Uses of Class
org.hipparchus.exception.NullArgumentException

Packages that use NullArgumentException

Package	Description
org.hipparchus	Common classes used throughout the Hipparchus library.
org.hipparchus.analysis.function	The function package contains function objects that wrap the methods contained in Math, as well as common mathematical functions such as the gaussian and sinc functions.
org.hipparchus.analysis.integration	Numerical integration (quadrature) algorithms for univariate real functions.
org.hipparchus.analysis.interpolation	Univariate real functions interpolation algorithms.
org.hipparchus.analysis.polynomials	Univariate real polynomials implementations, seen as differentiable univariate real functions.
org.hipparchus.analysis.solvers	Root finding algorithms, for univariate real functions.
org.hipparchus.clustering	Clustering algorithms.
org.hipparchus.complex	Complex number type and implementations of complex transcendental functions.
org.hipparchus.fraction	Fraction number type and fraction number formatting.
org.hipparchus.linear	Linear algebra support.
org.hipparchus.optim.nonlinear.scalar	Algorithms for optimizing a scalar function.
org.hipparchus.random	Random number and random data generators.
org.hipparchus.stat	Data storage, manipulation and summary routines.
org.hipparchus.stat.descriptive	Generic univariate and multivariate summary statistic objects.
org.hipparchus.stat.descriptive.moment	Summary statistics based on moments.
org.hipparchus.stat.descriptive.rank	Summary statistics based on ranks.
org.hipparchus.stat.descriptive.summary	Other summary statistics.
org.hipparchus.stat.fitting	Statistical methods for fitting distributions.
org.hipparchus.stat.inference	Classes providing hypothesis testing.
org.hipparchus.util	Convenience routines and common data structures used throughout the Hipparchus library.

Uses of NullArgumentException in org.hipparchus

Methods in org.hipparchus that throw NullArgumentException

Modifier and Type	Method	Description
T	FieldElement.add(T a)	Compute this + a.
T	FieldElement.divide(T a)	Compute this ÷ a.
T	FieldElement.multiply(T a)	Compute this × a.
T	FieldElement.subtract(T a)	Compute this - a.

Uses of NullArgumentException in org.hipparchus.analysis.function

Methods in org.hipparchus.analysis.function that throw NullArgumentException

Modifier and Type	Method	Description
double[]	Gaussian.Parametric.gradient(double x, double... param)	Computes the value of the gradient at x.
double[]	HarmonicOscillator.Parametric.gradient(double x, double... param)	Computes the value of the gradient at x.
double[]	Logistic.Parametric.gradient(double x, double... param)	Computes the value of the gradient at x.
double[]	Logit.Parametric.gradient(double x, double... param)	Computes the value of the gradient at x.
double[]	Sigmoid.Parametric.gradient(double x, double... param)	Computes the value of the gradient at x.
double	Gaussian.Parametric.value(double x, double... param)	Computes the value of the Gaussian at x.
double	HarmonicOscillator.Parametric.value(double x, double... param)	Computes the value of the harmonic oscillator at x.
double	Logistic.Parametric.value(double x, double... param)	Computes the value of the sigmoid at x.
double	Logit.Parametric.value(double x, double... param)	Computes the value of the logit at x.
double	Sigmoid.Parametric.value(double x, double... param)	Computes the value of the sigmoid at x.

Constructors in org.hipparchus.analysis.function that throw NullArgumentException

Constructor	Description
StepFunction(double[] x, double[] y)	Builds a step function from a list of arguments and the corresponding values.

Uses of NullArgumentException in org.hipparchus.analysis.integration

Methods in org.hipparchus.analysis.integration that throw NullArgumentException

Modifier and Type	Method	Description
T	BaseAbstractFieldUnivariateIntegrator.integrate(int maxEval, CalculusFieldUnivariateFunction<T> f, T lower, T upper)	Integrate the function in the given interval.
double	BaseAbstractUnivariateIntegrator.integrate(int maxEval, UnivariateFunction f, double lower, double upper)	Integrate the function in the given interval.
T	FieldUnivariateIntegrator.integrate(int maxEval, CalculusFieldUnivariateFunction<T> f, T min, T max)	Integrate the function in the given interval.
double	UnivariateIntegrator.integrate(int maxEval, UnivariateFunction f, double min, double max)	Integrate the function in the given interval.
protected void	BaseAbstractFieldUnivariateIntegrator.setup(int maxEval, CalculusFieldUnivariateFunction<T> f, T lower, T upper)	Prepare for computation.
protected void	BaseAbstractUnivariateIntegrator.setup(int maxEval, UnivariateFunction f, double lower, double upper)	Prepare for computation.

Uses of NullArgumentException in org.hipparchus.analysis.interpolation

Methods in org.hipparchus.analysis.interpolation that throw NullArgumentException

Modifier and Type	Method	Description
void	FieldHermiteInterpolator.addSamplePoint(T x, T[]... value)	Add a sample point.
T[][]	FieldHermiteInterpolator.derivatives(T x, int order)	Interpolate value and first derivatives at a specified abscissa.
double[][]	HermiteInterpolator.derivatives(double x, int order)	Interpolate value and first derivatives at a specified abscissa.
MultivariateFunction	MicrosphereProjectionInterpolator.interpolate(double[][] xval, double[] yval)	Computes an interpolating function for the data set.
MultivariateFunction	MultivariateInterpolator.interpolate(double[][] xval, double[] yval)	Computes an interpolating function for the data set.
PiecewiseBicubicSplineInterpolatingFunction	PiecewiseBicubicSplineInterpolator.interpolate(double[] xval, double[] yval, double[][] fval)	Compute an interpolating function for the dataset.
T[]	FieldHermiteInterpolator.value(T x)	Interpolate value at a specified abscissa.

