionEx2 - Class in com.imsl.test.example.stat
-
Finds the best regressions using Mallow's \(C_p\) criterion.
- SelectionRegressionEx2() - Constructor for class com.imsl.test.example.stat.SelectionRegressionEx2
-
- selectSplitVariable(double[][], double[], double[], double[], double[], int[]) - Method in class com.imsl.datamining.decisionTree.ALACART
-
Selects the split variable for the present node using the
CARTTM method.
- selectSplitVariable(double[][], double[], double[], double[], double[], int[]) - Method in class com.imsl.datamining.decisionTree.C45
-
Selects the split variable for the present node using the C45 method.
- selectSplitVariable(double[][], double[], double[], double[], double[], int[]) - Method in class com.imsl.datamining.decisionTree.CHAID
-
Selects the split variable for the current node using CHAID (chi-square
automatic interaction detection).
- selectSplitVariable(double[][], double[], double[], double[], double[], int[]) - Method in class com.imsl.datamining.decisionTree.DecisionTree
-
Abstract method for selecting the next split variable and split
definition for the node.
- selectSplitVariable(double[][], double[], double[], double[], double[], int[]) - Method in class com.imsl.datamining.decisionTree.DecisionTreeInfoGain
-
Abstract method for selecting the next split variable and split
definition for the node.
- selectSplitVariable(double[][], double[], double[], double[], double[], int[]) - Method in class com.imsl.datamining.decisionTree.QUEST
-
Selects the split variable for the present node using the QUEST method.
- SEMIANNUAL - Static variable in class com.imsl.finance.Bond
-
Coupon payments are made semiannually (twice per year).
- set(int, int, Complex) - Method in class com.imsl.math.ComplexSparseMatrix
-
Sets the value of an element in the matrix.
- set(int, int, double) - Method in class com.imsl.math.SparseMatrix
-
Sets the value of an element in the matrix.
- setA0Flag(boolean) - Method in class com.imsl.stat.VectorAutoregression
-
Sets the flag to include the leading autoregressive coefficient matrix in
the model.
- setAbsoluteError(double) - Method in class com.imsl.math.HyperRectangleQuadrature
-
Sets the absolute error tolerance.
- setAbsoluteError(double) - Method in class com.imsl.math.Quadrature
-
Sets the absolute error tolerance.
- setAbsoluteError(double) - Method in class com.imsl.math.ZeroFunction
-
Deprecated.
Sets first stopping criterion.
- setAbsoluteError(double) - Method in class com.imsl.math.ZerosFunction
-
Sets the second convergence criterion.
- setAbsoluteErrorTolerances(double[]) - Method in class com.imsl.math.FeynmanKac
-
Sets the absolute error tolerances.
- setAbsoluteErrorTolerances(double) - Method in class com.imsl.math.FeynmanKac
-
Sets the absolute error tolerances.
- setAbsoluteFcnTol(double) - Method in class com.imsl.math.BoundedLeastSquares
-
Sets the absolute function tolerance.
- setAbsoluteTolerance(double) - Method in class com.imsl.math.NonlinLeastSquares
-
Set the absolute function tolerance.
- setAbsoluteTolerance(double) - Method in class com.imsl.stat.NonlinearRegression
-
Sets the absolute function tolerance.
- setAccuracy(double) - Method in class com.imsl.math.MinConNonlin
-
Deprecated.
Set the final accuracy.
- setAccuracy(double) - Method in class com.imsl.math.MinUncon
-
Set the required absolute accuracy in the final value returned by member function
computeMin.
- setAccuracyTolerance(double) - Method in class com.imsl.stat.ARMAOutlierIdentification
-
Sets the tolerance value controlling the accuracy of the parameter estimates.
- setAccuracyTolerance(double) - Method in class com.imsl.stat.AutoARIMA
-
Sets the tolerance value controlling the accuracy of the parameter
estimates.
- setActivation(Activation) - Method in class com.imsl.datamining.neural.Perceptron
-
Sets the activation function.
- setAdditive() - Method in class com.imsl.stat.HoltWintersExponentialSmoothing
-
Specifies the use of the Additive time series model.
- setAlpha(double) - Method in class com.imsl.stat.MultipleComparisons
-
Sets the significance level of the test
- setAR(double[]) - Method in class com.imsl.stat.ARMAMaxLikelihood
-
Sets the initial values for the autoregressive terms to the
p values in ar.
- setARConstants(double[]) - Method in class com.imsl.stat.VectorAutoregression
-
Sets the constants for the autoregressive model.
- setARLag(int) - Method in class com.imsl.stat.VectorAutoregression
-
Sets the autoregressive lag parameter.
- setARLags(int[]) - Method in class com.imsl.stat.ARMA
-
Sets the order of the autoregressive parameters.
- setArmaInfo(double, double[], double[], double) - Method in class com.imsl.stat.ARMA
-
Sets the ARMA_Info Object to previously determined values
- setARModel(int[]) - Method in class com.imsl.stat.VectorAutoregression
-
Sets the form of the autoregressive terms of the model.
- setAutoPruningFlag(boolean) - Method in class com.imsl.datamining.decisionTree.DecisionTree
-
Sets the flag to automatically prune the tree during the fitting
procedure.
- setBackcasting(int, double) - Method in class com.imsl.stat.ARMA
-
Sets backcasting option.
- setBackwardOrigin(int) - Method in class com.imsl.stat.ARAutoUnivariate
-
Sets the maximum backward origin used in calculating the forecasts.
- setBackwardOrigin(int) - Method in class com.imsl.stat.ARMA
-
Sets the maximum backward origin.
- setBackwardOrigin(int) - Method in class com.imsl.stat.ARMAMaxLikelihood
-
Sets the maximum backward origin.
- setBias(double) - Method in class com.imsl.datamining.neural.Perceptron
-
Sets the bias for this Perceptron.
- setBias(double[]) - Method in class com.imsl.math.CsTCB
-
Sets the bias values at the data points.
- setBindingThreshold(double) - Method in class com.imsl.math.MinConNLP
-
Set the binding threshold for constraints.
- setBound(double) - Method in class com.imsl.math.MinUncon
-
Set the amount by which X may be changed from its initial value, xguess.
- setBounds(double, double, double, double) - Method in class com.imsl.datamining.neural.ScaleFilter
-
Sets bounds to be used during bounded scaling and unscaling.
- setBounds(double, double) - Method in class com.imsl.math.ZerosFunction
-
Sets the closed interval in which to search for the roots.
- setBoundViolationBound(double) - Method in class com.imsl.math.MinConNLP
-
Set the amount by which bounds may be violated during numerical
differentiation.
- setCalculateVariableImportance(boolean) - Method in class com.imsl.datamining.BootstrapAggregation
-
Sets the boolean to calculate variable importance.
- setCalculateVariableImportance(boolean) - Method in class com.imsl.datamining.decisionTree.RandomTrees
-
Sets the boolean to calculate variable importance.
- setCensor(int[]) - Method in class com.imsl.stat.KaplanMeierECDF
-
Set flags to note right-censoring
- setCensorColumn(int) - Method in class com.imsl.stat.CategoricalGenLinModel
-
Sets the column number in x which contains the interval
type for each observation.
- setCensorColumn(int) - Method in class com.imsl.stat.KaplanMeierEstimates
-
Sets the column index of x containing the
optional censoring code for each observation.
- setCensorColumn(int) - Method in class com.imsl.stat.ProportionalHazards
-
Sets the column index of x containing the
optional censoring code for each observation.
- setCenter(double) - Method in class com.imsl.datamining.neural.ScaleFilter
-
Set the measure of center to be used during z-score scaling.
- setCenter(boolean) - Method in class com.imsl.stat.ARMA
-
Sets center option.
- setCenter(int) - Method in class com.imsl.stat.ARSeasonalFit
-
Controls centering of the differenced series.
- setCenter(boolean) - Method in class com.imsl.stat.VectorAutoregression
-
Sets the flag to center the data.
- setChiSquaredTestNull(double) - Method in class com.imsl.stat.NormOneSample
-
Sets the null hypothesis value for the chi-squared test.
- setChiSquaredTestNull(double) - Method in class com.imsl.stat.NormTwoSample
-
Sets the null hypothesis value for the chi-squared test.
- setClassCounts(double[]) - Method in class com.imsl.datamining.PredictiveModel
-
Sets the counts of each class of the response variable.
- setClassificationMethod(int) - Method in class com.imsl.stat.DiscriminantAnalysis
-
Specifies the classification method to be either reclassification or
leave-out-one.
- setClassificationVariableColumn(int[]) - Method in class com.imsl.stat.CategoricalGenLinModel
-
Initializes an index vector to contain the column numbers in x
that are classification variables.
- setClassLabels(String[]) - Method in class com.imsl.datamining.PredictiveModel
-
Sets the class names or labels for a categorical response variable.
- setClassLabels(int[]) - Method in class com.imsl.datamining.supportvectormachine.SVModel
-
Sets the class labels.
- setClassPenaltyWeights(int[], double[]) - Method in class com.imsl.datamining.supportvectormachine.SVClassification
-
Sets the class penalty weights.
- setClassProbabilities(double[][]) - Method in class com.imsl.datamining.PredictiveModel
-
Sets the class probabilities.
- setClassVarColumns(int[]) - Method in class com.imsl.stat.ProportionalHazards
-
Sets the column indices of x that are the classification variables.
- setClosedForm(boolean) - Method in class com.imsl.stat.distributions.MaximumLikelihoodEstimation
-
Sets the flag indicating whether or not the closed form solution should
be used.
- setColumnClass(int, Class) - Method in class com.imsl.io.AbstractFlatFile
-
Sets a column class.
- setColumnClass(int, Class) - Method in class com.imsl.io.FlatFile
-
Sets a column class.
- setColumnLabels(String[]) - Method in class com.imsl.math.PrintMatrixFormat
-
Turns on column labeling using the given labels.
- setColumnName(int, String) - Method in class com.imsl.io.AbstractFlatFile
-
Sets a column name.
- setColumnParser(int, FlatFile.Parser) - Method in class com.imsl.io.FlatFile
-
Sets the Parser for the specified column.
- setColumnPermutationMethod(int) - Method in class com.imsl.math.ComplexSuperLU
-
Specifies how to permute the columns of the input matrix.
- setColumnPermutationMethod(int) - Method in class com.imsl.math.SuperLU
-
Specifies how to permute the columns of the input matrix.
- setColumnSpacing(int) - Method in class com.imsl.math.PrintMatrix
-
Sets the number of spaces between columns.
- setCombineFunction(TimeSeriesOperations.Function) - Method in class com.imsl.stat.TimeSeriesOperations
-
Sets the combine function to a user supplied function.
- setCombineMethod(TimeSeriesOperations.CombineMethod) - Method in class com.imsl.stat.TimeSeriesOperations
-
Sets the method for combining synchronous time series values.
- setConfidence(double) - Method in class com.imsl.stat.ARAutoUnivariate
-
Sets the confidence level for calculating confidence limit deviations
returned from getDeviations.
- setConfidence(double) - Method in class com.imsl.stat.ARMA
-
Sets the confidence level for calculating confidence limit deviations
returned from getDeviations.
- setConfidence(double) - Method in class com.imsl.stat.ARMAMaxLikelihood
-
Sets the confidence level for calculating confidence limit deviations
returned from getDeviations().
- setConfidence(double) - Method in class com.imsl.stat.ARMAOutlierIdentification
-
Sets the confidence level for calculating confidence
limit deviations returned from getDeviations.
- setConfidence(double) - Method in class com.imsl.stat.AutoARIMA
-
Sets the confidence level for calculating confidence limit deviations
returned by getDeviations.
- setConfidence(double) - Method in class com.imsl.stat.HoltWintersExponentialSmoothing
-
Sets the confidence level to use in the calculation of the prediction
intervals.
- setConfidenceMean(double) - Method in class com.imsl.stat.NormOneSample
-
Sets the confidence level (in percent) for a two-sided
interval estimate of the mean.
- setConfidenceMean(double) - Method in class com.imsl.stat.NormTwoSample
-
Sets the confidence level (in percent) for a two-sided
confidence interval for the difference in means,
\(\mu_x - \mu_y\).
- setConfidenceMean(double) - Method in class com.imsl.stat.WelchsTTest
-
Sets the confidence level for a two-sided confidence interval for the
difference in population means, \(\mu_x - \mu_y\).
- setConfidenceVariance(double) - Method in class com.imsl.stat.NormOneSample
-
Sets the confidence level (in percent) for two-sided interval
estimate of the variances.
- setConfidenceVariance(double) - Method in class com.imsl.stat.NormTwoSample
-
Sets the confidence level for a two-sided interval
estimate for the common variance and for the ratios of variances.
- setConfiguration(PredictiveModel) - Method in class com.imsl.datamining.decisionTree.ALACART
-
- setConfiguration(PredictiveModel) - Method in class com.imsl.datamining.decisionTree.CHAID
-
Sets the configuration of PredictiveModel to that of the
input model.
- setConfiguration(PredictiveModel) - Method in class com.imsl.datamining.decisionTree.DecisionTree
-
Sets the configuration of PredictiveModel to that of the
input model.
- setConfiguration(PredictiveModel) - Method in class com.imsl.datamining.decisionTree.QUEST
-
Sets the configuration of PredictiveModel to that of the
input model.
- setConfiguration(PredictiveModel) - Method in class com.imsl.datamining.decisionTree.RandomTrees
-
Sets the configuration of RandomTrees to that of the input
model.
- setConfiguration(PredictiveModel) - Method in class com.imsl.datamining.PredictiveModel
-
Sets the configuration of PredictiveModel to that of the
input model.
- setConfiguration(PredictiveModel) - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine
-
Sets the configuration of PredictiveModel to that of the
input model.
- setConfiguration(PredictiveModel) - Method in class com.imsl.datamining.supportvectormachine.SVClassification
-
Sets the configuration to that of the input PredictiveModel.
- setConfiguration(PredictiveModel) - Method in class com.imsl.datamining.supportvectormachine.SVRegression
-
Sets the configuration to that of the input PredictiveModel.
- setConLevelMean(double) - Method in class com.imsl.stat.LinearRegression.CaseStatistics
-
Sets the confidence level for two-sided confidence intervals of the
population mean.
- setConLevelPred(double) - Method in class com.imsl.stat.LinearRegression.CaseStatistics
-
Sets the confidence level for two-sided prediction intervals.
- setConstant(double) - Method in class com.imsl.math.SparseLP
-
Sets the value of the constant term in the objective function.
- setConstant(double) - Method in class com.imsl.stat.ARMAMaxLikelihood
-
Sets the initial value for the constant term in the ARMA model.
- setConstantColumn(int) - Method in class com.imsl.stat.ProportionalHazards
-
Sets the column index of x containing the constant \(w_i\) to be added
to the linear response.
- setConstraintType(int[]) - Method in class com.imsl.math.DenseLP
-
Sets the types of general constraints in the matrix a.
- setConstraintType(int[]) - Method in class com.imsl.math.LinearProgramming
-
Deprecated.
Sets the types of general constraints in the matrix a.
- setConstraintType(int[]) - Method in class com.imsl.math.SparseLP
-
Sets the types of general constraints in the matrix A.
