Package | Description |
---|---|
org.apache.commons.math3 |
Common classes used throughout the commons-math library.
|
org.apache.commons.math3.analysis.differentiation |
This package holds the main interfaces and basic building block classes
dealing with differentiation.
|
org.apache.commons.math3.analysis.function |
The
function package contains function objects that wrap the
methods contained in Math , as well as common
mathematical functions such as the gaussian and sinc functions. |
org.apache.commons.math3.analysis.integration.gauss |
Gauss family of quadrature schemes.
|
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.complex |
Complex number type and implementations of complex transcendental
functions.
|
org.apache.commons.math3.dfp |
Decimal floating point library for Java
|
org.apache.commons.math3.distribution |
Implementations of common discrete and continuous distributions.
|
org.apache.commons.math3.distribution.fitting |
Fitting of parameters against distributions.
|
org.apache.commons.math3.filter |
Implementations of common discrete-time linear filters.
|
org.apache.commons.math3.genetics |
This package provides Genetic Algorithms components and implementations.
|
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.optim.nonlinear.scalar.noderiv |
This package provides optimization algorithms that do not require derivatives.
|
org.apache.commons.math3.optim.nonlinear.vector |
Algorithms for optimizing a vector function.
|
org.apache.commons.math3.optimization.direct |
This package provides optimization algorithms that don't require derivatives.
|
org.apache.commons.math3.stat |
Data storage, manipulation and summary routines.
|
org.apache.commons.math3.stat.correlation |
Correlations/Covariance computations.
|
org.apache.commons.math3.stat.descriptive |
Generic univariate summary statistic objects.
|
org.apache.commons.math3.stat.descriptive.moment |
Summary statistics based on moments.
|
org.apache.commons.math3.stat.inference |
Classes providing hypothesis testing and confidence interval
construction.
|
org.apache.commons.math3.transform |
Implementations of transform methods, including Fast Fourier transforms.
|
org.apache.commons.math3.util |
Convenience routines and common data structures used throughout the commons-math library.
|
Modifier and Type | Method and Description |
---|---|
T |
RealFieldElement.atan2(T x)
Two arguments arc tangent operation.
|
T |
RealFieldElement.hypot(T y)
Returns the hypotenuse of a triangle with sides
this and y
- sqrt(this2 +y2)avoiding intermediate overflow or underflow. |
T |
RealFieldElement.linearCombination(double[] a,
T[] b)
Compute a linear combination.
|
T |
RealFieldElement.linearCombination(T[] a,
T[] b)
Compute a linear combination.
|
T |
RealFieldElement.pow(T e)
Power operation.
|
T |
RealFieldElement.remainder(T a)
IEEE remainder operator.
|
Modifier and Type | Method and Description |
---|---|
DerivativeStructure |
DerivativeStructure.add(DerivativeStructure a)
Compute this + a.
|
DerivativeStructure |
DerivativeStructure.atan2(DerivativeStructure x)
Two arguments arc tangent operation.
|
static DerivativeStructure |
DerivativeStructure.atan2(DerivativeStructure y,
DerivativeStructure x)
Two arguments arc tangent operation.
|
void |
DSCompiler.checkCompatibility(DSCompiler compiler)
Check rules set compatibility.
|
DerivativeStructure |
DerivativeStructure.compose(double... f)
Compute composition of the instance by a univariate function.
|
DerivativeStructure |
DerivativeStructure.divide(DerivativeStructure a)
Compute this ÷ a.
|
double |
DerivativeStructure.getPartialDerivative(int... orders)
Get a partial derivative.
|
int |
DSCompiler.getPartialDerivativeIndex(int... orders)
Get the index of a partial derivative in the array.
|
DerivativeStructure |
DerivativeStructure.hypot(DerivativeStructure y)
Returns the hypotenuse of a triangle with sides
this and y
- sqrt(this2 +y2)avoiding intermediate overflow or underflow. |
static DerivativeStructure |
DerivativeStructure.hypot(DerivativeStructure x,
DerivativeStructure y)
Returns the hypotenuse of a triangle with sides
x and y
- sqrt(x2 +y2)avoiding intermediate overflow or underflow. |
DerivativeStructure |
DerivativeStructure.linearCombination(DerivativeStructure[] a,
DerivativeStructure[] b)
Compute a linear combination.
|
DerivativeStructure |
DerivativeStructure.linearCombination(DerivativeStructure a1,
DerivativeStructure b1,
DerivativeStructure a2,
DerivativeStructure b2)
Compute a linear combination.
|
DerivativeStructure |
DerivativeStructure.linearCombination(DerivativeStructure a1,
DerivativeStructure b1,
DerivativeStructure a2,
DerivativeStructure b2,
DerivativeStructure a3,
DerivativeStructure b3)
Compute a linear combination.
|
DerivativeStructure |
DerivativeStructure.linearCombination(DerivativeStructure a1,
DerivativeStructure b1,
DerivativeStructure a2,
DerivativeStructure b2,
DerivativeStructure a3,
DerivativeStructure b3,
DerivativeStructure a4,
DerivativeStructure b4)
Compute a linear combination.
|
DerivativeStructure |
DerivativeStructure.linearCombination(double[] a,
DerivativeStructure[] b)
Compute a linear combination.
|
DerivativeStructure |
DerivativeStructure.linearCombination(double a1,
DerivativeStructure b1,
double a2,
DerivativeStructure b2)
Compute a linear combination.
|
DerivativeStructure |
DerivativeStructure.linearCombination(double a1,
DerivativeStructure b1,
double a2,
DerivativeStructure b2,
double a3,
DerivativeStructure b3)
Compute a linear combination.
|
DerivativeStructure |
DerivativeStructure.linearCombination(double a1,
DerivativeStructure b1,
double a2,
DerivativeStructure b2,
double a3,
DerivativeStructure b3,
double a4,
DerivativeStructure b4)
Compute a linear combination.
|
DerivativeStructure |
DerivativeStructure.multiply(DerivativeStructure a)
Compute this × a.