Constructors in org.hipparchus.analysis.interpolation that throw NullArgumentException

Constructor	Description
PiecewiseBicubicSplineInterpolatingFunction(double[] x, double[] y, double[][] f)	Simple constructor.

Uses of NullArgumentException in org.hipparchus.analysis.polynomials

Methods in org.hipparchus.analysis.polynomials that throw NullArgumentException

Modifier and Type	Method	Description
protected static <T extends CalculusFieldElement<T>> T[]	FieldPolynomialFunction.differentiate(T[] coefficients)	Returns the coefficients of the derivative of the polynomial with the given coefficients.
protected static double[]	PolynomialFunction.differentiate(double[] coefficients)	Returns the coefficients of the derivative of the polynomial with the given coefficients.
protected static <T extends CalculusFieldElement<T>> T	FieldPolynomialFunction.evaluate(T[] coefficients, T argument)	Uses Horner's Method to evaluate the polynomial with the given coefficients at the argument.
protected static double	PolynomialFunction.evaluate(double[] coefficients, double argument)	Uses Horner's Method to evaluate the polynomial with the given coefficients at the argument.
static double	PolynomialFunctionNewtonForm.evaluate(double[] a, double[] c, double z)	Evaluate the Newton polynomial using nested multiplication.
<T extends Derivative<T>> T	PolynomialFunction.value(T t)	Compute the value for the function.
<T extends CalculusFieldElement<T>> T	PolynomialFunction.value(T t)	Compute the value of the function.
protected static void	PolynomialFunctionNewtonForm.verifyInputArray(double[] a, double[] c)	Verifies that the input arrays are valid.

Constructors in org.hipparchus.analysis.polynomials that throw NullArgumentException

Constructor	Description
FieldPolynomialFunction(T[] c)	Construct a polynomial with the given coefficients.
FieldPolynomialSplineFunction(T[] knots, FieldPolynomialFunction<T>[] polynomials)	Construct a polynomial spline function with the given segment delimiters and interpolating polynomials.
PolynomialFunction(double[] c)	Construct a polynomial with the given coefficients.
PolynomialFunctionNewtonForm(double[] a, double[] c)	Construct a Newton polynomial with the given a[] and c[].
PolynomialSplineFunction(double[] knots, PolynomialFunction[] polynomials)	Construct a polynomial spline function with the given segment delimiters and interpolating polynomials.

Uses of NullArgumentException in org.hipparchus.analysis.solvers

Methods in org.hipparchus.analysis.solvers that throw NullArgumentException

Modifier and Type	Method	Description
static <T extends CalculusFieldElement<T>> T[]	UnivariateSolverUtils.bracket(CalculusFieldUnivariateFunction<T> function, T initial, T lowerBound, T upperBound)	This method simply calls bracket(function, initial, lowerBound, upperBound, q, r, maximumIterations) with q and r set to 1.0 and maximumIterations set to Integer.MAX_VALUE.
static <T extends CalculusFieldElement<T>> T[]	UnivariateSolverUtils.bracket(CalculusFieldUnivariateFunction<T> function, T initial, T lowerBound, T upperBound, int maximumIterations)	This method simply calls bracket(function, initial, lowerBound, upperBound, q, r, maximumIterations) with q and r set to 1.0.
static double[]	UnivariateSolverUtils.bracket(UnivariateFunction function, double initial, double lowerBound, double upperBound)	This method simply calls bracket(function, initial, lowerBound, upperBound, q, r, maximumIterations) with q and r set to 1.0 and maximumIterations set to Integer.MAX_VALUE.
static double[]	UnivariateSolverUtils.bracket(UnivariateFunction function, double initial, double lowerBound, double upperBound, int maximumIterations)	This method simply calls bracket(function, initial, lowerBound, upperBound, q, r, maximumIterations) with q and r set to 1.0.
static boolean	UnivariateSolverUtils.isBracketing(UnivariateFunction function, double lower, double upper)	Check whether the interval bounds bracket a root.
protected void	BaseAbstractUnivariateSolver.setup(int maxEval, F f, double min, double max, double startValue)	Prepare for computation.
T	FieldBracketingNthOrderBrentSolver.solve(int maxEval, CalculusFieldUnivariateFunction<T> f, T min, T max, AllowedSolution allowedSolution)	Solve for a zero in the given interval.
T	FieldBracketingNthOrderBrentSolver.solve(int maxEval, CalculusFieldUnivariateFunction<T> f, T min, T max, T startValue, AllowedSolution allowedSolution)	Solve for a zero in the given interval, start at startValue.
static double	UnivariateSolverUtils.solve(UnivariateFunction function, double x0, double x1)	Convenience method to find a zero of a univariate real function.
static double	UnivariateSolverUtils.solve(UnivariateFunction function, double x0, double x1, double absoluteAccuracy)	Convenience method to find a zero of a univariate real function.
Complex[]	LaguerreSolver.solveAllComplex(double[] coefficients, double initial)	Find all complex roots for the polynomial with the given coefficients, starting from the given initial value.
Complex	LaguerreSolver.solveComplex(double[] coefficients, double initial)	Find a complex root for the polynomial with the given coefficients, starting from the given initial value.
protected void	BaseAbstractUnivariateSolver.verifyBracketing(double lower, double upper)	Check that the endpoints specify an interval and the function takes opposite signs at the endpoints.
static void	UnivariateSolverUtils.verifyBracketing(UnivariateFunction function, double lower, double upper)	Check that the endpoints specify an interval and the end points bracket a root.

Uses of NullArgumentException in org.hipparchus.clustering

Methods in org.hipparchus.clustering that throw NullArgumentException

Modifier and Type	Method	Description
List<Cluster<T>>	DBSCANClusterer.cluster(Collection<T> points)	Performs DBSCAN cluster analysis.

