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FastMath.abs method wrapped as a ComposableFunction.
BigFraction.
FieldMatrix methods regardless of the underlying storage.FractionFormat and BigFractionFormat.RandomGenerator interface.RealVector interface.StorelessUnivariateStatistic interface.UnivariateStatistic interface.FastMath.abs method wrapped as a ComposableFunction.
Adams-Bashforth and
Adams-Moulton integrators.BinaryFunction.
BigInteger,
returning the result in reduced form.
v.
v.
m.
m.
v.
v.
m.
m.
m.
m.
m.
v.
m.
v.
v.
data.
x, y) to list of observed points
with a weight of 1.0.
x, y) to list of observed points
with a weight of weight.
Frequency.addValue(Comparable) instead
SummaryStatistics from several data sets or
data set partitions.SummaryStatistics for aggregation.double[]
arrays.
double[]
arrays.
double[]
arrays.
double[]
arrays.
FieldElement[][] array to store entries.v as the
data for the unique column of the v.length x 1 matrix
created.
v as the
data for the unique column of the v.length x 1 matrix
created.
FieldVector interface with a FieldElement array.RealVector interface with a double array.FastMath.asin method wrapped as a ComposableFunction.
Cluster.
FastMath.atan method wrapped as a ComposableFunction.
FastMath.atan2 method wrapped as a BinaryFunction.
BigDecimal.
BigDecimal following the passed
rounding mode.
BigDecimal following the passed scale
and rounding mode.
BigFraction equivalent to the passed BigInteger, ie
"num / 1".
BigFraction given the numerator and denominator as
BigInteger.
BigFraction equivalent to the passed int, ie
"num / 1".
BigFraction given the numerator and denominator as simple
int.
BigFraction equivalent to the passed long, ie "num / 1".
BigFraction given the numerator and denominator as simple
long.
FieldMatrix/BigFraction matrix to a RealMatrix.
FieldMatrix with a BigReal parameterArray2DRowFieldMatrix with a BigReal parameterd as the underlying
data array.
d as the underlying
data array.
d as the underlying data array.
v as the
data for the unique column of the v.length x 1 matrix
created.
BinaryChromosomes.n choose k", the number of
k-element subsets that can be selected from an
n-element set.
double representation of the Binomial
Coefficient, "n choose k", the number of
k-element subsets that can be selected from an
n-element set.
log of the Binomial
Coefficient, "n choose k", the number of
k-element subsets that can be selected from an
n-element set.
BinomialDistribution.BisectionSolver.solve(UnivariateRealFunction, double, double) or
UnivariateRealSolver.solve(UnivariateRealFunction, double, double, double)
method.
lowerBound <= a < initial < b <= upperBound
f(a) * f(b) < 0
If f is continuous on [a,b], this means that a
and b bracket a root of f.
lowerBound <= a < initial < b <= upperBound
f(a) * f(b) <= 0
If f is continuous on [a,b], this means that a
and b bracket a root of f.
100, 50 (see the
other constructor).
BrentSolver.solve(UnivariateRealFunction, double, double) or
UnivariateRealSolver.solve(UnivariateRealFunction, double, double, double)
method.
CauchyDistribution.FastMath.cbrt method wrapped as a ComposableFunction.
FastMath.ceil method wrapped as a ComposableFunction.
checkOrder method). To be removed in 3.0.
representation can represent a valid chromosome.
representation can represent a valid chromosome.
representation can represent a valid chromosome.
observed and expected
frequency counts.
counts
array, viewed as a two-way table.
observed and expected
frequency counts.
observed1 and observed2.
ChiSquaredDistributionobserved
frequency counts to those in the expected array.
alpha.
counts
array, viewed as a two-way table.
alpha.
observed
frequency counts to those in the expected array.
alpha.
observed1 and
observed2.
UnknownDistributionChiSquareTest interface.Chromosome objects.AbstractRandomGenerator.nextGaussian().
valuesFileURL after use in REPLAY_MODE.
Clusterable points.data sorted by comparator.
Comparable arguments.
Complex utilities functions.UnivariateRealFunction that can be composed with other functions.valuesFileURL, using the default number of bins.
valuesFileURL and binCount bins.
replacement instead.
NonLinearConjugateGradientOptimizer.ranks.
ConvergenceException.ConvergenceException(Localizable, Object...)
ConvergenceException.ConvergenceException(Throwable, Localizable, Object...)
IterativeAlgorithm. The concept of "accuracy" is
currently is also contained in SimpleRealPointChecker
and similar classes.RandomVectorGenerator that generates vectors with with
correlated components.FastMath.cos method wrapped as a ComposableFunction.
FastMath.cosh method wrapped as a ComposableFunction.
Random using the supplied
RandomGenerator.
MathRuntimeException.createArithmeticException(Localizable, Object...)
ArithmeticException with specified formatted detail message.
MathRuntimeException.createArrayIndexOutOfBoundsException(Localizable, Object...)
ArrayIndexOutOfBoundsException with specified formatted detail message.
MatrixUtils.createFieldIdentityMatrix(Field, int)
MatrixUtils.createFieldMatrix(FieldElement[][])
MatrixUtils.createFieldMatrix(FieldElement[][])
MatrixUtils.createFieldMatrix(FieldElement[][])
MatrixUtils.createFieldMatrix(FieldElement[][])
MatrixUtils.createColumnFieldMatrix(FieldElement[])
MatrixUtils.createColumnFieldMatrix(FieldElement[])
MatrixUtils.createColumnFieldMatrix(FieldElement[])
FieldMatrix using the data from the input
array.
RealMatrix using the data from the input
array.
MathRuntimeException.createConcurrentModificationException(Localizable, Object...)
ConcurrentModificationException with specified formatted detail message.
SummaryStatistics whose data will be
aggregated with those of this AggregateSummaryStatistics.
MathRuntimeException.createEOFException(Localizable, Object...)
EOFException with specified formatted detail message.
dimension x dimension identity matrix.
FieldMatrix with specified dimensions.
FieldMatrix whose entries are the the values in the
the input array.
FieldVector using the data from the input array.
MathRuntimeException.createIllegalArgumentException(Localizable, Object...)
IllegalArgumentException with specified formatted detail message.
IllegalArgumentException with specified nested
Throwable root cause.
MathRuntimeException.createIllegalStateException(Localizable, Object...)
IllegalStateException with specified formatted detail message.
RuntimeException for an internal error.
IOException with specified nested
Throwable root cause.
MathRuntimeException.createNoSuchElementException(Localizable, Object...)
NoSuchElementException with specified formatted detail message.
MathRuntimeException.createNullPointerException(Localizable, Object...)
GaussianParametersGuesser
instance initialized with the specified observations.
MathRuntimeException.createParseException(int, Localizable, Object...)
ParseException with specified
formatted detail message.
dimension x dimension identity matrix.
RealMatrix with specified dimensions.
RealMatrix whose entries are the the values in the
the input array.
RealVector using the data from the input array.
MatrixUtils.createRowFieldMatrix(FieldElement[])
MatrixUtils.createRowFieldMatrix(FieldElement[])
MatrixUtils.createRowFieldMatrix(FieldElement[])
FieldMatrix using the data from the input
array.
RealMatrix using the data from the input
array.
MathUnsupportedOperationException
instead.
Comparator for comparing
WeightedObservedPoint instances.
OnePointCrossover.crossover(Chromosome, Chromosome).
x).
x).
x).
x).
AbstractRealVector.SparseEntryIterator.next()
sequence of objects of type T according to the
permutation this chromosome represents.
sequence of objects of type T according to the
permutation this chromosome represents.
representation and
returns a (generic) list with the permuted values.
FieldMatrixChangingVisitor interface.FieldMatrixPreservingVisitor interface.RealMatrixChangingVisitor interface.RealMatrixPreservingVisitor interface.RealMatrix field in a class.
RealVector field in a class.
Dfp which hides the radix-10000 artifacts of the superclass.Dfp.MultivariateRealFunction representing a differentiable
multivariate real function.scalar differentiable objective
functions.MultivariateVectorialFunction representing a differentiable
multivariate vectorial function.vectorial differentiable objective functions.UnivariateMatrixFunction representing a differentiable univariate matrix function.UnivariateRealFunction representing a differentiable univariate real function.UnivariateVectorialFunction representing a differentiable univariate vectorial function.org.apache.commons.math.exception.i first or last elements of the array,
depending on the value of front.
- discardFrontElements(int) -
Method in class org.apache.commons.math.util.ResizableDoubleArray
- Discards the
i initial elements of the array.
- discardMostRecentElements(int) -
Method in class org.apache.commons.math.util.ResizableDoubleArray
- Discards the
i last elements of the array.
- DiscreteDistribution - Interface in org.apache.commons.math.distribution
- Base interface for discrete distributions.
- dispatchCompoundState(double[], double[], double[][], double[][]) -
Static method in class org.apache.commons.math.ode.jacobians.FirstOrderIntegratorWithJacobians
- Deprecated. Dispatch a compound state array into state and jacobians arrays.
- distance(Rotation, Rotation) -
Static method in class org.apache.commons.math.geometry.Rotation
- Compute the distance between two rotations.
- distance(Vector3D, Vector3D) -
Static method in class org.apache.commons.math.geometry.Vector3D
- Compute the distance between two vectors according to the L2 norm.
- distance(double[], double[]) -
Static method in class org.apache.commons.math.util.MathUtils
- Calculates the L2 (Euclidean) distance between two points.
- distance(int[], int[]) -
Static method in class org.apache.commons.math.util.MathUtils
- Calculates the L2 (Euclidean) distance between two points.
- distance1(Vector3D, Vector3D) -
Static method in class org.apache.commons.math.geometry.Vector3D
- Compute the distance between two vectors according to the L1 norm.
- distance1(double[], double[]) -
Static method in class org.apache.commons.math.util.MathUtils
- Calculates the L1 (sum of abs) distance between two points.
- distance1(int[], int[]) -
Static method in class org.apache.commons.math.util.MathUtils
- Calculates the L1 (sum of abs) distance between two points.
- distanceFrom(T) -
Method in interface org.apache.commons.math.stat.clustering.Clusterable
- Returns the distance from the given point.
- distanceFrom(EuclideanIntegerPoint) -
Method in class org.apache.commons.math.stat.clustering.EuclideanIntegerPoint
- Returns the distance from the given point.
