Uses of Class
org.apache.commons.math4.legacy.exception.MathIllegalArgumentException

Packages that use MathIllegalArgumentException

Package	Description
org.apache.commons.math4.legacy.analysis.differentiation	This package holds the main interfaces and basic building block classes dealing with differentiation.
org.apache.commons.math4.legacy.analysis.interpolation	Univariate real functions interpolation algorithms.
org.apache.commons.math4.legacy.analysis.solvers	Root finding algorithms, for univariate real functions.
org.apache.commons.math4.legacy.exception	Specialized exceptions for algorithms errors.
org.apache.commons.math4.legacy.genetics	This package provides Genetic Algorithms components and implementations.
org.apache.commons.math4.legacy.linear	Linear algebra support.
org.apache.commons.math4.legacy.ode	This package provides classes to solve Ordinary Differential Equations problems.
org.apache.commons.math4.legacy.special	Implementations of special functions such as Beta and Gamma.
org.apache.commons.math4.legacy.stat	Data storage, manipulation and summary routines.
org.apache.commons.math4.legacy.stat.correlation	Correlations/Covariance computations.
org.apache.commons.math4.legacy.stat.descriptive	Generic univariate summary statistic objects.
org.apache.commons.math4.legacy.stat.descriptive.moment	Summary statistics based on moments.
org.apache.commons.math4.legacy.stat.descriptive.rank	Summary statistics based on ranks.
org.apache.commons.math4.legacy.stat.descriptive.summary	Other summary statistics.
org.apache.commons.math4.legacy.stat.inference	Classes providing hypothesis testing.
org.apache.commons.math4.legacy.stat.regression	Statistical routines involving multivariate data.
org.apache.commons.math4.legacy.util	Convenience routines and common data structures used throughout the commons-math library.

Uses of MathIllegalArgumentException in org.apache.commons.math4.legacy.analysis.differentiation

Methods in org.apache.commons.math4.legacy.analysis.differentiation that throw MathIllegalArgumentException

Modifier and Type	Method	Description
DerivativeStructure[]	MultivariateDifferentiableVectorFunction.value(DerivativeStructure[] point)	Compute the value for the function at the given point.
DerivativeStructure[][]	UnivariateDifferentiableMatrixFunction.value(DerivativeStructure x)	Compute the value for the function.
DerivativeStructure[]	UnivariateDifferentiableVectorFunction.value(DerivativeStructure x)	Compute the value for the function.

Uses of MathIllegalArgumentException in org.apache.commons.math4.legacy.analysis.interpolation

Methods in org.apache.commons.math4.legacy.analysis.interpolation that throw MathIllegalArgumentException

Modifier and Type	Method	Description
MultivariateFunction	MultivariateInterpolator.interpolate(double[][] xval, double[] yval)	Computes an interpolating function for the data set.

Uses of MathIllegalArgumentException in org.apache.commons.math4.legacy.analysis.solvers

Methods in org.apache.commons.math4.legacy.analysis.solvers that throw MathIllegalArgumentException

Modifier and Type	Method	Description
double	BaseUnivariateSolver.solve(int maxEval, FUNC f, double min, double max)	Solve for a zero root in the given interval.
double	BaseUnivariateSolver.solve(int maxEval, FUNC f, double min, double max, double startValue)	Solve for a zero in the given interval, start at startValue.

Uses of MathIllegalArgumentException in org.apache.commons.math4.legacy.exception

Subclasses of MathIllegalArgumentException in org.apache.commons.math4.legacy.exception

Modifier and Type	Class	Description
class	DimensionMismatchException	Exception to be thrown when two dimensions differ.
class	InsufficientDataException	Exception to be thrown when there is insufficient data to perform a computation.
class	MathIllegalNumberException	Base class for exceptions raised by a wrong number.
class	MultiDimensionMismatchException	Exception to be thrown when two sets of dimensions differ.
class	NoBracketingException	Exception to be thrown when function values have the same sign at both ends of an interval.
class	NoDataException	Exception to be thrown when the required data is missing.
class	NonMonotonicSequenceException	Exception to be thrown when the a sequence of values is not monotonically increasing or decreasing.
class	NotANumberException	Exception to be thrown when a number is not a number.
class	NotFiniteNumberException	Exception to be thrown when a number is not finite.
class	NotPositiveException	Exception to be thrown when the argument is negative.
class	NotStrictlyPositiveException	Exception to be thrown when the argument is not greater than 0.
class	NumberIsTooLargeException	Exception to be thrown when a number is too large.
class	NumberIsTooSmallException	Exception to be thrown when a number is too small.
class	OutOfRangeException	Exception to be thrown when some argument is out of range.
class	ZeroException	Exception to be thrown when zero is provided where it is not allowed.

