| Package | Description |
|---|---|
| org.hipparchus |
Common classes used throughout the Hipparchus library.
|
| org.hipparchus.analysis.function |
The
function package contains function objects that wrap the
methods contained in Math, as well as common
mathematical functions such as the gaussian and sinc functions. |
| org.hipparchus.analysis.integration |
Numerical integration (quadrature) algorithms for univariate real functions.
|
| org.hipparchus.analysis.interpolation |
Univariate real functions interpolation algorithms.
|
| org.hipparchus.analysis.polynomials |
Univariate real polynomials implementations, seen as differentiable
univariate real functions.
|
| org.hipparchus.analysis.solvers |
Root finding algorithms, for univariate real functions.
|
| org.hipparchus.clustering |
Clustering algorithms.
|
| org.hipparchus.complex |
Complex number type and implementations of complex transcendental
functions.
|
| org.hipparchus.fraction |
Fraction number type and fraction number formatting.
|
| org.hipparchus.linear |
Linear algebra support.
|
| org.hipparchus.optim.nonlinear.scalar |
Algorithms for optimizing a scalar function.
|
| org.hipparchus.random |
Random number and random data generators.
|
| org.hipparchus.stat |
Data storage, manipulation and summary routines.
|
| org.hipparchus.stat.descriptive |
Generic univariate and multivariate summary statistic objects.
|
| org.hipparchus.stat.descriptive.moment |
Summary statistics based on moments.
|
| org.hipparchus.stat.descriptive.rank |
Summary statistics based on ranks.
|
| org.hipparchus.stat.descriptive.summary |
Other summary statistics.
|
| org.hipparchus.stat.fitting |
Statistical methods for fitting distributions.
|
| org.hipparchus.stat.inference |
Classes providing hypothesis testing.
|
| org.hipparchus.util |
Convenience routines and common data structures used throughout the Hipparchus library.
|
| Modifier and Type | Method and 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.
|
| Modifier and Type | Method and Description |
|---|---|
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[] |
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[] |
Sigmoid.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 |
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. |
double |
Sigmoid.Parametric.value(double x,
double... param)
Computes the value of the sigmoid at
x. |
| Constructor and Description |
|---|
StepFunction(double[] x,
double[] y)
Builds a step function from a list of arguments and the corresponding
values.
|
| Modifier and Type | Method and Description |
|---|---|
T |
FieldUnivariateIntegrator.integrate(int maxEval,
CalculusFieldUnivariateFunction<T> f,
T min,
T max)
Integrate the function in the given interval.
|
T |
BaseAbstractFieldUnivariateIntegrator.integrate(int maxEval,
CalculusFieldUnivariateFunction<T> f,
T lower,
T 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.
|
double |
BaseAbstractUnivariateIntegrator.integrate(int maxEval,
UnivariateFunction f,
double lower,
double upper)
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.
|
| Modifier and Type | Method and Description |
|---|---|
void |
FieldHermiteInterpolator.addSamplePoint(T x,
T[]... value)
Add a sample point.
|
double[][] |
HermiteInterpolator.derivatives(double x,
int order)
Interpolate value and first derivatives at a specified abscissa.
|
T[][] |
FieldHermiteInterpolator.derivatives(T x,
int order)
Interpolate value and first derivatives at a specified abscissa.
|
MultivariateFunction |
MultivariateInterpolator.interpolate(double[][] xval,
double[] yval)
Computes an interpolating function for the data set.
|
MultivariateFunction |
MicrosphereProjectionInterpolator.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.
|
| Constructor and Description |
|---|
PiecewiseBicubicSplineInterpolatingFunction(double[] x,
double[] y,
double[][] f) |
| Modifier and Type | Method and Description |
|---|---|
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>> |
FieldPolynomialFunction.differentiate(T[] 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.
|
static double |
PolynomialFunctionNewtonForm.evaluate(double[] a,
double[] c,
double z)
Evaluate the Newton polynomial using nested multiplication.
|
protected static <T extends CalculusFieldElement<T>> |
FieldPolynomialFunction.evaluate(T[] coefficients,
T argument)
Uses Horner's Method to evaluate the polynomial with the given coefficients at
the argument.
|
<T extends Derivative<T>> |
PolynomialFunction.value(T t)
Compute the value for the function.
|
<T extends CalculusFieldElement<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.
|
| Constructor and 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.
|
| Modifier and Type | Method and Description |
|---|---|
static <T extends CalculusFieldElement<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>> |
UnivariateSolverUtils.bracket(CalculusFieldUnivariateFunction<T> function,
T initial,
T lowerBound,
T upperBound,
int maximumIterations)
This method simply calls
bracket(function, initial, lowerBound, upperBound, q, r, maximumIterations)
with q and r set to 1.0. |
static double[] |
UnivariateSolverUtils.bracket(UnivariateFunction function,
double initial,
double lowerBound,
double upperBound)
This method simply calls
bracket(function, initial, lowerBound, upperBound, q, r, maximumIterations)
with q and r set to 1.0 and maximumIterations set to Integer.MAX_VALUE. |
static double[] |
UnivariateSolverUtils.bracket(UnivariateFunction function,
double initial,
double lowerBound,
double upperBound,
int maximumIterations)
This method simply calls
bracket(function, initial, lowerBound, upperBound, q, r, maximumIterations)
with q and r set to 1.0. |
static boolean |
UnivariateSolverUtils.isBracketing(UnivariateFunction function,
double lower,
double upper)
Check whether the interval bounds bracket a root.