- setContinuity(double[]) - Method in class com.imsl.math.CsTCB
-
Sets the continuity values at the data points.
- setContinuousSmoothingValue(double) - Method in class com.imsl.datamining.NaiveBayesClassifier
-
Parameter for calculating smoothed estimates of conditional probabilities
for continuous attributes.
- setContractionCoefficient(double) - Method in class com.imsl.math.NelderMead
-
Sets the value for the contraction coefficient.
- setConvergenceCriterion1(double) - Method in class com.imsl.stat.FactorAnalysis
-
Sets the convergence criterion used to terminate the iterations.
- setConvergenceCriterion2(double) - Method in class com.imsl.stat.FactorAnalysis
-
Sets the convergence criterion used to switch to exact second derivatives.
- setConvergenceTol(double) - Method in class com.imsl.stat.ProportionalHazards
-
Set the convergence tolerance.
- setConvergenceTolerance(double) - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine
-
Sets the convergence tolerance.
- setConvergenceTolerance(double) - Method in class com.imsl.stat.ARAutoUnivariate
-
Sets the tolerance level used to determine convergence of the nonlinear
least-squares and maximum likelihood algorithms.
- setConvergenceTolerance(double) - Method in class com.imsl.stat.ARMA
-
Sets the tolerance level used to determine convergence of the nonlinear
least-squares algorithm.
- setConvergenceTolerance(double) - Method in class com.imsl.stat.ARMAEstimateMissing
-
Sets the covergence tolerance used by the AR_1 and
AR_P missing value estimation methods.
- setConvergenceTolerance(double) - Method in class com.imsl.stat.CategoricalGenLinModel
-
Set the convergence criterion.
- setCostComplexityValues(double[]) - Method in class com.imsl.datamining.decisionTree.DecisionTree
-
Sets the cost-complexity values.
- setCostMatrix(double[][]) - Method in class com.imsl.datamining.PredictiveModel
-
Specifies the cost matrix for a categorical response variable.
- setCovarianceComputation(int) - Method in class com.imsl.stat.DiscriminantAnalysis
-
Specifies the covariance matrix computation to be either pooled or
pooled, group.
- setCriterionOption(int) - Method in class com.imsl.stat.SelectionRegression
-
Sets the Criterion to be used.
- setCriticalValue(double) - Method in class com.imsl.stat.ARMAOutlierIdentification
-
Sets the critical value used as a threshold during outlier detection.
- setCriticalValue(double) - Method in class com.imsl.stat.AutoARIMA
-
Sets the critical value used as a threshold during outlier detection.
- setCutpoints(double[]) - Method in class com.imsl.stat.ChiSquaredTest
-
Sets the cutpoints.
- setDateColumnParser(int, String, Locale) - Method in class com.imsl.io.FlatFile
-
Creates for a pattern string and sets the Parser for the specified
column.
- setDateIncrement(int) - Method in class com.imsl.stat.TimeSeries
-
Sets the date increment in number of days.
- setDateIncrementInMillis(long) - Method in class com.imsl.stat.TimeSeries
-
Sets the date increment in milliseconds.
- setDates() - Method in class com.imsl.stat.TimeSeries
-
Sets the date array using the start date and date increment.
- setDates(Date[]) - Method in class com.imsl.stat.TimeSeries
-
Sets the date array equal to user supplied dates.
- setDegreesOfFreedom(int) - Method in class com.imsl.stat.FactorAnalysis
-
Sets the number of degrees of freedom.
- setDelta(double) - Method in class com.imsl.stat.ARMAOutlierIdentification
-
Sets the dampening effect parameter.
- setDelta(double) - Method in class com.imsl.stat.AutoARIMA
-
Sets the dampening effect parameter.
- setDerivtol(double) - Method in class com.imsl.math.MinUncon
-
Set the derivative tolerance used by member function computeMin to decide
if the current point is a local minimum.
- setDiagonalPivotThreshold(double) - Method in class com.imsl.math.ComplexSuperLU
-
Specifies the threshold used for a diagonal entry to be an
acceptable pivot.
- setDiagonalPivotThreshold(double) - Method in class com.imsl.math.SuperLU
-
Specifies the threshold used for a diagonal entry to be an
acceptable pivot.
- setDiagonalScalingMatrix(double[]) - Method in class com.imsl.math.BoundedLeastSquares
-
Sets the diagonal scaling matrix for the functions.
- setDifferenceOrders(int[]) - Method in class com.imsl.stat.AutoARIMA
-
Defines the orders of the periodic differences used in the determination
of the optimum model.
- setDifferencingMethods(int[]) - Method in class com.imsl.math.NumericalDerivatives
-
Sets the methods used to compute the derivatives
- setDifferentiationType(int) - Method in class com.imsl.math.MinConNLP
-
Set the type of numerical differentiation to be used.
- setDigits(double) - Method in class com.imsl.math.MinUnconMultiVar
-
Set the number of good digits in the function.
- setDigits(int) - Method in class com.imsl.math.NonlinLeastSquares
-
Set the number of good digits in the function.
- setDigits(int) - Method in class com.imsl.stat.NonlinearRegression
-
Sets the number of good digits in the residuals.
- setDInitial(int[][]) - Method in class com.imsl.stat.ARSeasonalFit
-
Sets the candidate values for selecting the optimum seasonal adjustment
prior to calling the compute method.
- setDiscreteSmoothingValue(double) - Method in class com.imsl.datamining.NaiveBayesClassifier
-
Parameter for calculating smoothed estimates of conditional probabilities
for discrete (nominal) attributes.
- setDiscriminationMethod(int) - Method in class com.imsl.stat.DiscriminantAnalysis
-
Specifies the discrimination method used to be either linear or quadratic
discrimination.
- setDistanceMethod(int) - Method in class com.imsl.stat.ClusterKNN
-
Sets the distance calculation method to be used.
- setDistanceMethod(int) - Method in class com.imsl.stat.Dissimilarities
-
Sets the method to be used in computing the dissimilarities or similarities.
- setDualInfeasibilityTolerance(double) - Method in class com.imsl.math.SparseLP
-
Sets the dual infeasibility tolerance.
- setDualTolerance(double) - Method in class com.imsl.math.NonNegativeLeastSquares
-
Sets the dual tolerance.
- setDummyMethod(int) - Method in class com.imsl.stat.RegressorsForGLM
-
Sets the dummy method.
- setEffects(int[], int[]) - Method in class com.imsl.stat.CategoricalGenLinModel
-
Initializes an index vector to contain the column numbers in x
associated with each effect.
- setEffects(int[][]) - Method in class com.imsl.stat.RegressorsForGLM
-
Set the effects.
- setEOM(boolean) - Method in class com.imsl.finance.DayCountBasis
-
Specifies whether to use the End-Of-Month rule.
- setEpochNumber(int) - Method in class com.imsl.datamining.neural.LeastSquaresTrainer
-
Sets the epoch number for the trainer.
- setEpochNumber(int) - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer
-
Sets the epoch number for the trainer.
- setEpochSize(int) - Method in class com.imsl.datamining.neural.EpochTrainer
-
Sets the number of randomly selected training patterns in stage 1 epoch.
- setEqualColumnWidths(boolean) - Method in class com.imsl.math.PrintMatrix
-
Force all of the columns to have the same width.
- setEqualWeights(double[][]) - Method in class com.imsl.datamining.neural.FeedForwardNetwork
-
Initializes network weights using equal weighting.
- setEquilibrate(boolean) - Method in class com.imsl.math.ComplexSuperLU
-
Specifies if input matrix A should be equilibrated
before factorization.
- setEquilibrate(boolean) - Method in class com.imsl.math.SuperLU
-
Determines if input matrix A should be equilibrated
before factorization.
- setError(QuasiNewtonTrainer.Error) - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer
-
Sets the function used to compute the network error.
- setError(double) - Method in class com.imsl.math.ZerosFunction
-
Sets the first convergence criterion.
- setErrorIncludeType(int) - Method in class com.imsl.stat.ANOVAFactorial
-
Sets error included type.
- setEstimationMethod(int) - Method in class com.imsl.stat.ARAutoUnivariate
-
Sets the estimation method used for estimating the final estimates for
the autoregressive coefficients.
- setEstimationMethod(int) - Method in class com.imsl.stat.ARMAEstimateMissing
-
Sets the method used for estimating the autoregressive coefficients for
missing value estimation methods AR_1 and AR_P.
- setExact(boolean) - Method in class com.imsl.stat.distributions.MaximumLikelihoodEstimation
-
- setExclude(boolean) - Method in class com.imsl.stat.ARSeasonalFit
-
Controls whether to exclude or replace the inital values in the
transformed series.
- setExpansionCoefficient(double) - Method in class com.imsl.math.NelderMead
-
Sets the value for the expansion coefficient.
- setExtendedLikelihoodObservations(int[]) - Method in class com.imsl.stat.CategoricalGenLinModel
-
Initializes a vector indicating which observations are to be included in
the extended likelihood.
- setExtrapolation(boolean) - Method in class com.imsl.math.Quadrature
-
If true, the epsilon-algorithm for extrapolation is enabled.
- setFactorLoadingEstimationMethod(int) - Method in class com.imsl.stat.FactorAnalysis
-
Sets the factor loading estimation method.
- setFalseConvergenceTolerance(double) - Method in class com.imsl.datamining.neural.LeastSquaresTrainer
-
Set the false convergence tolerance.
- setFalseConvergenceTolerance(double) - Method in class com.imsl.math.NonlinLeastSquares
-
Set the false convergence tolerance.
- setFalseConvergenceTolerance(double) - Method in class com.imsl.stat.NonlinearRegression
-
Sets the false convergence tolerance.
- setFetchDirection(int) - Method in class com.imsl.io.AbstractFlatFile
-
Gives a hint as to the direction in which the rows in this
ResultSet object is processed.
- setFetchSize(int) - Method in class com.imsl.io.AbstractFlatFile
-
Gives the JDBC driver a hint as to the number of rows that should be
fetched from the database when more rows are needed for this
ResultSet object.
- setFirstColumnNumber(int) - Method in class com.imsl.math.PrintMatrixFormat
-
Turns on column labeling with index numbers and sets the
index for the label of the first column.
- setFirstRowNumber(int) - Method in class com.imsl.math.PrintMatrixFormat
-
Turns on row labeling with index numbers and sets the
index for the label of the first row.
- setFixedParameterColumn(int) - Method in class com.imsl.stat.CategoricalGenLinModel
-
Sets the column number in x that contains a fixed parameter
for each observation that is added to the linear response prior to
computing the model parameter.
- setFloor(double) - Method in class com.imsl.math.ODE
-
Sets the value used in the norm computation.
- setForce(int) - Method in class com.imsl.stat.StepwiseRegression
-
Forces independent variables into the model based on their level assigned
from setlevels(int[]).
- setForcingTerm(FeynmanKac.ForcingTerm) - Method in class com.imsl.math.FeynmanKac
-
Sets the user-supplied method that computes approximations to the forcing term
\(\phi(x)\) and its derivative \(\partial \phi/\partial y\)
used in the FeynmanKac PDE.
- setFrequencies(double[]) - Method in class com.imsl.stat.ClusterKMeans
-
Sets the frequency for each observation.
- setFrequencies(double[]) - Method in class com.imsl.stat.Covariances
-
Sets the frequency for each observation.
- setFrequencies(double[]) - Method in class com.imsl.stat.TableMultiWay
-
Sets the frequencies for each observation in x.
- setFrequency(int[]) - Method in class com.imsl.stat.KaplanMeierECDF
-
Sets the frequency for each entry in t
- setFrequencyColumn(int) - Method in class com.imsl.stat.CategoricalGenLinModel
-
Sets the column number in x that contains the frequency of
response for each observation.
- setFrequencyColumn(int) - Method in class com.imsl.stat.KaplanMeierEstimates
-
Sets the column index of x containing the frequency of
response for each observation.
- setFrequencyColumn(int) - Method in class com.imsl.stat.ProportionalHazards
-
Sets the column index of x containing the frequency of
response for each observation.
- setFscale(double) - Method in class com.imsl.math.MinUnconMultiVar
-
Set the function scaling value for scaling the gradient.
- setFscale(double[]) - Method in class com.imsl.math.NonlinLeastSquares
-
Set the diagonal scaling matrix for the functions.
- setFunctionPrecision(double) - Method in class com.imsl.math.MinConNLP
-
Set the relative precision of the function evaluation routine.
- setFuzz(double) - Method in class com.imsl.stat.Ranks
-
Sets the fuzz factor used in determining ties.
- setFuzz(double) - Method in class com.imsl.stat.WilcoxonRankSum
-
Sets the nonnegative constant used to determine ties in computing
ranks in the combined samples.
- setGainCriteria(DecisionTreeInfoGain.GainCriteria) - Method in class com.imsl.datamining.decisionTree.DecisionTreeInfoGain
-
Specifies which criteria to use in gain calculations in order to
determine the best split at each node.
- setGaussLegendreDegree(int) - Method in class com.imsl.math.FeynmanKac
-
Sets the number of quadrature points used in the
Gauss-Legendre quadrature formula.
- setGoodDigit(int) - Method in class com.imsl.math.BoundedLeastSquares
-
Sets the number of good digits in the function.
- setGradientPrecision(double) - Method in class com.imsl.math.MinConNLP
-
Set the relative precision in gradients.
- setGradientTol(double) - Method in class com.imsl.math.BoundedLeastSquares
-
Sets the scaled gradient tolerance.
- setGradientTolerance(double) - Method in class com.imsl.datamining.neural.LeastSquaresTrainer
-
Set the gradient tolerance.
- setGradientTolerance(double) - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer
-
Set the gradient tolerance.
- setGradientTolerance(double) - Method in class com.imsl.math.MinUnconMultiVar
-
Sets the gradient tolerance.
- setGradientTolerance(double) - Method in class com.imsl.math.NonlinLeastSquares
-
Set the scaled gradient tolerance stopping critierion.
- setGradientTolerance(double) - Method in class com.imsl.stat.ARMAMaxLikelihood
-
Sets the tolerance for the convergence algorithm.
- setGradientTolerance(double) - Method in class com.imsl.stat.NonlinearRegression
-
Sets the gradient tolerance used to compute the gradient.
- setGridType(int) - Method in class com.imsl.datamining.KohonenSOM
-
Sets the grid type.
- setGuess(double[]) - Method in class com.imsl.math.BoundedLeastSquares
-
Sets the initial guess of the solution.
- setGuess(double[]) - Method in class com.imsl.math.GenMinRes
-
Set the initial guess of the solution.
- setGuess(double[]) - Method in class com.imsl.math.MinConGenLin
-
Sets an initial guess of the solution.
- setGuess(double[]) - Method in class com.imsl.math.MinConNLP
-
Set the initial guess of the minimum point of the input function.
- setGuess(double[]) - Method in class com.imsl.math.MinConNonlin
-
Deprecated.
Set the initial guess of the minimum point of the input function.
- setGuess(double) - Method in class com.imsl.math.MinUncon
-
Set the initial guess of the minimum point of the input function.