- distanceInf(Vector3D, Vector3D) -
Static method in class org.apache.commons.math.geometry.Vector3D
- Compute the distance between two vectors according to the L∞ norm.
- distanceInf(double[], double[]) -
Static method in class org.apache.commons.math.util.MathUtils
- Calculates the L∞ (max of abs) distance between two points.
- distanceInf(int[], int[]) -
Static method in class org.apache.commons.math.util.MathUtils
- Calculates the L∞ (max of abs) distance between two points.
- distanceSq(Vector3D, Vector3D) -
Static method in class org.apache.commons.math.geometry.Vector3D
- Compute the square of the distance between two vectors.
- Distribution - Interface in org.apache.commons.math.distribution
- Base interface for probability distributions.
- distribution -
Variable in class org.apache.commons.math.stat.inference.ChiSquareTestImpl
- Distribution used to compute inference statistics.
- distribution -
Variable in class org.apache.commons.math.stat.inference.TTestImpl
- Deprecated. in 2.2 (to be removed in 3.0).
- distribution -
Variable in class org.apache.commons.math.stat.regression.SimpleRegression
- the distribution used to compute inference statistics.
- DIVIDE -
Static variable in class org.apache.commons.math.analysis.BinaryFunction
- Deprecated. The / operator method wrapped as a
BinaryFunction.
- divide(UnivariateRealFunction) -
Method in class org.apache.commons.math.analysis.ComposableFunction
- Return a function dividing the instance by another function.
- divide(Complex) -
Method in class org.apache.commons.math.complex.Complex
- Return the quotient of this complex number and the given complex number.
- divide(Dfp) -
Method in class org.apache.commons.math.dfp.Dfp
- Divide this by divisor.
- divide(int) -
Method in class org.apache.commons.math.dfp.Dfp
- Divide by a single digit less than radix.
- divide(T) -
Method in interface org.apache.commons.math.FieldElement
- Compute this ÷ a.
- divide(BigInteger) -
Method in class org.apache.commons.math.fraction.BigFraction
-
Divide the value of this fraction by the passed
BigInteger,
ie "this * 1 / bg", returning the result in reduced form.
- divide(int) -
Method in class org.apache.commons.math.fraction.BigFraction
-
Divide the value of this fraction by the passed int, ie
"this * 1 / i", returning the result in reduced form.
- divide(long) -
Method in class org.apache.commons.math.fraction.BigFraction
-
Divide the value of this fraction by the passed long, ie
"this * 1 / l", returning the result in reduced form.
- divide(BigFraction) -
Method in class org.apache.commons.math.fraction.BigFraction
-
Divide the value of this fraction by another, returning the result in
reduced form.
- divide(Fraction) -
Method in class org.apache.commons.math.fraction.Fraction
- Divide the value of this fraction by another.
- divide(int) -
Method in class org.apache.commons.math.fraction.Fraction
- Divide the fraction by an integer.
- divide(BigReal) -
Method in class org.apache.commons.math.util.BigReal
- Compute this ÷ a.
- DIVIDE_TRAP -
Static variable in class org.apache.commons.math.dfp.Dfp
- Name for traps triggered by division.
- DividedDifferenceInterpolator - Class in org.apache.commons.math.analysis.interpolation
- Implements the
Divided Difference Algorithm for interpolation of real univariate
functions.
- DividedDifferenceInterpolator() -
Constructor for class org.apache.commons.math.analysis.interpolation.DividedDifferenceInterpolator
-
- divideRow(int, double) -
Method in class org.apache.commons.math.optimization.linear.SimplexTableau
- Subtracts a multiple of one row from another.
- doCopy() -
Method in class org.apache.commons.math.ode.nonstiff.ClassicalRungeKuttaStepInterpolator
- Really copy the finalized instance.
- doCopy() -
Method in class org.apache.commons.math.ode.nonstiff.DormandPrince54StepInterpolator
- Really copy the finalized instance.
- doCopy() -
Method in class org.apache.commons.math.ode.nonstiff.DormandPrince853StepInterpolator
- Really copy the finalized instance.
- doCopy() -
Method in class org.apache.commons.math.ode.nonstiff.EulerStepInterpolator
- Really copy the finalized instance.
- doCopy() -
Method in class org.apache.commons.math.ode.nonstiff.GillStepInterpolator
- Really copy the finalized instance.
- doCopy() -
Method in class org.apache.commons.math.ode.nonstiff.GraggBulirschStoerStepInterpolator
- Really copy the finalized instance.
- doCopy() -
Method in class org.apache.commons.math.ode.nonstiff.HighamHall54StepInterpolator
- Really copy the finalized instance.
- doCopy() -
Method in class org.apache.commons.math.ode.nonstiff.MidpointStepInterpolator
- Really copy the finalized instance.
- doCopy() -
Method in class org.apache.commons.math.ode.nonstiff.ThreeEighthesStepInterpolator
- Really copy the finalized instance.
- doCopy() -
Method in class org.apache.commons.math.ode.sampling.AbstractStepInterpolator
- Really copy the finalized instance.
- doCopy() -
Method in class org.apache.commons.math.ode.sampling.DummyStepInterpolator
- Really copy the finalized instance.
- doCopy() -
Method in class org.apache.commons.math.ode.sampling.NordsieckStepInterpolator
- Really copy the finalized instance.
- doFinalize() -
Method in class org.apache.commons.math.ode.nonstiff.DormandPrince853StepInterpolator
- Really finalize the step.
- doFinalize() -
Method in class org.apache.commons.math.ode.sampling.AbstractStepInterpolator
- Really finalize the step.
- doIteration(SimplexTableau) -
Method in class org.apache.commons.math.optimization.linear.SimplexSolver
- Runs one iteration of the Simplex method on the given model.
- doOptimize() -
Method in class org.apache.commons.math.optimization.direct.PowellOptimizer
- Perform the bulk of optimization algorithm.
- doOptimize() -
Method in class org.apache.commons.math.optimization.general.AbstractLeastSquaresOptimizer
- Perform the bulk of optimization algorithm.
- doOptimize() -
Method in class org.apache.commons.math.optimization.general.AbstractScalarDifferentiableOptimizer
- Perform the bulk of optimization algorithm.
- doOptimize() -
Method in class org.apache.commons.math.optimization.general.GaussNewtonOptimizer
- Perform the bulk of optimization algorithm.
- doOptimize() -
Method in class org.apache.commons.math.optimization.general.LevenbergMarquardtOptimizer
- Perform the bulk of optimization algorithm.
- doOptimize() -
Method in class org.apache.commons.math.optimization.general.NonLinearConjugateGradientOptimizer
- Perform the bulk of optimization algorithm.
- doOptimize() -
Method in class org.apache.commons.math.optimization.linear.AbstractLinearOptimizer
- Perform the bulk of optimization algorithm.
- doOptimize() -
Method in class org.apache.commons.math.optimization.linear.SimplexSolver
- Perform the bulk of optimization algorithm.
- doOptimize() -
Method in class org.apache.commons.math.optimization.univariate.AbstractUnivariateRealOptimizer
- Method for implementing actual optimization algorithms in derived
classes.
- doOptimize() -
Method in class org.apache.commons.math.optimization.univariate.BrentOptimizer
- Method for implementing actual optimization algorithms in derived
classes.
- doRemove(int) -
Method in class org.apache.commons.math.util.OpenIntToDoubleHashMap
- Remove an element at specified index.
- doRemove(int) -
Method in class org.apache.commons.math.util.OpenIntToFieldHashMap
- Remove an element at specified index.
- DormandPrince54Integrator - Class in org.apache.commons.math.ode.nonstiff
- This class implements the 5(4) Dormand-Prince integrator for Ordinary
Differential Equations.
- DormandPrince54Integrator(double, double, double, double) -
Constructor for class org.apache.commons.math.ode.nonstiff.DormandPrince54Integrator
- Simple constructor.
- DormandPrince54Integrator(double, double, double[], double[]) -
Constructor for class org.apache.commons.math.ode.nonstiff.DormandPrince54Integrator
- Simple constructor.
- DormandPrince54StepInterpolator - Class in org.apache.commons.math.ode.nonstiff
- This class represents an interpolator over the last step during an
ODE integration for the 5(4) Dormand-Prince integrator.
- DormandPrince54StepInterpolator() -
Constructor for class org.apache.commons.math.ode.nonstiff.DormandPrince54StepInterpolator
- Simple constructor.
- DormandPrince54StepInterpolator(DormandPrince54StepInterpolator) -
Constructor for class org.apache.commons.math.ode.nonstiff.DormandPrince54StepInterpolator
- Copy constructor.
- DormandPrince853Integrator - Class in org.apache.commons.math.ode.nonstiff
- This class implements the 8(5,3) Dormand-Prince integrator for Ordinary
Differential Equations.
- DormandPrince853Integrator(double, double, double, double) -
Constructor for class org.apache.commons.math.ode.nonstiff.DormandPrince853Integrator
- Simple constructor.
- DormandPrince853Integrator(double, double, double[], double[]) -
Constructor for class org.apache.commons.math.ode.nonstiff.DormandPrince853Integrator
- Simple constructor.
- DormandPrince853StepInterpolator - Class in org.apache.commons.math.ode.nonstiff
- This class represents an interpolator over the last step during an
ODE integration for the 8(5,3) Dormand-Prince integrator.
- DormandPrince853StepInterpolator() -
Constructor for class org.apache.commons.math.ode.nonstiff.DormandPrince853StepInterpolator
- Simple constructor.
- DormandPrince853StepInterpolator(DormandPrince853StepInterpolator) -
Constructor for class org.apache.commons.math.ode.nonstiff.DormandPrince853StepInterpolator
- Copy constructor.
- dotProduct(Vector3D, Vector3D) -
Static method in class org.apache.commons.math.geometry.Vector3D
- Compute the dot-product of two vectors.
- dotProduct(double[]) -
Method in class org.apache.commons.math.linear.AbstractRealVector
- Compute the dot product.
- dotProduct(RealVector) -
Method in class org.apache.commons.math.linear.AbstractRealVector
- Compute the dot product.
- dotProduct(FieldVector<T>) -
Method in class org.apache.commons.math.linear.ArrayFieldVector
- Compute the dot product.
