Uses of Class
org.hipparchus.exception.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
-
Uses of NullArgumentException in org.hipparchus.analysis.function
Modifier and TypeMethodDescriptiondouble[]
Gaussian.Parametric.gradient
(double x, double... param) Computes the value of the gradient atx
.double[]
HarmonicOscillator.Parametric.gradient
(double x, double... param) Computes the value of the gradient atx
.double[]
Logistic.Parametric.gradient
(double x, double... param) Computes the value of the gradient atx
.double[]
Logit.Parametric.gradient
(double x, double... param) Computes the value of the gradient atx
.double[]
Sigmoid.Parametric.gradient
(double x, double... param) Computes the value of the gradient atx
.double
Gaussian.Parametric.value
(double x, double... param) Computes the value of the Gaussian atx
.double
HarmonicOscillator.Parametric.value
(double x, double... param) Computes the value of the harmonic oscillator atx
.double
Logistic.Parametric.value
(double x, double... param) Computes the value of the sigmoid atx
.double
Logit.Parametric.value
(double x, double... param) Computes the value of the logit atx
.double
Sigmoid.Parametric.value
(double x, double... param) Computes the value of the sigmoid atx
.ModifierConstructorDescriptionStepFunction
(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 TypeMethodDescriptionBaseAbstractFieldUnivariateIntegrator.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.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 TypeMethodDescriptionfinal 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.MicrosphereProjectionInterpolator.interpolate
(double[][] xval, double[] yval) Computes an interpolating function for the data set.MultivariateInterpolator.interpolate
(double[][] xval, double[] yval) Computes an interpolating function for the data set.PiecewiseBicubicSplineInterpolator.interpolate
(double[] xval, double[] yval, double[][] fval) Compute an interpolating function for the dataset.T[]
Interpolate value at a specified abscissa.ModifierConstructorDescriptionPiecewiseBicubicSplineInterpolatingFunction
(double[] x, double[] y, double[][] f) Simple constructor. -
Uses of NullArgumentException in org.hipparchus.analysis.polynomials
Modifier and TypeMethodDescriptionprotected 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>>
TFieldPolynomialFunction.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>>
TPolynomialFunction.value
(T t) Compute the value for the function.<T extends CalculusFieldElement<T>>
TPolynomialFunction.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.ModifierConstructorDescriptionFieldPolynomialFunction
(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 TypeMethodDescriptionstatic <T extends CalculusFieldElement<T>>
T[]UnivariateSolverUtils.bracket
(CalculusFieldUnivariateFunction<T> function, T initial, T lowerBound, T upperBound) This method simply callsbracket(function, initial, lowerBound, upperBound, q, r, maximumIterations)
withq
andr
set to 1.0 andmaximumIterations
set toInteger.MAX_VALUE
.static <T extends CalculusFieldElement<T>>
T[]UnivariateSolverUtils.bracket
(CalculusFieldUnivariateFunction<T> function, T initial, T lowerBound, T upperBound, int maximumIterations) This method simply callsbracket(function, initial, lowerBound, upperBound, q, r, maximumIterations)
withq
andr
set to 1.0.static double[]
UnivariateSolverUtils.bracket
(UnivariateFunction function, double initial, double lowerBound, double upperBound) This method simply callsbracket(function, initial, lowerBound, upperBound, q, r, maximumIterations)
withq
andr
set to 1.0 andmaximumIterations
set toInteger.MAX_VALUE
.static double[]
UnivariateSolverUtils.bracket
(UnivariateFunction function, double initial, double lowerBound, double upperBound, int maximumIterations) This method simply callsbracket(function, initial, lowerBound, upperBound, q, r, maximumIterations)
withq
andr
set to 1.0.static boolean
UnivariateSolverUtils.isBracketing
(UnivariateFunction function, double lower, double upper) Check whether the interval bounds bracket a root.protected void
Prepare for computation.FieldBracketingNthOrderBrentSolver.solve
(int maxEval, CalculusFieldUnivariateFunction<T> f, T min, T max, AllowedSolution allowedSolution) Solve for a zero in the given interval.FieldBracketingNthOrderBrentSolver.solve
