Index (com.irurueta:irurueta-numerical 1.4.0-SNAPSHOT API)

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
All Classes and Interfaces|All Packages|Constant Field Values|Serialized Form

A

a - Variable in class com.irurueta.numerical.fitting.MultiDimensionFitter: Estimated parameters of linear single dimensional function
a - Variable in class com.irurueta.numerical.fitting.MultiVariateFitter: Estimated parameters of linear single dimensional function.
a - Variable in class com.irurueta.numerical.fitting.SingleDimensionFitter: Estimated parameters of single dimensional function.
a - Variable in class com.irurueta.numerical.fitting.StraightLineFitter: Estimated "a" parameter of line following equation y = a + b*x
a - Variable in class com.irurueta.numerical.integration.DoubleExponentialRuleMatrixQuadrature: Lower limit of integration.
a - Variable in class com.irurueta.numerical.integration.DoubleExponentialRuleQuadrature: Lower limit of integration.
a - Variable in class com.irurueta.numerical.integration.MidPointMatrixQuadrature: Lower limit of integration.
a - Variable in class com.irurueta.numerical.integration.MidPointQuadrature: Lower limit of integration.
a - Variable in class com.irurueta.numerical.integration.TrapezoidalMatrixQuadrature: Lower limit of integration.
a - Variable in class com.irurueta.numerical.integration.TrapezoidalQuadrature: Lower limit of integration.
ACCURATE_MAXIMUM_LIKELIHOOD_ESTIMATOR - Enum constant in enum class com.irurueta.numerical.MaximumLikelihoodEstimatorMethod: MLE method that refines the histogram method by using Gaussian interpolation.
AccurateInterpolatingPolynomialEstimator - Class in com.irurueta.numerical.interpolation: Estimates coefficients of a polynomial passing through provided set of x and y points.
AccurateInterpolatingPolynomialEstimator() - Constructor for class com.irurueta.numerical.interpolation.AccurateInterpolatingPolynomialEstimator
AccurateMaximumLikelihoodEstimator - Class in com.irurueta.numerical: Class to estimate the most likely value from a series of samples assumed to be normally distributed.
AccurateMaximumLikelihoodEstimator() - Constructor for class com.irurueta.numerical.AccurateMaximumLikelihoodEstimator: Empty constructor.
AccurateMaximumLikelihoodEstimator(double[], double, boolean) - Constructor for class com.irurueta.numerical.AccurateMaximumLikelihoodEstimator: Constructor
AccurateMaximumLikelihoodEstimator(double, boolean) - Constructor for class com.irurueta.numerical.AccurateMaximumLikelihoodEstimator: Constructor.
AccurateMaximumLikelihoodEstimator(double, double, double[], double, boolean) - Constructor for class com.irurueta.numerical.AccurateMaximumLikelihoodEstimator: Constructor.
AccurateMaximumLikelihoodEstimator.EvaluatorListener - Class in com.irurueta.numerical: Internal class used by the BrentSingleOptimizer in order to evaluate the aggregation of Gaussians for all the samples in input data array with a high degree of precision.
add(Polynomial) - Method in class com.irurueta.numerical.polynomials.Polynomial: Adds another polynomial to this polynomial.
add(Polynomial, Polynomial) - Method in class com.irurueta.numerical.polynomials.Polynomial: Adds this polynomial to another one and stores the result into provided instance.
addAndReturnNew(Polynomial) - Method in class com.irurueta.numerical.polynomials.Polynomial: Adds this polynomial to another one and returns a new polynomial as a result.
adjustCovariance - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Indicates whether covariance must be adjusted or not.
adjustCovariance - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Indicates whether covariance must be adjusted or not.
adjustCovariance - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Indicates whether covariance must be adjusted or not.
adjustCovariance() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Adjusts covariance.
adjustCovariance() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Adjusts covariance.
adjustCovariance() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Adjusts covariance.
afunc - Variable in class com.irurueta.numerical.fitting.MultiDimensionLinearFitter: Array where results of function evaluations are stored
afunc - Variable in class com.irurueta.numerical.fitting.SingleDimensionLinearFitter: Array where results of function evaluations are stored.
ALF - Static variable in class com.irurueta.numerical.optimization.QuasiNewtonMultiOptimizer
allowLMSESolution - Variable in class com.irurueta.numerical.polynomials.estimators.LMSEPolynomialEstimator: Indicates if an LMSE (Least Mean Square Error) solution is allowed if more evaluations than the required minimum are provided.
alph - Variable in class com.irurueta.numerical.interpolation.KrigingInterpolator.Variogram: Estimated alpha of variogram.
alpha - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Curvature matrix.
alpha - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Curvature matrix.
alpha - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Curvature matrix.
amotry(Matrix, double[], double[], int, double, MultiDimensionFunctionEvaluatorListener) - Method in class com.irurueta.numerical.optimization.SimplexMultiOptimizer: Internal method to move simplex around.
ans - Variable in class com.irurueta.numerical.interpolation.CurveInterpolator: Result of interpolation.
aOrig - Variable in class com.irurueta.numerical.integration.LowerSquareRootMidPointMatrixQuadrature: Original lower bound of integration.
aOrig - Variable in class com.irurueta.numerical.integration.LowerSquareRootMidPointQuadrature: Original lower bound of integration.
areBracketEvaluationsAvailable() - Method in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Returns boolean indicating whether bracket evaluations are available for retrieval.
areDirectionsAvailable() - Method in class com.irurueta.numerical.optimization.PowellMultiOptimizer: Returns boolean indicating whether set of directions is available for retrieval.
areFunctionEvaluationsAvailable() - Method in class com.irurueta.numerical.optimization.SimplexMultiOptimizer: Returns boolean indicating whether function evaluations at simplex vertices are available for retrieval.
areMinMaxAvailable - Variable in class com.irurueta.numerical.MaximumLikelihoodEstimator: Boolean indicating whether minimum and maximum values in array are already available.
areMinMaxValuesAvailable() - Method in class com.irurueta.numerical.MaximumLikelihoodEstimator: Returns boolean indicating whether minimum and maximum values in array are already available.
arePolynomialParametersAvailable() - Method in class com.irurueta.numerical.roots.FirstDegreePolynomialRootsEstimator: Returns boolean indicating whether REAL polynomial parameters have been provided and is available for retrieval.
arePolynomialParametersAvailable() - Method in class com.irurueta.numerical.roots.PolynomialRootsEstimator: Returns boolean indicating whether polynomial parameters have been provided and are available for retrieval.
arePolynomialParametersAvailable() - Method in class com.irurueta.numerical.roots.SecondDegreePolynomialRootsEstimator: Returns boolean indicating whether REAL polynomial parameters have been provided and is available for retrieval.
arePolynomialParametersAvailable() - Method in class com.irurueta.numerical.roots.ThirdDegreePolynomialRootsEstimator: Returns boolean indicating whether REAL polynomial parameters have been provided and is available for retrieval.
areRootsAvailable() - Method in class com.irurueta.numerical.roots.PolynomialRootsEstimator: Returns boolean indicating whether roots have been estimated and are available for retrieval.
areRootsPolished() - Method in class com.irurueta.numerical.roots.LaguerrePolynomialRootsEstimator: Returns boolean indicating whether roots are refined after an initial estimation.
areWeightsAvailable() - Method in class com.irurueta.numerical.polynomials.estimators.WeightedPolynomialEstimator: Returns boolean indicating whether weights have been provided and are available for retrieval.
ARTIFACT_ID_KEY - Static variable in class com.irurueta.numerical.BuildInfo: Key to obtain artifactId of this library from properties file.
artifactId - Variable in class com.irurueta.numerical.BuildInfo: ArtifactId of this library.
as - Variable in class com.irurueta.numerical.ExponentialMatrixEstimator: Scaled version of provided input matrix.
ax - Variable in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Minimum evaluation point inside the bracket.
ax - Variable in class com.irurueta.numerical.PadeApproximantEstimator: Contains product of "a" and "x" matrices to iteratively improve LU solution.

B

b - Variable in class com.irurueta.numerical.fitting.StraightLineFitter: Estimated "b" parameter of line following equation y = a + b*X
b - Variable in class com.irurueta.numerical.integration.DoubleExponentialRuleMatrixQuadrature: Upper limit of integration.
b - Variable in class com.irurueta.numerical.integration.DoubleExponentialRuleQuadrature: Upper limit of integration.
b - Variable in class com.irurueta.numerical.integration.MidPointMatrixQuadrature: Upper limit of integration.
b - Variable in class com.irurueta.numerical.integration.MidPointQuadrature: Upper limit of integration.
b - Variable in class com.irurueta.numerical.integration.TrapezoidalMatrixQuadrature: Upper limit of integration.
b - Variable in class com.irurueta.numerical.integration.TrapezoidalQuadrature: Upper limit of integration.
BarycentricRationalInterpolator - Class in com.irurueta.numerical.interpolation: Computes barycentric rational interpolation.
BarycentricRationalInterpolator(double[], double[], int) - Constructor for class com.irurueta.numerical.interpolation.BarycentricRationalInterpolator: Constructor.
BaseInterpolator - Class in com.irurueta.numerical.interpolation: Abstract base class used by all interpolation implementations.
BaseInterpolator(double[], double[], int) - Constructor for class com.irurueta.numerical.interpolation.BaseInterpolator: Constructor.
BaseInterpolator(double[], double[], int, boolean) - Constructor for class com.irurueta.numerical.interpolation.BaseInterpolator: Constructor.
BaseRadialBasisFunctionInterpolator - Class in com.irurueta.numerical.interpolation: Base class for interpolation methods based on Radial Basis Functions to interpolate sparse points.
BaseRadialBasisFunctionInterpolator(Matrix) - Constructor for class com.irurueta.numerical.interpolation.BaseRadialBasisFunctionInterpolator: Constructor.
bestInliersData - Variable in class com.irurueta.numerical.robust.LMedSRobustEstimator: Data related to inliers found for best result.
bestInliersData - Variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Data related to inliers found for best result.
bestInliersData - Variable in class com.irurueta.numerical.robust.PROSACRobustEstimator: Data related to inliers found for best result.
bestInliersData - Variable in class com.irurueta.numerical.robust.RANSACRobustEstimator: Data related to inliers found for best result.
bestMedianResidual - Variable in class com.irurueta.numerical.robust.LMedSRobustEstimator.LMedSInliersData: Best median of error found so far taking into account all provided samples.
bestMedianResidual - Variable in class com.irurueta.numerical.robust.MSACRobustEstimator.MSACInliersData: Best median of error found so far taking into account all provided samples.
bestMedianResidual - Variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator.PROMedSInliersData: Best median of error found so far taking into account all provided samples.
bestNumberInliersData - Variable in class com.irurueta.numerical.robust.MSACRobustEstimator: Data related to solution producing the largest number of inliers.
bestResult - Variable in class com.irurueta.numerical.robust.LMedSRobustEstimator: Best solution that has been found so far during an estimation.
bestResult - Variable in class com.irurueta.numerical.robust.MSACRobustEstimator: Best solution that has been found so far during an estimation.
bestResult - Variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Best solution that has been found so far during an estimation.
bestResult - Variable in class com.irurueta.numerical.robust.PROSACRobustEstimator: Best solution that has been found so far during an estimation.
bestResult - Variable in class com.irurueta.numerical.robust.RANSACRobustEstimator: Best solution that has been found so far during an estimation.
bestResultInliersData - Variable in class com.irurueta.numerical.robust.MSACRobustEstimator: Data related to inliers found for best result.
bet - Variable in class com.irurueta.numerical.interpolation.KrigingInterpolator.Variogram: Beta of variogram.
beta - Variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: beta is the probability that a match is declared inlier by mistake, i.e. the ratio of the "inlier" surface by the total surface.
beta - Variable in class com.irurueta.numerical.robust.PROSACRobustEstimator: beta is the probability that a match is declared inlier by mistake, i.e. the ratio of the "inlier" surface by the total surface.
BicubicSpline2DInterpolator - Class in com.irurueta.numerical.interpolation: Computes bicubic spline interpolation in two dimensions.
BicubicSpline2DInterpolator(double[], double[], Matrix) - Constructor for class com.irurueta.numerical.interpolation.BicubicSpline2DInterpolator: Constructor.
BilinearInterpolator - Class in com.irurueta.numerical.interpolation: Interpolation in two dimensions.
BilinearInterpolator(double[], double[], Matrix) - Constructor for class com.irurueta.numerical.interpolation.BilinearInterpolator: Constructor.
bins - Variable in class com.irurueta.numerical.HistogramMaximumLikelihoodEstimator: Number of samples contained on each bin.
BisectionSingleRootEstimator - Class in com.irurueta.numerical.roots: This class searches for a single REAL root on a single dimension function (i.e. f(x) ).
BisectionSingleRootEstimator() - Constructor for class com.irurueta.numerical.roots.BisectionSingleRootEstimator: Empty constructor.
BisectionSingleRootEstimator(SingleDimensionFunctionEvaluatorListener, double, double, double) - Constructor for class com.irurueta.numerical.roots.BisectionSingleRootEstimator: Constructor.
borig - Variable in class com.irurueta.numerical.integration.UpperSquareRootMidPointMatrixQuadrature: Original upper bound of integration.
borig - Variable in class com.irurueta.numerical.integration.UpperSquareRootMidPointQuadrature: Original upper bound of integration.
BRACKET_EPS - Static variable in class com.irurueta.numerical.roots.BracketedSingleRootEstimator: Constant defining the value by which the largest bracket evaluation value is increased respect the minimum.
bracketAvailable - Variable in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Boolean indicating whether a bracket has been provided or computed.
bracketAvailable - Variable in class com.irurueta.numerical.roots.BracketedSingleRootEstimator: Boolean indicating whether a bracket has been computed and is available.
BracketedSingleOptimizer - Class in com.irurueta.numerical.optimization: This class searches for brackets of values containing a minimum in a single dimension function.
BracketedSingleOptimizer() - Constructor for class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Empty Constructor.
BracketedSingleOptimizer(double, double, double) - Constructor for class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Constructor.
BracketedSingleOptimizer(SingleDimensionFunctionEvaluatorListener, double, double, double) - Constructor for class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Constructor.
BracketedSingleRootEstimator - Class in com.irurueta.numerical.roots: Computes a root for a single dimension function inside a given bracket of values, in other words, root will only be searched within provided minimum and maximum evaluation points.
BracketedSingleRootEstimator() - Constructor for class com.irurueta.numerical.roots.BracketedSingleRootEstimator: Empty constructor.
BracketedSingleRootEstimator(SingleDimensionFunctionEvaluatorListener, double, double) - Constructor for class com.irurueta.numerical.roots.BracketedSingleRootEstimator: Constructor.
bracketEvaluationAvailable - Variable in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Boolean indicating whether function evaluation at bracket limits and middle point are available or not.
branch - Variable in class com.irurueta.numerical.BuildInfo: Build branch.
BRANCH_KEY - Static variable in class com.irurueta.numerical.BuildInfo: Key to obtain build branch from properties file.
brent - Variable in class com.irurueta.numerical.optimization.LineMultiOptimizer: Internal optimizer to find a minimum of a function along a line of input values.
BrentSingleOptimizer - Class in com.irurueta.numerical.optimization: This class uses Brent algorithm to determine a local function minimum for single dimension functions.
BrentSingleOptimizer() - Constructor for class com.irurueta.numerical.optimization.BrentSingleOptimizer: Empty constructor.
BrentSingleOptimizer(double, double, double, double) - Constructor for class com.irurueta.numerical.optimization.BrentSingleOptimizer: Constructor.
BrentSingleOptimizer(SingleDimensionFunctionEvaluatorListener, double, double, double, double) - Constructor for class com.irurueta.numerical.optimization.BrentSingleOptimizer: Constructor.
BrentSingleRootEstimator - Class in com.irurueta.numerical.roots: This class estimates the root of a single dimension continuous function using Brent's method.
BrentSingleRootEstimator() - Constructor for class com.irurueta.numerical.roots.BrentSingleRootEstimator: Empty constructor.
BrentSingleRootEstimator(SingleDimensionFunctionEvaluatorListener, double, double, double) - Constructor for class com.irurueta.numerical.roots.BrentSingleRootEstimator: Constructor.
BUILD_INFO_PROPERTIES - Static variable in class com.irurueta.numerical.BuildInfo: Location of properties file that contains build data.
BUILD_NUMBER_KEY - Static variable in class com.irurueta.numerical.BuildInfo: Key to obtain build number from properties file.
BUILD_TIMESTAMP_KEY - Static variable in class com.irurueta.numerical.BuildInfo: Key to obtain build timestamp from properties file.
buildDirections() - Method in class com.irurueta.numerical.optimization.PowellMultiOptimizer: Internal method to build or rebuild the set of directions if needed.
BuildInfo - Class in com.irurueta.numerical: Contains build data of this library.
BuildInfo() - Constructor for class com.irurueta.numerical.BuildInfo: Constructor.
buildNumber - Variable in class com.irurueta.numerical.BuildInfo: Build number.
buildTimestamp - Variable in class com.irurueta.numerical.BuildInfo: Build timestamp.
bx - Variable in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Middle evaluation point inside the bracket.

C

C - Static variable in class com.irurueta.numerical.optimization.GoldenSingleOptimizer: Golden ratio.
cache - Variable in class com.irurueta.numerical.DoubleFactorialEstimator: Cache of values.
cache - Variable in class com.irurueta.numerical.LongFactorialEstimator: Cache of values.
CACHE_SIZE - Static variable in class com.irurueta.numerical.DoubleFactorialEstimator: Cache size for factorial values that can be represented without overflowing with double precision.
CACHE_SIZE - Static variable in class com.irurueta.numerical.LongFactorialEstimator: Cache size for factorial values that can be represented without overflowing with long precision.
cast(QuadratureIntegrator<?>) - Static method in class com.irurueta.numerical.integration.QuadratureIntegrator: Casts integrator to a quadrature integrator without wildcard parameter. .
cast(QuadratureMatrixIntegrator<?>) - Static method in class com.irurueta.numerical.integration.QuadratureMatrixIntegrator: Cast integrator to a quadrature integrator without wildcard parameter.
cast(RombergIntegrator<?>) - Static method in class com.irurueta.numerical.integration.RombergIntegrator: Casts integrator to a quadrature integrator without wildcard parameter. .
cast(RombergMatrixIntegrator<?>) - Static method in class com.irurueta.numerical.integration.RombergMatrixIntegrator: Casts integrator to a quadrature integrator without wildcard parameter.
cast(SimpsonIntegrator<?>) - Static method in class com.irurueta.numerical.integration.SimpsonIntegrator: Casts integrator to a quadrature integrator without wildcard parameter.
cast(SimpsonMatrixIntegrator<?>) - Static method in class com.irurueta.numerical.integration.SimpsonMatrixIntegrator: Casts integrator to a quadrature integrator without wildcard parameter.
CGOLD - Static variable in class com.irurueta.numerical.optimization.BrentSingleOptimizer: Is the golden ratio.
CHI_SQUARED - Static variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Chi squared.
CHI_SQUARED - Static variable in class com.irurueta.numerical.robust.PROSACRobustEstimator: Chi squared.
chi2 - Variable in class com.irurueta.numerical.fitting.StraightLineFitter: Estimated chi square value.
chisq - Variable in class com.irurueta.numerical.fitting.MultiDimensionFitter: Estimated chi square value of input data
chisq - Variable in class com.irurueta.numerical.fitting.MultiVariateFitter: Estimated chi square value of input data.
chisq - Variable in class com.irurueta.numerical.fitting.SingleDimensionFitter: Estimated chi square value of input data.
cls - Variable in class com.irurueta.numerical.interpolation.CurveInterpolator: True indicates a closed curve, false indicates an open one.
columns - Variable in class com.irurueta.numerical.integration.DoubleExponentialRuleMatrixQuadrature: Number of columns of quadrature result.
columns - Variable in class com.irurueta.numerical.integration.MidPointMatrixQuadrature: Number of columns of quadrature result.
columns - Variable in class com.irurueta.numerical.integration.TrapezoidalMatrixQuadrature: Number of columns of quadrature result.
com.irurueta.numerical - package com.irurueta.numerical: This library contains packages for:
com.irurueta.numerical.fitting - package com.irurueta.numerical.fitting
com.irurueta.numerical.integration - package com.irurueta.numerical.integration
com.irurueta.numerical.interpolation - package com.irurueta.numerical.interpolation
com.irurueta.numerical.optimization - package com.irurueta.numerical.optimization
com.irurueta.numerical.polynomials - package com.irurueta.numerical.polynomials
com.irurueta.numerical.polynomials.estimators - package com.irurueta.numerical.polynomials.estimators
com.irurueta.numerical.robust - package com.irurueta.numerical.robust: This package contains robust estimators that can be used to discard outliers for cases where a model of the data is known (i.e. estimating lines, planes or many other geometric objects, etc.)
com.irurueta.numerical.roots - package com.irurueta.numerical.roots: This package contains classes to find function roots.
com.irurueta.numerical.signal.processing - package com.irurueta.numerical.signal.processing
commit - Variable in class com.irurueta.numerical.BuildInfo: Build commit.
COMMIT_KEY - Static variable in class com.irurueta.numerical.BuildInfo: Key to obtain build commit from properties file.
ComplexPolynomialEvaluator - Class in com.irurueta.numerical: Utility class to evaluate complex polynomials.
ComplexPolynomialEvaluator(Complex[]) - Constructor for class com.irurueta.numerical.ComplexPolynomialEvaluator: Constructor.
computeAndKeepInliers - Variable in class com.irurueta.numerical.robust.PROSACRobustEstimator: Indicates whether inliers must be computed and kept.
computeAndKeepInliers - Variable in class com.irurueta.numerical.robust.RANSACRobustEstimator: Indicates whether inliers must be computed and kept.
computeAndKeepResiduals - Variable in class com.irurueta.numerical.robust.PROSACRobustEstimator: Indicates whether residuals must be computed and kept.
computeAndKeepResiduals - Variable in class com.irurueta.numerical.robust.RANSACRobustEstimator: Indicates whether residuals must be computed and kept.
computeBracket() - Method in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Computes a bracket of values using the whole range of possible values as an initial guess.
computeBracket() - Method in class com.irurueta.numerical.roots.BracketedSingleRootEstimator: Starting at zero, this method expands the range (i.e. bracket of values) until a zero crossing is found where a root is present or until the bracket becomes unacceptably large, where an exception will be raised.
computeBracket(double) - Method in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Computes a bracket of values using provided value as a starting point, and assuming that bracket finishes at Double.MAX_VALUE.
computeBracket(double) - Method in class com.irurueta.numerical.roots.BracketedSingleRootEstimator: Starting from provided point, this method expands the range (i.e.
computeBracket(double, double) - Method in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Computes a bracket of values using provided values as a starting point.
computeBracket(double, double) - Method in class com.irurueta.numerical.roots.BracketedSingleRootEstimator: Starting from provided minimum and maximum values, this method expands the range (i.e. bracket of values) until a zero crossing is found where a root is present or until the bracket becomes unacceptably large, where an exception will be raised.
computeGaussian(double[], double) - Method in class com.irurueta.numerical.HistogramMaximumLikelihoodEstimator: Internal method to compute values of Gaussian vector assumed to be centered at provided value.
computeInliers(T, double, double[], LMedSRobustEstimatorListener<T>, Sorter<Double>, MSACRobustEstimator.MSACInliersData) - Static method in class com.irurueta.numerical.robust.MSACRobustEstimator: Computes inliers data for current iteration.
computeInliers(T, double, BitSet, int, PROSACRobustEstimatorListener<T>, double[]) - Static method in class com.irurueta.numerical.robust.PROSACRobustEstimator: Computes inliers data for current iteration.
computeInliers(T, int, double, boolean, double, double[], LMedSRobustEstimatorListener<T>, Sorter<Double>, PROMedSRobustEstimator.PROMedSInliersData) - Static method in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Computes inliers data for current iteration.
computeInliers(T, int, double, double[], LMedSRobustEstimatorListener<T>, Sorter<Double>, LMedSRobustEstimator.LMedSInliersData) - Static method in class com.irurueta.numerical.robust.LMedSRobustEstimator: Computes inliers data for current iteration.
computeIterations(double, int, double) - Static method in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Computes number of required iterations to achieve required confidence with current probability of inlier and sample subset size.
computeIterations(double, int, double) - Static method in class com.irurueta.numerical.robust.PROSACRobustEstimator: Computes number of required iterations to achieve required confidence with current probability of inlier and sample subset size.
computeMinMaxValues() - Method in class com.irurueta.numerical.MaximumLikelihoodEstimator: Internal method to compute minimum and maximum values of provided input data array.
computeRandomSubsets(int) - Method in class com.irurueta.numerical.robust.SubsetSelector: Computes a random subset of indices within range of number of samples to be used on robust estimators.
computeRandomSubsets(int, int[]) - Method in class com.irurueta.numerical.robust.FastRandomSubsetSelector: Computes a random subset of indices within range of number of samples to be used on robust estimators.
computeRandomSubsets(int, int[]) - Method in class com.irurueta.numerical.robust.SubsetSelector: Computes a random subset of indices within range of number of samples to be used on robust estimators.
computeRandomSubsetsInRange(int, int, int, boolean) - Method in class com.irurueta.numerical.robust.SubsetSelector: Computes a random subset of indices within provided range of positions to be used on robust estimators.
computeRandomSubsetsInRange(int, int, int, boolean, int[]) - Method in class com.irurueta.numerical.robust.FastRandomSubsetSelector: Computes a random subset of indices within provided range of positions to be used on robust estimators.
computeRandomSubsetsInRange(int, int, int, boolean, int[]) - Method in class com.irurueta.numerical.robust.SubsetSelector: Computes a random subset of indices within provided range of positions to be used on robust estimators.
computeResidual(T, int) - Method in interface com.irurueta.numerical.robust.LMedSRobustEstimatorListener: Computes residual for sample located at i-th position using estimation on current iteration.
computeSortedQualityIndices(double[]) - Static method in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Obtains indices of samples corresponding to samples ordered in descending quality scores.
computeSortedQualityIndices(double[]) - Static method in class com.irurueta.numerical.robust.PROSACRobustEstimator: Obtains indices of samples corresponding to samples ordered in descending quality scores.
confidence - Variable in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Amount of confidence expressed as a value between 0.0 and 1.0 (which is equivalent to 100%).
confidence - Variable in class com.irurueta.numerical.robust.LMedSRobustEstimator: Amount of confidence expressed as a value between 0 and 1.0 (which is equivalent to 100%).
confidence - Variable in class com.irurueta.numerical.robust.MSACRobustEstimator: Amount of confidence expressed as a value between 0 and 1.0 (which is equivalent to 100%).
confidence - Variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Amount of confidence expressed as a value between 0 and 1.0 (which is equivalent to 100%).
confidence - Variable in class com.irurueta.numerical.robust.PROSACRobustEstimator: Amount of confidence expressed as a value between 0 and 1.0 (which is equivalent to 100%).
confidence - Variable in class com.irurueta.numerical.robust.RANSACRobustEstimator: Amount of confidence expressed as a value between 0 and 1.0 (which is equivalent to 100%).
ConjugateGradientMultiOptimizer - Class in com.irurueta.numerical.optimization: This class searches for a multi dimension function local minimum.
ConjugateGradientMultiOptimizer() - Constructor for class com.irurueta.numerical.optimization.ConjugateGradientMultiOptimizer: Empty constructor.
ConjugateGradientMultiOptimizer(MultiDimensionFunctionEvaluatorListener, GradientFunctionEvaluatorListener, double[], double[], double, boolean) - Constructor for class com.irurueta.numerical.optimization.ConjugateGradientMultiOptimizer: Constructor.
ConjugateGradientMultiOptimizer(MultiDimensionFunctionEvaluatorListener, GradientFunctionEvaluatorListener, double[], double, boolean) - Constructor for class com.irurueta.numerical.optimization.ConjugateGradientMultiOptimizer: Constructor.
CONSTANT_EDGE - Enum constant in enum class com.irurueta.numerical.signal.processing.ConvolverEdgeMethod: When convolution kernel reaches edge of signal being convoluted, it is assumed that the signal has a constant value (with a value that can be setup).
constants - Variable in class com.irurueta.numerical.polynomials.estimators.IntegralIntervalPolynomialEvaluation: Constant terms of integral.
constants - Variable in class com.irurueta.numerical.polynomials.estimators.IntegralPolynomialEvaluation: Constant terms of integral.
constantValue - Variable in class com.irurueta.numerical.signal.processing.Convolver1D: Constant value to use during edge extension when CONSTANT_EDGE method is being used.
controlMatrix - Variable in class com.irurueta.numerical.signal.processing.KalmanFilter: Control matrix (B) (it is not used if there is no control).
convolve() - Method in class com.irurueta.numerical.signal.processing.Convolver1D: Convolves provided signal with provided kernel using provided configuration for edge extension.
convolve(double[]) - Method in class com.irurueta.numerical.signal.processing.Convolver1D: Convolves provided signal with provided kernel using provided configuration for edge extension.
convolve(double[], double[]) - Static method in class com.irurueta.numerical.signal.processing.Convolver1D: Convolves provided signal with provided kernel assuming a zero value extension method and kernel center located at its origin.
convolve(double[], double[], double[]) - Static method in class com.irurueta.numerical.signal.processing.Convolver1D: Convolves provided signal with provided kernel assuming a zero value extension method and kernel center located at its origin.
convolve(double[], double[], double[], Convolver1D.Convolver1DListener) - Static method in class com.irurueta.numerical.signal.processing.Convolver1D: Convolves provided signal with provided kernel assuming a zero value extension method and kernel center located at its origin.
convolve(double[], double[], int) - Static method in class com.irurueta.numerical.signal.processing.Convolver1D: Convolves provided signal with provided kernel assuming a zero value extension method.
convolve(double[], double[], int, double[]) - Static method in class com.irurueta.numerical.signal.processing.Convolver1D: Convolves provided signal with provided kernel assuming a zero value extension method.
convolve(double[], double[], int, double[], Convolver1D.Convolver1DListener) - Static method in class com.irurueta.numerical.signal.processing.Convolver1D: Convolves provided signal with provided kernel assuming a zero value extension method.
convolve(double[], double[], int, Convolver1D.Convolver1DListener) - Static method in class com.irurueta.numerical.signal.processing.Convolver1D: Convolves provided signal with provided kernel assuming a zero value extension method.
convolve(double[], double[], int, ConvolverEdgeMethod) - Static method in class com.irurueta.numerical.signal.processing.Convolver1D: Convolves provided signal with provided kernel assuming a zero value when constant edge extension is used (which makes it equivalent to zero extension method).
convolve(double[], double[], int, ConvolverEdgeMethod, double) - Static method in class com.irurueta.numerical.signal.processing.Convolver1D: Convolves provided signal with provided kernel.
convolve(double[], double[], int, ConvolverEdgeMethod, double[]) - Static method in class com.irurueta.numerical.signal.processing.Convolver1D: Convolves provided signal with provided kernel assuming a zero value when constant edge extension is used (which makes it equivalent to zero extension method).
convolve(double[], double[], int, ConvolverEdgeMethod, double[], Convolver1D.Convolver1DListener) - Static method in class com.irurueta.numerical.signal.processing.Convolver1D: Convolves provided signal with provided kernel assuming a zero value when constant edge extension is used (which makes it equivalent to zero extension method).
convolve(double[], double[], int, ConvolverEdgeMethod, double, double[]) - Static method in class com.irurueta.numerical.signal.processing.Convolver1D: Convolves provided signal with provided kernel.
convolve(double[], double[], int, ConvolverEdgeMethod, double, double[], Convolver1D.Convolver1DListener) - Static method in class com.irurueta.numerical.signal.processing.Convolver1D: Convolves provided signal with provided kernel.
convolve(double[], double[], int, ConvolverEdgeMethod, double, Convolver1D.Convolver1DListener) - Static method in class com.irurueta.numerical.signal.processing.Convolver1D: Convolves provided signal with provided kernel.
convolve(double[], double[], int, ConvolverEdgeMethod, Convolver1D.Convolver1DListener) - Static method in class com.irurueta.numerical.signal.processing.Convolver1D: Convolves provided signal with provided kernel assuming a zero value when constant edge extension is used (which makes it equivalent to zero extension method).
convolve(double[], double[], Convolver1D.Convolver1DListener) - Static method in class com.irurueta.numerical.signal.processing.Convolver1D: Convolves provided signal with provided kernel assuming a zero value extension method and kernel center located at its origin.
Convolver1D - Class in com.irurueta.numerical.signal.processing: Convolves a 1D signal with a 1D kernel.
Convolver1D() - Constructor for class com.irurueta.numerical.signal.processing.Convolver1D: Constructor.
Convolver1D(double[], double[]) - Constructor for class com.irurueta.numerical.signal.processing.Convolver1D: Constructor.
Convolver1D.Convolver1DListener - Interface in com.irurueta.numerical.signal.processing: Interface defining events produced by this class.
ConvolverEdgeMethod - Enum Class in com.irurueta.numerical.signal.processing: This enumerator indicates how edges should be treated during convolution.
ConvolverEdgeMethod() - Constructor for enum class com.irurueta.numerical.signal.processing.ConvolverEdgeMethod
cor - Variable in class com.irurueta.numerical.interpolation.BaseInterpolator
correct(Matrix) - Method in class com.irurueta.numerical.signal.processing.KalmanFilter: Adjusts model state.
covar - Variable in class com.irurueta.numerical.fitting.MultiDimensionFitter: Covariance of estimated parameters of linear single dimensional function
covar - Variable in class com.irurueta.numerical.fitting.MultiVariateFitter: Covariance of estimated parameters of linear single dimensional function.
covar - Variable in class com.irurueta.numerical.fitting.SingleDimensionFitter: Covariance of estimated parameters of single dimensional function.
covsrt(Matrix) - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Expand in storage the covariance matrix covar, to take into account parameters that are being held fixed.
covsrt(Matrix) - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Expand in storage the covariance matrix covar, to take into account parameters that are being held fixed.
covsrt(Matrix) - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Expand in storage the covariance matrix covar, to take into account parameters that are being held fixed.
cp - Variable in class com.irurueta.numerical.signal.processing.KalmanFilter: Number of control vector dimensions (control parameters).
create() - Static method in class com.irurueta.numerical.MaximumLikelihoodEstimator: Creates an instance of a subclass of this class using default maximum likelihood estimation method and default Gaussian sigma.
create() - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Creates an instance of a polynomial estimator using default type.
create() - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Creates a robust polynomial estimator using default method.
create(double) - Static method in class com.irurueta.numerical.MaximumLikelihoodEstimator: Creates an instance of a subclass of this class using default maximum likelihood estimation method and provided Gaussian sigma.
create(double[]) - Static method in class com.irurueta.numerical.MaximumLikelihoodEstimator: Creates an instance of a subclass of this class using default maximum likelihood method and Gaussian sigma, and provided input data array
create(double[], double) - Static method in class com.irurueta.numerical.MaximumLikelihoodEstimator: Creates an instance of a subclass of this class using default maximum likelihood method, provided Gaussian sigma and input data array
create(double[], double, MaximumLikelihoodEstimatorMethod) - Static method in class com.irurueta.numerical.MaximumLikelihoodEstimator: Creates an instance of a subclass of this class based on provided method and using provided Gaussian sigma and input data array.
create(double, double, double[]) - Static method in class com.irurueta.numerical.MaximumLikelihoodEstimator: Creates an instance of a subclass of this class using default maximum likelihood method and default Gaussian sigma, and using provided input data array and minimum/maximum values assumed to be contained in provided array.
create(double, double, double[], double) - Static method in class com.irurueta.numerical.MaximumLikelihoodEstimator: Creates an instance of a subclass of this class using default maximum likelihood method and using provided Gaussian sigma, input data array and minimum/maximum values assumed to be contained in provided array.
create(double, double, double[], double, MaximumLikelihoodEstimatorMethod) - Static method in class com.irurueta.numerical.MaximumLikelihoodEstimator: Creates an instance of a subclass of this class based on provided method and using provided Gaussian sigma, input data array and minimum/maximum values assumed to be contained in provided array.
create(double, double, MatrixSingleDimensionFunctionEvaluatorListener) - Static method in class com.irurueta.numerical.integration.MatrixIntegrator: Creates an integrator using default integrator and quadrature types with default accuracy.
create(double, double, MatrixSingleDimensionFunctionEvaluatorListener) - Static method in class com.irurueta.numerical.integration.QuadratureMatrixIntegrator: Creates a quadrature integrator using default accuracy and quadrature type.
create(double, double, MatrixSingleDimensionFunctionEvaluatorListener) - Static method in class com.irurueta.numerical.integration.RombergMatrixIntegrator: Creates an integrator using Romberg's method and having default accuracy and quadrature type.
create(double, double, MatrixSingleDimensionFunctionEvaluatorListener) - Static method in class com.irurueta.numerical.integration.SimpsonMatrixIntegrator: Creates an integrator using Simpson's method and having default accuracy and quadrature type.
create(double, double, MatrixSingleDimensionFunctionEvaluatorListener, double) - Static method in class com.irurueta.numerical.integration.MatrixIntegrator: Creates an integrator using default integrator and quadrature types.
create(double, double, MatrixSingleDimensionFunctionEvaluatorListener, double) - Static method in class com.irurueta.numerical.integration.QuadratureMatrixIntegrator: Creates a quadrature integrator using default quadrature type.
create(double, double, MatrixSingleDimensionFunctionEvaluatorListener, double) - Static method in class com.irurueta.numerical.integration.RombergMatrixIntegrator: Creates an integrator using Romberg's method and default quadrature type.
create(double, double, MatrixSingleDimensionFunctionEvaluatorListener, double) - Static method in class com.irurueta.numerical.integration.SimpsonMatrixIntegrator: Creates an integrator using Simpson's method and default quadrature type.
create(double, double, MatrixSingleDimensionFunctionEvaluatorListener, double, IntegratorType) - Static method in class com.irurueta.numerical.integration.MatrixIntegrator: Creates an integrator using provided integrator type and using default quadrature type.
create(double, double, MatrixSingleDimensionFunctionEvaluatorListener, double, IntegratorType, QuadratureType) - Static method in class com.irurueta.numerical.integration.MatrixIntegrator: Creates an integrator using provided integrator and quadrature types.
create(double, double, MatrixSingleDimensionFunctionEvaluatorListener, double, QuadratureType) - Static method in class com.irurueta.numerical.integration.QuadratureMatrixIntegrator: Creates a quadrature integrator.
create(double, double, MatrixSingleDimensionFunctionEvaluatorListener, double, QuadratureType) - Static method in class com.irurueta.numerical.integration.RombergMatrixIntegrator: Creates an integrator using Romberg's method.
create(double, double, MatrixSingleDimensionFunctionEvaluatorListener, double, QuadratureType) - Static method in class com.irurueta.numerical.integration.SimpsonMatrixIntegrator: Creates an integrator using Simpson's method.
create(double, double, MatrixSingleDimensionFunctionEvaluatorListener, IntegratorType) - Static method in class com.irurueta.numerical.integration.MatrixIntegrator: Creates an integrator using provided integrator type with default accuracy and using default quadrature type.
create(double, double, MatrixSingleDimensionFunctionEvaluatorListener, IntegratorType, QuadratureType) - Static method in class com.irurueta.numerical.integration.MatrixIntegrator: Creates an integrator using provided integrator and quadrature types with default accuracy.
create(double, double, MatrixSingleDimensionFunctionEvaluatorListener, QuadratureType) - Static method in class com.irurueta.numerical.integration.QuadratureMatrixIntegrator: Creates a quadrature integrator with default accuracy.
create(double, double, MatrixSingleDimensionFunctionEvaluatorListener, QuadratureType) - Static method in class com.irurueta.numerical.integration.RombergMatrixIntegrator: Creates an integrator using Romberg's method and having default accuracy.
create(double, double, MatrixSingleDimensionFunctionEvaluatorListener, QuadratureType) - Static method in class com.irurueta.numerical.integration.SimpsonMatrixIntegrator: Creates an integrator using Simpson's method and having default accuracy.
create(double, double, SingleDimensionFunctionEvaluatorListener) - Static method in class com.irurueta.numerical.integration.Integrator: Creates an integrator using default integrator and quadrature types with default accuracy.
create(double, double, SingleDimensionFunctionEvaluatorListener) - Static method in class com.irurueta.numerical.integration.QuadratureIntegrator: Creates a quadrature integrator using default accuracy and quadrature type.
create(double, double, SingleDimensionFunctionEvaluatorListener) - Static method in class com.irurueta.numerical.integration.RombergIntegrator: Creates an integrator using Romberg's method and having default accuracy and quadrature type.
create(double, double, SingleDimensionFunctionEvaluatorListener) - Static method in class com.irurueta.numerical.integration.SimpsonIntegrator: Creates an integrator using Simpson's method and having default accuracy and quadrature type.
create(double, double, SingleDimensionFunctionEvaluatorListener, double) - Static method in class com.irurueta.numerical.integration.Integrator: Creates an integrator using default integrator and quadrature types.
create(double, double, SingleDimensionFunctionEvaluatorListener, double) - Static method in class com.irurueta.numerical.integration.QuadratureIntegrator: Creates a quadrature integrator using default quadrature type.
create(double, double, SingleDimensionFunctionEvaluatorListener, double) - Static method in class com.irurueta.numerical.integration.RombergIntegrator: Creates an integrator using Romberg's method and default quadrature type.
create(double, double, SingleDimensionFunctionEvaluatorListener, double) - Static method in class com.irurueta.numerical.integration.SimpsonIntegrator: Creates an integrator using Simpson's method and default quadrature type.
create(double, double, SingleDimensionFunctionEvaluatorListener, double, IntegratorType) - Static method in class com.irurueta.numerical.integration.Integrator: Creates an integrator using provided integrator type and using default quadrature type.
create(double, double, SingleDimensionFunctionEvaluatorListener, double, IntegratorType, QuadratureType) - Static method in class com.irurueta.numerical.integration.Integrator: Creates an integrator using provided integrator and quadrature types.
create(double, double, SingleDimensionFunctionEvaluatorListener, double, QuadratureType) - Static method in class com.irurueta.numerical.integration.QuadratureIntegrator: Creates a quadrature integrator.
create(double, double, SingleDimensionFunctionEvaluatorListener, double, QuadratureType) - Static method in class com.irurueta.numerical.integration.RombergIntegrator: Creates an integrator using Romberg's method.
create(double, double, SingleDimensionFunctionEvaluatorListener, double, QuadratureType) - Static method in class com.irurueta.numerical.integration.SimpsonIntegrator: Creates an integrator using Simpson's method.
create(double, double, SingleDimensionFunctionEvaluatorListener, IntegratorType) - Static method in class com.irurueta.numerical.integration.Integrator: Creates an integrator using provided integrator type with default accuracy and using default quadrature type.
create(double, double, SingleDimensionFunctionEvaluatorListener, IntegratorType, QuadratureType) - Static method in class com.irurueta.numerical.integration.Integrator: Creates an integrator using provided integrator and quadrature types with default accuracy.
create(double, double, SingleDimensionFunctionEvaluatorListener, QuadratureType) - Static method in class com.irurueta.numerical.integration.QuadratureIntegrator: Creates a quadrature integrator with default accuracy.
create(double, double, SingleDimensionFunctionEvaluatorListener, QuadratureType) - Static method in class com.irurueta.numerical.integration.RombergIntegrator: Creates an integrator using Romberg's method and having default accuracy.
create(double, double, SingleDimensionFunctionEvaluatorListener, QuadratureType) - Static method in class com.irurueta.numerical.integration.SimpsonIntegrator: Creates an integrator using Simpson's method and having default accuracy.
create(double, MaximumLikelihoodEstimatorMethod) - Static method in class com.irurueta.numerical.MaximumLikelihoodEstimator: Creates an instance of a subclass of this class based on provided method and using provided Gaussian sigma.
create(int) - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Creates an instance of a polynomial estimator using provided degree and default type.
create(int) - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Creates a robust polynomial estimator using provided degree and default method.
create(int) - Static method in class com.irurueta.numerical.robust.SubsetSelector: Creates a new subset selector instance using provided total number of samples and default subset selector type.
create(int, PolynomialEstimatorListener) - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Creates an instance of a polynomial estimator using provided degree, listener and default type.
create(int, PolynomialEstimatorListener, PolynomialEstimatorType) - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Creates an instance of a polynomial estimator using provided degree, listener and type.
create(int, PolynomialEstimatorType) - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Creates an instance of a polynomial estimator using provided degree and type.
create(int, PolynomialRobustEstimatorListener) - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Creates a robust polynomial estimator using provided degree, listener and default method.
create(int, PolynomialRobustEstimatorListener, RobustEstimatorMethod) - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Creates a robust polynomial estimator using provided degree, listener and method.
create(int, RobustEstimatorMethod) - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Creates a robust polynomial estimator using provided degree and method.
create(int, List<PolynomialEvaluation>) - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Creates an instance of a polynomial estimator using provided degree, polynomial evaluations and default type.
create(int, List<PolynomialEvaluation>) - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Creates a robust polynomial estimator using provided degree, evaluations and default method.
create(int, List<PolynomialEvaluation>, PolynomialEstimatorListener) - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Creates an instance of a polynomial estimator using provided degree, evaluations, listener and default type.
create(int, List<PolynomialEvaluation>, PolynomialEstimatorListener, PolynomialEstimatorType) - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Creates an instance of a polynomial estimator using provided degree, evaluations, listener and type.
create(int, List<PolynomialEvaluation>, PolynomialEstimatorType) - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Creates an instance of a polynomial estimator using provided degree, evaluations and type.
create(int, List<PolynomialEvaluation>, PolynomialRobustEstimatorListener) - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Creates a robust polynomial estimator using provided degree, evaluations, listener and default method.
create(int, List<PolynomialEvaluation>, PolynomialRobustEstimatorListener, RobustEstimatorMethod) - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Creates a robust polynomial estimator using provided degree, evaluations, listener and method.
create(int, List<PolynomialEvaluation>, RobustEstimatorMethod) - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Creates a robust polynomial estimator using provided degree, evaluations and method.
create(PolynomialEstimatorListener) - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Creates an instance of a polynomial estimator using provided listener and default type and degree.
create(PolynomialEstimatorListener, PolynomialEstimatorType) - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Creates an instance of a polynomial estimator using provided listener and type.
create(PolynomialEstimatorType) - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Creates an instance of a polynomial estimator using provided type and default degree.
create(PolynomialRobustEstimatorListener) - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Creates a robust polynomial estimator using provided listener and default method.
create(PolynomialRobustEstimatorListener, RobustEstimatorMethod) - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Creates a robust polynomial estimator using provided listener and method.
create(RobustEstimatorMethod) - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Creates a robust polynomial estimator using provided method.
create(List<PolynomialEvaluation>) - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Creates an instance of a polynomial estimator using provided evaluations, and default type and degree.
create(List<PolynomialEvaluation>) - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Creates a robust polynomial estimator using provided evaluations and default method.
create(List<PolynomialEvaluation>, PolynomialEstimatorListener) - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Creates an instance of a polynomial estimator using provided evaluations, listener and default type.
create(List<PolynomialEvaluation>, PolynomialEstimatorListener, PolynomialEstimatorType) - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Creates an instance of a polynomial estimator using provided evaluations, listener and type.
create(List<PolynomialEvaluation>, PolynomialEstimatorType) - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Creates an instance of a polynomial estimator using provided evaluations and type.
create(List<PolynomialEvaluation>, PolynomialRobustEstimatorListener) - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Creates a robust polynomial estimator using provided evaluations, listener and default method.
create(List<PolynomialEvaluation>, PolynomialRobustEstimatorListener, RobustEstimatorMethod) - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Creates a robust polynomial estimator using provided evaluations, listener and method.
create(List<PolynomialEvaluation>, RobustEstimatorMethod) - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Creates a robust polynomial estimator using provided evaluations and method.
createCopy() - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator.PROMedSInliersData: Creates a copy of inlier data.
createInitialParametersArray() - Method in interface com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFunctionEvaluator: Creates array where estimated parameters will be stored.
createInitialParametersArray() - Method in interface com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFunctionEvaluator: Creates array where estimated parameters will be stored.
createInitialParametersArray() - Method in interface com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFunctionEvaluator: Creates array where estimated parameters will be stored.
createRandomizer(boolean) - Method in class com.irurueta.numerical.robust.FastRandomSubsetSelector: Initializes randomizer for an instance of this class.
createResultArray() - Method in interface com.irurueta.numerical.fitting.LinearFitterMultiDimensionFunctionEvaluator: Creates array where basis function results will be stored
createResultArray() - Method in interface com.irurueta.numerical.fitting.LinearFitterSingleDimensionFunctionEvaluator: Creates array where basis function results will be stored
cubeRoot(double) - Method in class com.irurueta.numerical.roots.ThirdDegreePolynomialRootsEstimator: Computes the cube root or x^(1/3) of provided value x
CubicSplineInterpolator - Class in com.irurueta.numerical.interpolation: Computes cubic spline interpolation.
CubicSplineInterpolator(double[], double[]) - Constructor for class com.irurueta.numerical.interpolation.CubicSplineInterpolator: Constructor with x and y vectors and default values for first derivative at the endpoints.
CubicSplineInterpolator(double[], double[], double, double) - Constructor for class com.irurueta.numerical.interpolation.CubicSplineInterpolator: Constructor with x and y vectors, and values of the first derivative at the endpoints.
CurveInterpolator - Class in com.irurueta.numerical.interpolation: Computes curve interpolation of multidimensional points using cubic splines.
CurveInterpolator(Matrix) - Constructor for class com.irurueta.numerical.interpolation.CurveInterpolator: Constructor assuming that curve is NOT closed.
CurveInterpolator(Matrix, boolean) - Constructor for class com.irurueta.numerical.interpolation.CurveInterpolator: Constructor.
cx - Variable in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Maximum evaluation point inside the bracket.
cX - Variable in class com.irurueta.numerical.ExponentialMatrixEstimator: Internal matrix reused for efficiency while provided input matrices keep the same size.