- dotProduct(T[]) -
Method in class org.apache.commons.math.linear.ArrayFieldVector
- Compute the dot product.
- dotProduct(ArrayFieldVector<T>) -
Method in class org.apache.commons.math.linear.ArrayFieldVector
- Compute the dot product.
- dotProduct(RealVector) -
Method in class org.apache.commons.math.linear.ArrayRealVector
- Compute the dot product.
- dotProduct(double[]) -
Method in class org.apache.commons.math.linear.ArrayRealVector
- Compute the dot product.
- dotProduct(ArrayRealVector) -
Method in class org.apache.commons.math.linear.ArrayRealVector
- Compute the dot product.
- dotProduct(FieldVector<T>) -
Method in interface org.apache.commons.math.linear.FieldVector
- Compute the dot product.
- dotProduct(T[]) -
Method in interface org.apache.commons.math.linear.FieldVector
- Compute the dot product.
- dotProduct(OpenMapRealVector) -
Method in class org.apache.commons.math.linear.OpenMapRealVector
- Optimized method to compute the dot product with an OpenMapRealVector.
- dotProduct(RealVector) -
Method in class org.apache.commons.math.linear.OpenMapRealVector
- Compute the dot product.
- dotProduct(RealVector) -
Method in interface org.apache.commons.math.linear.RealVector
- Compute the dot product.
- dotProduct(double[]) -
Method in interface org.apache.commons.math.linear.RealVector
- Compute the dot product.
- dotProduct(FieldVector<T>) -
Method in class org.apache.commons.math.linear.SparseFieldVector
- Compute the dot product.
- dotProduct(T[]) -
Method in class org.apache.commons.math.linear.SparseFieldVector
- Compute the dot product.
- dotrap(int, String, Dfp, Dfp) -
Method in class org.apache.commons.math.dfp.Dfp
- Raises a trap.
- DoubleArray - Interface in org.apache.commons.math.util
- Provides a standard interface for double arrays.
- doubleHighPart(double) -
Static method in class org.apache.commons.math.util.FastMath
- Get the high order bits from the mantissa.
- doubleValue() -
Method in class org.apache.commons.math.fraction.BigFraction
-
Gets the fraction as a double.
- doubleValue() -
Method in class org.apache.commons.math.fraction.Fraction
- Gets the fraction as a double.
- doubleValue() -
Method in class org.apache.commons.math.util.BigReal
- Get the double value corresponding to the instance.
- DOWNSIDE_VARIANCE -
Static variable in class org.apache.commons.math.stat.descriptive.moment.SemiVariance
- The DOWNSIDE Direction is used to specify that the observations below
the cutoff point will be used to calculate SemiVariance
- dropPhase1Objective() -
Method in class org.apache.commons.math.optimization.linear.SimplexTableau
- Removes the phase 1 objective function, positive cost non-artificial variables,
and the non-basic artificial variables from this tableau.
- DummyLocalizable - Class in org.apache.commons.math.exception.util
- Dummy implementation of the
Localizable interface, without localization. - DummyLocalizable(String) -
Constructor for class org.apache.commons.math.exception.util.DummyLocalizable
- Simple constructor.
- DummyStepHandler - Class in org.apache.commons.math.ode.sampling
- This class is a step handler that does nothing.
- DummyStepHandler() -
Constructor for class org.apache.commons.math.ode.sampling.DummyStepHandler
- Private constructor.
- DummyStepHandler.LazyHolder - Class in org.apache.commons.math.ode.sampling
- Holder for the instance.
- DummyStepHandler.LazyHolder() -
Constructor for class org.apache.commons.math.ode.sampling.DummyStepHandler.LazyHolder
-
- DummyStepInterpolator - Class in org.apache.commons.math.ode.sampling
- This class is a step interpolator that does nothing.
- DummyStepInterpolator() -
Constructor for class org.apache.commons.math.ode.sampling.DummyStepInterpolator
- Simple constructor.
- DummyStepInterpolator(double[], double[], boolean) -
Constructor for class org.apache.commons.math.ode.sampling.DummyStepInterpolator
- Simple constructor.
- DummyStepInterpolator(DummyStepInterpolator) -
Constructor for class org.apache.commons.math.ode.sampling.DummyStepInterpolator
- Copy constructor.
- DuplicateSampleAbscissaException - Exception in org.apache.commons.math
- Exception thrown when a sample contains several entries at the same abscissa.
- DuplicateSampleAbscissaException(double, int, int) -
Constructor for exception org.apache.commons.math.DuplicateSampleAbscissaException
- Construct an exception indicating the duplicate abscissa.
- dydp -
Variable in class org.apache.commons.math.ode.jacobians.FirstOrderIntegratorWithJacobians.EventHandlerWrapper
- Deprecated. Jacobian with respect to parameters dy/dp.
- dydp -
Variable in class org.apache.commons.math.ode.jacobians.FirstOrderIntegratorWithJacobians.StepInterpolatorWrapper
- Deprecated. Jacobian with respect to parameters dy/dp.
- dydpDot -
Variable in class org.apache.commons.math.ode.jacobians.FirstOrderIntegratorWithJacobians.StepInterpolatorWrapper
- Deprecated. Time derivative of the jacobian with respect to parameters dy/dp.
- dydy0 -
Variable in class org.apache.commons.math.ode.jacobians.FirstOrderIntegratorWithJacobians.EventHandlerWrapper
- Deprecated. Jacobian with respect to initial state dy/dy0.
- dydy0 -
Variable in class org.apache.commons.math.ode.jacobians.FirstOrderIntegratorWithJacobians.StepInterpolatorWrapper
- Deprecated. Jacobian with respect to initial state dy/dy0.
- dydy0Dot -
Variable in class org.apache.commons.math.ode.jacobians.FirstOrderIntegratorWithJacobians.StepInterpolatorWrapper
- Deprecated. Time derivative of the sacobian with respect to initial state dy/dy0.
Dfp with value e.
EmpiricalDistribution interface.DataAdapter for data provided as array of doubles.sampleStats and
beanStats abstracting the source of data.DataAdapter objects.DataAdapter for data provided through some input streamobject is a
FieldMatrix instance with the same dimensions as this
and all corresponding matrix entries are equal.
object is a
RealMatrix instance with the same dimensions as this
and all corresponding matrix entries are equal.
object is a
BigMatrixImpl instance with the same dimensions as this
and all corresponding matrix entries are equal.
a.subtract(b} to be the zero vector, while
a.equals(b) == false.
object is an
AbstractStorelessUnivariateStatistic returning the same
values as this for getResult() and getN()
object is a
SummaryStatistics instance and all statistics have the
same values as this.
object is a MultivariateSummaryStatistics
instance and all statistics have the same values as this.
object is a
StatisticalSummaryValues instance and all statistics have
the same values as this.
object is a
SummaryStatistics instance and all statistics have the
same values as this.
object is a MultivariateSummaryStatistics
instance and all statistics have the same values as this.
object is a
SummaryStatistics instance and all statistics have the
same values as this.
NaN == NaN. In release
3.0, the semantics will change in order to comply with IEEE754 where it
is specified that NaN != NaN.
New methods have been added for those cases wher the old semantics is
useful (see e.g. equalsIncludingNaN.
NaN == NaN. In release
3.0, the semantics will change in order to comply with IEEE754 where it
is specified that NaN != NaN.
New methods have been added for those cases where the old semantics is
useful (see e.g. equalsIncludingNaN.
MathUtils.equals(double,double).
equals(x, y, 1).
equals(x, y, maxUlps).
MathUtils.equalsIncludingNaN(float,float).
equals(x, y, 1).
equals(x, y, maxUlps.
MathUtils.equalsIncludingNaN(double,double).
Dfp array with value e split in two pieces.
Clusterable for points with integer coordinates.AbstractStorelessUnivariateStatistic.clear(), then invokes
AbstractStorelessUnivariateStatistic.increment(double) in a loop over the the input array, and then uses
AbstractStorelessUnivariateStatistic.getResult() to compute the return value.
AbstractStorelessUnivariateStatistic.clear(), then invokes
AbstractStorelessUnivariateStatistic.increment(double) in a loop over the specified portion of the input
array, and then uses AbstractStorelessUnivariateStatistic.getResult() to compute the return value.
Double.NaN if the designated subarray
is empty.
Double.NaN if the designated subarray
is empty.
SemiVariance for the entire array against the mean, using
instance properties varianceDirection and biasCorrection.
SemiVariance of the designated values against the mean, using
instance properties varianceDirection and biasCorrection.
SemiVariance for the entire array against the mean, using
the current value of the biasCorrection instance property.
SemiVariance of the designated values against the cutoff, using
instance properties variancDirection and biasCorrection.
SemiVariance of the designated values against the cutoff in the
given direction, using the current value of the biasCorrection instance property.
SemiVariance of the designated values against the cutoff
in the given direction with the provided bias correction.
Double.NaN if the array is empty.
Double.NaN if the designated subarray
is empty.
Double.NaN if the array is empty.
Double.NaN if the designated subarray
is empty.
Double.NaN if the designated subarray
is empty.
Double.NaN if the designated subarray
is empty.
Double.NaN if the designated subarray
is empty.
pth percentile of the values
in the values array.
quantileth percentile of the
designated values in the values array.
pth percentile of the values
in the values array, starting with the element in (0-based)
position begin in the array and including length
values.
Double.NaN if the designated subarray
is empty.
Double.NaN if the designated subarray
is empty.
Double.NaN if the designated subarray
is empty.
Double.NaN if the designated subarray
is empty.
Double.NaN if the designated subarray
is empty.
Double.NaN if the designated subarray
is empty.
EventHandlerEventException.EventException(Localizable, Object...)
event handler during integration steps.FastMath.exp method wrapped as a ComposableFunction.
expansionFactor
is additive or multiplicative.
FastMath.expm1 method wrapped as a ComposableFunction.
ExponentialDistribution.UnivariateRealIntegrator.integrate(UnivariateRealFunction, double, double)method.
UnivariateRealSolver.solve(UnivariateRealFunction, double, double) or
UnivariateRealSolver.solve(UnivariateRealFunction, double, double, double)
method.
StrictMath.FDistribution.BracketFinder.hi.
length with values generated
using getNext() repeatedly.
data[i] = value for each i in tiesTrace.
population for another chromosome with the same
representation.
