A B C D E F G H I J K L M N O P Q R S T U V W X Y

A

AbstractChrono - Class in umontreal.iro.lecuyer.util

AbstractChrono is a class that acts as an interface to the system clock and calculates the CPU or system time consumed by parts of a program.

AbstractChrono() - Constructor for class umontreal.iro.lecuyer.util.AbstractChrono

Accumulate - Class in umontreal.iro.lecuyer.simevents

A subclass of StatProbe, for collecting statistics on a variable that evolves in simulation time, with a piecewise-constant trajectory.

Accumulate() - Constructor for class umontreal.iro.lecuyer.simevents.Accumulate

Constructs a new Accumulate statistical probe using the default simulator and initializes it by invoking init().

Accumulate(Simulator) - Constructor for class umontreal.iro.lecuyer.simevents.Accumulate

Constructs a new Accumulate statistical probe linked to the given simulator, and initializes it by invoking init().

Accumulate(String) - Constructor for class umontreal.iro.lecuyer.simevents.Accumulate

Construct and initializes a new Accumulate statistical probe with name name and initial time 0, using the default simulator.

Accumulate(Simulator, String) - Constructor for class umontreal.iro.lecuyer.simevents.Accumulate

Construct and initializes a new Accumulate statistical probe with name name and initial time 0.

actions() - Method in class umontreal.iro.lecuyer.simevents.Event

This is the method that is executed when this event occurs.

actions() - Method in class umontreal.iro.lecuyer.simprocs.SimProcess

This is the method that is called when this process is executing.

activeTests - Static variable in class umontreal.iro.lecuyer.gof.GofFormat

The set of EDF tests that are to be performed when calling the methods activeTests, formatActiveTests, etc.

activeTests(DoubleArrayList, double[], double[]) - Static method in class umontreal.iro.lecuyer.gof.GofFormat

Computes the EDF test statistics by calling tests, then computes the p-values of those that currently belong to activeTests, and return these quantities in sVal and pVal, respectively.

activeTests(DoubleArrayList, ContinuousDistribution, double[], double[]) - Static method in class umontreal.iro.lecuyer.gof.GofFormat

The observations are in data, not necessarily sorted, and we want to compare their empirical distribution with the distribution dist.

AD - Static variable in class umontreal.iro.lecuyer.gof.GofFormat

Anderson-Darling test

add(double[]) - Method in class umontreal.iro.lecuyer.charts.BoxChart

Adds a data series into the series collection.

add(double[], int) - Method in class umontreal.iro.lecuyer.charts.BoxChart

Adds a data series into the series collection.

add(double[]) - Method in class umontreal.iro.lecuyer.charts.BoxSeriesCollection

Adds a data series into the series collection.

add(double[], int) - Method in class umontreal.iro.lecuyer.charts.BoxSeriesCollection

Adds a data series into the series collection.

add(double[]) - Method in class umontreal.iro.lecuyer.charts.EmpiricalSeriesCollection

Adds a data series into the series collection.

add(double[], int) - Method in class umontreal.iro.lecuyer.charts.EmpiricalSeriesCollection

Adds a data series into the series collection.

add(DoubleArrayList) - Method in class umontreal.iro.lecuyer.charts.EmpiricalSeriesCollection

Adds a data series into the series collection.

add(double[]) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection

Adds a data series into the series collection.

add(double[], int) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection

Adds a data series into the series collection.

add(DoubleArrayList) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection

Adds a data series into the series collection.

add(SSJXYSeriesCollection) - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart

Adds a new dataset to the chart at the end of the list and returns its position.

add(double[], double[], String, String) - Method in class umontreal.iro.lecuyer.charts.ScatterChart

Adds a data series into the series collection.

add(double[], double[]) - Method in class umontreal.iro.lecuyer.charts.ScatterChart

Adds a data series into the series collection.

add(double[], double[], int) - Method in class umontreal.iro.lecuyer.charts.ScatterChart

Adds a data series into the series collection.

add(double[], double[], String, String) - Method in class umontreal.iro.lecuyer.charts.XYLineChart

Adds a data series into the series collection.

add(double[], double[]) - Method in class umontreal.iro.lecuyer.charts.XYLineChart

Adds a data series into the series collection.

add(double[], double[], int) - Method in class umontreal.iro.lecuyer.charts.XYLineChart

Adds a data series into the series collection.

add(double[][]) - Method in class umontreal.iro.lecuyer.charts.XYLineChart

Adds the new collection of data series data into the series collection.

add(double[][], int) - Method in class umontreal.iro.lecuyer.charts.XYLineChart

Adds the new collection of data series data into the series collection.

add(double[], double[]) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection

Adds a data series into the series collection.

add(double[], double[], int) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection

Adds a data series into the series collection.

add(double[][]) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection

Adds a data series into the series collection.

add(double[][], int) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection

Adds data series into the series collection.

add(DoubleArrayList) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection

Adds a data series into the series collection.

add(RandomStream) - Method in class umontreal.iro.lecuyer.rng.RandomStreamManager

Adds the given stream to the internal list of this random stream manager and returns the added stream.

add(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree

add(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked

add(Event) - Method in interface umontreal.iro.lecuyer.simevents.eventlist.EventList

Adds a new event in the event list, according to the time of ev.

add(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen

add(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree

add(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree

add(int, E) - Method in class umontreal.iro.lecuyer.simevents.ListWithStat

add(E) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

add(int, E) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

add(double[]) - Method in class umontreal.iro.lecuyer.stat.list.ListOfTallies

Adds the observation x[i] in tally i of this list, for i = 0, ..., size() - 1.

add(double[]) - Method in class umontreal.iro.lecuyer.stat.list.ListOfTalliesWithCovariance

Adds a new vector of observations x to this list of tallies, and updates the internal data structures computing averages, and sums of products.

add(DoubleMatrix1D) - Method in class umontreal.iro.lecuyer.stat.list.ListOfTalliesWithCovariance

add(double) - Method in class umontreal.iro.lecuyer.stat.Tally

Gives a new observation x to the statistical collector.

add(double) - Method in class umontreal.iro.lecuyer.stat.TallyStore

add(int, OE) - Method in class umontreal.iro.lecuyer.util.TransformingList

addAfter(Event, Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree

addAfter(Event, Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked

addAfter(Event, Event) - Method in interface umontreal.iro.lecuyer.simevents.eventlist.EventList

Same as add, but adds the new event ev immediately after the event other in the list.

addAfter(Event, Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen

addAfter(Event, Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree

addAfter(Event, Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree

addAll(Collection<? extends E>) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

addAll(int, Collection<? extends E>) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

addArrayOfObservationListener(ArrayOfObservationListener) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

Adds the observation listener l to the list of observers of this list of statistical probes.

addBefore(Event, Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree

addBefore(Event, Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked

addBefore(Event, Event) - Method in interface umontreal.iro.lecuyer.simevents.eventlist.EventList

Same as add, but adds the new event ev immediately before the event other in the list.