- setGuess(double[]) - Method in class com.imsl.math.MinUnconMultiVar
-
Set the initial guess of the minimum point of the input function.
- setGuess(double[]) - Method in class com.imsl.math.NelderMead
-
Sets an initial guess of the solution.
- setGuess(double[]) - Method in class com.imsl.math.NonlinLeastSquares
-
Set the initial guess of the minimum point of the input function.
- setGuess(double[]) - Method in class com.imsl.math.NonNegativeLeastSquares
-
Sets the initial guess.
- setGuess(double[]) - Method in class com.imsl.math.ZerosFunction
-
Sets the initial guess for the zeros.
- setGuess(double[]) - Method in class com.imsl.math.ZeroSystem
-
Sets the initial estimate of the root.
- setGuess(double...) - Method in class com.imsl.stat.distributions.MaximumLikelihoodEstimation
-
Sets the guess or starting values of the parameters.
- setGuess(double[]) - Method in class com.imsl.stat.NonlinearRegression
-
Sets the initial guess of the parameter values
- setHessianOption(boolean) - Method in class com.imsl.stat.ProportionalHazards
-
Set the option to have the Hessian and gradient be computed at the initial estimates.
- setHypothesis(WelchsTTest.Hypothesis) - Method in class com.imsl.stat.WelchsTTest
-
Sets the direction of the null/alternative test.
- setIhess(int) - Method in class com.imsl.math.MinUnconMultiVar
-
Set the Hessian initialization parameter.
- setIndex(int) - Method in class com.imsl.datamining.supportvectormachine.DataNode
-
Sets the index of the node.
- setIndex(int[]) - Method in class com.imsl.stat.Dissimilarities
-
Sets the indices of the rows (columns).
- setInfiniteEstimateMethod(int) - Method in class com.imsl.stat.CategoricalGenLinModel
-
Sets the method to be used for handling infinite estimates.
- setInitialComplex(double[][]) - Method in class com.imsl.math.NelderMead
-
Defines the initial complex.
- setInitialConditionalVariance(double) - Method in class com.imsl.stat.ExtendedGARCH
-
Sets the initial value of the conditional variance.
- setInitialData(FeynmanKac.InitialData) - Method in class com.imsl.math.FeynmanKac
-
Sets the user-supplied method for adjustment of initial data or as an opportunity
for output during the integration steps.
- setInitialEstimates(double[], double[]) - Method in class com.imsl.stat.ARMA
-
Sets preliminary estimates for the LEAST_SQUARES estimation
method.
- setInitialEstimates(int, double[]) - Method in class com.imsl.stat.CategoricalGenLinModel
-
Sets the initial parameter estimates option.
- setInitialEstimates(double[]) - Method in class com.imsl.stat.ProportionalHazards
-
Sets the initial parameter estimates.
- setInitialF(double[]) - Method in class com.imsl.math.NumericalDerivatives
-
Set the initial function values.
- setInitialStepsize(double) - Method in class com.imsl.math.FeynmanKac
-
Sets the starting stepsize for the integration.
- setInitialStepsize(double) - Method in class com.imsl.math.ODE
-
Sets the initial internal step size.
- setInitialTrustRegion(double) - Method in class com.imsl.datamining.neural.LeastSquaresTrainer
-
Sets the intial trust region.
- setInitialTrustRegion(double) - Method in class com.imsl.math.NonlinLeastSquares
-
Set the initial trust region radius.
- setInitialTrustRegion(double) - Method in class com.imsl.stat.NonlinearRegression
-
Sets the initial trust region radius.
- setInitialValues(double[][]) - Method in class com.imsl.stat.HoltWintersExponentialSmoothing
-
Sets the initial values for the level, trend, and
seasonal component sequences.
- setInsensitivityBand(double) - Method in class com.imsl.datamining.supportvectormachine.SVRegression
-
Sets the insensitivity band parameter, \(\epsilon\), in
the standard formulation of the SVM regression problem.
- setIntegrationMethod(int) - Method in class com.imsl.math.OdeAdamsGear
-
Indicates which integration method is to be used.
- setInternalScale() - Method in class com.imsl.math.BoundedLeastSquares
-
Sets the internal variable scaling option.
- setIterations(int) - Method in class com.imsl.datamining.KohonenSOMTrainer
-
Sets the number of iterations to be used for training.
- setIterationsArray(int[]) - Method in class com.imsl.datamining.GradientBoosting
-
Sets the array of different numbers of iterations.
- setIterativeRefinement(boolean) - Method in class com.imsl.math.ComplexSuperLU
-
Specifies whether to perform iterative refinement.
- setIterativeRefinement(boolean) - Method in class com.imsl.math.SuperLU
-
Specifies whether to perform iterative refinement.
- setJacobi(double[]) - Method in class com.imsl.math.ConjugateGradient
-
Defines a Jacobi preconditioner as the preconditioning matrix, that is,
M is the diagonal of A.
- setJacobian(BoundedLeastSquares.Jacobian) - Method in class com.imsl.math.BoundedLeastSquares
-
Sets the Jacobian.
- setKernel(Kernel) - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine
-
Sets the kernel to be used in the optimization.
- setKernelParameters(double[]) - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine
-
Sets the kernel parameters.
- setLeftEndTangent(double) - Method in class com.imsl.math.CsTCB
-
Sets the value of the tangent at the left endpoint.
- setLevels(int[]) - Method in class com.imsl.stat.StepwiseRegression
-
Sets the levels of priority for variables entering and leaving the
regression.
- setLossFunctionType(GradientBoosting.LossFunctionType) - Method in class com.imsl.datamining.GradientBoosting
-
Sets the loss function type for the gradient boosting algorithm.
- setLowerBound(double[]) - Method in class com.imsl.math.DenseLP
-
Sets the lower bound, \(x_l\), on the variables.
- setLowerBound(double[]) - Method in class com.imsl.math.LinearProgramming
-
Deprecated.
Sets the lower bound on the variables.
- setLowerBound(double[]) - Method in class com.imsl.math.SparseLP
-
Sets the lower bound on the variables.
- setLowerBounds(double[]) - Method in class com.imsl.stat.HoltWintersExponentialSmoothing
-
Sets the lower bounds for each of the smoothing parameters, (\(
\alpha\), \(\beta\), \(\gamma
\)).
- setLowerEndpointColumn(int) - Method in class com.imsl.stat.CategoricalGenLinModel
-
Sets the column number in x that contains the lower
endpoint of the observation interval for full interval and right
interval observations.
- setMA(double[]) - Method in class com.imsl.stat.ARMAMaxLikelihood
-
Sets the initial values for the moving average terms to the
q values in ma.
- setMALags(int[]) - Method in class com.imsl.stat.ARMA
-
Sets the order of the moving average parameters.
- setMatrixType(int) - Method in class com.imsl.math.PrintMatrix
-
Set matrix type.
- setMaxClass(int) - Method in class com.imsl.stat.ProportionalHazards
-
Sets an upper bound on the sum of the number of distinct values found among
the classification variables in x.
- setMaxDepth(int) - Method in class com.imsl.datamining.decisionTree.DecisionTree
-
Sets the maximum tree depth allowed.
- setMaxEvaluations(int) - Method in class com.imsl.math.ZerosFunction
-
Sets the maximum number of function evaluations allowed.
- setMaxFunctionEvaluations(int) - Method in class com.imsl.stat.ARMA
-
Sets the maximum number of function evaluations.
- setMaximumARLag(int) - Method in class com.imsl.stat.AutoARIMA
-
Defines the maximum AR lag used in the determination of the optimum (s,d)
combination of method compute(int[] arOrders, int[] maOrders).
- setMaximumBDFOrder(int) - Method in class com.imsl.math.FeynmanKac
-
Sets the maximum order of the BDF formulas.
- setMaximumBestFound(int) - Method in class com.imsl.stat.SelectionRegression
-
Sets the maximum number of best regressions to be found.
- setMaximumFunctionEvals(int) - Method in class com.imsl.math.BoundedLeastSquares
-
Sets the maximum number of function evaluations.
- setMaximumFunctionEvaluations(int) - Method in class com.imsl.math.OdeAdamsGear
-
Sets the maximum number of function evaluations of \(y'\) allowed.
- setMaximumGoodSaved(int) - Method in class com.imsl.stat.SelectionRegression
-
Sets the maximum number of good regressions for each subset size saved.
- setMaximumIteration(int) - Method in class com.imsl.math.BoundedLeastSquares
-
Sets the maximum number of iterations.
- setMaximumIteration(int) - Method in class com.imsl.math.LinearProgramming
-
Deprecated.
Sets the maximum number of iterations.
- setMaximumJacobianEvals(int) - Method in class com.imsl.math.BoundedLeastSquares
-
Sets the maximum number of Jacobian evaluations.
- setMaximumNumberOfFunctionEvaluations(int) - Method in class com.imsl.math.NelderMead
-
Sets the maximum number of allowed function iterations.
- setMaximumStepsize(double) - Method in class com.imsl.datamining.neural.LeastSquaresTrainer
-
Sets the maximum step size.
- setMaximumStepsize(double) - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer
-
Sets the maximum step size.
- setMaximumStepSize(double) - Method in class com.imsl.math.BoundedLeastSquares
-
Sets the maximum allowable step size.
- setMaximumStepsize(double) - Method in class com.imsl.math.FeynmanKac
-
Sets the maximum internal step size used by the integrator.
- setMaximumStepsize(double) - Method in class com.imsl.math.MinUnconMultiVar
-
Set the maximum allowable stepsize to use.
- setMaximumStepsize(double) - Method in class com.imsl.math.NonlinLeastSquares
-
Set the maximum allowable stepsize to use.
- setMaximumStepsize(double) - Method in class com.imsl.math.ODE
-
Sets the maximum internal step size.
- setMaximumStepsize(double) - Method in class com.imsl.math.OdeAdamsGear
-
Sets the maximum internal step size.
- setMaximumStepsize(double) - Method in class com.imsl.math.OdeRungeKutta
-
Sets the maximum internal step size.
- setMaximumSubsetSize(int) - Method in class com.imsl.stat.SelectionRegression
-
Sets the maximum subset size if \(R^2\) criterion is
used.
- setMaximumTime(long) - Method in class com.imsl.math.MinConNLP
-
Sets the maximum time allowed for the solve step.
- setMaximumTime(long) - Method in class com.imsl.math.NonNegativeLeastSquares
-
Sets the maximum time allowed for the solve step.
- setMaximumTrainingIterations(int) - Method in class com.imsl.datamining.neural.LeastSquaresTrainer
-
Sets the maximum number of iterations used by the nonlinear least
squares solver.
- setMaximumTrainingIterations(int) - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer
-
Sets the maximum number of iterations to use in a training.
- setMaxIterations(int) - Method in class com.imsl.math.BoundedVariableLeastSquares
-
Sets the maximum number of iterations.
- setMaxIterations(int) - Method in class com.imsl.math.ComplexEigen
-
Set the maximum number of iterations allowed.
- setMaxIterations(int) - Method in class com.imsl.math.ConjugateGradient
-
Sets the maximum number of iterations allowed.
- setMaxIterations(int) - Method in class com.imsl.math.Eigen
-
Set the maximum number of iterations allowed.
- setMaxIterations(int) - Method in class com.imsl.math.GenMinRes
-
Set the maximum number of iterations allowed.
- setMaxIterations(int) - Method in class com.imsl.math.MinConNLP
-
Set the maximum number of iterations allowed.
- setMaxIterations(int) - Method in class com.imsl.math.MinConNonlin
-
Deprecated.
Set the maximum number of iterations allowed.
- setMaxIterations(int) - Method in class com.imsl.math.MinUnconMultiVar
-
Set the maximum number of iterations allowed.
- setMaxIterations(int) - Method in class com.imsl.math.NonlinLeastSquares
-
Set the maximum number of iterations allowed.
- setMaxIterations(int) - Method in class com.imsl.math.NonNegativeLeastSquares
-
Sets the maximum number of iterations.
- setMaxIterations(int) - Method in class com.imsl.math.SparseLP
-
Sets the maximum number of iterations allowed for the primal-dual solver.
- setMaxIterations(int) - Method in class com.imsl.math.ZeroFunction
-
Deprecated.
Sets the maximum number of iterations allowed per root.
- setMaxIterations(int) - Method in class com.imsl.math.ZeroPolynomial
-
Sets the maximum number of iterations allowed.
- setMaxIterations(int) - Method in class com.imsl.math.ZeroSystem
-
Sets the maximum number of iterations allowed.
- setMaxIterations(int) - Method in class com.imsl.stat.ARAutoUnivariate
-
Sets the maximum number of iterations used for estimating the
autoregressive coefficients.
- setMaxIterations(int) - Method in class com.imsl.stat.ARMA
-
Sets the maximum number of iterations.
- setMaxIterations(int) - Method in class com.imsl.stat.ARMAEstimateMissing
-
Sets the maximum number of estimation iterations for missing value
estimation methods AR_1 and AR_P.
- setMaxIterations(int) - Method in class com.imsl.stat.ARMAMaxLikelihood
-
Sets the maximum number of iterations.
- setMaxIterations(int) - Method in class com.imsl.stat.CategoricalGenLinModel
-
Set the maximum number of iterations allowed.
- setMaxIterations(int) - Method in class com.imsl.stat.ClusterKMeans
-
Sets the maximum number of iterations.
- setMaxIterations(int) - Method in class com.imsl.stat.distributions.MaximumLikelihoodEstimation
-
Sets the maximum number of iterations for the solver to use.
- setMaxIterations(int) - Method in class com.imsl.stat.FactorAnalysis
-
Sets the maximum number of iterations in the iterative procedure.
- setMaxIterations(int) - Method in class com.imsl.stat.NonlinearRegression
-
Sets the maximum number of iterations allowed during optimization
- setMaxIterations(int) - Method in class com.imsl.stat.ProportionalHazards
-
Set the maximum number of iterations allowed.
- setMaxKrylovDim(int) - Method in class com.imsl.math.GenMinRes
-
Set the maximum Krylov subspace dimension, i.e., the maximum allowable
number of GMRES iterations allowed before restarting.
- setMaxlag(int) - Method in class com.imsl.stat.ARMAEstimateMissing
-
Sets the maximum number of autoregressive lags when method
AR_P is selected as the missing value estimation method.
- setMaxLag(int) - Method in class com.imsl.stat.VectorAutoregression
-
Sets the maximum lag.
- setMaxNodes(int) - Method in class com.imsl.datamining.decisionTree.DecisionTree
-
Sets the maximum number of nodes allowed in a tree.
- setMaxNumberOfCategories(int) - Method in class com.imsl.datamining.PredictiveModel
-
Sets the maximum number of categories allowed within categorical
predictor and response variables.
- setMaxNumberOfEvaluations(int) - Method in class com.imsl.stat.ExtendedGARCH
-
Sets the maximum number of function evaluations to allow in the
Nelder-Mead method.
- setMaxNumberOfIterations(int) - Method in class com.imsl.datamining.PredictiveModel
-
Sets the maximum number of iterations allowed for the fitting procedure
or training algorithm.
- setMaxOrder(int) - Method in class com.imsl.math.OdeAdamsGear
-
Sets the highest order formula to use of implicit METHOD_ADAMS type or METHOD_BDF type.