FirstMoment identical
to the original
BracketFinder.lo.
FastMath.floor method wrapped as a ComposableFunction.
BracketFinder.mid.
Complex object to produce a string.
BigFraction object to produce a string.
Fraction object to produce a string.
BigFraction object to produce a string.
Fraction object to produce a string.
Vector3D object to produce a string.
RealVector object to produce a string.
Format.format(Object) on a default instance of
ComplexFormat.
Format.format(Object) on a default instance of
RealVectorFormat.
Format.format(Object) on a default instance of
Vector3DFormat.
FourthMoment identical
to the original
FieldMatrix/Fraction matrix to a RealMatrix.
GammaDistribution.GaussianFunction.GaussianFunction).a, b, c, and d)
of a ParametricGaussianFunction based on the specified observed
points.StoppingCondition in the last run.
GeometricMean identical
to the original
Double.NaN if the array is empty.
Double.NaN if the designated subarray
is empty.
SummaryStatistics
containing statistics describing the values in each of the bins.
SummaryStatistics instances containing
statistics describing the values in each of the bins.
col as an array.
col as an array.
col as an array.
col as an array.
col as an array.
col as an array.
col as an array.
col as an array.
col as an array
of double values.
col as an array
of double values.
column
as a column matrix.
column
as a column matrix.
column
as a column matrix.
column
as a column matrix.
column
as a column matrix.
column
as a column matrix.
column
as a column matrix.
column
as a column matrix.
column
as a vector.
column
as a vector.
column
as a vector.
column
as a vector.
column
as a vector.
column
as a vector.
getCorrelationStandardErrors().getEntry(i,j) is the standard
error associated with getCorrelationMatrix.getEntry(i,j)
Frequency.getCount(Comparable) as of 2.0
Frequency.getCumFreq(Comparable) as of 2.0
Frequency.getCumPct(Comparable) as of 2.0
BigInteger.
new LUDecompositionImpl(m).getDeterminant()
p, used to
bracket a CDF root.
p, used to
bracket a PDF root.
p, used to
bracket a CDF root.
p, used to
bracket a PDF root.
p, used to
bracket a CDF root.
p, used to
bracket a CDF root.
p, used to
bracket a CDF root.
p, used to
bracket a CDF root.
p, used to
bracket a CDF root.
p, used to
bracket a PDF root.
p, used to
bracket a CDF root.
p, used to
bracket a PDF root.
p, used to
bracket a CDF root.
p, used to
bracket a CDF root.
p, used to
bracket a CDF root.
p, used to
bracket a PDF root.
p, used to
bracket a CDF root.
p, used to
bracket a PDF root.
p, used to
bracket a CDF root.
p, used to
bracket a PDF root.
p, used to
bracket a CDF root.
p, used to
bracket a CDF root.
p, used to
bracket a CDF root.
p, used to
bracket a CDF root.
p, used to
bracket a CDF root.
p, used to
bracket a PDF root.
p, used to
bracket a CDF root.
p, used to
bracket a PDF root.
p, used to
bracket a CDF root.
p, used to
bracket a CDF root.
p, used to
bracket a CDF root.
p, used to
bracket a PDF root.
DoubleArray.
ResizableArray.
expansionMode determines whether the internal storage
array grows additively (ADDITIVE_MODE) or multiplicatively
(MULTIPLICATIVE_MODE) when it is expanded.
BracketFinder.getHi().
Field to which the instance belongs.
Field (really a DfpField) to which the instance belongs.
Field to which the instance belongs.
Field to which the instance belongs.
Field to which the instance belongs.
Field to which the instance belongs.
BracketFinder.getLo().
BracketFinder.getMid().
StoppingCondition in the last run.
MultivariateSummaryStatistics.addValue(double[])
MultivariateSummaryStatistics.addValue(double[])
p, used to
bracket a CDF root.
p, used to
bracket a CDF root.
p, used to
bracket a CDF root.
p, used to
bracket a CDF root.
p, used to
bracket a CDF root.
p, used to
bracket a CDF root.
p, used to
bracket a CDF root.
p, used to
bracket a CDF root.
p, used to
bracket a CDF root.
p, used to
bracket a CDF root.
MultivariateSummaryStatistics.addValue(double[])
MultivariateSummaryStatistics.addValue(double[])
MultivariateSummaryStatistics.addValue(double[])
MultivariateSummaryStatistics.addValue(double[])
MultivariateSummaryStatistics.addValue(double[])
MultivariateSummaryStatistics.addValue(double[])
ranks is NaN.
Cluster to the given point
valuesFileURL.
BigInteger.
optimize.
optimize.
optimize.
optimize.
optimize.
theoretical value according to the parameter.
MathException.getSpecificPattern() and MathException.getGeneralPattern()
MathRuntimeException.getSpecificPattern() and MathRuntimeException.getGeneralPattern()
Frequency.getPct(Comparable) as of 2.0
Cluster with the largest number of points
Cluster with the largest distance variance.
Dfp instances built by this factory.
PearsonsCorrelation instance constructed from the
ranked input data.
BigFraction instance with the 2 parts of a fraction
Y/Z.
Fraction instance with the 2 parts
of a fraction Y/Z.
BigDecimal.ROUND_HALF_UP
RoundingMode.HALF_UP
row as an array.
row as an array.
row as an array.
row as an array.
row as an array.
row as an array.
row as an array.
row as an array.
row as an array
of double values.
row as an array
of double values.
row
as a row matrix.
row
as a row matrix.
row
as a row matrix.
row
as a row matrix.
row
as a row matrix.
row
as a row matrix.
row
as a row matrix.
row
as a row matrix.
row
as a vector.
row
as a vector.
row
as a vector.
row
as a vector.
row
as a vector.
row
as a vector.
StatisticalSummary
describing this distribution.
StatisticalSummary describing this distribution.
OpenMapRealVector.getSparsity()
MultivariateSummaryStatistics.addValue(double[])
MultivariateSummaryStatistics.addValue(double[])
MultivariateSummaryStatistics.addValue(double[])
MultivariateSummaryStatistics.addValue(double[])
MultivariateSummaryStatistics.addValue(double[])
MultivariateSummaryStatistics.addValue(double[])
StatisticalSummaryValues instance reporting current
aggregate statistics.
StatisticalSummaryValues instance reporting current
statistics.
StatisticalSummaryValues instance reporting current
statistics.
MultivariateSummaryStatistics.addValue(double[])
MultivariateSummaryStatistics.addValue(double[])
ResizableDoubleArray.getInternalValues() as of 2.0
valuesFileURL
- getVariance() -
Method in class org.apache.commons.math.stat.descriptive.AggregateSummaryStatistics
- Returns the variance of the available values.
- getVariance() -
Method in class org.apache.commons.math.stat.descriptive.DescriptiveStatistics
- Returns the variance of the available values.
- getVariance() -
Method in interface org.apache.commons.math.stat.descriptive.StatisticalSummary
- Returns the variance of the available values.
- getVariance() -
Method in class org.apache.commons.math.stat.descriptive.StatisticalSummaryValues
-
- getVariance() -
Method in class org.apache.commons.math.stat.descriptive.SummaryStatistics
- Returns the variance of the values that have been added.
- getVariance() -
Method in class org.apache.commons.math.stat.descriptive.SynchronizedSummaryStatistics
- Returns the variance of the values that have been added.
- getVarianceDirection() -
Method in class org.apache.commons.math.stat.descriptive.moment.SemiVariance
- Returns the varianceDirection property.
- getVarianceImpl() -
Method in class org.apache.commons.math.stat.descriptive.DescriptiveStatistics
- Returns the currently configured variance implementation.
- getVarianceImpl() -
Method in class org.apache.commons.math.stat.descriptive.SummaryStatistics
- Returns the currently configured variance implementation
- getVarianceImpl() -
Method in class org.apache.commons.math.stat.descriptive.SynchronizedSummaryStatistics
- Returns the currently configured variance implementation
- getVT() -
Method in interface org.apache.commons.math.linear.EigenDecomposition
- Returns the transpose of the matrix V of the decomposition.
- getVT() -
Method in class org.apache.commons.math.linear.EigenDecompositionImpl
- Returns the transpose of the matrix V of the decomposition.
- getVT() -
Method in interface org.apache.commons.math.linear.SingularValueDecomposition
- Returns the transpose of the matrix V of the decomposition.
- getVT() -
Method in class org.apache.commons.math.linear.SingularValueDecompositionImpl
- Returns the transpose of the matrix V of the decomposition.
- getWeight() -
Method in class org.apache.commons.math.estimation.WeightedMeasurement
- Deprecated. Get the weight of the measurement in the least squares problem
- getWeight() -
Method in class org.apache.commons.math.optimization.fitting.WeightedObservedPoint
- Get the weight of the measurement in the fitting process.
- getWholeFormat() -
Method in class org.apache.commons.math.fraction.ProperBigFractionFormat
- Access the whole format.
- getWholeFormat() -
Method in class org.apache.commons.math.fraction.ProperFractionFormat
- Access the whole format.
- getWidth() -
Method in class org.apache.commons.math.optimization.linear.SimplexTableau
- Get the width of the tableau.
- getWindowSize() -
Method in class org.apache.commons.math.stat.descriptive.DescriptiveStatistics
- Returns the maximum number of values that can be stored in the
dataset, or INFINITE_WINDOW (-1) if there is no limit.
- getWindowSize() -
Method in class org.apache.commons.math.stat.descriptive.SynchronizedDescriptiveStatistics
- Returns the maximum number of values that can be stored in the
dataset, or INFINITE_WINDOW (-1) if there is no limit.
- getX() -
Method in class org.apache.commons.math.geometry.Vector3D
- Get the abscissa of the vector.
- getX() -
Method in class org.apache.commons.math.optimization.fitting.WeightedObservedPoint
- Get the abscissa of the point.
- getXSumSquares() -
Method in class org.apache.commons.math.stat.regression.SimpleRegression
- Returns the sum of squared deviations of the x values about their mean.
- getY() -
Method in class org.apache.commons.math.geometry.Vector3D
- Get the ordinate of the vector.
- getY() -
Method in class org.apache.commons.math.optimization.fitting.WeightedObservedPoint
- Get the observed value of the function at x.