Uses of MathIllegalArgumentException in org.apache.commons.math4.legacy.genetics

Subclasses of MathIllegalArgumentException in org.apache.commons.math4.legacy.genetics

Modifier and Type	Class	Description
class	InvalidRepresentationException	Exception indicating that the representation of a chromosome is not valid.

Methods in org.apache.commons.math4.legacy.genetics that throw MathIllegalArgumentException

Modifier and Type	Method	Description
ChromosomePair	CrossoverPolicy.crossover(Chromosome first, Chromosome second)	Perform a crossover operation on the given chromosomes.
ChromosomePair	CycleCrossover.crossover(Chromosome first, Chromosome second)	Perform a crossover operation on the given chromosomes.
ChromosomePair	NPointCrossover.crossover(Chromosome first, Chromosome second)	Performs a N-point crossover.
ChromosomePair	OnePointCrossover.crossover(Chromosome first, Chromosome second)	Performs one point crossover.
ChromosomePair	OrderedCrossover.crossover(Chromosome first, Chromosome second)	Perform a crossover operation on the given chromosomes.
ChromosomePair	UniformCrossover.crossover(Chromosome first, Chromosome second)	Perform a crossover operation on the given chromosomes.
static <S> List<Double>	RandomKey.inducedPermutation(List<S> originalData, List<S> permutedData)	Generates a representation of a permutation corresponding to a permutation which yields permutedData when applied to originalData.
Chromosome	BinaryMutation.mutate(Chromosome original)	Mutate the given chromosome.
Chromosome	MutationPolicy.mutate(Chromosome original)	Mutate the given chromosome.
Chromosome	RandomKeyMutation.mutate(Chromosome original)	Mutate the given chromosome.
ChromosomePair	SelectionPolicy.select(Population population)	Select two chromosomes from the population.
ChromosomePair	TournamentSelection.select(Population population)	Select two chromosomes from the population.

Uses of MathIllegalArgumentException in org.apache.commons.math4.legacy.linear

Subclasses of MathIllegalArgumentException in org.apache.commons.math4.legacy.linear

Modifier and Type	Class	Description
class	IllConditionedOperatorException	An exception to be thrown when the condition number of a RealLinearOperator is too high.
class	MatrixDimensionMismatchException	Exception to be thrown when either the number of rows or the number of columns of a matrix do not match the expected values.
class	NonPositiveDefiniteMatrixException	Exception to be thrown when a positive definite matrix is expected.
class	NonPositiveDefiniteOperatorException	Exception to be thrown when a symmetric, definite positive RealLinearOperator is expected.
class	NonSelfAdjointOperatorException	Exception to be thrown when a self-adjoint RealLinearOperator is expected.
class	NonSquareMatrixException	Exception to be thrown when a square matrix is expected.
class	NonSquareOperatorException	Exception to be thrown when a square linear operator is expected.
class	NonSymmetricMatrixException	Exception to be thrown when a symmetric matrix is expected.
class	SingularMatrixException	Exception to be thrown when a non-singular matrix is expected.
class	SingularOperatorException	Exception to be thrown when trying to invert a singular operator.

Uses of MathIllegalArgumentException in org.apache.commons.math4.legacy.ode

Subclasses of MathIllegalArgumentException in org.apache.commons.math4.legacy.ode

Modifier and Type	Class	Description
static class	JacobianMatrices.MismatchedEquations	Special exception for equations mismatch.
class	UnknownParameterException	Exception to be thrown when a parameter is unknown.