addBefore(Event, Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen

addBefore(Event, Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree

addBefore(Event, Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree

addFaurePermutations() - Method in class umontreal.iro.lecuyer.hups.HaltonSequence

Permutes the digits using Faure permutations for all coordinates.

addFaurePermutations() - Method in class umontreal.iro.lecuyer.hups.HammersleyPointSet

Permutes the digits using Faure permutations for all coordinates.

addFirst(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree

addFirst(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked

addFirst(Event) - Method in interface umontreal.iro.lecuyer.simevents.eventlist.EventList

Adds a new event at the beginning of the event list.

addFirst(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen

addFirst(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree

addFirst(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree

addFirst(E) - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat

addLast(E) - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat

addObservationListener(ObservationListener) - Method in class umontreal.iro.lecuyer.stat.StatProbe

Adds the observation listener l to the list of observers of this statistical probe.

addRandomShift(int, int, RandomStream) - Method in class umontreal.iro.lecuyer.hups.ContainerPointSet

Calls addRandomShift(d1, d2, stream) of the contained point set.

addRandomShift(RandomStream) - Method in class umontreal.iro.lecuyer.hups.ContainerPointSet

Calls addRandomShift(stream) of the contained point set.

addRandomShift(int, int, RandomStream) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet

Adds a random shift to all the points of the point set, using stream stream to generate the random numbers, for coordinates d1 to d2 - 1.

addRandomShift(int, int, RandomStream) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSetBase2

Adds a random digital shift in base 2 to all the points of the point set, using stream stream to generate the random numbers, for coordinates d1 to d2 - 1.

addRandomShift(int, int, RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNet

Adds a random digital shift to all the points of the point set, using stream stream to generate the random numbers.

addRandomShift(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNet

Same as addRandomShift(0, dim, stream), where dim is the dimension of the digital net.

addRandomShift(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2

addRandomShift(int, int, RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2

addRandomShift(int, int, RandomStream) - Method in class umontreal.iro.lecuyer.hups.PointSet

This method does nothing for this generic class.

addRandomShift(RandomStream) - Method in class umontreal.iro.lecuyer.hups.PointSet

This method does nothing for this generic class.

addRandomShift(int, int) - Method in class umontreal.iro.lecuyer.hups.PointSet

Deprecated.

addRandomShift() - Method in class umontreal.iro.lecuyer.hups.PointSet

Deprecated.

addRandomShift(int, int, RandomStream) - Method in class umontreal.iro.lecuyer.hups.RandShiftedPointSet

Changes the stream used for the random shifts to stream, then refreshes the shift for coordinates d1 to d2-1.

addRandomShift(RandomStream) - Method in class umontreal.iro.lecuyer.hups.RandShiftedPointSet

Changes the stream used for the random shifts to stream, then refreshes all coordinates of the random shift, up to its current dimension.

addRandomShift(int, int) - Method in class umontreal.iro.lecuyer.hups.RandShiftedPointSet

Deprecated.

addRandomShift() - Method in class umontreal.iro.lecuyer.hups.RandShiftedPointSet

Deprecated.

addRandomShift(int, int, RandomStream) - Method in class umontreal.iro.lecuyer.hups.Rank1Lattice

Adds a random shift to all the points of the point set, using stream stream to generate the random numbers.

addSeries(Comparable, double[], int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset

Adds a series to the dataset, using the specified number of bins.

addSeries(Comparable, double[], int, int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset

Adds a series to the dataset, using the specified number of bins.

addSeries(Comparable, double[], int, double, double) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset

Adds a series to the dataset.

addSeries(Comparable, double[], int, int, double, double) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset

Adds a series to the dataset.

addSeries(Comparable, double[], HistogramBin[]) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset

Adds a series to the dataset.

addSeries(Comparable, double[], int, HistogramBin[]) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset

Adds a series to the dataset.

advanceState(int, int) - Method in class umontreal.iro.lecuyer.rng.RandMrg

Deprecated.

Advances the state of this stream by k values, without modifying the states of other streams (as in setSeed), nor the values of B_g and I_g associated with this stream.

afterEachStep() - Method in class umontreal.iro.lecuyer.simevents.Continuous

This method is executed after each integration step for this Continuous variable.

and(BitMatrix) - Method in class umontreal.iro.lecuyer.util.BitMatrix

Returns the BitMatrix resulting from the application of the and operator on the original BitMatrix and that.

and(BitVector) - Method in class umontreal.iro.lecuyer.util.BitVector

Returns a BitVector which is the result of the and operator with both the this and that BitVector's.

andersonDarling(int, double) - Static method in class umontreal.iro.lecuyer.gof.FBar

Deprecated.

andersonDarling(int, double) - Static method in class umontreal.iro.lecuyer.gof.FDist

Deprecated.

andersonDarling(DoubleArrayList) - Static method in class umontreal.iro.lecuyer.gof.GofStat

Computes and returns the Anderson-Darling statistic A_N².

AndersonDarlingDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the Anderson-Darling distribution (see).

AndersonDarlingDist(int) - Constructor for class umontreal.iro.lecuyer.probdist.AndersonDarlingDist

Constructs an Anderson-Darling distribution for a sample of size n.

AndersonDarlingDistQuick - Class in umontreal.iro.lecuyer.probdist

Extends the class AndersonDarlingDist for the distribution (see).

AndersonDarlingDistQuick(int) - Constructor for class umontreal.iro.lecuyer.probdist.AndersonDarlingDistQuick

Constructs an distribution for a sample of size n.

AntitheticPointSet - Class in umontreal.iro.lecuyer.hups

This container class provides antithetic points.

AntitheticPointSet(PointSet) - Constructor for class umontreal.iro.lecuyer.hups.AntitheticPointSet

Constructs an antithetic point set from the given point set P.

AntitheticStream - Class in umontreal.iro.lecuyer.rng

This container class allows the user to force any RandomStream to return antithetic variates.

AntitheticStream(RandomStream) - Constructor for class umontreal.iro.lecuyer.rng.AntitheticStream

Constructs a new antithetic stream, using the random numbers from the base stream stream.

append(String) - Method in class umontreal.iro.lecuyer.util.PrintfFormat

Appends str to the buffer.

append(int, String) - Method in class umontreal.iro.lecuyer.util.PrintfFormat

Uses the s static method to append str to the buffer.

append(double) - Method in class umontreal.iro.lecuyer.util.PrintfFormat

Appends x to the buffer.

append(int, double) - Method in class umontreal.iro.lecuyer.util.PrintfFormat

Uses the f static method to append x to the buffer.

append(int, int, double) - Method in class umontreal.iro.lecuyer.util.PrintfFormat

Uses the f static method to append x to the buffer.

append(int) - Method in class umontreal.iro.lecuyer.util.PrintfFormat

Appends x to the buffer.

append(int, int) - Method in class umontreal.iro.lecuyer.util.PrintfFormat

Uses the d static method to append x to the buffer.

append(long) - Method in class umontreal.iro.lecuyer.util.PrintfFormat

Appends x to the buffer.

append(int, long) - Method in class umontreal.iro.lecuyer.util.PrintfFormat

Uses the d static method to append x to the buffer.

append(int, int, int, double) - Method in class umontreal.iro.lecuyer.util.PrintfFormat

Uses the format static method with the same four arguments to append x to the buffer.

append(char) - Method in class umontreal.iro.lecuyer.util.PrintfFormat

Appends a single character to the buffer.

append(CharSequence) - Method in class umontreal.iro.lecuyer.util.PrintfFormat

append(CharSequence, int, int) - Method in class umontreal.iro.lecuyer.util.PrintfFormat

apply(RandomStream) - Method in interface umontreal.iro.lecuyer.hups.Randomization

Deprecated.