- getZ() -
Method in class org.apache.commons.math.geometry.Vector3D
- Get the height of the vector.
- getZero() -
Method in class org.apache.commons.math.complex.ComplexField
- Get the additive identity of the field.
- getZero() -
Method in class org.apache.commons.math.dfp.Dfp
- Get the constant 0.
- getZero() -
Method in class org.apache.commons.math.dfp.DfpField
- Get the constant 0.
- getZero() -
Method in interface org.apache.commons.math.Field
- Get the additive identity of the field.
- getZero() -
Method in class org.apache.commons.math.fraction.BigFractionField
- Get the additive identity of the field.
- getZero() -
Method in class org.apache.commons.math.fraction.FractionField
- Get the additive identity of the field.
- getZero() -
Method in class org.apache.commons.math.util.BigRealField
- Get the additive identity of the field.
- GillIntegrator - Class in org.apache.commons.math.ode.nonstiff
- This class implements the Gill fourth order Runge-Kutta
integrator for Ordinary Differential Equations .
- GillIntegrator(double) -
Constructor for class org.apache.commons.math.ode.nonstiff.GillIntegrator
- Simple constructor.
- GillStepInterpolator - Class in org.apache.commons.math.ode.nonstiff
- This class implements a step interpolator for the Gill fourth
order Runge-Kutta integrator.
- GillStepInterpolator() -
Constructor for class org.apache.commons.math.ode.nonstiff.GillStepInterpolator
- Simple constructor.
- GillStepInterpolator(GillStepInterpolator) -
Constructor for class org.apache.commons.math.ode.nonstiff.GillStepInterpolator
- Copy constructor.
- globalCurrentTime -
Variable in class org.apache.commons.math.ode.sampling.AbstractStepInterpolator
- global current time
- globalPreviousTime -
Variable in class org.apache.commons.math.ode.sampling.AbstractStepInterpolator
- global previous time
- GLSMultipleLinearRegression - Class in org.apache.commons.math.stat.regression
- The GLS implementation of the multiple linear regression.
- GLSMultipleLinearRegression() -
Constructor for class org.apache.commons.math.stat.regression.GLSMultipleLinearRegression
-
- goal -
Variable in class org.apache.commons.math.optimization.general.AbstractScalarDifferentiableOptimizer
- Deprecated.
- goal -
Variable in class org.apache.commons.math.optimization.linear.AbstractLinearOptimizer
- Type of optimization goal: either
GoalType.MAXIMIZE or GoalType.MINIMIZE.
- GoalType - Enum in org.apache.commons.math.optimization
- Goal type for an optimization problem.
- GoalType() -
Constructor for enum org.apache.commons.math.optimization.GoalType
-
- GOLD -
Static variable in class org.apache.commons.math.optimization.univariate.BracketFinder
- Golden section.
- GOLDEN_SECTION -
Static variable in class org.apache.commons.math.optimization.univariate.BrentOptimizer
- Golden section.
- gradient() -
Method in interface org.apache.commons.math.analysis.DifferentiableMultivariateRealFunction
- Returns the gradient function.
- gradient(double, double[]) -
Method in class org.apache.commons.math.optimization.fitting.HarmonicFitter.ParametricHarmonicFunction
- Compute the gradient of the function with respect to its parameters.
- gradient(double, double[]) -
Method in class org.apache.commons.math.optimization.fitting.ParametricGaussianFunction
- Computes the gradient vector for a four variable version of the function
where the parameters, a, b, c, and d,
are considered the variables, not x.
- gradient(double, double[]) -
Method in interface org.apache.commons.math.optimization.fitting.ParametricRealFunction
- Compute the gradient of the function with respect to its parameters.
- gradient(double, double[]) -
Method in class org.apache.commons.math.optimization.fitting.PolynomialFitter.ParametricPolynomial
- Compute the gradient of the function with respect to its parameters.
- gradient -
Variable in class org.apache.commons.math.optimization.general.AbstractScalarDifferentiableOptimizer
- Objective function gradient.
- gradientEvaluations -
Variable in class org.apache.commons.math.optimization.general.AbstractScalarDifferentiableOptimizer
- Number of gradient evaluations.
- GraggBulirschStoerIntegrator - Class in org.apache.commons.math.ode.nonstiff
- This class implements a Gragg-Bulirsch-Stoer integrator for
Ordinary Differential Equations.
- GraggBulirschStoerIntegrator(double, double, double, double) -
Constructor for class org.apache.commons.math.ode.nonstiff.GraggBulirschStoerIntegrator
- Simple constructor.
- GraggBulirschStoerIntegrator(double, double, double[], double[]) -
Constructor for class org.apache.commons.math.ode.nonstiff.GraggBulirschStoerIntegrator
- Simple constructor.
- GraggBulirschStoerStepInterpolator - Class in org.apache.commons.math.ode.nonstiff
- This class implements an interpolator for the Gragg-Bulirsch-Stoer
integrator.
- GraggBulirschStoerStepInterpolator() -
Constructor for class org.apache.commons.math.ode.nonstiff.GraggBulirschStoerStepInterpolator
- Simple constructor.
- GraggBulirschStoerStepInterpolator(double[], double[], double[], double[], double[][], boolean) -
Constructor for class org.apache.commons.math.ode.nonstiff.GraggBulirschStoerStepInterpolator
- Simple constructor.
- GraggBulirschStoerStepInterpolator(GraggBulirschStoerStepInterpolator) -
Constructor for class org.apache.commons.math.ode.nonstiff.GraggBulirschStoerStepInterpolator
- Copy constructor.
- GREATER_THAN_TRAP -
Static variable in class org.apache.commons.math.dfp.Dfp
- Name for traps triggered by greaterThan.
- greaterThan(Dfp) -
Method in class org.apache.commons.math.dfp.Dfp
- Check if instance is greater than x.
- growLimit -
Variable in class org.apache.commons.math.optimization.univariate.BracketFinder
- Factor for expanding the interval.
- growTable() -
Method in class org.apache.commons.math.util.OpenIntToDoubleHashMap
- Grow the tables.
- growTable() -
Method in class org.apache.commons.math.util.OpenIntToFieldHashMap
- Grow the tables.
- guess() -
Method in class org.apache.commons.math.optimization.fitting.GaussianParametersGuesser
- Guesses the parameters based on the observed points.
- guess() -
Method in class org.apache.commons.math.optimization.fitting.HarmonicCoefficientsGuesser
- Estimate a first guess of the coefficients.
- guessAOmega() -
Method in class org.apache.commons.math.optimization.fitting.HarmonicCoefficientsGuesser
- Estimate a first guess of the a and ω coefficients.
- guessParametersErrors(EstimationProblem) -
Method in class org.apache.commons.math.estimation.AbstractEstimator
- Deprecated. Guess the errors in unbound estimated parameters.
- guessParametersErrors(EstimationProblem) -
Method in interface org.apache.commons.math.estimation.Estimator
- Deprecated. Guess the errors in estimated parameters.
- guessParametersErrors() -
Method in class org.apache.commons.math.optimization.general.AbstractLeastSquaresOptimizer
- Guess the errors in optimized parameters.
- guessPhi() -
Method in class org.apache.commons.math.optimization.fitting.HarmonicCoefficientsGuesser
- Estimate a first guess of the φ coefficient.
f (t) = a cos (ω t + φ).StatisticalSummary instances, under the
assumption of equal subpopulation variances.
StatisticalSummary instances, under the
assumption of equal subpopulation variances.
sample1
and sample2 are drawn from populations with the same mean,
with significance level alpha, assuming that the
subpopulation variances are equal.
sample1
and sample2 are drawn from populations with the same mean,
with significance level alpha, assuming that the
subpopulation variances are equal.
HypergeometricDistribution.x and y
- sqrt(x2 +y2)AbstractStorelessUnivariateStatistic.increment(double) in a loop over
the input array.
AbstractStorelessUnivariateStatistic.increment(double) in a loop over
the specified portion of the input array.
permutedData when applied to
originalData.
UnivariateRealIntegrator.integrate(UnivariateRealFunction, double, double)
since 2.0
IntegratorException.IntegratorException(Localizable, Object...)
SplineInterpolator
on the resulting fit.
InvalidMatrixException.InvalidMatrixException(Localizable, Object...)
new LUDecompositionImpl(m).getSolver().getInverse()
p.
p.
p.
p.
p.
p.
p.
p.
p.
p.
p.
p.
p.
ComposableFunction.
Double.POSITIVE_INFINITY or
Double.NEGATIVE_INFINITY) and neither part
is NaN.
NaN.
NaN.
NaN.
NaN.
true iff another has the same
representation and therefore the same fitness.
- isSame(Chromosome) -
Method in class org.apache.commons.math.genetics.Chromosome
- Returns
true iff another has the same
representation and therefore the same fitness.
- isSame(Chromosome) -
Method in class org.apache.commons.math.genetics.RandomKey
- Returns
true iff another is a RandomKey and
encodes the same permutation.
- isSatisfied(Population) -
Method in class org.apache.commons.math.genetics.FixedGenerationCount
- Determine whether or not the given number of generations have passed.
- isSatisfied(Population) -
Method in interface org.apache.commons.math.genetics.StoppingCondition
- Determine whether or not the given population satisfies the stopping
condition.
- isSequence(double, double, double) -
Method in class org.apache.commons.math.analysis.solvers.UnivariateRealSolverImpl
- Deprecated. Returns true if the arguments form a (strictly) increasing sequence
- isSingular() -
Method in class org.apache.commons.math.linear.AbstractRealMatrix
- Deprecated.
- isSingular() -
Method in class org.apache.commons.math.linear.BigMatrixImpl
- Deprecated. Is this a singular matrix?
- isSingular() -
Method in interface org.apache.commons.math.linear.RealMatrix
- Deprecated. as of release 2.0, replaced by the boolean negation of
new LUDecompositionImpl(m).getSolver().isNonSingular() - isSquare() -
Method in class org.apache.commons.math.linear.AbstractFieldMatrix
- Is this a square matrix?
- isSquare() -
Method in class org.apache.commons.math.linear.AbstractRealMatrix
- Is this a square matrix?
- isSquare() -
Method in interface org.apache.commons.math.linear.AnyMatrix
- Is this a square matrix?
