| Package | Description |
|---|---|
| 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.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.filter |
Implementations of common discrete-time linear filters.
|
| org.apache.commons.math3.linear |
Linear algebra support.
|
| org.apache.commons.math3.stat |
Data storage, manipulation and summary routines.
|
| org.apache.commons.math3.stat.inference |
Classes providing hypothesis testing.
|
| org.apache.commons.math3.stat.regression |
Statistical routines involving multivariate data.
|
| org.apache.commons.math3.util |
Convenience routines and common data structures used throughout the commons-math library.
|
| 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[][] |
FieldHermiteInterpolator.derivatives(T x,
int order)
Interpolate value and first derivatives at a specified abscissa.
|
PolynomialFunction[] |
HermiteInterpolator.getPolynomials()
Compute the interpolation polynomials.
|
MultivariateFunction |
MultivariateInterpolator.interpolate(double[][] xval,
double[] yval)
Computes an interpolating function for the data set.
|
MultivariateFunction |
MicrosphereInterpolator.interpolate(double[][] xval,
double[] yval)
Deprecated.
Computes an interpolating function for the data set.
|
MultivariateFunction |
MicrosphereProjectionInterpolator.interpolate(double[][] xval,
double[] yval)
Computes an interpolating function for the data set.
|
PolynomialSplineFunction |
LoessInterpolator.interpolate(double[] xval,
double[] yval)
Compute an interpolating function by performing a loess fit
on the data at the original abscissae and then building a cubic spline
with a
SplineInterpolator
on the resulting fit. |
BicubicSplineInterpolatingFunction |
SmoothingPolynomialBicubicSplineInterpolator.interpolate(double[] xval,
double[] yval,
double[][] fval)
Deprecated.
Compute an interpolating function for the dataset.
|
BicubicInterpolatingFunction |
BicubicInterpolator.interpolate(double[] xval,
double[] yval,
double[][] fval)
Compute an interpolating function for the dataset.
|
BicubicSplineInterpolatingFunction |
BicubicSplineInterpolator.interpolate(double[] xval,
double[] yval,
double[][] fval)
Deprecated.
Compute an interpolating function for the dataset.
|
PiecewiseBicubicSplineInterpolatingFunction |
PiecewiseBicubicSplineInterpolator.interpolate(double[] xval,
double[] yval,
double[][] fval)
Compute an interpolating function for the dataset.
|
BivariateFunction |
BivariateGridInterpolator.interpolate(double[] xval,
double[] yval,
double[][] fval)
Compute an interpolating function for the dataset.
|
TricubicInterpolatingFunction |
TricubicInterpolator.interpolate(double[] xval,
double[] yval,
double[] zval,
double[][][] fval)
Compute an interpolating function for the dataset.
|
TrivariateFunction |
TrivariateGridInterpolator.interpolate(double[] xval,
double[] yval,
double[] zval,
double[][][] fval)
Compute an interpolating function for the dataset.
|
TricubicSplineInterpolatingFunction |
TricubicSplineInterpolator.interpolate(double[] xval,
double[] yval,
double[] zval,
double[][][] fval)
Deprecated.
Compute an interpolating function for the dataset.
|
double[] |
LoessInterpolator.smooth(double[] xval,
double[] yval)
Compute a loess fit on the data at the original abscissae.
|
double[] |
LoessInterpolator.smooth(double[] xval,
double[] yval,
double[] weights)
Compute a weighted loess fit on the data at the original abscissae.
|
DerivativeStructure[] |
HermiteInterpolator.value(DerivativeStructure x)
Interpolate value at a specified abscissa.
|
double[] |
HermiteInterpolator.value(double x)
Interpolate value at a specified abscissa.
|
T[] |
FieldHermiteInterpolator.value(T x)
Interpolate value at a specified abscissa.
|
| Constructor and Description |
|---|
BicubicInterpolatingFunction(double[] x,
double[] y,
double[][] f,
double[][] dFdX,
double[][] dFdY,
double[][] d2FdXdY) |
BicubicSplineInterpolatingFunction(double[] x,
double[] y,
double[][] f,
double[][] dFdX,
double[][] dFdY,
double[][] d2FdXdY)
Deprecated.
|
BicubicSplineInterpolatingFunction(double[] x,
double[] y,
double[][] f,
double[][] dFdX,
double[][] dFdY,
double[][] d2FdXdY,
boolean initializeDerivatives)
Deprecated.
|
MicrosphereInterpolatingFunction(double[][] xval,
double[] yval,
int brightnessExponent,
int microsphereElements,
UnitSphereRandomVectorGenerator rand)
Deprecated.