|
DerivativeStructure |
DerivativeStructure.pow(DerivativeStructure e)
Power operation.
|
DerivativeStructure |
DerivativeStructure.remainder(DerivativeStructure a)
IEEE remainder operator.
|
DerivativeStructure |
DerivativeStructure.subtract(DerivativeStructure a)
Compute this - a.
|
DerivativeStructure |
UnivariateDifferentiableFunction.value(DerivativeStructure t)
Simple mathematical function.
|
Constructor and Description |
---|
DerivativeStructure(double a1,
DerivativeStructure ds1,
double a2,
DerivativeStructure ds2)
Linear combination constructor.
|
DerivativeStructure(double a1,
DerivativeStructure ds1,
double a2,
DerivativeStructure ds2,
double a3,
DerivativeStructure ds3)
Linear combination constructor.
|
DerivativeStructure(double a1,
DerivativeStructure ds1,
double a2,
DerivativeStructure ds2,
double a3,
DerivativeStructure ds3,
double a4,
DerivativeStructure ds4)
Linear combination constructor.
|
DerivativeStructure(int parameters,
int order,
double... derivatives)
Build an instance from all its derivatives.
|
Modifier and Type | Method and Description |
---|---|
double[] |
Gaussian.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[] |
Sigmoid.Parametric.gradient(double x,
double... param)
Computes the value of the gradient at
x . |
double[] |
HarmonicOscillator.Parametric.gradient(double x,
double... param)
Computes the value of the gradient at
x . |
DerivativeStructure |
Gaussian.value(DerivativeStructure t)
Simple mathematical function.
|
DerivativeStructure |
Sigmoid.value(DerivativeStructure t)
Simple mathematical function.
|
DerivativeStructure |
HarmonicOscillator.value(DerivativeStructure t)
Simple mathematical function.
|
DerivativeStructure |
Sinc.value(DerivativeStructure t)
Simple mathematical function.
|
double |
Gaussian.Parametric.value(double x,
double... param)
Computes the value of the Gaussian 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 |
Sigmoid.Parametric.value(double x,
double... param)
Computes the value of the sigmoid at
x . |
double |
HarmonicOscillator.Parametric.value(double x,
double... param)
Computes the value of the harmonic oscillator 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 |
---|---|
protected void |
BaseRuleFactory.addRule(Pair<T[],T[]> rule)
Stores a rule.
|
protected Pair<BigDecimal[],BigDecimal[]> |
LegendreHighPrecisionRuleFactory.computeRule(int numberOfPoints)
Computes the rule for the given order.
|
protected Pair<Double[],Double[]> |
LegendreRuleFactory.computeRule(int numberOfPoints)
Computes the rule for the given order.
|
protected abstract Pair<T[],T[]> |
BaseRuleFactory.computeRule(int numberOfPoints)
Computes the rule for the given order.
|
Pair<double[],double[]> |
BaseRuleFactory.getRule(int numberOfPoints)
Gets a copy of the quadrature rule with the given number of integration
points.
|
protected Pair<T[],T[]> |
BaseRuleFactory.getRuleInternal(int numberOfPoints)
Gets a rule.
|
Constructor and Description |
---|
GaussIntegrator(double[] points,
double[] weights)
Creates an integrator from the given
points and weights . |
Modifier and Type | Method and Description |
---|---|
void |
FieldHermiteInterpolator.addSamplePoint(T x,
T[]... value)
Add a sample point.
|
protected static double[] |
DividedDifferenceInterpolator.computeDividedDifference(double[] x,
double[] y)
Return a copy of the divided difference array.
|
MultivariateFunction |
MicrosphereInterpolator.interpolate(double[][] xval,
double[] yval)
Computes an interpolating function for the data set.
|
MultivariateFunction |
MultivariateInterpolator.interpolate(double[][] xval,
double[] yval)
Computes an interpolating function for the data set.
|
PolynomialFunctionNewtonForm |
DividedDifferenceInterpolator.interpolate(double[] x,
double[] y)
Compute an interpolating function for the dataset.
|
PolynomialFunctionLagrangeForm |
NevilleInterpolator.interpolate(double[] x,
double[] y)
Computes an interpolating function for the data set.
|
UnivariateFunction |
UnivariateInterpolator.interpolate(double[] xval,
double[] yval)
Compute an interpolating function for the dataset.
|
PolynomialSplineFunction |
SplineInterpolator.interpolate(double[] x,
double[] y)
Computes an interpolating function for the data set.
|
PolynomialSplineFunction |
LinearInterpolator.interpolate(double[] x,
double[] y)
Computes a linear 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 |
BicubicSplineInterpolator.interpolate(double[] xval,
double[] yval,
double[][] fval)
Compute an interpolating function for the dataset.
|
BicubicSplineInterpolatingFunction |
SmoothingPolynomialBicubicSplineInterpolator.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.
|
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)
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.
|
double |
MicrosphereInterpolatingFunction.value(double[] point) |
Constructor and Description |
---|
BicubicSplineInterpolatingFunction(double[] x,
double[] y,
double[][] f,
double[][] dFdX,
double[][] dFdY,
double[][] d2FdXdY) |
MicrosphereInterpolatingFunction(double[][] xval,
double[] yval,
int brightnessExponent,
int microsphereElements,
UnitSphereRandomVectorGenerator rand) |
TricubicSplineInterpolatingFunction(double[] x,
double[] y,
double[] z,
double[][][] f,
double[][][] dFdX,
double[][][] dFdY,
double[][][] dFdZ,
double[][][] d2FdXdY,
double[][][] d2FdXdZ,
double[][][] d2FdYdZ,
double[][][] d3FdXdYdZ) |
Modifier and Type | Method and Description |
---|---|
static double |
PolynomialFunctionLagrangeForm.evaluate(double[] x,
double[] y,
double z)
Evaluate the Lagrange polynomial using
Neville's Algorithm.
|
static double |
PolynomialFunctionNewtonForm.evaluate(double[] a,
double[] c,
double z)
Evaluate the Newton polynomial using nested multiplication.