Uses of NullArgumentException in org.hipparchus.complex

Methods in org.hipparchus.complex that throw NullArgumentException

Modifier and Type	Method	Description
Complex	Complex.add(Complex addend)	Returns a Complex whose value is (this + addend).
FieldComplex<T>	FieldComplex.add(FieldComplex<T> addend)	Returns a Complex whose value is (this + addend).
Complex	Complex.divide(Complex divisor)	Returns a Complex whose value is (this / divisor).
FieldComplex<T>	FieldComplex.divide(FieldComplex<T> divisor)	Returns a Complex whose value is (this / divisor).
static ComplexFormat	ComplexFormat.getComplexFormat(String imaginaryCharacter, Locale locale)	Returns the default complex format for the given locale.
Complex	Complex.multiply(Complex factor)	Returns a Complex whose value is this * factor.
FieldComplex<T>	FieldComplex.multiply(FieldComplex<T> factor)	Returns a Complex whose value is this * factor.
Complex	Complex.pow(Complex x)	Returns of value of this complex number raised to the power of x.
FieldComplex<T>	FieldComplex.pow(FieldComplex<T> x)	Returns of value of this complex number raised to the power of x.
Complex	Complex.subtract(Complex subtrahend)	Returns a Complex whose value is (this - subtrahend).
FieldComplex<T>	FieldComplex.subtract(FieldComplex<T> subtrahend)	Returns a Complex whose value is (this - subtrahend).

Constructors in org.hipparchus.complex that throw NullArgumentException

Constructor	Description
ComplexFormat(String imaginaryCharacter)	Create an instance with a custom imaginary character, and the default number format for both real and imaginary parts.
ComplexFormat(String imaginaryCharacter, NumberFormat format)	Create an instance with a custom imaginary character, and a custom number format for both real and imaginary parts.
ComplexFormat(String imaginaryCharacter, NumberFormat realFormat, NumberFormat imaginaryFormat)	Create an instance with a custom imaginary character, a custom number format for the real part, and a custom number format for the imaginary part.
ComplexFormat(NumberFormat format)	Create an instance with a custom number format for both real and imaginary parts.
ComplexFormat(NumberFormat realFormat, NumberFormat imaginaryFormat)	Create an instance with a custom number format for the real part and a custom number format for the imaginary part.

Uses of NullArgumentException in org.hipparchus.fraction

Methods in org.hipparchus.fraction that throw NullArgumentException

Modifier and Type	Method	Description
BigFraction	BigFraction.add(BigInteger bg)	Adds the value of this fraction to the passed BigInteger, returning the result in reduced form.