Applies this randomization to the point set using stream stream.

areAllNumberObsEqual() - Method in class umontreal.iro.lecuyer.stat.list.ListOfTallies

Tests that every tally in this list contains the same number of observations.

ArithmeticMod - Class in umontreal.iro.lecuyer.util

This class provides facilities to compute multiplications of scalars, of vectors and of matrices modulo m.

ArrayOfObservationListener - Interface in umontreal.iro.lecuyer.stat.list

Represents an object that can listen to observations broadcast by lists of statistical probes.

average() - Method in class umontreal.iro.lecuyer.simevents.Accumulate

Returns the time-average since the last initialization to the last call to update.

average(double[]) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

For each probe in this list, computes the average by calling average, and stores the results into the array a.

average(double[]) - Method in class umontreal.iro.lecuyer.stat.list.ListOfTallies

Computes the average for each tally in this list, and stores the averages in the array r.

average() - Method in class umontreal.iro.lecuyer.stat.StatProbe

Returns the average for this collector.

average() - Method in class umontreal.iro.lecuyer.stat.Tally

AverageMathFunction - Class in umontreal.iro.lecuyer.functions

Represents a function computing the average of several functions.

AverageMathFunction(MathFunction...) - Constructor for class umontreal.iro.lecuyer.functions.AverageMathFunction

Constructs a function computing the average of the functions in the array func.

Axis - Class in umontreal.iro.lecuyer.charts

Represents an axis of a chart encapsulated by an instance of XYChart.

Axis(NumberAxis, boolean) - Constructor for class umontreal.iro.lecuyer.charts.Axis

Create a new Axis instance from an existing NumberAxis instance with vertical (y-axis) or horizontal (x-axis) orientation.

B

BakerTransformedPointSet - Class in umontreal.iro.lecuyer.hups

This container class embodies a point set to which a Baker transformation is applied.

BakerTransformedPointSet(PointSet) - Constructor for class umontreal.iro.lecuyer.hups.BakerTransformedPointSet

Constructs a Baker-transformed point set from the given point set P.

BakerTransformedStream - Class in umontreal.iro.lecuyer.rng

This container class permits one to apply the baker's transformation to the output of any RandomStream.

BakerTransformedStream(RandomStream) - Constructor for class umontreal.iro.lecuyer.rng.BakerTransformedStream

Constructs a new baker transformed stream, using the random numbers from the base stream stream.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDist

barF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDist

Computes the complementary distribution function bar(F)_n(x) with parameter n.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDistQuick

barF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDistQuick

Computes the complementary distribution function bar(F)_n(x) with parameter n.

barF(double, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist

Same as barF (alpha, beta, 0, 1, d, x).

barF(double, double, double, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist

Computes the complementary distribution function.

barF(double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist

Equivalent to 1 - cdf(alpha, d, x).

barF(int) - Method in class umontreal.iro.lecuyer.probdist.BinomialDist

barF(double) - Method in class umontreal.iro.lecuyer.probdist.CauchyDist

barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.CauchyDist

Computes the complementary distribution.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.ChiDist

barF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiDist

Computes the complementary distribution.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.ChiSquareDist

barF(int, int, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareDist

Computes the complementary chi-square distribution function with n degrees of freedom, evaluated at x.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist

barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist

Computes the complementary noncentral chi-square distribution function with ν = nu degrees of freedom and parameter λ = lambda.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution

Returns the complementary distribution function.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist

barF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist

Computes the complementary distribution function bar(F)_n(x) with parameter n.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistribution

barF(double) - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistributionInt

Returns bar(F)(x), the complementary distribution function.

barF(int) - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistributionInt

Returns bar(F)(x), the complementary distribution function.

barF(double) - Method in interface umontreal.iro.lecuyer.probdist.Distribution

Returns bar(F)(x) = 1 - F(x).

barF(double) - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist

barF(int, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.ErlangDist

Computes the complementary distribution function.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.ExponentialDist

barF(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ExponentialDist

Computes the complementary distribution function.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist

Deprecated.

barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist

Deprecated.

Computes the complementary distribution function.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist

barF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist

Computes the complementary distribution function of the fatigue life distribution with parameters μ, β and γ.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.FisherFDist

barF(int, int, int, double) - Static method in class umontreal.iro.lecuyer.probdist.FisherFDist

Computes the complementary distribution function of the Fisher F distribution with parameters n and m, evaluated at x, with roughly d decimal digits of precision.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist

barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist

Computes the complementary distribution function.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.FrechetDist

barF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FrechetDist

Computes and returns the complementary distribution function 1 - F(x).

barF(double) - Method in class umontreal.iro.lecuyer.probdist.GammaDist

barF(double, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.GammaDist

Computes the complementary distribution function.

barF(double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.GammaDist

Same as barF (alpha, 1.0, d, x).

barF(int) - Method in class umontreal.iro.lecuyer.probdist.GeometricDist

barF(double, int) - Static method in class umontreal.iro.lecuyer.probdist.GeometricDist

Computes the complementary distribution function.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.GumbelDist

barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.GumbelDist

Computes and returns the complementary distribution function 1 - F(x).

barF(double) - Method in class umontreal.iro.lecuyer.probdist.HalfNormalDist

barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.HalfNormalDist

Computes the complementary distribution function.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist

barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist

Computes the complementary distribution function of the hyperbolic secant distribution with parameters μ and σ.

barF(int) - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist

barF(int, int, int, int) - Static method in class umontreal.iro.lecuyer.probdist.HypergeometricDist

Computes the complementary distribution function.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist

barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist

Computes the complementary distribution function of the inverse gaussian distribution with parameters μ and λ, evaluated at x.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist

barF(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist

Computes the complementary distribution.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist

barF(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist

Computes the complementary distribution function 1 - F(x).

barF(double) - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDist

barF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDist

Computes the complementary distribution function bar(F)(x) with parameter n.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDistQuick

barF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDistQuick

Computes the complementary distribution P[D_n >= x] with parameter n, in a form that is more precise in the upper tail.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist

barF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist

Computes the complementary distribution function bar(F)_n(x) with parameter n.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.LaplaceDist

barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LaplaceDist

Computes the Laplace complementary distribution function.

barF(int) - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist

barF(double, int) - Static method in class umontreal.iro.lecuyer.probdist.LogarithmicDist

Computes the complementary distribution function.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.LogisticDist

barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LogisticDist

Computes the complementary distribution function 1 - F(x).