|
protected static void |
PolynomialFunctionNewtonForm.verifyInputArray(double[] a,
double[] c)
Verifies that the input arrays are valid.
|
static boolean |
PolynomialFunctionLagrangeForm.verifyInterpolationArray(double[] x,
double[] y,
boolean abort)
Check that the interpolation arrays are valid.
|
Constructor and Description |
---|
PolynomialFunctionLagrangeForm(double[] x,
double[] y)
Construct a Lagrange polynomial with the given abscissas and function
values.
|
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.
|
Constructor and Description |
---|
Quaternion(double scalar,
double[] v)
Builds a quaternion from scalar and vector parts.
|
Modifier and Type | Method and Description |
---|---|
Dfp |
Dfp.atan2(Dfp x)
Two arguments arc tangent operation.
|
Dfp |
Dfp.linearCombination(Dfp[] a,
Dfp[] b)
Compute a linear combination.
|
Dfp |
Dfp.linearCombination(double[] a,
Dfp[] b)
Compute a linear combination.
|
Modifier and Type | Method and Description |
---|---|
double |
MultivariateNormalDistribution.density(double[] vals)
Returns the probability density function (PDF) of this distribution
evaluated at the specified point
x . |
Constructor and Description |
---|
EnumeratedIntegerDistribution(int[] singletons,
double[] probabilities)
Create a discrete distribution using the given probability mass function
definition.
|
EnumeratedIntegerDistribution(RandomGenerator rng,
int[] singletons,
double[] probabilities)
Create a discrete distribution using the given random number generator
and probability mass function definition.
|
EnumeratedRealDistribution(double[] singletons,
double[] probabilities)
Create a discrete distribution using the given probability mass function
enumeration.
|
EnumeratedRealDistribution(RandomGenerator rng,
double[] singletons,
double[] probabilities)
Create a discrete distribution using the given random number generator
and probability mass function enumeration.
|
MixtureMultivariateNormalDistribution(RandomGenerator rng,
List<Pair<Double,MultivariateNormalDistribution>> components)
Creates a mixture model from a list of distributions and their
associated weights.
|
MultivariateNormalDistribution(double[] means,
double[][] covariances)
Creates a multivariate normal distribution with the given mean vector and
covariance matrix.
|
MultivariateNormalDistribution(RandomGenerator rng,
double[] means,
double[][] covariances)
Creates a multivariate normal distribution with the given mean vector and
covariance matrix.
|
Modifier and Type | Method and Description |
---|---|
static MixtureMultivariateNormalDistribution |
MultivariateNormalMixtureExpectationMaximization.estimate(double[][] data,
int numComponents)
Helper method to create a multivariate normal mixture model which can be
used to initialize
MultivariateNormalMixtureExpectationMaximization.fit(MixtureMultivariateNormalDistribution) . |
void |
MultivariateNormalMixtureExpectationMaximization.fit(MixtureMultivariateNormalDistribution initialMixture,
int maxIterations,
double threshold)
Fit a mixture model to the data supplied to the constructor.
|
Constructor and Description |
---|
MultivariateNormalMixtureExpectationMaximization(double[][] data)
Creates an object to fit a multivariate normal mixture model to data.
|
Modifier and Type | Method and Description |
---|---|
void |
KalmanFilter.correct(double[] z)
Correct the current state estimate with an actual measurement.
|
void |
KalmanFilter.correct(RealVector z)
Correct the current state estimate with an actual measurement.
|
void |
KalmanFilter.predict(double[] u)
Predict the internal state estimation one time step ahead.
|
void |
KalmanFilter.predict(RealVector u)
Predict the internal state estimation one time step ahead.
|
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. |
KalmanFilter(ProcessModel process,
MeasurementModel measurement)
Creates a new Kalman filter with the given process and measurement models.
|
Modifier and Type | Method and Description |
---|---|
ChromosomePair |
OrderedCrossover.crossover(Chromosome first,
Chromosome second)
Perform a crossover operation on the given chromosomes.
|
ChromosomePair |
NPointCrossover.crossover(Chromosome first,
Chromosome second)
Performs a N-point crossover.
|
ChromosomePair |
OnePointCrossover.crossover(Chromosome first,
Chromosome second)
Performs one point crossover.
|
ChromosomePair |
CycleCrossover.crossover(Chromosome first,
Chromosome second)
Perform a crossover operation on the given chromosomes.
|
ChromosomePair |
UniformCrossover.crossover(Chromosome first,
Chromosome second)
Perform a crossover operation on the given chromosomes.
|
static <S> List<Double> |
RandomKey.inducedPermutation(List<S> originalData,
List<S> permutedData)
Generates a representation of a permutation corresponding to a
permutation which yields
permutedData when applied to
originalData . |
protected ChromosomePair |
OrderedCrossover.mate(AbstractListChromosome<T> first,
AbstractListChromosome<T> second)
|
protected ChromosomePair |
CycleCrossover.mate(AbstractListChromosome<T> first,
AbstractListChromosome<T> second)
Helper for
CycleCrossover.crossover(Chromosome, Chromosome) . |
Constructor and Description |
---|
FieldVector3D(T[] v)
Simple constructor.
|
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 |
---|---|
ArrayFieldVector<T> |
ArrayFieldVector.add(ArrayFieldVector<T> v)
Compute the sum of
this and v . |
FieldVector<T> |
ArrayFieldVector.add(FieldVector<T> v)
Compute the sum of
this and v . |
FieldVector<T> |
FieldVector.add(FieldVector<T> v)
Compute the sum of
this and v . |
FieldVector<T> |
SparseFieldVector.add(FieldVector<T> v)
Deprecated.
Compute the sum of
this and v . |
OpenMapRealVector |
OpenMapRealVector.add(OpenMapRealVector v)
Deprecated.
Optimized method to add two OpenMapRealVectors.
|
RealVector |
RealVector.add(RealVector v)
Compute the sum of this vector and
v . |
ArrayRealVector |
ArrayRealVector.add(RealVector v)
Compute the sum of this vector and
v . |
RealVector |
OpenMapRealVector.add(RealVector v)
Deprecated.