- setMaxSigma(double) - Method in class com.imsl.stat.GARCH
-
Sets the value of the upperbound on the first element (sigma) of the
array of returned estimated coefficients.
- setMaxStep(int) - Method in class com.imsl.stat.FactorAnalysis
-
Sets the maximum number of step halvings allowed during an iteration.
- setMaxSteps(int) - Method in class com.imsl.math.FeynmanKac
-
Sets the maximum number of internal steps allowed.
- setMaxSteps(int) - Method in class com.imsl.math.ODE
-
Sets the maximum number of internal steps allowed.
- setMaxStepsize(double) - Method in class com.imsl.stat.NonlinearRegression
-
Sets the maximum allowable stepsize.
- setMaxSubintervals(int) - Method in class com.imsl.math.Quadrature
-
Sets the maximum number of subintervals allowed.
- setMean(double) - Method in class com.imsl.stat.ARAutoUnivariate
-
Sets the estimate of the mean used for centering the time series z
.
- setMean(double) - Method in class com.imsl.stat.ARMA
-
Sets an initial estimate of the mean of the time series z.
- setMean(double) - Method in class com.imsl.stat.ARMAEstimateMissing
-
Sets the mean value used to center the series.
- setMean(double) - Method in class com.imsl.stat.ARMAMaxLikelihood
-
Sets the mean used for centering the series.
- setMean(double) - Method in class com.imsl.stat.AutoCorrelation
-
Estimate mean of the time series x.
- setMeanEstimate(double) - Method in class com.imsl.stat.ARMA
-
- setMeanModel(int[]) - Method in class com.imsl.stat.ExtendedGARCH
-
Sets the mean ARMA model specification as an integer array \(\{p,d,q\}\),
where \(p\) is the number of AR lags and \(q\) is the number of MA lags.
- setMeans(double[]) - Method in class com.imsl.stat.StepwiseRegression
-
Sets the means of the variables.
- setMeanX(double) - Method in class com.imsl.stat.CrossCorrelation
-
Estimate of the mean of time series x.
- setMeanX(double[]) - Method in class com.imsl.stat.MultiCrossCorrelation
-
Estimate of the mean of each channel of x.
- setMeanY(double) - Method in class com.imsl.stat.CrossCorrelation
-
Estimate of the mean of time series y.
- setMeanY(double[]) - Method in class com.imsl.stat.MultiCrossCorrelation
-
Estimate of the mean of each channel of y.
- setMergeCategoriesSignificanceLevel(double) - Method in class com.imsl.datamining.decisionTree.CHAID
-
Sets the significance level for merging categories.
- setMergeRule(TimeSeriesOperations.MergeRule) - Method in class com.imsl.stat.TimeSeriesOperations
-
Sets the rule that defines how two time series are merged.
- setMethod(int) - Method in class com.imsl.math.GenMinRes
-
Set the implementation method to be used.
- setMethod(int) - Method in class com.imsl.stat.ARMA
-
Sets the estimation method used for estimating the ARMA parameters.
- setMethod(int) - Method in class com.imsl.stat.ClusterHierarchical
-
Sets the clustering method to be used.
- setMethod(int) - Method in class com.imsl.stat.StepwiseRegression
-
Specifies the stepwise selection method, forward, backward, or stepwise
Regression.
- setMinCostComplexityValue(double) - Method in class com.imsl.datamining.decisionTree.DecisionTree
-
Sets the value of the minimum cost-complexity value.
- setMinimumSeparation(double) - Method in class com.imsl.math.ZerosFunction
-
Sets the minimum separation between accepted roots.
- setMinimumStepsize(double) - Method in class com.imsl.math.ODE
-
Sets the minimum internal step size.
- setMinObsPerChildNode(int) - Method in class com.imsl.datamining.decisionTree.DecisionTree
-
Sets the minimum number of observations that a child node must have in
order to split.
- setMinObsPerNode(int) - Method in class com.imsl.datamining.decisionTree.DecisionTree
-
Sets the minimum number of observations a node must have to allow a
split.
- setMissingTestYFlag(boolean) - Method in class com.imsl.datamining.GradientBoosting
-
Sets the flag indicating whether the test data is missing the response
variable data.
- setMissingValueMethod(int) - Method in class com.imsl.stat.ARMAEstimateMissing
-
Sets the current missing value estimation method to MEDIAN,
CUBIC_SPLINE, AR_1, or AR_P.
- setMissingValueMethod(int) - Method in class com.imsl.stat.Covariances
-
Sets the method used to exclude missing values in x from the computations,
where Double.NaN is interpreted as the missing value code.
- setModelIntercept(int) - Method in class com.imsl.stat.CategoricalGenLinModel
-
Sets the intercept option.
- setModelOrder(int) - Method in class com.imsl.stat.ANOVAFactorial
-
Sets the number of factors to be included in the highest-way interaction
in the model.
- setModelOrder(int) - Method in class com.imsl.stat.RegressorsForGLM
-
Sets the order of the model.
- setModelSelectionCriterion(int) - Method in class com.imsl.stat.AutoARIMA
-
Sets the model selection criterion.
- setMullerTolerance(double) - Method in class com.imsl.math.ZerosFunction
-
Sets the tolerance used during refinement to determine if Müllers method is
started.
- setMultiplier(int) - Method in class com.imsl.stat.Random
-
Sets the multiplier for a linear congruential random number generator.
- setMultiplierError(double) - Method in class com.imsl.math.MinConNLP
-
Set the error allowed in the multipliers.
- setMustEstimate(boolean) - Method in class com.imsl.stat.ExtendedGARCH
-
Sets the value of mustEstimateFlag.
- setMustFitModel(boolean) - Method in class com.imsl.datamining.PredictiveModel
-
Sets the flag of whether or not the model needs to be fit or re-estimated
because of a change in the data or configuration.
- setNameBounds(String) - Method in class com.imsl.io.MPSReader
-
Sets the name of the BOUNDS set to be used.
- setNameObjective(String) - Method in class com.imsl.io.MPSReader
-
Sets the name of the free row containing the objective.
- setNameRanges(String) - Method in class com.imsl.io.MPSReader
-
Sets the name of the RANGES set to be used.
- setNameRHS(String) - Method in class com.imsl.io.MPSReader
-
Sets the name of the RHS set to be used.
- setNeighborhoodType(int) - Method in class com.imsl.datamining.KohonenSOM
-
Sets the neighborhood type.
- setNoColumnLabels() - Method in class com.imsl.math.PrintMatrixFormat
-
Turns off column labels.
- setNonseasonalConstant() - Method in class com.imsl.stat.HoltWintersExponentialSmoothing
-
Remove the trend and the seasonal components and fit only the level
component.
- setNonseasonalTrend() - Method in class com.imsl.stat.HoltWintersExponentialSmoothing
-
Remove the seasonal component and fit only the level and trend
components.
- setNorm(int) - Method in class com.imsl.math.ODE
-
Sets the switch for determining the error norm.
- setNormTolerance(double) - Method in class com.imsl.math.NonNegativeLeastSquares
-
Sets the residual norm tolerance.
- setNoRowLabels() - Method in class com.imsl.math.PrintMatrixFormat
-
Turns off row labels.
- setNuFormulation(boolean) - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine
-
Sets the boolean to perform the \(\nu\)-formulation of
the optimization problem.
- setNumberEval(int) - Method in class com.imsl.stat.HoltWintersExponentialSmoothing
-
Sets the number of evaluations of the residual norm that are sampled to
obtain starting values for the smoothing parameters, (\(\alpha
\), \(\beta\), \(\gamma\)).
- setNumberForecasts(int) - Method in class com.imsl.stat.HoltWintersExponentialSmoothing
-
Sets the number of forecasts desired past the series data.
- setNumberFormat(NumberFormat) - Method in class com.imsl.math.PrintMatrixFormat
-
Sets the NumberFormat to be used in formatting double
and Complex entries.
- setNumberOfClasses(int) - Method in class com.imsl.datamining.PredictiveModel
-
Sets the number of distinct classes of the response variable.
- setNumberOfEpochs(int) - Method in class com.imsl.datamining.neural.EpochTrainer
-
Sets the number of epochs.
- setNumberOfIterations(int) - Method in class com.imsl.datamining.GradientBoosting
-
Sets the number of iterations.
- setNumberOfObservations(int) - Method in class com.imsl.stat.HoltWintersExponentialSmoothing
-
Sets the number of equally spaced series values.
- setNumberOfParameters(int) - Method in class com.imsl.stat.ExtendedGARCH
-
Sets the number of parameters in the Extended GARCH model.
- setNumberOfRandomFeatures(int) - Method in class com.imsl.datamining.decisionTree.DecisionTree
-
Sets the number of predictors in the random subset to select from at each
node.
- setNumberOfRandomFeatures(int) - Method in class com.imsl.datamining.decisionTree.RandomTrees
-
Sets the number of random features used in the splitting rules.
- setNumberOfRoots(int) - Method in class com.imsl.math.ZerosFunction
-
Sets the number of roots to be found.
- setNumberOfSampleFolds(int) - Method in class com.imsl.datamining.CrossValidation
-
Sets the number of folds to use in cross validation.
- setNumberOfSamples(int) - Method in class com.imsl.datamining.BootstrapAggregation
-
Sets the number of bootstrap samples.
- setNumberOfSurrogateSplits(int) - Method in class com.imsl.datamining.decisionTree.ALACART
-
Sets the number of surrogate splits.
- setNumberOfSurrogateSplits(int) - Method in interface com.imsl.datamining.decisionTree.DecisionTreeSurrogateMethod
-
Indicates the number of surrogate splits.
- setNumberOfThreads(int) - Method in class com.imsl.datamining.BootstrapAggregation
-
Sets the maximum number of java.lang.Thread instances that
may be used for parallel processing.
- setNumberOfThreads(int) - Method in class com.imsl.datamining.CrossValidation
-
Sets the maximum number of java.lang.Thread instances that
may be used for parallel processing.
- setNumberOfThreads(int) - Method in class com.imsl.datamining.decisionTree.RandomTrees
-
Sets the maximum number of java.lang.Thread instances that
may be used for parallel processing.
- setNumberOfThreads(int) - Method in class com.imsl.datamining.KohonenSOMTrainer
-
Sets the number of java.lang.Thread instances to be used for
parallel processing.
- setNumberOfThreads(int) - Method in class com.imsl.datamining.neural.EpochTrainer
-
Sets the number of java.lang.Thread instances to be used for
parallel processing.
- setNumberOfThreads(int) - Method in class com.imsl.math.BoundedLeastSquares
-
Sets the number of java.lang.Thread instances to be used for
parallel processing.
- setNumberOfThreads(int) - Method in class com.imsl.math.MinConGenLin
-
Sets the number of java.lang.Thread instances to be used for
parallel processing.
- setNumberOfThreads(int) - Method in class com.imsl.math.MinConNLP
-
Sets the number of java.lang.Thread instances to be used for
parallel processing.
- setNumberOfThreads(int) - Method in class com.imsl.math.MinUnconMultiVar
-
Sets the number of java.lang.Thread instances to be used for
parallel processing.
- setNumberOfThreads(int) - Method in class com.imsl.math.NelderMead
-
Sets the number of java.lang.Thread instances to be used for
parallel processing.
- setNumberOfThreads(int) - Method in class com.imsl.math.NonlinLeastSquares
-
Sets the number of java.lang.Thread instances to be used for
parallel processing.
- setNumberOfThreads(int) - Method in class com.imsl.stat.AutoCorrelation
-
Sets the number of java.lang.Thread instances to be used for
parallel processing.
- setNumberOfThreads(int) - Method in class com.imsl.stat.ExtendedGARCH
-
Sets the number of java.lang.Thread instances to be used for
parallel processing.
- setNumberOfTrees(int) - Method in class com.imsl.datamining.decisionTree.RandomTrees
-
Sets the number of trees to generate in the random forest.
- setNumericFactor(ComplexSparseCholesky.NumericFactor) - Method in class com.imsl.math.ComplexSparseCholesky
-
Sets the numeric Cholesky factor to use in solving a sparse complex
Hermitian positive definite system of linear equations \(Ax=b\).
- setNumericFactor(SparseCholesky.NumericFactor) - Method in class com.imsl.math.SparseCholesky
-
Sets the numeric Cholesky factor to use in solving of a sparse
positive definite system of linear equations \(Ax=b\).
- setNumericFactorizationMethod(int) - Method in class com.imsl.math.ComplexSparseCholesky
-
Defines the method used in the numerical factorization of the permuted
input matrix.
- setNumericFactorizationMethod(int) - Method in class com.imsl.math.SparseCholesky
-
Defines the method used in the numerical factorization of the permuted
input matrix.
- setNuParameter(double) - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine
-
Sets the value of \(\nu\) in the
\(\nu\)-formulation of the optimization problem.
- setObservationMax(int) - Method in class com.imsl.stat.CategoricalGenLinModel
-
Sets the maximum number of observations that can be handled in the
linear programming.
- setOptionalDistributionParameterColumn(int) - Method in class com.imsl.stat.CategoricalGenLinModel
-
Sets the column number in x that contains an optional
distribution parameter for each observation.
- setOrders(int[]) - Method in class com.imsl.stat.Difference
-
Sets the orders for the Difference object
- setOut(PrintStream) - Static method in class com.imsl.Warning
-
Reassigns the output stream.
- setOut(PrintStream) - Method in class com.imsl.WarningObject
-
Reassigns the output stream.
- setPageWidth(int) - Method in class com.imsl.math.PrintMatrix
-
Sets the page width.
- setParallelMode(ArrayList[]) - Method in class com.imsl.datamining.neural.LeastSquaresTrainer
-
Sets the trainer to be used in multi-threaded EpochTainer.
- setParallelMode(ArrayList[]) - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer
-
Sets the trainer to be used in multi-threaded EpochTainer.
- setParameterLowerBounds(double[]) - Method in class com.imsl.stat.ExtendedGARCH
-
Sets the lower bounds for the parameter estimates.
- setParameters(double...) - Method in class com.imsl.datamining.supportvectormachine.Kernel
-
Sets the kernel parameters.
- setParameters(double...) - Method in class com.imsl.datamining.supportvectormachine.PolynomialKernel
-
Sets the parameters for the polynomial kernel.
- setParameters(double...) - Method in class com.imsl.datamining.supportvectormachine.RadialBasisKernel
-
Sets the parameters for the radial basis kernel.
- setParameters(double...) - Method in class com.imsl.datamining.supportvectormachine.SigmoidKernel
-
Sets the parameters for the sigmoid kernel.
- setParameters(double[]) - Method in class com.imsl.stat.ExtendedGARCH
-
Sets the values of the parameters.
- setParameters(double[]) - Method in class com.imsl.stat.HoltWintersExponentialSmoothing
-
Sets the values of the smoothing parameters for the level (\(
\alpha\)), the trend (\(\beta\)), and the
seasonal (\(\gamma\)) component sequences.
- setParameterUpperBounds(double[]) - Method in class com.imsl.stat.ExtendedGARCH
-
Sets the upper bounds for the parameter estimates.