- isSquare() -
Method in class org.apache.commons.math.linear.BigMatrixImpl
- Deprecated. Is this a square matrix?
- isStrictlyIncreasing(double[]) -
Static method in class org.apache.commons.math.analysis.polynomials.PolynomialSplineFunction
- Determines if the given array is ordered in a strictly increasing
fashion.
- isSupportLowerBoundInclusive() -
Method in class org.apache.commons.math.distribution.AbstractIntegerDistribution
- Use this method to get information about whether the lower bound
of the support is inclusive or not.
- isSupportUpperBoundInclusive() -
Method in class org.apache.commons.math.distribution.AbstractIntegerDistribution
- Use this method to get information about whether the upper bound
of the support is inclusive or not.
- isSymmetric(RealMatrix) -
Method in class org.apache.commons.math.linear.EigenDecompositionImpl
- Check if a matrix is symmetric.
- isUpperBiDiagonal() -
Method in class org.apache.commons.math.linear.BiDiagonalTransformer
- Check if the matrix is transformed to upper bi-diagonal.
- iter -
Variable in class org.apache.commons.math.linear.OpenMapRealVector.OpenMapEntry
- Iterator pointing to the entry.
- iter -
Variable in class org.apache.commons.math.linear.OpenMapRealVector.OpenMapSparseIterator
- Underlying iterator.
- iterateSimplex(Comparator<RealPointValuePair>) -
Method in class org.apache.commons.math.optimization.direct.DirectSearchOptimizer
- Compute the next simplex of the algorithm.
- iterateSimplex(Comparator<RealPointValuePair>) -
Method in class org.apache.commons.math.optimization.direct.MultiDirectional
- Compute the next simplex of the algorithm.
- iterateSimplex(Comparator<RealPointValuePair>) -
Method in class org.apache.commons.math.optimization.direct.NelderMead
- Compute the next simplex of the algorithm.
- iterationCount -
Variable in class org.apache.commons.math.ConvergingAlgorithmImpl
- Deprecated. The last iteration count.
- iterations -
Variable in class org.apache.commons.math.optimization.direct.DirectSearchOptimizer
- Number of iterations already performed.
- iterations -
Variable in class org.apache.commons.math.optimization.general.AbstractLeastSquaresOptimizer
- Number of iterations already performed.
- iterations -
Variable in class org.apache.commons.math.optimization.general.AbstractScalarDifferentiableOptimizer
- Number of iterations already performed.
- iterations -
Variable in class org.apache.commons.math.optimization.linear.AbstractLinearOptimizer
- Number of iterations already performed.
- iterations -
Variable in class org.apache.commons.math.optimization.univariate.BracketFinder
- Number of iterations.
- iterator() -
Method in class org.apache.commons.math.genetics.ListPopulation
- Chromosome list iterator
- iterator() -
Method in class org.apache.commons.math.linear.AbstractRealVector
- Generic dense iterator.
- iterator() -
Method in interface org.apache.commons.math.linear.RealVector
- Generic dense iterator.
- iterator() -
Method in class org.apache.commons.math.util.MultidimensionalCounter
- Create an iterator over this counter.
- iterator() -
Method in class org.apache.commons.math.util.OpenIntToDoubleHashMap
- Get an iterator over map elements.
- iterator() -
Method in class org.apache.commons.math.util.OpenIntToFieldHashMap
- Get an iterator over map elements.
java.util.Random to implement
RandomGenerator.Kurtosis identical
to the original
LaguerreSolver.solve(UnivariateRealFunction, double, double) or
UnivariateRealSolver.solve(UnivariateRealFunction, double, double, double)
method.
lcm(a,b) = (a / gcd(a,b)) * b.
lcm(a,b) = (a / gcd(a,b)) * b.
vectorial
objective functions to scalar objective functions
when the goal is to minimize them.List.Dfp with value ln(10).
Dfp with value ln(2).
Dfp array with value ln(2) split in two pieces.
Dfp with value ln(5).
Dfp array with value ln(5) split in two pieces.
(lo, hi).
LoessInterpolator
with a bandwidth of LoessInterpolator.DEFAULT_BANDWIDTH,
LoessInterpolator.DEFAULT_ROBUSTNESS_ITERS robustness iterations
and an accuracy of {#link #DEFAULT_ACCURACY}.
LoessInterpolator
with given bandwidth and number of robustness iterations.
LoessInterpolator
with given bandwidth, number of robustness iterations and accuracy.
FastMath.log method wrapped as a ComposableFunction.
b of x.
FastMath.log10 method wrapped as a ComposableFunction.
FastMath.log1p method wrapped as a ComposableFunction.
LUDecomposition
Math.abs(double) function to each entry.
Math.abs(double) function to each entry.
Math.abs(double) function to each entry.
Math.acos(double) function to each entry.
Math.acos(double) function to each entry.
Math.acos(double) function to each entry.
Math.asin(double) function to each entry.
Math.asin(double) function to each entry.
Math.asin(double) function to each entry.
Math.atan(double) function to each entry.
Math.atan(double) function to each entry.
Math.atan(double) function to each entry.
Math.cbrt(double) function to each entry.
Math.cbrt(double) function to each entry.
Math.cbrt(double) function to each entry.
Math.ceil(double) function to each entry.
Math.ceil(double) function to each entry.
Math.ceil(double) function to each entry.
Math.cos(double) function to each entry.
Math.cosh(double) function to each entry.
Math.cosh(double) function to each entry.
Math.cosh(double) function to each entry.
Math.cos(double) function to each entry.
Math.cos(double) function to each entry.
Math.exp(double) function to each entry.
Math.expm1(double) function to each entry.
Math.expm1(double) function to each entry.
Math.expm1(double) function to each entry.
Math.exp(double) operation to each entry.
Math.exp(double) operation to each entry.
Math.floor(double) function to each entry.
Math.floor(double) function to each entry.
Math.floor(double) function to each entry.
Math.log(double) function to each entry.
Math.log10(double) function to each entry.
Math.log10(double) function to each entry.
Math.log10(double) function to each entry.
Math.log1p(double) function to each entry.
Math.log1p(double) function to each entry.
Math.log1p(double) function to each entry.
Math.log(double) function to each entry.
Math.log(double) function to each entry.
Math.rint(double) function to each entry.
Math.rint(double) function to each entry.
Math.rint(double) function to each entry.
Math.signum(double) function to each entry.
Math.signum(double) function to each entry.
Math.signum(double) function to each entry.
Math.sin(double) function to each entry.
Math.sinh(double) function to each entry.
Math.sinh(double) function to each entry.
Math.sinh(double) function to each entry.
Math.sin(double) function to each entry.
Math.sin(double) function to each entry.
Math.sqrt(double) function to each entry.
Math.sqrt(double) function to each entry.
Math.sqrt(double) function to each entry.
Math.tan(double) function to each entry.
Math.tanh(double) function to each entry.
Math.tanh(double) function to each entry.
Math.tanh(double) function to each entry.
Math.tan(double) function to each entry.
Math.tan(double) function to each entry.
Math.ulp(double) function to each entry.
Math.ulp(double) function to each entry.
Math.ulp(double) function to each entry.
MathException with no
detail message.
MathException.MathException(Localizable, Object...)
MathException with specified
formatted detail message.
MathException with specified
nested Throwable root cause.
MathException.MathException(Throwable, Localizable, Object...)
MathException with specified
formatted detail message and nested Throwable root cause.
MathRuntimeException.MathRuntimeException(Localizable, Object...)
MathRuntimeException with specified
formatted detail message.
MathRuntimeException with specified
nested Throwable root cause.
MathRuntimeException.MathRuntimeException(Throwable, Localizable, Object...)
MathRuntimeException with specified
formatted detail message and nested Throwable root cause.
Math.MatrixIndexException.MatrixIndexException(Localizable, Object...)
FieldMatrix/BigFraction.FieldMatrix/Fraction.Max identical
to the original
Double.NaN if the array is empty.
Double.NaN if the designated subarray
is empty.
MaxEvaluationsExceededException.MaxEvaluationsExceededException(int, Localizable, Object...)
MaxIterationsExceededException.MaxIterationsExceededException(int, Localizable, Object...)
Mean identical
to the original
Double.NaN if the array is empty.
Double.NaN if the designated subarray
is empty.
Median identical
to the original
Min identical
to the original
Double.NaN if the array is empty.
Double.NaN if the designated subarray
is empty.
MullerSolver.solve(UnivariateRealFunction, double, double) or
UnivariateRealSolver.solve(UnivariateRealFunction, double, double, double)
method.
BinaryFunction.
BigInteger, returning the result in reduced form.
m.
m.
m.
m.
m.
DifferentiableMultivariateRealOptimizer interface adding
multi-start features to an existing optimizer.DifferentiableMultivariateVectorialOptimizer interface adding
multi-start features to an existing optimizer.MultivariateRealOptimizer interface adding
multi-start features to an existing optimizer.UnivariateRealOptimizer interface adding
multi-start features to an existing optimizer.scalar objective functions.addValue method.ComposableFunction.
UnivariateRealSolver.
UnivariateRealSolver.
UnivariateRealSolver.
UnivariateRealSolver.
UnivariateRealSolver.
UnivariateRealSolver.
Dfp with a value of 0.
Dfp given a String representation.
Dfp with a non-finite value.
this is, with a
given arrayRepresentation.
UnivariateRealSolver.
UnivariateRealSolver.
UnivariateRealSolver.
UnivariateRealSolver.
NewtonSolver.solve(UnivariateRealFunction, double, double) or
UnivariateRealSolver.solve(UnivariateRealFunction, double, double, double)
method.
AbstractRealVector.SparseEntryIterator.next() to return.
FastMath.nextAfter(double, double)
which handles Infinities differently, and returns direction if d and direction compare equal.
Beta Distribution.
Binomial Distribution.
boolean value from this random number generator's
sequence.
boolean value from this random number generator's
sequence.
boolean value from this random number generator's
sequence.
boolean value from this random number generator's
sequence.
Cauchy Distribution.
ChiSquare Distribution.
double value between 0.0 and
1.0 from this random number generator's sequence.
double value between 0.0 and
1.0 from this random number generator's sequence.
double value between 0.0 and
1.0 from this random number generator's sequence.
double value between 0.0 and
1.0 from this random number generator's sequence.
mean.