Compute the sum of this vector and
v . |
FieldVector<T> |
SparseFieldVector.add(SparseFieldVector<T> v)
Deprecated.
Optimized method to add sparse vectors.
|
static void |
MatrixUtils.checkMultiplicationCompatible(AnyMatrix left,
AnyMatrix right)
Check if matrices are multiplication compatible
|
protected void |
AbstractFieldMatrix.checkMultiplicationCompatible(FieldMatrix<T> m)
Check if a matrix is multiplication compatible with the instance.
|
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. |
protected void |
ArrayFieldVector.checkVectorDimensions(FieldVector<T> v)
Check if instance and specified vectors have the same dimension.
|
protected void |
RealVector.checkVectorDimensions(int n)
Check if instance dimension is equal to some expected value.
|
protected void |
ArrayFieldVector.checkVectorDimensions(int n)
Check if instance dimension is equal to some expected value.
|
protected void |
ArrayRealVector.checkVectorDimensions(int n)
Check if instance dimension is equal to some expected value.
|
protected void |
SparseFieldVector.checkVectorDimensions(int n)
Deprecated.
Check if instance dimension is equal to some expected value.
|
protected void |
RealVector.checkVectorDimensions(RealVector v)
Check if instance and specified vectors have the same dimension.
|
protected void |
ArrayRealVector.checkVectorDimensions(RealVector v)
Check if instance and specified vectors have the same dimension.
|
RealVector |
RealVector.combine(double a,
double b,
RealVector y)
Returns a new vector representing
a * this + b * y , the linear
combination of this and y . |
ArrayRealVector |
ArrayRealVector.combine(double a,
double b,
RealVector y)
Returns a new vector representing
a * this + b * y , the linear
combination of this and y . |
RealVector |
RealVector.combineToSelf(double a,
double b,
RealVector y)
Updates
this with the linear combination of this and
y . |
ArrayRealVector |
ArrayRealVector.combineToSelf(double a,
double b,
RealVector y)
Updates
this with the linear combination of this and
y . |
double |
RealVector.cosine(RealVector v)
Computes the cosine of the angle between this vector and the
argument.
|
static <T extends FieldElement<T>> |
MatrixUtils.createFieldMatrix(T[][] data)
Returns a
FieldMatrix whose entries are the the values in the
the input array. |
RealMatrix |
DiagonalMatrix.createMatrix(int rowDimension,
int columnDimension)
Create a new RealMatrix of the same type as the instance with the
supplied
row and column dimensions.
|
static RealMatrix |
MatrixUtils.createRealMatrix(double[][] data)
Returns a
RealMatrix whose entries are the the values in the
the input array. |
T |
ArrayFieldVector.dotProduct(ArrayFieldVector<T> v)
Compute the dot product.
|
T |
ArrayFieldVector.dotProduct(FieldVector<T> v)
Compute the dot product.
|
T |
FieldVector.dotProduct(FieldVector<T> v)
Compute the dot product.
|
T |
SparseFieldVector.dotProduct(FieldVector<T> v)
Deprecated.
Compute the dot product.
|
double |
OpenMapRealVector.dotProduct(OpenMapRealVector v)
Deprecated.
as of 3.1 (to be removed in 4.0). The computation is
performed by the parent class. The method must be kept to maintain
backwards compatibility.
|
double |
RealVector.dotProduct(RealVector v)
Compute the dot product of this vector with
v . |
double |
ArrayRealVector.dotProduct(RealVector v)
Compute the dot product of this vector with
v . |
ArrayFieldVector<T> |
ArrayFieldVector.ebeDivide(ArrayFieldVector<T> v)
Element-by-element division.
|
FieldVector<T> |
ArrayFieldVector.ebeDivide(FieldVector<T> v)
Element-by-element division.
|
FieldVector<T> |
FieldVector.ebeDivide(FieldVector<T> v)
Element-by-element division.
|
FieldVector<T> |
SparseFieldVector.ebeDivide(FieldVector<T> v)
Deprecated.
Element-by-element division.
|
abstract RealVector |
RealVector.ebeDivide(RealVector v)
Deprecated.
As of version 3.1, this method is deprecated, and will be
removed in version 4.0. This decision follows the discussion reported in
MATH-803.
Uses of this method involving sparse implementations of
RealVector might lead to wrong results. Since there is no
satisfactory correction to this bug, this method is deprecated. Users who
want to preserve this feature are advised to implement
RealVectorPreservingVisitor (possibly ignoring corner cases for
the sake of efficiency). |
ArrayRealVector |
ArrayRealVector.ebeDivide(RealVector v)
Element-by-element division.
|
OpenMapRealVector |
OpenMapRealVector.ebeDivide(RealVector v)
Deprecated.
Element-by-element division.
|
ArrayFieldVector<T> |
ArrayFieldVector.ebeMultiply(ArrayFieldVector<T> v)
Element-by-element multiplication.
|
FieldVector<T> |
ArrayFieldVector.ebeMultiply(FieldVector<T> v)
Element-by-element multiplication.
|
FieldVector<T> |
FieldVector.ebeMultiply(FieldVector<T> v)
Element-by-element multiplication.
|
FieldVector<T> |
SparseFieldVector.ebeMultiply(FieldVector<T> v)
Deprecated.
Element-by-element multiplication.
|
abstract RealVector |
RealVector.ebeMultiply(RealVector v)
Deprecated.
As of version 3.1, this method is deprecated, and will be
removed in version 4.0. This decision follows the discussion reported in
MATH-803.
Uses of this method involving sparse implementations of
RealVector might lead to wrong results. Since there is no
satisfactory correction to this bug, this method is deprecated. Users who
want to preserve this feature are advised to implement
RealVectorPreservingVisitor (possibly ignoring corner cases for
the sake of efficiency). |
ArrayRealVector |
ArrayRealVector.ebeMultiply(RealVector v)
Element-by-element multiplication.
|
OpenMapRealVector |
OpenMapRealVector.ebeMultiply(RealVector v)
Deprecated.