- setPenaltyBound(double) - Method in class com.imsl.math.MinConNLP
-
Set the universal bound for describing how much the unscaled penalty-term
may deviate from zero.
- setPercentage(double) - Method in class com.imsl.stat.SignTest
-
Sets the percentage percentile of the population.
- setPercentageFactor(double[]) - Method in class com.imsl.math.NumericalDerivatives
-
Sets the percentage factor for differencing
- setPercentages(double[]) - Method in class com.imsl.datamining.neural.UnsupervisedOrdinalFilter
-
Set the untransformed cumulative percentages used during encoding and decoding.
- setPercentile(double) - Method in class com.imsl.stat.SignTest
-
Sets the hypothesized percentile of the population.
- setPerformanceTuningParameters(int, int) - Method in class com.imsl.math.ComplexSuperLU
-
Sets performance tuning parameters.
- setPerformanceTuningParameters(int, int) - Method in class com.imsl.math.SuperLU
-
Sets performance tuning parameters.
- setPeriods(int[]) - Method in class com.imsl.stat.AutoARIMA
-
Defines the periods used in the determination of the optimum model.
- setPivotGrowth(boolean) - Method in class com.imsl.math.ComplexSuperLU
-
Specifies whether to compute the reciprocal pivot growth factor.
- setPivotGrowth(boolean) - Method in class com.imsl.math.SuperLU
-
Specifies whether to compute the reciprocal pivot growth factor.
- setPopulationSize(int) - Method in class com.imsl.stat.LifeTables
-
Sets the population size at the beginning of the first age interval in
requesting a population table.
- setPredictorIndex(int[]) - Method in class com.imsl.datamining.PredictiveModel
-
Sets the column indices of xy in which the predictor
variables reside.
- setPredictorTypes(PredictiveModel.VariableType[]) - Method in class com.imsl.datamining.PredictiveModel
-
Sets the VariableType objects that correspond to the
predictor data types in xy.
- setPreordering(int) - Method in class com.imsl.math.SparseLP
-
Sets the variant of the Minimum Degree Ordering (MDO) algorithm used in
the preordering of the normal equations or augmented system matrix.
- setPresolve(int) - Method in class com.imsl.math.SparseLP
-
Sets the presolve option.
- setPrimalInfeasibilityTolerance(double) - Method in class com.imsl.math.SparseLP
-
Sets the primal infeasibility tolerance.
- setPrintLevel(int) - Method in class com.imsl.datamining.BootstrapAggregation
-
Sets the print level for the predictive model.
- setPrintLevel(int) - Method in class com.imsl.datamining.PredictiveModel
-
Sets the print level for a PredictiveModel.
- setPrintLevel(int) - Method in class com.imsl.math.SparseLP
-
Sets the print level.
- setPrior(int) - Method in class com.imsl.stat.DiscriminantAnalysis
-
Specifies the prior probabilities to be calculated as either equal or
proportional priors.
- setPrior(double[]) - Method in class com.imsl.stat.DiscriminantAnalysis
-
Specifies user supplied prior probabilities.
- setPriorProbabilities(double[]) - Method in class com.imsl.datamining.PredictiveModel
-
Sets the prior probabilities for class membership.
- setProbability(boolean) - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine
-
Sets the boolean to calculate probability estimates.
- setPValueIn(double) - Method in class com.imsl.stat.StepwiseRegression
-
Defines the largest p-value for variables entering the model.
- setPValueOut(double) - Method in class com.imsl.stat.StepwiseRegression
-
Defines the smallest p-value for removing variables.
- setQ(double[][]) - Method in class com.imsl.stat.KalmanFilter
-
Sets the Q matrix.
- setRadialFunction(RadialBasis.Function) - Method in class com.imsl.math.RadialBasis
-
Sets the radial function.
- setRandom(Random) - Method in class com.imsl.datamining.neural.EpochTrainer
-
Sets the random number generator used to perturb the initial stage 1
guesses.
- setRandom(Random) - Method in class com.imsl.stat.Ranks
-
Sets the Random object.
- setRandomFeatureSelection(boolean) - Method in class com.imsl.datamining.decisionTree.DecisionTree
-
Sets the flag to select split variables from a random subset of the
features.
- setRandomObject(Random) - Method in class com.imsl.datamining.BootstrapAggregation
-
Sets a random object for the bootstrap random sampling scheme.
- setRandomObject(Random) - Method in class com.imsl.datamining.CrossValidation
-
Sets the random object to be used in the permutation of observation data.
- setRandomObject(Random) - Method in class com.imsl.datamining.PredictiveModel
-
Sets the random object to be used in the permutation of observation data.
- setRandomObject(Random) - Method in class com.imsl.math.NelderMead
-
Sets the random object to be used in the computation of the vertices of
the complex.
- setRandomObject(Random) - Method in class com.imsl.stat.distributions.MaximumLikelihoodEstimation
-
Sets the random object to be used in the Nelder-Mead method.
- setRandomObject(Random) - Method in class com.imsl.stat.ExtendedGARCH
-
Sets the random object.
- setRandomObject(Random) - Method in class com.imsl.stat.RandomSamples
-
Sets the seed for the random number generator.
- setRandomSamples(Random, Random) - Method in class com.imsl.datamining.neural.EpochTrainer
-
Sets the random number generators used to select random training
patterns in stage 1.
- setRandomWeights(double[][], Random) - Method in class com.imsl.datamining.neural.FeedForwardNetwork
-
Initializes network weights using random weights.
- setRange(double, double) - Method in class com.imsl.stat.ChiSquaredTest
-
Sets endpoints of the range of the distribution.
- setRangeOfX(double[]) - Method in class com.imsl.stat.distributions.ProbabilityDistribution
-
Sets the proper range of the random variable having the current
probability distribution.
- setRankTolerance(double) - Method in class com.imsl.math.NonNegativeLeastSquares
-
Sets the tolerance used for the incoming column rank deficient check.
- setRefinementType(int) - Method in class com.imsl.math.DenseLP
-
Set the type of refinement used.
- setReflectionCoefficient(double) - Method in class com.imsl.math.NelderMead
-
Sets the value for the reflection coefficient.
- setRegularizationParameter(double) - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine
-
Sets the regularization parameter, C.
- setRelativeError(double) - Method in class com.imsl.math.ConjugateGradient
-
Sets the relative error used for stopping the algorithm.
- setRelativeError(double) - Method in class com.imsl.math.GenMinRes
-
Set the stopping tolerance.
- setRelativeError(double) - Method in class com.imsl.math.HyperRectangleQuadrature
-
Sets the relative error tolerance.
- setRelativeError(double) - Method in class com.imsl.math.Quadrature
-
Sets the relative error tolerance.
- setRelativeError(double) - Method in class com.imsl.math.ZeroFunction
-
Deprecated.
Sets second stopping criterion is the relative error.
- setRelativeError(double) - Method in class com.imsl.math.ZeroSystem
-
Sets the relative error tolerance.
- setRelativeError(double) - Method in class com.imsl.stat.ARMA
-
Sets the stopping criterion for use in the nonlinear equation solver.
- setRelativeError(double) - Method in class com.imsl.stat.ARMAEstimateMissing
-
Sets the relative error used for the METHOD_OF_MOMENTS and
LEAST_SQUARES estimation methods.
- setRelativeError(double) - Method in class com.imsl.stat.ARMAOutlierIdentification
-
Sets the stopping criterion for use in the nonlinear equation solver.
- setRelativeError(double) - Method in class com.imsl.stat.AutoARIMA
-
Sets the stopping criterion for use in the nonlinear equation solver.
- setRelativeErrorTolerances(double[]) - Method in class com.imsl.math.FeynmanKac
-
Sets the relative error tolerances.
- setRelativeErrorTolerances(double) - Method in class com.imsl.math.FeynmanKac
-
Sets the relative error tolerances.
- setRelativeFcnTol(double) - Method in class com.imsl.math.BoundedLeastSquares
-
Sets the relative function tolerance.
- setRelativeOptimalityTolerance(double) - Method in class com.imsl.math.SparseLP
-
Sets the relative optimality tolerance.
- setRelativeTolerance(double) - Method in class com.imsl.datamining.neural.LeastSquaresTrainer
-
Sets the relative tolerance.
- setRelativeTolerance(double) - Method in class com.imsl.math.NonlinLeastSquares
-
Set the relative function tolerance.
- setRelativeTolerance(double) - Method in class com.imsl.stat.NonlinearRegression
-
Sets the relative function tolerance
- setResidualUpdating(int) - Method in class com.imsl.math.GenMinRes
-
Set the residual updating method to be used.
- setResponseColumn(int) - Method in class com.imsl.stat.KaplanMeierEstimates
-
Sets the column index of x containing the
response time for each observation.
- setResponseColumn(int) - Method in class com.imsl.stat.ProportionalHazards
-
Sets the column index of x containing the
response variable.
- setResponseColumnIndex(int) - Method in class com.imsl.datamining.PredictiveModel
-
Sets the column index in xy containing the response
variable.
- setRightEndTangent(double) - Method in class com.imsl.math.CsTCB
-
Sets the value of the tangent at the right endpoint.
- setRow(boolean) - Method in class com.imsl.stat.Dissimilarities
-
Identifies whether distances are computed between rows or columns of x.
- setRule(int) - Method in class com.imsl.math.Quadrature
-
Set the Gauss-Kronrod rule.
- setSample(double[]) - Method in class com.imsl.stat.distributions.MaximumLikelihoodEstimation
-
Sets the sample data to use in the estimation procedure.
- setSampleSizeProportion(double) - Method in class com.imsl.datamining.GradientBoosting
-
Sets the sample size proportion.
- setScale(double) - Method in class com.imsl.math.ODE
-
Sets the scaling factor.
- setScale(double[]) - Method in class com.imsl.stat.NonlinearRegression
-
Sets the scaling array for theta.
- setScale(boolean) - Method in class com.imsl.stat.VectorAutoregression
-
Sets the flag to scale the data.
- setScaledStepTol(double) - Method in class com.imsl.math.BoundedLeastSquares
-
Sets the scaled step tolerance.
- setScalingBound(double) - Method in class com.imsl.math.MinConNLP
-
Set the scaling bound for the internal automatic scaling of the
objective function.
- setScalingFactors(double[]) - Method in class com.imsl.math.NumericalDerivatives
-
Sets the scaling factors for the y values.
- setScalingOption(int) - Method in class com.imsl.stat.Dissimilarities
-
Sets the scaling option used if the L2_NORM, L1_NORM,
or INFINITY_NORM distance methods are specified.
- setScalingVariable(double) - Method in class com.imsl.math.MinConNonlin
-
Deprecated.
Set the scaling variable for the problem function.
- setScalingVector(double[]) - Method in class com.imsl.math.BoundedLeastSquares
-
Sets the scaling vector for the variables.
- setSeed(long) - Method in class com.imsl.stat.Random
-
Sets the seed.
- setSeriesIncrement(int) - Method in class com.imsl.stat.HoltWintersExponentialSmoothing
-
Sets the constant stride through the series data y.
- setSeriesValues(double[]) - Method in class com.imsl.stat.TimeSeries
-
Sets the values of a univariate time series and initializes the time
index.
- setSeriesValues(double[], int) - Method in class com.imsl.stat.TimeSeries
-
Sets the values of a multivariate time series and initializes the time
index array.
- setSeriesValues(double[][]) - Method in class com.imsl.stat.TimeSeries
-
Sets the values of the TimeSeries.
- setShrinkageParameter(double) - Method in class com.imsl.datamining.GradientBoosting
-
Sets the value of the shrinkage parameter.
- setShrinking(boolean) - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine
-
Sets the boolean to perform shrinking during optimization.
- setSolveMethod(int) - Method in class com.imsl.math.OdeAdamsGear
-
Indicates which method to use for solving the formula equations.
- setSolverMethod(ExtendedGARCH.Solver) - Method in class com.imsl.stat.ExtendedGARCH
-
Sets the solver to use for parameter estimation.
- setSorted(boolean) - Method in class com.imsl.stat.KaplanMeierEstimates
-
Sets the boolean to indicate that the column of response times in x are
already sorted.
- setSplitMergedCategoriesSigLevel(double) - Method in class com.imsl.datamining.decisionTree.CHAID
-
Sets the significance level for splitting previously merged categories.
- setSplitVariableSelectionCriterion(double) - Method in class com.imsl.datamining.decisionTree.QUEST
-
Sets the significance level for split variable selection.
- setSplitVariableSignificanceLevel(double) - Method in class com.imsl.datamining.decisionTree.CHAID
-
Sets the significance level for split variable selection.
- setSpread(double) - Method in class com.imsl.datamining.neural.ScaleFilter
-
Set the measure of spread to be used during z-score scaling.
- setSpread(double) - Method in class com.imsl.math.ZeroFunction
-
Deprecated.
Sets the spread.
- setSpreadTolerance(double) - Method in class com.imsl.math.ZeroFunction
-
Deprecated.
Sets the spread criteria for multiple zeros.
- setStartDate(Date) - Method in class com.imsl.stat.TimeSeries
-
Sets the start date of the series.
- setStep(double) - Method in class com.imsl.math.MinUncon
-
Set the stepsize to use when changing x.
- setStepControlMethod(int) - Method in class com.imsl.math.FeynmanKac
-
Sets the step control method used in the integration of the Feynman-Kac
PDE.
- setStepTolerance(double) - Method in class com.imsl.datamining.neural.LeastSquaresTrainer
-
Set the step tolerance used to step between weights.
- setStepTolerance(double) - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer
-
Sets the scaled step tolerance.
- setStepTolerance(double) - Method in class com.imsl.math.MinUnconMultiVar
-
Set the scaled step tolerance to use when changing x.
- setStepTolerance(double) - Method in class com.imsl.math.NonlinLeastSquares
-
Set the scaled step tolerance.
- setStepTolerance(double) - Method in class com.imsl.stat.NonlinearRegression
-
Sets the step tolerance used to step between two points.
- setStratifiedCrossValidation(boolean) - Method in class com.imsl.datamining.CrossValidation
-
Sets the flag to perform stratified cross-validation.
- setStratumColumn(int) - Method in class com.imsl.stat.KaplanMeierEstimates
-
Sets the column index of x containing the stratum number
for each observation.
- setStratumColumn(int) - Method in class com.imsl.stat.ProportionalHazards
-
Sets the column index of x containing the stratification
variable.
- setStratumRatio(double) - Method in class com.imsl.stat.ProportionalHazards
-
Set the ratio at which a stratum is split into two strata.
- setSymbolicFactor(ComplexSparseCholesky.SymbolicFactor) - Method in class com.imsl.math.ComplexSparseCholesky
-
Sets the symbolic Cholesky factor to use in solving a sparse complex
Hermitian positive definite system of linear equations \(Ax=b\).
- setSymbolicFactor(SparseCholesky.SymbolicFactor) - Method in class com.imsl.math.SparseCholesky
-
Sets the symbolic Cholesky factor to use in solving a sparse
positive definite system of linear equations \(Ax=b\).
- setSymmetricMode(boolean) - Method in class com.imsl.math.ComplexSuperLU
-
Specifies whether to use the symmetric mode.
