| Package | Description |
|---|---|
| org.hipparchus.analysis.integration |
Numerical integration (quadrature) algorithms for 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.geometry |
This package is the top level package for geometry.
|
| org.hipparchus.geometry.euclidean.oned |
This package provides basic 1D geometry components.
|
| org.hipparchus.geometry.euclidean.threed |
This package provides basic 3D geometry components.
|
| org.hipparchus.geometry.euclidean.twod |
This package provides basic 2D geometry components.
|
| org.hipparchus.geometry.euclidean.twod.hull |
This package provides algorithms to generate the convex hull
for a set of points in an two-dimensional euclidean space.
|
| org.hipparchus.geometry.hull |
This package provides interfaces and classes related to the convex hull problem.
|
| org.hipparchus.geometry.spherical.twod |
This package provides basic geometry components on the 2-sphere.
|
| org.hipparchus.linear |
Linear algebra support.
|
| org.hipparchus.migration.exception |
This package provides migration classes from Apache Commons Math to Hipparchus.
|
| org.hipparchus.migration.geometry.euclidean |
This package provides migration classes from Apache Commons Math to Hipparchus.
|
| org.hipparchus.migration.ode |
This package provides migration classes from Apache Commons Math to Hipparchus.
|
| org.hipparchus.migration.ode.sampling |
This package provides migration classes from Apache Commons Math to Hipparchus.
|
| org.hipparchus.migration.optim.linear |
This package provides migration classes from Apache Commons Math to Hipparchus.
|
| org.hipparchus.ode |
This package provides classes to solve Ordinary Differential Equations problems.
|
| org.hipparchus.ode.events |
Events
|
| org.hipparchus.ode.nonstiff |
This package provides classes to solve non-stiff Ordinary Differential Equations problems.
|
| org.hipparchus.ode.sampling |
This package provides classes to handle sampling steps during
Ordinary Differential Equations integration.
|
| org.hipparchus.optim |
Generally, optimizers are algorithms that will either
minimize or
maximize
a scalar function, called the
objective
function. |
| org.hipparchus.optim.linear |
Optimization algorithms for linear constrained problems.
|
| org.hipparchus.optim.nonlinear.scalar |
Algorithms for optimizing a scalar function.
|
| org.hipparchus.optim.nonlinear.scalar.gradient |
This package provides optimization algorithms that require derivatives.
|
| org.hipparchus.optim.nonlinear.scalar.noderiv |
This package provides optimization algorithms that do not require derivatives.
|
| org.hipparchus.optim.univariate |
One-dimensional optimization algorithms.
|
| org.hipparchus.random |
Random number and random data generators.
|
| org.hipparchus.special |
Implementations of special functions such as Beta and Gamma.
|
| org.hipparchus.stat.descriptive |
Generic univariate and multivariate summary statistic objects.
|
| 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 | Description |
|---|---|---|
protected double |
BaseAbstractUnivariateIntegrator.computeObjectiveValue(double point) |
Compute the objective function value.
|
protected abstract double |
BaseAbstractUnivariateIntegrator.doIntegrate() |
Method for implementing actual integration algorithms in derived
classes.
|
protected double |
IterativeLegendreGaussIntegrator.doIntegrate() |
Method for implementing actual integration algorithms in derived
classes.
|
protected double |
MidPointIntegrator.doIntegrate() |
Method for implementing actual integration algorithms in derived
classes.
|
protected double |
RombergIntegrator.doIntegrate() |
Method for implementing actual integration algorithms in derived
classes.
|
protected double |
SimpsonIntegrator.doIntegrate() |
Method for implementing actual integration algorithms in derived
classes.
|
protected double |
TrapezoidIntegrator.doIntegrate() |
Method for implementing actual integration algorithms in derived
classes.
|
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.
|
| Modifier and Type | Method | Description |
|---|---|---|
protected double |
BaseAbstractUnivariateSolver.computeObjectiveValue(double point) |
Compute the objective function value.
|
protected DerivativeStructure |
AbstractUnivariateDifferentiableSolver.computeObjectiveValueAndDerivative(double point) |
Compute the objective function value.
|
protected abstract double |
BaseAbstractUnivariateSolver.doSolve() |
Method for implementing actual optimization algorithms in derived
classes.
|
protected double |
BaseSecantSolver.doSolve() |
Method for implementing actual optimization algorithms in derived
classes.
|
protected double |
BisectionSolver.doSolve() |
Method for implementing actual optimization algorithms in derived
classes.
|
protected double |
BrentSolver.doSolve() |
Method for implementing actual optimization algorithms in derived
classes.