|
PiecewiseBicubicSplineInterpolatingFunction(double[] x,
double[] y,
double[][] f) |
TricubicInterpolatingFunction(double[] x,
double[] y,
double[] z,
double[][][] f,
double[][][] dFdX,
double[][][] dFdY,
double[][][] dFdZ,
double[][][] d2FdXdY,
double[][][] d2FdXdZ,
double[][][] d2FdYdZ,
double[][][] d3FdXdYdZ) |
TricubicSplineInterpolatingFunction(double[] x,
double[] y,
double[] z,
double[][][] f,
double[][][] dFdX,
double[][][] dFdY,
double[][][] dFdZ,
double[][][] d2FdXdY,
double[][][] d2FdXdZ,
double[][][] d2FdYdZ,
double[][][] d3FdXdYdZ)
Deprecated.
|
| 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 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.
|
DerivativeStructure |
PolynomialFunction.value(DerivativeStructure t)
Simple mathematical function.
|
double |
PolynomialFunction.Parametric.value(double x,
double... parameters)
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 |
|---|
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[].
|
| Modifier and Type | Method and Description |
|---|---|
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,
double initial,
int maxEval)
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.
|
Complex |
LaguerreSolver.solveComplex(double[] coefficients,
double initial,
int maxEval)
Find a complex root for the polynomial with the given coefficients,
starting from the given initial value.
|
| Modifier and Type | Method and Description |
|---|---|
static ComplexFormat |
ComplexFormat.getInstance(String imaginaryCharacter,
Locale locale)
Returns the default complex format for the given locale.
|
| Constructor and Description |
|---|
ComplexFormat(String imaginaryCharacter)
Create an instance with a custom imaginary character, and the default
number format for both real and imaginary parts.
|
ComplexFormat(String imaginaryCharacter,
NumberFormat format)
Create an instance with a custom imaginary character, and a custom number
format for both real and imaginary parts.
|
ComplexFormat(String imaginaryCharacter,
NumberFormat realFormat,
NumberFormat imaginaryFormat)
Create an instance with a custom imaginary character, a custom number
format for the real part, and a custom number format for the imaginary
part.
|
| Constructor and Description |
|---|
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. |
| Modifier and Type | Method and Description |
|---|---|
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 |
AbstractFieldMatrix.copySubMatrix(int[] selectedRows,
int[] selectedColumns,
T[][] destination)
Copy a submatrix.
|
void |
FieldMatrix.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.
|
protected static <T extends FieldElement<T>> |
AbstractFieldMatrix.extractField(T[][] d)
Get the elements type from an array.
|
RealMatrix |
AbstractRealMatrix.getSubMatrix(int[] selectedRows,
int[] selectedColumns)
Gets a submatrix.
|
FieldMatrix<T> |
AbstractFieldMatrix.getSubMatrix(int[] selectedRows,
int[] selectedColumns)
Get a submatrix.
|
RealMatrix |
RealMatrix.getSubMatrix(int[] selectedRows,
int[] selectedColumns)
Gets a submatrix.
|
FieldMatrix<T> |
FieldMatrix.getSubMatrix(int[] selectedRows,
int[] selectedColumns)
Get a submatrix.
|
void |
BlockRealMatrix.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 |
Array2DRowRealMatrix.setSubMatrix(double[][] subMatrix,
int row,
int column)
Replace the submatrix starting at
row, column using data in the
input subMatrix array. |
void |
Array2DRowFieldMatrix.setSubMatrix(T[][] subMatrix,
int row,
int column)
Replace the submatrix starting at
(row, column) using data in the
input subMatrix array. |
void |
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. |
void |
FieldMatrix.setSubMatrix(T[][] subMatrix,
int row,
int column)
Replace the submatrix starting at
(row, column) using data in the
input subMatrix array. |
| 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[] v)
Create a new (column)
FieldMatrix<T> using v as the
data for the unique column of the created matrix. |
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.
|
| Modifier and Type | Method and Description |
|---|---|
static double |
StatUtils.meanDifference(double[] sample1,
double[] sample2)
Returns the mean of the (signed) differences between corresponding elements of the
input arrays -- i.e., sum(sample1[i] - sample2[i]) / sample1.length.
|
static double |
StatUtils.sumDifference(double[] sample1,
double[] sample2)
Returns the sum of the (signed) differences between corresponding elements of the
input arrays -- i.e., sum(sample1[i] - sample2[i]).
|
| Modifier and Type | Method and Description |
|---|---|
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.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. |
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.
|
| Modifier and Type | Method and Description |
|---|---|
RegressionResults |
SimpleRegression.regress()
Performs a regression on data present in buffers and outputs a RegressionResults object.
|
RegressionResults |
UpdatingMultipleLinearRegression.regress()
Performs a regression on data present in buffers and outputs a RegressionResults object
|
| Modifier and Type | Method and Description |
|---|---|
static double[] |
MathArrays.convolve(double[] x,
double[] h)
Calculates the
convolution between two sequences.
|
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