|
protected void |
BaseAbstractUnivariateSolver.setup(int maxEval,
F f,
double min,
double max,
double startValue)
Prepare for computation.
|
T |
FieldBracketingNthOrderBrentSolver.solve(int maxEval,
CalculusFieldUnivariateFunction<T> f,
T min,
T max,
AllowedSolution allowedSolution)
Solve for a zero in the given interval.
|
T |
FieldBracketingNthOrderBrentSolver.solve(int maxEval,
CalculusFieldUnivariateFunction<T> f,
T min,
T max,
T startValue,
AllowedSolution allowedSolution)
Solve for a zero in the given interval, start at
startValue. |
static double |
UnivariateSolverUtils.solve(UnivariateFunction function,
double x0,
double x1)
Convenience method to find a zero of a univariate real function.
|
static double |
UnivariateSolverUtils.solve(UnivariateFunction function,
double x0,
double x1,
double absoluteAccuracy)
Convenience method to find a zero of a univariate real function.
|
Complex[] |
LaguerreSolver.solveAllComplex(double[] coefficients,
double initial)
Find all complex roots for the polynomial with the given
coefficients, starting from the given initial value.
|
Complex |
LaguerreSolver.solveComplex(double[] coefficients,
double initial)
Find a complex root for the polynomial with the given coefficients,
starting from the given initial value.
|
protected void |
BaseAbstractUnivariateSolver.verifyBracketing(double lower,
double upper)
Check that the endpoints specify an interval and the function takes
opposite signs at the endpoints.
|
static void |
UnivariateSolverUtils.verifyBracketing(UnivariateFunction function,
double lower,
double upper)
Check that the endpoints specify an interval and the end points
bracket a root.
|
| Modifier and Type | Method and Description |
|---|---|
List<Cluster<T>> |
DBSCANClusterer.cluster(Collection<T> points)
Performs DBSCAN cluster analysis.
|
| Modifier and Type | Method and 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). |
| Constructor and Description |
|---|
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.
|
| Modifier and Type | Method and Description |
|---|---|
BigFraction |
BigFraction.add(BigInteger bg)
Adds the value of this fraction to the passed
BigInteger,
returning the result in reduced form. |
| Modifier and Type | Method and Description |
|---|---|
FieldVector<T> |
SparseFieldVector.append(T d)
Construct a vector by appending a T to this vector.
|
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 |
RealMatrix.copySubMatrix(int[] selectedRows,
int[] selectedColumns,
double[][] 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 |
AbstractFieldMatrix.copySubMatrix(int[] selectedRows,
int[] selectedColumns,
T[][] destination)
Copy a submatrix.
|
static <T extends FieldElement<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>> |
MatrixUtils.createFieldMatrix(T[][] data)
Returns a
FieldMatrix whose entries are the the values in the
the input array. |
static <T extends FieldElement<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>> |
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>> |
AbstractFieldMatrix.extractField(T[][] d)
Get the elements type from an array.
|
RealMatrix |
RealMatrix.getSubMatrix(int[] selectedRows,
int[] selectedColumns)
Gets a submatrix.
|
FieldMatrix<T> |
FieldMatrix.getSubMatrix(int[] selectedRows,
int[] selectedColumns)
Get a submatrix.
|
RealMatrix |
AbstractRealMatrix.getSubMatrix(int[] selectedRows,
int[] selectedColumns)
Gets a submatrix.
|
FieldMatrix<T> |
AbstractFieldMatrix.getSubMatrix(int[] selectedRows,
int[] selectedColumns)
Get 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> |
SparseFieldVector.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> |
ArrayFieldVector.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> |
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)
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)
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)
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)
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)
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)
Map a subtraction operation to each entry.
|
FieldVector<T> |
FieldVector.mapSubtractToSelf(T d)
Map a subtraction operation to each entry.
|
void |
SparseFieldVector.setEntry(int index,
T value)
Set a single element.
|
void |
RealMatrix.setSubMatrix(double[][] 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 |
AbstractRealMatrix.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 |
BlockFieldMatrix.setSubMatrix(T[][] 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. |
RealVector |
SymmLQ.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)
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 |
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,
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 |
PreconditionedIterativeLinearSolver.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 |
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,
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.
|
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 |
ConjugateGradient.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,
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.
|
abstract RealVector |
IterativeLinearSolver.solveInPlace(RealLinearOperator a,
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.
|
| Constructor and 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(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(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(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[] v1,
FieldVector<T> v2)
Construct a vector by appending one vector to another vector.