F Distribution.
float
value between 0.0 and 1.0 from this random
number generator's sequence.
float
value between 0.0 and 1.0 from this random
number generator's sequence.
float
value between 0.0 and 1.0 from this random
number generator's sequence.
float
value between 0.0 and 1.0 from this random
number generator's sequence.
Gamma Distribution.
double value with mean 0.0 and standard
deviation 1.0 from this random number generator's sequence.
double value with mean 0.0 and standard
deviation 1.0 from this random number generator's sequence.
double value with mean 0.0 and standard
deviation 1.0 from this random number generator's sequence.
double value with mean 0.0 and standard
deviation 1.0 from this random number generator's sequence.
len.
len.
Hypergeometric Distribution.
int
value from this random number generator's sequence.
int value
between 0 (inclusive) and the specified value (exclusive), drawn from
this random number generator's sequence.
int
value from this random number generator's sequence.
int
value from this random number generator's sequence.
lower and upper (endpoints included).
lower and upper, inclusive.
int
value from this random number generator's sequence.
long
value from this random number generator's sequence.
long
value from this random number generator's sequence.
long
value from this random number generator's sequence.
lower and upper (endpoints included).
lower and upper, inclusive.
long
value from this random number generator's sequence.
Pascal Distribution.
k whose entries
are selected randomly, without repetition, from the integers
0 through n-1 (inclusive).
k whose entries are
selected randomly, without repetition, from the integers
0 through n-1 (inclusive).
k objects selected randomly
from the Collection c.
lower and upper (endpoints included)
from a secure random sequence.
lower and upper, inclusive.
lower
and upper (endpoints included).
lower and upper, inclusive.
T Distribution.
lower,upper) (i.e., endpoints excluded).
lower,upper) (i.e., endpoints excluded).
Weibull Distribution.
Zipf Distribution.
NormalDistribution.NotARotationMatrixException.NotARotationMatrixException(Localizable, Object...)
null argument must throw
this exception.Dfp with value 1.
BigInteger representation of 100.
OneWayAnovaImpl
interface.RealVector interface with a OpenIntToDoubleHashMap backing store.Entry optimized for OpenMap.v.
v.
v.
v.
v.
v.
v.
v.
v.
v.
v.
v.
v.
v.
v.
v.
OptimizationException.OptimizationException(Localizable, Object...)
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.
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.
Complex object.
Complex object.
BigFraction object.
BigFraction object.
Fraction object.
Fraction object.
BigFraction object.
Fraction object.
Vector3D object.
Vector3D object.
RealVector object.
RealVector object.
source until a non-whitespace character is found.
source until a non-whitespace character is found.
source for an expected fixed string.
BigInteger.
source until a non-whitespace character is found.
source until a non-whitespace character is found.
source for special double values.
source for a number.
PascalDistribution.Covariance.
Percentile identical
to the original
pth percentile of the values
in the values array.
pth percentile of the values
in the values array, starting with the element in (0-based)
position begin in the array and including length
values.
Dfp with value π.
Dfp array with value π split in two pieces.
PoissonDistribution.FastMath.pow method wrapped as a BinaryFunction.
x.
BigFraction whose value is
(thisexponent), returning the result in reduced form.
BigFraction whose value is
(thisexponent), returning the result in reduced form.
double whose value is
(thisexponent), returning the result in reduced form.
other constructor).
other constructor).
y value associated with the
supplied x value, based on the data that has been
added to the model when this method is activated.
m.
v.
v.
m.
v.
v.
v.
v.
m.
v.
m.
v.
v.
v.
m.
v.
v.
m.
v.
v.
v.
Product identical
to the original
Double.NaN if the array is empty.
Double.NaN if the designated subarray
is empty.
java.util.Random wrapping a
RandomGenerator.length.
RandomData interface using a RandomGenerator
instance to generate non-secure data and a SecureRandom
instance to provide data for the nextSecureXxx methods.RandomGenerator as
the source of (non-secure) random data.
java.util.Random.RandomKeys.data using the natural ordering on Doubles, with
NaN values handled according to nanStrategy and ties
resolved using tiesStrategy.
matrix
using the current rankingAlgorithm
optimization
algorithm has converged.Array2DRowRealMatrixv as the
data for the unique column of the v.length x 1 matrix
created.
BigFraction to its lowest terms.
LinearConstraint.data.
valuesFileURL.
DoubleArray implementation that automatically
handles expanding and contracting its internal storage array as elements
are added and removed.TiesStrategy.
ranks[i] = Double.NaN for each i in nanPositions.
RiddersSolver.solve(UnivariateRealFunction, double, double) or
UnivariateRealSolver.solve(UnivariateRealFunction, double, double, double)
method.
FastMath.rint method wrapped as a ComposableFunction.
RombergIntegrator.integrate(UnivariateRealFunction, double, double)method.
d
FastMath.scalb(double, int)
f(p + alpha * d).
SecantSolver.solve(UnivariateRealFunction, double, double) or
UnivariateRealSolver.solve(UnivariateRealFunction, double, double, double)
method.
SecondMoment identical
to the original
biasCorrected
property and default (Downside) varianceDirection property.
biasCorrected
property and default (Downside) varianceDirection property.
Direction property
and default (true) biasCorrected property
isBiasCorrected
property and the specified Direction property.
SemiVariance identical
to the original
RealMatrix.
RealVector.
column
as a column matrix.
column
as a column matrix.
column
as a column matrix.
column
as a column matrix.
column
as a column matrix.
column
as a column matrix.
column
as a column matrix.
column
as a column matrix.
column
as a column matrix.
column
as a column matrix.
column
as a column matrix.
column
as a column matrix.
column
as a column matrix.
column
as a column matrix.
column
as a vector.
column
as a vector.
column
as a vector.
column
as a vector.
column
as a vector.
column
as a vector.
expansionMode.
DescriptiveStatistics.getPercentile(double).
row
as a row matrix.
row
as a row matrix.
row
as a row matrix.
row
as a row matrix.
row
as a row matrix.
row
as a row matrix.
row
as a row matrix.
row
as a row matrix.
row
as a row matrix.
row
as a row matrix.
row
as a row matrix.
row
as a row matrix.
row
as a row matrix.
row
as a row matrix.
row
as a vector.
row
as a vector.
row
as a vector.
row
as a vector.
row
as a vector.
row
as a vector.
int seed.
int array seed.
long seed.
int seed.
int array seed.
long seed.
int seed.
int array seed.
int seed.
int array seed.
long seed.
int seed.
int array seed.
long seed.
row, column using data in
the input subMatrix array.
row, column using data in
the input subMatrix array.
row, column using data in
the input subMatrix array.
row, column using data in
the input subMatrix array.
row, column using data in
the input subMatrix array.
row, column using data in
the input subMatrix array.
row, column using data in
the input subMatrix array.
row, column using data in
the input subMatrix array.
row, column using data in
the input subMatrix array.
row, column using data in
the input subMatrix array.
valuesFileURL using a string URL representation
valuesFileURL
x.
x.
x.
x.
x.
x.
FastMath.signum method wrapped as a ComposableFunction.
RealConvergenceChecker interface using
only point coordinates.other constructor instead.
RealConvergenceChecker interface using
only objective function values.VectorialConvergenceChecker interface using
only point coordinates.VectorialConvergenceChecker interface using
only objective function values.SimpsonIntegrator.integrate(UnivariateRealFunction, double, double)method.
FastMath.sin method wrapped as a ComposableFunction.
FastMath.sinh method wrapped as a ComposableFunction.
Skewness identical
to the original
BicubicSplineInterpolator
instead. If smoothing is desired, a tentative implementation is provided in class
SmoothingPolynomialBicubicSplineInterpolator.
This class will be removed in math 3.0.min and max.
startValue.
UnivariateRealSolver.solve(UnivariateRealFunction, double, double)
since 2.0
UnivariateRealSolver.solve(UnivariateRealFunction, double, double, double)
since 2.0
b.
b.
b.
b.
b.
DecompositionSolver.solve(double[])
DecompositionSolver.solve(RealMatrix)
MullerSolver.solve2(UnivariateRealFunction, double, double)
since 2.0
FieldVector interface with a OpenIntToFieldHashMap backing store.RealMatrix implementations that require sparse backing storageDfp's.
Dfp into 2 Dfp's such that their sum is equal to the input Dfp.
Dfp with value √2.
Dfp with value √2 / 2.
Dfp array with value √2 split in two pieces.
Dfp with value √3.
Dfp with value √3 / 3.
FastMath.sqrt method wrapped as a ComposableFunction.
this2 for this complex
number.
StandardDeviation identical
to the original
isBiasCorrected property.
isBiasCorrected property and the supplied external moment.
FixedStepHandler
into a StepHandler.UnivariateStatistic with
StorelessUnivariateStatistic.increment(double) and StorelessUnivariateStatistic.incrementAll(double[]) methods for adding
values and updating internal state.value for the most recently added value.
BinaryFunction.
BigInteger from the value of this one,
returning the result in reduced form.
v from this vector.
v from this vector.
m.
m.
v from this vector.
v from this vector.
m.
m.
m.
m.
m.
v from this vector.
v from this vector.
m.
v from this vector.
v from this vector.
Sum identical
to the original
Double.NaN if the array is empty.
Double.NaN if the designated subarray
is empty.
Double.NaN if the array is empty.
Double.NaN if the designated subarray
is empty.
addValue method.SumOfLogs identical
to the original
SumOfSquares identical
to the original
Double.NaN if the array is empty.
Double.NaN if the designated subarray
is empty.
DescriptiveStatistics that
is safe to use in a multithreaded environment.MultivariateSummaryStatistics that
is safe to use in a multithreaded environment.SummaryStatistics that
is safe to use in a multithreaded environment.sampleStats to mu.
StatisticalSummary instances, without the
assumption of equal subpopulation variances.
sampleStats to mu.
StatisticalSummary instances, without the
assumption of equal subpopulation variances.
FastMath.tan method wrapped as a ComposableFunction.
FastMath.tanh method wrapped as a ComposableFunction.
TDistribution.evaluate(double[], int, int) methods
to verify that the input parameters designate a subarray of positive length.
evaluate(double[], double[], int, int) methods
to verify that the begin and length parameters designate a subarray of positive length
and the weights are all non-negative, non-NaN, finite, and not all zero.