(int maxEval, CalculusFieldUnivariateFunction<T> f, T min, T max, T startValue, AllowedSolution allowedSolution) Solve for a zero in the given interval, start atstartValue
.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.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
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Uses of NullArgumentException in org.hipparchus.complex
Modifier and TypeMethodDescriptionReturns aComplex
whose value is(this + addend)
.FieldComplex.add
(FieldComplex<T> addend) Returns aComplex
whose value is(this + addend)
.Returns aComplex
whose value is(this / divisor)
.FieldComplex.divide
(FieldComplex<T> divisor) Returns aComplex
whose value is(this / divisor)
.static ComplexFormat
ComplexFormat.getComplexFormat
(String imaginaryCharacter, Locale locale) Returns the default complex format for the given locale.Returns aComplex
whose value isthis * factor
.FieldComplex.multiply
(FieldComplex<T> factor) Returns aComplex
whose value isthis * factor
.Returns of value of this complex number raised to the power ofx
.FieldComplex.pow
(FieldComplex<T> x) Returns of value of this complex number raised to the power ofx
.Returns aComplex
whose value is(this - subtrahend)
.FieldComplex.subtract
(FieldComplex<T> subtrahend) Returns aComplex
whose value is(this - subtrahend)
.ModifierConstructorDescriptionComplexFormat
(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 TypeMethodDescriptionBigFraction.add
(BigInteger bg) Adds the value of this fraction to the passedBigInteger
, returning the result in reduced form. -
Uses of NullArgumentException in org.hipparchus.linear
Modifier and TypeMethodDescriptionConstruct 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 ofsolve
andsolveInPlace
, 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 ofsolve
andsolveInPlace
, 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 columnFieldMatrix
using the data from the input array.static RealMatrix
MatrixUtils.createColumnRealMatrix
(double[] columnData) Creates a columnRealMatrix
using the data from the input array.static <T extends FieldElement<T>>
FieldMatrix<T>MatrixUtils.createFieldMatrix
(T[][] data) Returns aFieldMatrix
whose entries are the the values in the the input array.static <T extends FieldElement<T>>
FieldVector<T>MatrixUtils.createFieldVector
(T[] data) Creates aFieldVector
using the data from the input array.static RealMatrix
MatrixUtils.createRealMatrix
(double[][] data) Returns aRealMatrix
whose entries are the the values in the the input array.static RealVector
MatrixUtils.createRealVector
(double[] data) Creates aRealVector
using the data from the input array.static <T extends FieldElement<T>>
FieldMatrix<T>MatrixUtils.createRowFieldMatrix
(T[] rowData) Create a rowFieldMatrix
using the data from the input array.static RealMatrix
MatrixUtils.createRowRealMatrix
(double[] rowData) Create a rowRealMatrix
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.AbstractFieldMatrix.getSubMatrix
(int[] selectedRows, int[] selectedColumns) Get a submatrix.AbstractRealMatrix.getSubMatrix
(int[] selectedRows, int[] selectedColumns) Gets a submatrix.FieldMatrix.getSubMatrix
(int[] selectedRows, int[] selectedColumns) Get a submatrix.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.Map an addition operation to each entry.Map an addition operation to each entry.Map an addition operation to each entry.ArrayFieldVector.mapAddToSelf
(T d) Map an addition operation to each entry.FieldVector.mapAddToSelf
(T d) Map an addition operation to each entry.SparseFieldVector.mapAddToSelf
(T d) Map an addition operation to each entry.Map a division operation to each entry.Map a division operation to each entry.Map a division operation to each entry.ArrayFieldVector.mapDivideToSelf
(T d) Map a division operation to each entry.FieldVector.mapDivideToSelf
(T d) Map a division operation to each entry.SparseFieldVector.mapDivideToSelf
(T d) Map a division operation to each entry.ArrayFieldVector.mapMultiply
(T d) Map a multiplication operation to each entry.FieldVector.mapMultiply
(T d) Map a multiplication operation to each entry.SparseFieldVector.mapMultiply
(T d) Map a multiplication operation to each entry.ArrayFieldVector.mapMultiplyToSelf
(T d) Map a multiplication operation to each entry.FieldVector.mapMultiplyToSelf