D

d - Variable in class com.irurueta.numerical.ExponentialMatrixEstimator: Denominator of Padé approximant.
d - Variable in class com.irurueta.numerical.interpolation.BarycentricRationalInterpolator: Order of desired approximation.
dbrent - Variable in class com.irurueta.numerical.optimization.DerivativeLineMultiOptimizer: Internal optimizer to find a minimum of a function along a line of input values.
DEFAULT_ADJUST_COVARIANCE - Static variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Indicates whether covariance must be adjusted or not after fitting is finished.
DEFAULT_ADJUST_COVARIANCE - Static variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Indicates whether covariance must be adjusted or not after fitting is finished.
DEFAULT_ADJUST_COVARIANCE - Static variable in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Indicates whether covariance must be adjusted or not after fitting is finished.
DEFAULT_ALLOW_LMSE_SOLUTION - Static variable in class com.irurueta.numerical.polynomials.estimators.LMSEPolynomialEstimator: Indicates if by default an LMSE (Least Mean Square Error) solution is allowed if more evaluations than the required minimum are provided.
DEFAULT_BETA - Static variable in class com.irurueta.numerical.interpolation.KrigingInterpolator.Variogram: Default Beta value to be used.
DEFAULT_BETA - Static variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Defines the default value for beta, which is the probability that a match is declared inlier by mistake, i.e. the ratio of the "inlier" surface by the total surface.
DEFAULT_BETA - Static variable in class com.irurueta.numerical.robust.PROSACRobustEstimator: Defines the default value for beta, which is the probability that a match is declared inlier by mistake, i.e. the ratio of the "inlier" surface by the total surface.
DEFAULT_COMPUTE_AND_KEEP_INLIERS - Static variable in class com.irurueta.numerical.robust.PROSACRobustEstimator: Indicates that by default inliers will only be computed but not kept.
DEFAULT_COMPUTE_AND_KEEP_INLIERS - Static variable in class com.irurueta.numerical.robust.RANSACRobustEstimator: Indicates that by default inliers will only be computed but not kept.
DEFAULT_COMPUTE_AND_KEEP_RESIDUALS - Static variable in class com.irurueta.numerical.robust.PROSACRobustEstimator: Indicates that by default residuals will only be computed but not kept.
DEFAULT_COMPUTE_AND_KEEP_RESIDUALS - Static variable in class com.irurueta.numerical.robust.RANSACRobustEstimator: Indicates that by default residuals will only be computed but not kept.
DEFAULT_CONFIDENCE - Static variable in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Constant defining default confidence of the estimated result, which is 99%.
DEFAULT_CONFIDENCE - Static variable in class com.irurueta.numerical.robust.LMedSRobustEstimator: Constant defining default confidence of the estimated result, which is 99%.
DEFAULT_CONFIDENCE - Static variable in class com.irurueta.numerical.robust.MSACRobustEstimator: Constant defining default confidence of estimated result, which is 99%.
DEFAULT_CONFIDENCE - Static variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Constant defining default confidence of the estimated result, which is 99%.
DEFAULT_CONFIDENCE - Static variable in class com.irurueta.numerical.robust.PROSACRobustEstimator: Constant defining default confidence of the estimated result, which is 99%.
DEFAULT_CONFIDENCE - Static variable in class com.irurueta.numerical.robust.RANSACRobustEstimator: Constant defining default confidence of the estimated result, which is 99%.
DEFAULT_CONSTANT_VALUE - Static variable in class com.irurueta.numerical.signal.processing.Convolver1D: Default constant value to use during convolution if CONSTANT_EDGE method is being used for signal's edge extension.
DEFAULT_EDGE_METHOD - Static variable in class com.irurueta.numerical.signal.processing.Convolver1D: Default method to use for signal's edge extension.
DEFAULT_ESTIMATOR_TYPE - Static variable in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Default estimator type.
DEFAULT_ETA0 - Static variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Defines the default value for the maximum probability that a solution with more than inliersNStar in U_nStar exist and was not found after k samples.
DEFAULT_ETA0 - Static variable in class com.irurueta.numerical.robust.PROSACRobustEstimator: Defines the default value for the maximum probability that a solution with more than inliersNStar in U_nStar exist and was not found after k samples.
DEFAULT_GAUSSIAN_SIGMA - Static variable in class com.irurueta.numerical.MaximumLikelihoodEstimator: Default Gaussian sigma assigned to each sample.
DEFAULT_HMAX - Static variable in class com.irurueta.numerical.integration.DoubleExponentialRuleMatrixQuadrature: Default transformation of range of integration.
DEFAULT_HMAX - Static variable in class com.irurueta.numerical.integration.DoubleExponentialRuleQuadrature: Default transformation of range of integration.
DEFAULT_IMPROVEMENT_TIMES - Static variable in class com.irurueta.numerical.PadeApproximantEstimator: Number of times to iteratively improve LU decomposition solution by default.
DEFAULT_INLIER_FACTOR - Static variable in class com.irurueta.numerical.robust.LMedSRobustEstimator: Default factor to normalize threshold to determine inliers.
DEFAULT_INLIER_FACTOR - Static variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Default factor to normalize threshold to determine inliers.
DEFAULT_INTEGRATOR_TYPE - Static variable in class com.irurueta.numerical.integration.Integrator: Default integrator type.
DEFAULT_INTEGRATOR_TYPE - Static variable in class com.irurueta.numerical.integration.MatrixIntegrator: Default integrator type.
DEFAULT_ITMAX - Static variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Default maximum number of iterations.
DEFAULT_ITMAX - Static variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Default maximum number of iterations.
DEFAULT_ITMAX - Static variable in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Default maximum number of iterations.
DEFAULT_KERNEL_CENTER - Static variable in class com.irurueta.numerical.signal.processing.Convolver1D: Default position of kernel center.
DEFAULT_MAX_EVAL_POINT - Static variable in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Default maximum evaluation point where the bracket is supposed to start By default, if no bracket is computed, the whole range of values is used for minimum estimation.
DEFAULT_MAX_EVAL_POINT - Static variable in class com.irurueta.numerical.roots.BracketedSingleRootEstimator: Default maximum evaluation point.
DEFAULT_MAX_EVALUATIONS - Static variable in class com.irurueta.numerical.polynomials.estimators.WeightedPolynomialEstimator: Default number of evaluations to be weighted and taken into account.
DEFAULT_MAX_ITERATIONS - Static variable in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Default maximum allowed number of iterations.
DEFAULT_MAX_ITERATIONS - Static variable in class com.irurueta.numerical.robust.LMedSRobustEstimator: Default maximum allowed number of iterations.
DEFAULT_MAX_ITERATIONS - Static variable in class com.irurueta.numerical.robust.MSACRobustEstimator: Default maximum allowed number of iterations.
DEFAULT_MAX_ITERATIONS - Static variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Default maximum allowed number of iterations.
DEFAULT_MAX_ITERATIONS - Static variable in class com.irurueta.numerical.robust.PROSACRobustEstimator: Default maximum allowed number of iterations.
DEFAULT_MAX_ITERATIONS - Static variable in class com.irurueta.numerical.robust.RANSACRobustEstimator: Default maximum allowed number of iterations.
DEFAULT_MAX_OUTLIERS_PROPORTION - Static variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Default maximum allowed outliers proportion in the input data.
DEFAULT_MAX_OUTLIERS_PROPORTION - Static variable in class com.irurueta.numerical.robust.PROSACRobustEstimator: Default maximum allowed outliers proportion in the input data.
DEFAULT_MEASUREMENT_NOISE_VARIANCE - Static variable in class com.irurueta.numerical.signal.processing.KalmanFilter: Independent measurement noise variance assumed when no measurement noise covariance matrix is provided.
DEFAULT_METHOD - Static variable in class com.irurueta.numerical.MaximumLikelihoodEstimator: Default method to find the most likely value.
DEFAULT_MIDDLE_EVAL_POINT - Static variable in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Default middle evaluation point where the bracket is supposed to start By default, if no bracket is computed, the whole range of values is used for minimum estimation.
DEFAULT_MIN_EVAL_POINT - Static variable in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Default minimum evaluation point where the bracket is supposed to start By default, if no bracket is computed, the whole range of values is used for minimum estimation.
DEFAULT_MIN_EVAL_POINT - Static variable in class com.irurueta.numerical.roots.BracketedSingleRootEstimator: Default minimum evaluation point.
DEFAULT_NDONE - Static variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Default convergence parameter.
DEFAULT_NDONE - Static variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Default convergence parameter.
DEFAULT_NDONE - Static variable in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Default convergence parameter.
DEFAULT_NUG - Static variable in class com.irurueta.numerical.interpolation.KrigingInterpolator.Variogram: Default offset to use.
DEFAULT_NUMBER_OF_BINS - Static variable in class com.irurueta.numerical.HistogramMaximumLikelihoodEstimator: Default number of bins to be used on the histogram.
DEFAULT_POLISH_ROOTS - Static variable in class com.irurueta.numerical.roots.LaguerrePolynomialRootsEstimator: Constant indicating whether roots will be refined.
DEFAULT_PROCESS_NOISE_VARIANCE - Static variable in class com.irurueta.numerical.signal.processing.KalmanFilter: Independent process noise variance assumed when no process noise covariance matrix is provided.
DEFAULT_PROGRESS_DELTA - Static variable in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Default amount of progress variation before notifying a change in estimation progress.
DEFAULT_PROGRESS_DELTA - Static variable in class com.irurueta.numerical.robust.RobustEstimator: Default amount of progress variation before notifying a change in estimation progress.
DEFAULT_QUADRATURE_TYPE - Static variable in class com.irurueta.numerical.integration.Integrator: Default quadrature type.
DEFAULT_QUADRATURE_TYPE - Static variable in class com.irurueta.numerical.integration.MatrixIntegrator: Default quadrature type.
DEFAULT_ROBUST_METHOD - Static variable in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Default robust estimator method when none is provided.
DEFAULT_SEED_RANDOMIZER_WITH_TIME - Static variable in class com.irurueta.numerical.robust.FastRandomSubsetSelector: Constant defining whether randomizer needs to be initialized with system timer.
DEFAULT_SORT_WEIGHTS - Static variable in class com.irurueta.numerical.polynomials.estimators.WeightedPolynomialEstimator: Indicates if weights are sorted by default so that largest weighted evaluations are used first.
DEFAULT_STOP_THRESHOLD - Static variable in class com.irurueta.numerical.polynomials.estimators.LMedSPolynomialRobustEstimator: Default value to be used for stop threshold.
DEFAULT_STOP_THRESHOLD - Static variable in class com.irurueta.numerical.polynomials.estimators.PROMedSPolynomialRobustEstimator: Default value to be used for stop threshold.
DEFAULT_STOP_THRESHOLD - Static variable in class com.irurueta.numerical.robust.LMedSRobustEstimator: Default value to be used for stop threshold.
DEFAULT_STOP_THRESHOLD_ENABLED - Static variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Indicates whether the algorithm must stop prematurely when dynamically computed threshold using median of residuals has a value lower than provided threshold in listener.
DEFAULT_SUBSET_SELECTOR_TYPE - Static variable in class com.irurueta.numerical.robust.SubsetSelector: Defines default subset selector type.
DEFAULT_THRESHOLD - Static variable in class com.irurueta.numerical.polynomials.estimators.MSACPolynomialRobustEstimator: Constant defining default threshold to determine whether polynomials are inliers or not.
DEFAULT_THRESHOLD - Static variable in class com.irurueta.numerical.polynomials.estimators.PROSACPolynomialRobustEstimator: Constant defining default threshold to determine whether polynomials are inliers or not.
DEFAULT_THRESHOLD - Static variable in class com.irurueta.numerical.polynomials.estimators.RANSACPolynomialRobustEstimator: Constant defining default threshold to determine whether polynomials are inliers or not.
DEFAULT_TOL - Static variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Default tolerance to reach convergence.
DEFAULT_TOL - Static variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Default tolerance to reach convergence.
DEFAULT_TOL - Static variable in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Default tolerance to reach convergence.
DEFAULT_TOL - Static variable in class com.irurueta.numerical.fitting.SvdMultiDimensionLinearFitter: Default tolerance.
DEFAULT_TOL - Static variable in class com.irurueta.numerical.fitting.SvdSingleDimensionLinearFitter: Default tolerance.
DEFAULT_TOLERANCE - Static variable in class com.irurueta.numerical.optimization.BrentSingleOptimizer: Constant defining the default accuracy of the estimated minimum.
DEFAULT_TOLERANCE - Static variable in class com.irurueta.numerical.optimization.ConjugateGradientMultiOptimizer: Constant defining default tolerance or accuracy to be achieved on the minimum being estimated by this class.
DEFAULT_TOLERANCE - Static variable in class com.irurueta.numerical.optimization.DerivativeBrentSingleOptimizer: Default tolerance.
DEFAULT_TOLERANCE - Static variable in class com.irurueta.numerical.optimization.DerivativeConjugateGradientMultiOptimizer: Constant defining default tolerance or accuracy to be achieved on the minimum being estimated by this class.
DEFAULT_TOLERANCE - Static variable in class com.irurueta.numerical.optimization.GoldenSingleOptimizer: Constant defining the default accuracy of the estimated minimum.
DEFAULT_TOLERANCE - Static variable in class com.irurueta.numerical.optimization.PowellMultiOptimizer: Constant defining default tolerance or accuracy to be achieved on the minimum being estimated by this class.
DEFAULT_TOLERANCE - Static variable in class com.irurueta.numerical.optimization.QuasiNewtonMultiOptimizer: Constant defining default tolerance or accuracy to be achieved on the minimum being estimated by this class.
DEFAULT_TOLERANCE - Static variable in class com.irurueta.numerical.optimization.SimplexMultiOptimizer: Constant defining default tolerance or accuracy to be achieved on the minimum being estimated by this class.
DEFAULT_TOLERANCE - Static variable in class com.irurueta.numerical.roots.BisectionSingleRootEstimator: Constant defining default tolerance to find a root.
DEFAULT_TOLERANCE - Static variable in class com.irurueta.numerical.roots.BrentSingleRootEstimator: Constant defining default accuracy of the estimated root.
DEFAULT_TOLERANCE - Static variable in class com.irurueta.numerical.roots.FalsePositionSingleRootEstimator: Constant defining default tolerance to find a root.
DEFAULT_TOLERANCE - Static variable in class com.irurueta.numerical.roots.NewtonRaphsonSingleRootEstimator: Constant defining default accuracy of the estimated root.
DEFAULT_TOLERANCE - Static variable in class com.irurueta.numerical.roots.RidderSingleRootEstimator: Constant defining default accuracy of the estimated root.
DEFAULT_TOLERANCE - Static variable in class com.irurueta.numerical.roots.SafeNewtonRaphsonSingleRootEstimator: Constant defining default accuracy of the estimated root.
DEFAULT_TOLERANCE - Static variable in class com.irurueta.numerical.roots.SecantSingleRootEstimator: Constant defining default accuracy of the estimated root.
DEFAULT_USE_GEOMETRIC_DISTANCE - Static variable in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Flag indicating whether geometric distance is used by default or not to find outliers.
DEFAULT_USE_HISTOGRAM_INITIAL_SOLUTION - Static variable in class com.irurueta.numerical.AccurateMaximumLikelihoodEstimator: Boolean indicating if an initial solution should be obtained first by using the Histogram method.
DEFAULT_USE_INLIER_THRESHOLD - Static variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Indicates whether the inlier threshold will be used to find inliers along with their median of residuals.
DEFAULT_USE_POLAK_RIBIERE - Static variable in class com.irurueta.numerical.optimization.ConjugateGradientMultiOptimizer: Defines whether Polak-Ribiere is used if true, otherwise Fletcher-Reeves will be used.
DEFAULT_USE_POLAK_RIBIERE - Static variable in class com.irurueta.numerical.optimization.DerivativeConjugateGradientMultiOptimizer: Defines whether Polak-Ribiere is used if true, otherwise Fletcher-Reeves will be used.
degree - Variable in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Degree of polynomial to be estimated.
denominators - Variable in class com.irurueta.numerical.PadeApproximantEstimator.Result: Denominator coefficients.
derivative() - Method in class com.irurueta.numerical.polynomials.Polynomial: Replaces this instance by its derivative.
derivative(double) - Method in class com.irurueta.numerical.DerivativeEstimator: Computes the function derivative at provided point x.
derivative(double) - Method in class com.irurueta.numerical.SavitzkyGolayDerivativeEstimator: Computes the function derivative at provided point x.
derivative(double) - Method in class com.irurueta.numerical.SymmetricDerivativeEstimator: Computes the function derivative at provided point x.
derivative(Polynomial) - Method in class com.irurueta.numerical.polynomials.Polynomial: Computes derivative of polynomial.
DERIVATIVE_EVALUATION - Enum constant in enum class com.irurueta.numerical.polynomials.estimators.PolynomialEvaluationType: Evaluation of the nth-derivative of a polynomial.
derivativeAndReturnNew() - Method in class com.irurueta.numerical.polynomials.Polynomial: Computes derivative of polynomial.
DerivativeBrentSingleOptimizer - Class in com.irurueta.numerical.optimization: Class to compute local minimum on single dimension functions using a modification of Brent's algorithm that takes into account the function's derivative.
DerivativeBrentSingleOptimizer() - Constructor for class com.irurueta.numerical.optimization.DerivativeBrentSingleOptimizer: Empty constructor.
DerivativeBrentSingleOptimizer(SingleDimensionFunctionEvaluatorListener, SingleDimensionFunctionEvaluatorListener, double, double, double, double) - Constructor for class com.irurueta.numerical.optimization.DerivativeBrentSingleOptimizer: Constructor.
DerivativeConjugateGradientMultiOptimizer - Class in com.irurueta.numerical.optimization: This class searches for a multi dimension function local minimum.
DerivativeConjugateGradientMultiOptimizer() - Constructor for class com.irurueta.numerical.optimization.DerivativeConjugateGradientMultiOptimizer: Empty constructor.
DerivativeConjugateGradientMultiOptimizer(MultiDimensionFunctionEvaluatorListener, GradientFunctionEvaluatorListener, double[], double[], double, boolean) - Constructor for class com.irurueta.numerical.optimization.DerivativeConjugateGradientMultiOptimizer: Constructor.
DerivativeEstimator - Class in com.irurueta.numerical: Class to estimate the derivative of a single dimension function at a given point.
DerivativeEstimator(SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.DerivativeEstimator: Constructor
DerivativeLineMultiOptimizer - Class in com.irurueta.numerical.optimization: Class to find a minimum on a multidimensional function along a given line of input values.
DerivativeLineMultiOptimizer() - Constructor for class com.irurueta.numerical.optimization.DerivativeLineMultiOptimizer: Empty constructor.
DerivativeLineMultiOptimizer(MultiDimensionFunctionEvaluatorListener, GradientFunctionEvaluatorListener, double[], double[]) - Constructor for class com.irurueta.numerical.optimization.DerivativeLineMultiOptimizer: Constructor.
derivativeListener - Variable in class com.irurueta.numerical.optimization.DerivativeBrentSingleOptimizer: Listener to evaluate the functions derivative.
derivativeListener - Variable in class com.irurueta.numerical.roots.DerivativeSingleRootEstimator: Listener to evaluate a function's derivative.
derivativeOrder - Variable in class com.irurueta.numerical.polynomials.estimators.DerivativePolynomialEvaluation: Order of derivative.
DerivativePolynomialEvaluation - Class in com.irurueta.numerical.polynomials.estimators: Contains an evaluation of the derivative of a given order of a polynomial and the point where such derivative has been evaluated.
DerivativePolynomialEvaluation() - Constructor for class com.irurueta.numerical.polynomials.estimators.DerivativePolynomialEvaluation: Constructor.
DerivativePolynomialEvaluation(double, double) - Constructor for class com.irurueta.numerical.polynomials.estimators.DerivativePolynomialEvaluation
DerivativePolynomialEvaluation(double, double, int) - Constructor for class com.irurueta.numerical.polynomials.estimators.DerivativePolynomialEvaluation: Constructor.
DerivativeSingleRootEstimator - Class in com.irurueta.numerical.roots: Abstract class to find function roots of a single dimension function using also its derivative information.
DerivativeSingleRootEstimator() - Constructor for class com.irurueta.numerical.roots.DerivativeSingleRootEstimator: Empty constructor.
DerivativeSingleRootEstimator(SingleDimensionFunctionEvaluatorListener, double, double) - Constructor for class com.irurueta.numerical.roots.DerivativeSingleRootEstimator: Constructor.
DerivativeSingleRootEstimator(SingleDimensionFunctionEvaluatorListener, SingleDimensionFunctionEvaluatorListener, double, double) - Constructor for class com.irurueta.numerical.roots.DerivativeSingleRootEstimator: Constructor
dft - Variable in class com.irurueta.numerical.DirectionalDerivativeEvaluator: Array containing gradient at point p.
differentiateAt(double) - Method in class com.irurueta.numerical.DirectionalDerivativeEvaluator: Computes derivative on current direction of a function at distance x from current point and using current listener and gradient listener.
dim - Variable in class com.irurueta.numerical.interpolation.BaseRadialBasisFunctionInterpolator: Dimension of points to be interpolated.
dim - Variable in class com.irurueta.numerical.interpolation.CurveInterpolator: Number of points dimensions.
DIRECT_EVALUATION - Enum constant in enum class com.irurueta.numerical.polynomials.estimators.PolynomialEvaluationType: A direct evaluation of a polynomial.
direction - Variable in class com.irurueta.numerical.DirectionalEvaluator: Vector indicating the direction of the line where the function is evaluated.
DirectionalDerivativeEvaluator - Class in com.irurueta.numerical: This class evaluates a multidimensional function and obtains its gradient along a line; such line is defined by an input point and a given direction.
DirectionalDerivativeEvaluator(MultiDimensionFunctionEvaluatorListener, GradientFunctionEvaluatorListener, double[], double[]) - Constructor for class com.irurueta.numerical.DirectionalDerivativeEvaluator: Constructor.
DirectionalEvaluator - Class in com.irurueta.numerical: This class evaluates a multidimensional function along a line, such line is defined by an input point and a given direction.
DirectionalEvaluator(MultiDimensionFunctionEvaluatorListener, double[], double[]) - Constructor for class com.irurueta.numerical.DirectionalEvaluator: Constructor.
DirectPolynomialEvaluation - Class in com.irurueta.numerical.polynomials.estimators: Contains an evaluation of a polynomial and the point where the polynomial has been evaluated.
DirectPolynomialEvaluation() - Constructor for class com.irurueta.numerical.polynomials.estimators.DirectPolynomialEvaluation: Constructor.
DirectPolynomialEvaluation(double, double) - Constructor for class com.irurueta.numerical.polynomials.estimators.DirectPolynomialEvaluation: Constructor.
dj - Variable in class com.irurueta.numerical.interpolation.BaseInterpolator
DOUBLE_EXPONENTIAL_RULE - Enum constant in enum class com.irurueta.numerical.integration.QuadratureType: Double exponential rule.
DoubleExponentialMatrixSingleDimensionFunctionEvaluatorListener - Interface in com.irurueta.numerical.integration: Interface to define how matrix (multivariate) single dimension functions can be evaluated in Double Exponential Rule Quadrature function integrators.
DoubleExponentialRuleMatrixQuadrature - Class in com.irurueta.numerical.integration: Implementation of quadrature using double exponential, which allows integration with a variable transformation.
DoubleExponentialRuleMatrixQuadrature(DoubleExponentialMatrixSingleDimensionFunctionEvaluatorListener, double, double) - Constructor for class com.irurueta.numerical.integration.DoubleExponentialRuleMatrixQuadrature: Constructor.
DoubleExponentialRuleMatrixQuadrature(DoubleExponentialMatrixSingleDimensionFunctionEvaluatorListener, double, double, double) - Constructor for class com.irurueta.numerical.integration.DoubleExponentialRuleMatrixQuadrature: Constructor.
DoubleExponentialRuleMatrixQuadrature(MatrixSingleDimensionFunctionEvaluatorListener, double, double) - Constructor for class com.irurueta.numerical.integration.DoubleExponentialRuleMatrixQuadrature: Constructor.
DoubleExponentialRuleMatrixQuadrature(MatrixSingleDimensionFunctionEvaluatorListener, double, double, double) - Constructor for class com.irurueta.numerical.integration.DoubleExponentialRuleMatrixQuadrature: Constructor.
DoubleExponentialRuleQuadrature - Class in com.irurueta.numerical.integration: Implementation of quadrature using double exponential, which allows integration with a variable transformation.
DoubleExponentialRuleQuadrature(DoubleExponentialSingleDimensionFunctionEvaluatorListener, double, double) - Constructor for class com.irurueta.numerical.integration.DoubleExponentialRuleQuadrature: Constructor with default maximum step size.
DoubleExponentialRuleQuadrature(DoubleExponentialSingleDimensionFunctionEvaluatorListener, double, double, double) - Constructor for class com.irurueta.numerical.integration.DoubleExponentialRuleQuadrature: Constructor.
DoubleExponentialRuleQuadrature(SingleDimensionFunctionEvaluatorListener, double, double) - Constructor for class com.irurueta.numerical.integration.DoubleExponentialRuleQuadrature: Constructor with default maximum step size.
DoubleExponentialRuleQuadrature(SingleDimensionFunctionEvaluatorListener, double, double, double) - Constructor for class com.irurueta.numerical.integration.DoubleExponentialRuleQuadrature: Constructor.
DoubleExponentialRuleQuadratureIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using double exponential quadrature.
DoubleExponentialRuleQuadratureIntegrator(double, double, double, DoubleExponentialSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.DoubleExponentialRuleQuadratureIntegrator: Constructor with default accuracy.
DoubleExponentialRuleQuadratureIntegrator(double, double, double, DoubleExponentialSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.DoubleExponentialRuleQuadratureIntegrator: Constructor.
DoubleExponentialRuleQuadratureIntegrator(double, double, double, SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.DoubleExponentialRuleQuadratureIntegrator: Constructor with default accuracy.
DoubleExponentialRuleQuadratureIntegrator(double, double, double, SingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.DoubleExponentialRuleQuadratureIntegrator: Constructor.
DoubleExponentialRuleQuadratureIntegrator(double, double, DoubleExponentialSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.DoubleExponentialRuleQuadratureIntegrator: Constructor with default accuracy and default maximum step size.
DoubleExponentialRuleQuadratureIntegrator(double, double, DoubleExponentialSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.DoubleExponentialRuleQuadratureIntegrator: Constructor with default maximum step size.
DoubleExponentialRuleQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.DoubleExponentialRuleQuadratureIntegrator: Constructor with default accuracy and default maximum step size.
DoubleExponentialRuleQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.DoubleExponentialRuleQuadratureIntegrator: Constructor.
DoubleExponentialRuleQuadratureMatrixIntegrator - Class in com.irurueta.numerical.integration: Computes matrix function integration by using double exponential quadrature.
DoubleExponentialRuleQuadratureMatrixIntegrator(double, double, double, DoubleExponentialMatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.DoubleExponentialRuleQuadratureMatrixIntegrator: Constructor with default accuracy.
DoubleExponentialRuleQuadratureMatrixIntegrator(double, double, double, DoubleExponentialMatrixSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.DoubleExponentialRuleQuadratureMatrixIntegrator: Constructor.
DoubleExponentialRuleQuadratureMatrixIntegrator(double, double, double, MatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.DoubleExponentialRuleQuadratureMatrixIntegrator: Constructor with default accuracy.
DoubleExponentialRuleQuadratureMatrixIntegrator(double, double, double, MatrixSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.DoubleExponentialRuleQuadratureMatrixIntegrator: Constructor.
DoubleExponentialRuleQuadratureMatrixIntegrator(double, double, DoubleExponentialMatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.DoubleExponentialRuleQuadratureMatrixIntegrator: Constructor with default accuracy and default maximum step size.
DoubleExponentialRuleQuadratureMatrixIntegrator(double, double, DoubleExponentialMatrixSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.DoubleExponentialRuleQuadratureMatrixIntegrator: Constructor with default maximum step size.
DoubleExponentialRuleQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.DoubleExponentialRuleQuadratureMatrixIntegrator: Constructor with default accuracy and default maximum step size.
DoubleExponentialRuleQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.DoubleExponentialRuleQuadratureMatrixIntegrator: Constructor.
DoubleExponentialSingleDimensionFunctionEvaluatorListener - Interface in com.irurueta.numerical.integration: Interface to define how single dimension functions can be evaluated in Double Exponential Rule Quadrature function integrators.
DoubleFactorialEstimator - Class in com.irurueta.numerical: Estimates factorial values as double precision floating point values.
DoubleFactorialEstimator() - Constructor for class com.irurueta.numerical.DoubleFactorialEstimator: Constructor with default cache size.
DoubleFactorialEstimator(int) - Constructor for class com.irurueta.numerical.DoubleFactorialEstimator: Constructor.
dp - Variable in class com.irurueta.numerical.signal.processing.KalmanFilter: Number of state vector dimensions (dynamic parameters).
dstar - Variable in class com.irurueta.numerical.interpolation.KrigingInterpolator
dy - Variable in class com.irurueta.numerical.interpolation.PolynomialInterpolator: An indication of interpolation error on the y values of the last call to BaseInterpolator.interpolate(double).
dy - Variable in class com.irurueta.numerical.interpolation.RationalInterpolator: An indication of interpolation error on the y values of the last call to BaseInterpolator.interpolate(double).

E

edgeMethod - Variable in class com.irurueta.numerical.signal.processing.Convolver1D: Edge extension method to use during convolution when parts of the kernel are required to lie outside the signal's boundaries.
endX - Variable in class com.irurueta.numerical.polynomials.estimators.IntegralIntervalPolynomialEvaluation: End point of interval being integrated.
eps - Variable in class com.irurueta.numerical.integration.QuadratureIntegrator: Required accuracy.
eps - Variable in class com.irurueta.numerical.integration.QuadratureMatrixIntegrator: Required accuracy.
eps - Variable in class com.irurueta.numerical.integration.RombergIntegrator: Required accuracy.
eps - Variable in class com.irurueta.numerical.integration.RombergMatrixIntegrator: Required accuracy.
eps - Variable in class com.irurueta.numerical.integration.SimpsonIntegrator: Required accuracy.
eps - Variable in class com.irurueta.numerical.integration.SimpsonMatrixIntegrator: Required accuracy.
EPS - Static variable in class com.irurueta.numerical.AccurateMaximumLikelihoodEstimator: Value to be considered as the machine precision.
EPS - Static variable in class com.irurueta.numerical.DerivativeEstimator: Constant defining machine precision for this algorithm.
EPS - Static variable in class com.irurueta.numerical.GradientEstimator: Constant considered as machine precision.
EPS - Static variable in class com.irurueta.numerical.HistogramMaximumLikelihoodEstimator: Value to be considered as the machine precision.
EPS - Static variable in class com.irurueta.numerical.integration.QuadratureIntegrator: Default accuracy.
EPS - Static variable in class com.irurueta.numerical.integration.QuadratureMatrixIntegrator: Default accuracy.
EPS - Static variable in class com.irurueta.numerical.integration.RombergIntegrator: Default accuracy.
EPS - Static variable in class com.irurueta.numerical.integration.RombergMatrixIntegrator: Default accuracy.
EPS - Static variable in class com.irurueta.numerical.integration.RombergTrapezoidalQuadratureIntegrator: Default accuracy.
EPS - Static variable in class com.irurueta.numerical.integration.RombergTrapezoidalQuadratureMatrixIntegrator: Default accuracy.
EPS - Static variable in class com.irurueta.numerical.integration.SimpsonIntegrator: Default accuracy.
EPS - Static variable in class com.irurueta.numerical.integration.SimpsonMatrixIntegrator: Default accuracy.
EPS - Static variable in class com.irurueta.numerical.JacobianEstimator: Constant considered as machine precision.
EPS - Static variable in class com.irurueta.numerical.optimization.ConjugateGradientMultiOptimizer: Constant defining a value to be considered as machine precision.
EPS - Static variable in class com.irurueta.numerical.optimization.DerivativeConjugateGradientMultiOptimizer: Constant defining a value to be considered as machine precision.
EPS - Static variable in class com.irurueta.numerical.optimization.QuasiNewtonMultiOptimizer: Machine precision.
EPS - Static variable in class com.irurueta.numerical.polynomials.Polynomial: Constant defining machine precision
EPS - Static variable in class com.irurueta.numerical.roots.BrentSingleRootEstimator: Constant defining a small value which is considered as machine precision.
EPS - Static variable in class com.irurueta.numerical.roots.FirstDegreePolynomialRootsEstimator: Constant defining machine precision.
EPS - Static variable in class com.irurueta.numerical.roots.LaguerrePolynomialRootsEstimator: Constant considered as machine precision.
EPS - Static variable in class com.irurueta.numerical.roots.SecondDegreePolynomialRootsEstimator: Constant defining machine precision.
EPS - Static variable in class com.irurueta.numerical.roots.ThirdDegreePolynomialRootsEstimator: Constant defining machine precision.
errorCovPost - Variable in class com.irurueta.numerical.signal.processing.KalmanFilter: Posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k)
errorCovPre - Variable in class com.irurueta.numerical.signal.processing.KalmanFilter: Priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q)
estimate() - Method in class com.irurueta.numerical.AccurateMaximumLikelihoodEstimator: Starts the estimation of the most likely value contained within provided input data array.
estimate() - Method in class com.irurueta.numerical.HistogramMaximumLikelihoodEstimator: Starts the estimation of the most likely value contained within provided input data array.
estimate() - Method in class com.irurueta.numerical.MaximumLikelihoodEstimator: Starts the estimation of the most likely value contained within provided input data array.
estimate() - Method in class com.irurueta.numerical.polynomials.estimators.LMedSPolynomialRobustEstimator: Estimates polynomial.
estimate() - Method in class com.irurueta.numerical.polynomials.estimators.LMSEPolynomialEstimator: Estimates a polynomial based on provided evaluations.
estimate() - Method in class com.irurueta.numerical.polynomials.estimators.MSACPolynomialRobustEstimator: Estimates polynomial.
estimate() - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Estimates a polynomial based on provided evaluations.
estimate() - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Estimates polynomial.
estimate() - Method in class com.irurueta.numerical.polynomials.estimators.PROMedSPolynomialRobustEstimator: Estimates polynomial.
estimate() - Method in class com.irurueta.numerical.polynomials.estimators.PROSACPolynomialRobustEstimator: Estimates polynomial.
estimate() - Method in class com.irurueta.numerical.polynomials.estimators.RANSACPolynomialRobustEstimator: Estimates polynomial.
estimate() - Method in class com.irurueta.numerical.polynomials.estimators.WeightedPolynomialEstimator: Estimates a polynomial based on provided evaluations.
estimate() - Method in class com.irurueta.numerical.robust.LMedSRobustEstimator: Robustly estimates an instance of T.
estimate() - Method in class com.irurueta.numerical.robust.MSACRobustEstimator: Robustly estimates an instance of T.
estimate() - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Robustly estimates an instance of T.
estimate() - Method in class com.irurueta.numerical.robust.PROSACRobustEstimator: Robustly estimates an instance of T.
estimate() - Method in class com.irurueta.numerical.robust.RANSACRobustEstimator: Robustly estimates an instance of T.
estimate() - Method in class com.irurueta.numerical.robust.RobustEstimator: Robustly estimates an instance of T.
estimate() - Method in class com.irurueta.numerical.roots.BisectionSingleRootEstimator: Estimates a single root of the provided single dimension function contained within a given bracket of values.
estimate() - Method in class com.irurueta.numerical.roots.BrentSingleRootEstimator: Estimates a local root for a given single dimension function being evaluated by provided listener.
estimate() - Method in class com.irurueta.numerical.roots.FalsePositionSingleRootEstimator: Estimates a single root of the provided single dimension function contained within a given bracket of values.
estimate() - Method in class com.irurueta.numerical.roots.FirstDegreePolynomialRootsEstimator: Estimates the root of provided polynomial.
estimate() - Method in class com.irurueta.numerical.roots.LaguerrePolynomialRootsEstimator: Estimates the roots of provided polynomial.
estimate() - Method in class com.irurueta.numerical.roots.NewtonRaphsonSingleRootEstimator: Estimates a local root for a given single dimension function being evaluated by provided listener.
estimate() - Method in class com.irurueta.numerical.roots.RidderSingleRootEstimator: Estimates a local root for a given single dimension function being evaluated by provided listener.
estimate() - Method in class com.irurueta.numerical.roots.RootEstimator: Estimates the root or roots for a given function.
estimate() - Method in class com.irurueta.numerical.roots.SafeNewtonRaphsonSingleRootEstimator: Estimates a local root for a given single dimension function being evaluated by provided listener.
estimate() - Method in class com.irurueta.numerical.roots.SecantSingleRootEstimator: Estimates a local root for a given single dimension function being evaluated by provided listener.
estimate() - Method in class com.irurueta.numerical.roots.SecondDegreePolynomialRootsEstimator: Estimates the roots of provided polynomial.
estimate() - Method in class com.irurueta.numerical.roots.ThirdDegreePolynomialRootsEstimator: Estimates the roots of provided polynomial.
estimate(double[], double[]) - Method in class com.irurueta.numerical.interpolation.InterpolatingPolynomialEstimator: Estimates polynomial from provided x and y points.
estimate(double[], double[], double[]) - Method in class com.irurueta.numerical.interpolation.AccurateInterpolatingPolynomialEstimator: Estimates polynomial coefficients from provided x and y points.
estimate(double[], double[], double[]) - Method in class com.irurueta.numerical.interpolation.InterpolatingPolynomialEstimator: Estimates polynomial coefficients from provided x and y points.
estimate(double[], double[], double[]) - Method in class com.irurueta.numerical.interpolation.SimpleInterpolatingPolynomialEstimator: Estimates polynomial coefficients from provided x and y points.
estimate(double[], double[], Polynomial) - Method in class com.irurueta.numerical.interpolation.InterpolatingPolynomialEstimator: Estimates polynomial from provided x and y points.
estimateCoefficients(double[], double[]) - Method in class com.irurueta.numerical.interpolation.InterpolatingPolynomialEstimator: Estimates polynomial coefficients from provided x and y points.
estimatedThreshold - Variable in class com.irurueta.numerical.robust.LMedSRobustEstimator.LMedSInliersData: Estimated threshold to determine whether samples are inliers or not.
estimatedThreshold - Variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator.PROMedSInliersData: Estimated threshold to determine whether samples are inliers or not.
estimatePadeCoefficients(double[]) - Method in class com.irurueta.numerical.PadeApproximantEstimator: Estimates Padé coefficients for provided Taylor power series ones.
estimatePadeCoefficients(double[], double[], double[]) - Method in class com.irurueta.numerical.PadeApproximantEstimator: Estimates Padé coefficients for provided Taylor power series ones.
estimatePadeCoefficients(double[], int, double[], double[]) - Method in class com.irurueta.numerical.PadeApproximantEstimator: Estimates Padé coefficients for provided Taylor power series ones.
estimatePreliminarSolutions(int[], List<T>) - Method in interface com.irurueta.numerical.robust.LMedSRobustEstimatorListener: Estimates a list of possible preliminary solutions to be used during an iteration of LMedS robust estimator.
eta0 - Variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: eta0 is the maximum probability that a solution with more than inliersNStar inliers in U_nStar exists and was not found after k samples (typically set to 5%).
eta0 - Variable in class com.irurueta.numerical.robust.PROSACRobustEstimator: eta0 is the maximum probability that a solution with more than inliersNStar inliers in U_nStar exists and was not found after k samples (typically set to 5%).
evaluate(double) - Method in class com.irurueta.numerical.AccurateMaximumLikelihoodEstimator.EvaluatorListener: Evaluates the aggregation of Gaussians for all the samples in input data array, by assuming that each sample has an associated small Gaussian centered at the sample value and with a small sigma value.
evaluate(double) - Method in class com.irurueta.numerical.interpolation.GaussianRadialBasisFunction: Evaluates RBF at provided distance between two points.
evaluate(double) - Method in class com.irurueta.numerical.interpolation.InverseMultiQuadricRadialBasisFunction: Evaluates RBF at provided distance between two points.
evaluate(double) - Method in class com.irurueta.numerical.interpolation.KrigingInterpolator.Variogram: Evaluates variogram at provided distance.
evaluate(double) - Method in class com.irurueta.numerical.interpolation.MultiQuadricRadialBasisFunction: Evaluates RBF at provided distance between two points.
evaluate(double) - Method in interface com.irurueta.numerical.interpolation.RadialBasisFunction: Evaluates RBF at provided distance between two points.
evaluate(double) - Method in class com.irurueta.numerical.interpolation.ThinPlateRadialBasisFunction: Evaluates RBF at provided distance between two points.
evaluate(double) - Method in class com.irurueta.numerical.polynomials.Polynomial: Evaluates polynomial at provided value.
evaluate(double) - Method in class com.irurueta.numerical.RealPolynomialEvaluator: Evaluates polynomial at provided point x.
evaluate(double) - Method in interface com.irurueta.numerical.SingleDimensionFunctionEvaluatorListener: Evaluates a single dimension function such as f(x) at provided point and returns the result.
evaluate(double[]) - Method in interface com.irurueta.numerical.MultiDimensionFunctionEvaluatorListener: Evaluates a multi dimension function such as f([x1, x2, ..., xn]) at provided multidimensional point and returns the result as a scalar value.
evaluate(double[], double) - Static method in class com.irurueta.numerical.PolynomialEvaluator: Evaluates polynomial formed by provided polynomial parameters at provided point x.
evaluate(double[], double[]) - Method in interface com.irurueta.numerical.fitting.LinearFitterMultiDimensionFunctionEvaluator: Evaluates a linear multi dimension function at provided point and returns the evaluations of the basis functions at such point
evaluate(double[], double[]) - Method in interface com.irurueta.numerical.MultiVariateFunctionEvaluatorListener: Evaluates a multi variate function such as f1(x1, x2, ...), f2(x1, x2, ...) at provided multidimensional point and returns the result as a vectorial value
evaluate(double, double) - Method in interface com.irurueta.numerical.integration.DoubleExponentialSingleDimensionFunctionEvaluatorListener: Evaluates a single dimension function such as f(x) at provided point and returns the result.
evaluate(double, double[]) - Method in interface com.irurueta.numerical.fitting.LinearFitterSingleDimensionFunctionEvaluator: Evaluates a linear single dimension function at provided point and returns the evaluations of the basis functions at such point
evaluate(double, double, Matrix) - Method in interface com.irurueta.numerical.integration.DoubleExponentialMatrixSingleDimensionFunctionEvaluatorListener: Evaluates a single dimension function such as f(x) at provided point and returns the result.
evaluate(double, Matrix) - Method in interface com.irurueta.numerical.integration.MatrixSingleDimensionFunctionEvaluatorListener: Evaluates a matrix function such as f(x1) at provided point and returns the result as a matrix.
evaluate(int, double[], double[], double[]) - Method in interface com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFunctionEvaluator: Evaluates a non-linear multi dimension function at provided point using provided parameters and returns its evaluation and derivatives of the function respect the function parameters.
evaluate(int, double[], double[], double[], Matrix) - Method in interface com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFunctionEvaluator: Evaluates a non-linear multi variate function at provided point using provided parameters and returns its evaluation and jacobian of the function respect the function parameters
evaluate(int, double, double[], double[]) - Method in interface com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFunctionEvaluator: Evaluates a non-linear single dimension function at provided point using provided parameters and returns its evaluation and derivatives of the function respect the function parameters
evaluate(Complex) - Method in class com.irurueta.numerical.ComplexPolynomialEvaluator: Evaluates polynomial at provided point x.
evaluate(Complex[], Complex) - Static method in class com.irurueta.numerical.PolynomialEvaluator: Evaluates polynomial formed by provided polynomial parameters at provided point x.
evaluateAt(double) - Method in class com.irurueta.numerical.DirectionalEvaluator: Evaluates a function using current listener at a distance x from current point using current direction
evaluateBracket() - Method in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Computes function evaluations at provided or estimated bracket locations.
evaluateDerivative(double) - Method in class com.irurueta.numerical.polynomials.Polynomial: Evaluates derivative of polynomial at provided value.
evaluateGradient(double[], double[]) - Method in interface com.irurueta.numerical.GradientFunctionEvaluatorListener: Computes/retrieves a multidimensional function's gradient.
evaluateNthDerivative(double, int) - Method in class com.irurueta.numerical.polynomials.Polynomial: Evaluates nth-derivative of polynomial at provided value.
evaluateSecondDerivative(double) - Method in class com.irurueta.numerical.polynomials.Polynomial: Evaluates second derivative of polynomial at provided value.
evaluation - Variable in class com.irurueta.numerical.polynomials.estimators.PolynomialEvaluation: Evaluation of polynomial at point x.
EvaluationException - Exception in com.irurueta.numerical: Exception raised when function evaluation fails.
EvaluationException() - Constructor for exception com.irurueta.numerical.EvaluationException: Constructor.
EvaluationException(String) - Constructor for exception com.irurueta.numerical.EvaluationException: Constructor with String containing message.
EvaluationException(String, Throwable) - Constructor for exception com.irurueta.numerical.EvaluationException: Constructor with message and cause.
EvaluationException(Throwable) - Constructor for exception com.irurueta.numerical.EvaluationException: Constructor with cause.
evaluations - Variable in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Collection of polynomial evaluations and their corresponding point of evaluation used to determine a polynomial of required degree.
evaluations - Variable in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Collection of polynomial evaluations and their corresponding point of evaluation used to determine a polynomial of required degree.
evaluator - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Evaluator of functions.
evaluator - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Evaluator of functions.
evaluator - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Evaluator of functions.
evaluator - Variable in class com.irurueta.numerical.fitting.MultiDimensionLinearFitter: Evaluator of functions
evaluator - Variable in class com.irurueta.numerical.fitting.SingleDimensionLinearFitter: Evaluator of functions.
evaluator - Variable in class com.irurueta.numerical.optimization.DerivativeLineMultiOptimizer: Class in charge of evaluating a function through a given line.
evaluator - Variable in class com.irurueta.numerical.optimization.LineMultiOptimizer: Class in charge of evaluating a function through a given line.
EvaluatorListener() - Constructor for class com.irurueta.numerical.AccurateMaximumLikelihoodEstimator.EvaluatorListener
exponential(Matrix) - Method in class com.irurueta.numerical.ExponentialMatrixEstimator: Estimates exponential of provided matrix with default error tolerance.
exponential(Matrix, double) - Method in class com.irurueta.numerical.ExponentialMatrixEstimator: Estimates exponential of provided matrix.
exponential(Matrix, Matrix) - Method in class com.irurueta.numerical.ExponentialMatrixEstimator: Estimates exponential of provided matrix with default error tolerance.
exponential(Matrix, Matrix, double) - Method in class com.irurueta.numerical.ExponentialMatrixEstimator: Estimates exponential of provided matrix.
EXPONENTIAL_MID_POINT - Enum constant in enum class com.irurueta.numerical.integration.QuadratureType: Exponential mid-point quadrature.
ExponentialMatrixEstimator - Class in com.irurueta.numerical: Estimates exponential of a square matrix.
ExponentialMatrixEstimator() - Constructor for class com.irurueta.numerical.ExponentialMatrixEstimator
ExponentialMidPointMatrixQuadrature - Class in com.irurueta.numerical.integration: This is an exact replacement for MidPointMatrixQuadrature, except that upper limit is assumed to be infinite.
ExponentialMidPointMatrixQuadrature(double, MatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.ExponentialMidPointMatrixQuadrature: Constructor.
ExponentialMidPointQuadrature - Class in com.irurueta.numerical.integration: This is an exact replacement for MidPointQuadrature, except that upper limit is assumed to be infinite.
ExponentialMidPointQuadrature(double, SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.ExponentialMidPointQuadrature: Constructor.