|
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.
|
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.
|
| Constructor and Description |
|---|
MultiStartMultivariateOptimizer(MultivariateOptimizer optimizer,
int starts,
RandomVectorGenerator generator)
Create a multi-start optimizer from a single-start optimizer.
|
| Constructor and 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.
|
| Modifier and Type | Method and 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.
|
| Modifier and Type | Method and Description |
|---|---|
void |
AggregatableStatistic.aggregate(T other)
Aggregates the provided instance into this instance.
|
| Constructor and 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. |
| Modifier and Type | Method and Description |
|---|---|
void |
RandomPercentile.aggregate(RandomPercentile other)
Aggregates the provided instance into this instance.
|
| Constructor and 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 |
| Constructor and 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. |
| Modifier and Type | Method and 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.
|
| Modifier and Type | Method and 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.
|
static double |
InferenceTestUtils.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 |
InferenceTestUtils.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 |
InferenceTestUtils.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. |
double |
TTest.homoscedasticT(double[] sample1,
double[] sample2)
Computes a 2-sample t statistic, under the hypothesis of equal
subpopulation variances.
|
static double |
InferenceTestUtils.homoscedasticT(double[] sample1,
double[] sample2) |
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.homoscedasticT(StatisticalSummary sampleStats1,
StatisticalSummary sampleStats2) |
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 double |
InferenceTestUtils.homoscedasticTTest(double[] sample1,
double[] sample2) |
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 boolean |
InferenceTestUtils.homoscedasticTTest(double[] sample1,
double[] sample2,
double alpha) |
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.homoscedasticTTest(StatisticalSummary sampleStats1,
StatisticalSummary sampleStats2) |
static double |
InferenceTestUtils.kolmogorovSmirnovStatistic(double[] x,
double[] y) |
static double |
InferenceTestUtils.kolmogorovSmirnovStatistic(RealDistribution dist,
double[] data) |
static double |
InferenceTestUtils.kolmogorovSmirnovTest(double[] x,
double[] y) |
static double |
InferenceTestUtils.kolmogorovSmirnovTest(double[] x,
double[] y,
boolean strict) |
static double |
InferenceTestUtils.kolmogorovSmirnovTest(RealDistribution dist,
double[] data) |
static double |
InferenceTestUtils.kolmogorovSmirnovTest(RealDistribution dist,
double[] data,
boolean strict) |
static boolean |
InferenceTestUtils.kolmogorovSmirnovTest(RealDistribution dist,
double[] data,
double alpha) |
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) |
static double |
InferenceTestUtils.oneWayAnovaPValue(Collection<double[]> categoryData) |
static boolean |
InferenceTestUtils.oneWayAnovaTest(Collection<double[]> categoryData,
double alpha) |
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.pairedT(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 double |
InferenceTestUtils.pairedTTest(double[] sample1,
double[] sample2) |
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 boolean |
InferenceTestUtils.pairedTTest(double[] sample1,
double[] sample2,
double alpha) |
double |
TTest.t(double[] sample1,
double[] sample2)
Computes a 2-sample t statistic, without the hypothesis of equal
subpopulation variances.
|
static double |
InferenceTestUtils.t(double[] sample1,
double[] sample2) |
double |
TTest.t(double mu,
double[] observed)
Computes a
t statistic given observed values and a comparison constant.
|
static double |
InferenceTestUtils.t(double mu,
double[] observed) |
double |
TTest.t(double mu,
StatisticalSummary sampleStats)
|
static double |
InferenceTestUtils.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 two
StatisticalSummary instances, without the
assumption of equal subpopulation variances. |
static double |
InferenceTestUtils.t(StatisticalSummary sampleStats1,
StatisticalSummary sampleStats2) |
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 double |
InferenceTestUtils.tTest(double[] sample1,
double[] sample2) |
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 boolean |
InferenceTestUtils.tTest(double[] sample1,
double[] sample2,
double 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. |
static double |
InferenceTestUtils.tTest(double mu,
double[] sample) |
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 boolean |
InferenceTestUtils.tTest(double mu,
double[] sample,
double alpha) |
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 double |
InferenceTestUtils.tTest(double mu,
StatisticalSummary sampleStats) |
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 boolean |
InferenceTestUtils.tTest(double mu,
StatisticalSummary sampleStats,
double alpha) |
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 double |
InferenceTestUtils.tTest(StatisticalSummary sampleStats1,
StatisticalSummary sampleStats2) |
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. |
static boolean |
InferenceTestUtils.tTest(StatisticalSummary sampleStats1,
StatisticalSummary sampleStats2,
double 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.
|
| Modifier and Type | Method and 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.
|
| Constructor and Description |
|---|
Incrementor(int max,
Incrementor.MaxCountExceededCallback cb)
Creates an Incrementor.
|
KthSelector(PivotingStrategy pivotingStrategy)
Constructor with specified pivoting strategy
|
ResizableDoubleArray(ResizableDoubleArray original)
Copy constructor.
|
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