(T d) Map a multiplication operation to each entry.SparseFieldVector.mapMultiplyToSelf
(T d) Map a multiplication operation to each entry.ArrayFieldVector.mapSubtract
(T d) Map a subtraction operation to each entry.FieldVector.mapSubtract
(T d) Map a subtraction operation to each entry.SparseFieldVector.mapSubtract
(T d) Map a subtraction operation to each entry.ArrayFieldVector.mapSubtractToSelf
(T d) Map a subtraction operation to each entry.FieldVector.mapSubtractToSelf
(T d) Map a subtraction operation to each entry.SparseFieldVector.mapSubtractToSelf
(T d) Map a subtraction operation to each entry.void
Set a single element.void
AbstractFieldMatrix.setSubMatrix
(T[][] subMatrix, int row, int column) Replace the submatrix starting at(row, column)
using data in the inputsubMatrix
array.void
AbstractRealMatrix.setSubMatrix
(double[][] subMatrix, int row, int column) Replace the submatrix starting atrow, column
using data in the inputsubMatrix
array.void
Array2DRowFieldMatrix.setSubMatrix
(T[][] subMatrix, int row, int column) Replace the submatrix starting at(row, column)
using data in the inputsubMatrix
array.void
Array2DRowRealMatrix.setSubMatrix
(double[][] subMatrix, int row, int column) Replace the submatrix starting atrow, column
using data in the inputsubMatrix
array.void
BlockFieldMatrix.setSubMatrix
(T[][] subMatrix, int row, int column) Replace the submatrix starting at(row, column)
using data in the inputsubMatrix
array.void
BlockRealMatrix.setSubMatrix
(double[][] subMatrix, int row, int column) Replace the submatrix starting atrow, column
using data in the inputsubMatrix
array.void
FieldMatrix.setSubMatrix
(T[][] subMatrix, int row, int column) Replace the submatrix starting at(row, column)
using data in the inputsubMatrix
array.void
RealMatrix.setSubMatrix
(double[][] subMatrix, int row, int column) Replace the submatrix starting atrow, column
using data in the inputsubMatrix
array.IterativeLinearSolver.solve
(RealLinearOperator a, RealVector b) Returns an estimate of the solution to the linear system A · x = b.IterativeLinearSolver.solve
(RealLinearOperator a, RealVector b, RealVector x0) Returns an estimate of the solution to the linear system A · x = b.PreconditionedIterativeLinearSolver.solve
(RealLinearOperator a, RealLinearOperator m, RealVector b) Returns an estimate of the solution to the linear system A · x = b.PreconditionedIterativeLinearSolver.solve
(RealLinearOperator a, RealLinearOperator m, RealVector b, RealVector x0) Returns an estimate of the solution to the linear system A · x = b.PreconditionedIterativeLinearSolver.solve
(RealLinearOperator a, RealVector b) Returns an estimate of the solution to the linear system A · x = b.PreconditionedIterativeLinearSolver.solve
(RealLinearOperator a, RealVector b, RealVector x0) Returns an estimate of the solution to the linear system A · x = b.SymmLQ.solve
(RealLinearOperator a, RealLinearOperator m, RealVector b) Returns an estimate of the solution to the linear system A · x = b.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.SymmLQ.solve
(RealLinearOperator a, RealLinearOperator m, RealVector b, RealVector x) Returns an estimate of the solution to the linear system A · x = b.SymmLQ.solve
(RealLinearOperator a, RealVector b) Returns an estimate of the solution to the linear system A · x = b.SymmLQ.solve
(RealLinearOperator a, RealVector b, boolean goodb, double shift) Returns the solution to the system (A - shift · I) · x = b.SymmLQ.solve
(RealLinearOperator a, RealVector b, RealVector x) Returns an estimate of the solution to the linear system A · x = b.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.PreconditionedIterativeLinearSolver.solveInPlace
(RealLinearOperator a, RealVector b, RealVector x0) Returns an estimate of the solution to the linear system A · x = b.SymmLQ.solveInPlace
(RealLinearOperator a, RealLinearOperator m, RealVector b, RealVector x) Returns an estimate of the solution to the linear system A · x = b.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.SymmLQ.solveInPlace
(RealLinearOperator a, RealVector b, RealVector x) Returns an estimate of the solution to the linear system A · x = b.ModifierConstructorDescriptionArray2DRowFieldMatrix
(Field<T> field, T[][] d) Create a newFieldMatrix<T>
using the input array as the underlying data array.Array2DRowFieldMatrix