F

f - Variable in class com.irurueta.numerical.ExponentialMatrixEstimator: Contains copy of estimated result.
fa - Variable in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Function evaluation value at minimum evaluation point inside the bracket.
FACTOR - Static variable in class com.irurueta.numerical.roots.BracketedSingleRootEstimator: Factor to use to modify initial values when searching for a bracket.
factorial(int) - Method in class com.irurueta.numerical.DoubleFactorialEstimator: Gets factorial of provided value represented with double precision.
factorial(int) - Method in class com.irurueta.numerical.LongFactorialEstimator: Gets factorial of provided value represented with double precision.
factorialEstimator - Variable in class com.irurueta.numerical.ExponentialMatrixEstimator: Estimates factorial values.
FalsePositionSingleRootEstimator - Class in com.irurueta.numerical.roots: Computes a root for a single dimension function inside a given bracket of values, in other words, root will only be searched within provided minimum and maximum evaluation points.
FalsePositionSingleRootEstimator() - Constructor for class com.irurueta.numerical.roots.FalsePositionSingleRootEstimator: Empty constructor.
FalsePositionSingleRootEstimator(SingleDimensionFunctionEvaluatorListener, double, double, double) - Constructor for class com.irurueta.numerical.roots.FalsePositionSingleRootEstimator: Constructor.
FAST_RANDOM_SUBSET_SELECTOR - Enum constant in enum class com.irurueta.numerical.robust.SubsetSelectorType
FastRandomSubsetSelector - Class in com.irurueta.numerical.robust: This class computes indices of subsets of samples using a uniform randomizer to pick random samples as fast as possible.
FastRandomSubsetSelector(int) - Constructor for class com.irurueta.numerical.robust.FastRandomSubsetSelector: Constructor.
FastRandomSubsetSelector(int, boolean) - Constructor for class com.irurueta.numerical.robust.FastRandomSubsetSelector: Constructor.
fb - Variable in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Function evaluation value at middle evaluation point inside the bracket.
fc - Variable in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Function evaluation value at maximum evaluation point inside the bracket.
fillDerivativeEvaluation(DerivativePolynomialEvaluation, Matrix, Matrix, int) - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Fills row of system of equations for a derivative polynomial evaluation.
fillDirectEvaluation(DirectPolynomialEvaluation, Matrix, Matrix, int) - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Fills row of system of equations for a direct polynomial evaluation.
fillIntegralEvaluation(IntegralPolynomialEvaluation, Matrix, Matrix, int) - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Fills row of system of equations for an integral polynomial evaluation.
fillIntegralIntervalEvaluation(IntegralIntervalPolynomialEvaluation, Matrix, Matrix, int) - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Fills row of system of equations for a polynomial evaluation of an interval integration.
findQForRelativeError(double) - Method in class com.irurueta.numerical.ExponentialMatrixEstimator: Finds required order of Padé approximant for provided maximum allowed relative error to be achieved.
findQForTolerance(double, double) - Method in class com.irurueta.numerical.ExponentialMatrixEstimator: Finds required order of Padé approximant for provided absolute error tolerance to be achieved.
FirstDegreePolynomialRootsEstimator - Class in com.irurueta.numerical.roots: Class to estimate the root of a first degree polynomial along with other polynomial properties.
FirstDegreePolynomialRootsEstimator() - Constructor for class com.irurueta.numerical.roots.FirstDegreePolynomialRootsEstimator: Empty constructor.
FirstDegreePolynomialRootsEstimator(double[]) - Constructor for class com.irurueta.numerical.roots.FirstDegreePolynomialRootsEstimator: Constructor.
fit() - Method in class com.irurueta.numerical.fitting.Fitter: Fits a function to provided data so that parameters associated to that function can be estimated along with their covariance matrix and chi square value
fit() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Fits a function to provided data so that parameters associated to that function can be estimated along with their covariance matrix and chi square value.
fit() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Fits a function to provided data so that parameters associated to that function can be estimated along with their covariance matrix and chi square value.
fit() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Fits a function to provided data so that parameters associated to that function can be estimated along with their covariance matrix and chi square value.
fit() - Method in class com.irurueta.numerical.fitting.SimpleSingleDimensionLinearFitter: Fits a function to provided data so that parameters associated to that function can be estimated along with their covariance matrix and chi square value.
fit() - Method in class com.irurueta.numerical.fitting.StraightLineFitter: Fits a straight line following equation y = a + b*x to provided data (x, y) so that parameters associated a, b can be estimated along with their variances, covariance and chi square value.
fit() - Method in class com.irurueta.numerical.fitting.SvdMultiDimensionLinearFitter: Fits a function to provided data so that parameters associated to that function can be estimated along with their covariance matrix and chi square value.
fit() - Method in class com.irurueta.numerical.fitting.SvdSingleDimensionLinearFitter: Fits a function to provided data so that parameters associated to that function can be estimated along with their covariance matrix and chi square value.
Fitter - Class in com.irurueta.numerical.fitting: Base class for function fitters used to estimate function parameters along with their covariance matrix and chi square value
Fitter() - Constructor for class com.irurueta.numerical.fitting.Fitter
FittingException - Exception in com.irurueta.numerical.fitting: Raised when a fitter fails to fit a function to provided data.
FittingException() - Constructor for exception com.irurueta.numerical.fitting.FittingException: Constructor
FittingException(String) - Constructor for exception com.irurueta.numerical.fitting.FittingException: Constructor with String containing message
FittingException(String, Throwable) - Constructor for exception com.irurueta.numerical.fitting.FittingException: Constructor with message and cause
FittingException(Throwable) - Constructor for exception com.irurueta.numerical.fitting.FittingException: Constructor with cause
fitWithoutSig() - Method in class com.irurueta.numerical.fitting.StraightLineFitter: Fits data when standard deviations of input data is not provided.
fitWithSig() - Method in class com.irurueta.numerical.fitting.StraightLineFitter: Fits data when standard deviations of input data is provided.
fmin - Variable in class com.irurueta.numerical.optimization.MultiOptimizer: Function value at estimated minimum.
fmin - Variable in class com.irurueta.numerical.optimization.SingleOptimizer: Function evaluation at minimum that has been found.
fn - Variable in class com.irurueta.numerical.interpolation.RadialBasisFunctionInterpolator: Radial basis function defining a value based on the distance of two points.
fprime(double[], double[], int, int, int) - Method in class com.irurueta.numerical.interpolation.CurveInterpolator: Utility for estimating the derivatives at the endpoints, x and y point to the abscissa and ordinate of the endpoint.
frac - Static variable in class com.irurueta.numerical.roots.LaguerrePolynomialRootsEstimator: Array containing values for Laguerre method.
free(int) - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Releases parameter at position i of linear combination of basis functions, so it can be modified again if needed.
free(int) - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Releases parameter at position i of linear combination of basis functions, so it can be modified again if needed.
free(int) - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Releases parameter at position i of linear combination of basis functions, so it can be modified again if needed.
free(int) - Method in class com.irurueta.numerical.fitting.SimpleSingleDimensionLinearFitter: Releases parameter at position i of linear combination of basis functions, so it can be modified again if needed.
fret - Variable in class com.irurueta.numerical.optimization.ConjugateGradientMultiOptimizer: Value of the function at the minimum.
fret - Variable in class com.irurueta.numerical.optimization.DerivativeConjugateGradientMultiOptimizer: Value of the function at the minimum.
fret - Variable in class com.irurueta.numerical.optimization.PowellMultiOptimizer: Value of the function at the minimum.
fret - Variable in class com.irurueta.numerical.optimization.QuasiNewtonMultiOptimizer: value of the function at the minimum.
ftol - Variable in class com.irurueta.numerical.optimization.SimplexMultiOptimizer: The fractional tolerance in the function value such that failure to decrease by more than this amount on one iteration signals doneness.
func(double) - Method in class com.irurueta.numerical.integration.ExponentialMidPointQuadrature: Evaluates function at f(-log(x))/x.
func(double) - Method in class com.irurueta.numerical.integration.InfinityMidPointQuadrature: Evaluates function at 1/x.
func(double) - Method in class com.irurueta.numerical.integration.LowerSquareRootMidPointQuadrature: Evaluates function at 2*x*f(a0+x^2).
func(double) - Method in class com.irurueta.numerical.integration.MidPointQuadrature: Evaluates function at x.
func(double) - Method in class com.irurueta.numerical.integration.UpperSquareRootMidPointQuadrature: Evaluates function at 2*x*f(a0+x^2).
func(double, Matrix) - Method in class com.irurueta.numerical.integration.ExponentialMidPointMatrixQuadrature: Evaluates function at f(-log(x))/x.
func(double, Matrix) - Method in class com.irurueta.numerical.integration.InfinityMidPointMatrixQuadrature: Evaluates function at 1/x.
func(double, Matrix) - Method in class com.irurueta.numerical.integration.LowerSquareRootMidPointMatrixQuadrature: Evaluates function at 2*x*f(a0+x^2).
func(double, Matrix) - Method in class com.irurueta.numerical.integration.MidPointMatrixQuadrature: Evaluates matrix function at x.
func(double, Matrix) - Method in class com.irurueta.numerical.integration.UpperSquareRootMidPointMatrixQuadrature: Evaluates function at 2*x*f(a0+x^2).

G

gain - Variable in class com.irurueta.numerical.signal.processing.KalmanFilter: Kalman gain matrix (K(k)): K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)
gaussian - Variable in class com.irurueta.numerical.HistogramMaximumLikelihoodEstimator: Array containing the Gaussian values corresponding to each bin for a single sample.
GaussianRadialBasisFunction - Class in com.irurueta.numerical.interpolation: Gaussian Radial Basis Function implementation.
GaussianRadialBasisFunction() - Constructor for class com.irurueta.numerical.interpolation.GaussianRadialBasisFunction: Constructor.
GaussianRadialBasisFunction(double) - Constructor for class com.irurueta.numerical.interpolation.GaussianRadialBasisFunction: Constructor.
gaussianSigma - Variable in class com.irurueta.numerical.MaximumLikelihoodEstimator: Actual Gaussian sigma to be used on each sample when aggregating Gaussian functions centered at each input data sample value.
getA() - Method in class com.irurueta.numerical.fitting.MultiDimensionFitter: Returns estimated parameters of linear single dimensional function
getA() - Method in class com.irurueta.numerical.fitting.MultiVariateFitter: Returns estimated parameters of linear single dimensional function.
getA() - Method in class com.irurueta.numerical.fitting.SingleDimensionFitter: Returns estimated parameters of linear single dimensional function.
getA() - Method in class com.irurueta.numerical.fitting.StraightLineFitter: Returns estimated "a" parameter of line following equation y = a + b*x
getA() - Method in class com.irurueta.numerical.integration.MidPointMatrixQuadrature: Gets lower limit of integration.
getA() - Method in class com.irurueta.numerical.integration.MidPointQuadrature: Gets lower limit of integration.
getA() - Method in class com.irurueta.numerical.integration.TrapezoidalMatrixQuadrature: Gets lower limit of integration.
getA() - Method in class com.irurueta.numerical.integration.TrapezoidalQuadrature: Gets lower limit of integration.
getAlgebraicDistance(DerivativePolynomialEvaluation, Polynomial) - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Computes algebraic distance of a derivative between provided polynomial and evaluation.
getAlgebraicDistance(DirectPolynomialEvaluation, Polynomial) - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Computes algebraic distance of between provided polynomial and direct evaluation.
getAlgebraicDistance(IntegralIntervalPolynomialEvaluation, Polynomial) - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Computes algebraic distance of an integration interval between provided polynomial and evaluation.
getAlgebraicDistance(IntegralPolynomialEvaluation, Polynomial) - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Computes algebraic distance of an integral between provided polynomial and evaluation.
getAlgebraicDistance(PolynomialEvaluation, Polynomial) - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Computes algebraic distance between provided polynomial and evaluation.
getAlpha() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Returns curvature matrix.
getAlpha() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Returns curvature matrix.
getAlpha() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Returns curvature matrix.
getArtifactId() - Method in class com.irurueta.numerical.BuildInfo: Obtains artifactId of this library.
getB() - Method in class com.irurueta.numerical.fitting.StraightLineFitter: Returns estimated "b" parameter of line following equation y = a + b*x
getB() - Method in class com.irurueta.numerical.integration.MidPointMatrixQuadrature: Gets upper limit of integration.
getB() - Method in class com.irurueta.numerical.integration.MidPointQuadrature: Gets upper limit of integration.
getB() - Method in class com.irurueta.numerical.integration.TrapezoidalMatrixQuadrature: Gets upper limit of integration.
getB() - Method in class com.irurueta.numerical.integration.TrapezoidalQuadrature: Gets upper limit of integration.
getBestInliersData() - Method in class com.irurueta.numerical.robust.LMedSRobustEstimator: Returns data related to inliers found for best result.
getBestInliersData() - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Returns data related to inliers found for best result.
getBestInliersData() - Method in class com.irurueta.numerical.robust.PROSACRobustEstimator: Gets data related to inliers found for best result.
getBestInliersData() - Method in class com.irurueta.numerical.robust.RANSACRobustEstimator: Gets data related to inliers found for best result.
getBestMedianResidual() - Method in class com.irurueta.numerical.robust.LMedSRobustEstimator.LMedSInliersData: Returns best median of error found so far taking into account all provided samples.
getBestMedianResidual() - Method in class com.irurueta.numerical.robust.MSACRobustEstimator.MSACInliersData: Returns best median of error found so far taking into account all provided samples.
getBestMedianResidual() - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator.PROMedSInliersData: Returns best median of error found so far taking into account all provided samples.
getBestNumberInliersData() - Method in class com.irurueta.numerical.robust.MSACRobustEstimator: Returns data related to solution producing the largest number of inliers.
getBestResult() - Method in class com.irurueta.numerical.robust.LMedSRobustEstimator: Returns best solution that has been found so far during an estimation.
getBestResult() - Method in class com.irurueta.numerical.robust.MSACRobustEstimator: Returns best solution that has been found so far during an estimation.
getBestResult() - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Returns best solution that has been found so far during an estimation.
getBestResult() - Method in class com.irurueta.numerical.robust.PROSACRobustEstimator: Returns best solution that has been found so far during an estimation.
getBestResult() - Method in class com.irurueta.numerical.robust.RANSACRobustEstimator: Returns best solution that has been found so far during an estimation.
getBestResultInliersData() - Method in class com.irurueta.numerical.robust.MSACRobustEstimator: Returns data related to the best inliers found for best result.
getBeta() - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Returns beta, which is the probability that a match is declared inlier by mistake, i.e. the ratio of the "inlier" surface by the total surface.
getBeta() - Method in class com.irurueta.numerical.robust.PROSACRobustEstimator: Returns beta, which is the probability that a match is declared inlier by mistake, i.e. the ratio of the "inlier" surface by the total surface.
getBranch() - Method in class com.irurueta.numerical.BuildInfo: Obtains build branch.
getBuildNumber() - Method in class com.irurueta.numerical.BuildInfo: Obtains build number.
getBuildTimestamp() - Method in class com.irurueta.numerical.BuildInfo: Obtains build timestamp.
getCacheSize() - Method in class com.irurueta.numerical.DoubleFactorialEstimator: Gets current cache size.
getCacheSize() - Method in class com.irurueta.numerical.LongFactorialEstimator: Gets current cache size.
getChi2() - Method in class com.irurueta.numerical.fitting.StraightLineFitter: Returns estimated chi square value.
getChisq() - Method in class com.irurueta.numerical.fitting.MultiDimensionFitter: Returns estimated chi square value of input data
getChisq() - Method in class com.irurueta.numerical.fitting.MultiVariateFitter: Returns estimated chi square value of input data.
getChisq() - Method in class com.irurueta.numerical.fitting.SingleDimensionFitter: Returns estimated chi square value of input data.
getChisqDegreesOfFreedom() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Returns degrees of freedom of computed chi square value.
getChisqDegreesOfFreedom() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Returns degrees of freedom of computed chi square value.
getChisqDegreesOfFreedom() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Returns degrees of freedom of computed chi square value.
getColumns() - Method in interface com.irurueta.numerical.integration.DoubleExponentialMatrixSingleDimensionFunctionEvaluatorListener: Gets number of columns of matrix result of function f.
getColumns() - Method in class com.irurueta.numerical.integration.DoubleExponentialRuleMatrixQuadrature: Gets number of columns of quadrature result.
getColumns() - Method in class com.irurueta.numerical.integration.MatrixQuadrature: Gets number of columns of quadrature result.
getColumns() - Method in interface com.irurueta.numerical.integration.MatrixSingleDimensionFunctionEvaluatorListener: Gets number of columns of matrix result of function f.
getColumns() - Method in class com.irurueta.numerical.integration.MidPointMatrixQuadrature: Gets number of columns of quadrature result.
getColumns() - Method in class com.irurueta.numerical.integration.TrapezoidalMatrixQuadrature: Gets number of columns of quadrature result.
getCommit() - Method in class com.irurueta.numerical.BuildInfo: Obtains build commit.
getConfidence() - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Returns amount of confidence expressed as a value between 0.0 and 1.0 (which is equivalent to 100%).
getConfidence() - Method in class com.irurueta.numerical.robust.LMedSRobustEstimator: Returns amount of confidence expressed as a value between 0 and 1.0 (which is equivalent to 100%).
getConfidence() - Method in class com.irurueta.numerical.robust.MSACRobustEstimator: Returns amount of confidence expressed as a value between 0 and 1.0 (which is equivalent to 100%).
getConfidence() - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Returns amount of confidence expressed as a value between 0 and 1.0 (which is equivalent to 100%).
getConfidence() - Method in class com.irurueta.numerical.robust.PROSACRobustEstimator: Returns amount of confidence expressed as a value between 0 and 1.0 (which is equivalent to 100%).
getConfidence() - Method in class com.irurueta.numerical.robust.RANSACRobustEstimator: Returns amount of confidence expressed as a value between 0 and 1.0 (which is equivalent to 100%).
getConstants() - Method in class com.irurueta.numerical.polynomials.estimators.IntegralIntervalPolynomialEvaluation: Gets constant terms of integral.
getConstants() - Method in class com.irurueta.numerical.polynomials.estimators.IntegralPolynomialEvaluation: Gets constant terms of integral.
getConstantValue() - Method in class com.irurueta.numerical.signal.processing.Convolver1D: Gets constant value to use during edge extension when CONSTANT_EDGE method is being used.
getControlMatrix() - Method in class com.irurueta.numerical.signal.processing.KalmanFilter: Obtains the control matrix (B) (it is not used if there is no control).
getControlParameters() - Method in class com.irurueta.numerical.signal.processing.KalmanFilter: Obtains the number of control vector dimensions (control parameters).
getCovar() - Method in class com.irurueta.numerical.fitting.MultiDimensionFitter: Returns covariance of estimated parameters of linear single dimensional function
getCovar() - Method in class com.irurueta.numerical.fitting.MultiVariateFitter: Returns covariance of estimated parameters of linear single dimensional function.
getCovar() - Method in class com.irurueta.numerical.fitting.SingleDimensionFitter: Returns covariance of estimated parameters of linear single dimensional function.
getD() - Method in class com.irurueta.numerical.interpolation.BarycentricRationalInterpolator: Gets order of desired approximation.
getDegree() - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Gets degree of polynomial to be estimated.
getDegree() - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Gets degree of polynomial to be estimated.
getDegree() - Method in class com.irurueta.numerical.polynomials.Polynomial: Gets degree of polynomial.
getDenominators() - Method in class com.irurueta.numerical.PadeApproximantEstimator.Result: Gets denominator coefficients.
getDerivativeListener() - Method in class com.irurueta.numerical.optimization.DerivativeBrentSingleOptimizer: Returns derivative listener to get function derivative.
getDerivativeListener() - Method in class com.irurueta.numerical.roots.DerivativeSingleRootEstimator: Returns derivative listener to evaluate a function's derivative.
getDerivativeOrder() - Method in class com.irurueta.numerical.polynomials.estimators.DerivativePolynomialEvaluation: Gets order of derivative.
getDirection() - Method in class com.irurueta.numerical.DirectionalEvaluator: Returns array indicating the direction of the line where the function is evaluated.
getDirection() - Method in class com.irurueta.numerical.optimization.DerivativeLineMultiOptimizer: Returns direction to start looking for a minimum.
getDirection() - Method in class com.irurueta.numerical.optimization.LineMultiOptimizer: Returns direction to start looking for a minimum.
getDirections() - Method in class com.irurueta.numerical.optimization.PowellMultiOptimizer: Returns set of directions to start looking for a minimum.
getDiscriminant(double[]) - Static method in class com.irurueta.numerical.roots.SecondDegreePolynomialRootsEstimator: Internal method to compute the discriminant of a 2nd degree polynomial.
getDiscriminant(double[]) - Static method in class com.irurueta.numerical.roots.ThirdDegreePolynomialRootsEstimator: Internal method to compute the discriminant of a 3rd degree polynomial.
getDistance(PolynomialEvaluation, Polynomial) - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Computes geometric or algebraic distance between provided polynomial and evaluation.
getDy() - Method in class com.irurueta.numerical.interpolation.PolynomialInterpolator: Gets an indication of the error of interpolation on the y values.
getDy() - Method in class com.irurueta.numerical.interpolation.RationalInterpolator: Gets an indication of the error of interpolation on the y values.
getDynamicParameters() - Method in class com.irurueta.numerical.signal.processing.KalmanFilter: Obtains the number of state vector dimensions (dynamic parameters).
getEdgeMethod() - Method in class com.irurueta.numerical.signal.processing.Convolver1D: Gets edge extension method to use during convolution when parts of the kernel are required to lie outside the signal's boundaries.
getEndX() - Method in class com.irurueta.numerical.polynomials.estimators.IntegralIntervalPolynomialEvaluation: Gets end point of interval being integrated.
getErrorCovPost() - Method in class com.irurueta.numerical.signal.processing.KalmanFilter: Obtains the posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k).
getErrorCovPre() - Method in class com.irurueta.numerical.signal.processing.KalmanFilter: Obtains the priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q).
getEstimatedThreshold() - Method in class com.irurueta.numerical.robust.LMedSRobustEstimator.LMedSInliersData: Returns estimated threshold to determine whether samples are inliers or not.
getEstimatedThreshold() - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator.PROMedSInliersData: Returns estimated threshold to determine whether samples are inliers or not.
getEta0() - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Return eta0, which is the maximum probability that a solution with more than inliersNStar inliers in U_nStar exists and was not found after k samples (typically set to 5%).
getEta0() - Method in class com.irurueta.numerical.robust.PROSACRobustEstimator: Return eta0, which is the maximum probability that a solution with more than inliersNStar inliers in U_nStar exists and was not found after k samples (typically set to 5%).
getEvaluation() - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialEvaluation: Gets evaluation of polynomial at point x.
getEvaluationAtMax() - Method in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Returns single dimension function evaluation at provided or computed maximum evaluation point where the bracket finishes.
getEvaluationAtMiddle() - Method in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Returns single dimension function evaluation at provided or computed middle evaluation point within the bracket.
getEvaluationAtMin() - Method in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Returns single dimension function evaluation at provided or computed minimum evaluation point where the bracket starts.
getEvaluationAtResult() - Method in class com.irurueta.numerical.optimization.MultiOptimizer: Returns function evaluation at estimated minimum point.
getEvaluationAtResult() - Method in class com.irurueta.numerical.optimization.SingleOptimizer: Returns function evaluation at minimum that has been found.
getEvaluations() - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Gets collection of polynomial evaluations and their corresponding point of evaluation used to determine a polynomial of required degree.
getEvaluations() - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Gets collection of polynomial evaluations and their corresponding point of evaluation used to determine a polynomial of required degree.
getEvaluationsAtSimplex() - Method in class com.irurueta.numerical.optimization.SimplexMultiOptimizer: Returns function evaluations at simplex points or vertices.
getExtrema() - Method in class com.irurueta.numerical.polynomials.Polynomial: Gets location of minima or maxima (i.e. extrema) in this polynomial.
getExtrema(double) - Method in class com.irurueta.numerical.polynomials.Polynomial: Gets location of minima or maxima (i.e. extrema) in this polynomial.
getFunctionEvaluator() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Returns function evaluator to evaluate function at a given point and obtain function derivatives respect to each provided parameter.
getFunctionEvaluator() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Returns function evaluator to evaluate function at a given point and obtain function jacobian respect to each provided parameter.
getFunctionEvaluator() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Returns function evaluator to evaluate function at a given point and obtain function derivatives respect to each provided parameter
getFunctionEvaluator() - Method in class com.irurueta.numerical.fitting.MultiDimensionLinearFitter: Returns function evaluator to evaluate function at a given point and obtain the evaluation of function basis at such point
getFunctionEvaluator() - Method in class com.irurueta.numerical.fitting.SingleDimensionLinearFitter: Returns function evaluator to evaluate function at a given point and obtain the evaluation of function basis at such point.
getGain() - Method in class com.irurueta.numerical.signal.processing.KalmanFilter: Obtains the Kalman gain matrix (K(k)): K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R).
getGaussianSigma() - Method in class com.irurueta.numerical.MaximumLikelihoodEstimator: Returns Gaussian sigma to be used on each sample when aggregating Gaussian functions centered at each input data sample value.
getGeometricDistance(DirectPolynomialEvaluation, Polynomial) - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Computes distance of evaluation respect to provided polynomial in a geometric sense by computing a tangent line to polynomial at point x and comparing the distance of such line to provided evaluation point.
getGeometricOrAlgebraicDistance(PolynomialEvaluation, Polynomial) - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Commutes distance of evaluation respect to provided polynomial in a geometric sense if evaluation is direct, otherwise returns algebraic distance.
getGradientListener() - Method in class com.irurueta.numerical.DirectionalDerivativeEvaluator: Returns gradient listener that evaluates a multidimensional function gradient.
getGradientListener() - Method in class com.irurueta.numerical.optimization.ConjugateGradientMultiOptimizer: Returns gradient listener in charge of obtaining gradient values for the function to be evaluated.
getGradientListener() - Method in class com.irurueta.numerical.optimization.DerivativeLineMultiOptimizer: Returns gradient listener.
getGradientListener() - Method in class com.irurueta.numerical.optimization.QuasiNewtonMultiOptimizer: Returns gradient listener in charge of obtaining gradient values for the function to be evaluated.
getGroupId() - Method in class com.irurueta.numerical.BuildInfo: Obtains groupId of this library.
getInlierFactor() - Method in class com.irurueta.numerical.robust.LMedSRobustEstimator: Returns factor to normalize or adjust threshold to determine inliers.
getInlierFactor() - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Returns factor to normalize or adjust threshold to determine inliers.
getInliers() - Method in class com.irurueta.numerical.robust.InliersData: Returns efficient array indicating which samples are considered inliers and which ones aren't or null if inliers are not kept.
getInliers() - Method in class com.irurueta.numerical.robust.LMedSRobustEstimator.LMedSInliersData: Returns efficient array indicating which samples are considered inliers and which ones aren't.
getInliers() - Method in class com.irurueta.numerical.robust.MSACRobustEstimator.MSACInliersData: Returns efficient array indicating which samples are considered inliers and which ones aren't.
getInliers() - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator.PROMedSInliersData: Returns efficient array indicating which samples are considered inliers and which ones aren't.
getInliers() - Method in class com.irurueta.numerical.robust.PROSACRobustEstimator.PROSACInliersData: Returns efficient array indicating which samples are considered inliers and which ones aren't.
getInliers() - Method in class com.irurueta.numerical.robust.RANSACRobustEstimator.RANSACInliersData: Returns efficient array indicating which samples are considered inliers and which ones aren't.
getInliersData() - Method in class com.irurueta.numerical.robust.LMedSRobustEstimator: Returns data about inliers once estimation has been done.
getInliersData() - Method in class com.irurueta.numerical.robust.MSACRobustEstimator: Returns data about inliers once estimation has been done.
getInliersData() - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Returns data about inliers once estimation has been done.
getInliersData() - Method in class com.irurueta.numerical.robust.PROSACRobustEstimator: Returns data about inliers once estimation has been done.
getInliersData() - Method in class com.irurueta.numerical.robust.RANSACRobustEstimator: Returns data about inliers once estimation has been done.
getInliersData() - Method in class com.irurueta.numerical.robust.RobustEstimator: Returns data about inliers once estimation has been done.
getInliersLMedS() - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator.PROMedSInliersData: Returns inliers considering LMedS model.
getInliersMSAC() - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator.PROMedSInliersData: Returns inliers considering MSAC model.
getInputData() - Method in class com.irurueta.numerical.MaximumLikelihoodEstimator: Returns array containing input data to be used to find the most likely value.
getInstance() - Static method in class com.irurueta.numerical.BuildInfo: Obtains singleton instance.
getIntegralOrder() - Method in class com.irurueta.numerical.polynomials.estimators.IntegralIntervalPolynomialEvaluation: Gets integral order.
getIntegralOrder() - Method in class com.irurueta.numerical.polynomials.estimators.IntegralPolynomialEvaluation: Gets integral order.
getIntegratorType() - Method in class com.irurueta.numerical.integration.Integrator: Gets type of integrator.
getIntegratorType() - Method in class com.irurueta.numerical.integration.MatrixIntegrator: Gets type of integrator.
getIntegratorType() - Method in class com.irurueta.numerical.integration.QuadratureIntegrator: Gets type of integrator.
getIntegratorType() - Method in class com.irurueta.numerical.integration.QuadratureMatrixIntegrator: Gets type of integrator.
getIntegratorType() - Method in class com.irurueta.numerical.integration.RombergIntegrator: Gets type of integrator.
getIntegratorType() - Method in class com.irurueta.numerical.integration.RombergMatrixIntegrator: Gets type of integrator.
getIntegratorType() - Method in class com.irurueta.numerical.integration.SimpsonIntegrator: Gets type of integrator.
getIntegratorType() - Method in class com.irurueta.numerical.integration.SimpsonMatrixIntegrator: Gets type of integrator.
getIterations() - Method in class com.irurueta.numerical.optimization.ConjugateGradientMultiOptimizer: Return number of iterations that were needed to estimate a minimum.
getIterations() - Method in class com.irurueta.numerical.optimization.DerivativeConjugateGradientMultiOptimizer: Return number of iterations that were needed to estimate a minimum.
getIterations() - Method in class com.irurueta.numerical.optimization.QuasiNewtonMultiOptimizer: Return number of iterations that were needed to estimate a minimum.
getItmax() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Returns maximum number of iterations.
getItmax() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Returns maximum number of iterations.
getItmax() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Returns maximum number of iterations.
getKernel() - Method in class com.irurueta.numerical.signal.processing.Convolver1D: Gets kernel to convolve the signal with.
getKernelCenter() - Method in class com.irurueta.numerical.signal.processing.Convolver1D: Gets position of kernel center.
getLastErr() - Method in class com.irurueta.numerical.interpolation.KrigingInterpolator: Gets most recently computed error.
getLastVal() - Method in class com.irurueta.numerical.interpolation.KrigingInterpolator: Gets most recently computed interpolated value.
getListener() - Method in class com.irurueta.numerical.DirectionalEvaluator: Returns listener to evaluate a multidimensional function.
getListener() - Method in class com.irurueta.numerical.optimization.MultiOptimizer: Returns listener to evaluate a multidimensional function
getListener() - Method in class com.irurueta.numerical.optimization.SingleOptimizer: Returns listener to evaluate a single dimension function.
getListener() - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Gets listener to be notified of events such as when estimation starts, ends or estimation progress changes.
getListener() - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Gets listener to be notified of events such as when estimation starts, ends or its progress significantly changes.
getListener() - Method in class com.irurueta.numerical.robust.RobustEstimator: Returns reference to listener to be notified of events such as when estimation starts, ends or its progress significantly changes.
getListener() - Method in class com.irurueta.numerical.roots.SingleRootEstimator: Returns listener that evaluates a single dimension function in order to find its root.
getListener() - Method in class com.irurueta.numerical.signal.processing.Convolver1D: Gets listener in charge of attending events generated by this instance.
getM() - Method in class com.irurueta.numerical.interpolation.BicubicSpline2DInterpolator: Gets length of x1v array.
getM() - Method in class com.irurueta.numerical.interpolation.BilinearInterpolator: Gets length of x1v array.
getM() - Method in class com.irurueta.numerical.interpolation.Polynomial2DInterpolator: Gets length of x1v array.
getMaxEvaluationPoint() - Method in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Returns maximum evaluation point whether the bracket finishes.
getMaxEvaluationPoint() - Method in class com.irurueta.numerical.roots.BracketedSingleRootEstimator: Returns largest value inside the bracket of values where the root will be searched.
getMaxEvaluations() - Method in class com.irurueta.numerical.polynomials.estimators.WeightedPolynomialEstimator: Returns maximum number of evaluations to be weighted and taken into account.
getMaxima() - Method in class com.irurueta.numerical.polynomials.Polynomial: Gets location of maxima in this polynomial.
getMaxima(double) - Method in class com.irurueta.numerical.polynomials.Polynomial: Gets location of maxima in this polynomial.
getMaxIterations() - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Returns maximum allowed number of iterations.
getMaxIterations() - Method in class com.irurueta.numerical.robust.LMedSRobustEstimator: Maximum allowed number of iterations.
getMaxIterations() - Method in class com.irurueta.numerical.robust.MSACRobustEstimator: Maximum allowed number of iterations.
getMaxIterations() - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Maximum allowed number of iterations.
getMaxIterations() - Method in class com.irurueta.numerical.robust.PROSACRobustEstimator: Maximum allowed number of iterations.
getMaxIterations() - Method in class com.irurueta.numerical.robust.RANSACRobustEstimator: Maximum allowed number of iterations.
getMaxOutliersProportion() - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Returns maximum allowed outliers proportion in the input data.
getMaxOutliersProportion() - Method in class com.irurueta.numerical.robust.PROSACRobustEstimator: Returns maximum allowed outliers proportion in the input data.
getMaxValue() - Method in class com.irurueta.numerical.MaximumLikelihoodEstimator: Returns maximum value found on provided input data array.
getMeasurementMatrix() - Method in class com.irurueta.numerical.signal.processing.KalmanFilter: Obtains measurement matrix (H).
getMeasurementNoiseCov() - Method in class com.irurueta.numerical.signal.processing.KalmanFilter: Obtains the measurement noise covariance matrix (R).
getMeasurementNoiseCov() - Method in class com.irurueta.numerical.signal.processing.MeasurementNoiseCovarianceEstimator: Obtains estimated measurement noise covariance matrix.
getMeasureParameters() - Method in class com.irurueta.numerical.signal.processing.KalmanFilter: Obtains the number of measurement vector dimensions (measure parameters).
getMeasureParams() - Method in class com.irurueta.numerical.signal.processing.MeasurementNoiseCovarianceEstimator: Obtains the number of measurement vector dimensions (measure parameters).
getMethod() - Method in class com.irurueta.numerical.AccurateMaximumLikelihoodEstimator: Returns method to be used for maximum likelihood estimation, which for this class is MaximumLikelihoodEstimatorMethod.
getMethod() - Method in class com.irurueta.numerical.HistogramMaximumLikelihoodEstimator: Returns method to be used for maximum likelihood estimation, which for this class is MaximumLikelihoodEstimatorMethod.
getMethod() - Method in class com.irurueta.numerical.MaximumLikelihoodEstimator: Returns method to be used for maximum likelihood estimation on subclasses of this class.
getMethod() - Method in class com.irurueta.numerical.polynomials.estimators.LMedSPolynomialRobustEstimator: Returns method being used for robust estimation.
getMethod() - Method in class com.irurueta.numerical.polynomials.estimators.MSACPolynomialRobustEstimator: Returns method being used for robust estimation.
getMethod() - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Returns method being used for robust estimation.
getMethod() - Method in class com.irurueta.numerical.polynomials.estimators.PROMedSPolynomialRobustEstimator: Returns method being used for robust estimation.
getMethod() - Method in class com.irurueta.numerical.polynomials.estimators.PROSACPolynomialRobustEstimator: Returns method being used for robust estimation.
getMethod() - Method in class com.irurueta.numerical.polynomials.estimators.RANSACPolynomialRobustEstimator: Returns method being used for robust estimation.
getMethod() - Method in class com.irurueta.numerical.robust.LMedSRobustEstimator: Returns method being used for robust estimation.
getMethod() - Method in class com.irurueta.numerical.robust.MSACRobustEstimator: Returns method being used for robust estimation.
getMethod() - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Returns method being used for robust estimation.
getMethod() - Method in class com.irurueta.numerical.robust.PROSACRobustEstimator: Returns method being used for robust estimation.
getMethod() - Method in class com.irurueta.numerical.robust.RANSACRobustEstimator: Returns method being used for robust estimation.
getMethod() - Method in class com.irurueta.numerical.robust.RobustEstimator: Returns method being used for robust estimation.
getMiddleEvaluationPoint() - Method in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Returns middle evaluation point within the bracket.
getMinEvaluationPoint() - Method in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Returns minimum evaluation point where the bracket starts
getMinEvaluationPoint() - Method in class com.irurueta.numerical.roots.BracketedSingleRootEstimator: Returns smallest value inside the bracket of values where the root will be searched.
getMinima() - Method in class com.irurueta.numerical.polynomials.Polynomial: Gets location of minima in this polynomial.
getMinima(double) - Method in class com.irurueta.numerical.polynomials.Polynomial: Gets location of minima in this polynomial.
getMinNumberOfEvaluations() - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Gets minimum number of evaluations required to estimate a polynomial of the specified degree.
getMinNumberOfEvaluations() - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Gets minimum number of evaluations required to estimate a polynomial of the specified degree.
getMinNumberOfEvaluations(int) - Static method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Gets minimum number of evaluations required to estimate a polynomial of the specified degree.
getMinValue() - Method in class com.irurueta.numerical.MaximumLikelihoodEstimator: Returns minimum value found on provided input data array.
getMm() - Method in class com.irurueta.numerical.interpolation.Polynomial2DInterpolator: Gets number of rows of sub-block of ym values to be processed.
getMse() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Gets mean square error produced by estimated parameters respect to provided sample data.
getMse() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Gets mean square error produced by estimated parameters respect to provided sample data.
getMse() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Gets mean square error produced by estimated parameters respect to provided sample data.
getN() - Method in class com.irurueta.numerical.integration.MatrixQuadrature: Gets current level of refinement.
getN() - Method in class com.irurueta.numerical.integration.Quadrature: Gets current level of refinement.
getN() - Method in class com.irurueta.numerical.interpolation.BicubicSpline2DInterpolator: Gets length of x2v array.
getN() - Method in class com.irurueta.numerical.interpolation.BilinearInterpolator: Gets length of x2v array.
getN() - Method in class com.irurueta.numerical.interpolation.Polynomial2DInterpolator: Gets length of x2v array.
getNdim() - Method in class com.irurueta.numerical.interpolation.KrigingInterpolator: Gets number of dimensions of each point.
getNdone() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Returns convergence parameter.
getNdone() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Returns convergence parameter.
getNdone() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Returns convergence parameter.
getNIters() - Method in class com.irurueta.numerical.robust.LMedSRobustEstimator: Returns number of iterations to be done to obtain required confidence.
getNIters() - Method in class com.irurueta.numerical.robust.MSACRobustEstimator: Returns number of iterations to be done to obtain required confidence.
getNIters() - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Returns number of iterations to be done to obtain required confidence.
getNIters() - Method in class com.irurueta.numerical.robust.PROSACRobustEstimator: Returns number of iterations to be done to obtain required confidence.
getNIters() - Method in class com.irurueta.numerical.robust.RANSACRobustEstimator: Returns number of iterations to be done to obtain required confidence.
getNn() - Method in class com.irurueta.numerical.interpolation.Polynomial2DInterpolator: Gets number of columns of sub-block of ym values to be processed.
getNpt() - Method in class com.irurueta.numerical.interpolation.KrigingInterpolator: Gets number of provided points.
getNumberOfBins() - Method in class com.irurueta.numerical.HistogramMaximumLikelihoodEstimator: Returns number of bins to be used on the histogram.
getNumberOfDimensions() - Method in interface com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFunctionEvaluator: Number of dimensions of points (i.e. length of arrays) evaluated by this function evaluator.
getNumberOfDimensions() - Method in interface com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFunctionEvaluator: Number of dimensions of points (i.e. length of input points arrays) evaluated by this function evaluator
getNumberOfDimensions() - Method in interface com.irurueta.numerical.fitting.LinearFitterMultiDimensionFunctionEvaluator: Number of dimensions of points (i.e. length of arrays) evaluated by this function evaluator
getNumberOfVariables() - Method in interface com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFunctionEvaluator: Number of variables of function f.
getNumberOfVariables() - Method in interface com.irurueta.numerical.MultiVariateFunctionEvaluatorListener: Number of variables of function f.
getNumerators() - Method in class com.irurueta.numerical.PadeApproximantEstimator.Result: Gets numerator coefficients.
getNumInliers() - Method in class com.irurueta.numerical.robust.InliersData: Returns number of inliers found.
getNumSamples() - Method in class com.irurueta.numerical.robust.SubsetSelector: Returns number of samples to select subsets from.
getNumSelected() - Method in class com.irurueta.numerical.robust.WeightSelection: Returns number of correspondences that have been selected.
getOnIterationCompletedListener() - Method in class com.irurueta.numerical.optimization.Optimizer: Gets listener to handle minimization events.
getP() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Gets the probability of finding a smaller chi square value.
getP() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Gets the probability of finding a smaller chi square value.
getP() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Gets the probability of finding a smaller chi square value.
getPoint() - Method in class com.irurueta.numerical.DirectionalEvaluator: Returns point used as a reference to determine the function's input parameters along a line.
getPolynomialParameters() - Method in class com.irurueta.numerical.roots.FirstDegreePolynomialRootsEstimator: This method will always raise a NotAvailableException because this class only supports REAL polynomial parameters.
getPolynomialParameters() - Method in class com.irurueta.numerical.roots.PolynomialRootsEstimator: Returns array containing polynomial parameters.
getPolynomialParameters() - Method in class com.irurueta.numerical.roots.SecondDegreePolynomialRootsEstimator: This method will always raise a NotAvailableException because this class only supports REAL polynomial parameters
getPolynomialParameters() - Method in class com.irurueta.numerical.roots.ThirdDegreePolynomialRootsEstimator: This method will always raise a NotAvailableException because this class only supports REAL polynomial parameters.
getPolyParams() - Method in class com.irurueta.numerical.ComplexPolynomialEvaluator: Gets polynomial parameters.
getPolyParams() - Method in class com.irurueta.numerical.polynomials.Polynomial: Gets array defining parameters of polynomial.
getPolyParams() - Method in class com.irurueta.numerical.RealPolynomialEvaluator: Gets polynomial parameters.
getProcessNoiseCov() - Method in class com.irurueta.numerical.signal.processing.KalmanFilter: Obtains the process noise covariance matrix (Q).
getProgressDelta() - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Returns amount of progress variation before notifying a progress change during estimation.
getProgressDelta() - Method in class com.irurueta.numerical.robust.RobustEstimator: Returns amount of progress variation before notifying a progress change during estimation.
getPsum(Matrix, double[]) - Method in class com.irurueta.numerical.optimization.SimplexMultiOptimizer: Computes the sum of the elements of each matrix column.
getQ() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Gets a measure of quality of estimated fit as a value between 0.0 and 1.0.
getQ() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Gets a measure of quality of estimated fit as a value between 0.0 and 1.0.
getQ() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Gets a measure of quality of estimated fit as a value between 0.0 and 1.0.
getQ() - Method in class com.irurueta.numerical.fitting.StraightLineFitter: Returns estimated goodness-of-fit probability (i.e. that the fit would have a chi square value equal or larger than the estimated one).
getQuadratureType() - Method in class com.irurueta.numerical.integration.DoubleExponentialRuleQuadratureIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.DoubleExponentialRuleQuadratureMatrixIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.InfinityMidPointQuadratureIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.InfinityMidPointQuadratureMatrixIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.Integrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.LowerSquareRootMidPointQuadratureIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.LowerSquareRootMidPointQuadratureMatrixIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.MatrixIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.MidPointQuadratureIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.MidPointQuadratureMatrixIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.RombergDoubleExponentialRuleQuadratureIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.RombergDoubleExponentialRuleQuadratureMatrixIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.RombergExponentialMidPointQuadratureIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.RombergExponentialMidPointQuadratureMatrixIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.RombergInfinityMidPointQuadratureIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.RombergInfinityMidPointQuadratureMatrixIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.RombergLowerSquareRootMidPointQuadratureIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.RombergLowerSquareRootMidPointQuadratureMatrixIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.RombergMidPointQuadratureIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.RombergMidPointQuadratureMatrixIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.RombergTrapezoidalQuadratureIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.RombergTrapezoidalQuadratureMatrixIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.RombergUpperSquareRootMidPointQuadratureIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.RombergUpperSquareRootMidPointQuadratureMatrixIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.SimpsonDoubleExponentialRuleQuadratureIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.SimpsonDoubleExponentialRuleQuadratureMatrixIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.SimpsonInfinityMidPointQuadratureIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.SimpsonInfinityMidPointQuadratureMatrixIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.SimpsonLowerSquareRootMidPointQuadratureIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.SimpsonLowerSquareRootMidPointQuadratureMatrixIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.SimpsonMidPointQuadratureIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.SimpsonMidPointQuadratureMatrixIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.SimpsonTrapezoidalQuadratureIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.SimpsonTrapezoidalQuadratureMatrixIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.SimpsonUpperSquareRootMidPointQuadratureIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.SimpsonUpperSquareRootMidPointQuadratureMatrixIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.TrapezoidalQuadratureIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.TrapezoidalQuadratureMatrixIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.UpperSquareRootMidPointQuadratureIntegrator: Gets type of quadrature.
getQuadratureType() - Method in class com.irurueta.numerical.integration.UpperSquareRootMidPointQuadratureMatrixIntegrator: Gets type of quadrature.
getQualityScores() - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Returns quality scores corresponding to each polynomial evaluation.
getQualityScores() - Method in class com.irurueta.numerical.polynomials.estimators.PROMedSPolynomialRobustEstimator: Returns quality scores corresponding to each provided point.
getQualityScores() - Method in class com.irurueta.numerical.polynomials.estimators.PROSACPolynomialRobustEstimator: Returns quality scores corresponding to each provided point.
getQualityScores() - Method in interface com.irurueta.numerical.robust.PROSACRobustEstimatorListener: Returns quality scores corresponding to each sample.
getRandomizer() - Method in class com.irurueta.numerical.robust.FastRandomSubsetSelector: Returns internal randomizer to generate uniformly distributed random values.
getRealPolynomialParameters() - Method in class com.irurueta.numerical.roots.FirstDegreePolynomialRootsEstimator: Returns array of first degree polynomial parameters.
getRealPolynomialParameters() - Method in class com.irurueta.numerical.roots.SecondDegreePolynomialRootsEstimator: Returns array of second degree polynomial parameters.
getRealPolynomialParameters() - Method in class com.irurueta.numerical.roots.ThirdDegreePolynomialRootsEstimator: Returns array of third degree polynomial parameters.
getResiduals() - Method in class com.irurueta.numerical.robust.InliersData: Returns residuals obtained for each sample of data or null if residuals are not kept.
getResult() - Method in class com.irurueta.numerical.optimization.MultiOptimizer: Returns minimum point that was found.
getResult() - Method in class com.irurueta.numerical.optimization.SingleOptimizer: Returns value of the minimum that has been found.
getRoot() - Method in class com.irurueta.numerical.roots.SingleRootEstimator: Returns estimated root for a single dimension function inside a given bracket of values.
getRoots() - Method in class com.irurueta.numerical.polynomials.Polynomial: Gets roots of polynomial.
getRoots() - Method in class com.irurueta.numerical.roots.PolynomialRootsEstimator: Returns array of estimated polynomial roots.
getRows() - Method in interface com.irurueta.numerical.integration.DoubleExponentialMatrixSingleDimensionFunctionEvaluatorListener: Gets number of rows of matrix result of function f.
getRows() - Method in class com.irurueta.numerical.integration.DoubleExponentialRuleMatrixQuadrature: Gets number of rows of quadrature result.
getRows() - Method in class com.irurueta.numerical.integration.MatrixQuadrature: Gets number of rows of quadrature result.
getRows() - Method in interface com.irurueta.numerical.integration.MatrixSingleDimensionFunctionEvaluatorListener: Gets number of rows of matrix result of function f.
getRows() - Method in class com.irurueta.numerical.integration.MidPointMatrixQuadrature: Gets number of rows of quadrature result.
getRows() - Method in class com.irurueta.numerical.integration.TrapezoidalMatrixQuadrature: Gets number of rows of quadrature result.
getS() - Method in class com.irurueta.numerical.integration.MidPointMatrixQuadrature: Gets current value of integral.
getS() - Method in class com.irurueta.numerical.integration.MidPointQuadrature: Gets current value of integral.
getS() - Method in class com.irurueta.numerical.integration.TrapezoidalMatrixQuadrature: Gets current value of integral.
getS() - Method in class com.irurueta.numerical.integration.TrapezoidalQuadrature: Gets current value of integral.
getSampleAverage() - Method in class com.irurueta.numerical.signal.processing.MeasurementNoiseCovarianceEstimator: Obtains estimated sample average.
getSampleCount() - Method in class com.irurueta.numerical.signal.processing.MeasurementNoiseCovarianceEstimator: Obtains number of samples used for estimation.
getSelected() - Method in class com.irurueta.numerical.robust.WeightSelection: Returns array indicating which correspondences have been selected (i.e. have a true value), and which ones hasn't (have a false value).
getSig() - Method in class com.irurueta.numerical.fitting.MultiDimensionFitter: Returns standard deviations of each pair of points (x,y).
getSig() - Method in class com.irurueta.numerical.fitting.MultiVariateFitter: Returns standard deviations of each pair of points (x,y).
getSig() - Method in class com.irurueta.numerical.fitting.SingleDimensionFitter: Returns standard deviations of each pair of points (x,y).
getSig() - Method in class com.irurueta.numerical.fitting.StraightLineFitter: Returns standard deviations of each pair of points (x,y).
getSigA() - Method in class com.irurueta.numerical.fitting.StraightLineFitter: Returns estimated standard deviation of parameter "a".
getSigB() - Method in class com.irurueta.numerical.fitting.StraightLineFitter: Returns estimated standard deviation of parameter "b".
getSigdat() - Method in class com.irurueta.numerical.fitting.StraightLineFitter: Returns estimated standard deviation of provided input data.
getSignal() - Method in class com.irurueta.numerical.signal.processing.Convolver1D: Gets signal to be convolved.
getSignalValueConstant(double[], int, double) - Static method in class com.irurueta.numerical.signal.processing.Convolver1D: Internal method to determine signal value even when a position outside its boundaries is requested for constant value edge extension.
getSignalValueMirror(double[], int) - Static method in class com.irurueta.numerical.signal.processing.Convolver1D: Internal method to determine signal value even when a position outside its boundaries is requested for mirror edge extension.
getSignalValueRepeat(double[], int) - Static method in class com.irurueta.numerical.signal.processing.Convolver1D: Internal method to determine signal value even when a position outside its boundaries is requested for repeat edge extension.
getSignalValueZero(double[], int) - Static method in class com.irurueta.numerical.signal.processing.Convolver1D: Internal method to determine signal value even when a position outside its boundaries is requested when zero edge extension is being used.
getSimplex() - Method in class com.irurueta.numerical.optimization.SimplexMultiOptimizer: Returns current simplex.
getStandardDeviation() - Method in class com.irurueta.numerical.robust.LMedSRobustEstimator.LMedSInliersData: Returns standard deviation of error among all provided samples respect to currently estimated result.
getStandardDeviation() - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator.PROMedSInliersData: Returns standard deviation of error among all provided samples respect to currently estimated result.
getStartPoint() - Method in class com.irurueta.numerical.optimization.DerivativeLineMultiOptimizer: Returns start point where algorithm will be started.
getStartPoint() - Method in class com.irurueta.numerical.optimization.LineMultiOptimizer: Returns start point where algorithm will be started.
getStartPoint() - Method in class com.irurueta.numerical.optimization.QuasiNewtonMultiOptimizer: Returns start point where algorithm will be started.
getStartX() - Method in class com.irurueta.numerical.polynomials.estimators.IntegralIntervalPolynomialEvaluation: Gets start point of interval being integrated.
getStatePost() - Method in class com.irurueta.numerical.signal.processing.KalmanFilter: Obtains corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k)).
getStatePre() - Method in class com.irurueta.numerical.signal.processing.KalmanFilter: Obtains predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k).
getStopThreshold() - Method in class com.irurueta.numerical.polynomials.estimators.LMedSPolynomialRobustEstimator: Returns threshold to be used to keep the algorithm iterating in case that best estimated threshold using median of residuals is not small enough.
getStopThreshold() - Method in class com.irurueta.numerical.polynomials.estimators.PROMedSPolynomialRobustEstimator: Returns threshold to be used to keep the algorithm iterating in case that best estimated threshold using median of residuals is not small enough.
getStopThreshold() - Method in class com.irurueta.numerical.robust.LMedSRobustEstimator: Returns threshold to be used to keep the algorithm iterating in case that best threshold is not small enough.
getSubsetSize() - Method in interface com.irurueta.numerical.robust.LMedSRobustEstimatorListener: Returns size of subsets of samples to be selected.
getThreshold() - Method in class com.irurueta.numerical.polynomials.estimators.MSACPolynomialRobustEstimator: Returns threshold to determine whether polynomials are inliers or not when testing possible estimation solutions.
getThreshold() - Method in class com.irurueta.numerical.polynomials.estimators.PROSACPolynomialRobustEstimator: Returns threshold to determine whether polynomials are inliers or not when testing possible estimation solutions.
getThreshold() - Method in class com.irurueta.numerical.polynomials.estimators.RANSACPolynomialRobustEstimator: Returns threshold to determine whether polynomials are inliers or not when testing possible estimation solutions.
getThreshold() - Method in interface com.irurueta.numerical.robust.RANSACRobustEstimatorListener: Threshold to determine whether samples are inliers or not.
getTol() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Returns tolerance to reach convergence.
getTol() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Returns tolerance to reach convergence.
getTol() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Returns tolerance to reach convergence.
getTol() - Method in class com.irurueta.numerical.fitting.SvdMultiDimensionLinearFitter: Returns tolerance to define convergence threshold for SVD.
getTol() - Method in class com.irurueta.numerical.fitting.SvdSingleDimensionLinearFitter: Returns tolerance to define convergence threshold for SVD.
getTolerance() - Method in class com.irurueta.numerical.optimization.BrentSingleOptimizer: Returns tolerance value, which is the accuracy to be obtained when a minimum is estimated.
getTolerance() - Method in class com.irurueta.numerical.optimization.ConjugateGradientMultiOptimizer: Returns tolerance or accuracy to be expected on estimated local minimum.
getTolerance() - Method in class com.irurueta.numerical.optimization.DerivativeBrentSingleOptimizer: Returns tolerance value.
getTolerance() - Method in class com.irurueta.numerical.optimization.DerivativeConjugateGradientMultiOptimizer: Returns tolerance or accuracy to be expected on estimated local minimum.
getTolerance() - Method in class com.irurueta.numerical.optimization.GoldenSingleOptimizer: Returns tolerance value, which is the accuracy to be obtained when a minimum is estimated.
getTolerance() - Method in class com.irurueta.numerical.optimization.PowellMultiOptimizer: Returns tolerance or accuracy to be expected on estimated local minimum.
getTolerance() - Method in class com.irurueta.numerical.optimization.QuasiNewtonMultiOptimizer: Returns tolerance or accuracy to be expected on estimated local minimum.
getTolerance() - Method in class com.irurueta.numerical.optimization.SimplexMultiOptimizer: Returns tolerance or accuracy to be expected on estimated local minimum.
getTolerance() - Method in class com.irurueta.numerical.roots.BisectionSingleRootEstimator: Returns tolerance to find a root.
getTolerance() - Method in class com.irurueta.numerical.roots.BrentSingleRootEstimator: Returns tolerance value.
getTolerance() - Method in class com.irurueta.numerical.roots.FalsePositionSingleRootEstimator: Returns tolerance to find a root.
getTolerance() - Method in class com.irurueta.numerical.roots.NewtonRaphsonSingleRootEstimator: Returns tolerance value.
getTolerance() - Method in class com.irurueta.numerical.roots.RidderSingleRootEstimator: Returns tolerance value.
getTolerance() - Method in class com.irurueta.numerical.roots.SafeNewtonRaphsonSingleRootEstimator: Returns tolerance value.
getTolerance() - Method in class com.irurueta.numerical.roots.SecantSingleRootEstimator: Returns tolerance value.
getTotalSamples() - Method in interface com.irurueta.numerical.robust.LMedSRobustEstimatorListener: Returns total number of samples to be randomly processed.
getTransitionMatrix() - Method in class com.irurueta.numerical.signal.processing.KalmanFilter: Obtains the state transition matrix (A).
getType() - Method in class com.irurueta.numerical.integration.DoubleExponentialRuleMatrixQuadrature: Gets type of quadrature.
getType() - Method in class com.irurueta.numerical.integration.DoubleExponentialRuleQuadrature: Gets type of quadrature.
getType() - Method in class com.irurueta.numerical.integration.ExponentialMidPointMatrixQuadrature: Gets type of quadrature.
getType() - Method in class com.irurueta.numerical.integration.ExponentialMidPointQuadrature: Gets type of quadrature.
getType() - Method in class com.irurueta.numerical.integration.InfinityMidPointMatrixQuadrature: Gets type of quadrature.
getType() - Method in class com.irurueta.numerical.integration.InfinityMidPointQuadrature: Gets type of quadrature.
getType() - Method in class com.irurueta.numerical.integration.LowerSquareRootMidPointMatrixQuadrature: Gets type of quadrature.
getType() - Method in class com.irurueta.numerical.integration.LowerSquareRootMidPointQuadrature: Gets type of quadrature.
getType() - Method in class com.irurueta.numerical.integration.MatrixQuadrature: Gets type of quadrature.
getType() - Method in class com.irurueta.numerical.integration.MidPointMatrixQuadrature: Gets type of quadrature.
getType() - Method in class com.irurueta.numerical.integration.MidPointQuadrature: Gets type of quadrature.
getType() - Method in class com.irurueta.numerical.integration.Quadrature: Gets type of quadrature.
getType() - Method in class com.irurueta.numerical.integration.TrapezoidalMatrixQuadrature: Gets type of quadrature.
getType() - Method in class com.irurueta.numerical.integration.TrapezoidalQuadrature: Gets type of quadrature.
getType() - Method in class com.irurueta.numerical.integration.UpperSquareRootMidPointMatrixQuadrature: Gets type of quadrature.
getType() - Method in class com.irurueta.numerical.integration.UpperSquareRootMidPointQuadrature: Gets type of quadrature.
getType() - Method in class com.irurueta.numerical.polynomials.estimators.DerivativePolynomialEvaluation: Gets type of polynomial evaluation.
getType() - Method in class com.irurueta.numerical.polynomials.estimators.DirectPolynomialEvaluation: Gets type of polynomial evaluation.
getType() - Method in class com.irurueta.numerical.polynomials.estimators.IntegralIntervalPolynomialEvaluation: Gets type of polynomial evaluation.
getType() - Method in class com.irurueta.numerical.polynomials.estimators.IntegralPolynomialEvaluation: Gets type of polynomial evaluation.
getType() - Method in class com.irurueta.numerical.polynomials.estimators.LMSEPolynomialEstimator: Returns type of polynomial estimator.
getType() - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Returns type of polynomial estimator.
getType() - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialEvaluation: Gets type of polynomial evaluation.
getType() - Method in class com.irurueta.numerical.polynomials.estimators.WeightedPolynomialEstimator: Returns type of polynomial estimator.
getType() - Method in class com.irurueta.numerical.robust.FastRandomSubsetSelector: Returns type of this subset selector.
getType() - Method in class com.irurueta.numerical.robust.SubsetSelector: Returns type of this subset selector.
getVersion() - Method in class com.irurueta.numerical.BuildInfo: Obtains version of this library.
getWeights() - Method in class com.irurueta.numerical.polynomials.estimators.WeightedPolynomialEstimator: Returns array containing a weight amount for each polynomial evaluation.
getX() - Method in class com.irurueta.numerical.fitting.MultiDimensionFitter: Returns input points x where a multidimensional function f(x1, x2, ...)
getX() - Method in class com.irurueta.numerical.fitting.MultiVariateFitter: Returns input points x where a multi variate function f(x1, x2, ...)
getX() - Method in class com.irurueta.numerical.fitting.SingleDimensionFitter: Returns input points x where function f(x) is evaluated.
getX() - Method in class com.irurueta.numerical.fitting.StraightLineFitter: Returns array containing x coordinates of input data to be fitted to a straight line.
getX() - Method in class com.irurueta.numerical.polynomials.estimators.DerivativePolynomialEvaluation: Gets point where polynomial derivative has been evaluated.
getX() - Method in class com.irurueta.numerical.polynomials.estimators.DirectPolynomialEvaluation: Gets point where polynomial has been evaluated.
getX() - Method in class com.irurueta.numerical.polynomials.estimators.IntegralPolynomialEvaluation: Gets point where nth-polynomial integral has been evaluated.
getY() - Method in class com.irurueta.numerical.fitting.MultiDimensionFitter: Returns result of evaluation of multidimensional function f(x) at provided x points.
getY() - Method in class com.irurueta.numerical.fitting.MultiVariateFitter: Returns result of evaluation of multi variate function f(x) at provided x points.
getY() - Method in class com.irurueta.numerical.fitting.SingleDimensionFitter: Returns result of evaluation of linear single dimensional function f(x) at provided x points.
getY() - Method in class com.irurueta.numerical.fitting.StraightLineFitter: Returns array containing y coordinates of input data to be fitted to a straight line.
GLIMIT - Static variable in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: The maximum magnification allowed for a parabolic-fit step.
GOLD - Static variable in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: The default ratio by which intervals are magnified and.
GoldenSingleOptimizer - Class in com.irurueta.numerical.optimization: This class for a single dimensional function's local minimum.
GoldenSingleOptimizer() - Constructor for class com.irurueta.numerical.optimization.GoldenSingleOptimizer: Empty constructor.
GoldenSingleOptimizer(SingleDimensionFunctionEvaluatorListener, double, double, double, double) - Constructor for class com.irurueta.numerical.optimization.GoldenSingleOptimizer: Constructor.
gradient(double[]) - Method in class com.irurueta.numerical.GradientEstimator: Returns the gradient of a multidimensional function at provided point.
gradient(double[], double[]) - Method in class com.irurueta.numerical.GradientEstimator: Sets estimated gradient in provided result array of a multidimensional function at provided point.
gradient(double[], double[]) - Method in class com.irurueta.numerical.SavitzkyGolayGradientEstimator: Sets estimated gradient in provided result array of a multidimensional function at provided point.
gradient(double[], double[]) - Method in class com.irurueta.numerical.SymmetricGradientEstimator: Sets estimated gradient in provided result array of a multidimensional function at provided point.
GradientEstimator - Class in com.irurueta.numerical: Class to estimate the gradient of a multidimensional function.
GradientEstimator(MultiDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.GradientEstimator: Constructor.
GradientFunctionEvaluatorListener - Interface in com.irurueta.numerical: Listener to evaluate/retrieve a multidimensional function's gradient.
gradientListener - Variable in class com.irurueta.numerical.DirectionalDerivativeEvaluator: Listener to evaluate a multidimensional function's gradient.
gradientListener - Variable in class com.irurueta.numerical.optimization.ConjugateGradientMultiOptimizer: Listener to obtain gradient values for the multi dimension function being evaluated.
gradientListener - Variable in class com.irurueta.numerical.optimization.DerivativeLineMultiOptimizer: Listener to evaluate the function's gradient.
gradientListener - Variable in class com.irurueta.numerical.optimization.QuasiNewtonMultiOptimizer: Listener to obtain gradient values for the multi dimension function being evaluated.
GROUP_ID_KEY - Static variable in class com.irurueta.numerical.BuildInfo: Key to obtain groupID of this library from properties file.
groupId - Variable in class com.irurueta.numerical.BuildInfo: GroupId of this library.
GTOL - Static variable in class com.irurueta.numerical.optimization.ConjugateGradientMultiOptimizer: Convergence criterion for the zero gradient test.
GTOL - Static variable in class com.irurueta.numerical.optimization.DerivativeConjugateGradientMultiOptimizer: Convergence criterion for the zero gradient test.