Methods in org.apache.commons.math4.legacy.ode that throw MathIllegalArgumentException

Modifier and Type	Method	Description
void	ContinuousOutputFieldModel.append(ContinuousOutputFieldModel<T> model)	Append another model at the end of the instance.
void	ContinuousOutputModel.append(ContinuousOutputModel model)	Append another model at the end of the instance.
T[]	FieldEquationsMapper.extractEquationData(int index, T[] complete)	Extract equation data from a complete state or derivative array.
void	SecondOrderIntegrator.integrate(SecondOrderDifferentialEquations equations, double t0, double[] y0, double[] yDot0, double t, double[] y, double[] yDot)	Integrate the differential equations up to the given time.

Uses of MathIllegalArgumentException in org.apache.commons.math4.legacy.special

Methods in org.apache.commons.math4.legacy.special that throw MathIllegalArgumentException

Modifier and Type	Method	Description
double	BesselJ.value(double x)	Returns the value of the constructed Bessel function of the first kind, for the passed argument.
static double	BesselJ.value(double order, double x)	Returns the first Bessel function, \(J_{order}(x)\).

Uses of MathIllegalArgumentException in org.apache.commons.math4.legacy.stat

Methods in org.apache.commons.math4.legacy.stat that throw MathIllegalArgumentException

Modifier and Type	Method	Description
static double	StatUtils.geometricMean(double[] values)	Returns the geometric mean of the entries in the input array, or Double.NaN if the array is empty.
static double	StatUtils.geometricMean(double[] values, int begin, int length)	Returns the geometric mean of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
static double	StatUtils.max(double[] values)	Returns the maximum of the entries in the input array, or Double.NaN if the array is empty.
static double	StatUtils.max(double[] values, int begin, int length)	Returns the maximum of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
static double	StatUtils.mean(double[] values)	Returns the arithmetic mean of the entries in the input array, or Double.NaN if the array is empty.
static double	StatUtils.mean(double[] values, int begin, int length)	Returns the arithmetic mean of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
static double	StatUtils.min(double[] values)	Returns the minimum of the entries in the input array, or Double.NaN if the array is empty.
static double	StatUtils.min(double[] values, int begin, int length)	Returns the minimum of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
static double[]	StatUtils.mode(double[] sample)	Returns the sample mode(s).
static double	StatUtils.percentile(double[] values, double p)	Returns an estimate of the pth percentile of the values in the values array.
static double	StatUtils.percentile(double[] values, int begin, int length, double p)	Returns an estimate of the pth percentile of the values in the values array, starting with the element in (0-based) position begin in the array and including length values.
static double	StatUtils.populationVariance(double[] values)	Returns the population variance of the entries in the input array, or Double.NaN if the array is empty.
static double	StatUtils.populationVariance(double[] values, double mean)	Returns the population variance of the entries in the input array, using the precomputed mean value.
static double	StatUtils.populationVariance(double[] values, double mean, int begin, int length)	Returns the population variance of the entries in the specified portion of the input array, using the precomputed mean value.
static double	StatUtils.populationVariance(double[] values, int begin, int length)	Returns the population variance of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
static double	StatUtils.product(double[] values)	Returns the product of the entries in the input array, or Double.NaN if the array is empty.
static double	StatUtils.product(double[] values, int begin, int length)	Returns the product of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
static double	StatUtils.sum(double[] values)	Returns the sum of the values in the input array, or Double.NaN if the array is empty.
static double	StatUtils.sum(double[] values, int begin, int length)	Returns the sum of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
static double	StatUtils.sumLog(double[] values)	Returns the sum of the natural logs of the entries in the input array, or Double.NaN if the array is empty.
static double	StatUtils.sumLog(double[] values, int begin, int length)	Returns the sum of the natural logs of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
static double	StatUtils.sumSq(double[] values)	Returns the sum of the squares of the entries in the input array, or Double.NaN if the array is empty.
static double	StatUtils.sumSq(double[] values, int begin, int length)	Returns the sum of the squares of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
static double	StatUtils.variance(double[] values)	Returns the variance of the entries in the input array, or Double.NaN if the array is empty.
static double	StatUtils.variance(double[] values, double mean)	Returns the variance of the entries in the input array, using the precomputed mean value.
static double	StatUtils.variance(double[] values, double mean, int begin, int length)	Returns the variance of the entries in the specified portion of the input array, using the precomputed mean value.
static double	StatUtils.variance(double[] values, int begin, int length)	Returns the variance of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.

