Package | Description |
---|---|
org.apache.commons.math3.analysis.interpolation |
Univariate real functions interpolation algorithms.
|
org.apache.commons.math3.geometry.euclidean.threed |
This package provides basic 3D geometry components.
|
org.apache.commons.math3.geometry.euclidean.twod |
This package provides basic 2D geometry components.
|
org.apache.commons.math3.linear |
Linear algebra support.
|
org.apache.commons.math3.ode |
This package provides classes to solve Ordinary Differential Equations problems.
|
org.apache.commons.math3.ode.nonstiff |
This package provides classes to solve non-stiff Ordinary Differential Equations problems.
|
org.apache.commons.math3.stat.correlation |
Correlations/Covariance computations.
|
org.apache.commons.math3.stat.descriptive.moment |
Summary statistics based on moments.
|
org.apache.commons.math3.stat.inference |
Classes providing hypothesis testing and confidence interval
construction.
|
org.apache.commons.math3.transform |
Implementations of transform methods, including Fast Fourier transforms.
|
Constructor and Description |
---|
BicubicSplineInterpolatingFunction(double[] x,
double[] y,
double[][] f,
double[][] dFdX,
double[][] dFdY,
double[][] d2FdXdY) |
Constructor and Description |
---|
Vector3D(double[] v)
Simple constructor.
|
Constructor and Description |
---|
Vector2D(double[] v)
Simple constructor.
|
Modifier and Type | Class and Description |
---|---|
class |
NonSquareMatrixException
Exception to be thrown when a square matrix is expected.
|
class |
NonSquareOperatorException
Exception to be thrown when a square linear operator is expected.
|
Modifier and Type | Method and Description |
---|---|
protected static void |
PreconditionedIterativeLinearSolver.checkParameters(RealLinearOperator a,
RealLinearOperator minv,
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. |
RealVector |
RealLinearOperator.operateTranspose(RealVector x)
Returns the result of multiplying the transpose of
this operator
by the vector x (optional operation). |
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. |
RealVector |
PreconditionedIterativeLinearSolver.solve(RealLinearOperator a,
RealLinearOperator minv,
RealVector b)
Returns an estimate of the solution to the linear system A · x =
b.
|
RealVector |
SymmLQ.solve(RealLinearOperator a,
RealLinearOperator minv,
RealVector b)
Returns an estimate of the solution to the linear system A · x =
b.
|
RealVector |
SymmLQ.solve(RealLinearOperator a,
RealLinearOperator minv,
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 minv,
RealVector b,
RealVector x0)
Returns an estimate of the solution to the linear system A · x =
b.
|
RealVector |
SymmLQ.solve(RealLinearOperator a,
RealLinearOperator minv,
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 |
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 |
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 |
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 |
ConjugateGradient.solveInPlace(RealLinearOperator a,
RealLinearOperator minv,
RealVector b,
RealVector x0)
Returns an estimate of the solution to the linear system A · x =
b.
|
abstract RealVector |
PreconditionedIterativeLinearSolver.solveInPlace(RealLinearOperator a,
RealLinearOperator minv,
RealVector b,
RealVector x0)
Returns an estimate of the solution to the linear system A · x =
b.
|
RealVector |
SymmLQ.solveInPlace(RealLinearOperator a,
RealLinearOperator minv,
RealVector b,
RealVector x)
Returns an estimate of the solution to the linear system A · x =
b.
|
RealVector |
SymmLQ.solveInPlace(RealLinearOperator a,
RealLinearOperator minv,
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.
|
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.
|
Constructor and Description |
---|
Array2DRowFieldMatrix(Field<T> field,
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. |
Modifier and Type | Method and Description |
---|---|
void |
EquationsMapper.extractEquationData(double[] complete,
double[] equationData)
Extract equation data from a complete state or derivative array.
|
void |
EquationsMapper.insertEquationData(double[] equationData,
double[] complete)
Insert equation data into a complete state or derivative array.
|
void |
ExpandableStatefulODE.setCompleteState(double[] completeState)
Set the complete current state.
|
void |
JacobianMatrices.setInitialMainStateJacobian(double[][] dYdY0)
Set the initial value of the Jacobian matrix with respect to state.
|
void |
ExpandableStatefulODE.setPrimaryState(double[] primaryState)
Set primary part of the current state.
|
void |
ExpandableStatefulODE.setSecondaryState(int index,
double[] secondaryState)
Set secondary part of the current state.
|
Modifier and Type | Method and Description |
---|---|
protected void |
AdaptiveStepsizeIntegrator.sanityChecks(ExpandableStatefulODE equations,
double t)
Check the integration span.
|
Modifier and Type | Method and Description |
---|---|
void |
StorelessCovariance.increment(double[] data)
Increment the covariance matrix with one row of data.
|
Modifier and Type | Method and Description |
---|---|
void |
VectorialCovariance.increment(double[] v)
Add a new vector to the sample.
|
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. |
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(double[] expected,
long[] observed) |
double |
ChiSquareTest.chiSquare(double[] expected,
long[] observed)
|
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.chiSquareDataSetsComparison(long[] observed1,
long[] observed2) |
double |
ChiSquareTest.chiSquareDataSetsComparison(long[] observed1,
long[] observed2)
Computes a
Chi-Square two sample test statistic comparing bin frequency counts
in
observed1 and observed2 . |
static double |
TestUtils.chiSquareTest(double[] expected,
long[] observed) |
double |
ChiSquareTest.chiSquareTest(double[] expected,
long[] observed)
Returns the observed significance level, or
p-value, associated with a
Chi-square goodness of fit test comparing the
observed
frequency counts to those in the expected array. |
static boolean |
TestUtils.chiSquareTest(double[] expected,
long[] observed,
double alpha) |
boolean |
ChiSquareTest.chiSquareTest(double[] expected,
long[] observed,
double alpha)
Performs a
Chi-square goodness of fit test evaluating the null hypothesis that the
observed counts conform to the frequency distribution described by the expected
counts, with significance level
alpha . |
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.chiSquareTestDataSetsComparison(long[] observed1,
long[] observed2) |
double |
ChiSquareTest.chiSquareTestDataSetsComparison(long[] observed1,
long[] observed2)
Returns the observed significance level, or
p-value, associated with a Chi-Square two sample test comparing
bin frequency counts in
observed1 and
observed2 . |
static boolean |
TestUtils.chiSquareTestDataSetsComparison(long[] observed1,
long[] observed2,
double alpha) |
boolean |
ChiSquareTest.chiSquareTestDataSetsComparison(long[] observed1,
long[] observed2,
double alpha)
Performs a Chi-Square two sample test comparing two binned data
sets.
|
static double |
TestUtils.oneWayAnovaFValue(Collection<double[]> categoryData) |
static double |
TestUtils.oneWayAnovaPValue(Collection<double[]> categoryData) |
static boolean |
TestUtils.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 |
TestUtils.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 |
TestUtils.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 |
TestUtils.pairedTTest(double[] sample1,
double[] sample2,
double 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 |
---|---|
static Complex[] |
TransformUtils.createComplexArray(double[][] dataRI)
Builds a new array of
Complex from the specified two dimensional
array of real and imaginary parts. |
Copyright © 2003-2012 Apache Software Foundation. All Rights Reserved.