H

h - Variable in class com.irurueta.numerical.integration.RombergIntegrator: Successive trapezoidal step sizes.
h - Variable in class com.irurueta.numerical.integration.RombergMatrixIntegrator: Successive trapezoidal step sizes.
h - Variable in class com.irurueta.numerical.integration.RombergTrapezoidalQuadratureIntegrator: Successive trapezoidal step sizes.
h - Variable in class com.irurueta.numerical.integration.RombergTrapezoidalQuadratureMatrixIntegrator: Successive trapezoidal step sizes.
hasDoubleRoot() - Method in class com.irurueta.numerical.roots.SecondDegreePolynomialRootsEstimator: Returns boolean indicating whether this second degree polynomial has multiple roots (for the 2nd degree case this means 2 equal roots) This is true for polynomials of the form (x - r)^2 = x^2 - 2 * r * x + r^2, where r is the double root
hasDoubleRoot(double[]) - Static method in class com.irurueta.numerical.roots.SecondDegreePolynomialRootsEstimator: Returns boolean indicating whether a second degree polynomial has multiple roots (for the 2nd degree case this means 2 equal roots) This is true for polynomials of the form (x - r)^2 = x^2 - 2 * r * x + r^2, where r is the double root
hasMultipleRealRoot() - Method in class com.irurueta.numerical.roots.ThirdDegreePolynomialRootsEstimator: Returns boolean indicating whether the polynomial has two real and equal roots and a third different one (multiplicity 2), or all three roots are real and equal (multiplicity 3).
hasMultipleRealRoot(double[]) - Static method in class com.irurueta.numerical.roots.ThirdDegreePolynomialRootsEstimator: Returns boolean indicating whether the polynomial has two real and equal roots and a third different one (multiplicity 2), or all three roots are real and equal (multiplicity 3).
hasOneRealRootAndTwoComplexConjugateRoots() - Method in class com.irurueta.numerical.roots.ThirdDegreePolynomialRootsEstimator: Returns boolean indicating whether the polynomial has one real root and two complex conjugate roots.
hasOneRealRootAndTwoComplexConjugateRoots(double[]) - Static method in class com.irurueta.numerical.roots.ThirdDegreePolynomialRootsEstimator: Returns boolean indicating whether the polynomial has one real root and two complex conjugate roots.
hasThreeDistinctRealRoots() - Method in class com.irurueta.numerical.roots.ThirdDegreePolynomialRootsEstimator: Returns boolean indicating whether the roots of the polynomial are three distinct and real roots or not.
hasThreeDistinctRealRoots(double[]) - Static method in class com.irurueta.numerical.roots.ThirdDegreePolynomialRootsEstimator: Returns boolean indicating whether the roots of the polynomial are three distinct and real roots or not.
hasTwoComplexConjugateRoots() - Method in class com.irurueta.numerical.roots.SecondDegreePolynomialRootsEstimator: Returns boolean indicating whether the roots of the polynomial are two complex conjugate roots or not.
hasTwoComplexConjugateRoots(double[]) - Static method in class com.irurueta.numerical.roots.SecondDegreePolynomialRootsEstimator: Returns boolean indicating whether the roots of the polynomial are two complex conjugate roots or not.
hasTwoDistinctRealRoots() - Method in class com.irurueta.numerical.roots.SecondDegreePolynomialRootsEstimator: Returns boolean indicating whether the roots of the polynomial are two distinct and real roots or not.
hasTwoDistinctRealRoots(double[]) - Method in class com.irurueta.numerical.roots.SecondDegreePolynomialRootsEstimator: Returns boolean indicating whether the roots of the polynomial are two distinct and real roots or not.
HISTOGRAM_MAXIMUM_LIKELIHOOD_ESTIMATOR - Enum constant in enum class com.irurueta.numerical.MaximumLikelihoodEstimatorMethod: MLE method based on a histogram of all samples.
HistogramMaximumLikelihoodEstimator - Class in com.irurueta.numerical: Class to estimate the most likely value from a series of samples assumed to be normally distributed.
HistogramMaximumLikelihoodEstimator() - Constructor for class com.irurueta.numerical.HistogramMaximumLikelihoodEstimator: Empty constructor.
HistogramMaximumLikelihoodEstimator(double[], double, int) - Constructor for class com.irurueta.numerical.HistogramMaximumLikelihoodEstimator: Constructor.
HistogramMaximumLikelihoodEstimator(double, double, double[], double, int) - Constructor for class com.irurueta.numerical.HistogramMaximumLikelihoodEstimator: Constructor.
HistogramMaximumLikelihoodEstimator(double, int) - Constructor for class com.irurueta.numerical.HistogramMaximumLikelihoodEstimator: Constructor.
hmax - Variable in class com.irurueta.numerical.integration.DoubleExponentialRuleMatrixQuadrature: Maximum step size.
hmax - Variable in class com.irurueta.numerical.integration.DoubleExponentialRuleQuadrature: Maximum step size.
hold(int, double) - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Prevents parameter at position i of linear combination of basis functions to be modified during function fitting.
hold(int, double) - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Prevents parameter at position i of linear combination of basis functions to be modified during function fitting.
hold(int, double) - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Prevents parameter at position i of linear combination of basis functions to be modified during function fitting.
hold(int, double) - Method in class com.irurueta.numerical.fitting.SimpleSingleDimensionLinearFitter: Prevents parameter at position i of linear combination of basis functions to be modified during function fitting.
hunt(double) - Method in class com.irurueta.numerical.interpolation.BaseInterpolator: Given a value x, returns a value j such that x is (insofar as possible) centered in the subrange xx[j..j+mm-1], where xx is the stored array.

I

ia - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Determines which parameters can be modified during estimation (if true) and which ones are locked (if false).
ia - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Determines which parameters can be modified during estimation (if true) and which ones are locked (if false).
ia - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Determines which parameters can be modified during estimation (if true) and which ones are locked (if false).
ia - Variable in class com.irurueta.numerical.fitting.SimpleSingleDimensionLinearFitter: Determines which parameters can be modified during estimation (if true) and which ones are locked (if false)
imin(int, int, double) - Static method in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Non randomness states that i-m (where i is the cardinal of the set of inliers for a wrong model) follows the binomial distribution B(n,beta).
imin(int, int, double) - Static method in class com.irurueta.numerical.robust.PROSACRobustEstimator: Non randomness states that i-m (where i is the cardinal of the set of inliers for a wrong model) follows the binomial distribution B(n,beta).
improvedX - Variable in class com.irurueta.numerical.PadeApproximantEstimator: Improved LU solution in one iteration.
improveLuSolve(Matrix, Matrix, Matrix, Matrix) - Method in class com.irurueta.numerical.PadeApproximantEstimator: One step to iteratively improve LU solve solution.
improveTimes - Variable in class com.irurueta.numerical.PadeApproximantEstimator: Number of times to iteratively improve LU decomposition.
INFINITY_MID_POINT - Enum constant in enum class com.irurueta.numerical.integration.QuadratureType: Infinity mid-point.
InfinityMidPointMatrixQuadrature - Class in com.irurueta.numerical.integration: This is an exact replacement for MidPointQuadrature i.e., returns the nth stage of refinement of the integral of a function from "a" to "b", except that the function is evaluated at evenly spaced points in 1=x rather than in "x".
InfinityMidPointMatrixQuadrature(double, double, MatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.InfinityMidPointMatrixQuadrature: Constructor.
InfinityMidPointQuadrature - Class in com.irurueta.numerical.integration: This is an exact replacement for MidPointQuadrature i.e., returns the nth stage of refinement of the integral of a function from "a" to "b", except that the function is evaluated at evenly spaced points in 1=x rather than in x.
InfinityMidPointQuadrature(double, double, SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.InfinityMidPointQuadrature: Constructor.
InfinityMidPointQuadratureIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using an infinity mid-point quadrature.
InfinityMidPointQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.InfinityMidPointQuadratureIntegrator: Constructor.
InfinityMidPointQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.InfinityMidPointQuadratureIntegrator: Constructor.
InfinityMidPointQuadratureMatrixIntegrator - Class in com.irurueta.numerical.integration: Computes matrix (multivariate) function integration by using an infinity mid-point quadrature.
InfinityMidPointQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.InfinityMidPointQuadratureMatrixIntegrator: Constructor with default accuracy.
InfinityMidPointQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.InfinityMidPointQuadratureMatrixIntegrator: Constructor.
initialize(int) - Method in class com.irurueta.numerical.ExponentialMatrixEstimator: Initializes matrices being reused as long as number of rows is preserved for multiple provided input matrices for efficiency purposes.
initialize(int) - Method in class com.irurueta.numerical.PadeApproximantEstimator: Initializes required matrices.
inlierFactor - Variable in class com.irurueta.numerical.robust.LMedSRobustEstimator: Factor to normalize threshold to determine inliers.
inlierFactor - Variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Factor to normalize threshold to determine inliers.
inliers - Variable in class com.irurueta.numerical.robust.LMedSRobustEstimator.LMedSInliersData: Efficiently stores which samples are considered inliers and which ones aren't.
inliers - Variable in class com.irurueta.numerical.robust.MSACRobustEstimator.MSACInliersData: Efficiently stores which samples are considered inliers and which ones aren't.
inliers - Variable in class com.irurueta.numerical.robust.PROSACRobustEstimator.PROSACInliersData: Efficiently stores which samples are considered inliers and which ones aren't.
inliers - Variable in class com.irurueta.numerical.robust.RANSACRobustEstimator.RANSACInliersData: Efficiently stores which samples are considered inliers and which ones aren't.
InliersData - Class in com.irurueta.numerical.robust: Base class defining inlier data for a robust estimator.
InliersData() - Constructor for class com.irurueta.numerical.robust.InliersData
inliersLmeds - Variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator.PROMedSInliersData: Inliers considering LMedS model.
inliersMsac - Variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator.PROMedSInliersData: Inliers considering MSAC model.
inputData - Variable in class com.irurueta.numerical.MaximumLikelihoodEstimator: Array containing input data to be used to find the most likely value.
INTEGRAL_EVALUATION - Enum constant in enum class com.irurueta.numerical.polynomials.estimators.PolynomialEvaluationType: Evaluation of the nth-integral of a polynomial (assuming a given constant value)
INTEGRAL_INTERVAL - Enum constant in enum class com.irurueta.numerical.polynomials.estimators.PolynomialEvaluationType: Interval nth-integration of a polynomial.
IntegralIntervalPolynomialEvaluation - Class in com.irurueta.numerical.polynomials.estimators: Contains an evaluation of an interval of the nth-integral of a polynomial.
IntegralIntervalPolynomialEvaluation() - Constructor for class com.irurueta.numerical.polynomials.estimators.IntegralIntervalPolynomialEvaluation: Constructor.
IntegralIntervalPolynomialEvaluation(double, double, double) - Constructor for class com.irurueta.numerical.polynomials.estimators.IntegralIntervalPolynomialEvaluation: Constructor.
IntegralIntervalPolynomialEvaluation(double, double, double, int) - Constructor for class com.irurueta.numerical.polynomials.estimators.IntegralIntervalPolynomialEvaluation: Constructor.
integralOrder - Variable in class com.irurueta.numerical.polynomials.estimators.IntegralIntervalPolynomialEvaluation: Order of integral.
integralOrder - Variable in class com.irurueta.numerical.polynomials.estimators.IntegralPolynomialEvaluation: Order of integral.
IntegralPolynomialEvaluation - Class in com.irurueta.numerical.polynomials.estimators: Contains an evaluation of the nth-integral of a polynomial and the point where such integral has been evaluated.
IntegralPolynomialEvaluation() - Constructor for class com.irurueta.numerical.polynomials.estimators.IntegralPolynomialEvaluation: Constructor.
IntegralPolynomialEvaluation(double, double) - Constructor for class com.irurueta.numerical.polynomials.estimators.IntegralPolynomialEvaluation: Constructor.
IntegralPolynomialEvaluation(double, double, double[]) - Constructor for class com.irurueta.numerical.polynomials.estimators.IntegralPolynomialEvaluation: Constructor.
IntegralPolynomialEvaluation(double, double, double[], int) - Constructor for class com.irurueta.numerical.polynomials.estimators.IntegralPolynomialEvaluation: Constructor.
IntegralPolynomialEvaluation(double, double, int) - Constructor for class com.irurueta.numerical.polynomials.estimators.IntegralPolynomialEvaluation: Constructor.
integrate() - Method in class com.irurueta.numerical.integration.Integrator: Integrates function between provided lower and upper limits.
integrate() - Method in class com.irurueta.numerical.integration.QuadratureIntegrator: Integrates function between provided lower and upper limits.
integrate() - Method in class com.irurueta.numerical.integration.RombergIntegrator: Integrates function between lower and upper limits defined by provided quadrature.
integrate() - Method in class com.irurueta.numerical.integration.RombergTrapezoidalQuadratureIntegrator: Integrates function between lower and upper limits defined by provided quadrature.
integrate() - Method in class com.irurueta.numerical.integration.SimpsonIntegrator: Integrates function between provided lower and upper limits.
integrate(Matrix) - Method in class com.irurueta.numerical.integration.MatrixIntegrator: Integrates function between provided lower and upper limits.
integrate(Matrix) - Method in class com.irurueta.numerical.integration.QuadratureMatrixIntegrator: Integrates function between provided lower and upper limits.
integrate(Matrix) - Method in class com.irurueta.numerical.integration.RombergMatrixIntegrator: Integrates function between provided lower and upper limits.
integrate(Matrix) - Method in class com.irurueta.numerical.integration.RombergTrapezoidalQuadratureMatrixIntegrator: Integrates function between provided lower and upper limits.
integrate(Matrix) - Method in class com.irurueta.numerical.integration.SimpsonMatrixIntegrator: Integrates function between provided lower and upper limits.
integrateInterval(double, double) - Method in class com.irurueta.numerical.polynomials.Polynomial: Integrate polynomial within provided interval.
integration() - Method in class com.irurueta.numerical.polynomials.Polynomial: Updates this instance to contain its integration using a zero constant term.
integration(double) - Method in class com.irurueta.numerical.polynomials.Polynomial: Updates this instance to contain its integration.
integration(Polynomial) - Method in class com.irurueta.numerical.polynomials.Polynomial: Computes polynomial containing the integration of current one and assuming a zero constant term.
integration(Polynomial, double) - Method in class com.irurueta.numerical.polynomials.Polynomial: Computes polynomial containing the integration of current one.
integrationAndReturnNew() - Method in class com.irurueta.numerical.polynomials.Polynomial: Computes polynomial containing the integration of current one and assuming a zero constant term.
integrationAndReturnNew(double) - Method in class com.irurueta.numerical.polynomials.Polynomial: Computes polynomial containing the integration of current one.
IntegrationException - Exception in com.irurueta.numerical.integration: Exception raised when function integration fails.
IntegrationException() - Constructor for exception com.irurueta.numerical.integration.IntegrationException: Constructor.
IntegrationException(String) - Constructor for exception com.irurueta.numerical.integration.IntegrationException: Constructor with String containing message.
IntegrationException(String, Throwable) - Constructor for exception com.irurueta.numerical.integration.IntegrationException: Constructor with message and cause.
IntegrationException(Throwable) - Constructor for exception com.irurueta.numerical.integration.IntegrationException: Constructor with cause.
Integrator - Class in com.irurueta.numerical.integration: Integrates single dimension functions over a specified interval.
Integrator() - Constructor for class com.irurueta.numerical.integration.Integrator
IntegratorType - Enum Class in com.irurueta.numerical.integration: Indicates type of integrator.
IntegratorType() - Constructor for enum class com.irurueta.numerical.integration.IntegratorType
internalConvolveConstant(double[], double[], int, double, double[], Convolver1D.Convolver1DListener) - Static method in class com.irurueta.numerical.signal.processing.Convolver1D: Internal method to convolve signal using constant value edge extension method.
internalConvolveMirror(double[], double[], int, double[], Convolver1D.Convolver1DListener) - Static method in class com.irurueta.numerical.signal.processing.Convolver1D: Internal method to convolve signal using a mirror extension method.
internalConvolveRepeat(double[], double[], int, double[], Convolver1D.Convolver1DListener) - Static method in class com.irurueta.numerical.signal.processing.Convolver1D: Internal method to convolve signal using a repeat extension method.
internalConvolveZero(double[], double[], int, double[], Convolver1D.Convolver1DListener) - Static method in class com.irurueta.numerical.signal.processing.Convolver1D: Internal method to convolve signal using zero edge extension method.
internalEstimator - Variable in class com.irurueta.numerical.AccurateMaximumLikelihoodEstimator: Internal maximum likelihood estimator based on the Histogram method.
internalLaguer(Complex[], Complex, int[]) - Method in class com.irurueta.numerical.roots.LaguerrePolynomialRootsEstimator: Internal method to compute a root after decomposing and decreasing the degree of the polynomial.
internalSetBracket(double, double) - Method in class com.irurueta.numerical.roots.BracketedSingleRootEstimator: Internal method to set the bracket of values (i.e. range of values) where the root will be searched.
internalSetBracket(double, double, double) - Method in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Internal method to set a bracket of values.
internalSetDegree(int) - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Internal method to set degree of polynomial to be estimated.
internalSetEvaluations(List<PolynomialEvaluation>) - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Sets list of polynomial evaluations.
internalSetEvaluationsAndWeights(List<PolynomialEvaluation>, double[]) - Method in class com.irurueta.numerical.polynomials.estimators.WeightedPolynomialEstimator: Internal method to set evaluations and weights.
internalSetFunctionEvaluator(LevenbergMarquardtMultiDimensionFunctionEvaluator) - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Internal method to set function evaluator to evaluate function at a given point and obtain function derivatives respect to each provided parameter.
internalSetFunctionEvaluator(LevenbergMarquardtMultiVariateFunctionEvaluator) - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Internal method to set function evaluator to evaluate function at a given point and obtain function jacobian respect to each provided parameter.
internalSetFunctionEvaluator(LevenbergMarquardtSingleDimensionFunctionEvaluator) - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Internal method to set function evaluator to evaluate function at a given point and obtain function derivatives respect to each provided parameter
internalSetFunctionEvaluator(LinearFitterMultiDimensionFunctionEvaluator) - Method in class com.irurueta.numerical.fitting.MultiDimensionLinearFitter: Internal method to set function evaluator to evaluate function at a given point and obtain the evaluation of function basis at such point
internalSetFunctionEvaluator(LinearFitterSingleDimensionFunctionEvaluator) - Method in class com.irurueta.numerical.fitting.SingleDimensionLinearFitter: Internal method to set function evaluator to evaluate function at a given point and obtain the evaluation of function basis at such point.
internalSetGaussianSigma(double) - Method in class com.irurueta.numerical.MaximumLikelihoodEstimator: Internal method to set Gaussian sigma to be used on each sample when aggregating Gaussian functions centered at each input data sample value.
internalSetMinMaxValues(double, double) - Method in class com.irurueta.numerical.MaximumLikelihoodEstimator: Method to set internally minimum and maximum value found in input data array.
internalSetNumberOfBins(int) - Method in class com.irurueta.numerical.HistogramMaximumLikelihoodEstimator: Internal method to set number of bins to be used on the histogram.
internalSetPointAndDirections(double[], Matrix) - Method in class com.irurueta.numerical.optimization.PowellMultiOptimizer: Internal method to set start point and set of directions to start looking for minimum.
internalSetPolynomialParameters(double[]) - Method in class com.irurueta.numerical.roots.FirstDegreePolynomialRootsEstimator: Internal method to set array of first degree polynomial parameters.
internalSetPolynomialParameters(double[]) - Method in class com.irurueta.numerical.roots.SecondDegreePolynomialRootsEstimator: Internal method to set array of second degree polynomial parameters.
internalSetPolynomialParameters(double[]) - Method in class com.irurueta.numerical.roots.ThirdDegreePolynomialRootsEstimator: Internal method to set array of third degree polynomial parameters.
internalSetPolynomialParameters(Complex[]) - Method in class com.irurueta.numerical.roots.FirstDegreePolynomialRootsEstimator: This method will always raise an IllegalArgumentException because this class only supports REAL polynomial parameters.
internalSetPolynomialParameters(Complex[]) - Method in class com.irurueta.numerical.roots.LaguerrePolynomialRootsEstimator: Internal method to set parameters of a polynomial, taking into account that a polynomial of degree n is defined as: p(x) = a0 * x^n + a1 * x^(n - 1) + ... a(n-1) * x + an then the array of parameters is [an, a(n - 1), ... a1, a0] Polynomial parameters can be either real or complex values This method does not check if this class is locked.
internalSetPolynomialParameters(Complex[]) - Method in class com.irurueta.numerical.roots.PolynomialRootsEstimator: Internal method to set parameters of a polynomial, taking into account that a polynomial of degree n is defined as: p(x) = a0 * x^n + a1 * x^(n - 1) + ... a(n-1) * x + an then the array of parameters is [a0, a1, ... a(n - 1), an] This method does not check if this class is locked.
internalSetPolynomialParameters(Complex[]) - Method in class com.irurueta.numerical.roots.SecondDegreePolynomialRootsEstimator: This method will always raise an IllegalArgumentException because this class only supports REAL polynomial parameters
internalSetPolynomialParameters(Complex[]) - Method in class com.irurueta.numerical.roots.ThirdDegreePolynomialRootsEstimator: This method will always raise an IllegalArgumentException because this class only supports REAL polynomial parameters.
internalSetQualityScores(double[]) - Method in class com.irurueta.numerical.polynomials.estimators.PROMedSPolynomialRobustEstimator: Sets quality scores corresponding to each provided polynomial evaluation.
internalSetQualityScores(double[]) - Method in class com.irurueta.numerical.polynomials.estimators.PROSACPolynomialRobustEstimator: Sets quality scores corresponding to each provided polynomial evaluation.
internalSetSimplex(double[], double) - Method in class com.irurueta.numerical.optimization.SimplexMultiOptimizer: Internal method to set a simplex as a central point and a set of surrounding points at distance delta.
internalSetSimplex(double[], double[]) - Method in class com.irurueta.numerical.optimization.SimplexMultiOptimizer: Internal method to set a simplex defined as a central point and a set of surrounding points at their corresponding distance deltas[i], where "i" corresponds to one position of provided array of distances.
internalSetSimplex(Matrix) - Method in class com.irurueta.numerical.optimization.SimplexMultiOptimizer: Internal method to Set simplex as a matrix containing on each row a point of the simplex.
internalSetStartPoint(double[]) - Method in class com.irurueta.numerical.optimization.ConjugateGradientMultiOptimizer: Internal method to set start point where local minimum is searched nearby.
internalSetStartPoint(double[]) - Method in class com.irurueta.numerical.optimization.DerivativeConjugateGradientMultiOptimizer: Internal method to set start point where local minimum is searched nearby.
internalSetStartPointAndDirection(double[], double[]) - Method in class com.irurueta.numerical.optimization.DerivativeLineMultiOptimizer: Internal method to set start point and direction to start the search for a local minimum.
internalSetStartPointAndDirection(double[], double[]) - Method in class com.irurueta.numerical.optimization.LineMultiOptimizer: Internal method to set start point and direction to start the search for a local minimum.
internalSetTolerance(double) - Method in class com.irurueta.numerical.optimization.BrentSingleOptimizer: Internal method to set algorithm tolerance.
internalSetTolerance(double) - Method in class com.irurueta.numerical.optimization.ConjugateGradientMultiOptimizer: Internal method to set tolerance or accuracy to be expected on estimated local minimum.
internalSetTolerance(double) - Method in class com.irurueta.numerical.optimization.DerivativeBrentSingleOptimizer: Internal method to set tolerance.
internalSetTolerance(double) - Method in class com.irurueta.numerical.optimization.DerivativeConjugateGradientMultiOptimizer: Internal method to set tolerance or accuracy to be expected on estimated local minimum.
internalSetTolerance(double) - Method in class com.irurueta.numerical.optimization.GoldenSingleOptimizer: Internal method to set algorithm tolerance.
internalSetTolerance(double) - Method in class com.irurueta.numerical.optimization.PowellMultiOptimizer: Internal method to set tolerance or accuracy to be expected on estimated local minimum.
internalSetTolerance(double) - Method in class com.irurueta.numerical.optimization.QuasiNewtonMultiOptimizer: Internal method to set tolerance or accuracy to be expected on estimated local minimum.
internalSetTolerance(double) - Method in class com.irurueta.numerical.optimization.SimplexMultiOptimizer: Internal method to set tolerance or accuracy to be expected on estimated local minimum.
internalSetTolerance(double) - Method in class com.irurueta.numerical.roots.BisectionSingleRootEstimator: Internal method to set tolerance to find a root.
internalSetTolerance(double) - Method in class com.irurueta.numerical.roots.BrentSingleRootEstimator: Internal method to set tolerance value.
internalSetTolerance(double) - Method in class com.irurueta.numerical.roots.FalsePositionSingleRootEstimator: Internal method to set tolerance to find a root.
internalSetTolerance(double) - Method in class com.irurueta.numerical.roots.NewtonRaphsonSingleRootEstimator: Internal method to set tolerance value.
internalSetTolerance(double) - Method in class com.irurueta.numerical.roots.RidderSingleRootEstimator: Internal method to set tolerance value.
internalSetTolerance(double) - Method in class com.irurueta.numerical.roots.SafeNewtonRaphsonSingleRootEstimator: Internal method to set tolerance value.
internalSetTolerance(double) - Method in class com.irurueta.numerical.roots.SecantSingleRootEstimator: Internal method to set tolerance value.
interpolate(double) - Method in class com.irurueta.numerical.interpolation.BarycentricRationalInterpolator: Given a value x, returns an interpolated value, using data pointed to by BaseInterpolator.xx and BaseInterpolator.yy.
interpolate(double) - Method in class com.irurueta.numerical.interpolation.BaseInterpolator: Given a value x, returns an interpolated value, using data pointed to by BaseInterpolator.xx and BaseInterpolator.yy.
interpolate(double) - Method in class com.irurueta.numerical.interpolation.CurveInterpolator: Interpolates a point on the stored curve.
interpolate(double[]) - Method in class com.irurueta.numerical.interpolation.BaseRadialBasisFunctionInterpolator: Returns the interpolated function value at a dim-dimensional point pt.
interpolate(double[]) - Method in class com.irurueta.numerical.interpolation.KrigingInterpolator: Returns interpolated value at provided point.
interpolate(double[]) - Method in class com.irurueta.numerical.interpolation.RadialBasisFunctionInterpolator: Returns the interpolated function value at a dim-dimensional point pt.
interpolate(double[]) - Method in class com.irurueta.numerical.interpolation.ShepardInterpolator: Returns the interpolated function value at a dim-dimensional point pt.
interpolate(double[], double[]) - Method in class com.irurueta.numerical.interpolation.KrigingInterpolator: Returns interpolated value at provided point, and estimated error.
interpolate(double, double) - Method in class com.irurueta.numerical.interpolation.BicubicSpline2DInterpolator: Given values x1p an x2p, returns an interpolated value.
interpolate(double, double) - Method in class com.irurueta.numerical.interpolation.BilinearInterpolator: Given values x1p an x2p, returns an interpolated value.
interpolate(double, double) - Method in class com.irurueta.numerical.interpolation.Polynomial2DInterpolator: Given values x1p an x2p, returns an interpolated value.
InterpolatingPolynomialEstimator - Class in com.irurueta.numerical.interpolation: Base class for interpolating polynomial estimators.
InterpolatingPolynomialEstimator() - Constructor for class com.irurueta.numerical.interpolation.InterpolatingPolynomialEstimator
InterpolationException - Exception in com.irurueta.numerical.interpolation: Exception raised when function interpolation fails.
InterpolationException() - Constructor for exception com.irurueta.numerical.interpolation.InterpolationException: Constructor.
InterpolationException(String) - Constructor for exception com.irurueta.numerical.interpolation.InterpolationException: Constructor with String containing message.
InterpolationException(String, Throwable) - Constructor for exception com.irurueta.numerical.interpolation.InterpolationException: Constructor with message and cause.
InterpolationException(Throwable) - Constructor for exception com.irurueta.numerical.interpolation.InterpolationException: Constructor with cause.
interpolator - Variable in class com.irurueta.numerical.integration.RombergIntegrator: Polynomial interpolator.
interpolator - Variable in class com.irurueta.numerical.integration.RombergTrapezoidalQuadratureIntegrator: Polynomial interpolator.
InvalidBracketRangeException - Exception in com.irurueta.numerical: Exception raised when provided bracket of values is not valid.
InvalidBracketRangeException() - Constructor for exception com.irurueta.numerical.InvalidBracketRangeException: Constructor.
InvalidBracketRangeException(String) - Constructor for exception com.irurueta.numerical.InvalidBracketRangeException: Constructor with String containing message.
InvalidBracketRangeException(String, Throwable) - Constructor for exception com.irurueta.numerical.InvalidBracketRangeException: Constructor with message and cause.
InvalidBracketRangeException(Throwable) - Constructor for exception com.irurueta.numerical.InvalidBracketRangeException: Constructor with cause.
InvalidSubsetRangeException - Exception in com.irurueta.numerical.robust: Raised if provided range of samples to pick subsets from is invalid.
InvalidSubsetRangeException() - Constructor for exception com.irurueta.numerical.robust.InvalidSubsetRangeException: Constructor.
InvalidSubsetRangeException(String) - Constructor for exception com.irurueta.numerical.robust.InvalidSubsetRangeException: Constructor with String containing message.
InvalidSubsetRangeException(String, Throwable) - Constructor for exception com.irurueta.numerical.robust.InvalidSubsetRangeException: Constructor with message and cause.
InvalidSubsetRangeException(Throwable) - Constructor for exception com.irurueta.numerical.robust.InvalidSubsetRangeException: Constructor with cause.
InvalidSubsetSizeException - Exception in com.irurueta.numerical.robust: Raised if an invalid subset size is requested on a subset selector
InvalidSubsetSizeException() - Constructor for exception com.irurueta.numerical.robust.InvalidSubsetSizeException: Constructor.
InvalidSubsetSizeException(String) - Constructor for exception com.irurueta.numerical.robust.InvalidSubsetSizeException: Constructor with String containing message.
InvalidSubsetSizeException(String, Throwable) - Constructor for exception com.irurueta.numerical.robust.InvalidSubsetSizeException: Constructor with message and cause.
InvalidSubsetSizeException(Throwable) - Constructor for exception com.irurueta.numerical.robust.InvalidSubsetSizeException: Constructor with cause.
InverseMultiQuadricRadialBasisFunction - Class in com.irurueta.numerical.interpolation: Inverse Multi-quadric Radial Function Basis implementation.
InverseMultiQuadricRadialBasisFunction() - Constructor for class com.irurueta.numerical.interpolation.InverseMultiQuadricRadialBasisFunction: Constructor.
InverseMultiQuadricRadialBasisFunction(double) - Constructor for class com.irurueta.numerical.interpolation.InverseMultiQuadricRadialBasisFunction: Constructor.
isBracketAvailable() - Method in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Returns boolean indicating whether a bracket has been provided or computed and is available for retrieval.
isBracketAvailable() - Method in class com.irurueta.numerical.roots.BracketedSingleRootEstimator: Returns boolean indicating whether bracket has been set or not.
isComputeAndKeepInliersEnabled() - Method in class com.irurueta.numerical.robust.PROSACRobustEstimator: Indicates whether inliers must be computed and kept.
isComputeAndKeepInliersEnabled() - Method in class com.irurueta.numerical.robust.RANSACRobustEstimator: Indicates whether inliers must be computed and kept.
isComputeAndKeepResidualsEnabled() - Method in class com.irurueta.numerical.robust.PROSACRobustEstimator: Indicates whether residuals must be computed and kept.
isComputeAndKeepResidualsEnabled() - Method in class com.irurueta.numerical.robust.RANSACRobustEstimator: Indicates whether residuals must be computed and kept.
isCovarianceAdjusted() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Indicates whether covariance must be adjusted or not.
isCovarianceAdjusted() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Indicates whether covariance must be adjusted or not.
isCovarianceAdjusted() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Indicates whether covariance must be adjusted or not.
isDerivativeListenerAvailable() - Method in class com.irurueta.numerical.optimization.DerivativeBrentSingleOptimizer: Returns boolean indicating whether derivative listener has been provided and is available for retrieval.
isDerivativeListenerAvailable() - Method in class com.irurueta.numerical.roots.DerivativeSingleRootEstimator: Returns boolean indicating whether the derivative listener has been provided and is available for retrieval.
isDirectionAvailable() - Method in class com.irurueta.numerical.optimization.DerivativeLineMultiOptimizer: Returns boolean indicating whether direction has already been provided and is ready for retrieval.
isDirectionAvailable() - Method in class com.irurueta.numerical.optimization.LineMultiOptimizer: Returns boolean indicating whether direction has already been provided and is ready for retrieval.
isFirstDegree() - Method in class com.irurueta.numerical.roots.FirstDegreePolynomialRootsEstimator: Returns boolean indicating whether polynomial parameters provided to this instance correspond to a valid first degree polynomial.
isFirstDegree(double[]) - Static method in class com.irurueta.numerical.roots.FirstDegreePolynomialRootsEstimator: Returns boolean indicating whether provided array of polynomial parameters correspond to a valid first degree polynomial.
isGeometricDistanceUsed() - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Indicates whether geometric distance will be used to find outliers or algebraic distance will be used instead.
isGradientListenerAvailable() - Method in class com.irurueta.numerical.optimization.ConjugateGradientMultiOptimizer: Returns boolean indicating whether a gradient listener has already been provided and is available for retrieval.
isGradientListenerAvailable() - Method in class com.irurueta.numerical.optimization.DerivativeLineMultiOptimizer: Returns boolean indicating whether the gradient listener has been provided and is available for retrieval.
isGradientListenerAvailable() - Method in class com.irurueta.numerical.optimization.QuasiNewtonMultiOptimizer: Returns boolean indicating whether a gradient listener has already been provided and is available for retrieval.
isHistogramInitialSolutionUsed() - Method in class com.irurueta.numerical.AccurateMaximumLikelihoodEstimator: Returns boolean that indicates that an initial coarse solution will be computed first by using an internal HistogramMaximumLikelihoodEstimator in order to initialize the internal BrentSingleOptimizer to obtain a more accurate solution.
isInputDataAvailable() - Method in class com.irurueta.numerical.MaximumLikelihoodEstimator: Returns boolean indicating whether input data has already been provided or not.
isListenerAvailable() - Method in class com.irurueta.numerical.optimization.MultiOptimizer: Returns boolean indicating whether listener has been provided and is available for retrieval.
isListenerAvailable() - Method in class com.irurueta.numerical.optimization.SingleOptimizer: Returns boolean indicating whether a listener has been provided and is available for retrieval.
isListenerAvailable() - Method in class com.irurueta.numerical.robust.RobustEstimator: Indicates whether listener has been provided and is available for retrieval.
isListenerAvailable() - Method in class com.irurueta.numerical.roots.SingleRootEstimator: Returns boolean indicating whether a listener has been provided.
isLMedSInlierModelEnabled() - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator.PROMedSInliersData: Returns boolean indicating whether LMedS or MSAC inlier model is enabled.
isLMSESolutionAllowed() - Method in class com.irurueta.numerical.polynomials.estimators.LMSEPolynomialEstimator: Indicates if an LMSE (Least Mean Square Error) solution is allowed if more evaluations than the required minimum are provided.
isLocked() - Method in class com.irurueta.numerical.MaximumLikelihoodEstimator: Returns boolean indicating whether this instance is locked because some computations are being done.
isLocked() - Method in class com.irurueta.numerical.optimization.Optimizer: Returns boolean indicating whether this instance is locked.
isLocked() - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Indicates whether this instance is locked.
isLocked() - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Indicates if this estimator is locked because an estimation is being computed.
isLocked() - Method in class com.irurueta.numerical.robust.RobustEstimator: Indicates if this instance is locked because estimation is being computed.
isLocked() - Method in class com.irurueta.numerical.roots.RootEstimator: Returns boolean indicating whether this instance is locked.
isMedianResidualImproved() - Method in class com.irurueta.numerical.robust.LMedSRobustEstimator.LMedSInliersData: Returns boolean indicating whether median residual computed in current iteration has improved respect to previous iterations.
isMedianResidualImproved() - Method in class com.irurueta.numerical.robust.MSACRobustEstimator.MSACInliersData: Returns boolean indicating whether median residual computed in current iteration has improved respect to previous iterations.
isMedianResidualImproved() - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator.PROMedSInliersData: Returns boolean indicating whether median residual computed in current iteration has improved respect to previous iterations.
isPolakRibiereEnabled() - Method in class com.irurueta.numerical.optimization.ConjugateGradientMultiOptimizer: Returns boolean indicating whether Polak-Ribiere method is used or Fletcher-Reeves is used instead.
isPolakRibiereEnabled() - Method in class com.irurueta.numerical.optimization.DerivativeConjugateGradientMultiOptimizer: Returns boolean indicating whether Polak-Ribiere method is used or Fletcher-Reeves is used instead.
isReady() - Method in class com.irurueta.numerical.fitting.Fitter: Indicates whether this instance is ready because enough input data has been provided to start the fitting process
isReady() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Indicates whether provided instance has enough data to start the function fitting.
isReady() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Indicates whether provided instance has enough data to start the function fitting.
isReady() - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Indicates whether provided instance has enough data to start the function fitting.
isReady() - Method in class com.irurueta.numerical.fitting.MultiDimensionLinearFitter: Indicates whether provided instance has enough data to start the function fitting.
isReady() - Method in class com.irurueta.numerical.fitting.SingleDimensionLinearFitter: Indicates whether provided instance has enough data to start the function fitting.
isReady() - Method in class com.irurueta.numerical.fitting.StraightLineFitter: Indicates whether this instance is ready because enough input data has been provided to start the fitting process.
isReady() - Method in class com.irurueta.numerical.MaximumLikelihoodEstimator: Returns boolean indicating if enough parameters have been provided in order to start the computation of the maximum likelihood value.
isReady() - Method in class com.irurueta.numerical.optimization.BrentSingleOptimizer: Returns boolean indicating whether this instance is ready to start the estimation of a minimum or not.
isReady() - Method in class com.irurueta.numerical.optimization.ConjugateGradientMultiOptimizer: Returns boolean indicating whether this instance is ready to start the estimation of a local minimum.
isReady() - Method in class com.irurueta.numerical.optimization.DerivativeBrentSingleOptimizer: Returns boolean indicating whether this instance is ready to start the estimation of a local minimum.
isReady() - Method in class com.irurueta.numerical.optimization.DerivativeConjugateGradientMultiOptimizer: Returns boolean indicating whether this instance is ready to start the estimation of a local minimum.
isReady() - Method in class com.irurueta.numerical.optimization.DerivativeLineMultiOptimizer: Returns boolean indicating whether this instance is considered to be ready to start the estimation of a minimum.
isReady() - Method in class com.irurueta.numerical.optimization.GoldenSingleOptimizer: Returns boolean indicating whether this instance is ready to start the estimation of a minimum or not.
isReady() - Method in class com.irurueta.numerical.optimization.LineMultiOptimizer: Returns boolean indicating whether this instance is considered to be ready to start the estimation of a minimum.
isReady() - Method in class com.irurueta.numerical.optimization.MultiOptimizer: Returns boolean indicating whether this instance is ready to start the estimation of a minimum.
isReady() - Method in class com.irurueta.numerical.optimization.Optimizer: Returns boolean indicating whether this instance is ready.
isReady() - Method in class com.irurueta.numerical.optimization.PowellMultiOptimizer: Returns boolean indicating whether this instance is ready to start the estimation of a local minimum.
isReady() - Method in class com.irurueta.numerical.optimization.QuasiNewtonMultiOptimizer: Returns boolean indicating whether this instance is ready to start the estimation of a local minimum.
isReady() - Method in class com.irurueta.numerical.optimization.SimplexMultiOptimizer: Returns boolean indicating whether this instance is ready to start the estimation of a local minimum.
isReady() - Method in class com.irurueta.numerical.optimization.SingleOptimizer: Returns true if this instance is ready to start the minimum estimation, false otherwise.
isReady() - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Determines whether estimation is ready to start with the given data and required degree of polynomial to be estimated
isReady() - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Determines whether estimation is ready to start with the given data and required degree of polynomial to be estimated.
isReady() - Method in class com.irurueta.numerical.polynomials.estimators.PROMedSPolynomialRobustEstimator: Indicates if estimator is ready to start the polynomial estimation.
isReady() - Method in class com.irurueta.numerical.polynomials.estimators.PROSACPolynomialRobustEstimator: Indicates if estimator is ready to start the polynomial estimation.
isReady() - Method in class com.irurueta.numerical.polynomials.estimators.WeightedPolynomialEstimator: Indicates if this estimator is ready to start the estimation.
isReady() - Method in class com.irurueta.numerical.robust.LMedSRobustEstimator: Indicates if estimator is ready to start the estimation process.
isReady() - Method in class com.irurueta.numerical.robust.MSACRobustEstimator: Indicates if estimator is ready to start the estimation process.
isReady() - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Indicates if estimator is ready to start the estimation process.
isReady() - Method in class com.irurueta.numerical.robust.PROSACRobustEstimator: Indicates if estimator is ready to start the estimation process.
isReady() - Method in class com.irurueta.numerical.robust.RANSACRobustEstimator: Indicates if estimator is ready to start the estimation process.
isReady() - Method in class com.irurueta.numerical.robust.RobustEstimator: Indicates if estimator is ready to start the estimation process.
isReady() - Method in interface com.irurueta.numerical.robust.RobustEstimatorListener: Called to determine when a robust estimator is ready to start estimation.
isReady() - Method in class com.irurueta.numerical.roots.BisectionSingleRootEstimator: Returns boolean indicating whether this instance is ready to compute a root.
isReady() - Method in class com.irurueta.numerical.roots.BrentSingleRootEstimator: Returns boolean indicating whether this instance is ready to start estimating a root.
isReady() - Method in class com.irurueta.numerical.roots.DerivativeSingleRootEstimator: Returns boolean indicating whether enough parameters have been provided in order to start the estimation of the roots of a function.
isReady() - Method in class com.irurueta.numerical.roots.FalsePositionSingleRootEstimator: Returns boolean indicating whether this instance is ready to compute a root.
isReady() - Method in class com.irurueta.numerical.roots.PolynomialRootsEstimator: Returns boolean indicating whether this instance is ready to start the estimation of the polynomial roots.
isReady() - Method in class com.irurueta.numerical.roots.RidderSingleRootEstimator: Returns boolean indicating whether this instance is ready to start estimating a root.
isReady() - Method in class com.irurueta.numerical.roots.RootEstimator: Returns boolean indicating whether enough parameters have been provided in order to start the estimation of the roots of a function.
isReady() - Method in class com.irurueta.numerical.roots.SecantSingleRootEstimator: Returns boolean indicating whether this instance is ready to start estimating a root.
isReady() - Method in class com.irurueta.numerical.roots.SingleRootEstimator: Returns boolean indicating whether enough parameters have been provided in order to start the estimation of the roots of a function.
isReady() - Method in class com.irurueta.numerical.signal.processing.Convolver1D: Indicates whether this instance is ready to start a convolution.
isRealSolution() - Method in class com.irurueta.numerical.roots.FirstDegreePolynomialRootsEstimator: Returns boolean indicating whether estimated root is real.
isResultAvailable() - Method in class com.irurueta.numerical.fitting.Fitter: Returns boolean indicating whether result has been estimated and is available for retrieval
isResultAvailable() - Method in class com.irurueta.numerical.optimization.MultiOptimizer: Returns boolean indicating whether a minimum has been estimated and is available for retrieval.
isResultAvailable() - Method in class com.irurueta.numerical.optimization.SingleOptimizer: Returns boolean indicating whether the estimated minimum is available for retrieval.
isRootAvailable() - Method in class com.irurueta.numerical.roots.SingleRootEstimator: Returns boolean indicating whether a root has been estimated and is available for retrieval.
isSecondDegree() - Method in class com.irurueta.numerical.roots.SecondDegreePolynomialRootsEstimator: Returns boolean indicating whether polynomial parameters provided to this instance correspond to a valid second degree polynomial.
isSecondDegree(double[]) - Static method in class com.irurueta.numerical.roots.SecondDegreePolynomialRootsEstimator: Returns boolean indicating whether provided array of polynomial parameters correspond to a valid second degree polynomial.
isSimplexAvailable() - Method in class com.irurueta.numerical.optimization.SimplexMultiOptimizer: Returns boolean indicating whether a simplex has been provided and is available for retrieval.
isSortWeightsEnabled() - Method in class com.irurueta.numerical.polynomials.estimators.WeightedPolynomialEstimator: Indicates if weights are sorted by so that largest weighted evaluations are used first.
isStartPointAvailable() - Method in class com.irurueta.numerical.optimization.DerivativeLineMultiOptimizer: Returns boolean indicating whether start point has already been provided and is ready for retrieval.
isStartPointAvailable() - Method in class com.irurueta.numerical.optimization.LineMultiOptimizer: Returns boolean indicating whether start point has already been provided and is ready for retrieval.
isStartPointAvailable() - Method in class com.irurueta.numerical.optimization.QuasiNewtonMultiOptimizer: Returns boolean indicating whether start point has already been provided and is ready for retrieval.
isStopThresholdEnabled() - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Returns boolean indicating whether the algorithm must stop prematurely when dynamically computed threshold using median of residuals has a value lower than provided threshold in listener.
isThirdDegree() - Method in class com.irurueta.numerical.roots.ThirdDegreePolynomialRootsEstimator: Returns boolean indicating whether polynomial parameters provided to this instance correspond to a valid third degree polynomial.
isThirdDegree(double[]) - Static method in class com.irurueta.numerical.roots.ThirdDegreePolynomialRootsEstimator: Returns boolean indicating whether provided array of polynomial parameters correspond to a valid third degree polynomial.
isUseInlierThresholds() - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Returns flag indicating whether thresholds to determine inliers are used, or if only median of residuals is used.
iter - Variable in class com.irurueta.numerical.optimization.ConjugateGradientMultiOptimizer: Member contains number of iterations that were needed to estimate a minimum.
iter - Variable in class com.irurueta.numerical.optimization.DerivativeConjugateGradientMultiOptimizer: Member contains number of iterations that were needed to estimate a minimum.
iter - Variable in class com.irurueta.numerical.optimization.PowellMultiOptimizer: Member contains number of iterations that were needed to estimate a minimum.
iter - Variable in class com.irurueta.numerical.optimization.QuasiNewtonMultiOptimizer: Member contains number of iterations that were needed to estimate a minimum.
iterationCompletedListener - Variable in class com.irurueta.numerical.optimization.Optimizer: Listener to handle minimization events.
iters - Variable in class com.irurueta.numerical.robust.LMedSRobustEstimator: Number of iterations to be done to obtain required confidence.
iters - Variable in class com.irurueta.numerical.robust.MSACRobustEstimator: Number of iterations to be done to obtain required confidence.
iters - Variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Number of iterations to be done to obtain required confidence.
itmax - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Maximum number of iterations.
itmax - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Maximum number of iterations.
itmax - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Maximum number of iterations.
ITMAX - Static variable in class com.irurueta.numerical.optimization.BrentSingleOptimizer: Is the maximum allowed number of iterations.
ITMAX - Static variable in class com.irurueta.numerical.optimization.ConjugateGradientMultiOptimizer: Maximum allowed iterations.
ITMAX - Static variable in class com.irurueta.numerical.optimization.DerivativeBrentSingleOptimizer: Maximum number of iterations to perform.
ITMAX - Static variable in class com.irurueta.numerical.optimization.DerivativeConjugateGradientMultiOptimizer: Maximum allowed iterations.
ITMAX - Static variable in class com.irurueta.numerical.optimization.PowellMultiOptimizer: Maximum allowed iterations.
ITMAX - Static variable in class com.irurueta.numerical.optimization.QuasiNewtonMultiOptimizer: Maximum number of iterations.
ITMAX - Static variable in class com.irurueta.numerical.roots.BrentSingleRootEstimator: Constant defining maximum number of iterations.