barF(double) - Method in class umontreal.iro.lecuyer.probdist.LoglogisticDist

barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LoglogisticDist

Computes the complementary distribution function of the log-logistic distribution with parameters α and β.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.LognormalDist

barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LognormalDist

Computes the lognormal complementary distribution function bar(F)(x), using NormalDist.barF01.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.NakagamiDist

barF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NakagamiDist

Computes the complementary distribution function.

barF(int) - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist

barF(double) - Method in class umontreal.iro.lecuyer.probdist.NormalDist

barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist

Computes the complementary normal distribution function bar(F)(x) = 1 - Φ((x - μ)/σ), with mean μ and variance σ².

barF(double) - Method in class umontreal.iro.lecuyer.probdist.NormalDistQuick

barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDistQuick

Returns an approximation of 1 - Φ(x), where Φ is the standard normal distribution function, with mean 0 and variance 1.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist

barF(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist

NOT IMPLEMENTED.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.ParetoDist

barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ParetoDist

Computes the complementary distribution function.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.Pearson5Dist

barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson5Dist

Computes the complementary distribution function of a Pearson V distribution with shape parameter α and scale parameter β.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist

barF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson6Dist

Computes the complementary distribution function of a Pearson VI distribution with shape parameters α₁ and α₂, and scale parameter β.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist

barF(int) - Method in class umontreal.iro.lecuyer.probdist.PoissonDist

barF(double, int) - Static method in class umontreal.iro.lecuyer.probdist.PoissonDist

Computes and returns the value of the complementary Poisson distribution function, for λ = lambda.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.PowerDist

barF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.PowerDist

Computes the complementary distribution function.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.RayleighDist

barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist

Computes the complementary distribution function.

barF(double, double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist

Same as barF (0, beta, x).

barF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.StudentDist

Computes the complementary distribution function bar(F)(x).

barF(double) - Method in class umontreal.iro.lecuyer.probdist.TriangularDist

barF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.TriangularDist

Computes the complementary distribution function.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist

barF(double) - Method in class umontreal.iro.lecuyer.probdist.UniformDist

barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.UniformDist

Computes the uniform complementary distribution function bar(F)(x).

barF(int) - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist

barF(int, int, int) - Static method in class umontreal.iro.lecuyer.probdist.UniformIntDist

Computes the discrete uniform complementary distribution function bar(F)(x).

barF(double) - Method in class umontreal.iro.lecuyer.probdist.WatsonGDist

barF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.WatsonGDist

Computes the complementary distribution function bar(F)_n(x) with parameter n.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.WatsonUDist

barF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.WatsonUDist

Computes the complementary distribution function bar(F)_n(x), where F_n is the Watson U distribution with parameter n.

barF(double) - Method in class umontreal.iro.lecuyer.probdist.WeibullDist

barF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist

Computes the complementary distribution function.

barF(double, double) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist

Same as barF (alpha, 1, 0, x).

barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist

Computes the standard upper binormal distribution with μ₁ = μ₂ = 0 and σ₁ = σ₂ = 1.

barF(double, double) - Method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist

barF(double, double, double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist

Computes the upper binormal distribution function with parameters μ₁ = mu1, μ₂ = mu2, σ₁ = sigma1, σ₂ = sigma2 and ρ = rho.

barF(double, double, double, double, double, double, double, int) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDonnellyDist

Computes the upper binormal distribution function with parameters μ₁ = mu1, μ₂ = mu2, σ₁ = sigma1, σ₂ = sigma2, ρ = rho and ndig decimal digits of accuracy.

barF(double, double, double, int) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDonnellyDist

Computes the upper standard binormal distribution function with parameters ρ = rho and ndig decimal digits of accuracy.

barF(double, double) - Method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDonnellyDist

barF(double, double, double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDonnellyDist

barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDonnellyDist

barF(double, double) - Method in class umontreal.iro.lecuyer.probdistmulti.BiNormalGenzDist

barF(double, double, double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalGenzDist

barF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalGenzDist

barF(double, double) - Method in class umontreal.iro.lecuyer.probdistmulti.BiStudentDist

barF(int, double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiStudentDist

Computes the standard upper bivariate Student's t distribution.

barF(double, double) - Method in class umontreal.iro.lecuyer.probdistmulti.ContinuousDistribution2Dim

.

barF01(double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist

Same as barF (0, 1, x).

barF01(double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDistQuick

Same as barF (0.0, 1.0, x).

BasicRandomStreamFactory - Class in umontreal.iro.lecuyer.rng

Represents a basic random stream factory that can constructs new instances of a given RandomStream implementation via the newInstance method.

BasicRandomStreamFactory(Class) - Constructor for class umontreal.iro.lecuyer.rng.BasicRandomStreamFactory

Constructs a new basic random stream factory with random stream class rsClass.

BatchSort - Class in umontreal.iro.lecuyer.util

This class implements a MultiDimSort that performs a batch sort on the arrays.

BatchSort(int[]) - Constructor for class umontreal.iro.lecuyer.util.BatchSort

Constructs a BatchSort that will use batches.

BatchSort(double[]) - Constructor for class umontreal.iro.lecuyer.util.BatchSort

Constructs a BatchSort that will use batchesExponents.

bernoulliPoly(int, double) - Static method in class umontreal.iro.lecuyer.util.Num

Evaluates the Bernoulli polynomial B_n(x) of degree n at x.

besselK025(double) - Static method in class umontreal.iro.lecuyer.util.Num

Returns the value of K_1/4(x), where K_a is the modified Bessel's function of the second kind.

BetaDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the beta distribution with shape parameters α > 0 and β > 0, over the interval (a, b), where a < b.

BetaDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.BetaDist

Constructs a BetaDist object with parameters α = alpha and β = beta and default domain (0, 1).

BetaDist(double, double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.BetaDist

Constructs a BetaDist object with parameters α = alpha and β = beta, and domain (a, b).

BetaDist(double, double, int) - Constructor for class umontreal.iro.lecuyer.probdist.BetaDist

Constructs a BetaDist object with parameters α = alpha and β = beta, and approximations of roughly d decimal digits of precision when computing the distribution, complementary distribution, and inverse functions.

BetaDist(double, double, double, double, int) - Constructor for class umontreal.iro.lecuyer.probdist.BetaDist

Constructs a BetaDist object with parameters α = alpha and β = beta, and approximations of roughly d decimal digits of precision when computing distribution, complementary distribution, and inverse functions.

BetaGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators with the beta distribution with shape parameters α > 0 and β > 0, over the interval (a, b), where a < b.

BetaGen(RandomStream, double, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaGen

Creates a new beta generator with parameters α = alpha and β = beta, over the interval (a, b), using stream s.

BetaGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaGen

Creates a new beta generator with parameters α = alpha and β = beta, over the interval (0, 1), using stream s.

BetaGen(RandomStream, BetaDist) - Constructor for class umontreal.iro.lecuyer.randvar.BetaGen

Creates a new generator for the distribution dist, using stream s.