Uses of MathIllegalArgumentException in org.apache.commons.math4.legacy.stat.correlation

Methods in org.apache.commons.math4.legacy.stat.correlation that throw MathIllegalArgumentException

Modifier and Type	Method	Description
protected RealMatrix	Covariance.computeCovarianceMatrix(double[][] data)	Create a covariance matrix from a rectangular array whose columns represent covariates.
protected RealMatrix	Covariance.computeCovarianceMatrix(double[][] data, boolean biasCorrected)	Compute a covariance matrix from a rectangular array whose columns represent covariates.
protected RealMatrix	Covariance.computeCovarianceMatrix(RealMatrix matrix)	Create a covariance matrix from a matrix whose columns represent covariates.
protected RealMatrix	Covariance.computeCovarianceMatrix(RealMatrix matrix, boolean biasCorrected)	Compute a covariance matrix from a matrix whose columns represent covariates.
double	Covariance.covariance(double[] xArray, double[] yArray)	Computes the covariance between the two arrays, using the bias-corrected formula.
double	Covariance.covariance(double[] xArray, double[] yArray, boolean biasCorrected)	Computes the covariance between the two arrays.

Constructors in org.apache.commons.math4.legacy.stat.correlation that throw MathIllegalArgumentException

Constructor	Description
Covariance(double[][] data)	Create a Covariance matrix from a rectangular array whose columns represent covariates.
Covariance(double[][] data, boolean biasCorrected)	Create a Covariance matrix from a rectangular array whose columns represent covariates.
Covariance(RealMatrix matrix)	Create a covariance matrix from a matrix whose columns represent covariates.
Covariance(RealMatrix matrix, boolean biasCorrected)	Create a covariance matrix from a matrix whose columns represent covariates.
SpearmansCorrelation(RealMatrix dataMatrix, RankingAlgorithm rankingAlgorithm)	Create a SpearmansCorrelation with the given input data matrix and ranking algorithm.
SpearmansCorrelation(RankingAlgorithm rankingAlgorithm)	Create a SpearmansCorrelation with the given ranking algorithm.

Uses of MathIllegalArgumentException in org.apache.commons.math4.legacy.stat.descriptive

Methods in org.apache.commons.math4.legacy.stat.descriptive that throw MathIllegalArgumentException

Modifier and Type	Method	Description
double	AbstractStorelessUnivariateStatistic.evaluate(double[] values)	This default implementation creates a copy of this StorelessUnivariateStatistic instance, calls AbstractStorelessUnivariateStatistic.clear() on it, then calls AbstractStorelessUnivariateStatistic.incrementAll(double[]) with the specified portion of the input array, and then uses AbstractStorelessUnivariateStatistic.getResult() to compute the return value.
double	AbstractStorelessUnivariateStatistic.evaluate(double[] values, int begin, int length)	This default implementation creates a copy of this StorelessUnivariateStatistic instance, calls AbstractStorelessUnivariateStatistic.clear() on it, then calls AbstractStorelessUnivariateStatistic.incrementAll(double[]) with the specified portion of the input array, and then uses AbstractStorelessUnivariateStatistic.getResult() to compute the return value.
double	AbstractUnivariateStatistic.evaluate()	Returns the result of evaluating the statistic over the stored data.
double	AbstractUnivariateStatistic.evaluate(double[] values)	Returns the result of evaluating the statistic over the input array.
abstract double	AbstractUnivariateStatistic.evaluate(double[] values, int begin, int length)	Returns the result of evaluating the statistic over the specified entries in the input array.
double	UnivariateStatistic.evaluate(double[] values)	Returns the result of evaluating the statistic over the input array.
double	UnivariateStatistic.evaluate(double[] values, int begin, int length)	Returns the result of evaluating the statistic over the specified entries in the input array.
double	WeightedEvaluation.evaluate(double[] values, double[] weights)	Returns the result of evaluating the statistic over the input array, using the supplied weights.
double	WeightedEvaluation.evaluate(double[] values, double[] weights, int begin, int length)	Returns the result of evaluating the statistic over the specified entries in the input array, using corresponding entries in the supplied weights array.
double	DescriptiveStatistics.getPercentile(double p)	Returns an estimate for the pth percentile of the stored values.
void	AbstractStorelessUnivariateStatistic.incrementAll(double[] values)	This default implementation just calls AbstractStorelessUnivariateStatistic.increment(double) in a loop over the input array.
void	AbstractStorelessUnivariateStatistic.incrementAll(double[] values, int begin, int length)	This default implementation just calls AbstractStorelessUnivariateStatistic.increment(double) in a loop over the specified portion of the input array.
void	StorelessUnivariateStatistic.incrementAll(double[] values)	Updates the internal state of the statistic to reflect addition of all values in the values array.
void	StorelessUnivariateStatistic.incrementAll(double[] values, int start, int length)	Updates the internal state of the statistic to reflect addition of the values in the designated portion of the values array.
void	AbstractUnivariateStatistic.setData(double[] values, int begin, int length)	Set the data array.
void	DescriptiveStatistics.setPercentileImpl(UnivariateStatistic percentileImpl)	Sets the implementation to be used by DescriptiveStatistics.getPercentile(double).
void	DescriptiveStatistics.setWindowSize(int windowSize)	WindowSize controls the number of values that contribute to the reported statistics.
void	SynchronizedDescriptiveStatistics.setWindowSize(int windowSize)	WindowSize controls the number of values that contribute to the reported statistics.