J

jacobian - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Jacobian of function at a given point.
jacobian(double[]) - Method in class com.irurueta.numerical.JacobianEstimator: Returns the Jacobian of a multivariate function at provided point.
jacobian(double[], Matrix) - Method in class com.irurueta.numerical.JacobianEstimator: Sets estimated jacobian in provided result matrix of a multivariate function at provided point.
JacobianEstimator - Class in com.irurueta.numerical: Class to estimate the Jacobian of a multi variate and multidimensional function.
JacobianEstimator(MultiVariateFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.JacobianEstimator: Constructor.
JMAX - Static variable in class com.irurueta.numerical.integration.QuadratureIntegrator: Maximum number of allowed steps.
JMAX - Static variable in class com.irurueta.numerical.integration.QuadratureMatrixIntegrator: Maximum number of allowed steps.
JMAX - Static variable in class com.irurueta.numerical.integration.RombergIntegrator: Maximum number of allowed steps.
JMAX - Static variable in class com.irurueta.numerical.integration.RombergMatrixIntegrator: Maximum number of allowed steps.
JMAX - Static variable in class com.irurueta.numerical.integration.RombergTrapezoidalQuadratureIntegrator: Maximum number of allowed steps.
JMAX - Static variable in class com.irurueta.numerical.integration.RombergTrapezoidalQuadratureMatrixIntegrator: Maximum number of allowed steps.
JMAX - Static variable in class com.irurueta.numerical.integration.SimpsonIntegrator: Maximum number of allowed steps.
JMAX - Static variable in class com.irurueta.numerical.integration.SimpsonMatrixIntegrator: Maximum number of allowed steps.
JMAX - Static variable in class com.irurueta.numerical.roots.BisectionSingleRootEstimator: Constant defining maximum number of iterations to estimate a root.
JMAX - Static variable in class com.irurueta.numerical.roots.NewtonRaphsonSingleRootEstimator: Maximum number of iterations.
JMAXP - Static variable in class com.irurueta.numerical.integration.RombergIntegrator: Maximum number of allowed steps + 1.
JMAXP - Static variable in class com.irurueta.numerical.integration.RombergMatrixIntegrator: Maximum number of allowed steps + 1.
JMAXP - Static variable in class com.irurueta.numerical.integration.RombergTrapezoidalQuadratureIntegrator: Maximum number of allowed steps + 1.
JMAXP - Static variable in class com.irurueta.numerical.integration.RombergTrapezoidalQuadratureMatrixIntegrator: Maximum number of allowed steps + 1.
JMIN - Static variable in class com.irurueta.numerical.integration.QuadratureIntegrator: Minimum required number of steps.
JMIN - Static variable in class com.irurueta.numerical.integration.QuadratureMatrixIntegrator: Minimum required number of steps.
JMIN - Static variable in class com.irurueta.numerical.integration.SimpsonIntegrator: Minimum required number of steps.
JMIN - Static variable in class com.irurueta.numerical.integration.SimpsonMatrixIntegrator: Minimum required number of steps.
jsav - Variable in class com.irurueta.numerical.interpolation.BaseInterpolator

K

K - Static variable in class com.irurueta.numerical.integration.RombergIntegrator: Minimum required number of steps.
K - Static variable in class com.irurueta.numerical.integration.RombergMatrixIntegrator: Minimum required number of steps.
K - Static variable in class com.irurueta.numerical.integration.RombergTrapezoidalQuadratureIntegrator: Minimum required number of steps.
K - Static variable in class com.irurueta.numerical.integration.RombergTrapezoidalQuadratureMatrixIntegrator: Minimum required number of steps.
KalmanFilter - Class in com.irurueta.numerical.signal.processing: Implementation of a Kalman filter.
KalmanFilter(int, int) - Constructor for class com.irurueta.numerical.signal.processing.KalmanFilter: Constructor in case of no control parameters.
KalmanFilter(int, int, int) - Constructor for class com.irurueta.numerical.signal.processing.KalmanFilter: Allocates a Kalman filter and all its matrices and initializes them.
keepInliersData(PROMedSRobustEstimator.PROMedSInliersData, int) - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Keeps inliers data stored and initializes a new one with proper initial values.
kernel - Variable in class com.irurueta.numerical.signal.processing.Convolver1D: Kernel to convolve the signal with.
kernelCenter - Variable in class com.irurueta.numerical.signal.processing.Convolver1D: Position of kernel center.
KrigingInterpolator - Class in com.irurueta.numerical.interpolation: Interpolates sparsely defined points using D.G.
KrigingInterpolator(Matrix, double[]) - Constructor for class com.irurueta.numerical.interpolation.KrigingInterpolator: Constructor.
KrigingInterpolator(Matrix, double[], KrigingInterpolator.Variogram) - Constructor for class com.irurueta.numerical.interpolation.KrigingInterpolator: Constructor.
KrigingInterpolator(Matrix, double[], KrigingInterpolator.Variogram, double[]) - Constructor for class com.irurueta.numerical.interpolation.KrigingInterpolator: Constructor.
KrigingInterpolator.Variogram - Class in com.irurueta.numerical.interpolation: Variogram function.

L

LAGUER_EPS - Static variable in class com.irurueta.numerical.roots.LaguerrePolynomialRootsEstimator: Constant considered as machine precision for Laguerre method.
LaguerrePolynomialRootsEstimator - Class in com.irurueta.numerical.roots: This class estimates the roots of a polynomial of degree n.
LaguerrePolynomialRootsEstimator() - Constructor for class com.irurueta.numerical.roots.LaguerrePolynomialRootsEstimator: Empty constructor.
LaguerrePolynomialRootsEstimator(boolean) - Constructor for class com.irurueta.numerical.roots.LaguerrePolynomialRootsEstimator: Constructor.
LaguerrePolynomialRootsEstimator(Complex[]) - Constructor for class com.irurueta.numerical.roots.LaguerrePolynomialRootsEstimator: Constructor.
LaguerrePolynomialRootsEstimator(Complex[], boolean) - Constructor for class com.irurueta.numerical.roots.LaguerrePolynomialRootsEstimator: Constructor.
lasterr - Variable in class com.irurueta.numerical.interpolation.KrigingInterpolator: Most recently computed error.
lastval - Variable in class com.irurueta.numerical.interpolation.KrigingInterpolator: Most recently computed value.
LevenbergMarquardtMultiDimensionFitter - Class in com.irurueta.numerical.fitting: Fits provided data (x, y) to a generic non-linear function using Levenberg-Marquardt iterative algorithm.
LevenbergMarquardtMultiDimensionFitter() - Constructor for class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Constructor.
LevenbergMarquardtMultiDimensionFitter(Matrix, double[], double) - Constructor for class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Constructor.
LevenbergMarquardtMultiDimensionFitter(Matrix, double[], double[]) - Constructor for class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Constructor.
LevenbergMarquardtMultiDimensionFitter(LevenbergMarquardtMultiDimensionFunctionEvaluator) - Constructor for class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Constructor.
LevenbergMarquardtMultiDimensionFitter(LevenbergMarquardtMultiDimensionFunctionEvaluator, Matrix, double[], double) - Constructor for class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Constructor.
LevenbergMarquardtMultiDimensionFitter(LevenbergMarquardtMultiDimensionFunctionEvaluator, Matrix, double[], double[]) - Constructor for class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Constructor.
LevenbergMarquardtMultiDimensionFunctionEvaluator - Interface in com.irurueta.numerical.fitting: Interface to evaluate non-linear multidimensional functions.
LevenbergMarquardtMultiVariateFitter - Class in com.irurueta.numerical.fitting: Fits provided data (x, y) to a generic non-linear function using Levenberg-Marquardt iterative algorithm.
LevenbergMarquardtMultiVariateFitter() - Constructor for class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Constructor.
LevenbergMarquardtMultiVariateFitter(Matrix, Matrix, double) - Constructor for class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Constructor.
LevenbergMarquardtMultiVariateFitter(Matrix, Matrix, double[]) - Constructor for class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Constructor.
LevenbergMarquardtMultiVariateFitter(LevenbergMarquardtMultiVariateFunctionEvaluator) - Constructor for class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Constructor.
LevenbergMarquardtMultiVariateFitter(LevenbergMarquardtMultiVariateFunctionEvaluator, Matrix, Matrix, double) - Constructor for class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Constructor.
LevenbergMarquardtMultiVariateFitter(LevenbergMarquardtMultiVariateFunctionEvaluator, Matrix, Matrix, double[]) - Constructor for class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Constructor.
LevenbergMarquardtMultiVariateFunctionEvaluator - Interface in com.irurueta.numerical.fitting: Interface to evaluate non-linear multi variate and multidimensional functions.
LevenbergMarquardtSingleDimensionFitter - Class in com.irurueta.numerical.fitting: Fits provided data (x,y) to a generic non-linear function using Levenberg-Marquardt iterative algorithm.
LevenbergMarquardtSingleDimensionFitter() - Constructor for class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Constructor.
LevenbergMarquardtSingleDimensionFitter(double[], double[], double) - Constructor for class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Constructor.
LevenbergMarquardtSingleDimensionFitter(double[], double[], double[]) - Constructor for class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Constructor.
LevenbergMarquardtSingleDimensionFitter(LevenbergMarquardtSingleDimensionFunctionEvaluator) - Constructor for class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Constructor.
LevenbergMarquardtSingleDimensionFitter(LevenbergMarquardtSingleDimensionFunctionEvaluator, double[], double[], double) - Constructor for class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Constructor.
LevenbergMarquardtSingleDimensionFitter(LevenbergMarquardtSingleDimensionFunctionEvaluator, double[], double[], double[]) - Constructor for class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Constructor.
LevenbergMarquardtSingleDimensionFunctionEvaluator - Interface in com.irurueta.numerical.fitting: Interface to evaluate non-linear single dimensional functions.
LinearFitterMultiDimensionFunctionEvaluator - Interface in com.irurueta.numerical.fitting: Interface to evaluate linear multidimensional functions f(x1, x2, ...) = a * f0(x1, x2, ...) + b * f1(x1, x2, ...) + ...
LinearFitterSingleDimensionFunctionEvaluator - Interface in com.irurueta.numerical.fitting: Interface to evaluate linear single dimensional functions f(x) = a * f0(x) + b * f1(x) + ...
LinearInterpolator - Class in com.irurueta.numerical.interpolation: Computes linear interpolation.
LinearInterpolator(double[], double[]) - Constructor for class com.irurueta.numerical.interpolation.LinearInterpolator
LineMultiOptimizer - Class in com.irurueta.numerical.optimization: Abstract class to search for a local minimum on a multidimensional function along a given line of input parameters.
LineMultiOptimizer() - Constructor for class com.irurueta.numerical.optimization.LineMultiOptimizer: Empty constructor.
LineMultiOptimizer(MultiDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.optimization.LineMultiOptimizer: Constructor.
LineMultiOptimizer(MultiDimensionFunctionEvaluatorListener, double[], double[]) - Constructor for class com.irurueta.numerical.optimization.LineMultiOptimizer: Constructor.
linmin() - Method in class com.irurueta.numerical.optimization.DerivativeLineMultiOptimizer: Searches for a minimum along a given line of input values.
linmin() - Method in class com.irurueta.numerical.optimization.LineMultiOptimizer: Searches for a minimum along a given line of input values.
listener - Variable in class com.irurueta.numerical.DerivativeEstimator: Listener to evaluate a single dimension function.
listener - Variable in class com.irurueta.numerical.DirectionalEvaluator: Listener to evaluate a multidimensional function.
listener - Variable in class com.irurueta.numerical.GradientEstimator: Listener to evaluate a multidimensional function.
listener - Variable in class com.irurueta.numerical.integration.DoubleExponentialRuleMatrixQuadrature: Listener to evaluate single dimension functions at required points.
listener - Variable in class com.irurueta.numerical.integration.DoubleExponentialRuleQuadrature: Listener to evaluate single dimension functions at required points.
listener - Variable in class com.irurueta.numerical.integration.MidPointMatrixQuadrature: Listener to evaluate single dimension matrix functions at required points.
listener - Variable in class com.irurueta.numerical.integration.MidPointQuadrature: Listener to evaluate single dimension functions at required points.
listener - Variable in class com.irurueta.numerical.integration.TrapezoidalMatrixQuadrature: Listener to evaluate single dimension matrix functions at required points.
listener - Variable in class com.irurueta.numerical.integration.TrapezoidalQuadrature: Listener to evaluate single dimension functions at required points.
listener - Variable in class com.irurueta.numerical.JacobianEstimator: Listener to evaluate a multivariate function.
listener - Variable in class com.irurueta.numerical.optimization.MultiOptimizer: Listener to evaluate a multidimensional function.
listener - Variable in class com.irurueta.numerical.optimization.SingleOptimizer: Listener to evaluate single dimension functions.
listener - Variable in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Listener to be notified of events such as when estimation starts, ends or estimation progress changes.
listener - Variable in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Listener to be notified of events such as when estimation starts, ends or its progress significantly changes.
listener - Variable in class com.irurueta.numerical.robust.RobustEstimator: Listener to be notified of events such as when estimation starts, ends or its progress significantly changes.
listener - Variable in class com.irurueta.numerical.roots.SingleRootEstimator: Listener that evaluates a single dimension function in order to find its root.
listener - Variable in class com.irurueta.numerical.signal.processing.Convolver1D: Listener in charge of attending events generated by this instance.
LMEDS - Enum constant in enum class com.irurueta.numerical.robust.RobustEstimatorMethod: Least Median of Squares.
lmedsInlierModelEnabled - Variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator.PROMedSInliersData: Indicates whether LMedS or MSAC inlier model is enabled.
LMedSInliersData(int) - Constructor for class com.irurueta.numerical.robust.LMedSRobustEstimator.LMedSInliersData: Constructor.
LMedSPolynomialRobustEstimator - Class in com.irurueta.numerical.polynomials.estimators: Finds the best polynomial using LMedS algorithm.
LMedSPolynomialRobustEstimator() - Constructor for class com.irurueta.numerical.polynomials.estimators.LMedSPolynomialRobustEstimator: Constructor.
LMedSPolynomialRobustEstimator(int) - Constructor for class com.irurueta.numerical.polynomials.estimators.LMedSPolynomialRobustEstimator: Constructor.
LMedSPolynomialRobustEstimator(int, PolynomialRobustEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.LMedSPolynomialRobustEstimator: Constructor.
LMedSPolynomialRobustEstimator(int, List<PolynomialEvaluation>) - Constructor for class com.irurueta.numerical.polynomials.estimators.LMedSPolynomialRobustEstimator: Constructor.
LMedSPolynomialRobustEstimator(int, List<PolynomialEvaluation>, PolynomialRobustEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.LMedSPolynomialRobustEstimator: Constructor.
LMedSPolynomialRobustEstimator(PolynomialRobustEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.LMedSPolynomialRobustEstimator: Constructor.
LMedSPolynomialRobustEstimator(List<PolynomialEvaluation>) - Constructor for class com.irurueta.numerical.polynomials.estimators.LMedSPolynomialRobustEstimator: Constructor.
LMedSPolynomialRobustEstimator(List<PolynomialEvaluation>, PolynomialRobustEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.LMedSPolynomialRobustEstimator: Constructor.
LMedSRobustEstimator<T> - Class in com.irurueta.numerical.robust: This class implements LMedS (Least Median of Squares) algorithm to robustly estimate a data model.
LMedSRobustEstimator() - Constructor for class com.irurueta.numerical.robust.LMedSRobustEstimator: Constructor.
LMedSRobustEstimator(LMedSRobustEstimatorListener<T>) - Constructor for class com.irurueta.numerical.robust.LMedSRobustEstimator: Constructor with listener.
LMedSRobustEstimator.LMedSInliersData - Class in com.irurueta.numerical.robust: Contains data related to inliers estimated in one iteration.
LMedSRobustEstimatorListener<T> - Interface in com.irurueta.numerical.robust: Listener to get data samples and residuals for LMedS method.
LMSE_POLYNOMIAL_ESTIMATOR - Enum constant in enum class com.irurueta.numerical.polynomials.estimators.PolynomialEstimatorType: Polynomial estimator using LMSE (Least Mean Square Error) solutions.
LMSEPolynomialEstimator - Class in com.irurueta.numerical.polynomials.estimators: This class defines an LMSE (Least Mean Square Error) estimator of a polynomial of a given degree using points where polynomials (or its derivatives or integrals) are evaluated.
LMSEPolynomialEstimator() - Constructor for class com.irurueta.numerical.polynomials.estimators.LMSEPolynomialEstimator: Constructor.
LMSEPolynomialEstimator(int) - Constructor for class com.irurueta.numerical.polynomials.estimators.LMSEPolynomialEstimator: Constructor.
LMSEPolynomialEstimator(int, PolynomialEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.LMSEPolynomialEstimator: Constructor.
LMSEPolynomialEstimator(int, List<PolynomialEvaluation>) - Constructor for class com.irurueta.numerical.polynomials.estimators.LMSEPolynomialEstimator: Constructor.
LMSEPolynomialEstimator(int, List<PolynomialEvaluation>, PolynomialEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.LMSEPolynomialEstimator: Constructor.
LMSEPolynomialEstimator(PolynomialEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.LMSEPolynomialEstimator: Constructor.
LMSEPolynomialEstimator(List<PolynomialEvaluation>) - Constructor for class com.irurueta.numerical.polynomials.estimators.LMSEPolynomialEstimator: Constructor.
LMSEPolynomialEstimator(List<PolynomialEvaluation>, PolynomialEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.LMSEPolynomialEstimator: Constructor.
lnsrch(double[], double, double[], double[], double[], double[], double, boolean[]) - Method in class com.irurueta.numerical.optimization.QuasiNewtonMultiOptimizer: Internal method to search for a minimum along a line.
locate(double) - Method in class com.irurueta.numerical.interpolation.BaseInterpolator: Given a value x, returns a value j such that x is (insofar as possible) centered in the subrange xx[j..j+mm-1], where xx is the stored array.
locked - Variable in class com.irurueta.numerical.MaximumLikelihoodEstimator: Boolean indicating whether this instance is locked because some computations are being done.
locked - Variable in class com.irurueta.numerical.optimization.Optimizer: Boolean indicating whether this instance is locked because computations are being done.
locked - Variable in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: True when estimator is estimating radial distortion.
locked - Variable in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Indicates if this estimator is locked because an estimation is being computed.
locked - Variable in class com.irurueta.numerical.robust.RobustEstimator: Indicates if this estimator is locked because an estimation is being computed.
locked - Variable in class com.irurueta.numerical.roots.RootEstimator: Boolean indicating that this instance is locked because it is doing computations.
LockedException - Exception in com.irurueta.numerical: Exception raised when an instance is locked.
LockedException() - Constructor for exception com.irurueta.numerical.LockedException: Constructor.
LockedException(String) - Constructor for exception com.irurueta.numerical.LockedException: Constructor with String containing message.
LockedException(String, Throwable) - Constructor for exception com.irurueta.numerical.LockedException: Constructor with message and cause.
LockedException(Throwable) - Constructor for exception com.irurueta.numerical.LockedException: Constructor with cause.
LOGGER - Static variable in class com.irurueta.numerical.BuildInfo: This class logger.
LongFactorialEstimator - Class in com.irurueta.numerical: Estimates factorial values as long integer values.
LongFactorialEstimator() - Constructor for class com.irurueta.numerical.LongFactorialEstimator: Constructor with default cache size.
LongFactorialEstimator(int) - Constructor for class com.irurueta.numerical.LongFactorialEstimator: Constructor.
LOWER_SQUARE_ROOT_MID_POINT - Enum constant in enum class com.irurueta.numerical.integration.QuadratureType: Lower square root mid-point.
LowerSquareRootMidPointMatrixQuadrature - Class in com.irurueta.numerical.integration: This is an exact replacement for MidPointMatrixQuadrature, except that it allows for an inverse square-root singularity in the integrand at the lower limit "a".
LowerSquareRootMidPointMatrixQuadrature(double, double, MatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.LowerSquareRootMidPointMatrixQuadrature: Constructor.
LowerSquareRootMidPointQuadrature - Class in com.irurueta.numerical.integration: This is an exact replacement for MidPointQuadrature, except that it allows for an inverse square-root singularity in the integrand at the lower limit "a".
LowerSquareRootMidPointQuadrature(double, double, SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.LowerSquareRootMidPointQuadrature: Constructor.
LowerSquareRootMidPointQuadratureIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using Lower Square Root mid-point quadrature when lower bound integration bound lies at a function singularity.
LowerSquareRootMidPointQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.LowerSquareRootMidPointQuadratureIntegrator: Constructor.
LowerSquareRootMidPointQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.LowerSquareRootMidPointQuadratureIntegrator: Constructor.
LowerSquareRootMidPointQuadratureMatrixIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using Lower Square Root mid-point quadrature when lower bound integration bound lies at a function singularity.
LowerSquareRootMidPointQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.LowerSquareRootMidPointQuadratureMatrixIntegrator: Constructor.
LowerSquareRootMidPointQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.LowerSquareRootMidPointQuadratureMatrixIntegrator: Constructor.
luDecomposer - Variable in class com.irurueta.numerical.PadeApproximantEstimator: Computes LU decomposition to find denominator coefficients.

M

m - Variable in class com.irurueta.numerical.interpolation.BicubicSpline2DInterpolator: Length of x1v array.
m - Variable in class com.irurueta.numerical.interpolation.BilinearInterpolator: Length of x1v array.
m - Variable in class com.irurueta.numerical.interpolation.Polynomial2DInterpolator: Length of x1v array.
M - Static variable in class com.irurueta.numerical.interpolation.CubicSplineInterpolator: Length of x's and y's to take into account.
M - Static variable in class com.irurueta.numerical.interpolation.LinearInterpolator: Length of x's and y's to take into account.
ma - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Number of function parameters to be estimated.
ma - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Number of function parameters to be estimated.
ma - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Number of function parameters to be estimated.
ma - Variable in class com.irurueta.numerical.fitting.MultiDimensionLinearFitter: Number of function basis used as a linear combination of functions being fitted
ma - Variable in class com.irurueta.numerical.fitting.SingleDimensionLinearFitter: Number of function basis used as a linear combination of functions being fitted.
MatrixIntegrator - Class in com.irurueta.numerical.integration: Integrates single dimension matrix (multivariate) functions over a specified interval.
MatrixIntegrator() - Constructor for class com.irurueta.numerical.integration.MatrixIntegrator
MatrixQuadrature - Class in com.irurueta.numerical.integration: Abstract base class for elementary matrix quadrature algorithms used for matrix (multivariate) single dimension function integration.
MatrixQuadrature() - Constructor for class com.irurueta.numerical.integration.MatrixQuadrature
MatrixSingleDimensionFunctionEvaluatorListener - Interface in com.irurueta.numerical.integration: Interface to define how matrix (multivariate) single dimension functions can be evaluated.
MAX_BETA - Static variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Maximum allowed value for beta.
MAX_BETA - Static variable in class com.irurueta.numerical.robust.PROSACRobustEstimator: Maximum allowed value for beta.
MAX_CONFIDENCE - Static variable in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Maximum allowed confidence value.
MAX_CONFIDENCE - Static variable in class com.irurueta.numerical.robust.LMedSRobustEstimator: Maximum allowed confidence value.
MAX_CONFIDENCE - Static variable in class com.irurueta.numerical.robust.MSACRobustEstimator: Maximum allowed confidence value.
MAX_CONFIDENCE - Static variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Maximum allowed confidence value.
MAX_CONFIDENCE - Static variable in class com.irurueta.numerical.robust.PROSACRobustEstimator: Maximum allowed confidence value.
MAX_CONFIDENCE - Static variable in class com.irurueta.numerical.robust.RANSACRobustEstimator: Maximum allowed confidence value.
MAX_ETA0 - Static variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Maximum allowed value for eta0.
MAX_ETA0 - Static variable in class com.irurueta.numerical.robust.PROSACRobustEstimator: Maximum allowed value for eta0.
MAX_MAX_OUTLIERS_PROPORTION - Static variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Maximum allowed value for maximum allowed outliers proportion in the input data.
MAX_MAX_OUTLIERS_PROPORTION - Static variable in class com.irurueta.numerical.robust.PROSACRobustEstimator: Maximum allowed value for maximum allowed outliers proportion in the input data
MAX_PROGRESS_DELTA - Static variable in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Maximum allowed value for progress delta.
MAX_PROGRESS_DELTA - Static variable in class com.irurueta.numerical.robust.RobustEstimator: Maximum allowed value for progress delta.
maxEvalPoint - Variable in class com.irurueta.numerical.roots.BracketedSingleRootEstimator: Maximum evaluation point.
maxEvaluations - Variable in class com.irurueta.numerical.polynomials.estimators.WeightedPolynomialEstimator: Maximum number of evaluations to be weighted and taken into account.
MaximumLikelihoodEstimator - Class in com.irurueta.numerical: Abstract class to estimate the most likely value from a series of data assumed to be normally distributed.
MaximumLikelihoodEstimator() - Constructor for class com.irurueta.numerical.MaximumLikelihoodEstimator: Empty constructor.
MaximumLikelihoodEstimator(double) - Constructor for class com.irurueta.numerical.MaximumLikelihoodEstimator: Constructor.
MaximumLikelihoodEstimator(double[], double) - Constructor for class com.irurueta.numerical.MaximumLikelihoodEstimator: Constructor.
MaximumLikelihoodEstimator(double, double, double[], double) - Constructor for class com.irurueta.numerical.MaximumLikelihoodEstimator: Constructor.
MaximumLikelihoodEstimatorMethod - Enum Class in com.irurueta.numerical: Types of maximum likelihood estimation to determine the real value corresponding to a set of values.
MaximumLikelihoodEstimatorMethod() - Constructor for enum class com.irurueta.numerical.MaximumLikelihoodEstimatorMethod
MAXIT - Static variable in class com.irurueta.numerical.roots.FalsePositionSingleRootEstimator: Maximum allowed number of iterations.
MAXIT - Static variable in class com.irurueta.numerical.roots.LaguerrePolynomialRootsEstimator: Maximum number of iterations.
MAXIT - Static variable in class com.irurueta.numerical.roots.RidderSingleRootEstimator: Maximum number of iterations.
MAXIT - Static variable in class com.irurueta.numerical.roots.SafeNewtonRaphsonSingleRootEstimator: Maximum number of iterations.
MAXIT - Static variable in class com.irurueta.numerical.roots.SecantSingleRootEstimator: Maximum number of iterations.
maxIterations - Variable in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Maximum allowed number of iterations.
maxIterations - Variable in class com.irurueta.numerical.robust.LMedSRobustEstimator: Maximum allowed number of iterations.
maxIterations - Variable in class com.irurueta.numerical.robust.MSACRobustEstimator: Maximum allowed number of iterations.
maxIterations - Variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Maximum allowed number of iterations.
maxIterations - Variable in class com.irurueta.numerical.robust.PROSACRobustEstimator: Maximum allowed number of iterations.
maxIterations - Variable in class com.irurueta.numerical.robust.RANSACRobustEstimator: Maximum allowed number of iterations.
maxOutliersProportion - Variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: In this implementation, PROSAC won't stop before having reached the corresponding inliers rate on the complete data set.
maxOutliersProportion - Variable in class com.irurueta.numerical.robust.PROSACRobustEstimator: In this implementation, PROSAC won't stop before having reached the corresponding inliers rate on the complete data set.
maxValue - Variable in class com.irurueta.numerical.MaximumLikelihoodEstimator: Maximum value found on provided input data array.
measurementMatrix - Variable in class com.irurueta.numerical.signal.processing.KalmanFilter: Measurement matrix (H).
measurementNoiseCov - Variable in class com.irurueta.numerical.signal.processing.KalmanFilter: Measurement noise covariance matrix (R).
measurementNoiseCov - Variable in class com.irurueta.numerical.signal.processing.MeasurementNoiseCovarianceEstimator: Estimated measurement noise covariance matrix.
MeasurementNoiseCovarianceEstimator - Class in com.irurueta.numerical.signal.processing: Estimates noise covariance matrix for a given set of measures.
MeasurementNoiseCovarianceEstimator(int) - Constructor for class com.irurueta.numerical.signal.processing.MeasurementNoiseCovarianceEstimator: Constructor.
medianResidual - Variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator.PROMedSInliersData: Median of error found on current iteration among all provided samples.
medianResidualImproved - Variable in class com.irurueta.numerical.robust.LMedSRobustEstimator.LMedSInliersData: Indicates whether median residual computed in current iteration has improved respect to previous iterations.
medianResidualImproved - Variable in class com.irurueta.numerical.robust.MSACRobustEstimator.MSACInliersData: Indicates whether median residual computed in current iteration has improved respect to previous iterations.
medianResidualImproved - Variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator.PROMedSInliersData: Indicates whether median residual computed in current iteration has improved respect to previous iterations.
mfit - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Number of parameters ot be fitted.
mfit - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Number of parameters ot be fitted.
mfit - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Number of parameters to be fitted.
MID_POINT - Enum constant in enum class com.irurueta.numerical.integration.QuadratureType: Mid-point quadrature.
MidPointMatrixQuadrature - Class in com.irurueta.numerical.integration: Implementation of matrix quadrature using mid-point algorithm.
MidPointMatrixQuadrature(double, double, MatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.MidPointMatrixQuadrature: Constructor.
MidPointQuadrature - Class in com.irurueta.numerical.integration: Implementation of quadrature using mid-point algorithm.
MidPointQuadrature(double, double, SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.MidPointQuadrature: Constructor.
MidPointQuadratureIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using Mid-Point quadrature up to desired accuracy.
MidPointQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.MidPointQuadratureIntegrator: Constructor with default accuracy.
MidPointQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.MidPointQuadratureIntegrator: Constructor.
MidPointQuadratureMatrixIntegrator - Class in com.irurueta.numerical.integration: Computes single dimension matrix (multivariate) function integration by using Mid-Point quadrature up to desired accuracy.
MidPointQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.MidPointQuadratureMatrixIntegrator: Constructor with default accuracy.
MidPointQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.MidPointQuadratureMatrixIntegrator: Constructor.
MIN_BETA - Static variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Minimum allowed value for beta.
MIN_BETA - Static variable in class com.irurueta.numerical.robust.PROSACRobustEstimator: Minimum allowed value for beta.
MIN_CONFIDENCE - Static variable in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Minimum allowed confidence value.
MIN_CONFIDENCE - Static variable in class com.irurueta.numerical.robust.LMedSRobustEstimator: Minimum allowed confidence value.
MIN_CONFIDENCE - Static variable in class com.irurueta.numerical.robust.MSACRobustEstimator: Minimum allowed confidence value.
MIN_CONFIDENCE - Static variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Minimum allowed confidence value.
MIN_CONFIDENCE - Static variable in class com.irurueta.numerical.robust.PROSACRobustEstimator: Minimum allowed confidence value.
MIN_CONFIDENCE - Static variable in class com.irurueta.numerical.robust.RANSACRobustEstimator: Minimum allowed confidence value.
MIN_DEGREE - Static variable in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Minimum allowed degree to be estimated
MIN_DERIVATIVE_ORDER - Static variable in class com.irurueta.numerical.polynomials.estimators.DerivativePolynomialEvaluation: Minimum allowed derivative order.
MIN_ETA0 - Static variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Minimum allowed value for eta0.
MIN_ETA0 - Static variable in class com.irurueta.numerical.robust.PROSACRobustEstimator: Minimum allowed value for eta0.
MIN_GAUSSIAN_SIGMA - Static variable in class com.irurueta.numerical.MaximumLikelihoodEstimator: Minimum allowed Gaussian sigma to be set for each sample.
MIN_INLER_FACTOR - Static variable in class com.irurueta.numerical.robust.LMedSRobustEstimator: Minimum allowed value for inlier factor.
MIN_INLER_FACTOR - Static variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Minimum allowed value for inlier factor.
MIN_INTEGRAL_ORDER - Static variable in class com.irurueta.numerical.polynomials.estimators.IntegralIntervalPolynomialEvaluation: Minimum allowed integral order.
MIN_INTEGRAL_ORDER - Static variable in class com.irurueta.numerical.polynomials.estimators.IntegralPolynomialEvaluation: Minimum allowed integral order.
MIN_ITERATIONS - Static variable in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Minimum allowed number of iterations.
MIN_ITERATIONS - Static variable in class com.irurueta.numerical.robust.LMedSRobustEstimator: Minimum allowed number of iterations.
MIN_ITERATIONS - Static variable in class com.irurueta.numerical.robust.MSACRobustEstimator: Minimum allowed number of iterations.
MIN_ITERATIONS - Static variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Minimum allowed number of iterations.
MIN_ITERATIONS - Static variable in class com.irurueta.numerical.robust.PROSACRobustEstimator: Minimum allowed number of iterations.
MIN_ITERATIONS - Static variable in class com.irurueta.numerical.robust.RANSACRobustEstimator: Minimum allowed number of iterations.
MIN_MAX_OUTLIERS_PROPORTION - Static variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Minimum allowed value for maximum allowed outliers proportion in the input data.
MIN_MAX_OUTLIERS_PROPORTION - Static variable in class com.irurueta.numerical.robust.PROSACRobustEstimator: Minimum allowed value for maximum allowed outliers proportion in the input data.
MIN_NUM_SAMPLES - Static variable in class com.irurueta.numerical.robust.SubsetSelector: Constant defining minimum amount of allowed samples.
MIN_NUMBER_OF_BINS - Static variable in class com.irurueta.numerical.HistogramMaximumLikelihoodEstimator: Minimum number of bins allowed on the histogram.
MIN_ORDER - Static variable in class com.irurueta.numerical.polynomials.Polynomial: Minimum derivative / integration order.
MIN_PROGRESS_DELTA - Static variable in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Minimum allowed value for progress delta.
MIN_PROGRESS_DELTA - Static variable in class com.irurueta.numerical.robust.RobustEstimator: Minimum allowed value for progress delta
MIN_STOP_THRESHOLD - Static variable in class com.irurueta.numerical.polynomials.estimators.LMedSPolynomialRobustEstimator: Minimum value that can be set as stop threshold.
MIN_STOP_THRESHOLD - Static variable in class com.irurueta.numerical.polynomials.estimators.PROMedSPolynomialRobustEstimator: Minimum allowed stop threshold value
MIN_STOP_THRESHOLD - Static variable in class com.irurueta.numerical.robust.LMedSRobustEstimator: Minimum allowed stop threshold value.
MIN_THRESHOLD - Static variable in class com.irurueta.numerical.polynomials.estimators.MSACPolynomialRobustEstimator: Minimum value that can be set as threshold.
MIN_THRESHOLD - Static variable in class com.irurueta.numerical.polynomials.estimators.PROSACPolynomialRobustEstimator: Minimum value that can be set as threshold.
MIN_THRESHOLD - Static variable in class com.irurueta.numerical.polynomials.estimators.RANSACPolynomialRobustEstimator: Minimum value that can be set as threshold.
MIN_THRESHOLD - Static variable in class com.irurueta.numerical.robust.MSACRobustEstimator: Minimum allowed threshold to determine inliers.
MIN_THRESHOLD - Static variable in class com.irurueta.numerical.robust.PROSACRobustEstimator: Minimum allowed threshold to determine inliers.
MIN_THRESHOLD - Static variable in class com.irurueta.numerical.robust.RANSACRobustEstimator: Minimum allowed threshold to determine inliers.
MIN_TOLERANCE - Static variable in class com.irurueta.numerical.optimization.BrentSingleOptimizer: Minimum allowed tolerance.
MIN_TOLERANCE - Static variable in class com.irurueta.numerical.optimization.ConjugateGradientMultiOptimizer: Minimum allowed tolerance value.
MIN_TOLERANCE - Static variable in class com.irurueta.numerical.optimization.DerivativeBrentSingleOptimizer: Minimum allowed tolerance value.
MIN_TOLERANCE - Static variable in class com.irurueta.numerical.optimization.DerivativeConjugateGradientMultiOptimizer: Minimum allowed tolerance value.
MIN_TOLERANCE - Static variable in class com.irurueta.numerical.optimization.GoldenSingleOptimizer: Minimum allowed tolerance.
MIN_TOLERANCE - Static variable in class com.irurueta.numerical.optimization.PowellMultiOptimizer: Minimum allowed tolerance value.
MIN_TOLERANCE - Static variable in class com.irurueta.numerical.optimization.QuasiNewtonMultiOptimizer: Minimum allowed tolerance value.
MIN_TOLERANCE - Static variable in class com.irurueta.numerical.optimization.SimplexMultiOptimizer: Minimum allowed tolerance value.
MIN_TOLERANCE - Static variable in class com.irurueta.numerical.roots.BisectionSingleRootEstimator: Minimum allowed tolerance that can be set.
MIN_TOLERANCE - Static variable in class com.irurueta.numerical.roots.BrentSingleRootEstimator: Constant defining minimum allowed tolerance.
MIN_TOLERANCE - Static variable in class com.irurueta.numerical.roots.FalsePositionSingleRootEstimator: Minimum allowed tolerance that can be set.
MIN_TOLERANCE - Static variable in class com.irurueta.numerical.roots.NewtonRaphsonSingleRootEstimator: Constant defining minimum allowed tolerance.
MIN_TOLERANCE - Static variable in class com.irurueta.numerical.roots.RidderSingleRootEstimator: Constant defining minimum allowed tolerance.
MIN_TOLERANCE - Static variable in class com.irurueta.numerical.roots.SafeNewtonRaphsonSingleRootEstimator: Constant defining minimum allowed tolerance.
MIN_TOLERANCE - Static variable in class com.irurueta.numerical.roots.SecantSingleRootEstimator: Constant defining minimum allowed tolerance.
MIN_VALID_POLY_PARAMS_LENGTH - Static variable in class com.irurueta.numerical.polynomials.Polynomial: Minimum allowed length in polynomial parameters.
MIN_VALID_POLY_PARAMS_LENGTH - Static variable in class com.irurueta.numerical.roots.LaguerrePolynomialRootsEstimator: Minimum allowed length in polynomial parameters.
minEvalPoint - Variable in class com.irurueta.numerical.roots.BracketedSingleRootEstimator: Minimum evaluation point.
minimize() - Method in class com.irurueta.numerical.optimization.BrentSingleOptimizer: This function estimates a function minimum within provided or computed bracket of values.
minimize() - Method in class com.irurueta.numerical.optimization.ConjugateGradientMultiOptimizer: This function estimates a function minimum.
minimize() - Method in class com.irurueta.numerical.optimization.DerivativeBrentSingleOptimizer: This function estimates a function minimum within provided or computed bracket of values.
minimize() - Method in class com.irurueta.numerical.optimization.DerivativeConjugateGradientMultiOptimizer: This function estimates a function minimum.
minimize() - Method in class com.irurueta.numerical.optimization.GoldenSingleOptimizer: This function estimates a function minimum within provided or computed bracket of values.
minimize() - Method in class com.irurueta.numerical.optimization.Optimizer: This function estimates a function minimum.
minimize() - Method in class com.irurueta.numerical.optimization.PowellMultiOptimizer: This function estimates a function minimum.
minimize() - Method in class com.irurueta.numerical.optimization.QuasiNewtonMultiOptimizer: This function estimates a function minimum.
minimize() - Method in class com.irurueta.numerical.optimization.SimplexMultiOptimizer: This function estimates a function minimum.
minValue - Variable in class com.irurueta.numerical.MaximumLikelihoodEstimator: Minimum value found on provided input data array.
MIRROR_EDGE - Enum constant in enum class com.irurueta.numerical.signal.processing.ConvolverEdgeMethod: When convolution kernel reaches edge of signal being convoluted, it is assumed that the signal is mirrored.
mm - Variable in class com.irurueta.numerical.interpolation.BaseInterpolator: Length of data to be taken into account on x's and y's.
mm - Variable in class com.irurueta.numerical.interpolation.Polynomial2DInterpolator: Number of rows of sub-block of ym values to be processed.
mov3(double[], double[], double[], double, double, double) - Method in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Moves d, e and f into a[0], b[0] and c[0].
mp - Variable in class com.irurueta.numerical.signal.processing.KalmanFilter: Number of measurement vector dimensions (measure parameters).
mp - Variable in class com.irurueta.numerical.signal.processing.MeasurementNoiseCovarianceEstimator: Number of measurement vector dimensions (measure parameters).
mpts - Variable in class com.irurueta.numerical.optimization.SimplexMultiOptimizer: Number of points in the simplex.
MR - Static variable in class com.irurueta.numerical.roots.LaguerrePolynomialRootsEstimator: Constant that affects the number of iterations.
mrqcof(double[], Matrix, double[]) - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Used by fit to evaluate the linearized fitting matrix alpha, and vector beta to calculate chi square.
mrqcof(double[], Matrix, double[]) - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Used by fit to evaluate the linearized fitting matrix alpha, and vector beta to calculate chi square.
mrqcof(double[], Matrix, double[]) - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Used by LevenbergMarquardtSingleDimensionFitter.fit() to evaluate the linearized fitting matrix alpha, and vector beta to calculate chi square.
MSAC - Enum constant in enum class com.irurueta.numerical.robust.RobustEstimatorMethod: Median Sample Consensus.
MSACInliersData(int) - Constructor for class com.irurueta.numerical.robust.MSACRobustEstimator.MSACInliersData: Constructor.
MSACPolynomialRobustEstimator - Class in com.irurueta.numerical.polynomials.estimators: Finds the best polynomial using RANSAC algorithm.
MSACPolynomialRobustEstimator() - Constructor for class com.irurueta.numerical.polynomials.estimators.MSACPolynomialRobustEstimator: Constructor.
MSACPolynomialRobustEstimator(int) - Constructor for class com.irurueta.numerical.polynomials.estimators.MSACPolynomialRobustEstimator: Constructor.
MSACPolynomialRobustEstimator(int, PolynomialRobustEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.MSACPolynomialRobustEstimator: Constructor.
MSACPolynomialRobustEstimator(int, List<PolynomialEvaluation>) - Constructor for class com.irurueta.numerical.polynomials.estimators.MSACPolynomialRobustEstimator: Constructor.
MSACPolynomialRobustEstimator(int, List<PolynomialEvaluation>, PolynomialRobustEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.MSACPolynomialRobustEstimator: Constructor.
MSACPolynomialRobustEstimator(PolynomialRobustEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.MSACPolynomialRobustEstimator: Constructor.
MSACPolynomialRobustEstimator(List<PolynomialEvaluation>) - Constructor for class com.irurueta.numerical.polynomials.estimators.MSACPolynomialRobustEstimator: Constructor.
MSACPolynomialRobustEstimator(List<PolynomialEvaluation>, PolynomialRobustEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.MSACPolynomialRobustEstimator: Constructor.
MSACRobustEstimator<T> - Class in com.irurueta.numerical.robust: This class implements MSAC (Median SAmple Consensus) algorithm to robustly estimate a data model.
MSACRobustEstimator() - Constructor for class com.irurueta.numerical.robust.MSACRobustEstimator: Constructor.
MSACRobustEstimator(MSACRobustEstimatorListener<T>) - Constructor for class com.irurueta.numerical.robust.MSACRobustEstimator: Constructor.
MSACRobustEstimator.MSACInliersData - Class in com.irurueta.numerical.robust: Contains data related to inliers estimated in one iteration.
MSACRobustEstimatorListener<T> - Interface in com.irurueta.numerical.robust: Listener to get data samples and residuals for MSAC method
mse - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Mean square error.
mse - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Mean square error.
mse - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Mean square error.
MT - Static variable in class com.irurueta.numerical.roots.LaguerrePolynomialRootsEstimator: Constant that affects the number of iterations.
MultiDimensionFitter - Class in com.irurueta.numerical.fitting: Base class to fit a multi dimension function y = f(x1, x2, ...) by using provided data (x, y)
MultiDimensionFitter() - Constructor for class com.irurueta.numerical.fitting.MultiDimensionFitter: Constructor
MultiDimensionFitter(Matrix, double[], double) - Constructor for class com.irurueta.numerical.fitting.MultiDimensionFitter: Constructor
MultiDimensionFitter(Matrix, double[], double[]) - Constructor for class com.irurueta.numerical.fitting.MultiDimensionFitter: Constructor
MultiDimensionFunctionEvaluatorListener - Interface in com.irurueta.numerical: Interface to define how multi dimension functions can be evaluated.
MultiDimensionLinearFitter - Class in com.irurueta.numerical.fitting: Base class to fit provided multidimensional data (x1, x2, ..., y1, y2, ...)
MultiDimensionLinearFitter() - Constructor for class com.irurueta.numerical.fitting.MultiDimensionLinearFitter: Constructor
MultiDimensionLinearFitter(Matrix, double[], double) - Constructor for class com.irurueta.numerical.fitting.MultiDimensionLinearFitter: Constructor
MultiDimensionLinearFitter(Matrix, double[], double[]) - Constructor for class com.irurueta.numerical.fitting.MultiDimensionLinearFitter: Constructor
MultiDimensionLinearFitter(LinearFitterMultiDimensionFunctionEvaluator) - Constructor for class com.irurueta.numerical.fitting.MultiDimensionLinearFitter: Constructor
MultiDimensionLinearFitter(LinearFitterMultiDimensionFunctionEvaluator, Matrix, double[], double) - Constructor for class com.irurueta.numerical.fitting.MultiDimensionLinearFitter: Constructor
MultiDimensionLinearFitter(LinearFitterMultiDimensionFunctionEvaluator, Matrix, double[], double[]) - Constructor for class com.irurueta.numerical.fitting.MultiDimensionLinearFitter: Constructor
MultiOptimizer - Class in com.irurueta.numerical.optimization: Abstract class to search for minima on multidimensional classes.
MultiOptimizer() - Constructor for class com.irurueta.numerical.optimization.MultiOptimizer: Empty constructor.
MultiOptimizer(MultiDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.optimization.MultiOptimizer: Constructor.
multiply(Polynomial) - Method in class com.irurueta.numerical.polynomials.Polynomial: Multiplies this polynomial with another one.
multiply(Polynomial, Polynomial) - Method in class com.irurueta.numerical.polynomials.Polynomial: Multiplies two polynomials.
multiplyAndReturnNew(Polynomial) - Method in class com.irurueta.numerical.polynomials.Polynomial: Multiplies two polynomials and returns a new instance containing result.
multiplyByScalar(double) - Method in class com.irurueta.numerical.polynomials.Polynomial: Multiplies all parameters of this polynomial by provided scalar.
multiplyByScalar(double, Polynomial) - Method in class com.irurueta.numerical.polynomials.Polynomial: Multiplies all parameters of this polynomial by a scalar and stores the result into provided polynomial instance.
multiplyByScalarAndReturnNew(double) - Method in class com.irurueta.numerical.polynomials.Polynomial: Multiplies all parameters of this polynomial by a scalar and returns a new polynomial containing the result.
MultiQuadricRadialBasisFunction - Class in com.irurueta.numerical.interpolation: Multi-quadric Radial Function Basis implementation.
MultiQuadricRadialBasisFunction() - Constructor for class com.irurueta.numerical.interpolation.MultiQuadricRadialBasisFunction: Constructor.
MultiQuadricRadialBasisFunction(double) - Constructor for class com.irurueta.numerical.interpolation.MultiQuadricRadialBasisFunction: Constructor.
MultiVariateFitter - Class in com.irurueta.numerical.fitting: Base class to fit a multi variate function [y1, y2, ...] = f([x1, x2, ...])
MultiVariateFitter() - Constructor for class com.irurueta.numerical.fitting.MultiVariateFitter: Constructor.
MultiVariateFitter(Matrix, Matrix, double) - Constructor for class com.irurueta.numerical.fitting.MultiVariateFitter: Constructor.
MultiVariateFitter(Matrix, Matrix, double[]) - Constructor for class com.irurueta.numerical.fitting.MultiVariateFitter: Constructor.
MultiVariateFunctionEvaluatorListener - Interface in com.irurueta.numerical: Interface to define how multivariate functions can be evaluated.