Element-by-element multiplication.
|
double |
OpenMapRealVector.getDistance(OpenMapRealVector v)
Deprecated.
Optimized method to compute distance.
|
double |
RealVector.getDistance(RealVector v)
Distance between two vectors.
|
double |
ArrayRealVector.getDistance(RealVector v)
Distance between two vectors.
|
double |
OpenMapRealVector.getDistance(RealVector v)
Deprecated.
Distance between two vectors.
|
double |
OpenMapRealVector.getL1Distance(OpenMapRealVector v)
Deprecated.
Distance between two vectors.
|
double |
RealVector.getL1Distance(RealVector v)
Distance between two vectors.
|
double |
ArrayRealVector.getL1Distance(RealVector v)
Distance between two vectors.
|
double |
OpenMapRealVector.getL1Distance(RealVector v)
Deprecated.
Distance between two vectors.
|
double |
RealVector.getLInfDistance(RealVector v)
Distance between two vectors.
|
double |
ArrayRealVector.getLInfDistance(RealVector v)
Distance between two vectors.
|
double |
OpenMapRealVector.getLInfDistance(RealVector v)
Deprecated.
Distance between two vectors.
|
Array2DRowFieldMatrix<T> |
Array2DRowFieldMatrix.multiply(Array2DRowFieldMatrix<T> m)
Postmultiplying this matrix by
m . |
Array2DRowRealMatrix |
Array2DRowRealMatrix.multiply(Array2DRowRealMatrix m)
Returns the result of postmultiplying
this by m . |
BlockFieldMatrix<T> |
BlockFieldMatrix.multiply(BlockFieldMatrix<T> m)
Returns the result of postmultiplying
this by m . |
BlockRealMatrix |
BlockRealMatrix.multiply(BlockRealMatrix m)
Returns the result of postmultiplying this by
m . |
DiagonalMatrix |
DiagonalMatrix.multiply(DiagonalMatrix m)
Returns the result of postmultiplying
this by m . |
FieldMatrix<T> |
BlockFieldMatrix.multiply(FieldMatrix<T> m)
Postmultiply this matrix by
m . |
FieldMatrix<T> |
FieldMatrix.multiply(FieldMatrix<T> m)
Postmultiply this matrix by
m . |
FieldMatrix<T> |
AbstractFieldMatrix.multiply(FieldMatrix<T> m)
Postmultiply this matrix by
m . |
OpenMapRealMatrix |
OpenMapRealMatrix.multiply(OpenMapRealMatrix m)
Deprecated.
Postmultiply this matrix by
m . |
RealMatrix |
RealMatrix.multiply(RealMatrix m)
Returns the result of postmultiplying
this by m . |
RealMatrix |
AbstractRealMatrix.multiply(RealMatrix m)
Returns the result of postmultiplying
this by m . |
RealMatrix |
OpenMapRealMatrix.multiply(RealMatrix m)
Deprecated.
Returns the result of postmultiplying
this by m . |
RealMatrix |
DiagonalMatrix.multiply(RealMatrix m)
Returns the result of postmultiplying
this by m . |
BlockRealMatrix |
BlockRealMatrix.multiply(RealMatrix m)
Returns the result of postmultiplying
this by m . |
double[] |
RealMatrix.operate(double[] v)
Returns the result of multiplying this by the vector
v . |
double[] |
AbstractRealMatrix.operate(double[] v)
Returns the result of multiplying this by the vector
v . |
double[] |
DiagonalMatrix.operate(double[] v)
Returns the result of multiplying this by the vector
v . |
double[] |
BlockRealMatrix.operate(double[] v)
Returns the result of multiplying this by the vector
v . |
double[] |
Array2DRowRealMatrix.operate(double[] v)
Returns the result of multiplying this by the vector
v . |
FieldVector<T> |
FieldMatrix.operate(FieldVector<T> v)
Returns the result of multiplying this by the vector
v . |
FieldVector<T> |
AbstractFieldMatrix.operate(FieldVector<T> v)
Returns the result of multiplying this by the vector
v . |
RealVector |
RealMatrix.operate(RealVector v)
Returns the result of multiplying this by the vector
v . |
RealVector |
AbstractRealMatrix.operate(RealVector v)
Returns the result of multiplying
this by the vector x . |
abstract RealVector |
RealLinearOperator.operate(RealVector x)
Returns the result of multiplying
this by the vector x . |
T[] |
BlockFieldMatrix.operate(T[] v)
Returns the result of multiplying this by the vector
v . |
T[] |
FieldMatrix.operate(T[] v)
Returns the result of multiplying this by the vector
v . |
T[] |
Array2DRowFieldMatrix.operate(T[] v)
Returns the result of multiplying this by the vector
v . |
T[] |
AbstractFieldMatrix.operate(T[] v)
Returns the result of multiplying this by the vector
v . |
RealVector |
RealLinearOperator.operateTranspose(RealVector x)
Returns the result of multiplying the transpose of
this operator
by the vector x (optional operation). |
double[] |
RealMatrix.preMultiply(double[] v)
Returns the (row) vector result of premultiplying this by the vector
v . |
double[] |
AbstractRealMatrix.preMultiply(double[] v)
Returns the (row) vector result of premultiplying this by the vector
v . |
double[] |
DiagonalMatrix.preMultiply(double[] v)
Returns the (row) vector result of premultiplying this by the vector
v . |
double[] |
BlockRealMatrix.preMultiply(double[] v)
Returns the (row) vector result of premultiplying this by the vector
v . |
double[] |
Array2DRowRealMatrix.preMultiply(double[] v)
Returns the (row) vector result of premultiplying this by the vector
v . |
FieldMatrix<T> |
FieldMatrix.preMultiply(FieldMatrix<T> m)
Premultiply this matrix by
m . |
FieldMatrix<T> |
AbstractFieldMatrix.preMultiply(FieldMatrix<T> m)
Premultiply this matrix by
m . |
FieldVector<T> |
FieldMatrix.preMultiply(FieldVector<T> v)
Returns the (row) vector result of premultiplying this by the vector
v . |
FieldVector<T> |
AbstractFieldMatrix.preMultiply(FieldVector<T> v)
Returns the (row) vector result of premultiplying this by the vector
v . |
RealMatrix |
RealMatrix.preMultiply(RealMatrix m)
Returns the result of premultiplying
this by m . |
RealMatrix |
AbstractRealMatrix.preMultiply(RealMatrix m)
Returns the result of premultiplying
this by m . |
RealVector |
RealMatrix.preMultiply(RealVector v)
Returns the (row) vector result of premultiplying this by the vector
v . |
RealVector |
AbstractRealMatrix.preMultiply(RealVector v)
Returns the (row) vector result of premultiplying this by the vector
v . |
T[] |
BlockFieldMatrix.preMultiply(T[] v)
Returns the (row) vector result of premultiplying this by the vector
v . |
T[] |
FieldMatrix.preMultiply(T[] v)
Returns the (row) vector result of premultiplying this by the vector
v . |
T[] |
Array2DRowFieldMatrix.preMultiply(T[] v)
Returns the (row) vector result of premultiplying this by the vector
v . |
T[] |
AbstractFieldMatrix.preMultiply(T[] v)
Returns the (row) vector result of premultiplying this by the vector
v . |
ArrayFieldVector<T> |
ArrayFieldVector.projection(ArrayFieldVector<T> v)
Find the orthogonal projection of this vector onto another vector.