(Field<T> field, T[][] d, boolean copyArray) Create a newFieldMatrix<T>
using the input array as the underlying data array.Array2DRowFieldMatrix
(T[][] d) Create a newFieldMatrix<T>
using the input array as the underlying data array.Array2DRowFieldMatrix
(T[][] d, boolean copyArray) Create a newFieldMatrix<T>
using the input array as the underlying data array.Array2DRowRealMatrix
(double[][] d) Create a newRealMatrix
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.Construct a vector from another vector, using a deep copy.ArrayFieldVector
(ArrayFieldVector<T> v, boolean deep) Construct a vector from another vector.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.Construct a vector from another vector, using a deep copy.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.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
ModifierConstructorDescriptionMultiStartMultivariateOptimizer
(MultivariateOptimizer optimizer, int starts, RandomVectorGenerator generator) Create a multi-start optimizer from a single-start optimizer. -
Uses of NullArgumentException in org.hipparchus.random
ModifierConstructorDescriptionHaltonSequenceGenerator
(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 TypeMethodDescriptionvoid
Frequency.merge
(Collection<? extends Frequency<? extends T>> others) Merge aCollection
ofFrequency
objects into this instance.void
Merge another Frequency object's counts into this instance. -
Uses of NullArgumentException in org.hipparchus.stat.descriptive
-
Uses of NullArgumentException in org.hipparchus.stat.descriptive.moment
ModifierConstructorDescriptionGeometricMean
(GeometricMean original) Copy constructor, creates a newGeometricMean
identical to theoriginal
.Copy constructor, creates a newKurtosis
identical to theoriginal
.Copy constructor, creates a newMean
identical to theoriginal
.SecondMoment
(SecondMoment original) Copy constructor, creates a newSecondMoment
identical to theoriginal
.SemiVariance
(SemiVariance original) Copy constructor, creates a newSemiVariance
identical to theoriginal
.Copy constructor, creates a newSkewness
identical to theoriginal
.StandardDeviation
(StandardDeviation original) Copy constructor, creates a newStandardDeviation
identical to theoriginal
.Copy constructor, creates a newVariance
identical to theoriginal
. -
Uses of NullArgumentException in org.hipparchus.stat.descriptive.rank
Modifier and TypeMethodDescriptionvoid
RandomPercentile.aggregate
(RandomPercentile other) Aggregates the provided instance into this instance.ModifierConstructorDescriptionCopy constructor, creates a newMax
identical to theoriginal
.Copy constructor, creates a newMin
identical to theoriginal
.Percentile
(Percentile original) Copy constructor, creates a newPercentile
identical to theoriginal
-
Uses of NullArgumentException in org.hipparchus.stat.descriptive.summary
ModifierConstructorDescriptionCopy constructor, creates a newProduct
identical to theoriginal
.Copy constructor, creates a newSum
identical to theoriginal
.Copy constructor, creates a newSumOfLogs
identical to theoriginal
.SumOfSquares
(SumOfSquares original) Copy constructor, creates a newSumOfSquares
identical to theoriginal
. -
Uses of NullArgumentException in org.hipparchus.stat.fitting
Modifier and TypeMethodDescriptionvoid
EmpiricalDistribution.load
(double[] in) Computes the empirical distribution from the provided array of numbers.void
Computes the empirical distribution from the input file.void
Computes the empirical distribution using data read from a URL. -
Uses of NullArgumentException in org.hipparchus.stat.inference
Modifier and TypeMethodDescriptiondouble
OneWayAnova.anovaFValue
(Collection<double[]> categoryData) Computes the ANOVA F-value for a collection ofdouble[]
arrays.double
OneWayAnova.anovaPValue
(Collection<double[]> categoryData) Computes the ANOVA P-value for a collection ofdouble[]
arrays.double
OneWayAnova.anovaPValue
(Collection<StreamingStatistics> categoryData, boolean allowOneElementData) Computes the ANOVA P-value for a collection ofStreamingStatistics
.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 inputcounts
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 inputcounts
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 inputcounts
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 levelalpha
