Uses of Class
org.apache.commons.math3.exception.NullArgumentException

Packages that use NullArgumentException
org.apache.commons.math3 Common classes used throughout the commons-math library. 
org.apache.commons.math3.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.apache.commons.math3.analysis.integration Numerical integration (quadrature) algorithms for univariate real functions. 
org.apache.commons.math3.analysis.interpolation Univariate real functions interpolation algorithms. 
org.apache.commons.math3.analysis.polynomials Univariate real polynomials implementations, seen as differentiable univariate real functions. 
org.apache.commons.math3.analysis.solvers Root finding algorithms, for univariate real functions. 
org.apache.commons.math3.complex Complex number type and implementations of complex transcendental functions. 
org.apache.commons.math3.dfp Decimal floating point library for Java 
org.apache.commons.math3.filter Implementations of common discrete-time linear filters. 
org.apache.commons.math3.fraction Fraction number type and fraction number formatting. 
org.apache.commons.math3.genetics This package provides Genetic Algorithms components and implementations. 
org.apache.commons.math3.linear Linear algebra support. 
org.apache.commons.math3.optim.nonlinear.scalar Algorithms for optimizing a scalar function. 
org.apache.commons.math3.optim.nonlinear.vector Algorithms for optimizing a vector function. 
org.apache.commons.math3.random Random number and random data generators. 
org.apache.commons.math3.stat.clustering Clustering algorithms 
org.apache.commons.math3.stat.descriptive Generic univariate summary statistic objects. 
org.apache.commons.math3.stat.descriptive.moment Summary statistics based on moments. 
org.apache.commons.math3.stat.descriptive.rank Summary statistics based on ranks. 
org.apache.commons.math3.stat.descriptive.summary Other summary statistics. 
org.apache.commons.math3.stat.inference Classes providing hypothesis testing and confidence interval construction. 
org.apache.commons.math3.util Convenience routines and common data structures used throughout the commons-math library. 
 

Uses of NullArgumentException in org.apache.commons.math3
 

Methods in org.apache.commons.math3 that throw NullArgumentException
 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.apache.commons.math3.analysis.function
 

Methods in org.apache.commons.math3.analysis.function that throw NullArgumentException
 double[] HarmonicOscillator.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[] Logistic.Parametric.gradient(double x, double... param)
          Computes the value of the gradient at x.
 double[] Gaussian.Parametric.gradient(double x, double... param)
          Computes the value of the gradient at x.
 double HarmonicOscillator.Parametric.value(double x, double... param)
          Computes the value of the harmonic oscillator 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.
 double Logistic.Parametric.value(double x, double... param)
          Computes the value of the sigmoid at x.
 double Gaussian.Parametric.value(double x, double... param)
          Computes the value of the Gaussian at x.
 

Constructors in org.apache.commons.math3.analysis.function that throw NullArgumentException
StepFunction(double[] x, double[] y)
          Builds a step function from a list of arguments and the corresponding values.
 

Uses of NullArgumentException in org.apache.commons.math3.analysis.integration
 

Methods in org.apache.commons.math3.analysis.integration that throw NullArgumentException
 double BaseAbstractUnivariateIntegrator.integrate(int maxEval, UnivariateFunction f, double lower, double upper)
          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 BaseAbstractUnivariateIntegrator.setup(int maxEval, UnivariateFunction f, double lower, double upper)
          Prepare for computation.
 

Uses of NullArgumentException in org.apache.commons.math3.analysis.interpolation
 

Methods in org.apache.commons.math3.analysis.interpolation that throw NullArgumentException
 MultivariateFunction MicrosphereInterpolator.interpolate(double[][] xval, double[] yval)
          Computes an interpolating function for the data set.
 

Constructors in org.apache.commons.math3.analysis.interpolation that throw NullArgumentException
MicrosphereInterpolatingFunction(double[][] xval, double[] yval, int brightnessExponent, int microsphereElements, UnitSphereRandomVectorGenerator rand)
           
 

Uses of NullArgumentException in org.apache.commons.math3.analysis.polynomials
 

Methods in org.apache.commons.math3.analysis.polynomials that throw NullArgumentException
protected static double[] PolynomialFunction.differentiate(double[] coefficients)
          Returns the coefficients of the derivative of the polynomial with the given coefficients.
protected static double PolynomialFunction.evaluate(double[] coefficients, double argument)
          Uses Horner's Method to evaluate the polynomial with the given coefficients at the argument.
 DerivativeStructure PolynomialFunction.value(DerivativeStructure t)
          Simple mathematical function.
 