|
FieldVector<T> |
ArrayFieldVector.projection(FieldVector<T> v)
Find the orthogonal projection of this vector onto another vector.
|
FieldVector<T> |
FieldVector.projection(FieldVector<T> v)
Find the orthogonal projection of this vector onto another vector.
|
FieldVector<T> |
SparseFieldVector.projection(FieldVector<T> v)
Deprecated.
Find the orthogonal projection of this vector onto another vector.
|
RealVector |
RealVector.projection(RealVector v)
Find the orthogonal projection of this vector onto another vector.
|
void |
RealMatrix.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 |
Array2DRowRealMatrix.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 |
PreconditionedIterativeLinearSolver.solve(RealLinearOperator a,
RealVector b)
Returns an estimate of the solution to the linear system A · x =
b.
|
RealVector |
IterativeLinearSolver.solve(RealLinearOperator a,
RealVector b)
Returns an estimate of the solution to the linear system A · x =
b.
|
RealVector |
SymmLQ.solve(RealLinearOperator a,
RealVector b,
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 |
PreconditionedIterativeLinearSolver.solve(RealLinearOperator a,
RealVector b,
RealVector x0)
Returns an estimate of the solution to the linear system A · x =
b.
|
RealVector |
IterativeLinearSolver.solve(RealLinearOperator a,
RealVector b,
RealVector x0)
Returns an estimate of the solution to the linear system A · x =
b.
|
RealVector |
SymmLQ.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.
|
RealVector |
PreconditionedIterativeLinearSolver.solveInPlace(RealLinearOperator a,
RealVector b,
RealVector x0)
Returns an estimate of the solution to the linear system A · x =
b.
|
abstract RealVector |
IterativeLinearSolver.solveInPlace(RealLinearOperator a,
RealVector b,
RealVector x0)
Returns an estimate of the solution to the linear system A · x =
b.
|
static void |
MatrixUtils.solveLowerTriangularSystem(RealMatrix rm,
RealVector b)
Solve a system of composed of a Lower Triangular Matrix
RealMatrix . |
static void |
MatrixUtils.solveUpperTriangularSystem(RealMatrix rm,
RealVector b)
Solver a system composed of an Upper Triangular Matrix
RealMatrix . |
ArrayFieldVector<T> |
ArrayFieldVector.subtract(ArrayFieldVector<T> v)
Compute
this minus v . |
FieldVector<T> |
ArrayFieldVector.subtract(FieldVector<T> v)
Compute
this minus v . |
FieldVector<T> |
FieldVector.subtract(FieldVector<T> v)
Compute
this minus v . |
FieldVector<T> |
SparseFieldVector.subtract(FieldVector<T> v)
Deprecated.
Compute
this minus v . |
OpenMapRealVector |
OpenMapRealVector.subtract(OpenMapRealVector v)
Deprecated.
Optimized method to subtract OpenMapRealVectors.
|
RealVector |
RealVector.subtract(RealVector v)
Subtract
v from this vector. |
ArrayRealVector |
ArrayRealVector.subtract(RealVector v)
Subtract
v from this vector. |
RealVector |
OpenMapRealVector.subtract(RealVector v)
Deprecated.
Subtract
v from this vector. |
SparseFieldVector<T> |
SparseFieldVector.subtract(SparseFieldVector<T> v)
Deprecated.
Optimized method to compute
this minus v . |
static double[][] |
BlockRealMatrix.toBlocksLayout(double[][] rawData)
Convert a data array from raw layout to blocks layout.
|
static <T extends FieldElement<T>> |
BlockFieldMatrix.toBlocksLayout(T[][] rawData)
Convert a data array from raw layout to blocks layout.
|
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.
|
BlockFieldMatrix(int rows,
int columns,
T[][] blockData,
boolean copyArray)
Create a new dense matrix copying entries from block layout data.
|
BlockFieldMatrix(T[][] rawData)
Create a new dense matrix copying entries from raw layout data.
|
BlockRealMatrix(double[][] rawData)
Create a new dense matrix copying entries from raw layout data.
|
BlockRealMatrix(int rows,
int columns,
double[][] blockData,
boolean copyArray)
Create a new dense matrix copying entries from block layout data.
|
Modifier and Type | Method and Description |
---|---|
protected double |
AbstractIntegrator.acceptStep(AbstractStepInterpolator interpolator,
double[] y,
double[] yDot,
double tEnd)
Accept a step, triggering events and step handlers.