.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 inputcounts
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 levelalpha
.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 twoStatisticalSummary
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 twoStatisticalSummary
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 thatsample1
andsample2
are drawn from populations with the same mean, with significance levelalpha
, 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 thatsample1
andsample2
are drawn from populations with the same mean, with significance levelalpha
, 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 ofx
, \(m\) is the length ofy
, \(F_n\) is the empirical distribution that puts mass \(1/n\) at each of the values inx
and \(F_m\) is the empirical distribution of they
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 withdistribution
, \(n\) is the length ofdata
and \(F_n\) is the empirical distribution that puts mass \(1/n\) at each of the values indata
.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 thatx
andy
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 thatx
andy
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 thatdata
conforms todistribution
.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 thatdata
conforms todistribution
.static boolean
InferenceTestUtils.kolmogorovSmirnovTest
(RealDistribution dist, double[] data, double alpha) Performs a Kolmogorov-Smirnov test evaluating the null hypothesis thatdata
conforms todistribution
.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 ofdouble[]
arrays.static double
InferenceTestUtils.oneWayAnovaPValue
(Collection<double[]> categoryData) Computes the ANOVA P-value for a collection ofdouble[]
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 betweensample1
andsample2
is 0 in favor of the two-sided alternative that the mean paired difference is not equal to 0, with significance levelalpha
.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 betweensample1
andsample2
is 0 in favor of the two-sided alternative that the mean paired difference is not equal to 0, with significance levelalpha
.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) static double
InferenceTestUtils.t
(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2) Computes a 2-sample t statistic, comparing the means of the datasets described by twoStatisticalSummary
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) double
TTest.t
(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2) Computes a 2-sample t statistic, comparing the means of the datasets described by twoStatisticalSummary
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 thatsample1
andsample2
are drawn from populations with the same mean, with significance levelalpha
.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 constantmu
.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 whichsample
is drawn equalsmu
.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 bysampleStats
with the constantmu
.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 bystats
is drawn equalsmu
.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 thatsampleStats1
andsampleStats2
describe datasets drawn from populations with the same mean, with significance levelalpha
.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 thatsample1
andsample2
are drawn from populations with the same mean, with significance levelalpha
.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 constantmu
.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 whichsample
is drawn equalsmu
.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 bysampleStats
with the constantmu
.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 bystats
is drawn equalsmu
.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 thatsampleStats1
andsampleStats2
describe datasets drawn from populations with the same mean, with significance levelalpha
.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 TypeMethodDescriptionstatic 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.ModifierConstructorDescriptionIncrementor
(int max, Incrementor.MaxCountExceededCallback cb) Creates an Incrementor.KthSelector
(PivotingStrategy pivotingStrategy) Constructor with specified pivoting strategyResizableDoubleArray
(ResizableDoubleArray original) Copy constructor.