BetaRejectionLoglogisticGen - Class in umontreal.iro.lecuyer.randvar

Implements Beta random variate generators using the rejection method with log-logistic envelopes.

BetaRejectionLoglogisticGen(RandomStream, RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaRejectionLoglogisticGen

Creates a beta random variate generator with parameters α = alpha and β = beta over the interval (0, 1), using main stream s and auxiliary stream aux.

BetaRejectionLoglogisticGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaRejectionLoglogisticGen

Creates a beta random variate generator with parameters α = alpha and β = beta, over the interval (0, 1), using stream s.

BetaRejectionLoglogisticGen(RandomStream, RandomStream, double, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaRejectionLoglogisticGen

Creates a beta random variate generator with parameters α = alpha and β = beta over the interval (a, b), using main stream s and auxiliary stream aux.

BetaRejectionLoglogisticGen(RandomStream, double, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaRejectionLoglogisticGen

Creates a beta random variate generator with parameters α = alpha and β = beta, over the interval (a, b), using stream s.

BetaRejectionLoglogisticGen(RandomStream, RandomStream, BetaDist) - Constructor for class umontreal.iro.lecuyer.randvar.BetaRejectionLoglogisticGen

Creates a new generator for the distribution dist, using stream s and auxiliary stream aux.

BetaRejectionLoglogisticGen(RandomStream, BetaDist) - Constructor for class umontreal.iro.lecuyer.randvar.BetaRejectionLoglogisticGen

Same as BetaRejectionLoglogisticGen (s, s, dist).

BetaStratifiedRejectionGen - Class in umontreal.iro.lecuyer.randvar

This class implements Beta random variate generators using the stratified rejection/patchwork rejection method.

BetaStratifiedRejectionGen(RandomStream, RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaStratifiedRejectionGen

Creates a beta random variate generator with parameters α = alpha and β = beta, over the interval (0, 1), using main stream s and auxiliary stream aux.

BetaStratifiedRejectionGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaStratifiedRejectionGen

Creates a beta random variate generator with parameters α = alpha and β = beta, over the interval (0, 1), using stream s.

BetaStratifiedRejectionGen(RandomStream, RandomStream, double, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaStratifiedRejectionGen

Creates a beta random variate generator with parameters α = alpha and β = beta, over the interval (a, b), using main stream s and auxiliary stream aux.

BetaStratifiedRejectionGen(RandomStream, double, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaStratifiedRejectionGen

Creates a beta random variate generator with parameters α = alpha and β = beta, over the interval (a, b), using stream s.

BetaStratifiedRejectionGen(RandomStream, RandomStream, BetaDist) - Constructor for class umontreal.iro.lecuyer.randvar.BetaStratifiedRejectionGen

Creates a new generator for the distribution dist, using the given stream s and auxiliary stream aux.

BetaStratifiedRejectionGen(RandomStream, BetaDist) - Constructor for class umontreal.iro.lecuyer.randvar.BetaStratifiedRejectionGen

Same as BetaStratifiedRejectionGen(s, s, dist).

BetaSymmetricalBestGen - Class in umontreal.iro.lecuyer.randvar

This class implements symmetrical beta random variate generators using Devroye's one-liner method.

BetaSymmetricalBestGen(RandomStream, RandomStream, RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaSymmetricalBestGen

Creates a symmetrical beta random variate generator with parameter α = alpha, using stream s1 to generate U₁, stream s2 to generate U₂ and stream s3 to generate S, as given in equation.

BetaSymmetricalBestGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaSymmetricalBestGen

Creates a symmetrical beta random variate generator with parameter α = alpha, using only one stream s1 to generate U₁, U₂, and S as given in equation.

BetaSymmetricalBestGen(RandomStream, RandomStream, RandomStream, BetaSymmetricalDist) - Constructor for class umontreal.iro.lecuyer.randvar.BetaSymmetricalBestGen

Creates a new generator for the distribution dist, using stream s1 to generate U₁, stream s2 to generate U₂ and stream s3 to generate S as given in equation.

BetaSymmetricalBestGen(RandomStream, BetaSymmetricalDist) - Constructor for class umontreal.iro.lecuyer.randvar.BetaSymmetricalBestGen

Creates a new generator for the distribution dist, using only one stream s1.

BetaSymmetricalDist - Class in umontreal.iro.lecuyer.probdist

Specializes the class BetaDist to the case of a symmetrical beta distribution over the interval [0, 1], with shape parameters α = β.

BetaSymmetricalDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist

Constructs a BetaSymmetricalDist object with parameters α = β = alpha, over the unit interval (0, 1).

BetaSymmetricalDist(double, int) - Constructor for class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist

Same as BetaSymmetricalDist (alpha), but using approximations of roughly d decimal digits of precision when computing the distribution, complementary distribution, and inverse functions.

BetaSymmetricalGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators with the symmetrical beta distribution with shape parameters α = β, over the interval (0, 1).

BetaSymmetricalGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaSymmetricalGen

Creates a new symmetrical beta generator with parameters α = alpha, over the interval (0, 1), using stream s.

BetaSymmetricalGen(RandomStream, BetaSymmetricalDist) - Constructor for class umontreal.iro.lecuyer.randvar.BetaSymmetricalGen

Creates a new generator for the distribution dist, using stream s.

BetaSymmetricalPolarGen - Class in umontreal.iro.lecuyer.randvar

This class implements symmetrical beta random variate generators using Ulrich's polar method.

BetaSymmetricalPolarGen(RandomStream, RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaSymmetricalPolarGen

Creates a symmetrical beta random variate generator with parameter α = alpha, using stream s1 to generate x and stream s2 to generate y, as in above.

BetaSymmetricalPolarGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.BetaSymmetricalPolarGen

Creates a symmetrical beta random variate generator with parameter α = alpha, using stream s1 to generate x and y, as in above.

BetaSymmetricalPolarGen(RandomStream, RandomStream, BetaSymmetricalDist) - Constructor for class umontreal.iro.lecuyer.randvar.BetaSymmetricalPolarGen

Creates a new generator for the distribution dist, using stream s1 to generate x and stream s2 to generate y, as in above.

BetaSymmetricalPolarGen(RandomStream, BetaSymmetricalDist) - Constructor for class umontreal.iro.lecuyer.randvar.BetaSymmetricalPolarGen

Creates a new generator for the distribution dist, using only one stream s1.

Bin - Class in umontreal.iro.lecuyer.simprocs

A Bin corresponds to a pile of identical tokens, and a list of processes waiting for the tokens when the list is empty.

Bin() - Constructor for class umontreal.iro.lecuyer.simprocs.Bin

Constructs a new bin, initially empty, with service policy FIFO and linked with the default simulator.

Bin(ProcessSimulator) - Constructor for class umontreal.iro.lecuyer.simprocs.Bin

Constructs a new bin, initially empty, with service policy FIFO and linked with simulator sim.

Bin(String) - Constructor for class umontreal.iro.lecuyer.simprocs.Bin

Constructs a new bin, initially empty, with service policy FIFO, identifier name and linked with the default simulator.