ThirdMoment identical
to the original
String representing this fraction, ie
"num / dem" or just "num" if the denominator is one.
String representing this fraction, ie
"num / dem" or just "num" if the denominator is one.
TournamentSelection.select(Population).
TrapezoidIntegrator.integrate(UnivariateRealFunction, double, double)method.
Tricubic interpolation in three dimensions
F.- TricubicSplineInterpolatingFunction(double[], double[], double[], double[][][], double[][][], double[][][], double[][][], double[][][], double[][][], double[][][], double[][][]) - Constructor for class org.apache.commons.math.analysis.interpolation.TricubicSplineInterpolatingFunction
- TricubicSplineInterpolator - Class in org.apache.commons.math.analysis.interpolation
- Generates a tricubic interpolating function.
- TricubicSplineInterpolator() - Constructor for class org.apache.commons.math.analysis.interpolation.TricubicSplineInterpolator
- TriDiagonalTransformer - Class in org.apache.commons.math.linear
- Class transforming a symmetrical matrix to tridiagonal shape.
- TriDiagonalTransformer(RealMatrix) - Constructor for class org.apache.commons.math.linear.TriDiagonalTransformer
- Build the transformation to tridiagonal shape of a symmetrical matrix.
- trigamma(double) - Static method in class org.apache.commons.math.special.Gamma
- Computes the trigamma function of x.
- trimmedPrefix - Variable in class org.apache.commons.math.geometry.Vector3DFormat
- Trimmed prefix.
- trimmedPrefix - Variable in class org.apache.commons.math.linear.RealVectorFormat
- Trimmed prefix.
- trimmedSeparator - Variable in class org.apache.commons.math.geometry.Vector3DFormat
- Trimmed separator.
- trimmedSeparator - Variable in class org.apache.commons.math.linear.RealVectorFormat
- Trimmed separator.
- trimmedSuffix - Variable in class org.apache.commons.math.geometry.Vector3DFormat
- Trimmed suffix.
- trimmedSuffix - Variable in class org.apache.commons.math.linear.RealVectorFormat
- Trimmed suffix.
- TrivariateRealFunction - Interface in org.apache.commons.math.analysis
- An interface representing a trivariate real function.
- TrivariateRealGridInterpolator - Interface in org.apache.commons.math.analysis.interpolation
- Interface representing a trivariate real interpolating function where the sample points must be specified on a regular grid.
- trunc(DfpField.RoundingMode) - Method in class org.apache.commons.math.dfp.Dfp
- Does the integer conversions with the specified rounding.
- TRUNC_TRAP - Static variable in class org.apache.commons.math.dfp.Dfp
- Name for traps triggered by truncation.
- tryStep(double, double[], double, int, double[], double[][], double[], double[], double[]) - Method in class org.apache.commons.math.ode.nonstiff.GraggBulirschStoerIntegrator
- Perform integration over one step using substeps of a modified midpoint method.
- tTest - Static variable in class org.apache.commons.math.stat.inference.TestUtils
- Singleton TTest instance using default implementation.
- tTest(double, double[], double) - Static method in class org.apache.commons.math.stat.inference.TestUtils
- tTest(double, double[]) - Static method in class org.apache.commons.math.stat.inference.TestUtils
- tTest(double, StatisticalSummary, double) - Static method in class org.apache.commons.math.stat.inference.TestUtils
- tTest(double, StatisticalSummary) - Static method in class org.apache.commons.math.stat.inference.TestUtils
- tTest(double[], double[], double) - Static method in class org.apache.commons.math.stat.inference.TestUtils
- tTest(double[], double[]) - Static method in class org.apache.commons.math.stat.inference.TestUtils
- tTest(StatisticalSummary, StatisticalSummary, double) - Static method in class org.apache.commons.math.stat.inference.TestUtils
- tTest(StatisticalSummary, StatisticalSummary) - Static method in class org.apache.commons.math.stat.inference.TestUtils
- TTest - Interface in org.apache.commons.math.stat.inference
- An interface for Student's t-tests.
- tTest(double, double[]) - Method in interface org.apache.commons.math.stat.inference.TTest
- 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.- tTest(double, double[], double) - Method in interface org.apache.commons.math.stat.inference.TTest
- Performs a two-sided t-test evaluating the null hypothesis that the mean of the population from which
sampleis drawn equalsmu.- tTest(double, StatisticalSummary) - Method in interface org.apache.commons.math.stat.inference.TTest
- 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
sampleStatswith the constantmu.- tTest(double, StatisticalSummary, double) - Method in interface org.apache.commons.math.stat.inference.TTest
- Performs a two-sided t-test evaluating the null hypothesis that the mean of the population from which the dataset described by
statsis drawn equalsmu.- tTest(double[], double[]) - Method in interface org.apache.commons.math.stat.inference.TTest
- Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the input arrays.
- tTest(double[], double[], double) - Method in interface org.apache.commons.math.stat.inference.TTest
- Performs a two-sided t-test evaluating the null hypothesis that
sample1andsample2are drawn from populations with the same mean, with significance levelalpha.- tTest(StatisticalSummary, StatisticalSummary) - Method in interface org.apache.commons.math.stat.inference.TTest
- 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.
- tTest(StatisticalSummary, StatisticalSummary, double) - Method in interface org.apache.commons.math.stat.inference.TTest
- Performs a two-sided t-test evaluating the null hypothesis that
sampleStats1andsampleStats2describe datasets drawn from populations with the same mean, with significance levelalpha.- tTest(double, double[]) - Method in class org.apache.commons.math.stat.inference.TTestImpl
- 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.- tTest(double, double[], double) - Method in class org.apache.commons.math.stat.inference.TTestImpl
- Performs a two-sided t-test evaluating the null hypothesis that the mean of the population from which
sampleis drawn equalsmu.- tTest(double, StatisticalSummary) - Method in class org.apache.commons.math.stat.inference.TTestImpl
- 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
sampleStatswith the constantmu.- tTest(double, StatisticalSummary, double) - Method in class org.apache.commons.math.stat.inference.TTestImpl
- Performs a two-sided t-test evaluating the null hypothesis that the mean of the population from which the dataset described by
statsis drawn equalsmu.- tTest(double[], double[]) - Method in class org.apache.commons.math.stat.inference.TTestImpl
- Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the input arrays.
- tTest(double[], double[], double) - Method in class org.apache.commons.math.stat.inference.TTestImpl
- Performs a two-sided t-test evaluating the null hypothesis that
sample1andsample2are drawn from populations with the same mean, with significance levelalpha.- tTest(StatisticalSummary, StatisticalSummary) - Method in class org.apache.commons.math.stat.inference.TTestImpl
- 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.
- tTest(StatisticalSummary, StatisticalSummary, double) - Method in class org.apache.commons.math.stat.inference.TTestImpl
- Performs a two-sided t-test evaluating the null hypothesis that
sampleStats1andsampleStats2describe datasets drawn from populations with the same mean, with significance levelalpha.- tTest(double, double, double, double) - Method in class org.apache.commons.math.stat.inference.TTestImpl
- Computes p-value for 2-sided, 1-sample t-test.
- tTest(double, double, double, double, double, double) - Method in class org.apache.commons.math.stat.inference.TTestImpl
- Computes p-value for 2-sided, 2-sample t-test.
- TTestImpl - Class in org.apache.commons.math.stat.inference
- Implements t-test statistics defined in the
TTestinterface.- TTestImpl() - Constructor for class org.apache.commons.math.stat.inference.TTestImpl
- Default constructor.
- TTestImpl(TDistribution) - Constructor for class org.apache.commons.math.stat.inference.TTestImpl
- Deprecated. in 2.2 (to be removed in 3.0).
- two - Variable in class org.apache.commons.math.dfp.DfpField
- A
Dfpwith value 2.- TWO - Static variable in class org.apache.commons.math.fraction.BigFraction
- A fraction representing "2 / 1".
- TWO - Static variable in class org.apache.commons.math.fraction.Fraction
- A fraction representing "2 / 1".
- TWO_FIFTHS - Static variable in class org.apache.commons.math.fraction.BigFraction
- A fraction representing "2/5".
- TWO_FIFTHS - Static variable in class org.apache.commons.math.fraction.Fraction
- A fraction representing "2/5".
- TWO_MINUS_SQRT_2 - Static variable in class org.apache.commons.math.ode.nonstiff.GillStepInterpolator
- First Gill coefficient.
- TWO_PI - Static variable in class org.apache.commons.math.util.MathUtils
- 2 π.
- TWO_PLUS_SQRT_2 - Static variable in class org.apache.commons.math.ode.nonstiff.GillStepInterpolator
- Second Gill coefficient.
- TWO_POWER_52 - Static variable in class org.apache.commons.math.util.FastMath
- 2^52 - double numbers this large must be integral (no fraction) or NaN or Infinite
- TWO_QUARTERS - Static variable in class org.apache.commons.math.fraction.BigFraction
- A fraction representing "2/4".
- TWO_QUARTERS - Static variable in class org.apache.commons.math.fraction.Fraction
- A fraction representing "2/4".
- TWO_THIRDS - Static variable in class org.apache.commons.math.fraction.BigFraction
- A fraction representing "2/3".
- TWO_THIRDS - Static variable in class org.apache.commons.math.fraction.Fraction
- A fraction representing "2/3".
FastMath.ulp method wrapped as a ComposableFunction.
RandomVectorGenerator that generates vectors with uncorrelated
components.MersenneTwister),
in order to generate the individual components.
UnivariateRealIntegrator.integrate(UnivariateRealFunction, double, double)method.
UnivariateRealSolver instances.UnivariateRealSolverFactory.UnivariateRealSolver.solve(UnivariateRealFunction, double, double) or
UnivariateRealSolver.solve(UnivariateRealFunction, double, double, double)
method.
UnivariateRealSolver objects.value and gradient methods.
isBiasCorrected
property.
isBiasCorrected
property
isBiasCorrected
property and the supplied external second moment.
Variance identical
to the original
Double.NaN if the array is empty.
Double.NaN if the designated subarray
is empty.
optimization algorithm has converged.lower < initial < upper
throws IllegalArgumentException if not
WeibullDistribution.curve fitting.Dfp with value 0.
ZipfDistribution.
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