Constructors in org.apache.commons.math3.analysis.polynomials that throw NullArgumentException
PolynomialFunction(double[] c)
          Construct a polynomial with the given coefficients.
 

Uses of NullArgumentException in org.apache.commons.math3.analysis.solvers
 

Methods in org.apache.commons.math3.analysis.solvers that throw NullArgumentException
static double[] UnivariateSolverUtils.bracket(UnivariateFunction function, double initial, double lowerBound, double upperBound)
          This method attempts to find two values a and b satisfying lowerBound <= a < initial < b <= upperBound f(a) * f(b) < 0 If f is continuous on [a,b], this means that a and b bracket a root of f.
static double[] UnivariateSolverUtils.bracket(UnivariateFunction function, double initial, double lowerBound, double upperBound, int maximumIterations)
          This method attempts to find two values a and b satisfying lowerBound <= a < initial < b <= upperBound f(a) * f(b) <= 0 If f is continuous on [a,b], this means that a and b bracket a root of f.
static boolean UnivariateSolverUtils.isBracketing(UnivariateFunction function, double lower, double upper)
          Check whether the interval bounds bracket a root.
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.apache.commons.math3.complex
 

Methods in org.apache.commons.math3.complex that throw NullArgumentException
 Complex Complex.add(Complex addend)
          Returns a Complex whose value is (this + addend).
 Complex Complex.divide(Complex divisor)
          Returns a Complex whose value is (this / divisor).
static ComplexFormat ComplexFormat.getInstance(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.
 Complex Complex.pow(Complex 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).
 

Constructors in org.apache.commons.math3.complex that throw NullArgumentException
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.
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.
 

Uses of NullArgumentException in org.apache.commons.math3.dfp
 

Methods in org.apache.commons.math3.dfp that throw NullArgumentException
 Dfp BracketingNthOrderBrentSolverDFP.solve(int maxEval, UnivariateDfpFunction f, Dfp min, Dfp max, AllowedSolution allowedSolution)
          Solve for a zero in the given interval.
 Dfp BracketingNthOrderBrentSolverDFP.solve(int maxEval, UnivariateDfpFunction f, Dfp min, Dfp max, Dfp startValue, AllowedSolution allowedSolution)
          Solve for a zero in the given interval, start at startValue.
 

Uses of NullArgumentException in org.apache.commons.math3.filter
 

Methods in org.apache.commons.math3.filter that throw NullArgumentException
 void KalmanFilter.correct(double[] z)
          Correct the current state estimate with an actual measurement.
 void KalmanFilter.correct(RealVector z)
          Correct the current state estimate with an actual measurement.
 

Constructors in org.apache.commons.math3.filter that throw NullArgumentException
DefaultMeasurementModel(double[][] measMatrix, double[][] measNoise)
          Create a new MeasurementModel, taking double arrays as input parameters for the respective measurement matrix and noise.
DefaultProcessModel(double[][] stateTransition, double[][] control, double[][] processNoise)
          Create a new ProcessModel, taking double arrays as input parameters.
DefaultProcessModel(double[][] stateTransition, double[][] control, double[][] processNoise, double[] initialStateEstimate, double[][] initialErrorCovariance)
          Create a new ProcessModel, taking double arrays as input parameters.
KalmanFilter(ProcessModel process, MeasurementModel measurement)
          Creates a new Kalman filter with the given process and measurement models.
 

Uses of NullArgumentException in org.apache.commons.math3.fraction
 

Methods in org.apache.commons.math3.fraction that throw NullArgumentException
 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.apache.commons.math3.genetics
 

Methods in org.apache.commons.math3.genetics that throw NullArgumentException
 void ListPopulation.setChromosomes(List<Chromosome> chromosomes)
          Deprecated. use ListPopulation.addChromosomes(Collection) instead
 

Constructors in org.apache.commons.math3.genetics that throw NullArgumentException
ElitisticListPopulation(List<Chromosome> chromosomes, int populationLimit, double elitismRate)
          Creates a new ElitisticListPopulation instance.
ListPopulation(List<Chromosome> chromosomes, int populationLimit)
          Creates a new ListPopulation instance.
 