Bin(ProcessSimulator, String) - Constructor for class umontreal.iro.lecuyer.simprocs.Bin

Constructs a new bin, initially empty, with service policy FIFO, identifier name and linked with simulator sim.

BinaryTree - Class in umontreal.iro.lecuyer.simevents.eventlist

An implementation of EventList using a binary search tree.

BinaryTree() - Constructor for class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree

BinomialConvolutionGen - Class in umontreal.iro.lecuyer.randvar

Implements binomial random variate generators using the convolution method.

BinomialConvolutionGen(RandomStream, int, double) - Constructor for class umontreal.iro.lecuyer.randvar.BinomialConvolutionGen

Creates a binomial random variate generator with parameters n and p, using stream s.

BinomialConvolutionGen(RandomStream, BinomialDist) - Constructor for class umontreal.iro.lecuyer.randvar.BinomialConvolutionGen

Creates a random variate generator for the binomial distribution dist and stream s.

BinomialDist - Class in umontreal.iro.lecuyer.probdist

Extends the class DiscreteDistributionInt for the binomial distribution with parameters n and p, where n is a positive integer and 0 <= p <= 1.

BinomialDist(int, double) - Constructor for class umontreal.iro.lecuyer.probdist.BinomialDist

Creates an object that contains the binomial terms, for 0 <= x <= n, and the corresponding cumulative function.

BinomialGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators for the binomial distribution.

BinomialGen(RandomStream, int, double) - Constructor for class umontreal.iro.lecuyer.randvar.BinomialGen

Creates a binomial random variate generator with parameters n and p, using stream s.

BinomialGen(RandomStream, BinomialDist) - Constructor for class umontreal.iro.lecuyer.randvar.BinomialGen

Creates a random variate generator for the binomial distribution dist and the random stream s.

BiNormalDist - Class in umontreal.iro.lecuyer.probdistmulti

Extends the class ContinuousDistribution2Dim for the bivariate normal distribution.

BiNormalDist(double) - Constructor for class umontreal.iro.lecuyer.probdistmulti.BiNormalDist

Constructs a BiNormalDist object with default parameters μ₁ = μ₂ = 0, σ₁ = σ₂ = 1 and correlation ρ = rho.

BiNormalDist(double, double, double, double, double) - Constructor for class umontreal.iro.lecuyer.probdistmulti.BiNormalDist

Constructs a BiNormalDist object with parameters μ₁ = mu1, μ₂ = mu2, σ₁ = sigma1, σ₂ = sigma2 and ρ = rho.

BiNormalDonnellyDist - Class in umontreal.iro.lecuyer.probdistmulti

Extends the class BiNormalDist for the bivariate normal distribution using a translation of Donnelly's FORTRAN code.

BiNormalDonnellyDist(double, int) - Constructor for class umontreal.iro.lecuyer.probdistmulti.BiNormalDonnellyDist

Constructor with default parameters μ₁ = μ₂ = 0, σ₁ = σ₂ = 1, correlation ρ = rho, and d = ndig digits of accuracy (the absolute error is smaller than 10^-d).

BiNormalDonnellyDist(double) - Constructor for class umontreal.iro.lecuyer.probdistmulti.BiNormalDonnellyDist

Same as BiNormalDonnellyDist (rho, 15).

BiNormalDonnellyDist(double, double, double, double, double, int) - Constructor for class umontreal.iro.lecuyer.probdistmulti.BiNormalDonnellyDist

Constructor with parameters μ₁ = mu1, μ₂ = mu2, σ₁ = sigma1, σ₂ = sigma2, ρ = rho, and d = ndig digits of accuracy.

BiNormalDonnellyDist(double, double, double, double, double) - Constructor for class umontreal.iro.lecuyer.probdistmulti.BiNormalDonnellyDist

Same as BiNormalDonnellyDist (mu1, sigma1, mu2, sigma2, rho, 15).

BiNormalGenzDist - Class in umontreal.iro.lecuyer.probdistmulti

Extends the class BiNormalDist for the bivariate normal distribution using Genz's algorithm as described in.

BiNormalGenzDist(double) - Constructor for class umontreal.iro.lecuyer.probdistmulti.BiNormalGenzDist

Constructs a BiNormalGenzDist object with default parameters μ₁ = μ₂ = 0, σ₁ = σ₂ = 1 and correlation ρ = rho.

BiNormalGenzDist(double, double, double, double, double) - Constructor for class umontreal.iro.lecuyer.probdistmulti.BiNormalGenzDist

Constructs a BiNormalGenzDist object with parameters μ₁ = mu1, μ₂ = mu2, σ₁ = sigma1, σ₂ = sigma2 and ρ = rho.

BiStudentDist - Class in umontreal.iro.lecuyer.probdistmulti

Extends the class ContinuousDistribution2Dim for the standard bivariate Student's t distribution.

BiStudentDist(int, double) - Constructor for class umontreal.iro.lecuyer.probdistmulti.BiStudentDist

Constructs a BiStudentDist object with correlation ρ = rho and ν = nu degrees of freedom.

BitMatrix - Class in umontreal.iro.lecuyer.util

This class implements matrices of bits of arbitrary dimensions.

BitMatrix(int, int) - Constructor for class umontreal.iro.lecuyer.util.BitMatrix

Creates a new BitMatrix with r rows and c columns filled with 0's.

BitMatrix(BitVector[]) - Constructor for class umontreal.iro.lecuyer.util.BitMatrix

Creates a new BitMatrix using the data in rows.

BitMatrix(int[][], int, int) - Constructor for class umontreal.iro.lecuyer.util.BitMatrix

Creates a new BitMatrix with r rows and c columns using the data in data.

BitMatrix(BitMatrix) - Constructor for class umontreal.iro.lecuyer.util.BitMatrix

Copy constructor.

BitMatrix.IncompatibleDimensionException - Exception in umontreal.iro.lecuyer.util

Runtime exception raised when the dimensions of the BitMatrix are not appropriate for the operation.

BitVector - Class in umontreal.iro.lecuyer.util

This class implements vectors of bits and the operations needed to use them.

BitVector(int) - Constructor for class umontreal.iro.lecuyer.util.BitVector

Creates a new BitVector of length length with all its bits set to 0.

BitVector(int[], int) - Constructor for class umontreal.iro.lecuyer.util.BitVector

Creates a new BitVector of length length using the data in vect.

BitVector(int[]) - Constructor for class umontreal.iro.lecuyer.util.BitVector

Creates a new BitVector using the data in vect.

BitVector(BitVector) - Constructor for class umontreal.iro.lecuyer.util.BitVector

Creates a copy of the BitVector that.

BoxChart - Class in umontreal.iro.lecuyer.charts

This class provides tools to create and manage box-and-whisker plots.

BoxChart() - Constructor for class umontreal.iro.lecuyer.charts.BoxChart

Initializes a new BoxChart instance with an empty data set.

BoxChart(String, String, String, double[], int) - Constructor for class umontreal.iro.lecuyer.charts.BoxChart

Initializes a new BoxChart instance with data data.