Uses of NullArgumentException in org.hipparchus.linear

Methods in org.hipparchus.linear that throw NullArgumentException

Modifier and Type	Method	Description
FieldVector<T>	SparseFieldVector.append(T d)	Construct a vector by appending a T to this vector.
protected static void	IterativeLinearSolver.checkParameters(RealLinearOperator a, RealVector b, RealVector x0)	Performs all dimension checks on the parameters of solve and solveInPlace, and throws an exception if one of the checks fails.
protected static void	PreconditionedIterativeLinearSolver.checkParameters(RealLinearOperator a, RealLinearOperator m, RealVector b, RealVector x0)	Performs all dimension checks on the parameters of solve and solveInPlace, and throws an exception if one of the checks fails.
protected void	AbstractFieldMatrix.checkSubMatrixIndex(int[] selectedRows, int[] selectedColumns)	Check if submatrix ranges indices are valid.
static void	MatrixUtils.checkSubMatrixIndex(AnyMatrix m, int[] selectedRows, int[] selectedColumns)	Check if submatrix ranges indices are valid.
void	AbstractFieldMatrix.copySubMatrix(int[] selectedRows, int[] selectedColumns, T[][] destination)	Copy a submatrix.
void	AbstractRealMatrix.copySubMatrix(int[] selectedRows, int[] selectedColumns, double[][] destination)	Copy a submatrix.
void	FieldMatrix.copySubMatrix(int[] selectedRows, int[] selectedColumns, T[][] destination)	Copy a submatrix.
void	RealMatrix.copySubMatrix(int[] selectedRows, int[] selectedColumns, double[][] destination)	Copy a submatrix.
static <T extends FieldElement<T>> FieldMatrix<T>	MatrixUtils.createColumnFieldMatrix(T[] columnData)	Creates a column FieldMatrix using the data from the input array.
static RealMatrix	MatrixUtils.createColumnRealMatrix(double[] columnData)	Creates a column RealMatrix using the data from the input array.
static <T extends FieldElement<T>> FieldMatrix<T>	MatrixUtils.createFieldMatrix(T[][] data)	Returns a FieldMatrix whose entries are the the values in the the input array.
static <T extends FieldElement<T>> FieldVector<T>	MatrixUtils.createFieldVector(T[] data)	Creates a FieldVector using the data from the input array.
static RealMatrix	MatrixUtils.createRealMatrix(double[][] data)	Returns a RealMatrix whose entries are the the values in the the input array.
static RealVector	MatrixUtils.createRealVector(double[] data)	Creates a RealVector using the data from the input array.
static <T extends FieldElement<T>> FieldMatrix<T>	MatrixUtils.createRowFieldMatrix(T[] rowData)	Create a row FieldMatrix using the data from the input array.
static RealMatrix	MatrixUtils.createRowRealMatrix(double[] rowData)	Create a row RealMatrix using the data from the input array.
protected static <T extends FieldElement<T>> Field<T>	AbstractFieldMatrix.extractField(T[][] d)	Get the elements type from an array.
FieldMatrix<T>	AbstractFieldMatrix.getSubMatrix(int[] selectedRows, int[] selectedColumns)	Get a submatrix.
RealMatrix	AbstractRealMatrix.getSubMatrix(int[] selectedRows, int[] selectedColumns)	Gets a submatrix.
FieldMatrix<T>	FieldMatrix.getSubMatrix(int[] selectedRows, int[] selectedColumns)	Get a submatrix.
RealMatrix	RealMatrix.getSubMatrix(int[] selectedRows, int[] selectedColumns)	Gets a submatrix.
static RealMatrix	MatrixUtils.inverse(RealMatrix matrix)	Computes the inverse of the given matrix.
static RealMatrix	MatrixUtils.inverse(RealMatrix matrix, double threshold)	Computes the inverse of the given matrix.
FieldVector<T>	ArrayFieldVector.mapAdd(T d)	Map an addition operation to each entry.
FieldVector<T>	FieldVector.mapAdd(T d)	Map an addition operation to each entry.
FieldVector<T>	SparseFieldVector.mapAdd(T d)	Map an addition operation to each entry.
FieldVector<T>	ArrayFieldVector.mapAddToSelf(T d)	Map an addition operation to each entry.
FieldVector<T>	FieldVector.mapAddToSelf(T d)	Map an addition operation to each entry.
FieldVector<T>	SparseFieldVector.mapAddToSelf(T d)	Map an addition operation to each entry.
FieldVector<T>	ArrayFieldVector.mapDivide(T d)	Map a division operation to each entry.
FieldVector<T>	FieldVector.mapDivide(T d)	Map a division operation to each entry.
FieldVector<T>	SparseFieldVector.mapDivide(T d)	Map a division operation to each entry.
FieldVector<T>	ArrayFieldVector.mapDivideToSelf(T d)	Map a division operation to each entry.
FieldVector<T>	FieldVector.mapDivideToSelf(T d)	Map a division operation to each entry.
FieldVector<T>	SparseFieldVector.mapDivideToSelf(T d)	Map a division operation to each entry.
FieldVector<T>	ArrayFieldVector.mapMultiply(T d)	Map a multiplication operation to each entry.
FieldVector<T>	FieldVector.mapMultiply(T d)	Map a multiplication operation to each entry.
FieldVector<T>	SparseFieldVector.mapMultiply(T d)	Map a multiplication operation to each entry.
FieldVector<T>	ArrayFieldVector.mapMultiplyToSelf(T d)	Map a multiplication operation to each entry.
FieldVector<T>	FieldVector.mapMultiplyToSelf(T d)	Map a multiplication operation to each entry.
FieldVector<T>	SparseFieldVector.mapMultiplyToSelf(T d)	Map a multiplication operation to each entry.
FieldVector<T>	ArrayFieldVector.mapSubtract(T d)	Map a subtraction operation to each entry.
FieldVector<T>	FieldVector.mapSubtract(T d)	Map a subtraction operation to each entry.
FieldVector<T>	SparseFieldVector.mapSubtract(T d)	Map a subtraction operation to each entry.
FieldVector<T>	ArrayFieldVector.mapSubtractToSelf(T d)	Map a subtraction operation to each entry.
FieldVector<T>	FieldVector.mapSubtractToSelf(T d)	Map a subtraction operation to each entry.
FieldVector<T>	SparseFieldVector.mapSubtractToSelf(T d)	Map a subtraction operation to each entry.
void	SparseFieldVector.setEntry(int index, T value)	Set a single element.
void	AbstractFieldMatrix.setSubMatrix(T[][] subMatrix, int row, int column)	Replace the submatrix starting at (row, column) using data in the input subMatrix array.
void	AbstractRealMatrix.setSubMatrix(double[][] subMatrix, int row, int column)	Replace the submatrix starting at row, column using data in the input subMatrix array.
void	Array2DRowFieldMatrix.setSubMatrix(T[][] subMatrix, int row, int column)	Replace the submatrix starting at (row, column) using data in the input subMatrix array.
void	Array2DRowRealMatrix.setSubMatrix(double[][] subMatrix, int row, int column)	Replace the submatrix starting at row, column using data in the input subMatrix array.
void	BlockFieldMatrix.setSubMatrix(T[][] subMatrix, int row, int column)	Replace the submatrix starting at (row, column) using data in the input subMatrix array.
void	BlockRealMatrix.setSubMatrix(double[][] subMatrix, int row, int column)	Replace the submatrix starting at row, column using data in the input subMatrix array.
void	FieldMatrix.setSubMatrix(T[][] subMatrix, int row, int column)	Replace the submatrix starting at (row, column) using data in the input subMatrix array.
void	RealMatrix.setSubMatrix(double[][] subMatrix, int row, int column)	Replace the submatrix starting at row, column using data in the input subMatrix array.
RealVector	IterativeLinearSolver.solve(RealLinearOperator a, RealVector b)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	IterativeLinearSolver.solve(RealLinearOperator a, RealVector b, RealVector x0)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	PreconditionedIterativeLinearSolver.solve(RealLinearOperator a, RealLinearOperator m, RealVector b)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	PreconditionedIterativeLinearSolver.solve(RealLinearOperator a, RealLinearOperator m, RealVector b, RealVector x0)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	PreconditionedIterativeLinearSolver.solve(RealLinearOperator a, RealVector b)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	PreconditionedIterativeLinearSolver.solve(RealLinearOperator a, RealVector b, RealVector x0)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	SymmLQ.solve(RealLinearOperator a, RealLinearOperator m, RealVector b)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	SymmLQ.solve(RealLinearOperator a, RealLinearOperator m, RealVector b, boolean goodb, double shift)	Returns an estimate of the solution to the linear system (A - shift · I) · x = b.
RealVector	SymmLQ.solve(RealLinearOperator a, RealLinearOperator m, RealVector b, RealVector x)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	SymmLQ.solve(RealLinearOperator a, RealVector b)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	SymmLQ.solve(RealLinearOperator a, RealVector b, boolean goodb, double shift)	Returns the solution to the system (A - shift · I) · x = b.
RealVector	SymmLQ.solve(RealLinearOperator a, RealVector b, RealVector x)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	ConjugateGradient.solveInPlace(RealLinearOperator a, RealLinearOperator m, RealVector b, RealVector x0)	Returns an estimate of the solution to the linear system A · x = b.
abstract RealVector	IterativeLinearSolver.solveInPlace(RealLinearOperator a, RealVector b, RealVector x0)	Returns an estimate of the solution to the linear system A · x = b.
abstract RealVector	PreconditionedIterativeLinearSolver.solveInPlace(RealLinearOperator a, RealLinearOperator m, RealVector b, RealVector x0)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	PreconditionedIterativeLinearSolver.solveInPlace(RealLinearOperator a, RealVector b, RealVector x0)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	SymmLQ.solveInPlace(RealLinearOperator a, RealLinearOperator m, RealVector b, RealVector x)	Returns an estimate of the solution to the linear system A · x = b.
RealVector	SymmLQ.solveInPlace(RealLinearOperator a, RealLinearOperator m, RealVector b, RealVector x, boolean goodb, double shift)	Returns an estimate of the solution to the linear system (A - shift · I) · x = b.
RealVector	SymmLQ.solveInPlace(RealLinearOperator a, RealVector b, RealVector x)	Returns an estimate of the solution to the linear system A · x = b.