Uses of NullArgumentException in org.apache.commons.math3.linear
 

Methods in org.apache.commons.math3.linear that throw NullArgumentException
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 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.
static void MatrixUtils.checkSubMatrixIndex(AnyMatrix m, int[] selectedRows, int[] selectedColumns)
          Check if submatrix ranges indices are valid.
protected  void AbstractFieldMatrix.checkSubMatrixIndex(int[] selectedRows, int[] selectedColumns)
          Check if submatrix ranges indices are valid.
 void AbstractRealMatrix.copySubMatrix(int[] selectedRows, int[] selectedColumns, double[][] destination)
          Copy a submatrix.
 void RealMatrix.copySubMatrix(int[] selectedRows, int[] selectedColumns, double[][] destination)
          Copy a submatrix.
 void FieldMatrix.copySubMatrix(int[] selectedRows, int[] selectedColumns, T[][] destination)
          Copy a submatrix.
 void AbstractFieldMatrix.copySubMatrix(int[] selectedRows, int[] selectedColumns, T[][] 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.
 RealMatrix AbstractRealMatrix.getSubMatrix(int[] selectedRows, int[] selectedColumns)
          Gets a submatrix.
 RealMatrix RealMatrix.getSubMatrix(int[] selectedRows, int[] selectedColumns)
          Gets a submatrix.
 FieldMatrix<T> FieldMatrix.getSubMatrix(int[] selectedRows, int[] selectedColumns)
          Get a submatrix.
 FieldMatrix<T> AbstractFieldMatrix.getSubMatrix(int[] selectedRows, int[] selectedColumns)
          Get a submatrix.
 FieldVector<T> ArrayFieldVector.mapAdd(T d)
          Map an addition operation to each entry.
 FieldVector<T> SparseFieldVector.mapAdd(T d)
          Deprecated. Map an addition operation to each entry.
 FieldVector<T> FieldVector.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> SparseFieldVector.mapAddToSelf(T d)
          Deprecated. Map an addition operation to each entry.
 FieldVector<T> FieldVector.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> SparseFieldVector.mapDivide(T d)
          Deprecated. Map a division operation to each entry.
 FieldVector<T> FieldVector.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> SparseFieldVector.mapDivideToSelf(T d)
          Deprecated. Map a division operation to each entry.
 FieldVector<T> FieldVector.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> SparseFieldVector.mapMultiply(T d)
          Deprecated. Map a multiplication operation to each entry.
 FieldVector<T> FieldVector.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> SparseFieldVector.mapMultiplyToSelf(T d)
          Deprecated. Map a multiplication operation to each entry.
 FieldVector<T> FieldVector.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> SparseFieldVector.mapSubtract(T d)
          Deprecated. Map a subtraction operation to each entry.
 FieldVector<T> FieldVector.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> SparseFieldVector.mapSubtractToSelf(T d)
          Deprecated. Map a subtraction operation to each entry.
 FieldVector<T> FieldVector.mapSubtractToSelf(T d)
          Map a subtraction operation to each entry.
 void Array2DRowRealMatrix.setSubMatrix(double[][] 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 RealMatrix.setSubMatrix(double[][] 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 Array2DRowFieldMatrix.setSubMatrix(T[][] subMatrix, int row, int column)
          Replace the submatrix starting at (row, column) using data in the input subMatrix array.
 void AbstractFieldMatrix.setSubMatrix(T[][] 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.
 RealVector PreconditionedIterativeLinearSolver.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)
          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 PreconditionedIterativeLinearSolver.solve(RealLinearOperator a, RealLinearOperator m, 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, RealVector x)
          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 IterativeLinearSolver.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)
          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 PreconditionedIterativeLinearSolver.solve(RealLinearOperator a, RealVector b, RealVector x0)
          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 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 PreconditionedIterativeLinearSolver.solveInPlace(RealLinearOperator a, RealLinearOperator m, 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 PreconditionedIterativeLinearSolver.solveInPlace(RealLinearOperator a, 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.
 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.apache.commons.math3.linear that throw NullArgumentException
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(ArrayFieldVector<T> v)
          Construct a vector from another vector, using a deep copy.
ArrayFieldVector(ArrayFieldVector<T> v1, ArrayFieldVector<T> v2)
          Construct a vector by appending one vector to another vector.
ArrayFieldVector(ArrayFieldVector<T> v, boolean deep)
          Construct a vector from another vector.
ArrayFieldVector(ArrayFieldVector<T> v1, T[] v2)
          Construct a vector by appending one vector to another vector.
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(FieldVector<T> v)
          Construct a vector from another vector, using a deep copy.
ArrayFieldVector(T[] d)
          Construct a vector from an array, copying the input array.
ArrayFieldVector(T[] v1, ArrayFieldVector<T> v2)
          Construct a vector by appending one vector to another vector.
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, T[] v2)
          Construct a vector by appending one vector to another vector.
ArrayRealVector(ArrayRealVector v)
          Construct a vector from another vector, using a deep copy.
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(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.
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.
 