BoxChart(String, String, String, double[]...) - Constructor for class umontreal.iro.lecuyer.charts.BoxChart

Initializes a new BoxChart instance with data data.

BoxSeriesCollection - Class in umontreal.iro.lecuyer.charts

This class stores data used in a CategoryChart.

BoxSeriesCollection() - Constructor for class umontreal.iro.lecuyer.charts.BoxSeriesCollection

Creates a new BoxSeriesCollection instance with an empty dataset.

BoxSeriesCollection(double[], int) - Constructor for class umontreal.iro.lecuyer.charts.BoxSeriesCollection

Creates a new BoxSeriesCollection instance with default parameters and input series data.

BoxSeriesCollection(double[]...) - Constructor for class umontreal.iro.lecuyer.charts.BoxSeriesCollection

Creates a new BoxSeriesCollection instance with default parameters and given data series.

BoxSeriesCollection(DefaultBoxAndWhiskerCategoryDataset) - Constructor for class umontreal.iro.lecuyer.charts.BoxSeriesCollection

Creates a new BoxSeriesCollection instance with default parameters and given data series.

brentDekker(double, double, MathFunction, double) - Static method in class umontreal.iro.lecuyer.util.RootFinder

Computes a root x of the function in f using the Brent-Dekker method.

BrownianMotion - Class in umontreal.iro.lecuyer.stochprocess

This class represents a Brownian motion process {X(t) : t >= 0}, sampled at times 0 = t₀ < t₁ < ^...

BrownianMotion(double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.BrownianMotion

Constructs a new BrownianMotion with parameters μ = mu, σ = sigma and initial value X(t₀) = x0.

BrownianMotion(double, double, double, NormalGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.BrownianMotion

Constructs a new BrownianMotion with parameters μ = mu, σ = sigma and initial value X(t₀) = x0.

BrownianMotionBridge - Class in umontreal.iro.lecuyer.stochprocess

Represents a Brownian motion process {X(t) : t >= 0} sampled using the bridge sampling technique (see for example).

BrownianMotionBridge(double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.BrownianMotionBridge

Constructs a new BrownianMotionBridge with parameters μ = mu, σ = sigma and initial value X(t₀) = x0.

BrownianMotionBridge(double, double, double, NormalGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.BrownianMotionBridge

Constructs a new BrownianMotionBridge with parameters μ = mu, σ = sigma and initial value X(t₀) = x0.

BrownianMotionPCA - Class in umontreal.iro.lecuyer.stochprocess

A Brownian motion process {X(t) : t >= 0} sampled using the principal component decomposition (PCA).

BrownianMotionPCA(double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCA

Constructs a new BrownianMotionBridge with parameters μ = mu, σ = sigma and initial value X(t₀) = x0.

BrownianMotionPCA(double, double, double, NormalGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCA

Constructs a new BrownianMotionBridge with parameters μ = mu, σ = sigma and initial value X(t₀) = x0.

BrownianMotionPCAEqualSteps - Class in umontreal.iro.lecuyer.stochprocess

Same as BrownianMotionPCA, but uses a trick to speed up the calculation when the time steps are equidistant.

BrownianMotionPCAEqualSteps(double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCAEqualSteps

Constructs a new BrownianMotionPCAEqualSteps.

BrownianMotionPCAEqualSteps(double, double, double, NormalGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCAEqualSteps

Constructs a new BrownianMotionPCAEqualSteps.

BSpline - Class in umontreal.iro.lecuyer.functionfit

Represents a B-spline with control points at (X_i, Y_i).

BSpline(double[], double[], int) - Constructor for class umontreal.iro.lecuyer.functionfit.BSpline

Constructs a new uniform B-spline of degree degree with control points at (x[i], y[i]).

BSpline(double[], double[], double[]) - Constructor for class umontreal.iro.lecuyer.functionfit.BSpline

Constructs a new uniform B-spline with control points at (x[i], y[i]), and knot vector given by the array knots.

C

CachedPointSet - Class in umontreal.iro.lecuyer.hups

This container class caches a point set by precomputing and storing its points locally in an array.

CachedPointSet(PointSet, int, int) - Constructor for class umontreal.iro.lecuyer.hups.CachedPointSet

Creates a new PointSet object that contains an array storing the first dim coordinates of the first n points of P.

CachedPointSet(PointSet) - Constructor for class umontreal.iro.lecuyer.hups.CachedPointSet

Creates a new PointSet object that contains an array storing the points of P.

calcMatStirling(int, int) - Static method in class umontreal.iro.lecuyer.util.Num

Computes and returns the Stirling numbers of the second kind

cancel() - Method in class umontreal.iro.lecuyer.simevents.Event

Cancels this event before it occurs.

cancel(String) - Method in class umontreal.iro.lecuyer.simevents.Event

Finds the first occurence of an event of class ``type'' in the event list, and cancels it.

cancel() - Method in class umontreal.iro.lecuyer.simprocs.SimProcess

Cancels the activating event that was supposed to resume this process, and places the process in the SUSPENDED state.

CategoryChart - Class in umontreal.iro.lecuyer.charts

This class provides tools to create charts from data in a simple way.

CategoryChart() - Constructor for class umontreal.iro.lecuyer.charts.CategoryChart

CauchyDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the Cauchy distribution with location parameter α and scale parameter β > 0.

CauchyDist() - Constructor for class umontreal.iro.lecuyer.probdist.CauchyDist

Constructs a CauchyDist object with parameters α = 0 and β = 1.

CauchyDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.CauchyDist

Constructs a CauchyDist object with parameters α = alpha and β = beta.

CauchyGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators for the Cauchy distribution.

CauchyGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.CauchyGen

Creates a Cauchy random variate generator with parameters α = alpha and β = beta, using stream s.

CauchyGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.CauchyGen

Creates a Cauchy random variate generator with parameters α = 0 and β = 1, using stream s.

CauchyGen(RandomStream, CauchyDist) - Constructor for class umontreal.iro.lecuyer.randvar.CauchyGen

Create a new generator for the distribution dist, using stream s.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDist

cdf(int, double) - Static method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDist

Computes the Anderson-Darling distribution function F_n(x), with parameter n, using Marsaglia's and al.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDistQuick

cdf(int, double) - Static method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDistQuick

Computes the distribution function F_n(x) with parameter n.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.BetaDist

cdf(double, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist

Same as cdf (alpha, beta, 0, 1, d, x).

cdf(double, double, double, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist

Computes an approximation of the distribution function, with roughly d decimal digits of precision.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist

cdf(double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist

Same as cdf (alpha, alpha, d, x).

cdf(int) - Method in class umontreal.iro.lecuyer.probdist.BinomialDist

cdf(int, double, int) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist

Computes F(x), the distribution function of a binomial random variable with parameters n and p, evaluated at x.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.CauchyDist

cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.CauchyDist

Computes the distribution function.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.ChiDist

cdf(int, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiDist

Computes the distribution function by using the gamma distribution function.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.ChiSquareDist

cdf(int, int, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareDist

Computes the chi-square distribution function with n degrees of freedom, evaluated at x.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist

cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist

Computes the noncentral chi-square distribution function with ν = nu degrees of freedom and parameter λ = lambda.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist

cdf(int, double) - Static method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist

Computes the Cramér-von Mises distribution function with parameter n.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistribution

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistributionInt

Returns the distribution function F evaluated at x (see).

cdf(int) - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistributionInt

Returns the distribution function F evaluated at x (see).

cdf(double) - Method in interface umontreal.iro.lecuyer.probdist.Distribution

Returns the distribution function F(x).