|
double |
LaguerreSolver.doSolve() |
Method for implementing actual optimization algorithms in derived
classes.
|
protected double |
MullerSolver.doSolve() |
Method for implementing actual optimization algorithms in derived
classes.
|
protected double |
MullerSolver2.doSolve() |
Method for implementing actual optimization algorithms in derived
classes.
|
protected double |
NewtonRaphsonSolver.doSolve() |
Method for implementing actual optimization algorithms in derived
classes.
|
protected double |
RiddersSolver.doSolve() |
Method for implementing actual optimization algorithms in derived
classes.
|
protected double |
SecantSolver.doSolve() |
Method for implementing actual optimization algorithms in derived
classes.
|
protected BracketedUnivariateSolver.Interval |
BaseSecantSolver.doSolveInterval() |
Find a root and return the containing interval.
|
protected void |
BaseAbstractUnivariateSolver.incrementEvaluationCount() |
Increment the evaluation count by one.
|
double |
BaseAbstractUnivariateSolver.solve(int maxEval,
FUNC f,
double startValue) |
Solve for a zero in the vicinity of
startValue. |
double |
BaseAbstractUnivariateSolver.solve(int maxEval,
FUNC f,
double min,
double max,
double startValue) |
Solve for a zero in the given interval, start at
startValue. |
double |
BaseUnivariateSolver.solve(int maxEval,
FUNC f,
double min,
double max) |
Solve for a zero root in the given interval.
|
double |
BaseUnivariateSolver.solve(int maxEval,
FUNC f,
double min,
double max,
double startValue) |
Solve for a zero in the given interval, start at
startValue. |
double |
BracketingNthOrderBrentSolver.solve(int maxEval,
UnivariateFunction f,
double min,
double max,
double startValue,
AllowedSolution allowedSolution) |
Solve for a zero in the given interval, start at
startValue. |
double |
BracketingNthOrderBrentSolver.solve(int maxEval,
UnivariateFunction f,
double min,
double max,
AllowedSolution allowedSolution) |
Solve for a zero in the given interval.
|
double |
NewtonRaphsonSolver.solve(int maxEval,
UnivariateDifferentiableFunction f,
double min,
double max) |
Find a zero near the midpoint of
min and max. |
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.
|
BracketedUnivariateSolver.Interval |
BaseSecantSolver.solveInterval(int maxEval,
UnivariateFunction f,
double min,
double max,
double startValue) |
Solve for a zero in the given interval and return a tolerance interval surrounding
the root.
|
default BracketedRealFieldUnivariateSolver.Interval<T> |
BracketedRealFieldUnivariateSolver.solveInterval(int maxEval,
RealFieldUnivariateFunction<T> f,
T min,
T max) |
Solve for a zero in the given interval and return a tolerance interval surrounding
the root.
|
BracketedRealFieldUnivariateSolver.Interval<T> |
BracketedRealFieldUnivariateSolver.solveInterval(int maxEval,
RealFieldUnivariateFunction<T> f,
T min,
T max,
T startValue) |
Solve for a zero in the given interval and return a tolerance interval surrounding
the root.
|
default BracketedUnivariateSolver.Interval |
BracketedUnivariateSolver.solveInterval(int maxEval,
FUNC f,
double min,
double max) |
Solve for a zero in the given interval and return a tolerance interval surrounding
the root.
|
BracketedUnivariateSolver.Interval |
BracketedUnivariateSolver.solveInterval(int maxEval,
FUNC f,
double min,
double max,
double startValue) |
Solve for a zero in the given interval and return a tolerance interval surrounding
the root.
|
BracketedUnivariateSolver.Interval |
BracketingNthOrderBrentSolver.solveInterval(int maxEval,
UnivariateFunction f,
double min,
double max,
double startValue) |
Solve for a zero in the given interval and return a tolerance interval surrounding
the root.
|
BracketedRealFieldUnivariateSolver.Interval<T> |
FieldBracketingNthOrderBrentSolver.solveInterval(int maxEval,
RealFieldUnivariateFunction<T> f,
T min,
T max,
T startValue) |
Solve for a zero in the given interval and return a tolerance interval surrounding
the root.
|
| Modifier and Type | Method | Description |
|---|---|---|
abstract List<? extends Cluster<T>> |
Clusterer.cluster(Collection<T> points) |
Perform a cluster analysis on the given set of
Clusterable instances. |
List<CentroidCluster<T>> |
KMeansPlusPlusClusterer.cluster(Collection<T> points) |
Runs the K-means++ clustering algorithm.
|
List<CentroidCluster<T>> |
MultiKMeansPlusPlusClusterer.cluster(Collection<T> points) |
Runs the K-means++ clustering algorithm.