Constructors in org.apache.commons.math4.legacy.stat.descriptive that throw MathIllegalArgumentException

Constructor	Description
DescriptiveStatistics(int window)	Construct a DescriptiveStatistics instance with the specified window.
SynchronizedDescriptiveStatistics(int window)	Construct an instance with finite window.

Uses of MathIllegalArgumentException in org.apache.commons.math4.legacy.stat.descriptive.moment

Methods in org.apache.commons.math4.legacy.stat.descriptive.moment that throw MathIllegalArgumentException

Modifier and Type	Method	Description
double	GeometricMean.evaluate(double[] values, int begin, int length)	Returns the geometric mean of the entries in the specified portion of the input array.
double	Kurtosis.evaluate(double[] values, int begin, int length)	Returns the kurtosis of the entries in the specified portion of the input array.
double	Mean.evaluate(double[] values, double[] weights)	Returns the weighted arithmetic mean of the entries in the input array.
double	Mean.evaluate(double[] values, double[] weights, int begin, int length)	Returns the weighted arithmetic mean of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
double	Mean.evaluate(double[] values, int begin, int length)	Returns the arithmetic mean of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
double	SemiVariance.evaluate(double[] values, double cutoff)	Returns the SemiVariance of the designated values against the cutoff, using instance properties variancDirection and biasCorrection.
double	SemiVariance.evaluate(double[] values, double cutoff, SemiVariance.Direction direction)	Returns the SemiVariance of the designated values against the cutoff in the given direction, using the current value of the biasCorrection instance property.
double	SemiVariance.evaluate(double[] values, double cutoff, SemiVariance.Direction direction, boolean corrected, int start, int length)	Returns the SemiVariance of the designated values against the cutoff in the given direction with the provided bias correction.
double	SemiVariance.evaluate(double[] values, int start, int length)	Returns the SemiVariance of the designated values against the mean, using instance properties varianceDirection and biasCorrection.
double	SemiVariance.evaluate(double[] values, SemiVariance.Direction direction)	This method calculates SemiVariance for the entire array against the mean, using the current value of the biasCorrection instance property.
double	Skewness.evaluate(double[] values, int begin, int length)	Returns the Skewness of the entries in the specified portion of the input array.
double	StandardDeviation.evaluate(double[] values)	Returns the Standard Deviation of the entries in the input array, or Double.NaN if the array is empty.
double	StandardDeviation.evaluate(double[] values, double mean)	Returns the Standard Deviation of the entries in the input array, using the precomputed mean value.
double	StandardDeviation.evaluate(double[] values, double mean, int begin, int length)	Returns the Standard Deviation of the entries in the specified portion of the input array, using the precomputed mean value.
double	StandardDeviation.evaluate(double[] values, int begin, int length)	Returns the Standard Deviation of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
double	Variance.evaluate(double[] values)	Returns the variance of the entries in the input array, or Double.NaN if the array is empty.
double	Variance.evaluate(double[] values, double mean)	Returns the variance of the entries in the input array, using the precomputed mean value.
double	Variance.evaluate(double[] values, double[] weights)	Returns the weighted variance of the entries in the input array.
double	Variance.evaluate(double[] values, double[] weights, double mean)	Returns the weighted variance of the values in the input array, using the precomputed weighted mean value.
double	Variance.evaluate(double[] values, double[] weights, double mean, int begin, int length)	Returns the weighted variance of the entries in the specified portion of the input array, using the precomputed weighted mean value.
double	Variance.evaluate(double[] values, double[] weights, int begin, int length)	Returns the weighted variance of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
double	Variance.evaluate(double[] values, double mean, int begin, int length)	Returns the variance of the entries in the specified portion of the input array, using the precomputed mean value.
double	Variance.evaluate(double[] values, int begin, int length)	Returns the variance of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.

