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.
beta - Variable in class com.irurueta.numerical.robust.PROSACRobustEstimator: beta is the probability that a match is declared inlier by mistake, i.e.
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.
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.
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.
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.
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.
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.
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.
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.
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.
getExtrema(double) - Method in class com.irurueta.numerical.polynomials.Polynomial: Gets location of minima or maxima (i.e.
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.
getNumberOfDimensions() - Method in interface com.irurueta.numerical.fitting.LevenbergMarquardtMultiVariateFunctionEvaluator: Number of dimensions of points (i.e.
getNumberOfDimensions() - Method in interface com.irurueta.numerical.fitting.LinearFitterMultiDimensionFunctionEvaluator: Number of dimensions of points (i.e.
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.
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.
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, ...) is evaluated and 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
getX() - Method in class com.irurueta.numerical.fitting.MultiVariateFitter: Returns input points x where a multi variate function f(x1, x2, ...) is evaluated and 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.
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.
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) + ...
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) + ...
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, ...) to a function made of a linear combination of functions used as a basis (i.e.
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, ...]) by using provided data (x, y).
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) + ...
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.
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.
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.
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.
setBracket(double, double) - Method in class com.irurueta.numerical.roots.BracketedSingleRootEstimator: Sets the bracket of values (i.e.
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) + ...
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.
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.
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.
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.
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, ...) at provided x points.
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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