Constructors in org.hipparchus.linear that throw NullArgumentException

Constructor	Description
Array2DRowFieldMatrix(Field<T> field, T[][] d)	Create a new FieldMatrix<T> using the input array as the underlying data array.
Array2DRowFieldMatrix(Field<T> field, T[][] d, boolean copyArray)	Create a new FieldMatrix<T> using the input array as the underlying data array.
Array2DRowFieldMatrix(T[][] d)	Create a new FieldMatrix<T> using the input array as the underlying data array.
Array2DRowFieldMatrix(T[][] d, boolean copyArray)	Create a new FieldMatrix<T> using the input array as the underlying data array.
Array2DRowRealMatrix(double[][] d)	Create a new RealMatrix using the input array as the underlying data array.
Array2DRowRealMatrix(double[][] d, boolean copyArray)	Create a new RealMatrix using the input array as the underlying data array.
ArrayFieldVector(Field<T> field, T[] d)	Construct a vector from an array, copying the input array.
ArrayFieldVector(Field<T> field, T[] d, boolean copyArray)	Create a new ArrayFieldVector using the input array as the underlying data array.
ArrayFieldVector(Field<T> field, T[] d, int pos, int size)	Construct a vector from part of a array.
ArrayFieldVector(Field<T> field, T[] v1, T[] v2)	Construct a vector by appending one vector to another vector.
ArrayFieldVector(ArrayFieldVector<T> v)	Construct a vector from another vector, using a deep copy.
ArrayFieldVector(ArrayFieldVector<T> v, boolean deep)	Construct a vector from another vector.
ArrayFieldVector(FieldVector<T> v)	Construct a vector from another vector, using a deep copy.
ArrayFieldVector(FieldVector<T> v1, FieldVector<T> v2)	Construct a vector by appending one vector to another vector.
ArrayFieldVector(FieldVector<T> v1, T[] v2)	Construct a vector by appending one vector to another vector.
ArrayFieldVector(T[] d)	Construct a vector from an array, copying the input array.
ArrayFieldVector(T[] d, boolean copyArray)	Create a new ArrayFieldVector using the input array as the underlying data array.
ArrayFieldVector(T[] d, int pos, int size)	Construct a vector from part of a array.
ArrayFieldVector(T[] v1, FieldVector<T> v2)	Construct a vector by appending one vector to another vector.
ArrayFieldVector(T[] v1, T[] v2)	Construct a vector by appending one vector to another vector.
ArrayRealVector(double[] d, boolean copyArray)	Create a new ArrayRealVector using the input array as the underlying data array.
ArrayRealVector(double[] d, int pos, int size)	Construct a vector from part of a array.
ArrayRealVector(Double[] d, int pos, int size)	Construct a vector from part of an array.
ArrayRealVector(ArrayRealVector v)	Construct a vector from another vector, using a deep copy.
ArrayRealVector(RealVector v)	Construct a vector from another vector, using a deep copy.
ConjugateGradient(IterationManager manager, double delta, boolean check)	Creates a new instance of this class, with default stopping criterion and custom iteration manager.
DiagonalMatrix(double[] d, boolean copyArray)	Creates a matrix using the input array as the underlying data.
IterativeLinearSolver(IterationManager manager)	Creates a new instance of this class, with custom iteration manager.
PreconditionedIterativeLinearSolver(IterationManager manager)	Creates a new instance of this class, with custom iteration manager.
SparseFieldVector(Field<T> field, T[] values)	Create from a Field array.

Uses of NullArgumentException in org.hipparchus.optim.nonlinear.scalar

Constructors in org.hipparchus.optim.nonlinear.scalar that throw NullArgumentException

Constructor	Description
MultiStartMultivariateOptimizer(MultivariateOptimizer optimizer, int starts, RandomVectorGenerator generator)	Create a multi-start optimizer from a single-start optimizer.

Uses of NullArgumentException in org.hipparchus.random

Constructors in org.hipparchus.random that throw NullArgumentException

Constructor	Description
HaltonSequenceGenerator(int dimension, int[] bases, int[] weights)	Construct a new Halton sequence generator with the given base numbers and weights for each dimension.
StableRandomGenerator(RandomGenerator generator, double alpha, double beta)	Create a new generator.