|
| Modifier and Type | Method | Description |
|---|---|---|
double |
RootsOfUnity.getImaginary(int k) |
Get the imaginary part of the
k-th n-th root of unity. |
double |
RootsOfUnity.getReal(int k) |
Get the real part of the
k-th n-th root of unity. |
boolean |
RootsOfUnity.isCounterClockWise() |
|
Complex |
ComplexFormat.parse(String source) |
Parses a string to produce a
Complex object. |
| Modifier and Type | Method | Description |
|---|---|---|
StringBuffer |
FractionFormat.format(Object obj,
StringBuffer toAppendTo,
FieldPosition pos) |
Formats an object and appends the result to a StringBuffer.
|
BigFraction |
BigFractionFormat.parse(String source) |
Parses a string to produce a
BigFraction object. |
Fraction |
FractionFormat.parse(String source) |
Parses a string to produce a
Fraction object. |
| Constructor | Description |
|---|---|
BigFraction(double value,
double epsilon,
int maxIterations) |
Create a fraction given the double value and maximum error allowed.
|
BigFraction(double value,
int maxDenominator) |
Create a fraction given the double value and maximum denominator.
|
Fraction(double value) |
Create a fraction given the double value.
|
Fraction(double value,
double epsilon,
int maxIterations) |
Create a fraction given the double value and maximum error allowed.
|
Fraction(double value,
int maxDenominator) |
Create a fraction given the double value and maximum denominator.
|
| Modifier and Type | Method | Description |
|---|---|---|
abstract Vector<S> |
VectorFormat.parse(String source) |
Parses a string to produce a
Vector object. |
| Modifier and Type | Method | Description |
|---|---|---|
Vector1D |
Vector1DFormat.parse(String source) |
Parses a string to produce a
Vector object. |
| Modifier and Type | Method | Description |
|---|---|---|
T[] |
FieldRotation.getAngles(RotationOrder order) |
Deprecated.
as of 3.6, replaced with
FieldRotation.getAngles(RotationOrder, RotationConvention) |
T[] |
FieldRotation.getAngles(RotationOrder order,
RotationConvention convention) |
Get the Cardan or Euler angles corresponding to the instance.
|
double[] |
Rotation.getAngles(RotationOrder order) |
Deprecated.
as of 3.6, replaced with
Rotation.getAngles(RotationOrder, RotationConvention) |
double[] |
Rotation.getAngles(RotationOrder order,
RotationConvention convention) |
Get the Cardan or Euler angles corresponding to the instance.
|
Vector3D |
Vector3DFormat.parse(String source) |
Parses a string to produce a
Vector3D object. |
| Modifier and Type | Method | Description |
|---|---|---|
Vector2D |
Vector2DFormat.parse(String source) |
Parses a string to produce a
Vector object. |
| Modifier and Type | Method | Description |
|---|---|---|
ConvexHull2D |
ConvexHullGenerator2D.generate(Collection<Vector2D> points) |
Builds the convex hull from the set of input points.
|
| Modifier and Type | Method | Description |
|---|---|---|
ConvexHull<S,P> |
ConvexHullGenerator.generate(Collection<P> points) |
Builds the convex hull from the set of input points.
|
| Modifier and Type | Method | Description |
|---|---|---|
protected void |
SphericalPolygonsSet.computeGeometricalProperties() |
Compute some geometrical properties.
|
List<Vertex> |
SphericalPolygonsSet.getBoundaryLoops() |
Get the boundary loops of the polygon.
|
| Modifier and Type | Method | Description |
|---|---|---|
RealVector |
IterativeLinearSolver.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,
RealVector x0) |
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 |
PreconditionedIterativeLinearSolver.solve(RealLinearOperator a,
RealLinearOperator m,
RealVector b,
RealVector x0) |
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 |
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,
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 |
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 |
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 |
IterativeLinearSolver.solveInPlace(RealLinearOperator a,
RealVector b,
RealVector x0) |
Returns an estimate of the solution to the linear system A · x =
b.
|
abstract RealVector |
PreconditionedIterativeLinearSolver.solveInPlace(RealLinearOperator a,
RealLinearOperator m,
RealVector b,
RealVector x0) |
Returns an estimate of the solution to the linear system A · x =
b.
|
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,
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 |
SymmLQ.solveInPlace(RealLinearOperator a,
RealVector b,
RealVector x) |
Returns an estimate of the solution to the linear system A · x =
b.
|
| Modifier and Type | Class | Description |
|---|---|---|
class |
ConvergenceException |
Deprecated.