N

n - Variable in class com.irurueta.numerical.ExponentialMatrixEstimator: Numerator of Padé approximant.
n - Variable in class com.irurueta.numerical.integration.MatrixQuadrature: Current level of refinement.
n - Variable in class com.irurueta.numerical.integration.Quadrature: Current level of refinement.
n - Variable in class com.irurueta.numerical.interpolation.BaseInterpolator: Length of x and y values to be interpolated.
n - Variable in class com.irurueta.numerical.interpolation.BaseRadialBasisFunctionInterpolator: Number of points provided in a matrix as a basis for interpolation.
n - Variable in class com.irurueta.numerical.interpolation.BicubicSpline2DInterpolator: Length of x2v array.
n - Variable in class com.irurueta.numerical.interpolation.BilinearInterpolator: Length of x2v array.
n - Variable in class com.irurueta.numerical.interpolation.Polynomial2DInterpolator: Length of x2v array.
n - Variable in class com.irurueta.numerical.optimization.DerivativeLineMultiOptimizer: Number of dimensions on function being evaluated.
n - Variable in class com.irurueta.numerical.optimization.LineMultiOptimizer: Number of dimensions on function being evaluated.
n - Variable in class com.irurueta.numerical.PadeApproximantEstimator: Number of coefficients being processed based on provided Taylor power series ones.
N_POINTS - Static variable in class com.irurueta.numerical.SavitzkyGolayDerivativeEstimator: Number of required point to evaluate to compute derivative.
N_POINTS - Static variable in class com.irurueta.numerical.SavitzkyGolayGradientEstimator: Number of required point to evaluate to compute derivative.
ndat - Variable in class com.irurueta.numerical.fitting.MultiDimensionFitter: Number of samples (x, y) in provided input data
ndat - Variable in class com.irurueta.numerical.fitting.MultiVariateFitter: Number of samples (x, y) in provided input data.
ndat - Variable in class com.irurueta.numerical.fitting.SingleDimensionFitter: Number of samples (x, y) in provided input data.
ndim - Variable in class com.irurueta.numerical.interpolation.KrigingInterpolator: Number of dimensions of each point.
ndim - Variable in class com.irurueta.numerical.optimization.SimplexMultiOptimizer: Number of dimensions of current function being optimized.
ndone - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Convergence parameter.
ndone - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Convergence parameter.
ndone - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Convergence parameter.
NewtonRaphsonSingleRootEstimator - Class in com.irurueta.numerical.roots: Finds a single dimensional function's root within a bracket of values using Newton-Raphson's method.
NewtonRaphsonSingleRootEstimator() - Constructor for class com.irurueta.numerical.roots.NewtonRaphsonSingleRootEstimator: Empty constructor.
NewtonRaphsonSingleRootEstimator(SingleDimensionFunctionEvaluatorListener, double, double, double) - Constructor for class com.irurueta.numerical.roots.NewtonRaphsonSingleRootEstimator: Constructor.
NewtonRaphsonSingleRootEstimator(SingleDimensionFunctionEvaluatorListener, SingleDimensionFunctionEvaluatorListener, double, double, double) - Constructor for class com.irurueta.numerical.roots.NewtonRaphsonSingleRootEstimator: Constructor.
next() - Method in class com.irurueta.numerical.integration.DoubleExponentialRuleQuadrature: Returns the value of the integral at the nth stage of refinement.
next() - Method in class com.irurueta.numerical.integration.MidPointQuadrature: Returns the value of the integral at the nth stage of refinement.
next() - Method in class com.irurueta.numerical.integration.Quadrature: Returns the value of the integral at the nth stage of refinement.
next() - Method in class com.irurueta.numerical.integration.TrapezoidalQuadrature: Returns the value of the integral at the nth stage of refinement.
next(Matrix) - Method in class com.irurueta.numerical.integration.DoubleExponentialRuleMatrixQuadrature: Returns the value of the integral at the nth stage of refinement.
next(Matrix) - Method in class com.irurueta.numerical.integration.MatrixQuadrature: Returns the value of the integral at the nth stage of refinement.
next(Matrix) - Method in class com.irurueta.numerical.integration.MidPointMatrixQuadrature: Returns the value of the integral at the nth stage of refinement.
next(Matrix) - Method in class com.irurueta.numerical.integration.TrapezoidalMatrixQuadrature: Returns the value of the integral at the nth stage of refinement.
nfunc - Variable in class com.irurueta.numerical.optimization.SimplexMultiOptimizer: Number of function evaluations.
nIters - Variable in class com.irurueta.numerical.robust.PROSACRobustEstimator: Number of iterations to be done to obtain required confidence.
nIters - Variable in class com.irurueta.numerical.robust.RANSACRobustEstimator: Number of iterations to be done to obtain required confidence.
NMAX - Static variable in class com.irurueta.numerical.optimization.SimplexMultiOptimizer: Maximum number of iterations.
nn - Variable in class com.irurueta.numerical.interpolation.Polynomial2DInterpolator: Number of columns of sub-block of ym values to be processed.
norm - Variable in class com.irurueta.numerical.interpolation.RadialBasisFunctionInterpolator: Indicates whether normalized Radial Basis Function (RBF) must be used or not.
normalize() - Method in class com.irurueta.numerical.polynomials.Polynomial: Normalizes this polynomial so that the array of parameters has unitary norm.
normalize(Matrix, Matrix, int) - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Normalizes rows of system matrix and values matrix to increase accuracy of linear system of equations to be solved.
normalize(Matrix, Matrix, int, double) - Static method in class com.irurueta.numerical.polynomials.estimators.WeightedPolynomialEstimator: Normalizes rows of system matrix and values matrix to increase accuracy of linear system of equations to be solved.
normalize(Polynomial) - Method in class com.irurueta.numerical.polynomials.Polynomial: Normalizes parameters of this polynomial so that the array of parameters has unitary norm and stores result into provided instance.
normalizeAndReturnNew() - Method in class com.irurueta.numerical.polynomials.Polynomial: Normalizes parameters of this polynomial so that the array of parameters has unitary norm and returns result as a new polynomial instance.
normalizeHighestDegreeTerm() - Method in class com.irurueta.numerical.polynomials.Polynomial: Normalizes parameters of this polynomial so that the highest degree term becomes 1.0.
normalizeHighestDegreeTerm(Polynomial) - Method in class com.irurueta.numerical.polynomials.Polynomial: Normalizes parameters of this polynomial so that the highest degree term becomes 1.0 and stores result into provided instance.
normalizeHighestDegreeTermAndReturnNew() - Method in class com.irurueta.numerical.polynomials.Polynomial: Normalizes parameters of this polynomial so that the highest degree term becomes 1.0 and returns the result as a new instance.
normmax(Matrix) - Static method in class com.irurueta.numerical.ExponentialMatrixEstimator: Estimates infinite norm of provided matrix.
normMin(Matrix) - Static method in class com.irurueta.numerical.integration.QuadratureMatrixIntegrator: Estimates smallest norm of provided matrix.
normMin(Matrix) - Static method in class com.irurueta.numerical.integration.SimpsonMatrixIntegrator: Estimates smallest norm of provided matrix.
NotAvailableException - Exception in com.irurueta.numerical: Exception raised when some value cannot be retrieved, usually because it has not yet been provided or computed.
NotAvailableException() - Constructor for exception com.irurueta.numerical.NotAvailableException: Constructor.
NotAvailableException(String) - Constructor for exception com.irurueta.numerical.NotAvailableException: Constructor with String containing message.
NotAvailableException(String, Throwable) - Constructor for exception com.irurueta.numerical.NotAvailableException: Constructor with message and cause.
NotAvailableException(Throwable) - Constructor for exception com.irurueta.numerical.NotAvailableException: Constructor with cause.
NotEnoughSamplesException - Exception in com.irurueta.numerical.robust: Raised if there aren't enough samples to make a computation.
NotEnoughSamplesException() - Constructor for exception com.irurueta.numerical.robust.NotEnoughSamplesException: Constructor.
NotEnoughSamplesException(String) - Constructor for exception com.irurueta.numerical.robust.NotEnoughSamplesException: Constructor with String containing message.
NotEnoughSamplesException(String, Throwable) - Constructor for exception com.irurueta.numerical.robust.NotEnoughSamplesException: Constructor with message and cause.
NotEnoughSamplesException(Throwable) - Constructor for exception com.irurueta.numerical.robust.NotEnoughSamplesException: Constructor with cause.
NotReadyException - Exception in com.irurueta.numerical: Raised when attempting to do a certain operation and not all parameters have been provided or are correctly set.
NotReadyException() - Constructor for exception com.irurueta.numerical.NotReadyException: Constructor.
NotReadyException(String) - Constructor for exception com.irurueta.numerical.NotReadyException: Constructor with String containing message.
NotReadyException(String, Throwable) - Constructor for exception com.irurueta.numerical.NotReadyException: Constructor with message and cause.
NotReadyException(Throwable) - Constructor for exception com.irurueta.numerical.NotReadyException: Constructor with cause.
npt - Variable in class com.irurueta.numerical.interpolation.KrigingInterpolator: Number of provided points.
nthDerivative(int) - Method in class com.irurueta.numerical.polynomials.Polynomial: Replaces this instance by its nth-order derivative.
nthDerivative(int, Polynomial) - Method in class com.irurueta.numerical.polynomials.Polynomial: Computes nth-order derivative of polynomial.
nthDerivativeAndReturnNew(int) - Method in class com.irurueta.numerical.polynomials.Polynomial: Computes nth-order derivative of polynomial.
nthIntegration(int) - Method in class com.irurueta.numerical.polynomials.Polynomial: Computes polynomial containing the nth-order integration of current one.
nthIntegration(int, double[]) - Method in class com.irurueta.numerical.polynomials.Polynomial: Computes polynomial containing the nth-order integration of current one.
nthIntegration(int, Polynomial) - Method in class com.irurueta.numerical.polynomials.Polynomial: Computes polynomial containing the nth-order integration of current one.
nthIntegration(int, Polynomial, double[]) - Method in class com.irurueta.numerical.polynomials.Polynomial: Computes polynomial containing the nth-order integration of current one.
nthIntegrationAndReturnNew(int) - Method in class com.irurueta.numerical.polynomials.Polynomial: Computes polynomial containing the nth-order integration of current one.
nthIntegrationAndReturnNew(int, double[]) - Method in class com.irurueta.numerical.polynomials.Polynomial: Computes polynomial containing the nth-order integration of current one.
nthOrderIntegrateInterval(double, double, int) - Method in class com.irurueta.numerical.polynomials.Polynomial: Computes nth-integration over provided interval.
nthOrderIntegrateInterval(double, double, int, double[]) - Method in class com.irurueta.numerical.polynomials.Polynomial: Computes nth-integration over provided interval.
NTRY - Static variable in class com.irurueta.numerical.roots.BracketedSingleRootEstimator: Number tries to automatically compute a bracket of values for a given function.
nugsq - Variable in class com.irurueta.numerical.interpolation.KrigingInterpolator.Variogram: Squared offset.
numberOfBins - Variable in class com.irurueta.numerical.HistogramMaximumLikelihoodEstimator: Number of bins to be used on the histogram.
numerators - Variable in class com.irurueta.numerical.PadeApproximantEstimator.Result: Numerator coefficients.
NumericalException - Exception in com.irurueta.numerical: Base class for all the exceptions in this package.
NumericalException() - Constructor for exception com.irurueta.numerical.NumericalException: Constructor.
NumericalException(String) - Constructor for exception com.irurueta.numerical.NumericalException: Constructor with String containing message.
NumericalException(String, Throwable) - Constructor for exception com.irurueta.numerical.NumericalException: Constructor with message and cause.
NumericalException(Throwable) - Constructor for exception com.irurueta.numerical.NumericalException: Constructor with cause.
numInliers - Variable in class com.irurueta.numerical.robust.InliersData: Number of inliers found on current iteration.
numSamples - Variable in class com.irurueta.numerical.robust.SubsetSelector: Total number of samples to pick subsets from.
numSelected - Variable in class com.irurueta.numerical.robust.WeightSelection: Number of correspondences that have been selected.

O

onConvolveProgressChange(float) - Method in interface com.irurueta.numerical.signal.processing.Convolver1D.Convolver1DListener: Called when convolution progress changes.
onEstimateEnd(PolynomialEstimator) - Method in interface com.irurueta.numerical.polynomials.estimators.PolynomialEstimatorListener: Called when an estimator ends the polynomial estimation process.
onEstimateEnd(PolynomialRobustEstimator) - Method in interface com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimatorListener: Called when an estimator ends the polynomial estimation process.
onEstimateEnd(RobustEstimator<T>) - Method in interface com.irurueta.numerical.robust.RobustEstimatorListener: Called when estimation ends.
onEstimateNextIteration(PolynomialRobustEstimator, int) - Method in interface com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimatorListener: Called when estimator iterates to refine a possible solution.
onEstimateNextIteration(RobustEstimator<T>, int) - Method in interface com.irurueta.numerical.robust.RobustEstimatorListener: Called when estimator iterates to refine a possible solution.
onEstimateProgressChange(PolynomialRobustEstimator, float) - Method in interface com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimatorListener: Called when estimation progress changes significantly.
onEstimateProgressChange(RobustEstimator<T>, float) - Method in interface com.irurueta.numerical.robust.RobustEstimatorListener: Called when estimation progress changes significantly.
onEstimateStart(PolynomialEstimator) - Method in interface com.irurueta.numerical.polynomials.estimators.PolynomialEstimatorListener: Called when an estimator starts the polynomial estimation process.
onEstimateStart(PolynomialRobustEstimator) - Method in interface com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimatorListener: Called when an estimator starts the polynomial estimation process.
onEstimateStart(RobustEstimator<T>) - Method in interface com.irurueta.numerical.robust.RobustEstimatorListener: Called when estimation starts.
onFinishConvolution() - Method in interface com.irurueta.numerical.signal.processing.Convolver1D.Convolver1DListener: Called when convolution finishes.
onIterationCompleted(Optimizer, int, Integer) - Method in interface com.irurueta.numerical.optimization.OnIterationCompletedListener: Called when an iteration is completed.
OnIterationCompletedListener - Interface in com.irurueta.numerical.optimization: Notifies of events generated by an optimizer.
onStartConvolution() - Method in interface com.irurueta.numerical.signal.processing.Convolver1D.Convolver1DListener: Called when convolution starts.
OptimizationException - Exception in com.irurueta.numerical.optimization: Raised when an optimizer cannot find a minimum on a function, usually because of lack of convergence.
OptimizationException() - Constructor for exception com.irurueta.numerical.optimization.OptimizationException: Constructor.
OptimizationException(String) - Constructor for exception com.irurueta.numerical.optimization.OptimizationException: Constructor with String containing message.
OptimizationException(String, Throwable) - Constructor for exception com.irurueta.numerical.optimization.OptimizationException: Constructor with message and cause.
OptimizationException(Throwable) - Constructor for exception com.irurueta.numerical.optimization.OptimizationException: Constructor with cause.
optimizer - Variable in class com.irurueta.numerical.AccurateMaximumLikelihoodEstimator: Internal optimizer to find the true maximum of the probability distribution function.
Optimizer - Class in com.irurueta.numerical.optimization: Abstract class to find function minima.
Optimizer() - Constructor for class com.irurueta.numerical.optimization.Optimizer: Empty constructor.

P

p - Variable in class com.irurueta.numerical.DirectionalEvaluator: Point currently being evaluated in the multidimensional function.
p - Variable in class com.irurueta.numerical.optimization.DerivativeLineMultiOptimizer: n-dimensional point containing a minimum in a given line.
p - Variable in class com.irurueta.numerical.optimization.LineMultiOptimizer: n-dimensional point containing a minimum in a given line.
p - Variable in class com.irurueta.numerical.optimization.QuasiNewtonMultiOptimizer: n-dimensional start point.
p - Variable in class com.irurueta.numerical.optimization.SimplexMultiOptimizer: Current simplex.
PadeApproximantEstimator - Class in com.irurueta.numerical: Estimates the Padé approximant rational function by using a number of coefficients of a Taylor series.
PadeApproximantEstimator() - Constructor for class com.irurueta.numerical.PadeApproximantEstimator: Default constructor.
PadeApproximantEstimator(int) - Constructor for class com.irurueta.numerical.PadeApproximantEstimator: Constructor.
PadeApproximantEstimator.Result - Class in com.irurueta.numerical: Contains result of Padé approximant.
pi - Variable in class com.irurueta.numerical.interpolation.BaseRadialBasisFunctionInterpolator: ith point to make comparisons.
pneg - Variable in class com.irurueta.numerical.interpolation.ShepardInterpolator: Negative value of p parameter controlling Shepard power-law function phi(r) = r^-p.
point - Variable in class com.irurueta.numerical.DirectionalEvaluator: Point used as a reference to determine the function's input parameters along a line.
polishRoots - Variable in class com.irurueta.numerical.roots.LaguerrePolynomialRootsEstimator: Indicates if roots should be refined.
Polynomial - Class in com.irurueta.numerical.polynomials: Contains a polynomial and common operations done with polynomials.
Polynomial() - Constructor for class com.irurueta.numerical.polynomials.Polynomial: Constructor.
Polynomial(double...) - Constructor for class com.irurueta.numerical.polynomials.Polynomial: Constructor.
Polynomial(int) - Constructor for class com.irurueta.numerical.polynomials.Polynomial: Constructor.
Polynomial2DInterpolator - Class in com.irurueta.numerical.interpolation: Interpolation in two dimensions.
Polynomial2DInterpolator(double[], double[], Matrix) - Constructor for class com.irurueta.numerical.interpolation.Polynomial2DInterpolator: Constructor.
Polynomial2DInterpolator(double[], double[], Matrix, int, int) - Constructor for class com.irurueta.numerical.interpolation.Polynomial2DInterpolator: Constructor.
PolynomialEstimationException - Exception in com.irurueta.numerical.polynomials.estimators: Exception raised if polynomial estimation fails.
PolynomialEstimationException() - Constructor for exception com.irurueta.numerical.polynomials.estimators.PolynomialEstimationException: Constructor.
PolynomialEstimationException(String) - Constructor for exception com.irurueta.numerical.polynomials.estimators.PolynomialEstimationException: Constructor with String containing message.
PolynomialEstimationException(String, Throwable) - Constructor for exception com.irurueta.numerical.polynomials.estimators.PolynomialEstimationException: Constructor with message and cause.
PolynomialEstimationException(Throwable) - Constructor for exception com.irurueta.numerical.polynomials.estimators.PolynomialEstimationException: Constructor with cause.
polynomialEstimator - Variable in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Internal non robust estimator of polynomial estimator.
PolynomialEstimator - Class in com.irurueta.numerical.polynomials.estimators: This class defines the interface for an estimator of a polynomial of a given degree using points where polynomials are evaluated.
PolynomialEstimator() - Constructor for class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Constructor.
PolynomialEstimator(int) - Constructor for class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Constructor.
PolynomialEstimator(int, PolynomialEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Constructor.
PolynomialEstimator(int, List<PolynomialEvaluation>) - Constructor for class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Constructor.
PolynomialEstimator(int, List<PolynomialEvaluation>, PolynomialEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Constructor.
PolynomialEstimator(PolynomialEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Constructor.
PolynomialEstimator(List<PolynomialEvaluation>) - Constructor for class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Constructor.
PolynomialEstimator(List<PolynomialEvaluation>, PolynomialEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Constructor.
PolynomialEstimatorListener - Interface in com.irurueta.numerical.polynomials.estimators: Listener to be notified when estimation starts, finishes or any progress changes.
PolynomialEstimatorType - Enum Class in com.irurueta.numerical.polynomials.estimators: Polynomial estimator types.
PolynomialEstimatorType() - Constructor for enum class com.irurueta.numerical.polynomials.estimators.PolynomialEstimatorType
PolynomialEvaluation - Class in com.irurueta.numerical.polynomials.estimators: Contains an evaluation of a polynomial and the point where the polynomial has been evaluated.
PolynomialEvaluation() - Constructor for class com.irurueta.numerical.polynomials.estimators.PolynomialEvaluation: Constructor.
PolynomialEvaluation(double) - Constructor for class com.irurueta.numerical.polynomials.estimators.PolynomialEvaluation: Constructor.
PolynomialEvaluationType - Enum Class in com.irurueta.numerical.polynomials.estimators: Determines different types of polynomial evaluations that can be used to estimate a polynomial.
PolynomialEvaluationType() - Constructor for enum class com.irurueta.numerical.polynomials.estimators.PolynomialEvaluationType
PolynomialEvaluator - Class in com.irurueta.numerical: Utility class to evaluate polynomials having either real or complex coefficients.
PolynomialEvaluator() - Constructor for class com.irurueta.numerical.PolynomialEvaluator: Empty constructor.
PolynomialInterpolator - Class in com.irurueta.numerical.interpolation: Computes polynomial interpolation.
PolynomialInterpolator(double[], double[]) - Constructor for class com.irurueta.numerical.interpolation.PolynomialInterpolator: Constructor.
PolynomialInterpolator(double[], double[], int) - Constructor for class com.irurueta.numerical.interpolation.PolynomialInterpolator: Constructor.
PolynomialInterpolator(double[], double[], int, boolean) - Constructor for class com.irurueta.numerical.interpolation.PolynomialInterpolator: Constructor.
PolynomialRobustEstimator - Class in com.irurueta.numerical.polynomials.estimators: This is an abstract class for algorithms to robustly find the best Polynomial for provided collection of evaluations.
PolynomialRobustEstimator() - Constructor for class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Constructor.
PolynomialRobustEstimator(int) - Constructor for class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Constructor.
PolynomialRobustEstimator(int, PolynomialRobustEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Constructor.
PolynomialRobustEstimator(int, List<PolynomialEvaluation>) - Constructor for class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Constructor.
PolynomialRobustEstimator(int, List<PolynomialEvaluation>, PolynomialRobustEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Constructor.
PolynomialRobustEstimator(PolynomialRobustEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Constructor.
PolynomialRobustEstimator(List<PolynomialEvaluation>) - Constructor for class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Constructor.
PolynomialRobustEstimator(List<PolynomialEvaluation>, PolynomialRobustEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Constructor.
PolynomialRobustEstimatorListener - Interface in com.irurueta.numerical.polynomials.estimators: Listener to be notified of events such as when estimation starts, ends or when progress changes.
PolynomialRootsEstimator - Class in com.irurueta.numerical.roots: Abstract class to estimate the roots of a polynomial.
PolynomialRootsEstimator() - Constructor for class com.irurueta.numerical.roots.PolynomialRootsEstimator: Empty constructor.
PolynomialsException - Exception in com.irurueta.numerical.polynomials: Base exception for polynomials.
PolynomialsException() - Constructor for exception com.irurueta.numerical.polynomials.PolynomialsException: Constructor.
PolynomialsException(String) - Constructor for exception com.irurueta.numerical.polynomials.PolynomialsException: Constructor with String containing message.
PolynomialsException(String, Throwable) - Constructor for exception com.irurueta.numerical.polynomials.PolynomialsException: Constructor with message and cause.
PolynomialsException(Throwable) - Constructor for exception com.irurueta.numerical.polynomials.PolynomialsException: Constructor with cause.
polyParams - Variable in class com.irurueta.numerical.ComplexPolynomialEvaluator: Polynomial coefficients.
polyParams - Variable in class com.irurueta.numerical.polynomials.Polynomial: Array containing parameters defining a polynomial.
polyParams - Variable in class com.irurueta.numerical.RealPolynomialEvaluator: Polynomial coefficients.
polyParams - Variable in class com.irurueta.numerical.roots.PolynomialRootsEstimator: Array containing parameters of a polynomial, taking into account that a polynomial of degree n is defined as: p(x) = a0 * x^n + a1 * x^(n - 1) + ... a(n-1) * x + an then the array of parameters is [a0, a1, ... a(n - 1), an]
PowellMultiOptimizer - Class in com.irurueta.numerical.optimization: This class searches for a multi dimension function local minimum.
PowellMultiOptimizer() - Constructor for class com.irurueta.numerical.optimization.PowellMultiOptimizer: Empty constructor.
PowellMultiOptimizer(MultiDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.optimization.PowellMultiOptimizer: Constructor.
PowellMultiOptimizer(MultiDimensionFunctionEvaluatorListener, double[], Matrix, double) - Constructor for class com.irurueta.numerical.optimization.PowellMultiOptimizer: Constructor.
predict() - Method in class com.irurueta.numerical.signal.processing.KalmanFilter: Estimates subsequent model state without control parameters.
predict(Matrix) - Method in class com.irurueta.numerical.signal.processing.KalmanFilter: Estimates subsequent model state.
processNoiseCov - Variable in class com.irurueta.numerical.signal.processing.KalmanFilter: Process noise covariance matrix (Q).
progressDelta - Variable in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Amount of progress variation before notifying a progress change during estimation.
progressDelta - Variable in class com.irurueta.numerical.robust.RobustEstimator: Amount of progress variation before notifying a progress change during estimation.
PROMEDS - Enum constant in enum class com.irurueta.numerical.robust.RobustEstimatorMethod: Progressive Median of Squares.
PROMedSInliersData(int) - Constructor for class com.irurueta.numerical.robust.PROMedSRobustEstimator.PROMedSInliersData: Constructor.
PROMedSPolynomialRobustEstimator - Class in com.irurueta.numerical.polynomials.estimators: Finds the best polynomial using PROMedS algorithm.
PROMedSPolynomialRobustEstimator() - Constructor for class com.irurueta.numerical.polynomials.estimators.PROMedSPolynomialRobustEstimator: Constructor.
PROMedSPolynomialRobustEstimator(int) - Constructor for class com.irurueta.numerical.polynomials.estimators.PROMedSPolynomialRobustEstimator: Constructor.
PROMedSPolynomialRobustEstimator(int, PolynomialRobustEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.PROMedSPolynomialRobustEstimator: Constructor.
PROMedSPolynomialRobustEstimator(int, List<PolynomialEvaluation>) - Constructor for class com.irurueta.numerical.polynomials.estimators.PROMedSPolynomialRobustEstimator: Constructor.
PROMedSPolynomialRobustEstimator(int, List<PolynomialEvaluation>, PolynomialRobustEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.PROMedSPolynomialRobustEstimator: Constructor.
PROMedSPolynomialRobustEstimator(PolynomialRobustEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.PROMedSPolynomialRobustEstimator: Constructor.
PROMedSPolynomialRobustEstimator(List<PolynomialEvaluation>) - Constructor for class com.irurueta.numerical.polynomials.estimators.PROMedSPolynomialRobustEstimator: Constructor.
PROMedSPolynomialRobustEstimator(List<PolynomialEvaluation>, PolynomialRobustEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.PROMedSPolynomialRobustEstimator: Constructor.
PROMedSRobustEstimator<T> - Class in com.irurueta.numerical.robust: This class implements PROMedS (PROgressive least Median Sample) algorithm to robustly estimate a data model.
PROMedSRobustEstimator() - Constructor for class com.irurueta.numerical.robust.PROMedSRobustEstimator: Constructor.
PROMedSRobustEstimator(PROMedSRobustEstimatorListener<T>) - Constructor for class com.irurueta.numerical.robust.PROMedSRobustEstimator: Constructor with listener.
PROMedSRobustEstimator.PROMedSInliersData - Class in com.irurueta.numerical.robust: Contains data related to inliers estimated in one iteration.
PROMedSRobustEstimatorListener<T> - Interface in com.irurueta.numerical.robust: Listener to get data samples and residuals for PROMedS method.
PROSAC - Enum constant in enum class com.irurueta.numerical.robust.RobustEstimatorMethod: Progressive Sample Consensus.
PROSACInliersData(int, boolean, boolean) - Constructor for class com.irurueta.numerical.robust.PROSACRobustEstimator.PROSACInliersData
PROSACPolynomialRobustEstimator - Class in com.irurueta.numerical.polynomials.estimators: Finds the best polynomial using PROSAC algorithm.
PROSACPolynomialRobustEstimator() - Constructor for class com.irurueta.numerical.polynomials.estimators.PROSACPolynomialRobustEstimator: Constructor.
PROSACPolynomialRobustEstimator(int) - Constructor for class com.irurueta.numerical.polynomials.estimators.PROSACPolynomialRobustEstimator: Constructor.
PROSACPolynomialRobustEstimator(int, PolynomialRobustEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.PROSACPolynomialRobustEstimator: Constructor.
PROSACPolynomialRobustEstimator(int, List<PolynomialEvaluation>) - Constructor for class com.irurueta.numerical.polynomials.estimators.PROSACPolynomialRobustEstimator: Constructor.
PROSACPolynomialRobustEstimator(int, List<PolynomialEvaluation>, PolynomialRobustEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.PROSACPolynomialRobustEstimator: Constructor.
PROSACPolynomialRobustEstimator(PolynomialRobustEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.PROSACPolynomialRobustEstimator: Constructor.
PROSACPolynomialRobustEstimator(List<PolynomialEvaluation>) - Constructor for class com.irurueta.numerical.polynomials.estimators.PROSACPolynomialRobustEstimator: Constructor.
PROSACPolynomialRobustEstimator(List<PolynomialEvaluation>, PolynomialRobustEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.PROSACPolynomialRobustEstimator: Constructor.
PROSACRobustEstimator<T> - Class in com.irurueta.numerical.robust: This class implements PROSAC (PROgressive random SAmple Consensus) algorithm to robustly estimate a data model.
PROSACRobustEstimator() - Constructor for class com.irurueta.numerical.robust.PROSACRobustEstimator: Constructor.
PROSACRobustEstimator(PROSACRobustEstimatorListener<T>) - Constructor for class com.irurueta.numerical.robust.PROSACRobustEstimator: Constructor with listener.
PROSACRobustEstimator.PROSACInliersData - Class in com.irurueta.numerical.robust: Contains data related to estimated inliers.
PROSACRobustEstimatorListener<T> - Interface in com.irurueta.numerical.robust: Listener to get data samples and residuals for PROSAC method
pts - Variable in class com.irurueta.numerical.interpolation.BaseRadialBasisFunctionInterpolator: Matrix containing points to interpolate from.

Q

q - Variable in class com.irurueta.numerical.fitting.StraightLineFitter: Estimated goodness-of-fit probability (i.e. that the fit would have a chi square value equal or larger than the estimated one).
q - Variable in class com.irurueta.numerical.integration.QuadratureIntegrator: Quadrature used for integration.
q - Variable in class com.irurueta.numerical.integration.QuadratureMatrixIntegrator: Quadrature used for integration.
q - Variable in class com.irurueta.numerical.integration.RombergIntegrator: Quadrature used for integration.
q - Variable in class com.irurueta.numerical.integration.RombergMatrixIntegrator: Quadrature used for integration.
q - Variable in class com.irurueta.numerical.integration.SimpsonIntegrator: Quadrature used for integration.
q - Variable in class com.irurueta.numerical.integration.SimpsonMatrixIntegrator: Quadrature used for integration.
q - Variable in class com.irurueta.numerical.PadeApproximantEstimator: Contains matrix to solve Padé coefficients.
Quadrature - Class in com.irurueta.numerical.integration: Abstract base class for elementary quadrature algorithms used for function integration.
Quadrature() - Constructor for class com.irurueta.numerical.integration.Quadrature
QUADRATURE - Enum constant in enum class com.irurueta.numerical.integration.IntegratorType: Quadrature integrator.
QuadratureIntegrator<T extends Quadrature> - Class in com.irurueta.numerical.integration: Integrates functions given a quadrature implementation up to desired accuracy.
QuadratureIntegrator(T, double) - Constructor for class com.irurueta.numerical.integration.QuadratureIntegrator: Constructor.
QuadratureMatrixIntegrator<T extends MatrixQuadrature> - Class in com.irurueta.numerical.integration: Integrates matrix (multivariate) single dimension functions given a quadrature implementation up to desired accuracy.
QuadratureMatrixIntegrator(T, double) - Constructor for class com.irurueta.numerical.integration.QuadratureMatrixIntegrator: Constructor.
QuadratureType - Enum Class in com.irurueta.numerical.integration: Indicates type of quadrature.
QuadratureType() - Constructor for enum class com.irurueta.numerical.integration.QuadratureType
qualityScores - Variable in class com.irurueta.numerical.polynomials.estimators.PROMedSPolynomialRobustEstimator: Quality scores corresponding to each provided polynomial evaluation.
qualityScores - Variable in class com.irurueta.numerical.polynomials.estimators.PROSACPolynomialRobustEstimator: Quality scores corresponding to each provided polynomial evaluation.
QuasiNewtonMultiOptimizer - Class in com.irurueta.numerical.optimization: This class searches for a multi dimension function local minimum.
QuasiNewtonMultiOptimizer() - Constructor for class com.irurueta.numerical.optimization.QuasiNewtonMultiOptimizer: Empty constructor.
QuasiNewtonMultiOptimizer(MultiDimensionFunctionEvaluatorListener, GradientFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.optimization.QuasiNewtonMultiOptimizer: Constructor.
QuasiNewtonMultiOptimizer(MultiDimensionFunctionEvaluatorListener, GradientFunctionEvaluatorListener, double[], double) - Constructor for class com.irurueta.numerical.optimization.QuasiNewtonMultiOptimizer: Constructor.