- setSymmetricMode(boolean) - Method in class com.imsl.math.SuperLU
-
Specifies whether to use the symmetric mode.
- setTension(double[]) - Method in class com.imsl.math.CsTCB
-
Sets the tension values at the data points.
- setTestData(double[][], double[]) - Method in class com.imsl.datamining.BootstrapAggregation
-
Sets the test data to be predicted along with weights for each row in the
test data.
- setTestData(double[][]) - Method in class com.imsl.datamining.BootstrapAggregation
-
Sets the test data to be predicted.
- setTieBreaker(int) - Method in class com.imsl.stat.Ranks
-
Sets the tie breaker for Ranks.
- setTiesOption(int) - Method in class com.imsl.stat.ProportionalHazards
-
Sets the method for handling ties.
- setTimeBarrier(double) - Method in class com.imsl.math.FeynmanKac
-
Sets a barrier for the integration in the time direction.
- setTimeDependence(boolean[]) - Method in class com.imsl.math.FeynmanKac
-
Sets the time dependence of the coefficients, boundary conditions and
function \(\phi\) in the Feynman Kac equation.
- setTimeZone(TimeZone) - Method in class com.imsl.stat.TimeSeries
-
Sets the time zone for the time series to the given TimeZone.
- setTimeZone(int) - Method in class com.imsl.stat.TimeSeries
-
Sets the time zone for the time series to the time zone associated with
the given offset from GMT.
- setTimeZone(int, String) - Method in class com.imsl.stat.TimeSeries
-
Sets the time zone for the time series using the offset and String id.
- setTitle(String) - Method in class com.imsl.math.PrintMatrix
-
Sets matrix title
- setTolerance(double) - Method in class com.imsl.math.BoundedVariableLeastSquares
-
Sets the internal tolerance used to determine the relative linear
dependence of a column vector for a variable moved from its initial
value.
- setTolerance(double) - Method in class com.imsl.math.MinConGenLin
-
Sets the nonnegative tolerance on the first order conditions at the calculated solution.
- setTolerance(double) - Method in class com.imsl.math.MinConNLP
-
Set the desired precision of the solution.
- setTolerance(double) - Method in class com.imsl.math.NelderMead
-
Sets the convergence tolerance.
- setTolerance(double) - Method in class com.imsl.math.ODE
-
Sets the error tolerance.
- setTolerance(double) - Method in class com.imsl.stat.ARMAMaxLikelihood
-
Sets the tolerance for the convergence algorithm.
- setTolerance(double) - Method in class com.imsl.stat.CategoricalGenLinModel
-
Initializes the tolerance used in determining linear dependence.
- setTolerance(double) - Method in class com.imsl.stat.ExtendedGARCH
-
Sets the convergence tolerance.
- setTolerance(double) - Method in class com.imsl.stat.InverseCdf
-
Sets the tolerance to be used as the convergence criterion.
- setTolerance(double) - Method in class com.imsl.stat.KalmanFilter
-
Sets the tolerance used in determining linear dependence.
- setTolerance(double) - Method in class com.imsl.stat.StepwiseRegression
-
The tolerance used to detect linear dependence among the independent
variables.
- setTrainingData(double[][], int, PredictiveModel.VariableType[]) - Method in class com.imsl.datamining.GradientBoosting
-
Sets up the training data for the predictive model.
- setTrainingData(double[][], int, PredictiveModel.VariableType[]) - Method in class com.imsl.datamining.PredictiveModel
-
Sets up the training data for the predictive model.
- setTransformType(int) - Method in class com.imsl.stat.ClusterHierarchical
-
Sets the type of transformation.
- setTransitionMatrix(double[][]) - Method in class com.imsl.stat.KalmanFilter
-
Sets the transition matrix.
- setTrend(boolean) - Method in class com.imsl.stat.VectorAutoregression
-
Sets the flag to fit a trend parameter in the model.
- setTrustRegion(double) - Method in class com.imsl.math.BoundedLeastSquares
-
Sets the size of initial trust region radius.
- setTTestNull(double) - Method in class com.imsl.stat.NormOneSample
-
Sets the Null hypothesis value for t test for the mean.
- setTTestNull(double) - Method in class com.imsl.stat.NormTwoSample
-
Sets the Null hypothesis value for t-test for the mean.
- setTTestNull(double) - Method in class com.imsl.stat.WelchsTTest
-
Sets the null hypothesis value.
- setUnequalVariances(boolean) - Method in class com.imsl.stat.NormTwoSample
-
Specifies whether to return statistics based on equal or unequal
variances.
- setUpperBound(double[]) - Method in class com.imsl.math.DenseLP
-
Sets the upper bound, \(x_u\), on the variables.
- setUpperBound(double[]) - Method in class com.imsl.math.LinearProgramming
-
Deprecated.
Sets the upper bound on the variables.
- setUpperBound(double[]) - Method in class com.imsl.math.SparseLP
-
Sets the upper bound on the variables.
- setUpperBound(int) - Method in class com.imsl.stat.CategoricalGenLinModel
-
Sets the upper bound on the sum of the number of distinct values taken
on by each classification variable.
- setUpperBounds(double[]) - Method in class com.imsl.stat.HoltWintersExponentialSmoothing
-
Sets the upper bounds for each of the smoothing parameters, (\(
\alpha\), \(\beta\), \(\gamma
\)).
- setUpperEndpointColumn(int) - Method in class com.imsl.stat.CategoricalGenLinModel
-
Sets the column number in x that contains the upper
endpoint of the observation interval for full interval and left interval
observations.
- setUpperLimit(double[]) - Method in class com.imsl.math.DenseLP
-
Sets the upper limit of the constraints.
- setUpperLimit(double[]) - Method in class com.imsl.math.LinearProgramming
-
Deprecated.
Sets the upper limit of the constraints.
- setUpperLimit(double[]) - Method in class com.imsl.math.SparseLP
-
Sets the upper limit of the constraints that have both a lower and an
upper bound.
- setUseAnalytic(boolean) - Method in class com.imsl.stat.distributions.MaximumLikelihoodEstimation
-
Sets the flag indicating whether or not the PDF supplies the analytic
gradient and Hessian.
- setUseBackPropagation(boolean) - Method in class com.imsl.datamining.neural.QuasiNewtonTrainer
-
Sets whether or not to use the back propagation algorithm for gradient
calculations during network training.
- setUseRatio(boolean) - Method in class com.imsl.datamining.decisionTree.DecisionTreeInfoGain
-
Sets the flag to use or not use the gain ratio instead of the gain to
determine the best split.
- setValue(double) - Method in class com.imsl.datamining.neural.InputNode
-
Sets the value of this Node.
- setValue(double) - Method in class com.imsl.datamining.supportvectormachine.DataNode
-
Sets the value of the node.
- setValues(double[]) - Method in class com.imsl.stat.ExtendedGARCH
-
Sets the optional values needed in the specification for
gFunction.
- setVariableType(PredictiveModel.VariableType[]) - Method in class com.imsl.datamining.PredictiveModel
-
Sets the variable types for the data.
- setVarianceEstimationMethod(int) - Method in class com.imsl.stat.FactorAnalysis
-
Sets the variance estimation method.
- setVariances(double[]) - Method in class com.imsl.stat.FactorAnalysis
-
Sets the initial values of the unique variances.
- setVectorProducts(GenMinRes.VectorProducts) - Method in class com.imsl.math.GenMinRes
-
Sets the user-supplied functions for the inner product and,
optionally, the norm to be used in the Gram-Schmidt implementations.
- setViolationBound(double) - Method in class com.imsl.math.MinConNLP
-
Set the scalar which defines allowable constraint violations of the
final accepted result.
- setWarning(SQLWarning) - Method in class com.imsl.io.AbstractFlatFile
-
Sets a SQLWarning.
- setWarning(WarningObject) - Static method in class com.imsl.Warning
-
Sets a new WarningObject.
- setWeight(double) - Method in class com.imsl.datamining.neural.Link
-
Sets the weight for this Link.
- setWeights() - Method in class com.imsl.datamining.KohonenSOM
-
Sets the weights of the nodes using random numbers.
- setWeights(Random) - Method in class com.imsl.datamining.KohonenSOM
-
Sets the weights of the nodes using a Random object.
- setWeights(double[][][]) - Method in class com.imsl.datamining.KohonenSOM
-
Sets the weights of the nodes.
- setWeights(int, int, double[]) - Method in class com.imsl.datamining.KohonenSOM
-
Sets the weights of the node at (i, j) in the node grid.
- setWeights(double[]) - Method in class com.imsl.datamining.neural.FeedForwardNetwork
-
Sets the weights for the Links in this
Network.
- setWeights(double[]) - Method in class com.imsl.datamining.neural.Network
-
Sets the weights.
- setWeights(double[]) - Method in class com.imsl.datamining.PredictiveModel
-
Specifies the case weights.
- setWeights(double[]) - Method in class com.imsl.stat.ClusterKMeans
-
Sets the weight for each observation.
- setWeights(double[]) - Method in class com.imsl.stat.Covariances
-
Sets the weight for each observation.
- setWorkArraySize(double) - Method in class com.imsl.datamining.supportvectormachine.SupportVectorMachine
-
Sets the work array size.
- setXKnots(double[]) - Method in class com.imsl.math.Spline2DLeastSquares
-
Sets the knot sequences of the spline in the x-direction.
- setXlowerBound(double[]) - Method in class com.imsl.math.MinConNLP
-
Set the lower bounds on the variables.
- setXlowerBound(double[]) - Method in class com.imsl.math.MinConNonlin
-
Deprecated.
Set the lower bounds on the variables.
- setXOrder(int) - Method in class com.imsl.math.Spline2DLeastSquares
-
Sets the order of the spline in the x-direction.
- setXscale(double[]) - Method in class com.imsl.math.MinConNLP
-
Set the internal scaling of the variables.
- setXscale(double[]) - Method in class com.imsl.math.MinConNonlin
-
Deprecated.
Set the diagonal scaling matrix for the variables.
- setXscale(double[]) - Method in class com.imsl.math.MinUnconMultiVar
-
Set the diagonal scaling matrix for the variables.
- setXscale(double[]) - Method in class com.imsl.math.NonlinLeastSquares
-
Set the diagonal scaling matrix for the variables.
- setXScale(double) - Method in class com.imsl.math.ZerosFunction
-
Sets the scaling in the x-coordinate.
- setXupperBound(double[]) - Method in class com.imsl.math.MinConNLP
-
Set the upper bounds on the variables.
- setXupperBound(double[]) - Method in class com.imsl.math.MinConNonlin
-
Deprecated.
Set the upper bounds on the variables.
- setXWeights(double[]) - Method in class com.imsl.math.Spline2DLeastSquares
-
Sets the weights for the least-squares fit in the x-direction.
- setYKnots(double[]) - Method in class com.imsl.math.Spline2DLeastSquares
-
Sets the knot sequences of the spline in the y-direction.
- setYOrder(int) - Method in class com.imsl.math.Spline2DLeastSquares
-
Sets the order of the spline in the y-direction.
- setYWeights(double[]) - Method in class com.imsl.math.Spline2DLeastSquares
-
Sets the weights for the least-squares fit in the y-direction.
- setZDistribution(ExtendedGARCH.zDistribution) - Method in class com.imsl.stat.ExtendedGARCH
-
Sets the distribution to use for the random variable, \(z\), in the
extended GARCH model.
- setZeroCorrection(double) - Method in class com.imsl.datamining.NaiveBayesClassifier
-
Specifies the replacement value to be used for conditional probabilities
equal to zero.
- Sfun - Class in com.imsl.math
-
Collection of special functions.
- SfunEx1 - Class in com.imsl.test.example.math
-
Calculates various special functions.
- SfunEx1() - Constructor for class com.imsl.test.example.math.SfunEx1
-
- ShapiroWilkWTest() - Method in class com.imsl.stat.NormalityTest
-
Performs the Shapiro-Wilk W test.
- shortValue() - Method in class com.imsl.math.Complex
-
Returns the value of the real part as a short.
- sigma(double, double) - Method in interface com.imsl.math.FeynmanKac.PdeCoefficients
-
Returns the value of the \(\sigma\) coefficient at the given point.
- sigmaPrime(double, double) - Method in interface com.imsl.math.FeynmanKac.PdeCoefficients
-
Returns the value of \(\sigma^\prime=\frac{\partial \sigma(x,t)}{\partial x}\)
at the given point.
- SigmoidKernel - Class in com.imsl.datamining.supportvectormachine
-
Specifies the sigmoid kernel for support vector machines.
- SigmoidKernel() - Constructor for class com.imsl.datamining.supportvectormachine.SigmoidKernel
-
Constructor for the sigmoid kernel.
- SigmoidKernel(double, double) - Constructor for class com.imsl.datamining.supportvectormachine.SigmoidKernel
-
Constructs a sigmoid kernel.
- SigmoidKernel(SigmoidKernel) - Constructor for class com.imsl.datamining.supportvectormachine.SigmoidKernel
-
Constructs a copy of the input SigmoidKernel kernel.
- sign(double, double) - Static method in class com.imsl.math.Sfun
-
Returns the value of x with the sign of y.
- SignTest - Class in com.imsl.stat
-
Performs a sign test.
- SignTest(double[]) - Constructor for class com.imsl.stat.SignTest
-
Constructor for SignTest.
- SignTestEx1 - Class in com.imsl.test.example.stat
-
Performs the sign test on a small data set.
- SignTestEx1() - Constructor for class com.imsl.test.example.stat.SignTestEx1
-
- SignTestEx2 - Class in com.imsl.test.example.stat
-
Performs the sign test on a small data set.
- SignTestEx2() - Constructor for class com.imsl.test.example.stat.SignTestEx2
-
- sin(Complex) - Static method in class com.imsl.math.Complex
-
Returns the sine of a Complex.
- sin(double) - Static method in class com.imsl.math.JMath
-
Returns the sine of a double.
- SingularException(String) - Constructor for exception com.imsl.math.MinConNLP.SingularException
-
Constructs a SingularException object.
- SingularException(String, Object[]) - Constructor for exception com.imsl.math.MinConNLP.SingularException
-
Constructs a SingularException object.
- SingularException(String) - Constructor for exception com.imsl.stat.FactorAnalysis.SingularException
-
Constructs a SingularException object.
- SingularException(String, Object[]) - Constructor for exception com.imsl.stat.FactorAnalysis.SingularException
-
Constructs a SingularException object.
- SingularMatrixException(String) - Constructor for exception com.imsl.math.OdeAdamsGear.SingularMatrixException
-
Constructs a SingularMatrixException with the
specified detailed message.
- SingularMatrixException(String, Object[]) - Constructor for exception com.imsl.math.OdeAdamsGear.SingularMatrixException
-
Constructs a SingularMatrixException with the
specified detailed message.
- SingularMatrixException - Exception in com.imsl.math
-
The matrix is singular.
- SingularMatrixException() - Constructor for exception com.imsl.math.SingularMatrixException
-
- SingularPreconditionMatrixException(String) - Constructor for exception com.imsl.math.ConjugateGradient.SingularPreconditionMatrixException
-
Constructs a SingularPreconditionMatrixException object.