as of 1.0, this exception is replaced by
MathIllegalStateException |
class |
MathInternalError |
Deprecated.
as of 1.0, this exception is replaced by
MathIllegalStateException |
class |
MathParseException |
Deprecated.
as of 1.0, this exception is replaced by
MathIllegalStateException |
class |
MaxCountExceededException |
Deprecated.
as of 1.0, this exception is replaced by
MathIllegalStateException |
class |
TooManyEvaluationsException |
Deprecated.
as of 1.0, this exception is replaced by
MathIllegalArgumentException |
class |
TooManyIterationsException |
Deprecated.
as of 1.0, this exception is replaced by
MathIllegalArgumentException |
| Modifier and Type | Class | Description |
|---|---|---|
class |
CardanEulerSingularityException |
Deprecated.
as of 1.0, this exception is replaced by
MathIllegalStateException |
| Modifier and Type | Method | Description |
|---|---|---|
void |
FirstOrderDifferentialEquations.computeDerivatives(double t,
double[] y,
double[] yDot) |
Deprecated.
Get the current time derivative of the state vector.
|
default double[] |
SecondaryEquations.computeDerivatives(double t,
double[] primary,
double[] primaryDot,
double[] secondary) |
Deprecated.
Compute the derivatives related to the secondary state parameters.
|
void |
SecondaryEquations.computeDerivatives(double t,
double[] primary,
double[] primaryDot,
double[] secondary,
double[] secondaryDot) |
Deprecated.
Compute the derivatives related to the secondary state parameters.
|
double[][] |
MainStateJacobianProvider.computeMainStateJacobian(double t,
double[] y,
double[] yDot) |
Deprecated.
Compute the jacobian matrix of ODE with respect to main state.
|
default double[] |
ParameterJacobianProvider.computeParameterJacobian(double t,
double[] y,
double[] yDot,
String paramName) |
Deprecated.
Compute the Jacobian matrix of ODE with respect to one parameter.
|
void |
ParameterJacobianProvider.computeParameterJacobian(double t,
double[] y,
double[] yDot,
String paramName,
double[] dFdP) |
Deprecated.
Compute the Jacobian matrix of ODE with respect to one parameter.
|
double[] |
ContinuousOutputModel.getInterpolatedDerivatives() |
Deprecated.
Get the derivatives of the state vector of the interpolated point.
|
double[] |
ContinuousOutputModel.getInterpolatedSecondaryDerivatives(int secondaryStateIndex) |
Deprecated.
Get the interpolated secondary derivatives corresponding to the secondary equations.
|
double[] |
ContinuousOutputModel.getInterpolatedSecondaryState(int secondaryStateIndex) |
Deprecated.
Get the interpolated secondary state corresponding to the secondary equations.
|
double[] |
ContinuousOutputModel.getInterpolatedState() |
Deprecated.
Get the state vector of the interpolated point.
|
| Modifier and Type | Method | Description |
|---|---|---|
StepInterpolator |
StepInterpolator.copy() |
Deprecated.
Copy the instance.
|
double[] |
StepInterpolator.getInterpolatedDerivatives() |
Deprecated.
as of 1.0, replaced with
ODEStateInterpolator.getInterpolatedState(double).ODEStateAndDerivative.getPrimaryDerivative() |
double[] |
StepInterpolator.getInterpolatedSecondaryDerivatives(int index) |
Deprecated.
as of 1.0, replaced with
ODEStateInterpolator.getInterpolatedState(double).ODEStateAndDerivative.getSecondaryDerivative(int) |
double[] |
StepInterpolator.getInterpolatedSecondaryState(int index) |
Deprecated.
as of 1.0, replaced with
ODEStateInterpolator.getInterpolatedState(double).ODEState.getSecondaryState(int) |
double[] |
StepInterpolator.getInterpolatedState() |
Deprecated.
as of 1.0, replaced with
ODEStateInterpolator.getInterpolatedState(double).ODEState.getPrimaryState() |
void |
StepHandler.handleStep(org.hipparchus.migration.ode.sampling.MigrationStepInterpolator interpolator,
boolean isLast) |
Deprecated.
Handle the last accepted step
|
default void |
StepHandler.handleStep(ODEStateInterpolator interpolator,
boolean isLast) |
Deprecated.