Uses of NullArgumentException in org.apache.commons.math3.optim.nonlinear.scalar
 

Constructors in org.apache.commons.math3.optim.nonlinear.scalar that throw NullArgumentException
MultiStartMultivariateOptimizer(MultivariateOptimizer optimizer, int starts, RandomVectorGenerator generator)
          Create a multi-start optimizer from a single-start optimizer.
 

Uses of NullArgumentException in org.apache.commons.math3.optim.nonlinear.vector
 

Constructors in org.apache.commons.math3.optim.nonlinear.vector that throw NullArgumentException
MultiStartMultivariateVectorOptimizer(MultivariateVectorOptimizer optimizer, int starts, RandomVectorGenerator generator)
          Create a multi-start optimizer from a single-start optimizer.
 

Uses of NullArgumentException in org.apache.commons.math3.random
 

Methods in org.apache.commons.math3.random that throw NullArgumentException
 void ValueServer.computeDistribution()
          Computes the empirical distribution using values from the file in valuesFileURL, using the default number of bins.
 void ValueServer.computeDistribution(int binCount)
          Computes the empirical distribution using values from the file in valuesFileURL and binCount bins.
 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.
 

Constructors in org.apache.commons.math3.random that throw NullArgumentException
StableRandomGenerator(RandomGenerator generator, double alpha, double beta)
          Create a new generator.
 

Uses of NullArgumentException in org.apache.commons.math3.stat.clustering
 

Methods in org.apache.commons.math3.stat.clustering that throw NullArgumentException
 List<Cluster<T>> DBSCANClusterer.cluster(Collection<T> points)
          Performs DBSCAN cluster analysis.
 

Uses of NullArgumentException in org.apache.commons.math3.stat.descriptive
 

Methods in org.apache.commons.math3.stat.descriptive that throw NullArgumentException
static void DescriptiveStatistics.copy(DescriptiveStatistics source, DescriptiveStatistics dest)
          Copies source to dest.
static void SummaryStatistics.copy(SummaryStatistics source, SummaryStatistics dest)
          Copies source to dest.
static void SynchronizedDescriptiveStatistics.copy(SynchronizedDescriptiveStatistics source, SynchronizedDescriptiveStatistics dest)
          Copies source to dest.
static void SynchronizedSummaryStatistics.copy(SynchronizedSummaryStatistics source, SynchronizedSummaryStatistics dest)
          Copies source to dest.
 

Constructors in org.apache.commons.math3.stat.descriptive that throw NullArgumentException
AggregateSummaryStatistics(SummaryStatistics prototypeStatistics)
          Initializes a new AggregateSummaryStatistics with the specified statistics object as a prototype for contributing statistics and for the internal aggregate statistics.
DescriptiveStatistics(DescriptiveStatistics original)
          Copy constructor.
SummaryStatistics(SummaryStatistics original)
          A copy constructor.
SynchronizedDescriptiveStatistics(SynchronizedDescriptiveStatistics original)
          A copy constructor.
SynchronizedSummaryStatistics(SynchronizedSummaryStatistics original)
          A copy constructor.
 

Uses of NullArgumentException in org.apache.commons.math3.stat.descriptive.moment
 

Methods in org.apache.commons.math3.stat.descriptive.moment that throw NullArgumentException
static void GeometricMean.copy(GeometricMean source, GeometricMean dest)
          Copies source to dest.
static void Kurtosis.copy(Kurtosis source, Kurtosis dest)
          Copies source to dest.
static void Mean.copy(Mean source, Mean dest)
          Copies source to dest.
static void SecondMoment.copy(SecondMoment source, SecondMoment dest)
          Copies source to dest.
static void SemiVariance.copy(SemiVariance source, SemiVariance dest)
          Copies source to dest.
static void Skewness.copy(Skewness source, Skewness dest)
          Copies source to dest.
static void StandardDeviation.copy(StandardDeviation source, StandardDeviation dest)
          Copies source to dest.
static void Variance.copy(Variance source, Variance dest)
          Copies source to dest.
 

Constructors in org.apache.commons.math3.stat.descriptive.moment that throw NullArgumentException
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.apache.commons.math3.stat.descriptive.rank
 

Methods in org.apache.commons.math3.stat.descriptive.rank that throw NullArgumentException
static void Max.copy(Max source, Max dest)
          Copies source to dest.
static void Min.copy(Min source, Min dest)
          Copies source to dest.
static void Percentile.copy(Percentile source, Percentile dest)
          Copies source to dest.
 