Uses of MathIllegalArgumentException in org.apache.commons.math4.legacy.stat.descriptive.rank

Methods in org.apache.commons.math4.legacy.stat.descriptive.rank that throw MathIllegalArgumentException

Modifier and Type	Method	Description
double	Max.evaluate(double[] values, int begin, int length)	Returns the maximum of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
double	Min.evaluate(double[] values, int begin, int length)	Returns the minimum of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.

Uses of MathIllegalArgumentException in org.apache.commons.math4.legacy.stat.descriptive.summary

Methods in org.apache.commons.math4.legacy.stat.descriptive.summary that throw MathIllegalArgumentException

Modifier and Type	Method	Description
double	Product.evaluate(double[] values, double[] weights)	Returns the weighted product of the entries in the input array.
double	Product.evaluate(double[] values, double[] weights, int begin, int length)	Returns the weighted product of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
double	Product.evaluate(double[] values, int begin, int length)	Returns the product of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
double	Sum.evaluate(double[] values, double[] weights)	The weighted sum of the entries in the input array.
double	Sum.evaluate(double[] values, double[] weights, int begin, int length)	The weighted sum of the entries in the specified portion of the input array, or 0 if the designated subarray is empty.
double	Sum.evaluate(double[] values, int begin, int length)	The sum of the entries in the specified portion of the input array, or 0 if the designated subarray is empty.
double	SumOfLogs.evaluate(double[] values, int begin, int length)	Returns the sum of the natural logs of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
double	SumOfSquares.evaluate(double[] values, int begin, int length)	Returns the sum of the squares of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.

Uses of MathIllegalArgumentException in org.apache.commons.math4.legacy.stat.inference

Methods in org.apache.commons.math4.legacy.stat.inference that throw MathIllegalArgumentException

Modifier and Type	Method	Description
protected double	TTest.tTest(double m, double mu, double v, double n)	Computes p-value for 2-sided, 1-sample t-test.

Uses of MathIllegalArgumentException in org.apache.commons.math4.legacy.stat.regression

Subclasses of MathIllegalArgumentException in org.apache.commons.math4.legacy.stat.regression

Modifier and Type	Class	Description
class	ModelSpecificationException	Exception thrown when a regression model is not correctly specified.

Methods in org.apache.commons.math4.legacy.stat.regression that throw MathIllegalArgumentException

Modifier and Type	Method	Description
void	OLSMultipleLinearRegression.newSampleData(double[] y, double[][] x)	Loads model x and y sample data, overriding any previous sample.
RegressionResults	SimpleRegression.regress(int[] variablesToInclude)	Performs a regression on data present in buffers including only regressors.
RegressionResults	UpdatingMultipleLinearRegression.regress(int[] variablesToInclude)	Performs a regression on data present in buffers including only regressors.
protected void	AbstractMultipleLinearRegression.validateSampleData(double[][] x, double[] y)	Validates sample data.

Uses of MathIllegalArgumentException in org.apache.commons.math4.legacy.util

Methods in org.apache.commons.math4.legacy.util that throw MathIllegalArgumentException

Modifier and Type	Method	Description
StringBuffer	ComplexFormat.format(Object obj, StringBuffer toAppendTo, FieldPosition pos)	Formats a object to produce a string.