Handle the last accepted step
|
| Modifier and Type | Class | Description |
|---|---|---|
class |
NoFeasibleSolutionException |
Deprecated.
as of 1.0, this exception is replaced by
MathIllegalStateException |
class |
UnboundedSolutionException |
Deprecated.
as of 1.0, this exception is replaced by
MathIllegalStateException |
| Modifier and Type | Method | Description |
|---|---|---|
protected FieldODEStateAndDerivative<T> |
AbstractFieldIntegrator.acceptStep(AbstractFieldODEStateInterpolator<T> interpolator,
T tEnd) |
Accept a step, triggering events and step handlers.
|
protected ODEStateAndDerivative |
AbstractIntegrator.acceptStep(AbstractODEStateInterpolator interpolator,
double tEnd) |
Accept a step, triggering events and step handlers.
|
void |
DenseOutputModel.append(DenseOutputModel model) |
Append another model at the end of the instance.
|
void |
FieldDenseOutputModel.append(FieldDenseOutputModel<T> model) |
Append another model at the end of the instance.
|
T[] |
AbstractFieldIntegrator.computeDerivatives(T t,
T[] y) |
Compute the derivatives and check the number of evaluations.
|
double[] |
AbstractIntegrator.computeDerivatives(double t,
double[] y) |
Compute the derivatives and check the number of evaluations.
|
double[] |
ExpandableODE.computeDerivatives(double t,
double[] y) |
Get the current time derivative of the complete state vector.
|
T[] |
FieldExpandableODE.computeDerivatives(T t,
T[] y) |
Get the current time derivative of the complete state vector.
|
T[] |
FieldSecondaryODE.computeDerivatives(T t,
T[] primary,
T[] primaryDot,
T[] secondary) |
Compute the derivatives related to the secondary state parameters.
|
double[] |
SecondaryODE.computeDerivatives(double t,
double[] primary,
double[] primaryDot,
double[] secondary) |
Compute the derivatives related to the secondary state parameters.
|
double[][] |
ODEJacobiansProvider.computeMainStateJacobian(double t,
double[] y,
double[] yDot) |
Compute the Jacobian matrix of ODE with respect to state.
|
double[] |
NamedParameterJacobianProvider.computeParameterJacobian(double t,
double[] y,
double[] yDot,
String paramName) |
Compute the Jacobian matrix of ODE with respect to one parameter.
|
void |
DenseOutputModel.handleStep(ODEStateInterpolator interpolator,
boolean isLast) |
Handle the last accepted step.
|
void |
FieldDenseOutputModel.handleStep(FieldODEStateInterpolator<T> interpolator,
boolean isLast) |
Handle the last accepted step.
|
FieldODEStateAndDerivative<T> |
FieldODEIntegrator.integrate(FieldExpandableODE<T> equations,
FieldODEState<T> initialState,
T finalTime) |
Integrate the differential equations up to the given time.
|
ODEStateAndDerivative |
ODEIntegrator.integrate(ExpandableODE equations,
ODEState initialState,
double finalTime) |
Integrate the differential equations up to the given time.
|
default double |
ODEIntegrator.integrate(OrdinaryDifferentialEquation equations,
double t0,
double[] y0,
double t,
double[] y) |
Deprecated.
as of 1.0, replaced with
ODEIntegrator.integrate(ExpandableODE, ODEState, double) |
default ODEStateAndDerivative |
ODEIntegrator.integrate(OrdinaryDifferentialEquation equations,
ODEState initialState,
double finalTime) |
Integrate the differential equations up to the given time.
|
protected void |
MultistepFieldIntegrator.start(FieldExpandableODE<T> equations,
FieldODEState<T> initialState,
T t) |
Start the integration.
|
protected void |
MultistepIntegrator.start(ExpandableODE equations,
ODEState initialState,
double finalTime) |
Start the integration.
|
| Modifier and Type | Method | Description |
|---|---|---|
boolean |
EventState.evaluateStep(ODEStateInterpolator interpolator) |
Evaluate the impact of the proposed step on the event handler.
|
boolean |
FieldEventState.evaluateStep(FieldODEStateInterpolator<T> interpolator) |
Evaluate the impact of the proposed step on the event handler.
|
void |
EventState.reinitializeBegin(ODEStateInterpolator interpolator) |
Reinitialize the beginning of the step.
|
void |
FieldEventState.reinitializeBegin(FieldODEStateInterpolator<T> interpolator) |
Reinitialize the beginning of the step.
|
| Modifier and Type | Method | Description |
|---|---|---|
T |
AdaptiveStepsizeFieldIntegrator.initializeStep(boolean forward,
int order,
T[] scale,
FieldODEStateAndDerivative<T> state0,
FieldEquationsMapper<T> mapper) |
Initialize the integration step.
|
double |
AdaptiveStepsizeIntegrator.initializeStep(boolean forward,
int order,
double[] scale,
ODEStateAndDerivative state0,
EquationsMapper mapper) |
Initialize the integration step.