Uses of NullArgumentException in org.hipparchus.stat

Methods in org.hipparchus.stat that throw NullArgumentException

Modifier and Type	Method	Description
void	Frequency.merge(Collection<? extends Frequency<? extends T>> others)	Merge a Collection of Frequency objects into this instance.
void	Frequency.merge(Frequency<? extends T> other)	Merge another Frequency object's counts into this instance.

Uses of NullArgumentException in org.hipparchus.stat.descriptive

Methods in org.hipparchus.stat.descriptive that throw NullArgumentException

Modifier and Type	Method	Description
void	AggregatableStatistic.aggregate(T other)	Aggregates the provided instance into this instance.

Uses of NullArgumentException in org.hipparchus.stat.descriptive.moment

Constructors in org.hipparchus.stat.descriptive.moment that throw NullArgumentException

Constructor	Description
GeometricMean(GeometricMean original)	Copy constructor, creates a new GeometricMean identical to the original.
Kurtosis(Kurtosis original)	Copy constructor, creates a new Kurtosis identical to the original.
Mean(Mean original)	Copy constructor, creates a new Mean identical to the original.
SecondMoment(SecondMoment original)	Copy constructor, creates a new SecondMoment identical to the original.
SemiVariance(SemiVariance original)	Copy constructor, creates a new SemiVariance identical to the original.
Skewness(Skewness original)	Copy constructor, creates a new Skewness identical to the original.
StandardDeviation(StandardDeviation original)	Copy constructor, creates a new StandardDeviation identical to the original.
Variance(Variance original)	Copy constructor, creates a new Variance identical to the original.

Uses of NullArgumentException in org.hipparchus.stat.descriptive.rank

Methods in org.hipparchus.stat.descriptive.rank that throw NullArgumentException

Modifier and Type	Method	Description
void	RandomPercentile.aggregate(RandomPercentile other)	Aggregates the provided instance into this instance.

Constructors in org.hipparchus.stat.descriptive.rank that throw NullArgumentException

Constructor	Description
Max(Max original)	Copy constructor, creates a new Max identical to the original.
Min(Min original)	Copy constructor, creates a new Min identical to the original.
Percentile(Percentile original)	Copy constructor, creates a new Percentile identical to the original

Uses of NullArgumentException in org.hipparchus.stat.descriptive.summary

Constructors in org.hipparchus.stat.descriptive.summary that throw NullArgumentException

Constructor	Description
Product(Product original)	Copy constructor, creates a new Product identical to the original.
Sum(Sum original)	Copy constructor, creates a new Sum identical to the original.
SumOfLogs(SumOfLogs original)	Copy constructor, creates a new SumOfLogs identical to the original.
SumOfSquares(SumOfSquares original)	Copy constructor, creates a new SumOfSquares identical to the original.

Uses of NullArgumentException in org.hipparchus.stat.fitting

Methods in org.hipparchus.stat.fitting that throw NullArgumentException

Modifier and Type	Method	Description
void	EmpiricalDistribution.load(double[] in)	Computes the empirical distribution from the provided array of numbers.
void	EmpiricalDistribution.load(File file)	Computes the empirical distribution from the input file.
void	EmpiricalDistribution.load(URL url)	Computes the empirical distribution using data read from a URL.