|
void |
ExpandableStatefulODE.computeDerivatives(double t,
double[] y,
double[] yDot)
Get the current time derivative of the complete state vector.
|
void |
FirstOrderDifferentialEquations.computeDerivatives(double t,
double[] y,
double[] yDot)
Get the current time derivative of the state vector.
|
void |
AbstractIntegrator.computeDerivatives(double t,
double[] y,
double[] yDot)
Compute the derivatives and check the number of evaluations.
|
void |
SecondaryEquations.computeDerivatives(double t,
double[] primary,
double[] primaryDot,
double[] secondary,
double[] secondaryDot)
Compute the derivatives related to the secondary state parameters.
|
void |
MainStateJacobianProvider.computeMainStateJacobian(double t,
double[] y,
double[] yDot,
double[][] dFdY)
Compute the jacobian matrix of ODE with respect to main state.
|
void |
ParameterJacobianProvider.computeParameterJacobian(double t,
double[] y,
double[] yDot,
String paramName,
double[] dFdP)
Compute the Jacobian matrix of ODE with respect to one parameter.
|
void |
EquationsMapper.extractEquationData(double[] complete,
double[] equationData)
Extract equation data from a complete state or derivative array.
|
double[] |
ExpandableStatefulODE.getCompleteState()
Get the complete current state.
|
void |
EquationsMapper.insertEquationData(double[] equationData,
double[] complete)
Insert equation data into a complete state or derivative array.
|
abstract void |
AbstractIntegrator.integrate(ExpandableStatefulODE equations,
double t)
Integrate a set of differential equations up to the given time.
|
double |
FirstOrderIntegrator.integrate(FirstOrderDifferentialEquations equations,
double t0,
double[] y0,
double t,
double[] y)
Integrate the differential equations up to the given time.
|
double |
AbstractIntegrator.integrate(FirstOrderDifferentialEquations equations,
double t0,
double[] y0,
double t,
double[] y)
Integrate the differential equations up to the given time.
|
void |
JacobianMatrices.registerVariationalEquations(ExpandableStatefulODE expandable)
Register the variational equations for the Jacobians matrices to the expandable set.
|
protected void |
AbstractIntegrator.sanityChecks(ExpandableStatefulODE equations,
double t)
Check the integration span.
|
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 |
JacobianMatrices.setInitialParameterJacobian(String pName,
double[] dYdP)
Set the initial value of a column of the Jacobian matrix with respect to one parameter.
|
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.
|
protected void |
MultistepIntegrator.start(double t0,
double[] y0,
double t)
Start the integration.
|
Constructor and Description |
---|
JacobianMatrices(FirstOrderDifferentialEquations fode,
double[] hY,
String... parameters)
Simple constructor for a secondary equations set computing Jacobian matrices.
|
Modifier and Type | Method and Description |
---|---|
double |
AdaptiveStepsizeIntegrator.initializeStep(boolean forward,
int order,
double[] scale,
double t0,
double[] y0,
double[] yDot0,
double[] y1,
double[] yDot1)
Initialize the integration step.
|
abstract void |
AdaptiveStepsizeIntegrator.integrate(ExpandableStatefulODE equations,
double t)
Integrate a set of differential equations up to the given time.
|
abstract void |
AdamsIntegrator.integrate(ExpandableStatefulODE equations,
double t)
Integrate a set of differential equations up to the given time.
|
void |
AdamsBashforthIntegrator.integrate(ExpandableStatefulODE equations,
double t)
Integrate a set of differential equations up to the given time.
|
void |
GraggBulirschStoerIntegrator.integrate(ExpandableStatefulODE equations,
double t)
Integrate a set of differential equations up to the given time.
|
void |
RungeKuttaIntegrator.integrate(ExpandableStatefulODE equations,
double t)
Integrate a set of differential equations up to the given time.
|
void |
AdamsMoultonIntegrator.integrate(ExpandableStatefulODE equations,
double t)
Integrate a set of differential equations up to the given time.
|
void |
EmbeddedRungeKuttaIntegrator.integrate(ExpandableStatefulODE equations,
double t)
Integrate a set of differential equations up to the given time.
|
protected void |
AdaptiveStepsizeIntegrator.sanityChecks(ExpandableStatefulODE equations,
double t)
Check the integration span.
|
Modifier and Type | Method and Description |
---|---|
PointValuePair |
CMAESOptimizer.optimize(OptimizationData... optData)
Stores data and performs the optimization.
|
Modifier and Type | Method and Description |
---|---|
PointVectorValuePair |
JacobianMultivariateVectorOptimizer.optimize(OptimizationData... optData)
Stores data and performs the optimization.
|
PointVectorValuePair |
MultivariateVectorOptimizer.optimize(OptimizationData... optData)
Stores data and performs the optimization.
|
Modifier and Type | Method and Description |
---|---|
protected PointVectorValuePair |
BaseAbstractMultivariateVectorOptimizer.optimize(int maxEval,
FUNC f,
OptimizationData... optData)
Deprecated.
Optimize an objective function.
|
protected PointVectorValuePair |
BaseAbstractMultivariateVectorOptimizer.optimizeInternal(int maxEval,
FUNC f,
OptimizationData... optData)
Deprecated.