R

R - Static variable in class com.irurueta.numerical.optimization.GoldenSingleOptimizer: Golden ratio.
r0 - Variable in class com.irurueta.numerical.interpolation.GaussianRadialBasisFunction: Scale factor.
r0 - Variable in class com.irurueta.numerical.interpolation.ThinPlateRadialBasisFunction: Scale factor.
r02 - Variable in class com.irurueta.numerical.interpolation.InverseMultiQuadricRadialBasisFunction: Squared r0 value, which is a scale factor.
r02 - Variable in class com.irurueta.numerical.interpolation.MultiQuadricRadialBasisFunction: Squared r0 value, which is a scale factor.
rad(double[], double[]) - Method in class com.irurueta.numerical.interpolation.BaseRadialBasisFunctionInterpolator: Computes the euclidean distance between two points.
rad(double[], double[]) - Method in class com.irurueta.numerical.interpolation.CurveInterpolator: Computes the euclidean distance between two points.
RadialBasisFunction - Interface in com.irurueta.numerical.interpolation: Interface defining a Radial Basis Function (RBF) to be used for interpolation.
RadialBasisFunctionInterpolator - Class in com.irurueta.numerical.interpolation: Interpolates sparsely defined points of dimension "dim" using a Radial Basis Function.
RadialBasisFunctionInterpolator(Matrix, double[], RadialBasisFunction) - Constructor for class com.irurueta.numerical.interpolation.RadialBasisFunctionInterpolator: Constructor.
RadialBasisFunctionInterpolator(Matrix, double[], RadialBasisFunction, boolean) - Constructor for class com.irurueta.numerical.interpolation.RadialBasisFunctionInterpolator: Constructor.
randomizer - Variable in class com.irurueta.numerical.robust.FastRandomSubsetSelector: Randomizer to pick random indexes.
RANSAC - Enum constant in enum class com.irurueta.numerical.robust.RobustEstimatorMethod: Random Sample Consensus.
RANSACInliersData(int, boolean, boolean) - Constructor for class com.irurueta.numerical.robust.RANSACRobustEstimator.RANSACInliersData: Constructor.
RANSACPolynomialRobustEstimator - Class in com.irurueta.numerical.polynomials.estimators: Finds the best polynomial using RANSAC algorithm.
RANSACPolynomialRobustEstimator() - Constructor for class com.irurueta.numerical.polynomials.estimators.RANSACPolynomialRobustEstimator: Constructor.
RANSACPolynomialRobustEstimator(int) - Constructor for class com.irurueta.numerical.polynomials.estimators.RANSACPolynomialRobustEstimator: Constructor.
RANSACPolynomialRobustEstimator(int, PolynomialRobustEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.RANSACPolynomialRobustEstimator: Constructor.
RANSACPolynomialRobustEstimator(int, List<PolynomialEvaluation>) - Constructor for class com.irurueta.numerical.polynomials.estimators.RANSACPolynomialRobustEstimator: Constructor.
RANSACPolynomialRobustEstimator(int, List<PolynomialEvaluation>, PolynomialRobustEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.RANSACPolynomialRobustEstimator: Constructor.
RANSACPolynomialRobustEstimator(PolynomialRobustEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.RANSACPolynomialRobustEstimator: Constructor.
RANSACPolynomialRobustEstimator(List<PolynomialEvaluation>) - Constructor for class com.irurueta.numerical.polynomials.estimators.RANSACPolynomialRobustEstimator: Constructor.
RANSACPolynomialRobustEstimator(List<PolynomialEvaluation>, PolynomialRobustEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.RANSACPolynomialRobustEstimator: Constructor.
RANSACRobustEstimator<T> - Class in com.irurueta.numerical.robust: This class implements RANSAC (RANdom SAmple Consensus) algorithm to robustly estimate a data model.
RANSACRobustEstimator() - Constructor for class com.irurueta.numerical.robust.RANSACRobustEstimator: Constructor.
RANSACRobustEstimator(RANSACRobustEstimatorListener<T>) - Constructor for class com.irurueta.numerical.robust.RANSACRobustEstimator: Constructor with listener.
RANSACRobustEstimator.RANSACInliersData - Class in com.irurueta.numerical.robust: Contains data related to estimated inliers.
RANSACRobustEstimatorListener<T> - Interface in com.irurueta.numerical.robust: Listener to get data samples and residuals for RANSAC method
RationalInterpolator - Class in com.irurueta.numerical.interpolation: Computes rational interpolation.
RationalInterpolator(double[], double[]) - Constructor for class com.irurueta.numerical.interpolation.RationalInterpolator: Constructor.
RationalInterpolator(double[], double[], int) - Constructor for class com.irurueta.numerical.interpolation.RationalInterpolator: Constructor.
rawinterp(int, double) - Method in class com.irurueta.numerical.interpolation.BarycentricRationalInterpolator: Actual interpolation method.
rawinterp(int, double) - Method in class com.irurueta.numerical.interpolation.BaseInterpolator: Actual interpolation method to be implemented by subclasses.
rawinterp(int, double) - Method in class com.irurueta.numerical.interpolation.CubicSplineInterpolator: Actual interpolation method.
rawinterp(int, double) - Method in class com.irurueta.numerical.interpolation.LinearInterpolator: Actual interpolation method to be implemented by subclasses.
rawinterp(int, double) - Method in class com.irurueta.numerical.interpolation.PolynomialInterpolator: Actual interpolation method.
rawinterp(int, double) - Method in class com.irurueta.numerical.interpolation.RationalInterpolator: Actual interpolation method.
rdist(double[], double[]) - Method in class com.irurueta.numerical.interpolation.KrigingInterpolator: Computes euclidean distance between two points.
RealPolynomialEvaluator - Class in com.irurueta.numerical: Utility class to evaluate real polynomials.
RealPolynomialEvaluator(double[]) - Constructor for class com.irurueta.numerical.RealPolynomialEvaluator: Constructor.
realPolyParams - Variable in class com.irurueta.numerical.roots.FirstDegreePolynomialRootsEstimator: Array containing parameters of a first degree polynomial.
realPolyParams - Variable in class com.irurueta.numerical.roots.SecondDegreePolynomialRootsEstimator: Array containing parameters of a second degree polynomial.
realPolyParams - Variable in class com.irurueta.numerical.roots.ThirdDegreePolynomialRootsEstimator: Array containing parameters of a second degree polynomial.
reference - Static variable in class com.irurueta.numerical.BuildInfo: Singleton stored in a soft reference (to keep it cached in memory unless memory is claimed).
relativeError(int, int) - Method in class com.irurueta.numerical.ExponentialMatrixEstimator: Estimates relative error achieved by this algorithm for provided input values.
REPEAT_EDGE - Enum constant in enum class com.irurueta.numerical.signal.processing.ConvolverEdgeMethod: When convolution kernel reaches edge of signal being convoluted, it is assumed that the signal is repeated indefinitely.
residual - Variable in class com.irurueta.numerical.PadeApproximantEstimator: Contains residual to iteratively improve LU solution.
residuals - Variable in class com.irurueta.numerical.robust.InliersData: Residuals obtained for each sample of data.
Result(double[], double[]) - Constructor for class com.irurueta.numerical.PadeApproximantEstimator.Result: Constructor.
resultAvailable - Variable in class com.irurueta.numerical.fitting.Fitter: Indicates whether result has been estimated and is available for retrieval
resultAvailable - Variable in class com.irurueta.numerical.optimization.MultiOptimizer: Boolean indicating whether a minimum has already been found or not.
resultAvailable - Variable in class com.irurueta.numerical.optimization.SingleOptimizer: Boolean indicating whether a minimum has been found and is available for retrieval.
reverse(int[]) - Static method in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Reverses provided array.
reverse(int[]) - Static method in class com.irurueta.numerical.robust.PROSACRobustEstimator: Reverses provided array.
RidderSingleRootEstimator - Class in com.irurueta.numerical.roots: Computes a root for a single dimension function inside a given bracket of values, in other words, root will only be searched within provided minimum and maximum evaluation points.
RidderSingleRootEstimator() - Constructor for class com.irurueta.numerical.roots.RidderSingleRootEstimator: Empty constructor.
RidderSingleRootEstimator(SingleDimensionFunctionEvaluatorListener, double, double, double) - Constructor for class com.irurueta.numerical.roots.RidderSingleRootEstimator: Constructor.
RobustEstimator<T> - Class in com.irurueta.numerical.robust: Robust estimator to estimate some object in a robust manner
RobustEstimator() - Constructor for class com.irurueta.numerical.robust.RobustEstimator: Constructor.
RobustEstimator(RobustEstimatorListener<T>) - Constructor for class com.irurueta.numerical.robust.RobustEstimator: Constructor.
RobustEstimatorException - Exception in com.irurueta.numerical.robust: Raised if estimation on a RobustEstimator fails.
RobustEstimatorException() - Constructor for exception com.irurueta.numerical.robust.RobustEstimatorException: Constructor.
RobustEstimatorException(String) - Constructor for exception com.irurueta.numerical.robust.RobustEstimatorException: Constructor with String containing message.
RobustEstimatorException(String, Throwable) - Constructor for exception com.irurueta.numerical.robust.RobustEstimatorException: Constructor with message and cause.
RobustEstimatorException(Throwable) - Constructor for exception com.irurueta.numerical.robust.RobustEstimatorException: Constructor with cause.
RobustEstimatorListener<T> - Interface in com.irurueta.numerical.robust: Listener to be notified of events on a robust estimator such as when estimation starts, ends or when progress changes.
RobustEstimatorMethod - Enum Class in com.irurueta.numerical.robust: Enumerator containing different robust estimation algorithms.
RobustEstimatorMethod() - Constructor for enum class com.irurueta.numerical.robust.RobustEstimatorMethod
ROMBERG - Enum constant in enum class com.irurueta.numerical.integration.IntegratorType: Romberg integrator.
RombergDoubleExponentialRuleQuadratureIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using Romberg's method and double exponential quadrature.
RombergDoubleExponentialRuleQuadratureIntegrator(double, double, double, DoubleExponentialSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.RombergDoubleExponentialRuleQuadratureIntegrator: Constructor with default accuracy.
RombergDoubleExponentialRuleQuadratureIntegrator(double, double, double, DoubleExponentialSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.RombergDoubleExponentialRuleQuadratureIntegrator: Constructor.
RombergDoubleExponentialRuleQuadratureIntegrator(double, double, double, SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.RombergDoubleExponentialRuleQuadratureIntegrator: Constructor with default accuracy.
RombergDoubleExponentialRuleQuadratureIntegrator(double, double, double, SingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.RombergDoubleExponentialRuleQuadratureIntegrator: Constructor.
RombergDoubleExponentialRuleQuadratureIntegrator(double, double, DoubleExponentialSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.RombergDoubleExponentialRuleQuadratureIntegrator: Constructor with default accuracy and default maximum step size.
RombergDoubleExponentialRuleQuadratureIntegrator(double, double, DoubleExponentialSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.RombergDoubleExponentialRuleQuadratureIntegrator: Constructor with default maximum step size.
RombergDoubleExponentialRuleQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.RombergDoubleExponentialRuleQuadratureIntegrator: Constructor with default accuracy and default maximum step size.
RombergDoubleExponentialRuleQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.RombergDoubleExponentialRuleQuadratureIntegrator: Constructor.
RombergDoubleExponentialRuleQuadratureMatrixIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using Romberg's method and double exponential quadrature.
RombergDoubleExponentialRuleQuadratureMatrixIntegrator(double, double, double, DoubleExponentialMatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.RombergDoubleExponentialRuleQuadratureMatrixIntegrator: Constructor with default accuracy.
RombergDoubleExponentialRuleQuadratureMatrixIntegrator(double, double, double, DoubleExponentialMatrixSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.RombergDoubleExponentialRuleQuadratureMatrixIntegrator: Constructor.
RombergDoubleExponentialRuleQuadratureMatrixIntegrator(double, double, double, MatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.RombergDoubleExponentialRuleQuadratureMatrixIntegrator: Constructor with default accuracy.
RombergDoubleExponentialRuleQuadratureMatrixIntegrator(double, double, double, MatrixSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.RombergDoubleExponentialRuleQuadratureMatrixIntegrator: Constructor.
RombergDoubleExponentialRuleQuadratureMatrixIntegrator(double, double, DoubleExponentialMatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.RombergDoubleExponentialRuleQuadratureMatrixIntegrator: Constructor with default accuracy and default maximum step size.
RombergDoubleExponentialRuleQuadratureMatrixIntegrator(double, double, DoubleExponentialMatrixSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.RombergDoubleExponentialRuleQuadratureMatrixIntegrator: Constructor.
RombergDoubleExponentialRuleQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.RombergDoubleExponentialRuleQuadratureMatrixIntegrator: Constructor with default accuracy and default maximum step size.
RombergDoubleExponentialRuleQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.RombergDoubleExponentialRuleQuadratureMatrixIntegrator: Constructor.
RombergExponentialMidPointQuadratureIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using Romberg's method and exponential quadrature.
RombergExponentialMidPointQuadratureIntegrator(double, SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.RombergExponentialMidPointQuadratureIntegrator: Constructor with default accuracy.
RombergExponentialMidPointQuadratureIntegrator(double, SingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.RombergExponentialMidPointQuadratureIntegrator: Constructor.
RombergExponentialMidPointQuadratureMatrixIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using Romberg's method and exponential quadrature.
RombergExponentialMidPointQuadratureMatrixIntegrator(double, MatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.RombergExponentialMidPointQuadratureMatrixIntegrator: Constructor with default accuracy.
RombergExponentialMidPointQuadratureMatrixIntegrator(double, MatrixSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.RombergExponentialMidPointQuadratureMatrixIntegrator: Constructor.
RombergInfinityMidPointQuadratureIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using Romberg's method and Infinity mid-point quadrature.
RombergInfinityMidPointQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.RombergInfinityMidPointQuadratureIntegrator: Constructor.
RombergInfinityMidPointQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.RombergInfinityMidPointQuadratureIntegrator: Constructor.
RombergInfinityMidPointQuadratureMatrixIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using Romberg's method and Infinity mid-point quadrature.
RombergInfinityMidPointQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.RombergInfinityMidPointQuadratureMatrixIntegrator: Constructor.
RombergInfinityMidPointQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.RombergInfinityMidPointQuadratureMatrixIntegrator: Constructor.
RombergIntegrator<T extends Quadrature> - Class in com.irurueta.numerical.integration: Base integrator for implementations based on Romberg's method.
RombergIntegrator(T, double) - Constructor for class com.irurueta.numerical.integration.RombergIntegrator: Constructor.
RombergLowerSquareRootMidPointQuadratureIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using Romberg's method and Lower Square Root Mid-Point Quadrature when lower integration bound lies at a function singularity.
RombergLowerSquareRootMidPointQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.RombergLowerSquareRootMidPointQuadratureIntegrator: Constructor with default accuracy.
RombergLowerSquareRootMidPointQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.RombergLowerSquareRootMidPointQuadratureIntegrator: Constructor.
RombergLowerSquareRootMidPointQuadratureMatrixIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using Romberg's method and Lower Square Root Mid-Point Quadrature when lower integration bound lies at a function singularity.
RombergLowerSquareRootMidPointQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.RombergLowerSquareRootMidPointQuadratureMatrixIntegrator: Constructor with default accuracy.
RombergLowerSquareRootMidPointQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.RombergLowerSquareRootMidPointQuadratureMatrixIntegrator: Constructor.
RombergMatrixIntegrator<T extends MatrixQuadrature> - Class in com.irurueta.numerical.integration: Base integrator for implementations based on Romberg's method.
RombergMatrixIntegrator(T, double) - Constructor for class com.irurueta.numerical.integration.RombergMatrixIntegrator: Constructor.
RombergMidPointQuadratureIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using Romberg's method and Mid-Point Quadrature.
RombergMidPointQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.RombergMidPointQuadratureIntegrator: Constructor with default accuracy.
RombergMidPointQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.RombergMidPointQuadratureIntegrator: Constructor.
RombergMidPointQuadratureMatrixIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using Romberg's method and Mid-Point Quadrature.
RombergMidPointQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.RombergMidPointQuadratureMatrixIntegrator: Constructor with default accuracy.
RombergMidPointQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.RombergMidPointQuadratureMatrixIntegrator: Constructor.
RombergTrapezoidalQuadratureIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using Romberg integration.
RombergTrapezoidalQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.RombergTrapezoidalQuadratureIntegrator: Constructor with default accuracy.
RombergTrapezoidalQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.RombergTrapezoidalQuadratureIntegrator: Constructor.
RombergTrapezoidalQuadratureMatrixIntegrator - Class in com.irurueta.numerical.integration: Computes matrix function integration by using Romberg integration.
RombergTrapezoidalQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.RombergTrapezoidalQuadratureMatrixIntegrator: Constructor with default accuracy.
RombergTrapezoidalQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.RombergTrapezoidalQuadratureMatrixIntegrator: Constructor.
RombergUpperSquareRootMidPointQuadratureIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using Romberg's method and Upper Square Root Mid-Point Quadrature when upper integration bound lies at a function singularity.
RombergUpperSquareRootMidPointQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.RombergUpperSquareRootMidPointQuadratureIntegrator: Constructor with default accuracy.
RombergUpperSquareRootMidPointQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.RombergUpperSquareRootMidPointQuadratureIntegrator: Constructor.
RombergUpperSquareRootMidPointQuadratureMatrixIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using Romberg's method and Upper Square Root Mid-Point Quadrature when upper integration bound lies at a function singularity.
RombergUpperSquareRootMidPointQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.RombergUpperSquareRootMidPointQuadratureMatrixIntegrator: Constructor with default accuracy.
RombergUpperSquareRootMidPointQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.RombergUpperSquareRootMidPointQuadratureMatrixIntegrator: Constructor.
root - Variable in class com.irurueta.numerical.roots.SingleRootEstimator: Root that has been found.
ROOT_THREE - Static variable in class com.irurueta.numerical.roots.ThirdDegreePolynomialRootsEstimator: Constant defining the squared root of three.
rootAvailable - Variable in class com.irurueta.numerical.roots.SingleRootEstimator: Boolean indicating that a root has been computed and is available to be retrieved.
RootEstimationException - Exception in com.irurueta.numerical.roots: Raised when a root estimator cannot determine a root of a polynomial, usually because of lack of convergence
RootEstimationException() - Constructor for exception com.irurueta.numerical.roots.RootEstimationException: Constructor.
RootEstimationException(String) - Constructor for exception com.irurueta.numerical.roots.RootEstimationException: Constructor with String containing message.
RootEstimationException(String, Throwable) - Constructor for exception com.irurueta.numerical.roots.RootEstimationException: Constructor with message and cause.
RootEstimationException(Throwable) - Constructor for exception com.irurueta.numerical.roots.RootEstimationException: Constructor with cause.
RootEstimator - Class in com.irurueta.numerical.roots: Abstract class to find roots of functions.
RootEstimator() - Constructor for class com.irurueta.numerical.roots.RootEstimator: Constructor.
roots - Variable in class com.irurueta.numerical.roots.PolynomialRootsEstimator: Array containing estimated roots.
rows - Variable in class com.irurueta.numerical.ExponentialMatrixEstimator: Number of rows of matrix to be estimated.
rows - Variable in class com.irurueta.numerical.integration.DoubleExponentialRuleMatrixQuadrature: Number of rows of quadrature result.
rows - Variable in class com.irurueta.numerical.integration.MidPointMatrixQuadrature: Number of rows of quadrature result.
rows - Variable in class com.irurueta.numerical.integration.TrapezoidalMatrixQuadrature: Number of rows of quadrature result.

S

s - Variable in class com.irurueta.numerical.integration.DoubleExponentialRuleMatrixQuadrature: Value of the next stage of refinement.
s - Variable in class com.irurueta.numerical.integration.DoubleExponentialRuleQuadrature: Value of the next stage of refinement.
s - Variable in class com.irurueta.numerical.integration.MidPointMatrixQuadrature: Current value of integral.
s - Variable in class com.irurueta.numerical.integration.MidPointQuadrature: Current value of integral.
s - Variable in class com.irurueta.numerical.integration.RombergIntegrator: Successive trapezoidal approximations.
s - Variable in class com.irurueta.numerical.integration.RombergMatrixIntegrator: Successive trapezoidal approximations.
s - Variable in class com.irurueta.numerical.integration.RombergTrapezoidalQuadratureIntegrator: Successive trapezoidal approximations.
s - Variable in class com.irurueta.numerical.integration.RombergTrapezoidalQuadratureMatrixIntegrator: Successive trapezoidal approximations.
s - Variable in class com.irurueta.numerical.integration.TrapezoidalMatrixQuadrature: Current value of integral.
s - Variable in class com.irurueta.numerical.integration.TrapezoidalQuadrature: Current value of integral.
SafeNewtonRaphsonSingleRootEstimator - Class in com.irurueta.numerical.roots: Computes a root for a single dimension function inside a given bracket of values, in other words, root will only be searched within provided minimum and maximum evaluation points.
SafeNewtonRaphsonSingleRootEstimator() - Constructor for class com.irurueta.numerical.roots.SafeNewtonRaphsonSingleRootEstimator: Empty constructor.
SafeNewtonRaphsonSingleRootEstimator(SingleDimensionFunctionEvaluatorListener, double, double, double) - Constructor for class com.irurueta.numerical.roots.SafeNewtonRaphsonSingleRootEstimator: Constructor.
SafeNewtonRaphsonSingleRootEstimator(SingleDimensionFunctionEvaluatorListener, SingleDimensionFunctionEvaluatorListener, double, double, double) - Constructor for class com.irurueta.numerical.roots.SafeNewtonRaphsonSingleRootEstimator: Constructor.
sampleAverage - Variable in class com.irurueta.numerical.signal.processing.MeasurementNoiseCovarianceEstimator: Estimated sample average.
sampleCount - Variable in class com.irurueta.numerical.signal.processing.MeasurementNoiseCovarianceEstimator: Number of samples used for estimation.
sampleMatrix - Variable in class com.irurueta.numerical.signal.processing.MeasurementNoiseCovarianceEstimator: A sample expressed in matrix form.
sampleNoMean - Variable in class com.irurueta.numerical.signal.processing.MeasurementNoiseCovarianceEstimator: A sample after removing its mean.
SavitzkyGolayDerivativeEstimator - Class in com.irurueta.numerical: Class to estimate the derivative of a single dimension function at a given point.
SavitzkyGolayDerivativeEstimator(SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.SavitzkyGolayDerivativeEstimator: Constructor.
SavitzkyGolayGradientEstimator - Class in com.irurueta.numerical: Class to estimate the gradient of a multidimensional function.
SavitzkyGolayGradientEstimator(MultiDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.SavitzkyGolayGradientEstimator: Constructor.
SecantSingleRootEstimator - Class in com.irurueta.numerical.roots: Computes a root for a single dimension function inside a given bracket of values, in other words, root will only be searched within provided minimum and maximum evaluation points.
SecantSingleRootEstimator() - Constructor for class com.irurueta.numerical.roots.SecantSingleRootEstimator: Empty constructor.
SecantSingleRootEstimator(SingleDimensionFunctionEvaluatorListener, double, double, double) - Constructor for class com.irurueta.numerical.roots.SecantSingleRootEstimator: Constructor.
SecondDegreePolynomialRootsEstimator - Class in com.irurueta.numerical.roots: Class to estimate the roots of a second degree polynomial along with other polynomial properties.
SecondDegreePolynomialRootsEstimator() - Constructor for class com.irurueta.numerical.roots.SecondDegreePolynomialRootsEstimator: Empty constructor.
SecondDegreePolynomialRootsEstimator(double[]) - Constructor for class com.irurueta.numerical.roots.SecondDegreePolynomialRootsEstimator: Constructor.
secondDerivative() - Method in class com.irurueta.numerical.polynomials.Polynomial: Replaces this instance by its second derivative.
secondDerivative(Polynomial) - Method in class com.irurueta.numerical.polynomials.Polynomial: Computes second derivative of polynomial.
secondDerivativeAndReturnNew() - Method in class com.irurueta.numerical.polynomials.Polynomial: Computes second derivative of polynomial.
selected - Variable in class com.irurueta.numerical.robust.WeightSelection: Array indicating which correspondences have been selected (i.e. have a true value), and which ones hasn't (have a false value).
selectedIndices - Variable in class com.irurueta.numerical.robust.FastRandomSubsetSelector: Set containing selected indices on a given run.
selectWeights(double[], boolean, int) - Static method in class com.irurueta.numerical.robust.WeightSelection: Selects correspondences based on provided weights and creates a weight selection instance.
setBeta(double) - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Sets beta, which is the probability that a match is declared inlier by mistake, i.e. the ratio of the "inlier" surface by the total surface.
setBeta(double) - Method in class com.irurueta.numerical.robust.PROSACRobustEstimator: Sets beta, which is the probability that a match is declared inlier by mistake, i.e. the ratio of the "inlier" surface by the total surface.
setBracket(double, double) - Method in class com.irurueta.numerical.roots.BracketedSingleRootEstimator: Sets the bracket of values (i.e. range of values) where the root will be searched.
setBracket(double, double, double) - Method in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Sets a bracket of values to later search for a minimum.
setComputeAndKeepInliersEnabled(boolean) - Method in class com.irurueta.numerical.robust.PROSACRobustEstimator: Specifies whether inliers must be computed and kept.
setComputeAndKeepInliersEnabled(boolean) - Method in class com.irurueta.numerical.robust.RANSACRobustEstimator: Specifies whether inliers must be computed and kept.
setComputeAndKeepResidualsEnabled(boolean) - Method in class com.irurueta.numerical.robust.PROSACRobustEstimator: Specifies whether residuals must be computed and kept.
setComputeAndKeepResidualsEnabled(boolean) - Method in class com.irurueta.numerical.robust.RANSACRobustEstimator: Specifies whether residuals must be computed and kept.
setConfidence(double) - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Sets amount of confidence expressed as a value between 0.0 and 1.0 (which is equivalent to 100%).
setConfidence(double) - Method in class com.irurueta.numerical.robust.LMedSRobustEstimator: Sets amount of confidence expressed as a value between 0 and 1.0 (which is equivalent to 100%).
setConfidence(double) - Method in class com.irurueta.numerical.robust.MSACRobustEstimator: Sets amount of confidence expressed as a value between 0 and 1.0 (which is equivalent to 100%).
setConfidence(double) - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Sets amount of confidence expressed as a value between 0 and 1.0 (which is equivalent to 100%).
setConfidence(double) - Method in class com.irurueta.numerical.robust.PROSACRobustEstimator: Sets amount of confidence expressed as a value between 0 and 1.0 (which is equivalent to 100%).
setConfidence(double) - Method in class com.irurueta.numerical.robust.RANSACRobustEstimator: Sets amount of confidence expressed as a value between 0 and 1.0 (which is equivalent to 100%).
setConstant(double[]) - Method in class com.irurueta.numerical.polynomials.estimators.IntegralPolynomialEvaluation: Sets constant terms of integral.
setConstants(double[]) - Method in class com.irurueta.numerical.polynomials.estimators.IntegralIntervalPolynomialEvaluation: Sets constant terms of integral.
setConstantValue(double) - Method in class com.irurueta.numerical.signal.processing.Convolver1D: Sets constant value to use during edge extension when CONSTANT_EDGE method is being used.
setControlMatrix(Matrix) - Method in class com.irurueta.numerical.signal.processing.KalmanFilter: Sets the control matrix (B) (it is not used if there is no control).
setCovarianceAdjusted(boolean) - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Specifies whether covariance must be adjusted or not.
setCovarianceAdjusted(boolean) - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Specifies whether covariance must be adjusted or not.
setCovarianceAdjusted(boolean) - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Specifies whether covariance must be adjusted or not.
setDegree(int) - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Sets degree of polynomial to be estimated.
setDegree(int) - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Sets degree of polynomial to be estimated.
setDegreeAndEvaluations(int, List<PolynomialEvaluation>) - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Sets degree of polynomial to be estimated and collection of polynomial evaluations and their corresponding point of evaluation used to determine a polynomial of specified degree.
setDegreeEvaluationsAndWeights(int, List<PolynomialEvaluation>, double[]) - Method in class com.irurueta.numerical.polynomials.estimators.WeightedPolynomialEstimator: Sets degree of polynomial to be estimated and collection of polynomial evaluations and their corresponding point of evaluation used to determine a polynomial of specified degree.
setDerivativeListener(SingleDimensionFunctionEvaluatorListener) - Method in class com.irurueta.numerical.optimization.DerivativeBrentSingleOptimizer: Sets derivative listener that gets function derivative.
setDerivativeListener(SingleDimensionFunctionEvaluatorListener) - Method in class com.irurueta.numerical.roots.DerivativeSingleRootEstimator: Sets derivative listener to evaluate a function's derivative.
setDerivativeOrder(int) - Method in class com.irurueta.numerical.polynomials.estimators.DerivativePolynomialEvaluation: Sets order of derivative.
setEdgeMethod(ConvolverEdgeMethod) - Method in class com.irurueta.numerical.signal.processing.Convolver1D: Sets edge extension method to use during convolution when parts of the kernel are required to lie outside the signal's boundaries.
setEndX(double) - Method in class com.irurueta.numerical.polynomials.estimators.IntegralIntervalPolynomialEvaluation: Sets end point of interval being integrated.
setErrorCovPost(Matrix) - Method in class com.irurueta.numerical.signal.processing.KalmanFilter: Sets the posteriori error estimate covariance matrix (P(k)): P(k)=(I-K(k)*H)*P'(k).
setErrorCovPre(Matrix) - Method in class com.irurueta.numerical.signal.processing.KalmanFilter: Sets the priori error estimate covariance matrix (P'(k)): P'(k)=A*P(k-1)*At + Q).
setEta0(double) - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Sets eta0, which is the maximum probability that a solution with more than inliersNStar inliers in U_nStar exists and was not found after k samples (typically set to 5%).
setEta0(double) - Method in class com.irurueta.numerical.robust.PROSACRobustEstimator: Sets eta0, which is the maximum probability that a solution with more than inliersNStar inliers in U_nStar exists and was not found after k samples (typically set to 5%).
setEvaluation(double) - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialEvaluation: Sets evaluation of polynomial at point x.
setEvaluations(List<PolynomialEvaluation>) - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Sets collection of polynomial evaluations and their corresponding point of evaluation used to determine a polynomial of required degree.
setEvaluations(List<PolynomialEvaluation>) - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Sets collection of polynomial evaluations and their corresponding point of evaluation used to determine a polynomial of required degree.
setEvaluations(List<PolynomialEvaluation>) - Method in class com.irurueta.numerical.polynomials.estimators.WeightedPolynomialEstimator: Sets collection of polynomial evaluations and their corresponding point of evaluation used to determine a polynomial of required degree.
setEvaluationsAndWeights(List<PolynomialEvaluation>, double[]) - Method in class com.irurueta.numerical.polynomials.estimators.WeightedPolynomialEstimator: Sets collection of polynomial evaluations along with their corresponding weights.
setFunctionEvaluator(LevenbergMarquardtMultiDimensionFunctionEvaluator) - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Sets function evaluator to evaluate function at a given point and obtain function derivatives respect to each provided parameter.
setFunctionEvaluator(LevenbergMarquardtMultiVariateFunctionEvaluator) - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Sets function evaluator to evaluate function at a given point and obtain function jacobian respect to each provided parameter.
setFunctionEvaluator(LevenbergMarquardtSingleDimensionFunctionEvaluator) - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Sets function evaluator to evaluate function at a given point and obtain function derivatives respect to each provided parameter
setFunctionEvaluator(LinearFitterMultiDimensionFunctionEvaluator) - Method in class com.irurueta.numerical.fitting.MultiDimensionLinearFitter: Sets function evaluator to evaluate function at a given point and obtain the evaluation of function basis at such point
setFunctionEvaluator(LinearFitterSingleDimensionFunctionEvaluator) - Method in class com.irurueta.numerical.fitting.SimpleSingleDimensionLinearFitter: Sets function evaluator to evaluate function at a given point and obtain the evaluation of function basis at such point.
setFunctionEvaluator(LinearFitterSingleDimensionFunctionEvaluator) - Method in class com.irurueta.numerical.fitting.SingleDimensionLinearFitter: Sets function evaluator to evaluate function at a given point and obtain the evaluation of function basis at such point.
setGain(Matrix) - Method in class com.irurueta.numerical.signal.processing.KalmanFilter: Sets the Kalman gain matrix (K(k)): K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R).
setGaussianSigma(double) - Method in class com.irurueta.numerical.MaximumLikelihoodEstimator: Sets Gaussian sigma to be used on each sample when aggregating Gaussian functions centered at each input data sample value.
setGeometricDistanceUsed(boolean) - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Specifies whether geometric distance will be used to find outliers or algebraic distance will be used instead.
setGradientListener(GradientFunctionEvaluatorListener) - Method in class com.irurueta.numerical.DirectionalDerivativeEvaluator: Sets gradient listener that evaluates a multidimensional function gradient.
setGradientListener(GradientFunctionEvaluatorListener) - Method in class com.irurueta.numerical.optimization.ConjugateGradientMultiOptimizer: Sets gradient listener in charge of obtaining gradient values for the function to be evaluated.
setGradientListener(GradientFunctionEvaluatorListener) - Method in class com.irurueta.numerical.optimization.DerivativeLineMultiOptimizer: Sets gradient listener.
setGradientListener(GradientFunctionEvaluatorListener) - Method in class com.irurueta.numerical.optimization.QuasiNewtonMultiOptimizer: Sets gradient listener in charge of obtaining gradient values for the function to be evaluated.
setHistogramInitialSolutionUsed(boolean) - Method in class com.irurueta.numerical.AccurateMaximumLikelihoodEstimator: Sets boolean that indicates that an initial coarse solution will be computed first by using an internal HistogramMaximumLikelihoodEstimator in order to initialize the internal BrentSingleOptimizer to obtain a more accurate solution.
setInlierFactor(double) - Method in class com.irurueta.numerical.robust.LMedSRobustEstimator: Sets factor to normalize or adjust threshold to determine inliers.
setInlierFactor(double) - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Sets factor to normalize or adjust threshold to determine inliers.
setInputData(double[]) - Method in class com.irurueta.numerical.MaximumLikelihoodEstimator: Sets array containing input data to be used to find the most likely value.
setInputData(double[], double[]) - Method in class com.irurueta.numerical.fitting.StraightLineFitter: Sets input data to fit a straight line to.
setInputData(double[], double[], double) - Method in class com.irurueta.numerical.fitting.SingleDimensionFitter: Sets required input data to start function fitting and assuming constant standard deviation errors in input data.
setInputData(double[], double[], double[]) - Method in class com.irurueta.numerical.fitting.SingleDimensionFitter: Sets required input data to start function fitting.
setInputData(double[], double, double) - Method in class com.irurueta.numerical.MaximumLikelihoodEstimator: Sets array containing input data to be used to find the most likely value along with the minimum and maximum values assumed to be contained in it.
setInputData(Matrix, double[], double) - Method in class com.irurueta.numerical.fitting.MultiDimensionFitter: Sets required input data to start function fitting and assuming constant standard deviation errors in input data
setInputData(Matrix, double[], double[]) - Method in class com.irurueta.numerical.fitting.MultiDimensionFitter: Sets required input data to start function fitting
setInputData(Matrix, Matrix, double) - Method in class com.irurueta.numerical.fitting.MultiVariateFitter: Sets required input data to start function fitting and assuming constant standard deviation errors in input data.
setInputData(Matrix, Matrix, double[]) - Method in class com.irurueta.numerical.fitting.MultiVariateFitter: Sets required input data to start function fitting.
setInputDataAndStandardDeviations(double[], double[], double[]) - Method in class com.irurueta.numerical.fitting.StraightLineFitter: Sets input data and standard deviations of input data to fit a straight line to.
setIntegralOrder(int) - Method in class com.irurueta.numerical.polynomials.estimators.IntegralIntervalPolynomialEvaluation: Sets integral order.
setIntegralOrder(int) - Method in class com.irurueta.numerical.polynomials.estimators.IntegralPolynomialEvaluation: Sets integral order.
setItmax(int) - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Sets maximum number of iterations.
setItmax(int) - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Sets maximum number of iterations.
setItmax(int) - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Sets maximum number of iterations.
setKernel(double[]) - Method in class com.irurueta.numerical.signal.processing.Convolver1D: Sets kernel to convolve the signal with.
setKernelCenter(int) - Method in class com.irurueta.numerical.signal.processing.Convolver1D: Sets position of kernel center.
setListener(MultiDimensionFunctionEvaluatorListener) - Method in class com.irurueta.numerical.DirectionalEvaluator: Sets listener to evaluate a multidimensional function.
setListener(MultiDimensionFunctionEvaluatorListener) - Method in class com.irurueta.numerical.optimization.MultiOptimizer: Sets listener to evaluate a multidimensional function.
setListener(PolynomialEstimatorListener) - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialEstimator: Sets listener to be notified of events such as when estimation starts, ends or estimation progress changes.
setListener(PolynomialRobustEstimatorListener) - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Sets listener to be notified of events such as when estimation starts, ends or its progress significantly changes.
setListener(RobustEstimatorListener<T>) - Method in class com.irurueta.numerical.robust.RobustEstimator: Sets listener to be notified of events such as when estimation starts, ends or its progress significantly changes.
setListener(Convolver1D.Convolver1DListener) - Method in class com.irurueta.numerical.signal.processing.Convolver1D: Sets listener in charge of attending events generated by this instance.
setListener(SingleDimensionFunctionEvaluatorListener) - Method in class com.irurueta.numerical.optimization.SingleOptimizer: Sets listener.
setListener(SingleDimensionFunctionEvaluatorListener) - Method in class com.irurueta.numerical.roots.SingleRootEstimator: Sets listener that evaluates a single dimension function in order to find its root.
setLMSESolutionAllowed(boolean) - Method in class com.irurueta.numerical.polynomials.estimators.LMSEPolynomialEstimator: Specified if an LMSE (Least Mean Square Error) solution is allowed if more evaluations than the required minimum are provided.
setMaxEvaluations(int) - Method in class com.irurueta.numerical.polynomials.estimators.WeightedPolynomialEstimator: Sets maximum number of evaluations to be weighted and taken into account.
setMaxIterations(int) - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Sets maximum allowed number of iterations.
setMaxIterations(int) - Method in class com.irurueta.numerical.robust.LMedSRobustEstimator: Sets maximum allowed number of iterations.
setMaxIterations(int) - Method in class com.irurueta.numerical.robust.MSACRobustEstimator: Sets maximum allowed number of iterations.
setMaxIterations(int) - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Sets maximum allowed number of iterations.
setMaxIterations(int) - Method in class com.irurueta.numerical.robust.PROSACRobustEstimator: Sets maximum allowed number of iterations.
setMaxIterations(int) - Method in class com.irurueta.numerical.robust.RANSACRobustEstimator: Sets maximum allowed number of iterations.
setMaxOutliersProportion(double) - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Sets maximum allowed outliers proportion in the input data.
setMaxOutliersProportion(double) - Method in class com.irurueta.numerical.robust.PROSACRobustEstimator: Sets maximum allowed outliers proportion in the input data.
setMeasurementMatrix(Matrix) - Method in class com.irurueta.numerical.signal.processing.KalmanFilter: Sets measurement matrix (H).
setMeasurementNoiseCov(Matrix) - Method in class com.irurueta.numerical.signal.processing.KalmanFilter: Sets the measurement noise covariance matrix (R).
setMeasureParameters(int) - Method in class com.irurueta.numerical.signal.processing.KalmanFilter: Sets the number of measurement vector dimensions (measure parameters).
setMinMaxValues(double, double) - Method in class com.irurueta.numerical.MaximumLikelihoodEstimator: Sets minimum and maximum value assumed to be found in input data array.
setNdone(int) - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Sets convergence parameter.
setNdone(int) - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Sets convergence parameter.
setNdone(int) - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Sets convergence parameter.
setNumberOfBins(int) - Method in class com.irurueta.numerical.HistogramMaximumLikelihoodEstimator: Sets number of bins to be used on the histogram.
setNumSamples(int) - Method in class com.irurueta.numerical.robust.SubsetSelector: Sets number of samples to select subsets from.
setNumSelected(int) - Method in class com.irurueta.numerical.robust.WeightSelection: Sets number of correspondences that have been selected.
setOnIterationCompletedListener(OnIterationCompletedListener) - Method in class com.irurueta.numerical.optimization.Optimizer: Sets listener to handle minimization events.
setPointAndDirection(double[], double[]) - Method in class com.irurueta.numerical.DirectionalEvaluator: Sets point used as a reference to determine the function's input parameters along a line and the direction of the line where the function is evaluated.
setPointAndDirections(double[], Matrix) - Method in class com.irurueta.numerical.optimization.PowellMultiOptimizer: Sets start point and set of directions to start looking for minimum.
setPolishRootsEnabled(boolean) - Method in class com.irurueta.numerical.roots.LaguerrePolynomialRootsEstimator: Sets boolean indicating whether roots will be refined after an initial estimation.
setPolynomialParameters(double[]) - Method in class com.irurueta.numerical.roots.FirstDegreePolynomialRootsEstimator: Set array of first degree polynomial parameters.
setPolynomialParameters(double[]) - Method in class com.irurueta.numerical.roots.SecondDegreePolynomialRootsEstimator: Set array of second degree polynomial parameters.
setPolynomialParameters(double[]) - Method in class com.irurueta.numerical.roots.ThirdDegreePolynomialRootsEstimator: Set array of third degree polynomial parameters.
setPolynomialParameters(Complex[]) - Method in class com.irurueta.numerical.roots.PolynomialRootsEstimator: Sets parameters of a polynomial, taking into account that a polynomial of degree n is defined as: p(x) = a0 * x^n + a1 * x^(n - 1) + ... a(n-1) * x + an then the array of parameters is [a0, a1, ... a(n - 1), an]
setPolyParams(double...) - Method in class com.irurueta.numerical.polynomials.Polynomial: Sets array defining parameters of polynomial.
setProcessNoiseCov(Matrix) - Method in class com.irurueta.numerical.signal.processing.KalmanFilter: Sets the process noise covariance matrix (Q).
setProgressDelta(float) - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Sets amount of progress variation before notifying a progress change during estimation.
setProgressDelta(float) - Method in class com.irurueta.numerical.robust.RobustEstimator: Sets amount of progress variation before notifying a progress change during estimation.
setQualityScores(double[]) - Method in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Sets quality scores corresponding to each polynomial evaluation.
setQualityScores(double[]) - Method in class com.irurueta.numerical.polynomials.estimators.PROMedSPolynomialRobustEstimator: Sets quality scores corresponding to each provided point.
setQualityScores(double[]) - Method in class com.irurueta.numerical.polynomials.estimators.PROSACPolynomialRobustEstimator: Sets quality scores corresponding to each provided point.
setSelected(boolean[]) - Method in class com.irurueta.numerical.robust.WeightSelection: Sets array indicating which correspondences have been selected (i.e.
setSignal(double[]) - Method in class com.irurueta.numerical.signal.processing.Convolver1D: Sets signal to be convolved.
setSimplex(double[], double) - Method in class com.irurueta.numerical.optimization.SimplexMultiOptimizer: Sets a simplex defined as a central point and a set of surrounding points at distance delta.
setSimplex(double[], double[]) - Method in class com.irurueta.numerical.optimization.SimplexMultiOptimizer: Sets a simplex defined as a central point and a set of surrounding points at their corresponding distance deltas[i], where "i" corresponds to one position of provided array of distances.
setSimplex(Matrix) - Method in class com.irurueta.numerical.optimization.SimplexMultiOptimizer: Sets simplex as a matrix containing on each row a point of the simplex.
setSortWeightsEnabled(boolean) - Method in class com.irurueta.numerical.polynomials.estimators.WeightedPolynomialEstimator: Specifies whether weights are sorted by so that largest weighted evaluations are used first.
setStartPoint(double[]) - Method in class com.irurueta.numerical.optimization.ConjugateGradientMultiOptimizer: Sets start point where local minimum is searched nearby.
setStartPoint(double[]) - Method in class com.irurueta.numerical.optimization.DerivativeConjugateGradientMultiOptimizer: Sets start point where local minimum is searched nearby.
setStartPoint(double[]) - Method in class com.irurueta.numerical.optimization.PowellMultiOptimizer: Sets start point where local minimum is searched nearby.
setStartPoint(double[]) - Method in class com.irurueta.numerical.optimization.QuasiNewtonMultiOptimizer: Sets start point where algorithm will be started.
setStartPointAndDirection(double[], double[]) - Method in class com.irurueta.numerical.optimization.DerivativeLineMultiOptimizer: Internal method to set start point and direction to start the search for a local minimum.
setStartPointAndDirection(double[], double[]) - Method in class com.irurueta.numerical.optimization.LineMultiOptimizer: Internal method to set start point and direction to start the search for a local minimum.
setStartX(double) - Method in class com.irurueta.numerical.polynomials.estimators.IntegralIntervalPolynomialEvaluation: Sets start point of interval being integrated.
setStatePost(Matrix) - Method in class com.irurueta.numerical.signal.processing.KalmanFilter: Sets corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k)).
setStatePre(Matrix) - Method in class com.irurueta.numerical.signal.processing.KalmanFilter: Sets predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k).
setStopThreshold(double) - Method in class com.irurueta.numerical.polynomials.estimators.LMedSPolynomialRobustEstimator: Sets threshold to be used to keep the algorithm iterating in case that best estimated threshold using median of residuals is not small enough.
setStopThreshold(double) - Method in class com.irurueta.numerical.polynomials.estimators.PROMedSPolynomialRobustEstimator: Sets threshold to be used to keep the algorithm iterating in case that best estimated threshold using median of residuals is not small enough.
setStopThreshold(double) - Method in class com.irurueta.numerical.robust.LMedSRobustEstimator: Sets threshold to be used to keep the algorithm iterating in case that best threshold is not small enough.
setStopThresholdEnabled(boolean) - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Sets boolean indicating whether the algorithm must stop prematurely when dynamically computed threshold using median of residuals has a value lower than provided threshold in listener.
setThreshold(double) - Method in class com.irurueta.numerical.polynomials.estimators.MSACPolynomialRobustEstimator: Sets threshold to determine whether polynomials are inliers or not when testing possible estimation solutions.
setThreshold(double) - Method in class com.irurueta.numerical.polynomials.estimators.PROSACPolynomialRobustEstimator: Sets threshold to determine whether polynomials are inliers or not when testing possible estimation solutions.
setThreshold(double) - Method in class com.irurueta.numerical.polynomials.estimators.RANSACPolynomialRobustEstimator: Sets threshold to determine whether polynomials are inliers or not when testing possible estimation solutions.
setTol(double) - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Sets tolerance to reach convergence.
setTol(double) - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Sets tolerance to reach convergence.
setTol(double) - Method in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Sets tolerance to reach convergence.
setTol(double) - Method in class com.irurueta.numerical.fitting.SvdMultiDimensionLinearFitter: Sets tolerance to define convergence threshold for SVD.
setTol(double) - Method in class com.irurueta.numerical.fitting.SvdSingleDimensionLinearFitter: Sets tolerance to define convergence threshold for SVD.
setTolerance(double) - Method in class com.irurueta.numerical.optimization.BrentSingleOptimizer: Sets algorithm's tolerance.
setTolerance(double) - Method in class com.irurueta.numerical.optimization.ConjugateGradientMultiOptimizer: Sets tolerance or accuracy to be expected on estimated local minimum.
setTolerance(double) - Method in class com.irurueta.numerical.optimization.DerivativeBrentSingleOptimizer: Sets tolerance value.
setTolerance(double) - Method in class com.irurueta.numerical.optimization.DerivativeConjugateGradientMultiOptimizer: Sets tolerance or accuracy to be expected on estimated local minimum.
setTolerance(double) - Method in class com.irurueta.numerical.optimization.GoldenSingleOptimizer: Sets algorithm's tolerance.
setTolerance(double) - Method in class com.irurueta.numerical.optimization.PowellMultiOptimizer: Sets tolerance or accuracy to be expected on estimated local minimum.
setTolerance(double) - Method in class com.irurueta.numerical.optimization.QuasiNewtonMultiOptimizer: Sets tolerance or accuracy to be expected on estimated local minimum.
setTolerance(double) - Method in class com.irurueta.numerical.optimization.SimplexMultiOptimizer: Sets tolerance or accuracy to be expected on estimated local minimum.
setTolerance(double) - Method in class com.irurueta.numerical.roots.BisectionSingleRootEstimator: Sets tolerance to find a root.
setTolerance(double) - Method in class com.irurueta.numerical.roots.BrentSingleRootEstimator: Sets tolerance value.
setTolerance(double) - Method in class com.irurueta.numerical.roots.FalsePositionSingleRootEstimator: Sets tolerance to find a root.
setTolerance(double) - Method in class com.irurueta.numerical.roots.NewtonRaphsonSingleRootEstimator: Sets tolerance value.
setTolerance(double) - Method in class com.irurueta.numerical.roots.RidderSingleRootEstimator: Sets tolerance value.
setTolerance(double) - Method in class com.irurueta.numerical.roots.SafeNewtonRaphsonSingleRootEstimator: Sets tolerance value.
setTolerance(double) - Method in class com.irurueta.numerical.roots.SecantSingleRootEstimator: Sets tolerance value.
setTransitionMatrix(Matrix) - Method in class com.irurueta.numerical.signal.processing.KalmanFilter: Sets the state transition matrix (A).
setUseInlierThresholds(boolean) - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Sets flag indicating whether thresholds to determine inliers are used, or if only median of residuals is used.
setUsePolakRibiere(boolean) - Method in class com.irurueta.numerical.optimization.ConjugateGradientMultiOptimizer: Sets boolean indicating whether Polak-Ribiere method or Fletcher-Reeves method is used.
setUsePolakRibiere(boolean) - Method in class com.irurueta.numerical.optimization.DerivativeConjugateGradientMultiOptimizer: Sets boolean indicating whether Polak-Ribiere method or Fletcher-Reeves method is used.
setX(double) - Method in class com.irurueta.numerical.polynomials.estimators.DerivativePolynomialEvaluation: Sets point where polynomial derivative has been evaluated.
setX(double) - Method in class com.irurueta.numerical.polynomials.estimators.DirectPolynomialEvaluation: Sets point where polynomial has been evaluated.
setX(double) - Method in class com.irurueta.numerical.polynomials.estimators.IntegralPolynomialEvaluation: Sets point where nth-polynomial integral has been evaluated.
setY2(double[], double[], double, double) - Method in class com.irurueta.numerical.interpolation.CubicSplineInterpolator: This method stores an array y2[0..n-1] with second derivatives of the interpolating function at the tabulated points pointed to by xv, using function values pointed to by yv.
ShepardInterpolator - Class in com.irurueta.numerical.interpolation: Interpolates sparsely defined points of dimension "dim" using Shepard interpolation, which is a simplification of Radial Basis Function interpolation that achieves less accurate results but having less computational cost.
ShepardInterpolator(Matrix, double[]) - Constructor for class com.irurueta.numerical.interpolation.ShepardInterpolator: Constructor using default p parameter, which is equal to 2.0.
ShepardInterpolator(Matrix, double[], double) - Constructor for class com.irurueta.numerical.interpolation.ShepardInterpolator: Constructor.
shift2(double[], double[], double) - Method in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Pushes "b" value into "a", and "c" value into "b".
shift3(double[], double[], double[], double) - Method in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Pushes "b" value into "a", and "c" value into "b" and "d" value into "c".
sig - Variable in class com.irurueta.numerical.fitting.MultiDimensionFitter: Standard deviations of each pair of points (x, y).
sig - Variable in class com.irurueta.numerical.fitting.MultiVariateFitter: Standard deviations of each pair of points (x, y).
sig - Variable in class com.irurueta.numerical.fitting.SingleDimensionFitter: Standard deviations of each pair of points (x,y).
sig - Variable in class com.irurueta.numerical.fitting.StraightLineFitter: Standard deviations of each pair of points (x,y).
siga - Variable in class com.irurueta.numerical.fitting.StraightLineFitter: Estimated standard deviation of parameter "a".
sigb - Variable in class com.irurueta.numerical.fitting.StraightLineFitter: Estimated standard deviation of parameter "b".
sigdat - Variable in class com.irurueta.numerical.fitting.StraightLineFitter: Estimated standard deviation of provided input data.
sign(double, double) - Method in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Internal method to determine whether a and b have the same sign.
sign(double, double) - Method in class com.irurueta.numerical.roots.BracketedSingleRootEstimator: Internal method to determine whether a and b have the same sign.
signal - Variable in class com.irurueta.numerical.signal.processing.Convolver1D: Signal to be convolved.
SignalProcessingException - Exception in com.irurueta.numerical.signal.processing: Raised when something fails during signal processing.
SignalProcessingException() - Constructor for exception com.irurueta.numerical.signal.processing.SignalProcessingException: Constructor.
SignalProcessingException(String) - Constructor for exception com.irurueta.numerical.signal.processing.SignalProcessingException: Constructor with String containing message.
SignalProcessingException(String, Throwable) - Constructor for exception com.irurueta.numerical.signal.processing.SignalProcessingException: Constructor with message and cause.
SignalProcessingException(Throwable) - Constructor for exception com.irurueta.numerical.signal.processing.SignalProcessingException: Constructor with cause.
SimpleInterpolatingPolynomialEstimator - Class in com.irurueta.numerical.interpolation: Estimates coefficients of a polynomial passing through provided set of x and y points.
SimpleInterpolatingPolynomialEstimator() - Constructor for class com.irurueta.numerical.interpolation.SimpleInterpolatingPolynomialEstimator
SimpleSingleDimensionLinearFitter - Class in com.irurueta.numerical.fitting: Fits provided data (x,y) to a function made of a linear combination of functions used as a basis (i.e. f(x) = a * f0(x) + b * f1(x) + ...).
SimpleSingleDimensionLinearFitter() - Constructor for class com.irurueta.numerical.fitting.SimpleSingleDimensionLinearFitter: Constructor.
SimpleSingleDimensionLinearFitter(double[], double[], double) - Constructor for class com.irurueta.numerical.fitting.SimpleSingleDimensionLinearFitter: Constructor.
SimpleSingleDimensionLinearFitter(double[], double[], double[]) - Constructor for class com.irurueta.numerical.fitting.SimpleSingleDimensionLinearFitter: Constructor.
SimpleSingleDimensionLinearFitter(LinearFitterSingleDimensionFunctionEvaluator) - Constructor for class com.irurueta.numerical.fitting.SimpleSingleDimensionLinearFitter: Constructor.
SimpleSingleDimensionLinearFitter(LinearFitterSingleDimensionFunctionEvaluator, double[], double[], double) - Constructor for class com.irurueta.numerical.fitting.SimpleSingleDimensionLinearFitter: Constructor.
SimpleSingleDimensionLinearFitter(LinearFitterSingleDimensionFunctionEvaluator, double[], double[], double[]) - Constructor for class com.irurueta.numerical.fitting.SimpleSingleDimensionLinearFitter: Constructor.
SimplexMultiOptimizer - Class in com.irurueta.numerical.optimization: This class searches for a multi dimension function local minimum.
SimplexMultiOptimizer() - Constructor for class com.irurueta.numerical.optimization.SimplexMultiOptimizer: Empty constructor.
SimplexMultiOptimizer(MultiDimensionFunctionEvaluatorListener, double[], double[], double) - Constructor for class com.irurueta.numerical.optimization.SimplexMultiOptimizer: Constructor.
SimplexMultiOptimizer(MultiDimensionFunctionEvaluatorListener, double[], double, double) - Constructor for class com.irurueta.numerical.optimization.SimplexMultiOptimizer: Constructor.
SimplexMultiOptimizer(MultiDimensionFunctionEvaluatorListener, Matrix, double) - Constructor for class com.irurueta.numerical.optimization.SimplexMultiOptimizer: Constructor.
SIMPSON - Enum constant in enum class com.irurueta.numerical.integration.IntegratorType: Simpson integrator.
SimpsonDoubleExponentialRuleQuadratureIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using Simpson's method and double exponential quadrature.
SimpsonDoubleExponentialRuleQuadratureIntegrator(double, double, double, DoubleExponentialSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.SimpsonDoubleExponentialRuleQuadratureIntegrator: Constructor with default accuracy.
SimpsonDoubleExponentialRuleQuadratureIntegrator(double, double, double, DoubleExponentialSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.SimpsonDoubleExponentialRuleQuadratureIntegrator: Constructor.
SimpsonDoubleExponentialRuleQuadratureIntegrator(double, double, double, SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.SimpsonDoubleExponentialRuleQuadratureIntegrator: Constructor with default accuracy.
SimpsonDoubleExponentialRuleQuadratureIntegrator(double, double, double, SingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.SimpsonDoubleExponentialRuleQuadratureIntegrator: Constructor.
SimpsonDoubleExponentialRuleQuadratureIntegrator(double, double, DoubleExponentialSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.SimpsonDoubleExponentialRuleQuadratureIntegrator: Constructor with default accuracy and default maximum step size.
SimpsonDoubleExponentialRuleQuadratureIntegrator(double, double, DoubleExponentialSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.SimpsonDoubleExponentialRuleQuadratureIntegrator: Constructor with default maximum step size.
SimpsonDoubleExponentialRuleQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.SimpsonDoubleExponentialRuleQuadratureIntegrator: Constructor with default accuracy and default maximum step size.
SimpsonDoubleExponentialRuleQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.SimpsonDoubleExponentialRuleQuadratureIntegrator: Constructor.
SimpsonDoubleExponentialRuleQuadratureMatrixIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using Simpson's method and double exponential quadrature.
SimpsonDoubleExponentialRuleQuadratureMatrixIntegrator(double, double, double, DoubleExponentialMatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.SimpsonDoubleExponentialRuleQuadratureMatrixIntegrator: Constructor with default accuracy.
SimpsonDoubleExponentialRuleQuadratureMatrixIntegrator(double, double, double, DoubleExponentialMatrixSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.SimpsonDoubleExponentialRuleQuadratureMatrixIntegrator: Constructor.
SimpsonDoubleExponentialRuleQuadratureMatrixIntegrator(double, double, double, MatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.SimpsonDoubleExponentialRuleQuadratureMatrixIntegrator: Constructor with default accuracy.
SimpsonDoubleExponentialRuleQuadratureMatrixIntegrator(double, double, double, MatrixSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.SimpsonDoubleExponentialRuleQuadratureMatrixIntegrator: Constructor.
SimpsonDoubleExponentialRuleQuadratureMatrixIntegrator(double, double, DoubleExponentialMatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.SimpsonDoubleExponentialRuleQuadratureMatrixIntegrator: Constructor with default accuracy and default maximum step size.
SimpsonDoubleExponentialRuleQuadratureMatrixIntegrator(double, double, DoubleExponentialMatrixSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.SimpsonDoubleExponentialRuleQuadratureMatrixIntegrator: Constructor with default maximum step size.
SimpsonDoubleExponentialRuleQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.SimpsonDoubleExponentialRuleQuadratureMatrixIntegrator: Constructor with default accuracy and default maximum step size.
SimpsonDoubleExponentialRuleQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.SimpsonDoubleExponentialRuleQuadratureMatrixIntegrator: Constructor.
SimpsonInfinityMidPointQuadratureIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using Simpson's rule and infinity mid-point quadrature.
SimpsonInfinityMidPointQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.SimpsonInfinityMidPointQuadratureIntegrator: Constructor.
SimpsonInfinityMidPointQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.SimpsonInfinityMidPointQuadratureIntegrator: Constructor.
SimpsonInfinityMidPointQuadratureMatrixIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using Simpson's rule and infinity mid-point quadrature.
SimpsonInfinityMidPointQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.SimpsonInfinityMidPointQuadratureMatrixIntegrator: Constructor with default accuracy.
SimpsonInfinityMidPointQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.SimpsonInfinityMidPointQuadratureMatrixIntegrator: Constructor.
SimpsonIntegrator<T extends Quadrature> - Class in com.irurueta.numerical.integration: Base integrator for implementations based on Simpson's method.
SimpsonIntegrator(T, double) - Constructor for class com.irurueta.numerical.integration.SimpsonIntegrator: Constructor.
SimpsonLowerSquareRootMidPointQuadratureIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using Simpson's rule and Lower Square Root mid-point quadrature when lower integration bound lies at a function singularity.
SimpsonLowerSquareRootMidPointQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.SimpsonLowerSquareRootMidPointQuadratureIntegrator: Constructor.
SimpsonLowerSquareRootMidPointQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.SimpsonLowerSquareRootMidPointQuadratureIntegrator: Constructor.
SimpsonLowerSquareRootMidPointQuadratureMatrixIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using Simpson's rule and Lower Square Root mid-point quadrature when lower integration bound lies at a function singularity.
SimpsonLowerSquareRootMidPointQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.SimpsonLowerSquareRootMidPointQuadratureMatrixIntegrator: Constructor with default accuracy.
SimpsonLowerSquareRootMidPointQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.SimpsonLowerSquareRootMidPointQuadratureMatrixIntegrator: Constructor.
SimpsonMatrixIntegrator<T extends MatrixQuadrature> - Class in com.irurueta.numerical.integration: Base integrator for implementations based on Simpson's method.
SimpsonMatrixIntegrator(T, double) - Constructor for class com.irurueta.numerical.integration.SimpsonMatrixIntegrator: Constructor.
SimpsonMidPointQuadratureIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using Simpson's rule and mid-point quadrature.
SimpsonMidPointQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.SimpsonMidPointQuadratureIntegrator: Constructor.
SimpsonMidPointQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.SimpsonMidPointQuadratureIntegrator: Constructor.
SimpsonMidPointQuadratureMatrixIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using Simpson's rule and mid-point quadrature.
SimpsonMidPointQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.SimpsonMidPointQuadratureMatrixIntegrator: Constructor with default accuracy.
SimpsonMidPointQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.SimpsonMidPointQuadratureMatrixIntegrator: Constructor.
SimpsonTrapezoidalQuadratureIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using Simpson's rule and trapezoidal quadrature.
SimpsonTrapezoidalQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.SimpsonTrapezoidalQuadratureIntegrator: Constructor with default accuracy.
SimpsonTrapezoidalQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.SimpsonTrapezoidalQuadratureIntegrator: Constructor.
SimpsonTrapezoidalQuadratureMatrixIntegrator - Class in com.irurueta.numerical.integration: Computes matrix function integration by using Simpson's rule and trapezoidal quadrature.
SimpsonTrapezoidalQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.SimpsonTrapezoidalQuadratureMatrixIntegrator: Constructor with default accuracy.
SimpsonTrapezoidalQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.SimpsonTrapezoidalQuadratureMatrixIntegrator: Constructor.
SimpsonUpperSquareRootMidPointQuadratureIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using Simpson's rule an Upper Square Root mid-point quadrature when upper integration bound lies at a function singularity.
SimpsonUpperSquareRootMidPointQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.SimpsonUpperSquareRootMidPointQuadratureIntegrator: Constructor.
SimpsonUpperSquareRootMidPointQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.SimpsonUpperSquareRootMidPointQuadratureIntegrator: Constructor.
SimpsonUpperSquareRootMidPointQuadratureMatrixIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using Simpson's rule an Upper Square Root mid-point quadrature when upper integration bound lies at a function singularity.
SimpsonUpperSquareRootMidPointQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.SimpsonUpperSquareRootMidPointQuadratureMatrixIntegrator: Constructor with default accuracy.
SimpsonUpperSquareRootMidPointQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.SimpsonUpperSquareRootMidPointQuadratureMatrixIntegrator: Constructor.
singleCov - Variable in class com.irurueta.numerical.signal.processing.MeasurementNoiseCovarianceEstimator: A covariance matrix for a single sample.
SingleDimensionFitter - Class in com.irurueta.numerical.fitting: Base class to fit a single dimension function y = f(x) by using provided data (x, y)
SingleDimensionFitter() - Constructor for class com.irurueta.numerical.fitting.SingleDimensionFitter: Constructor.
SingleDimensionFitter(double[], double[], double) - Constructor for class com.irurueta.numerical.fitting.SingleDimensionFitter: Constructor.
SingleDimensionFitter(double[], double[], double[]) - Constructor for class com.irurueta.numerical.fitting.SingleDimensionFitter: Constructor.
SingleDimensionFunctionEvaluatorListener - Interface in com.irurueta.numerical: Interface to define how single dimension functions can be evaluated.
SingleDimensionLinearFitter - Class in com.irurueta.numerical.fitting: Base class to fit provided data (x,y) to a function made of a linear combination of functions used as a basis (i.e. f(x) = a * f0(x) + b * f1(x) + ...).
SingleDimensionLinearFitter() - Constructor for class com.irurueta.numerical.fitting.SingleDimensionLinearFitter: Constructor.
SingleDimensionLinearFitter(double[], double[], double) - Constructor for class com.irurueta.numerical.fitting.SingleDimensionLinearFitter: Constructor.
SingleDimensionLinearFitter(double[], double[], double[]) - Constructor for class com.irurueta.numerical.fitting.SingleDimensionLinearFitter: Constructor.
SingleDimensionLinearFitter(LinearFitterSingleDimensionFunctionEvaluator) - Constructor for class com.irurueta.numerical.fitting.SingleDimensionLinearFitter: Constructor.
SingleDimensionLinearFitter(LinearFitterSingleDimensionFunctionEvaluator, double[], double[], double) - Constructor for class com.irurueta.numerical.fitting.SingleDimensionLinearFitter: Constructor.
SingleDimensionLinearFitter(LinearFitterSingleDimensionFunctionEvaluator, double[], double[], double[]) - Constructor for class com.irurueta.numerical.fitting.SingleDimensionLinearFitter: Constructor.
SingleOptimizer - Class in com.irurueta.numerical.optimization: Abstract class to find minima on single dimension functions.
SingleOptimizer() - Constructor for class com.irurueta.numerical.optimization.SingleOptimizer: Empty constructor.
SingleOptimizer(SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.optimization.SingleOptimizer: Constructor with listener.
SingleRootEstimator - Class in com.irurueta.numerical.roots: Abstract class to find roots of single dimension functions.
SingleRootEstimator() - Constructor for class com.irurueta.numerical.roots.SingleRootEstimator: Empty constructor.
SingleRootEstimator(SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.roots.SingleRootEstimator: Constructor.
solveCubic(double, double, double, double, Complex, Complex, Complex) - Method in class com.irurueta.numerical.roots.ThirdDegreePolynomialRootsEstimator: Finds 3rd degree polynomial roots
solveLinear(double, double) - Method in class com.irurueta.numerical.roots.FirstDegreePolynomialRootsEstimator: Internal method to estimate a root on a first degree polynomial.
solveQuadratic(double, double, double, Complex, Complex) - Method in class com.irurueta.numerical.roots.SecondDegreePolynomialRootsEstimator: Finds 2nd degree polynomial roots
sortWeights - Variable in class com.irurueta.numerical.polynomials.estimators.WeightedPolynomialEstimator: Indicates if weights are sorted by default so that largest weighted evaluations are used first.
sqr(double) - Static method in class com.irurueta.numerical.interpolation.KrigingInterpolator: Computes squared value.
sqr(double) - Method in class com.irurueta.numerical.optimization.PowellMultiOptimizer: Computes the squared value of provided double.
sqr(double) - Method in class com.irurueta.numerical.optimization.QuasiNewtonMultiOptimizer: Computes the squared value of provided double.
srp - Variable in class com.irurueta.numerical.interpolation.BicubicSpline2DInterpolator: Array of one dimensional cubic spline interpolators.
srp - Variable in class com.irurueta.numerical.interpolation.CurveInterpolator: Array of one dimensional cubic spline interpolators.
standardDeviation - Variable in class com.irurueta.numerical.robust.LMedSRobustEstimator.LMedSInliersData: Standard deviation of error among all provided samples respect to currently estimated result.
standardDeviation - Variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator.PROMedSInliersData: Standard deviation of error among all provided samples respect to currently estimated result.
startX - Variable in class com.irurueta.numerical.polynomials.estimators.IntegralIntervalPolynomialEvaluation: Start point of interval being integrated.
statePost - Variable in class com.irurueta.numerical.signal.processing.KalmanFilter: Corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))
statePre - Variable in class com.irurueta.numerical.signal.processing.KalmanFilter: Predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k)
STD_CONSTANT - Static variable in class com.irurueta.numerical.robust.LMedSRobustEstimator: Constant to estimate standard deviation of residuals based on their median.
STD_CONSTANT - Static variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Constant to estimate standard deviation of residuals based on their median.
stopThreshold - Variable in class com.irurueta.numerical.polynomials.estimators.LMedSPolynomialRobustEstimator: Threshold to be used to keep the algorithm iterating in case that best estimated threshold using median of residuals is not small enough.
stopThreshold - Variable in class com.irurueta.numerical.polynomials.estimators.PROMedSPolynomialRobustEstimator: Threshold to be used to keep the algorithm iterating in case that best estimated threshold using median of residuals is not small enough.
stopThreshold - Variable in class com.irurueta.numerical.robust.LMedSRobustEstimator: Threshold to be used to keep the algorithm iterating in case that best threshold is not small enough.
stopThresholdEnabled - Variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Indicates whether the algorithm must stop prematurely when dynamically computed threshold using median of residuals has a value lower than provided threshold in listener.
STPMX - Static variable in class com.irurueta.numerical.optimization.QuasiNewtonMultiOptimizer: Scaled maximum step length allowed in line searches.
StraightLineFitter - Class in com.irurueta.numerical.fitting: Fits provided data (x,y) to a straight line following equation y = a + b*x, estimates parameters a and b their variances, covariance and their chi square value.
StraightLineFitter() - Constructor for class com.irurueta.numerical.fitting.StraightLineFitter: Constructor.
StraightLineFitter(double[], double[]) - Constructor for class com.irurueta.numerical.fitting.StraightLineFitter: Constructor.
StraightLineFitter(double[], double[], double[]) - Constructor for class com.irurueta.numerical.fitting.StraightLineFitter: Constructor.
subsetSelector - Variable in class com.irurueta.numerical.robust.LMedSRobustEstimator: Instance in charge of picking random subsets of samples.
subsetSelector - Variable in class com.irurueta.numerical.robust.MSACRobustEstimator: Instance in charge of picking random subsets of samples.
subsetSelector - Variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Instance in charge of picking random subsets of samples.
subsetSelector - Variable in class com.irurueta.numerical.robust.PROSACRobustEstimator: Instance in charge of picking random subsets of samples.
subsetSelector - Variable in class com.irurueta.numerical.robust.RANSACRobustEstimator: Instance in charge of picking random subsets of samples.
SubsetSelector - Class in com.irurueta.numerical.robust: Base class to pick subsets of samples.
SubsetSelector(int) - Constructor for class com.irurueta.numerical.robust.SubsetSelector: Constructor.
SubsetSelectorException - Exception in com.irurueta.numerical.robust: Raised if subset selection of samples fails.
SubsetSelectorException() - Constructor for exception com.irurueta.numerical.robust.SubsetSelectorException: Constructor.
SubsetSelectorException(String) - Constructor for exception com.irurueta.numerical.robust.SubsetSelectorException: Constructor with String containing message.
SubsetSelectorException(String, Throwable) - Constructor for exception com.irurueta.numerical.robust.SubsetSelectorException: Constructor with message and cause.
SubsetSelectorException(Throwable) - Constructor for exception com.irurueta.numerical.robust.SubsetSelectorException: Constructor with cause.
SubsetSelectorType - Enum Class in com.irurueta.numerical.robust: Enumerator containing supported types of subset selectors to pick random samples for robust estimators.
SubsetSelectorType() - Constructor for enum class com.irurueta.numerical.robust.SubsetSelectorType
subtract(Polynomial) - Method in class com.irurueta.numerical.polynomials.Polynomial: Subtracts another polynomial form this one.
subtract(Polynomial, Polynomial) - Method in class com.irurueta.numerical.polynomials.Polynomial: Subtract other polynomial from this one and stores the result into provided instance.
subtractAndReturnNew(Polynomial) - Method in class com.irurueta.numerical.polynomials.Polynomial: Subtract other polynomial from this one and returns a new polynomial as a result.
sum - Variable in class com.irurueta.numerical.integration.DoubleExponentialRuleMatrixQuadrature: Temporary value storing summation of evaluations.
sum - Variable in class com.irurueta.numerical.integration.MidPointMatrixQuadrature: Temporary value storing summation of evaluations.
sum - Variable in class com.irurueta.numerical.integration.TrapezoidalMatrixQuadrature: Temporary value storing summation of evaluations.
SvdMultiDimensionLinearFitter - Class in com.irurueta.numerical.fitting: Fits provided data (x,y) to a function made of a linear combination of functions used as a basis (i.e. f(x1, x2, ...) = a * f0(x1, x2, ...) + b * f1(x1, x2, ...) + ...).
SvdMultiDimensionLinearFitter() - Constructor for class com.irurueta.numerical.fitting.SvdMultiDimensionLinearFitter: Constructor.
SvdMultiDimensionLinearFitter(Matrix, double[], double) - Constructor for class com.irurueta.numerical.fitting.SvdMultiDimensionLinearFitter: Constructor.
SvdMultiDimensionLinearFitter(Matrix, double[], double[]) - Constructor for class com.irurueta.numerical.fitting.SvdMultiDimensionLinearFitter: Constructor.
SvdMultiDimensionLinearFitter(LinearFitterMultiDimensionFunctionEvaluator) - Constructor for class com.irurueta.numerical.fitting.SvdMultiDimensionLinearFitter: Constructor.
SvdMultiDimensionLinearFitter(LinearFitterMultiDimensionFunctionEvaluator, Matrix, double[], double) - Constructor for class com.irurueta.numerical.fitting.SvdMultiDimensionLinearFitter: Constructor.
SvdMultiDimensionLinearFitter(LinearFitterMultiDimensionFunctionEvaluator, Matrix, double[], double[]) - Constructor for class com.irurueta.numerical.fitting.SvdMultiDimensionLinearFitter: Constructor.
SvdSingleDimensionLinearFitter - Class in com.irurueta.numerical.fitting: Fits provided data (x,y) to a function made of a linear combination of functions used as a basis (i.e. f(x) = a * f0(x) + b * f1(x) + ...).
SvdSingleDimensionLinearFitter() - Constructor for class com.irurueta.numerical.fitting.SvdSingleDimensionLinearFitter: Constructor.
SvdSingleDimensionLinearFitter(double[], double[], double) - Constructor for class com.irurueta.numerical.fitting.SvdSingleDimensionLinearFitter: Constructor.
SvdSingleDimensionLinearFitter(double[], double[], double[]) - Constructor for class com.irurueta.numerical.fitting.SvdSingleDimensionLinearFitter: Constructor.
SvdSingleDimensionLinearFitter(LinearFitterSingleDimensionFunctionEvaluator) - Constructor for class com.irurueta.numerical.fitting.SvdSingleDimensionLinearFitter: Constructor.
SvdSingleDimensionLinearFitter(LinearFitterSingleDimensionFunctionEvaluator, double[], double[], double) - Constructor for class com.irurueta.numerical.fitting.SvdSingleDimensionLinearFitter: Constructor.
SvdSingleDimensionLinearFitter(LinearFitterSingleDimensionFunctionEvaluator, double[], double[], double[]) - Constructor for class com.irurueta.numerical.fitting.SvdSingleDimensionLinearFitter: Constructor.
swap(double[], double[]) - Method in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Internal method to swap two values.
swap(double[], double[]) - Static method in class com.irurueta.numerical.optimization.SimplexMultiOptimizer: Swaps a and b values.
swap(double[], double[]) - Method in class com.irurueta.numerical.roots.BracketedSingleRootEstimator: Internal method to swap two values.
swap(double[], double[], int, int) - Static method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Swaps values of arrays at provided positions.
swap(double[], double[], int, int) - Static method in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Swaps values of arrays at provided positions.
swap(double[], double[], int, int) - Static method in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Swaps values of arrays at provided positions.
swap(double[], double[], int, int) - Static method in class com.irurueta.numerical.fitting.SimpleSingleDimensionLinearFitter: Swaps values of arrays at provided positions.
SymmetricDerivativeEstimator - Class in com.irurueta.numerical: Class to estimate the derivative of a single dimension function at a given point.
SymmetricDerivativeEstimator(SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.SymmetricDerivativeEstimator: Constructor.
SymmetricGradientEstimator - Class in com.irurueta.numerical: Class to estimate the gradient of a multidimensional function.
SymmetricGradientEstimator(MultiDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.SymmetricGradientEstimator: Constructor.