- SingularPreconditionMatrixException(String, Object[]) - Constructor for exception com.imsl.math.ConjugateGradient.SingularPreconditionMatrixException
-
Constructs a SingularPreconditionMatrixException object.
- sinh(Complex) - Static method in class com.imsl.math.Complex
-
Returns the hyperbolic sine of a Complex.
- sinh(double) - Static method in class com.imsl.math.Hyperbolic
-
Returns the hyperbolic sine of its argument.
- skewness(double[]) - Static method in class com.imsl.stat.Summary
-
Returns the skewness of the given data set.
- skewness(double[], double[]) - Static method in class com.imsl.stat.Summary
-
Returns the skewness of the given data set and associated weights.
- SKIP - Static variable in class com.imsl.math.NumericalDerivatives
-
Indicates a variable to be skipped.
- skip(int) - Method in class com.imsl.stat.Random
-
Resets the seed to skip ahead in the base linear congruential generator.
- sln(double, double, int) - Static method in class com.imsl.finance.Finance
-
Returns the depreciation of an asset using the straight line method.
- SOFTMAX - Static variable in interface com.imsl.datamining.neural.Activation
-
The softmax activation function.
- SolutionNotFoundException() - Constructor for exception com.imsl.math.QuadraticProgramming.SolutionNotFoundException
-
A solution was not found.
- SolutionNotFoundException(String) - Constructor for exception com.imsl.math.QuadraticProgramming.SolutionNotFoundException
-
A solution was not found.
- solve() - Method in class com.imsl.math.BoundedLeastSquares
-
Solves a nonlinear least-squares problem subject to bounds on the
variables using a modified Levenberg-Marquardt algorithm.
- solve() - Method in class com.imsl.math.BoundedVariableLeastSquares
-
Find the solution x to the problem for the current constraints.
- solve(double[]) - Method in class com.imsl.math.Cholesky
-
Solve Ax = b where A is a positive definite matrix with elements of type
double.
- solve(boolean) - Method in class com.imsl.math.ComplexEigen
-
Solves for the eigenvalues and (optionally) the eigenvectors
of a complex square matrix.
- solve(Complex[]) - Method in class com.imsl.math.ComplexLU
-
Return the solution x of the linear system Ax = b using the LU
factorization of A.
- solve(Complex[][], Complex[]) - Static method in class com.imsl.math.ComplexLU
-
Solve Ax = b for x using the LU factorization of A.
- solve(Complex[]) - Method in class com.imsl.math.ComplexSparseCholesky
-
Computes the solution of a sparse Hermitian positive definite
system of linear equations \(Ax=b\).
- solve(Complex[]) - Method in class com.imsl.math.ComplexSuperLU
-
Computation of the solution vector for the system
\( Ax = b\).
- solve(double[]) - Method in class com.imsl.math.ConjugateGradient
-
Solves a real symmetric positive or negative definite system \(Ax=b\) using
a conjugate gradient method with or without preconditioning.
- solve() - Method in class com.imsl.math.DenseLP
-
Solves the problem using an active set method.
- solve(double[][], boolean) - Method in class com.imsl.math.Eigen
-
Solves for the eigenvalues and (optionally) the eigenvectors
of a real square matrix.
- solve(double[]) - Method in class com.imsl.math.GenMinRes
-
Generate an approximate solution to \(Ax=b\) using the Generalized
Residual Method.
- solve() - Method in class com.imsl.math.LinearProgramming
-
Deprecated.
Solves the program using the revised simplex algorithm.
- solve(double[]) - Method in class com.imsl.math.LU
-
Return the solution x of the linear system Ax = b using the
LU factorization of A.
- solve(double[][], double[]) - Static method in class com.imsl.math.LU
-
Solve Ax = b for x using the LU factorization of A.
- solve() - Method in class com.imsl.math.MinConGenLin
-
Minimizes a general objective function subject to linear equality/inequality constraints.
- solve(MinConNLP.Function) - Method in class com.imsl.math.MinConNLP
-
Solve a general nonlinear programming problem using the successive
quadratic programming algorithm with a finite-difference gradient or
with a user-supplied gradient.
- solve(MinConNonlin.Function) - Method in class com.imsl.math.MinConNonlin
-
Deprecated.
Solve a general nonlinear programming problem using the successive
quadratic programming algorithm with a finite-difference gradient or
with a user-supplied gradient.
- solve() - Method in class com.imsl.math.NelderMead
-
Solves a minimization problem using a Nelder-Mead type algorithm.
- solve(NonlinLeastSquares.Function) - Method in class com.imsl.math.NonlinLeastSquares
-
Solve a nonlinear least-squares problem using a modified Levenberg-Marquardt
algorithm and a Jacobian.
- solve() - Method in class com.imsl.math.NonNegativeLeastSquares
-
Finds the solution to the problem for the current constraints.
- solve(double, double, double[]) - Method in class com.imsl.math.OdeAdamsGear
-
Integrates the ODE system from t to tEnd.
- solve(double, double, double[]) - Method in class com.imsl.math.OdeRungeKutta
-
Integrates the ODE system from t to tEnd.
- solve(double[]) - Method in class com.imsl.math.QR
-
Returns the solution to the least-squares problem Ax = b.
- solve(double[], double) - Method in class com.imsl.math.QR
-
Returns the solution to the least-squares problem Ax = b using
an input tolerance.
- solve(double[]) - Method in class com.imsl.math.SparseCholesky
-
Computes the solution of a sparse real symmetric positive definite system of linear
equations \(Ax=b\).
- solve() - Method in class com.imsl.math.SparseLP
-
Solves the sparse linear programming problem by an infeasible primal-dual
interior-point method.
- solve(double[]) - Method in class com.imsl.math.SuperLU
-
Computation of the solution vector for the system \( Ax = b\).
- solve(ZeroSystem.Function) - Method in class com.imsl.math.ZeroSystem
-
Solve a system of nonlinear equations using the modified Powell hybrid
algorithm
- solve() - Method in class com.imsl.stat.CategoricalGenLinModel
-
Returns the parameter estimates and associated statistics for a
CategoricalGenLinModel object.
- solve(NonlinearRegression.Function) - Method in class com.imsl.stat.NonlinearRegression
-
Solves the least squares problem and returns the regression coefficients.
- SOLVE_CHORD_COMPUTED_DIAGONAL - Static variable in class com.imsl.math.OdeAdamsGear
-
A chord method and a diagonal matrix based on a directional directive
- SOLVE_CHORD_COMPUTED_JACOBIAN - Static variable in class com.imsl.math.OdeAdamsGear
-
A chord or modified Newton method and a divided differences Jacobian
- SOLVE_CHORD_USER_JACOBIAN - Static variable in class com.imsl.math.OdeAdamsGear
-
A chord or modified Newton method and a user-supplied Jacobian
- SOLVE_FUNCTION_ITERATION - Static variable in class com.imsl.math.OdeAdamsGear
-
A function iteration or successive substitution method
- solveConjugateTranspose(Complex[]) - Method in class com.imsl.math.ComplexSuperLU
-
Computation of the solution vector for the system
\( A^Hx = b\).
- solveTranspose(Complex[]) - Method in class com.imsl.math.ComplexLU
-
Return the solution x of the linear system \(A^T x = b\).
- solveTranspose(Complex[]) - Method in class com.imsl.math.ComplexSuperLU
-
Computation of the solution vector for the system
\( A^Tx = b\).
- solveTranspose(double[]) - Method in class com.imsl.math.LU
-
Return the solution x of the linear system \(A^T = b\).
- solveTranspose(double[]) - Method in class com.imsl.math.SuperLU
-
Computation of the solution vector for the system \( A^Tx = b\).
- SomeConstraintsDiscardedException() - Constructor for exception com.imsl.math.DenseLP.SomeConstraintsDiscardedException
-
Some constraints were discarded because they were too linearly
dependent on other active constraints.
- SomeConstraintsDiscardedException(String) - Constructor for exception com.imsl.math.DenseLP.SomeConstraintsDiscardedException
-
Some constraints were discarded because they were too linearly
dependent on other active constraints.
- SomeConstraintsDiscardedException(String, Object[]) - Constructor for exception com.imsl.math.DenseLP.SomeConstraintsDiscardedException
-
Some constraints were discarded because they were too linearly
dependent on other active constraints.
- Sort - Class in com.imsl.stat
-
A collection of sorting functions.
- SORTED_AS_PER_OBSERVATIONS - Static variable in class com.imsl.stat.ProportionalHazards
-
Failures are assumed to occur in the same order as the
observations input in x.
- SortEx1 - Class in com.imsl.test.example.stat
-
Sorts an array and computes the permutation.
- SortEx1() - Constructor for class com.imsl.test.example.stat.SortEx1
-
- SortEx2 - Class in com.imsl.test.example.stat
-
Sorts a matrix using columns as keys.
- SortEx2() - Constructor for class com.imsl.test.example.stat.SortEx2
-
- SparseArray() - Constructor for class com.imsl.math.ComplexSparseMatrix.SparseArray
-
- SparseArray() - Constructor for class com.imsl.math.SparseMatrix.SparseArray
-
- SparseCholesky - Class in com.imsl.math
-
Sparse Cholesky factorization of a matrix of type SparseMatrix.
- SparseCholesky(SparseMatrix) - Constructor for class com.imsl.math.SparseCholesky
-
Constructs the matrix structure for the Cholesky factorization of
a sparse symmetric positive definite matrix of type SparseMatrix.
- SparseCholesky.NotSPDException - Exception in com.imsl.math
-
The matrix is not symmetric, positive definite.
- SparseCholesky.NumericFactor - Class in com.imsl.math
-
The numeric Cholesky factorization of a matrix.
- SparseCholesky.SymbolicFactor - Class in com.imsl.math
-
The symbolic Cholesky factorization of a matrix.
- SparseCholeskyEx1 - Class in com.imsl.test.example.math
-
SparseCholesky Example 1: Computes the Cholesky factorization of a sparse
matrix.
- SparseCholeskyEx1() - Constructor for class com.imsl.test.example.math.SparseCholeskyEx1
-
- SparseLP - Class in com.imsl.math
-
Solves a sparse linear programming problem by an infeasible primal-dual
interior-point method.
- SparseLP(SparseMatrix, double[], double[]) - Constructor for class com.imsl.math.SparseLP
-
Constructs a SparseLP object.
- SparseLP(MPSReader) - Constructor for class com.imsl.math.SparseLP
-
Constructs a SparseLP object using an MPSReader object.
- SparseLP(int[], int[], double[], double[], double[]) - Constructor for class com.imsl.math.SparseLP
-
Constructs a SparseLP object using Compressed Sparse Column
(CSC), or Harwell-Boeing format.
- SparseLP.CholeskyFactorizationAccuracyException - Exception in com.imsl.math
-
The Cholesky factorization failed because of accuracy problems.
- SparseLP.DiagonalWeightMatrixException - Exception in com.imsl.math
-
A diagonal element of the diagonal weight matrix is too small.
- SparseLP.DualInfeasibleException - Exception in com.imsl.math
-
The dual problem is infeasible.
- SparseLP.IllegalBoundsException - Exception in com.imsl.math
-
The lower bound is greater than the upper bound.
- SparseLP.IncorrectlyActiveException - Exception in com.imsl.math
-
One or more LP variables are falsely characterized by the internal
presolver.
- SparseLP.IncorrectlyEliminatedException - Exception in com.imsl.math
-
One or more LP variables are falsely characterized by the internal
presolver.
- SparseLP.InitialSolutionInfeasibleException - Exception in com.imsl.math
-
The initial solution for the one-row linear program is infeasible.
- SparseLP.PrimalInfeasibleException - Exception in com.imsl.math
-
The primal problem is infeasible.
- SparseLP.PrimalUnboundedException - Exception in com.imsl.math
-
The primal problem is unbounded.
- SparseLP.ProblemUnboundedException - Exception in com.imsl.math
-
The problem is unbounded.
- SparseLP.TooManyIterationsException - Exception in com.imsl.math
-
The maximum number of iterations has been exceeded.
- SparseLP.ZeroColumnException - Exception in com.imsl.math
-
A column of the constraint matrix has no entries.
- SparseLP.ZeroRowException - Exception in com.imsl.math
-
A row of the constraint matrix has no entries.
- SparseLPEx1 - Class in com.imsl.test.example.math
-
Solves a linear programming problem with sparse representation.
- SparseLPEx1() - Constructor for class com.imsl.test.example.math.SparseLPEx1
-
- SparseLPEx2 - Class in com.imsl.test.example.math
-
SparseLP Example 2: Solves a linear programming problem defined in an MPS
file.
- SparseLPEx2() - Constructor for class com.imsl.test.example.math.SparseLPEx2
-
- SparseMatrix - Class in com.imsl.math
-
Sparse matrix of type double.
- SparseMatrix(int, int) - Constructor for class com.imsl.math.SparseMatrix
-
Creates a new instance of SparseMatrix.
- SparseMatrix(SparseMatrix) - Constructor for class com.imsl.math.SparseMatrix
-
Creates a new instance of SparseMatrix which is a copy of another SparseMatrix.
- SparseMatrix(SparseMatrix.SparseArray) - Constructor for class com.imsl.math.SparseMatrix
-
Constructs a sparse matrix from a SparseArray object.
- SparseMatrix(int, int, int[][], double[][]) - Constructor for class com.imsl.math.SparseMatrix
-
Constructs a sparse matrix from SparseArray (Java Sparse Array) data.
- SparseMatrix.SparseArray - Class in com.imsl.math
-
The SparseArray class uses public fields to hold
the data for a sparse matrix in the Java Sparse Array format.
- SparseMatrixEx1 - Class in com.imsl.test.example.math
-
SparseMatrix Example 1: Computes the matrix product of two sparse
matrices.
- SparseMatrixEx1() - Constructor for class com.imsl.test.example.math.SparseMatrixEx1
-
- SparseMatrixEx2 - Class in com.imsl.test.example.math
-
SparseMatrix Example 2: Converts a matrix in market format to a sparse matrix
format.
- SparseMatrixEx2() - Constructor for class com.imsl.test.example.math.SparseMatrixEx2
-
- SparseMatrixEx2.MTXReader - Class in com.imsl.test.example.math
-
Reads a file containing Market format data.
- Spline - Class in com.imsl.math
-
Spline represents and evaluates univariate piecewise polynomial splines.
- Spline() - Constructor for class com.imsl.math.Spline
-
- Spline2D - Class in com.imsl.math
-
Represents and evaluates tensor-product splines.
- Spline2D() - Constructor for class com.imsl.math.Spline2D
-
- Spline2DInterpolate - Class in com.imsl.math
-
Computes a two-dimensional, tensor-product spline interpolant from
two-dimensional, tensor-product data.
- Spline2DInterpolate(double[], double[], double[][]) - Constructor for class com.imsl.math.Spline2DInterpolate
-
Constructor for Spline2DInterpolate.
- Spline2DInterpolate(double[], double[], double[][], int, int) - Constructor for class com.imsl.math.Spline2DInterpolate
-
Constructor for Spline2DInterpolate.