Optimize an objective function.
|
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]).
|
static double |
StatUtils.varianceDifference(double[] sample1,
double[] sample2,
double meanDifference)
Returns the variance of the (signed) differences between corresponding elements of the
input arrays -- i.e., var(sample1[i] - sample2[i]).
|
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 |
MultivariateSummaryStatistics.addValue(double[] value)
Add an n-tuple to the data
|
void |
SynchronizedMultivariateSummaryStatistics.addValue(double[] value)
Add an n-tuple to the data
|
void |
MultivariateSummaryStatistics.setGeoMeanImpl(StorelessUnivariateStatistic[] geoMeanImpl)
Sets the implementation for the geometric mean.
|
void |
SynchronizedMultivariateSummaryStatistics.setGeoMeanImpl(StorelessUnivariateStatistic[] geoMeanImpl)
Sets the implementation for the geometric mean.
|
void |
MultivariateSummaryStatistics.setMaxImpl(StorelessUnivariateStatistic[] maxImpl)
Sets the implementation for the maximum.
|
void |
SynchronizedMultivariateSummaryStatistics.setMaxImpl(StorelessUnivariateStatistic[] maxImpl)
Sets the implementation for the maximum.
|
void |
MultivariateSummaryStatistics.setMeanImpl(StorelessUnivariateStatistic[] meanImpl)
Sets the implementation for the mean.
|
void |
SynchronizedMultivariateSummaryStatistics.setMeanImpl(StorelessUnivariateStatistic[] meanImpl)
Sets the implementation for the mean.
|
void |
MultivariateSummaryStatistics.setMinImpl(StorelessUnivariateStatistic[] minImpl)
Sets the implementation for the minimum.
|
void |
SynchronizedMultivariateSummaryStatistics.setMinImpl(StorelessUnivariateStatistic[] minImpl)
Sets the implementation for the minimum.
|
void |
MultivariateSummaryStatistics.setSumImpl(StorelessUnivariateStatistic[] sumImpl)
Sets the implementation for the Sum.
|
void |
SynchronizedMultivariateSummaryStatistics.setSumImpl(StorelessUnivariateStatistic[] sumImpl)
Sets the implementation for the Sum.
|
void |
MultivariateSummaryStatistics.setSumLogImpl(StorelessUnivariateStatistic[] sumLogImpl)
Sets the implementation for the sum of logs.
|
void |
SynchronizedMultivariateSummaryStatistics.setSumLogImpl(StorelessUnivariateStatistic[] sumLogImpl)
Sets the implementation for the sum of logs.
|
void |
MultivariateSummaryStatistics.setSumsqImpl(StorelessUnivariateStatistic[] sumsqImpl)
Sets the implementation for the sum of squares.
|
void |
SynchronizedMultivariateSummaryStatistics.setSumsqImpl(StorelessUnivariateStatistic[] sumsqImpl)
Sets the implementation for the sum of squares.
|
Modifier and Type | Method and Description |
---|---|
void |
VectorialMean.increment(double[] v)
Add a new vector to the sample.
|
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. |
double |
OneWayAnova.anovaPValue(Collection<SummaryStatistics> categoryData,
boolean allowOneElementData)
Computes the ANOVA P-value for a collection of
SummaryStatistics . |
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.chiSquare(double[] expected,
long[] observed)
|
static double |
TestUtils.chiSquare(double[] expected,
long[] observed) |
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.chiSquare(long[][] counts) |
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.chiSquareDataSetsComparison(long[] observed1,
long[] observed2) |
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 double |
TestUtils.chiSquareTest(double[] expected,
long[] observed) |
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 boolean |
TestUtils.chiSquareTest(double[] expected,
long[] observed,
double 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. |
static double |
TestUtils.chiSquareTest(long[][] counts) |
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 boolean |
TestUtils.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 . |
static double |
TestUtils.chiSquareTestDataSetsComparison(long[] observed1,
long[] observed2) |
boolean |
ChiSquareTest.chiSquareTestDataSetsComparison(long[] observed1,
long[] observed2,
double alpha)
Performs a Chi-Square two sample test comparing two binned data
sets.
|
static boolean |
TestUtils.chiSquareTestDataSetsComparison(long[] observed1,
long[] observed2,
double alpha) |
static double |
TestUtils.g(double[] expected,
long[] observed) |
double |
GTest.g(double[] expected,
long[] observed)
|
static double |
TestUtils.gDataSetsComparison(long[] observed1,
long[] observed2) |
double |
GTest.gDataSetsComparison(long[] observed1,
long[] observed2)
Computes a G (Log-Likelihood Ratio) two sample test statistic for
independence comparing frequency counts in
observed1 and observed2 . |
static double |
TestUtils.gTest(double[] expected,
long[] observed) |
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. |
static boolean |
TestUtils.gTest(double[] expected,
long[] observed,
double alpha) |
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 |
TestUtils.gTestDataSetsComparison(long[] observed1,
long[] observed2) |
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 . |
static boolean |
TestUtils.gTestDataSetsComparison(long[] observed1,
long[] observed2,
double alpha) |
boolean |
GTest.gTestDataSetsComparison(long[] observed1,
long[] observed2,
double alpha)
Performs a G-Test (Log-Likelihood Ratio Test) comparing two binned
data sets.
|
static double |
TestUtils.gTestIntrinsic(double[] expected,
long[] observed) |
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 |
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) |
static double |
TestUtils.rootLogLikelihoodRatio(long k11,
long k12,
long k21,
long k22) |
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. |
Modifier and Type | Method and Description |
---|---|
static void |
MathArrays.checkRectangular(long[][] in)
Throws DimensionMismatchException if the input array is not rectangular.
|
static double[] |
MathArrays.ebeAdd(double[] a,
double[] b)
Creates an array whose contents will be the element-by-element
addition of the arguments.
|
static double[] |
MathArrays.ebeDivide(double[] a,
double[] b)
Creates an array whose contents will be the element-by-element
division of the first argument by the second.
|
static double[] |
MathArrays.ebeMultiply(double[] a,
double[] b)
Creates an array whose contents will be the element-by-element
multiplication of the arguments.
|
static double[] |
MathArrays.ebeSubtract(double[] a,
double[] b)
Creates an array whose contents will be the element-by-element
subtraction of the second argument from the first.
|
int |
MultidimensionalCounter.getCount(int... c)
Convert to unidimensional counter.
|
Decimal64 |
Decimal64.linearCombination(Decimal64[] a,
Decimal64[] b)
Compute a linear combination.
|
Decimal64 |
Decimal64.linearCombination(double[] a,
Decimal64[] b)
Compute a linear combination.
|
static double |
MathArrays.linearCombination(double[] a,
double[] b)
Compute a linear combination accurately.
|
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.
|
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