# Uses of Classorg.hipparchus.exception.NullArgumentException

Packages that use NullArgumentException
Package
Description
Common classes used throughout the Hipparchus library.
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.
Numerical integration (quadrature) algorithms for univariate real functions.
Univariate real functions interpolation algorithms.
Univariate real polynomials implementations, seen as differentiable univariate real functions.
Root finding algorithms, for univariate real functions.
Clustering algorithms.
Complex number type and implementations of complex transcendental functions.
Fraction number type and fraction number formatting.
Linear algebra support.
Algorithms for optimizing a scalar function.
Random number and random data generators.
Data storage, manipulation and summary routines.
Generic univariate and multivariate summary statistic objects.
Summary statistics based on moments.
Summary statistics based on ranks.
Other summary statistics.
Statistical methods for fitting distributions.
Classes providing hypothesis testing.
Convenience routines and common data structures used throughout the Hipparchus library.
• ## Uses of NullArgumentException in org.hipparchus

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

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.
Modifier
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

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

Modifier and Type
Method
Description
final void
FieldHermiteInterpolator.addSamplePoint(T x, T[]... value)
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.
Modifier
Constructor
Description

PiecewiseBicubicSplineInterpolatingFunction(double[] x, double[] y, double[][] f)
Simple constructor.
• ## Uses of NullArgumentException in org.hipparchus.analysis.polynomials

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.
Modifier
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

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.solveAllComplex(double[] coefficients, int maxEval, 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

Modifier and Type
Method
Description
List<Cluster<T>>
DBSCANClusterer.cluster(Collection<T> points)
Performs DBSCAN cluster analysis.
• ## Uses of NullArgumentException in org.hipparchus.complex

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).
Modifier
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

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

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
Modifier
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

Modifier
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
Modifier
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

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

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

Modifier
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

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

Modifier
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

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

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

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
Modifier
Constructor
Description

Incrementor(int max, Incrementor.MaxCountExceededCallback cb)
Creates an Incrementor.

KthSelector(PivotingStrategy pivotingStrategy)
Constructor with specified pivoting strategy

ResizableDoubleArray(ResizableDoubleArray original)
Copy constructor.