|
FieldODEStateAndDerivative<T> |
AdamsBashforthFieldIntegrator.integrate(FieldExpandableODE<T> equations,
FieldODEState<T> initialState,
T finalTime) |
Integrate the differential equations up to the given time.
|
ODEStateAndDerivative |
AdamsBashforthIntegrator.integrate(ExpandableODE equations,
ODEState initialState,
double finalTime) |
Integrate the differential equations up to the given time.
|
abstract FieldODEStateAndDerivative<T> |
AdamsFieldIntegrator.integrate(FieldExpandableODE<T> equations,
FieldODEState<T> initialState,
T finalTime) |
Integrate the differential equations up to the given time.
|
abstract ODEStateAndDerivative |
AdamsIntegrator.integrate(ExpandableODE equations,
ODEState initialState,
double finalTime) |
Integrate the differential equations up to the given time.
|
FieldODEStateAndDerivative<T> |
AdamsMoultonFieldIntegrator.integrate(FieldExpandableODE<T> equations,
FieldODEState<T> initialState,
T finalTime) |
Integrate the differential equations up to the given time.
|
ODEStateAndDerivative |
AdamsMoultonIntegrator.integrate(ExpandableODE equations,
ODEState initialState,
double finalTime) |
Integrate the differential equations up to the given time.
|
FieldODEStateAndDerivative<T> |
EmbeddedRungeKuttaFieldIntegrator.integrate(FieldExpandableODE<T> equations,
FieldODEState<T> initialState,
T finalTime) |
Integrate the differential equations up to the given time.
|
ODEStateAndDerivative |
EmbeddedRungeKuttaIntegrator.integrate(ExpandableODE equations,
ODEState initialState,
double finalTime) |
Integrate the differential equations up to the given time.
|
ODEStateAndDerivative |
GraggBulirschStoerIntegrator.integrate(ExpandableODE equations,
ODEState initialState,
double finalTime) |
Integrate the differential equations up to the given time.
|
FieldODEStateAndDerivative<T> |
RungeKuttaFieldIntegrator.integrate(FieldExpandableODE<T> equations,
FieldODEState<T> initialState,
T finalTime) |
Integrate the differential equations up to the given time.
|
ODEStateAndDerivative |
RungeKuttaIntegrator.integrate(ExpandableODE equations,
ODEState initialState,
double finalTime) |
Integrate the differential equations up to the given time.
|
| Modifier and Type | Method | Description |
|---|---|---|
protected abstract FieldODEStateAndDerivative<T> |
AbstractFieldODEStateInterpolator.computeInterpolatedStateAndDerivatives(FieldEquationsMapper<T> equationsMapper,
T time,
T theta,
T thetaH,
T oneMinusThetaH) |
Compute the state and derivatives at the interpolated time.
|
protected abstract ODEStateAndDerivative |
AbstractODEStateInterpolator.computeInterpolatedStateAndDerivatives(EquationsMapper equationsMapper,
double time,
double theta,
double thetaH,
double oneMinusThetaH) |
Compute the state and derivatives at the interpolated time.
|
void |
FieldODEStepHandler.handleStep(FieldODEStateInterpolator<T> interpolator,
boolean isLast) |
Handle the last accepted step
|
void |
FieldStepNormalizer.handleStep(FieldODEStateInterpolator<T> interpolator,
boolean isLast) |
Handle the last accepted step
|
void |
ODEStepHandler.handleStep(ODEStateInterpolator interpolator,
boolean isLast) |
Handle the last accepted step
|
void |
StepNormalizer.handleStep(ODEStateInterpolator interpolator,
boolean isLast) |
Handle the last accepted step
|
| Modifier and Type | Method | Description |
|---|---|---|
protected void |
BaseOptimizer.incrementEvaluationCount() |
Increment the evaluation count.
|
protected void |
BaseOptimizer.incrementIterationCount() |
Increment the iteration count.
|
PAIR |
BaseOptimizer.optimize() |
Performs the optimization.
|
PAIR |
BaseOptimizer.optimize(OptimizationData... optData) |
Stores data and performs the optimization.
|
| Modifier and Type | Method | Description |
|---|---|---|
protected void |
SimplexSolver.doIteration(org.hipparchus.optim.linear.SimplexTableau tableau) |
Runs one iteration of the Simplex method on the given model.
|
PointValuePair |
SimplexSolver.doOptimize() |
Performs the bulk of the optimization algorithm.
|
PointValuePair |
LinearOptimizer.optimize(OptimizationData... optData) |
Stores data and performs the optimization.
|
PointValuePair |
SimplexSolver.optimize(OptimizationData... optData) |
Stores data and performs the optimization.