Uses of NullArgumentException in org.hipparchus.stat.inference

Methods in org.hipparchus.stat.inference that throw NullArgumentException

Modifier and Type	Method	Description
double	OneWayAnova.anovaFValue(Collection<double[]> categoryData)	Computes the ANOVA F-value for a collection of double[] arrays.
double	OneWayAnova.anovaPValue(Collection<double[]> categoryData)	Computes the ANOVA P-value for a collection of double[] arrays.
double	OneWayAnova.anovaPValue(Collection<StreamingStatistics> categoryData, boolean allowOneElementData)	Computes the ANOVA P-value for a collection of StreamingStatistics.
boolean	OneWayAnova.anovaTest(Collection<double[]> categoryData, double alpha)	Performs an ANOVA test, evaluating the null hypothesis that there is no difference among the means of the data categories.
double	ChiSquareTest.chiSquare(long[][] counts)	Computes the Chi-Square statistic associated with a chi-square test of independence based on the input counts array, viewed as a two-way table.
static double	InferenceTestUtils.chiSquare(long[][] counts)	Computes the Chi-Square statistic associated with a chi-square test of independence based on the input counts array, viewed as a two-way table.
double	ChiSquareTest.chiSquareTest(long[][] counts)	Returns the observed significance level, or p-value, associated with a chi-square test of independence based on the input counts array, viewed as a two-way table.
boolean	ChiSquareTest.chiSquareTest(long[][] counts, double alpha)	Performs a chi-square test of independence evaluating the null hypothesis that the classifications represented by the counts in the columns of the input 2-way table are independent of the rows, with significance level alpha.
static double	InferenceTestUtils.chiSquareTest(long[][] counts)	Returns the observed significance level, or p-value, associated with a chi-square test of independence based on the input counts array, viewed as a two-way table.
static boolean	InferenceTestUtils.chiSquareTest(long[][] counts, double alpha)	Performs a chi-square test of independence evaluating the null hypothesis that the classifications represented by the counts in the columns of the input 2-way table are independent of the rows, with significance level alpha.
static double	InferenceTestUtils.homoscedasticT(double[] sample1, double[] sample2)	Computes a 2-sample t statistic, under the hypothesis of equal subpopulation variances.
static double	InferenceTestUtils.homoscedasticT(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)	Computes a 2-sample t statistic, comparing the means of the datasets described by two StatisticalSummary instances, under the assumption of equal subpopulation variances.
double	TTest.homoscedasticT(double[] sample1, double[] sample2)	Computes a 2-sample t statistic, under the hypothesis of equal subpopulation variances.
double	TTest.homoscedasticT(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)	Computes a 2-sample t statistic, comparing the means of the datasets described by two StatisticalSummary instances, under the assumption of equal subpopulation variances.
static double	InferenceTestUtils.homoscedasticTTest(double[] sample1, double[] sample2)	Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the input arrays, under the assumption that the two samples are drawn from subpopulations with equal variances.
static boolean	InferenceTestUtils.homoscedasticTTest(double[] sample1, double[] sample2, double alpha)	Performs a two-sided t-test evaluating the null hypothesis that sample1 and sample2 are drawn from populations with the same mean, with significance level alpha, assuming that the subpopulation variances are equal.
static double	InferenceTestUtils.homoscedasticTTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)	Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the datasets described by two StatisticalSummary instances, under the hypothesis of equal subpopulation variances.
double	TTest.homoscedasticTTest(double[] sample1, double[] sample2)	Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the input arrays, under the assumption that the two samples are drawn from subpopulations with equal variances.
boolean	TTest.homoscedasticTTest(double[] sample1, double[] sample2, double alpha)	Performs a two-sided t-test evaluating the null hypothesis that sample1 and sample2 are drawn from populations with the same mean, with significance level alpha, assuming that the subpopulation variances are equal.
double	TTest.homoscedasticTTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)	Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the datasets described by two StatisticalSummary instances, under the hypothesis of equal subpopulation variances.
static double	InferenceTestUtils.kolmogorovSmirnovStatistic(double[] x, double[] y)	Computes the two-sample Kolmogorov-Smirnov test statistic, \(D_{n,m}=\sup_x |F_n(x)-F_m(x)|\) where \(n\) is the length of x, \(m\) is the length of y, \(F_n\) is the empirical distribution that puts mass \(1/n\) at each of the values in x and \(F_m\) is the empirical distribution of the y values.
static double	InferenceTestUtils.kolmogorovSmirnovStatistic(RealDistribution dist, double[] data)	Computes the one-sample Kolmogorov-Smirnov test statistic, \(D_n=\sup_x |F_n(x)-F(x)|\) where \(F\) is the distribution (cdf) function associated with distribution, \(n\) is the length of data and \(F_n\) is the empirical distribution that puts mass \(1/n\) at each of the values in data.
static double	InferenceTestUtils.kolmogorovSmirnovTest(double[] x, double[] y)	Computes the p-value, or observed significance level, of a two-sample Kolmogorov-Smirnov test evaluating the null hypothesis that x and y are samples drawn from the same probability distribution.
static double	InferenceTestUtils.kolmogorovSmirnovTest(double[] x, double[] y, boolean strict)	Computes the p-value, or observed significance level, of a two-sample Kolmogorov-Smirnov test evaluating the null hypothesis that x and y are samples drawn from the same probability distribution.
static double	InferenceTestUtils.kolmogorovSmirnovTest(RealDistribution dist, double[] data)	Computes the p-value, or observed significance level, of a one-sample Kolmogorov-Smirnov test evaluating the null hypothesis that data conforms to distribution.
static double	InferenceTestUtils.kolmogorovSmirnovTest(RealDistribution dist, double[] data, boolean strict)	Computes the p-value, or observed significance level, of a one-sample Kolmogorov-Smirnov test evaluating the null hypothesis that data conforms to distribution.
static boolean	InferenceTestUtils.kolmogorovSmirnovTest(RealDistribution dist, double[] data, double alpha)	Performs a Kolmogorov-Smirnov test evaluating the null hypothesis that data conforms to distribution.
double	MannWhitneyUTest.mannWhitneyU(double[] x, double[] y)	Computes the Mann-Whitney U statistic comparing means for two independent samples possibly of different lengths.
double	MannWhitneyUTest.mannWhitneyUTest(double[] x, double[] y)	Returns the asymptotic observed significance level, or p-value, associated with a Mann-Whitney U Test comparing means for two independent samples.
double	MannWhitneyUTest.mannWhitneyUTest(double[] x, double[] y, boolean exact)	Returns the asymptotic observed significance level, or p-value, associated with a Mann-Whitney U Test comparing means for two independent samples.
static double	InferenceTestUtils.oneWayAnovaFValue(Collection<double[]> categoryData)	Computes the ANOVA F-value for a collection of double[] arrays.
static double	InferenceTestUtils.oneWayAnovaPValue(Collection<double[]> categoryData)	Computes the ANOVA P-value for a collection of double[] arrays.
static boolean	InferenceTestUtils.oneWayAnovaTest(Collection<double[]> categoryData, double alpha)	Performs an ANOVA test, evaluating the null hypothesis that there is no difference among the means of the data categories.
static double	InferenceTestUtils.pairedT(double[] sample1, double[] sample2)	Computes a paired, 2-sample t-statistic based on the data in the input arrays.