Constructors in org.apache.commons.math3.stat.descriptive.rank that throw NullArgumentException
Max(Max original)
          Copy constructor, creates a new Max identical to the original
Median(Median original)
          Copy constructor, creates a new Median 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.apache.commons.math3.stat.descriptive.summary
 

Methods in org.apache.commons.math3.stat.descriptive.summary that throw NullArgumentException
static void Product.copy(Product source, Product dest)
          Copies source to dest.
static void SumOfLogs.copy(SumOfLogs source, SumOfLogs dest)
          Copies source to dest.
static void SumOfSquares.copy(SumOfSquares source, SumOfSquares dest)
          Copies source to dest.
static void Sum.copy(Sum source, Sum dest)
          Copies source to dest.
 

Constructors in org.apache.commons.math3.stat.descriptive.summary that throw NullArgumentException
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.apache.commons.math3.stat.inference
 

Methods in org.apache.commons.math3.stat.inference that throw NullArgumentException
 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.
 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.
static double TestUtils.chiSquare(long[][] counts)
           
 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 TestUtils.chiSquareTest(long[][] counts)
           
 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.
static boolean TestUtils.chiSquareTest(long[][] counts, double alpha)
           
 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 TestUtils.homoscedasticT(double[] sample1, double[] sample2)
           
 double TTest.homoscedasticT(double[] sample1, double[] sample2)
          Computes a 2-sample t statistic, under the hypothesis of equal subpopulation variances.
static double TestUtils.homoscedasticT(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)
           
 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 TestUtils.homoscedasticTTest(double[] sample1, double[] sample2)
           
 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.
static boolean TestUtils.homoscedasticTTest(double[] sample1, double[] sample2, double alpha)
           
 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.
static double TestUtils.homoscedasticTTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)
           
 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.
 double MannWhitneyUTest.mannWhitneyU(double[] x, double[] y)
          Computes the Mann-Whitney U statistic comparing mean for two independent samples possibly of different length.
 double MannWhitneyUTest.mannWhitneyUTest(double[] x, double[] y)
          Returns the asymptotic observed significance level, or p-value, associated with a Mann-Whitney U statistic comparing mean for two independent samples.
static double TestUtils.oneWayAnovaFValue(Collection<double[]> categoryData)
           
static double TestUtils.oneWayAnovaPValue(Collection<double[]> categoryData)
           
static boolean TestUtils.oneWayAnovaTest(Collection<double[]> categoryData, double alpha)
           
static double TestUtils.pairedT(double[] sample1, double[] sample2)
           
 double TTest.pairedT(double[] sample1, double[] sample2)
          Computes a paired, 2-sample t-statistic based on the data in the input arrays.
static double TestUtils.pairedTTest(double[] sample1, double[] sample2)
           
 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.
static boolean TestUtils.pairedTTest(double[] sample1, double[] sample2, double alpha)
           
 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 TestUtils.t(double[] sample1, double[] sample2)
           
 double TTest.t(double[] sample1, double[] sample2)
          Computes a 2-sample t statistic, without the hypothesis of equal subpopulation variances.
static double TestUtils.t(double mu, double[] observed)
           
 double TTest.t(double mu, double[] observed)
          Computes a t statistic given observed values and a comparison constant.
static double TestUtils.t(double mu, StatisticalSummary sampleStats)
           
 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.
static double TestUtils.t(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)
           
 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 TestUtils.tTest(double[] sample1, double[] sample2)
           
 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.
static boolean TestUtils.tTest(double[] sample1, double[] sample2, double alpha)
           
 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.
static double TestUtils.tTest(double mu, double[] sample)
           
 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.
static boolean TestUtils.tTest(double mu, double[] sample, double alpha)
           
 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.
static double TestUtils.tTest(double mu, StatisticalSummary sampleStats)
           
 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.
static boolean TestUtils.tTest(double mu, StatisticalSummary sampleStats, double alpha)
           
 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.
static double TestUtils.tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)
           
 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.
static boolean TestUtils.tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2, double alpha)
           
 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 mean 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.apache.commons.math3.util
 

Methods in org.apache.commons.math3.util that throw NullArgumentException
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 DimensionMismatchException if the input array is not rectangular.
static void ResizableDoubleArray.copy(ResizableDoubleArray source, ResizableDoubleArray dest)
          Copies source to dest, copying the underlying data, so dest is a new, independent copy of source.
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.
 double DefaultTransformer.transform(Object o)
           
 

Constructors in org.apache.commons.math3.util that throw NullArgumentException
ResizableDoubleArray(ResizableDoubleArray original)
          Copy constructor.
 



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