|
protected void |
SimplexSolver.solvePhase1(org.hipparchus.optim.linear.SimplexTableau tableau) |
Solves Phase 1 of the Simplex method.
|
| Modifier and Type | Method | Description |
|---|---|---|
PointValuePair |
GradientMultivariateOptimizer.optimize(OptimizationData... optData) |
Stores data and performs the optimization.
|
PointValuePair |
MultivariateOptimizer.optimize(OptimizationData... optData) |
Stores data and performs the optimization.
|
| Modifier and Type | Method | Description |
|---|---|---|
PointValuePair |
NonLinearConjugateGradientOptimizer.optimize(OptimizationData... optData) |
Stores data and performs the optimization.
|
| Modifier and Type | Method | Description |
|---|---|---|
PointValuePair |
CMAESOptimizer.optimize(OptimizationData... optData) |
Stores data and performs the optimization.
|
| Modifier and Type | Method | Description |
|---|---|---|
UnivariatePointValuePair |
UnivariateOptimizer.optimize(OptimizationData... optData) |
Stores data and performs the optimization.
|
| Constructor | Description |
|---|---|
SobolSequenceGenerator(int dimension,
InputStream is) |
Construct a new Sobol sequence generator for the given space dimension with
direction vectors loaded from the given stream.
|
| Modifier and Type | Method | Description |
|---|---|---|
double |
BesselJ.value(double x) |
Returns the value of the constructed Bessel function of the first kind,
for the passed argument.
|
static double |
BesselJ.value(double order,
double x) |
Returns the first Bessel function, \(J_{order}(x)\).
|
| Modifier and Type | Method | Description |
|---|---|---|
void |
DescriptiveStatistics.removeMostRecentValue() |
Removes the most recent value from the dataset.
|
double |
DescriptiveStatistics.replaceMostRecentValue(double v) |
Replaces the most recently stored value with the given value.
|
| Modifier and Type | Method | Description |
|---|---|---|
double |
EmpiricalDistribution.getNextValue() |
Generates a random value from this distribution.
|
| Modifier and Type | Method | Description |
|---|---|---|
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.
|
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. |
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. |
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. |
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 |
InferenceTestUtils.chiSquareTest(double[] expected,
long[] observed) |
|
static boolean |
InferenceTestUtils.chiSquareTest(double[] expected,
long[] observed,
double alpha) |
|
static double |
InferenceTestUtils.chiSquareTest(long[][] counts) |
|
static boolean |
InferenceTestUtils.chiSquareTest(long[][] counts,
double alpha) |
|
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. |
boolean |
ChiSquareTest.chiSquareTestDataSetsComparison(long[] observed1,
long[] observed2,
double alpha) |
Performs a Chi-Square two sample test comparing two binned data
sets.
|
static double |
InferenceTestUtils.chiSquareTestDataSetsComparison(long[] observed1,
long[] observed2) |
|
static boolean |
InferenceTestUtils.chiSquareTestDataSetsComparison(long[] observed1,
long[] observed2,
double alpha) |
|
double |
GTest.gTest(double[] expected,
long[] observed) |
Returns the observed significance level, or p-value,
associated with a G-Test for goodness of fit comparing the
observed frequency counts to those in the expected array. |
boolean |
GTest.gTest(double[] expected,
long[] observed,
double alpha) |
Performs a G-Test (Log-Likelihood Ratio Test) for goodness of fit
evaluating the null hypothesis that the observed counts conform to the
frequency distribution described by the expected counts, with
significance level
alpha. |
static double |
InferenceTestUtils.gTest(double[] expected,
long[] observed) |
|
static boolean |
InferenceTestUtils.gTest(double[] expected,
long[] observed,
double alpha) |
|
double |
GTest.gTestDataSetsComparison(long[] observed1,
long[] observed2) |
Returns the observed significance level, or
p-value, associated with a G-Value (Log-Likelihood Ratio) for two
sample test comparing bin frequency counts in
observed1 and
observed2. |
boolean |
GTest.gTestDataSetsComparison(long[] observed1,
long[] observed2,
double alpha) |
Performs a G-Test (Log-Likelihood Ratio Test) comparing two binned
data sets.
|
static double |
InferenceTestUtils.gTestDataSetsComparison(long[] observed1,
long[] observed2) |
|
static boolean |
InferenceTestUtils.gTestDataSetsComparison(long[] observed1,
long[] observed2,
double alpha) |
|
double |
GTest.gTestIntrinsic(double[] expected,
long[] observed) |
Returns the intrinsic (Hardy-Weinberg proportions) p-Value, as described
in p64-69 of McDonald, J.H.