T

temp1 - Variable in class com.irurueta.numerical.signal.processing.KalmanFilter: Temporary matrix 1.
temp2 - Variable in class com.irurueta.numerical.signal.processing.KalmanFilter: Temporary matrix 2.
temp3 - Variable in class com.irurueta.numerical.signal.processing.KalmanFilter: Temporary matrix 3.
temp4 - Variable in class com.irurueta.numerical.signal.processing.KalmanFilter: Temporary matrix 4.
temp5 - Variable in class com.irurueta.numerical.signal.processing.KalmanFilter: Temporary matrix 5.
temp6 - Variable in class com.irurueta.numerical.signal.processing.KalmanFilter: Temporary matrix 6.
temp7 - Variable in class com.irurueta.numerical.signal.processing.KalmanFilter: Temporary matrix 7.
temp8 - Variable in class com.irurueta.numerical.signal.processing.KalmanFilter: Temporary matrix 8.
ThinPlateRadialBasisFunction - Class in com.irurueta.numerical.interpolation: Thin-plate spline Radial Basis Function implementation.
ThinPlateRadialBasisFunction() - Constructor for class com.irurueta.numerical.interpolation.ThinPlateRadialBasisFunction: Constructor.
ThinPlateRadialBasisFunction(double) - Constructor for class com.irurueta.numerical.interpolation.ThinPlateRadialBasisFunction: Constructor.
THIRD - Static variable in class com.irurueta.numerical.roots.ThirdDegreePolynomialRootsEstimator: Constant defining one third.
ThirdDegreePolynomialRootsEstimator - Class in com.irurueta.numerical.roots: Class to estimate the roots of a third degree polynomial along with other polynomial properties.
ThirdDegreePolynomialRootsEstimator() - Constructor for class com.irurueta.numerical.roots.ThirdDegreePolynomialRootsEstimator: Empty constructor.
ThirdDegreePolynomialRootsEstimator(double[]) - Constructor for class com.irurueta.numerical.roots.ThirdDegreePolynomialRootsEstimator: Constructor.
threshold - Variable in class com.irurueta.numerical.polynomials.estimators.MSACPolynomialRobustEstimator: Threshold to determine whether polynomial evaluations are inliers or not when testing possible estimation solutions
threshold - Variable in class com.irurueta.numerical.polynomials.estimators.PROSACPolynomialRobustEstimator: Threshold to determine whether polynomial evaluations are inlers or not when testing possible estimation solutions
threshold - Variable in class com.irurueta.numerical.polynomials.estimators.RANSACPolynomialRobustEstimator: Threshold to determine whether polynomial evaluations are inliers or not when testing possible estimation solutions
TIMESTAMP_FORMAT - Static variable in class com.irurueta.numerical.BuildInfo: Format for build timestamp.
TINY - Static variable in class com.irurueta.numerical.interpolation.RationalInterpolator: A small number.
TINY - Static variable in class com.irurueta.numerical.optimization.BracketedSingleOptimizer: Small value representing machine precision.
TINY - Static variable in class com.irurueta.numerical.optimization.PowellMultiOptimizer: A small number.
TINY - Static variable in class com.irurueta.numerical.optimization.SimplexMultiOptimizer: Small value considered to be machine precision.
tmp - Variable in class com.irurueta.numerical.ExponentialMatrixEstimator: Internal matrix reused for efficiency while provided input matrices keep the same size.
tmpA - Variable in class com.irurueta.numerical.integration.DoubleExponentialRuleMatrixQuadrature: Temporary value storing evaluation at lower bound.
tmpA - Variable in class com.irurueta.numerical.integration.TrapezoidalMatrixQuadrature: Temporary value storing evaluation at point "a".
tmpB - Variable in class com.irurueta.numerical.integration.DoubleExponentialRuleMatrixQuadrature: Temporary value storing evaluation at upper bound.
tmpB - Variable in class com.irurueta.numerical.integration.TrapezoidalMatrixQuadrature: Temporary value storing evaluation at point "b".
tmpMid - Variable in class com.irurueta.numerical.integration.DoubleExponentialRuleMatrixQuadrature: Temporary value storing evaluation at mid-point.
tmpMid - Variable in class com.irurueta.numerical.integration.MidPointMatrixQuadrature: Temporary value storing evaluation at mid-point.
tmpX - Variable in class com.irurueta.numerical.integration.DoubleExponentialRuleMatrixQuadrature: Temporary value storing evaluation at point x.
tmpX - Variable in class com.irurueta.numerical.integration.MidPointMatrixQuadrature: Temporary value storing evaluation at point x.
tmpX - Variable in class com.irurueta.numerical.integration.TrapezoidalMatrixQuadrature: Temporary value storing evaluation at point x.
tol - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: Tolerance to reach convergence.
tol - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Tolerance to reach convergence.
tol - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtSingleDimensionFitter: Tolerance to reach convergence.
tol - Variable in class com.irurueta.numerical.fitting.SvdMultiDimensionLinearFitter: Tolerance to define convergence threshold for SVD.
tol - Variable in class com.irurueta.numerical.fitting.SvdSingleDimensionLinearFitter: Tolerance to define convergence threshold for SVD.
tolerance - Variable in class com.irurueta.numerical.optimization.BrentSingleOptimizer: Tolerance value.
tolerance - Variable in class com.irurueta.numerical.optimization.ConjugateGradientMultiOptimizer: The fractional tolerance in the function value such that failure to decrease by more than this amount on one iteration signals done-ness.
tolerance - Variable in class com.irurueta.numerical.optimization.DerivativeBrentSingleOptimizer: Tolerance.
tolerance - Variable in class com.irurueta.numerical.optimization.DerivativeConjugateGradientMultiOptimizer: The fractional tolerance in the function value such that failure to decrease by more than this amount on one iteration signals done-ness.
tolerance - Variable in class com.irurueta.numerical.optimization.GoldenSingleOptimizer: Tolerance value.
tolerance - Variable in class com.irurueta.numerical.optimization.PowellMultiOptimizer: The fractional tolerance in the function value such that failure to decrease by more than this amount on one iteration signals doneness.
tolerance - Variable in class com.irurueta.numerical.optimization.QuasiNewtonMultiOptimizer: The fractional tolerance in the function value such that failure to decrease by more than this amount on one iteration signals done-ness.
tolerance - Variable in class com.irurueta.numerical.roots.BisectionSingleRootEstimator: Tolerance to find a root.
tolerance - Variable in class com.irurueta.numerical.roots.BrentSingleRootEstimator: Tolerance value.
tolerance - Variable in class com.irurueta.numerical.roots.FalsePositionSingleRootEstimator: Tolerance to find a root.
tolerance - Variable in class com.irurueta.numerical.roots.NewtonRaphsonSingleRootEstimator: Tolerance value.
tolerance - Variable in class com.irurueta.numerical.roots.RidderSingleRootEstimator: Tolerance value.
tolerance - Variable in class com.irurueta.numerical.roots.SafeNewtonRaphsonSingleRootEstimator: Tolerance value.
tolerance - Variable in class com.irurueta.numerical.roots.SecantSingleRootEstimator: Tolerance value.
TOLERANCE - Static variable in class com.irurueta.numerical.ExponentialMatrixEstimator: Default error tolerance of estimated result element-wise.
TOLX - Static variable in class com.irurueta.numerical.optimization.QuasiNewtonMultiOptimizer: Convergence criterion on x values.
TOLX2 - Static variable in class com.irurueta.numerical.optimization.QuasiNewtonMultiOptimizer
transformIndices(int[], int[], int[]) - Static method in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Transforms indices picked by the subset selector into the indices where samples are actually localed by taking into account their original position before sorting quality scores.
transformIndices(int[], int[], int[]) - Static method in class com.irurueta.numerical.robust.PROSACRobustEstimator: Transforms indices picked by the subset selector into the indices where samples are actually localed by taking into account their original position before sorting quality scores.
transitionMatrix - Variable in class com.irurueta.numerical.signal.processing.KalmanFilter: State transition matrix (A).
transposedSampleMatrix - Variable in class com.irurueta.numerical.signal.processing.MeasurementNoiseCovarianceEstimator: The transposed sample matrix.
TRAPEZOIDAL - Enum constant in enum class com.irurueta.numerical.integration.QuadratureType: Trapezoidal quadrature.
TrapezoidalMatrixQuadrature - Class in com.irurueta.numerical.integration: Implementation of matrix quadrature using trapezoidal algorithm.
TrapezoidalMatrixQuadrature(double, double, MatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.TrapezoidalMatrixQuadrature: Constructor.
TrapezoidalQuadrature - Class in com.irurueta.numerical.integration: Implementation of quadrature using trapezoidal algorithm.
TrapezoidalQuadrature(double, double, SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.TrapezoidalQuadrature: Constructor.
TrapezoidalQuadratureIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using Trapezoidal quadrature up to desired accuracy.
TrapezoidalQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.TrapezoidalQuadratureIntegrator: Constructor with default accuracy.
TrapezoidalQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.TrapezoidalQuadratureIntegrator: Constructor.
TrapezoidalQuadratureMatrixIntegrator - Class in com.irurueta.numerical.integration: Computes single dimension matrix (multivariate) function integration by using Trapezoidal quadrature up to desired accuracy.
TrapezoidalQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.TrapezoidalQuadratureMatrixIntegrator: Constructor with default accuracy.
TrapezoidalQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.TrapezoidalQuadratureMatrixIntegrator: Constructor.
trim() - Method in class com.irurueta.numerical.polynomials.Polynomial: Trims this polynomial to remove all terms above degree that can be neglected.
trim(Polynomial) - Method in class com.irurueta.numerical.polynomials.Polynomial: Trims polynomial to remove all terms above degree that can be neglected.
trimAndReturnNew() - Method in class com.irurueta.numerical.polynomials.Polynomial: Trims this polynomial to remove all terms above degree that can be neglected and returns the result as a new polynomial.

U

update(double[]) - Method in class com.irurueta.numerical.signal.processing.MeasurementNoiseCovarianceEstimator: Updates currently estimated covariance matrix by adding provided sample data.
update(double, double, boolean, double[], int, double, double, boolean) - Method in class com.irurueta.numerical.robust.PROMedSRobustEstimator.PROMedSInliersData: Updates data contained in this instance.
update(double, double, BitSet, double[], int, double, boolean) - Method in class com.irurueta.numerical.robust.LMedSRobustEstimator.LMedSInliersData: Updates data contained in this instance.
update(double, BitSet, double[], int, boolean) - Method in class com.irurueta.numerical.robust.MSACRobustEstimator.MSACInliersData: Updates data contained in this instance.
update(BitSet, double[], int) - Method in class com.irurueta.numerical.robust.PROSACRobustEstimator.PROSACInliersData: Updates data contained in this instance.
update(BitSet, double[], int) - Method in class com.irurueta.numerical.robust.RANSACRobustEstimator.RANSACInliersData: Updates data contained in this instance.
UPPER_SQUARE_ROOT_MID_POINT - Enum constant in enum class com.irurueta.numerical.integration.QuadratureType: Upper square root mid-point.
UpperSquareRootMidPointMatrixQuadrature - Class in com.irurueta.numerical.integration: This is an exact replacement for MidPointMatrixQuadrature, except that it allows for an inverse square-root singularity in the integrand at the upper limit b.
UpperSquareRootMidPointMatrixQuadrature(double, double, MatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.UpperSquareRootMidPointMatrixQuadrature: Constructor.
UpperSquareRootMidPointQuadrature - Class in com.irurueta.numerical.integration: This is an exact replacement for MidPointQuadrature, except that it allows for an inverse square-root singularity in the integrand at the upper limit b.
UpperSquareRootMidPointQuadrature(double, double, SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.UpperSquareRootMidPointQuadrature: Constructor.
UpperSquareRootMidPointQuadratureIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using an Upper Square Root mid-point quadrature when upper integration bound lies at a function singularity.
UpperSquareRootMidPointQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.UpperSquareRootMidPointQuadratureIntegrator: Constructor.
UpperSquareRootMidPointQuadratureIntegrator(double, double, SingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.UpperSquareRootMidPointQuadratureIntegrator: Constructor.
UpperSquareRootMidPointQuadratureMatrixIntegrator - Class in com.irurueta.numerical.integration: Computes function integration by using an Upper Square Root mid-point quadrature when upper integration bound lies at a function singularity.
UpperSquareRootMidPointQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener) - Constructor for class com.irurueta.numerical.integration.UpperSquareRootMidPointQuadratureMatrixIntegrator: Constructor.
UpperSquareRootMidPointQuadratureMatrixIntegrator(double, double, MatrixSingleDimensionFunctionEvaluatorListener, double) - Constructor for class com.irurueta.numerical.integration.UpperSquareRootMidPointQuadratureMatrixIntegrator: Constructor.
useGeometricDistance - Variable in class com.irurueta.numerical.polynomials.estimators.PolynomialRobustEstimator: Indicates whether geometric distance will be used to find outliers or algebraic distance will be used instead.
useHistogramInitialSolution - Variable in class com.irurueta.numerical.AccurateMaximumLikelihoodEstimator: Boolean that indicates that an initial coarse solution will be computed first by using an internal HistogramMaximumLikelihoodEstimator in order to initialize the internal BrentSingleOptimizer to obtain a more accurate solution.
useInlierThresholds - Variable in class com.irurueta.numerical.robust.PROMedSRobustEstimator: Flag indicating whether thresholds to determine inliers are used, or if only median of residuals is used.
usePolakRibiere - Variable in class com.irurueta.numerical.optimization.ConjugateGradientMultiOptimizer: Boolean indicating whether Polak-Ribiere method is used if true, otherwise Fletcher-Reeves will be used.
usePolakRibiere - Variable in class com.irurueta.numerical.optimization.DerivativeConjugateGradientMultiOptimizer: Boolean indicating whether Polak-Ribiere method is used if true, otherwise Fletcher-Reeves will be used.

V

VALID_POLY_PARAMS_LENGTH - Static variable in class com.irurueta.numerical.roots.FirstDegreePolynomialRootsEstimator: Number of parameters valid for a first degree polynomial.
VALID_POLY_PARAMS_LENGTH - Static variable in class com.irurueta.numerical.roots.SecondDegreePolynomialRootsEstimator: Number of parameters valid for a second degree polynomial.
VALID_POLY_PARAMS_LENGTH - Static variable in class com.irurueta.numerical.roots.ThirdDegreePolynomialRootsEstimator: Number of parameters valid for a third degree polynomial.
vals - Variable in class com.irurueta.numerical.interpolation.ShepardInterpolator: Values of function at provided points.
valueOf(String) - Static method in enum class com.irurueta.numerical.integration.IntegratorType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class com.irurueta.numerical.integration.QuadratureType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class com.irurueta.numerical.MaximumLikelihoodEstimatorMethod: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class com.irurueta.numerical.polynomials.estimators.PolynomialEstimatorType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class com.irurueta.numerical.polynomials.estimators.PolynomialEvaluationType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class com.irurueta.numerical.robust.RobustEstimatorMethod: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class com.irurueta.numerical.robust.SubsetSelectorType: Returns the enum constant of this class with the specified name.
valueOf(String) - Static method in enum class com.irurueta.numerical.signal.processing.ConvolverEdgeMethod: Returns the enum constant of this class with the specified name.
values() - Static method in enum class com.irurueta.numerical.integration.IntegratorType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class com.irurueta.numerical.integration.QuadratureType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class com.irurueta.numerical.MaximumLikelihoodEstimatorMethod: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class com.irurueta.numerical.polynomials.estimators.PolynomialEstimatorType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class com.irurueta.numerical.polynomials.estimators.PolynomialEvaluationType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class com.irurueta.numerical.robust.RobustEstimatorMethod: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class com.irurueta.numerical.robust.SubsetSelectorType: Returns an array containing the constants of this enum class, in the order they are declared.
values() - Static method in enum class com.irurueta.numerical.signal.processing.ConvolverEdgeMethod: Returns an array containing the constants of this enum class, in the order they are declared.
Variogram(Matrix, double[]) - Constructor for class com.irurueta.numerical.interpolation.KrigingInterpolator.Variogram: Constructor using default beta = 1.5 and zero offset.
Variogram(Matrix, double[], double) - Constructor for class com.irurueta.numerical.interpolation.KrigingInterpolator.Variogram: Constructor using default zero offset.
Variogram(Matrix, double[], double, double) - Constructor for class com.irurueta.numerical.interpolation.KrigingInterpolator.Variogram: Constructor.
version - Variable in class com.irurueta.numerical.BuildInfo: Version of this library.
VERSION_KEY - Static variable in class com.irurueta.numerical.BuildInfo: Key to obtain version of this library from properties file.
vgram - Variable in class com.irurueta.numerical.interpolation.KrigingInterpolator: Variogram of provided data.
vi - Variable in class com.irurueta.numerical.interpolation.KrigingInterpolator: LU decomposer.
vstar - Variable in class com.irurueta.numerical.interpolation.KrigingInterpolator

W

w - Variable in class com.irurueta.numerical.interpolation.BarycentricRationalInterpolator: Weights for barycentric rational interpolation.
w - Variable in class com.irurueta.numerical.interpolation.RadialBasisFunctionInterpolator: Computed weights to compute interpolation from provided points.
WEIGHTED_POLYNOMIAL_ESTIMATOR - Enum constant in enum class com.irurueta.numerical.polynomials.estimators.PolynomialEstimatorType: Polynomial estimator using weighted evaluations.
WeightedPolynomialEstimator - Class in com.irurueta.numerical.polynomials.estimators: This class implements a polynomial estimator using weighted evaluations.
WeightedPolynomialEstimator() - Constructor for class com.irurueta.numerical.polynomials.estimators.WeightedPolynomialEstimator: Constructor.
WeightedPolynomialEstimator(int) - Constructor for class com.irurueta.numerical.polynomials.estimators.WeightedPolynomialEstimator: Constructor.
WeightedPolynomialEstimator(int, PolynomialEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.WeightedPolynomialEstimator: Constructor.
WeightedPolynomialEstimator(int, List<PolynomialEvaluation>, double[]) - Constructor for class com.irurueta.numerical.polynomials.estimators.WeightedPolynomialEstimator: Constructor.
WeightedPolynomialEstimator(int, List<PolynomialEvaluation>, double[], PolynomialEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.WeightedPolynomialEstimator: Constructor.
WeightedPolynomialEstimator(PolynomialEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.WeightedPolynomialEstimator: Constructor.
WeightedPolynomialEstimator(List<PolynomialEvaluation>, double[]) - Constructor for class com.irurueta.numerical.polynomials.estimators.WeightedPolynomialEstimator: Constructor.
WeightedPolynomialEstimator(List<PolynomialEvaluation>, double[], PolynomialEstimatorListener) - Constructor for class com.irurueta.numerical.polynomials.estimators.WeightedPolynomialEstimator: Constructor.
weights - Variable in class com.irurueta.numerical.polynomials.estimators.WeightedPolynomialEstimator: Array containing weights for all evaluations.
WeightSelection - Class in com.irurueta.numerical.robust: Class containing the selection that was made on a weighted algorithm.
WeightSelection() - Constructor for class com.irurueta.numerical.robust.WeightSelection: Constructor.

X

x - Variable in class com.irurueta.numerical.ExponentialMatrixEstimator: Internal matrix reused for efficiency while provided input matrices keep the same size.
x - Variable in class com.irurueta.numerical.fitting.MultiDimensionFitter: Input points x where a multidimensional function f(x1, x2, ...) is evaluated where each column of the matrix represents each dimension of the point and each row is related to each sample corresponding to provided y pairs of values
x - Variable in class com.irurueta.numerical.fitting.MultiVariateFitter: Input points x where a multidimensional function f(x1, x2, ...) is evaluated where each column of the matrix represents each dimension of the point and each row is related to each sample corresponding to provided y pairs of values.
x - Variable in class com.irurueta.numerical.fitting.SingleDimensionFitter: Input points x where function f(x) is evaluated.
x - Variable in class com.irurueta.numerical.fitting.StraightLineFitter: Array containing x coordinates of input data to be fitted to a straight line.
x - Variable in class com.irurueta.numerical.interpolation.KrigingInterpolator: Data to compute interpolations from.
x - Variable in class com.irurueta.numerical.PadeApproximantEstimator: Contains intermediate solution for denominator coefficients.
x - Variable in class com.irurueta.numerical.polynomials.estimators.DerivativePolynomialEvaluation: Point where derivative of a given order has been evaluated.
x - Variable in class com.irurueta.numerical.polynomials.estimators.DirectPolynomialEvaluation: Point where polynomial has been evaluated.
x - Variable in class com.irurueta.numerical.polynomials.estimators.IntegralPolynomialEvaluation: Point where integral of a given order has been evaluated.
x1 - Variable in class com.irurueta.numerical.interpolation.BicubicSpline2DInterpolator: Array of x1v.
x1terp - Variable in class com.irurueta.numerical.interpolation.BilinearInterpolator: One dimensional interpolator for x1v.
x1terp - Variable in class com.irurueta.numerical.interpolation.Polynomial2DInterpolator: One dimensional interpolator for x1v.
x2terp - Variable in class com.irurueta.numerical.interpolation.BilinearInterpolator: One dimensional interpolator for x2v.
x2terp - Variable in class com.irurueta.numerical.interpolation.Polynomial2DInterpolator: One dimensional interpolator for x2v.
xh - Variable in class com.irurueta.numerical.GradientEstimator: Internal array to hold input parameter's values.
xh - Variable in class com.irurueta.numerical.JacobianEstimator: Internal array to hold input parameter's values.
xh1 - Variable in class com.irurueta.numerical.SymmetricGradientEstimator: Internal array containing one point to sample close to the original one.
xh2 - Variable in class com.irurueta.numerical.SymmetricGradientEstimator: Internal array containing one point to sample close to the original one.
xi - Variable in class com.irurueta.numerical.interpolation.KrigingInterpolator: Values of i-th point.
xi - Variable in class com.irurueta.numerical.optimization.DerivativeLineMultiOptimizer: Direction to make the search.
xi - Variable in class com.irurueta.numerical.optimization.LineMultiOptimizer: Direction to make the search.
ximat - Variable in class com.irurueta.numerical.optimization.PowellMultiOptimizer: Set of directions.
xmin - Variable in class com.irurueta.numerical.optimization.MultiOptimizer: Minimum that was estimated.
xmin - Variable in class com.irurueta.numerical.optimization.SingleOptimizer: Value where minimum has been found.
xRow - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter: An input point to be evaluated.
xRow - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: An input point to be evaluated.
xx - Variable in class com.irurueta.numerical.interpolation.BaseInterpolator: X values to be used for interpolation estimation.

Y

y - Variable in class com.irurueta.numerical.fitting.MultiDimensionFitter: Result of evaluation of multidimensional function f(x1, x2, ...)
y - Variable in class com.irurueta.numerical.fitting.MultiVariateFitter: Result of evaluation of multi variate function f(x1, x2, ...) at provided x points.
y - Variable in class com.irurueta.numerical.fitting.SingleDimensionFitter: Result of evaluation of linear single dimensional function f(x) at provided x points.
y - Variable in class com.irurueta.numerical.fitting.StraightLineFitter: Array containing y coordinates of input data to be fitted to a straight line.
y - Variable in class com.irurueta.numerical.interpolation.BilinearInterpolator: Matrix of tabulated function values yij.
y - Variable in class com.irurueta.numerical.interpolation.Polynomial2DInterpolator: Matrix of tabulated function values yij.
y - Variable in class com.irurueta.numerical.optimization.SimplexMultiOptimizer: Function values at the vertices of the simples.
y - Variable in class com.irurueta.numerical.PadeApproximantEstimator: Contains intermediate solution for numerator coefficients.
y2 - Variable in class com.irurueta.numerical.interpolation.CubicSplineInterpolator
ymod - Variable in class com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFitter: Results of function evaluations.
YP1 - Static variable in class com.irurueta.numerical.interpolation.CubicSplineInterpolator
YPN - Static variable in class com.irurueta.numerical.interpolation.CubicSplineInterpolator
yv - Variable in class com.irurueta.numerical.interpolation.BicubicSpline2DInterpolator: Array of x2v.
yv - Variable in class com.irurueta.numerical.interpolation.Polynomial2DInterpolator: Temporary array containing interpolated values in one direction.
yvi - Variable in class com.irurueta.numerical.interpolation.KrigingInterpolator
yy - Variable in class com.irurueta.numerical.interpolation.BaseInterpolator: Y values to be used for interpolation estimation.

Z

ZEPS - Static variable in class com.irurueta.numerical.optimization.BrentSingleOptimizer: Small number that protects against trying to achieve fractional accuracy for a minimum that happens to be exactly zero.
ZEPS - Static variable in class com.irurueta.numerical.optimization.DerivativeBrentSingleOptimizer: Constant defining machine precision.
ZERO_EDGE - Enum constant in enum class com.irurueta.numerical.signal.processing.ConvolverEdgeMethod: When convolution kernel reaches edge of signal being convoluted, it is assumed that the signal value is zero.

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