- Spline2DInterpolate(double[], double[], double[][], int, int, double[], double[]) - Constructor for class com.imsl.math.Spline2DInterpolate
-
Constructor for Spline2DInterpolate.
- Spline2DInterpolateEx1 - Class in com.imsl.test.example.math
-
Spline2DInterpolate Example 1: Computes a tensor-product spline
interpolant.
- Spline2DInterpolateEx1() - Constructor for class com.imsl.test.example.math.Spline2DInterpolateEx1
-
- Spline2DInterpolateEx2 - Class in com.imsl.test.example.math
-
Spline2DInterpolate Example 2: Computes the tensor-product spline interpolant.
- Spline2DInterpolateEx2() - Constructor for class com.imsl.test.example.math.Spline2DInterpolateEx2
-
- Spline2DInterpolateEx3 - Class in com.imsl.test.example.math
-
Computes a spline interpolant on a function and evaluates the partial
derivatives.
- Spline2DInterpolateEx3() - Constructor for class com.imsl.test.example.math.Spline2DInterpolateEx3
-
- Spline2DInterpolateEx4 - Class in com.imsl.test.example.math
-
Spline2DInterpolate Example 4: Integrates a tensor-product spline.
- Spline2DInterpolateEx4() - Constructor for class com.imsl.test.example.math.Spline2DInterpolateEx4
-
- Spline2DLeastSquares - Class in com.imsl.math
-
Computes a two-dimensional, tensor-product spline approximant using least squares.
- Spline2DLeastSquares(double[], double[], double[][], int, int) - Constructor for class com.imsl.math.Spline2DLeastSquares
-
Constructor for Spline2DLeastSquares.
- Spline2DLeastSquaresEx1 - Class in com.imsl.test.example.math
-
Computes a tensor-product cubic spline least squares fit to a function.
- Spline2DLeastSquaresEx1() - Constructor for class com.imsl.test.example.math.Spline2DLeastSquaresEx1
-
- sqrt(Complex) - Static method in class com.imsl.math.Complex
-
Returns the square root of a Complex,
with a branch cut along the negative real axis.
- sqrt(double) - Static method in class com.imsl.math.JMath
-
Returns the square root of a double.
- SQUASH - Static variable in interface com.imsl.datamining.neural.Activation
-
The squash activation function,
\(g(x) = \frac{x}{1+|x|}\)
- stack(TimeSeries) - Method in class com.imsl.stat.TimeSeriesOperations
-
Stacks or vectorizes the values of a multivariate TimeSeries.
- STANDARD_METHOD - Static variable in class com.imsl.math.ComplexSparseCholesky
-
Indicates that the method of George/Liu (1981) is used for numeric factorization.
- STANDARD_METHOD - Static variable in class com.imsl.math.SparseCholesky
-
Indicates the method of George/Liu (1981) will be used for numeric
factorization.
- standardDeviation(double[]) - Static method in class com.imsl.stat.Summary
-
Returns the population standard deviation of the given data set.
- standardDeviation(double[], double[]) - Static method in class com.imsl.stat.Summary
-
Returns the population standard deviation of the given data set and associated weights.
- StateChangeException(String) - Constructor for exception com.imsl.datamining.PredictiveModel.StateChangeException
-
Constructs a StateChangeException and issues the
specified message.
- StateChangeException(String, Object[]) - Constructor for exception com.imsl.datamining.PredictiveModel.StateChangeException
-
Constructs a StateChangeException with the specified
detail message.
- STD_DEV - Static variable in class com.imsl.stat.Dissimilarities
-
Indicates scaling by the standard deviation.
- STDEV_CORRELATION_MATRIX - Static variable in class com.imsl.stat.Covariances
-
Indicates correlation matrix except for the diagonal elements which are
the standard deviations
- STEPWISE_REGRESSION - Static variable in class com.imsl.stat.StepwiseRegression
-
Indicates stepwise regression.
- StepwiseRegression - Class in com.imsl.stat
-
Builds multiple linear regression models using forward selection, backward
selection, or stepwise selection.
- StepwiseRegression(double[][], double[]) - Constructor for class com.imsl.stat.StepwiseRegression
-
Creates a new instance of StepwiseRegression.
- StepwiseRegression(double[][], double[], double[]) - Constructor for class com.imsl.stat.StepwiseRegression
-
Creates a new instance of weighted StepwiseRegression.
- StepwiseRegression(double[][], double[], double[], double[]) - Constructor for class com.imsl.stat.StepwiseRegression
-
Creates a new instance of weighted StepwiseRegression using
observation frequencies.
- StepwiseRegression(double[][], int) - Constructor for class com.imsl.stat.StepwiseRegression
-
Creates a new instance of StepwiseRegression from a
user-supplied variance-covariance matrix.
- StepwiseRegression.CoefficientTTests - Class in com.imsl.stat
-
CoefficientTTests() contains statistics related to the
student-t test, for each regression coefficient.
- StepwiseRegression.CyclingIsOccurringException - Exception in com.imsl.stat
-
Cycling is occurring.
- StepwiseRegression.NoVariablesEnteredException - Exception in com.imsl.stat
-
No Variables can enter the model.
- StepwiseRegressionEx1 - Class in com.imsl.test.example.stat
-
Performs stepwise regression variable selection.
- StepwiseRegressionEx1() - Constructor for class com.imsl.test.example.stat.StepwiseRegressionEx1
-
- STRICT_LOWER_TRIANGULAR - Static variable in class com.imsl.math.PrintMatrix
-
This flag as the argument to setMatrixType, indicates that only the
strict lower triangular elements of the matrix are to be printed.
- STRICT_UPPER_TRIANGULAR - Static variable in class com.imsl.math.PrintMatrix
-
This flag as the argument to setMatrixType, indicates that only the
strict upper triangular elements of the matrix are to be printed.
- studentsT(double, double) - Static method in class com.imsl.stat.Cdf
-
Evaluates the Student's t cumulative probability distribution function.
- studentsT(double, double) - Static method in class com.imsl.stat.InvCdf
-
Returns the inverse of the Student's t cumulative probability distribution function.
- subtract(Complex, Complex) - Static method in class com.imsl.math.Complex
-
Returns the difference of two Complex objects, x-y.
- subtract(Complex, double) - Static method in class com.imsl.math.Complex
-
Returns the difference of a Complex object and a double, x-y.
- subtract(double, Complex) - Static method in class com.imsl.math.Complex
-
Returns the difference of a double and a Complex object, x-y.
- subtract(Complex[][], Complex[][]) - Static method in class com.imsl.math.ComplexMatrix
-
Subtract two Complex rectangular arrays, a - b.
- subtract(double[][], double[][]) - Static method in class com.imsl.math.Matrix
-
Subtract two rectangular arrays, a - b.
- subtract(Physical, Physical) - Static method in class com.imsl.math.Physical
-
Subtract two compatible Physical objects.
- suffix - Static variable in class com.imsl.math.Complex
-
String used in converting Complex to String.
- sum(int[]...) - Static method in class com.imsl.datamining.Apriori
-
Sums up the itemset frequencies.
- SUM_OF_SQUARES - Static variable in class com.imsl.datamining.neural.QuasiNewtonTrainer
-
Compute the sum of squares error.
- SUM_TO_ZERO - Static variable in class com.imsl.stat.RegressorsForGLM
-
The \(n-1\) dummies are defined in terms of the indicator variables
so that for balanced data, the usual summation restrictions are
imposed on the regression coefficients.
- Summary - Class in com.imsl.stat
-
Computes basic univariate statistics.
- Summary() - Constructor for class com.imsl.stat.Summary
-
Constructs a new summary statistics object.
- SummaryEx1 - Class in com.imsl.test.example.stat
-
Computes summary statistics for a small data set.
- SummaryEx1() - Constructor for class com.imsl.test.example.stat.SummaryEx1
-
- SumOfProbabilitiesNotOneException(String) - Constructor for exception com.imsl.datamining.PredictiveModel.SumOfProbabilitiesNotOneException
-
Constructs a SumOfProbabilitiesNotOneException and
issues the specified message
- SumOfProbabilitiesNotOneException(String, Object[]) - Constructor for exception com.imsl.datamining.PredictiveModel.SumOfProbabilitiesNotOneException
-
Constructs a SumOfProbabilitiesNotOneException with the
specified detail message.
- SumOfWeightsNegException(String) - Constructor for exception com.imsl.stat.DiscriminantAnalysis.SumOfWeightsNegException
-
The sum of the weights have become negative.
- SumOfWeightsNegException(String, Object[]) - Constructor for exception com.imsl.stat.DiscriminantAnalysis.SumOfWeightsNegException
-
The sum of the weights have become negative.
- SuperLU - Class in com.imsl.math
-
Computes the LU factorization of a general sparse matrix of type
SparseMatrix by a column method and solves the real sparse linear
system of equations \(Ax=b\).
- SuperLU(SparseMatrix) - Constructor for class com.imsl.math.SuperLU
-
Constructor for SuperLU.
- SuperLUEx1 - Class in com.imsl.test.example.math
-
SuperLU Example 1: Computes the LU factorization of a sparse matrix.
- SuperLUEx1() - Constructor for class com.imsl.test.example.math.SuperLUEx1
-
- SupportVectorMachine - Class in com.imsl.datamining.supportvectormachine
-
Abstract class for generating a support vector machine.
- SupportVectorMachine(double[][], int, PredictiveModel.VariableType[]) - Constructor for class com.imsl.datamining.supportvectormachine.SupportVectorMachine
-
Constructs a support vector machine for a single response variable and
multiple predictor variables.
- SupportVectorMachine(double[][], int, PredictiveModel.VariableType[], Kernel) - Constructor for class com.imsl.datamining.supportvectormachine.SupportVectorMachine
-
Constructs a support vector machine for a single response variable and
multiple predictor variables.
- SupportVectorMachine.ReflectiveOperationException - Exception in com.imsl.datamining.supportvectormachine
-
Class that wraps exceptions thrown by reflective operations in core
reflection.
- SupportVectorMachineEx1 - Class in com.imsl.test.example.datamining.supportvectormachine
-
Trains a support vector machine on Fisher's iris data.
- SupportVectorMachineEx1() - Constructor for class com.imsl.test.example.datamining.supportvectormachine.SupportVectorMachineEx1
-
- SupportVectorMachineEx2 - Class in com.imsl.test.example.datamining.supportvectormachine
-
Classifies Fisher's iris data after first selecting
parameter values using cross-validation.
- SupportVectorMachineEx2() - Constructor for class com.imsl.test.example.datamining.supportvectormachine.SupportVectorMachineEx2
-
- SupportVectorMachineEx3 - Class in com.imsl.test.example.datamining.supportvectormachine
-
Performs goodness-of-fit using the one-class support vector machine.
- SupportVectorMachineEx3() - Constructor for class com.imsl.test.example.datamining.supportvectormachine.SupportVectorMachineEx3
-
- SupportVectorMachineEx4 - Class in com.imsl.test.example.datamining.supportvectormachine
-
Compares a regression and a classification support vector machine
for predicting a categorical response.
- SupportVectorMachineEx4() - Constructor for class com.imsl.test.example.datamining.supportvectormachine.SupportVectorMachineEx4
-
- SupportVectorMachineEx5 - Class in com.imsl.test.example.datamining.supportvectormachine
-
Illustrates the use of case weights on the
training data.
- SupportVectorMachineEx5() - Constructor for class com.imsl.test.example.datamining.supportvectormachine.SupportVectorMachineEx5
-
- SVClassification - Class in com.imsl.datamining.supportvectormachine
-
Specifies a support vector machine for classification (SVC).
- SVClassification(double[][], int, PredictiveModel.VariableType[]) - Constructor for class com.imsl.datamining.supportvectormachine.SVClassification
-
Constructs a support vector machine for classification (SVC).
- SVClassification(double[][], int, PredictiveModel.VariableType[], Kernel) - Constructor for class com.imsl.datamining.supportvectormachine.SVClassification
-
Constructs a support vector machine for classification (SVC).
- SVClassification(SVClassification) - Constructor for class com.imsl.datamining.supportvectormachine.SVClassification
-
Copy constructor.
- SVD - Class in com.imsl.math
-
Singular Value Decomposition (SVD) of a
rectangular matrix of type double.
- SVD(double[][], double) - Constructor for class com.imsl.math.SVD
-
Construct the singular value decomposition of a rectangular matrix with a given tolerance.
- SVD(double[][]) - Constructor for class com.imsl.math.SVD
-
Construct the singular value decomposition of a rectangular matrix with
default tolerance.
- SVD.DidNotConvergeException - Exception in com.imsl.math
-
The iteration did not converge
- SVDEx1 - Class in com.imsl.test.example.math
-
Computes the SVD factorization of a matrix.
- SVDEx1() - Constructor for class com.imsl.test.example.math.SVDEx1
-
- SVModel - Class in com.imsl.datamining.supportvectormachine
-
Class to contain model estimates after training a support vector machine.
- SVModel() - Constructor for class com.imsl.datamining.supportvectormachine.SVModel
-
- SVOneClass - Class in com.imsl.datamining.supportvectormachine
-
Specifies a support vector machine for the one class problem.
- SVOneClass(double[][], int, PredictiveModel.VariableType[]) - Constructor for class com.imsl.datamining.supportvectormachine.SVOneClass
-
Constructs a one class support vector machine.
- SVOneClass(double[][], int, PredictiveModel.VariableType[], Kernel) - Constructor for class com.imsl.datamining.supportvectormachine.SVOneClass
-
Constructs a one class support vector machine.
- SVOneClass(SVOneClass) - Constructor for class com.imsl.datamining.supportvectormachine.SVOneClass
-
Constructs a copy of the input SVOneClass predictive model.
- SVRegression - Class in com.imsl.datamining.supportvectormachine
-
Specifies a support vector machine for regression (SVR).
- SVRegression(double[][], int, PredictiveModel.VariableType[]) - Constructor for class com.imsl.datamining.supportvectormachine.SVRegression
-
Constructs a support vector machine for regression (SVR).
- SVRegression(SVRegression) - Constructor for class com.imsl.datamining.supportvectormachine.SVRegression
-
Constructs a copy of the input SVRegression predictive model.
- SVRegression(double[][], int, PredictiveModel.VariableType[], Kernel) - Constructor for class com.imsl.datamining.supportvectormachine.SVRegression
-
Constructs a support vector machine for regression (SVR).
- syd(double, double, int, int) - Static method in class com.imsl.finance.Finance
-
Returns the depreciation of an asset using the sum-of-years digits method.
- SymEigen - Class in com.imsl.math
-
Computes the eigenvalues and eigenvectors of a real
symmetric matrix.
- SymEigen(double[][]) - Constructor for class com.imsl.math.SymEigen
-
Constructs the eigenvalues and the eigenvectors
for a real symmetric matrix.
- SymEigen(double[][], boolean) - Constructor for class com.imsl.math.SymEigen
-
Constructs the eigenvalues and (optionally) the eigenvectors
for a real symmetric matrix.
- SymEigenEx1 - Class in com.imsl.test.example.math
-
Computes the eigenvalues and eigenvectors of a symmetric matrix.
- SymEigenEx1() - Constructor for class com.imsl.test.example.math.SymEigenEx1
-