double	TTest.pairedT(double[] sample1, double[] sample2)	Computes a paired, 2-sample t-statistic based on the data in the input arrays.
static double	InferenceTestUtils.pairedTTest(double[] sample1, double[] sample2)	Returns the observed significance level, or p-value, associated with a paired, two-sample, two-tailed t-test based on the data in the input arrays.
static boolean	InferenceTestUtils.pairedTTest(double[] sample1, double[] sample2, double alpha)	Performs a paired t-test evaluating the null hypothesis that the mean of the paired differences between sample1 and sample2 is 0 in favor of the two-sided alternative that the mean paired difference is not equal to 0, with significance level alpha.
double	TTest.pairedTTest(double[] sample1, double[] sample2)	Returns the observed significance level, or p-value, associated with a paired, two-sample, two-tailed t-test based on the data in the input arrays.
boolean	TTest.pairedTTest(double[] sample1, double[] sample2, double alpha)	Performs a paired t-test evaluating the null hypothesis that the mean of the paired differences between sample1 and sample2 is 0 in favor of the two-sided alternative that the mean paired difference is not equal to 0, with significance level alpha.
static double	InferenceTestUtils.t(double[] sample1, double[] sample2)	Computes a 2-sample t statistic, without the hypothesis of equal subpopulation variances.
static double	InferenceTestUtils.t(double mu, double[] observed)	Computes a t statistic given observed values and a comparison constant.
static double	InferenceTestUtils.t(double mu, StatisticalSummary sampleStats)	Computes a t statistic to use in comparing the mean of the dataset described by sampleStats to mu.
static double	InferenceTestUtils.t(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)	Computes a 2-sample t statistic, comparing the means of the datasets described by two StatisticalSummary instances, without the assumption of equal subpopulation variances.
double	TTest.t(double[] sample1, double[] sample2)	Computes a 2-sample t statistic, without the hypothesis of equal subpopulation variances.
double	TTest.t(double mu, double[] observed)	Computes a t statistic given observed values and a comparison constant.
double	TTest.t(double mu, StatisticalSummary sampleStats)	Computes a t statistic to use in comparing the mean of the dataset described by sampleStats to mu.
double	TTest.t(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)	Computes a 2-sample t statistic, comparing the means of the datasets described by two StatisticalSummary instances, without the assumption of equal subpopulation variances.
static double	InferenceTestUtils.tTest(double[] sample1, double[] sample2)	Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the input arrays.
static boolean	InferenceTestUtils.tTest(double[] sample1, double[] sample2, double alpha)	Performs a two-sided t-test evaluating the null hypothesis that sample1 and sample2 are drawn from populations with the same mean, with significance level alpha.
static double	InferenceTestUtils.tTest(double mu, double[] sample)	Returns the observed significance level, or p-value, associated with a one-sample, two-tailed t-test comparing the mean of the input array with the constant mu.
static boolean	InferenceTestUtils.tTest(double mu, double[] sample, double alpha)	Performs a two-sided t-test evaluating the null hypothesis that the mean of the population from which sample is drawn equals mu.
static double	InferenceTestUtils.tTest(double mu, StatisticalSummary sampleStats)	Returns the observed significance level, or p-value, associated with a one-sample, two-tailed t-test comparing the mean of the dataset described by sampleStats with the constant mu.
static boolean	InferenceTestUtils.tTest(double mu, StatisticalSummary sampleStats, double alpha)	Performs a two-sided t-test evaluating the null hypothesis that the mean of the population from which the dataset described by stats is drawn equals mu.
static double	InferenceTestUtils.tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)	Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the datasets described by two StatisticalSummary instances.
static boolean	InferenceTestUtils.tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2, double alpha)	Performs a two-sided t-test evaluating the null hypothesis that sampleStats1 and sampleStats2 describe datasets drawn from populations with the same mean, with significance level alpha.
double	TTest.tTest(double[] sample1, double[] sample2)	Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the input arrays.
boolean	TTest.tTest(double[] sample1, double[] sample2, double alpha)	Performs a two-sided t-test evaluating the null hypothesis that sample1 and sample2 are drawn from populations with the same mean, with significance level alpha.
double	TTest.tTest(double mu, double[] sample)	Returns the observed significance level, or p-value, associated with a one-sample, two-tailed t-test comparing the mean of the input array with the constant mu.
boolean	TTest.tTest(double mu, double[] sample, double alpha)	Performs a two-sided t-test evaluating the null hypothesis that the mean of the population from which sample is drawn equals mu.
double	TTest.tTest(double mu, StatisticalSummary sampleStats)	Returns the observed significance level, or p-value, associated with a one-sample, two-tailed t-test comparing the mean of the dataset described by sampleStats with the constant mu.
boolean	TTest.tTest(double mu, StatisticalSummary sampleStats, double alpha)	Performs a two-sided t-test evaluating the null hypothesis that the mean of the population from which the dataset described by stats is drawn equals mu.
double	TTest.tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)	Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the datasets described by two StatisticalSummary instances.
boolean	TTest.tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2, double alpha)	Performs a two-sided t-test evaluating the null hypothesis that sampleStats1 and sampleStats2 describe datasets drawn from populations with the same mean, with significance level alpha.
double	WilcoxonSignedRankTest.wilcoxonSignedRank(double[] x, double[] y)	Computes the Wilcoxon signed ranked statistic comparing means for two related samples or repeated measurements on a single sample.
double	WilcoxonSignedRankTest.wilcoxonSignedRankTest(double[] x, double[] y, boolean exactPValue)	Returns the observed significance level, or p-value, associated with a Wilcoxon signed ranked statistic comparing mean for two related samples or repeated measurements on a single sample.

Uses of NullArgumentException in org.hipparchus.util

Methods in org.hipparchus.util that throw NullArgumentException

Modifier and Type	Method	Description
static void	MathUtils.checkNotNull(Object o)	Checks that an object is not null.
static void	MathUtils.checkNotNull(Object o, Localizable pattern, Object... args)	Checks that an object is not null.
static void	MathArrays.checkRectangular(long[][] in)	Throws MathIllegalArgumentException if the input array is not rectangular.
static double[]	MathArrays.convolve(double[] x, double[] h)	Calculates the convolution between two sequences.
static void	MathArrays.sortInPlace(double[] x, double[]... yList)	Sort an array in ascending order in place and perform the same reordering of entries on other arrays.
static void	MathArrays.sortInPlace(double[] x, MathArrays.OrderDirection dir, double[]... yList)	Sort an array in place and perform the same reordering of entries on other arrays.

Constructors in org.hipparchus.util that throw NullArgumentException

Constructor	Description
Incrementor(int max, Incrementor.MaxCountExceededCallback cb)	Creates an Incrementor.
KthSelector(PivotingStrategy pivotingStrategy)	Constructor with specified pivoting strategy
ResizableDoubleArray(ResizableDoubleArray original)	Copy constructor.