|
static double |
InferenceTestUtils.gTestIntrinsic(double[] expected,
long[] observed) |
|
static double |
InferenceTestUtils.homoscedasticTTest(double[] sample1,
double[] sample2) |
|
static boolean |
InferenceTestUtils.homoscedasticTTest(double[] sample1,
double[] sample2,
double alpha) |
|
static double |
InferenceTestUtils.homoscedasticTTest(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.
|
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. |
protected double |
TTest.homoscedasticTTest(double m1,
double m2,
double v1,
double v2,
double n1,
double n2) |
Computes p-value for 2-sided, 2-sample t-test, under the assumption
of equal subpopulation variances.
|
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.oneWayAnovaPValue(Collection<double[]> categoryData) |
|
static boolean |
InferenceTestUtils.oneWayAnovaTest(Collection<double[]> categoryData,
double alpha) |
|
static double |
InferenceTestUtils.pairedTTest(double[] sample1,
double[] sample2) |
|
static boolean |
InferenceTestUtils.pairedTTest(double[] sample1,
double[] sample2,
double alpha) |
|
double |
TTest.pairedTTest(double[] sample1,
double[] sample2) |
Returns the observed significance level, or
p-value, associated with a paired, two-sample, two-tailed t-test
based on the data in the input arrays.
|
boolean |
TTest.pairedTTest(double[] sample1,
double[] sample2,
double alpha) |
Performs a paired t-test evaluating the null hypothesis that the
mean of the paired differences 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 |
InferenceTestUtils.tTest(double[] sample1,
double[] sample2) |
|
static boolean |
InferenceTestUtils.tTest(double[] sample1,
double[] sample2,
double alpha) |
|
static double |
InferenceTestUtils.tTest(double mu,
double[] sample) |
|
static boolean |
InferenceTestUtils.tTest(double mu,
double[] sample,
double alpha) |
|
static double |
InferenceTestUtils.tTest(double mu,
StatisticalSummary sampleStats) |
|
static boolean |
InferenceTestUtils.tTest(double mu,
StatisticalSummary sampleStats,
double alpha) |
|
static double |
InferenceTestUtils.tTest(StatisticalSummary sampleStats1,
StatisticalSummary sampleStats2) |
|
static boolean |
InferenceTestUtils.tTest(StatisticalSummary sampleStats1,
StatisticalSummary sampleStats2,
double alpha) |
|
double |
TTest.tTest(double[] sample1,
double[] sample2) |
Returns the observed significance level, or
p-value, associated with a two-sample, two-tailed t-test
comparing the means of the input arrays.
|
boolean |
TTest.tTest(double[] sample1,
double[] sample2,
double alpha) |
Performs a
two-sided t-test evaluating the null hypothesis that
sample1
and sample2 are drawn from populations with the same mean,
with significance level 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. |
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. |
protected double |
TTest.tTest(double m,
double mu,
double v,
double n) |
Computes p-value for 2-sided, 1-sample t-test.
|
protected double |
TTest.tTest(double m1,
double m2,
double v1,
double v2,
double n1,
double n2) |
Computes p-value for 2-sided, 2-sample t-test.
|
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. |
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. |
double |
TTest.tTest(StatisticalSummary sampleStats1,
StatisticalSummary sampleStats2) |
Returns the observed significance level, or
p-value, associated with a two-sample, two-tailed t-test
comparing the means of the datasets described by two StatisticalSummary
instances.
|
boolean |
TTest.tTest(StatisticalSummary sampleStats1,
StatisticalSummary sampleStats2,
double alpha) |
Performs a
two-sided t-test evaluating the null hypothesis that
sampleStats1 and sampleStats2 describe
datasets drawn from populations with the same mean, with significance
level alpha. |
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 | Description |
|---|---|---|
double |
ContinuedFraction.evaluate(double x) |
Evaluates the continued fraction at the value x.
|
double |
ContinuedFraction.evaluate(double x,
double epsilon) |
Evaluates the continued fraction at the value x.
|
double |
ContinuedFraction.evaluate(double x,
double epsilon,
int maxIterations) |
Evaluates the continued fraction at the value x.
|
double |
ContinuedFraction.evaluate(double x,
int maxIterations) |
Evaluates the continued fraction at the value x.
|
void |
IterationManager.incrementIterationCount() |
Increments the iteration count by one, and throws an exception if the
maximum number of iterations is reached.
|
double |
ResizableDoubleArray.substituteMostRecentElement(double value) |
Substitutes
value for the most recently added value. |
void |
Incrementor.MaxCountExceededCallback.trigger(int maximalCount) |
Function called when the maximal count has been reached.
|
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