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist

cdf(int, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.ErlangDist

Computes the distribution function using the gamma distribution function.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.ExponentialDist

cdf(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ExponentialDist

Computes the distribution function.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist

Deprecated.

cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist

Deprecated.

Computes the distribution function.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist

cdf(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist

Computes the fatigue life distribution function with parameters μ, β and γ.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.FisherFDist

cdf(int, int, int, double) - Static method in class umontreal.iro.lecuyer.probdist.FisherFDist

Computes the distribution function of the Fisher F distribution with parameters n and m, evaluated at x, with roughly d decimal digits of precision.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist

cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist

Computes the distribution function.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.FrechetDist

cdf(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FrechetDist

Computes and returns the distribution function.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.GammaDist

cdf(double, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.GammaDist

Returns an approximation of the gamma distribution function with parameters α = alpha and λ = lambda.

cdf(double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.GammaDist

Equivalent to cdf (alpha, 1.0, d, x).

cdf(int) - Method in class umontreal.iro.lecuyer.probdist.GeometricDist

cdf(double, int) - Static method in class umontreal.iro.lecuyer.probdist.GeometricDist

Computes the distribution function F(x).

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.GumbelDist

cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.GumbelDist

Computes and returns the distribution function.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.HalfNormalDist

cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.HalfNormalDist

Computes the distribution function.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist

cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist

Computes the distribution function of the hyperbolic secant distribution with parameters μ and σ.

cdf(int) - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist

cdf(int, int, int, int) - Static method in class umontreal.iro.lecuyer.probdist.HypergeometricDist

Computes the distribution function F(x).

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.InverseDistFromDensity

Computes the distribution function at x.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist

cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist

Computes the distribution function of the inverse gaussian distribution with parameters μ and λ, evaluated at x.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist

cdf(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist

Computes the distribution function.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist

cdf(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist

Computes the distribution function F(x).

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDist

cdf(int, double) - Static method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDist

Computes the distribution function F(x) with parameter n using Durbin's matrix formula.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDistQuick

cdf(int, double) - Static method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDistQuick

Computes the distribution function u = P[D_n <= x] with parameter n.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist

cdf(int, double) - Static method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist

Computes the Kolmogorov-Smirnov+ distribution function F_n(x) with parameter n.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.LaplaceDist

cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LaplaceDist

Computes the Laplace distribution function.

cdf(int) - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist

cdf(double, int) - Static method in class umontreal.iro.lecuyer.probdist.LogarithmicDist

Computes the distribution function F(x).

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.LogisticDist

cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LogisticDist

Computes the distribution function F(x).

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.LoglogisticDist

cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LoglogisticDist

Computes the distribution function of the log-logistic distribution with parameters α and β.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.LognormalDist

cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LognormalDist

Computes the lognormal distribution function, using cdf01.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.NakagamiDist

cdf(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NakagamiDist

Computes the distribution function.

cdf(int) - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist

cdf(double, double, int) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist

Computes the distribution function.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.NormalDist

cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist

Computes the normal distribution function with mean μ and variance σ².

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.NormalDistQuick

cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDistQuick

Returns an approximation of Φ(x), where Φ is the standard normal distribution function, with mean 0 and variance 1.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist

cdf(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist

NOT IMPLEMENTED.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.ParetoDist

cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ParetoDist

Computes the distribution function.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.Pearson5Dist

cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson5Dist

Computes the density function of a Pearson V distribution with shape parameter α and scale parameter β.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist

cdf(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson6Dist

Computes the distribution function of a Pearson VI distribution with shape parameters α₁ and α₂, and scale parameter β.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist

cdf(int) - Method in class umontreal.iro.lecuyer.probdist.PoissonDist

cdf(double, int) - Static method in class umontreal.iro.lecuyer.probdist.PoissonDist

Computes and returns the value of the Poisson distribution function F(x) for λ = lambda.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.PowerDist

cdf(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.PowerDist

Computes the distribution function.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.RayleighDist

cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist

Computes the distribution function.

cdf(double, double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist

Same as cdf (0, beta, x).

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.StudentDist

cdf(int, double) - Static method in class umontreal.iro.lecuyer.probdist.StudentDist

Returns an approximation for the Student-t distribution function with n degrees of freedom.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.TriangularDist

cdf(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.TriangularDist

Computes the distribution function.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.UniformDist

cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.UniformDist

Computes the uniform distribution function as in.

cdf(int) - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist

cdf(int, int, int) - Static method in class umontreal.iro.lecuyer.probdist.UniformIntDist

Computes the discrete uniform distribution function defined in.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.WatsonGDist

cdf(int, double) - Static method in class umontreal.iro.lecuyer.probdist.WatsonGDist

Computes the Watson G distribution function F_n(x), with parameter n.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.WatsonUDist

cdf(int, double) - Static method in class umontreal.iro.lecuyer.probdist.WatsonUDist

Computes the Watson U distribution function, i.e.

cdf(double) - Method in class umontreal.iro.lecuyer.probdist.WeibullDist

cdf(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist

Computes the distribution function.

cdf(double, double) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist

Same as cdf (alpha, 1, 0, x).

cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist

Computes the standard binormal distribution using the fast Drezner-Wesolowsky method described in.

cdf(double, double) - Method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist

cdf(double, double, double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist

Computes the binormal distribution function with parameters μ₁ = mu1, μ₂ = mu2, σ₁ = sigma1, σ₂ = sigma2 and ρ = rho.

cdf(double, double, double, int) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDonnellyDist

Computes the standard binormal distribution with the method described in, where ndig is the number of decimal digits of accuracy provided (ndig <= 15).

cdf(double, double, double, double, double, double, double, int) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDonnellyDist

Computes the binormal distribution function with parameters μ₁ = mu1, μ₂ = mu2, σ₁ = sigma1, σ₂ = sigma2, correlation ρ = rho and ndig decimal digits of accuracy.

cdf(double, double) - Method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDonnellyDist

cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDonnellyDist

cdf(double, double, double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDonnellyDist

cdf(double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalGenzDist

Computes the standard binormal distribution with the method described in.

cdf(double, double, double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalGenzDist

cdf(double, double) - Method in class umontreal.iro.lecuyer.probdistmulti.BiNormalGenzDist

cdf(double, double) - Method in class umontreal.iro.lecuyer.probdistmulti.BiStudentDist

cdf(int, double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiStudentDist

Computes the standard bivariate Student's t distribution using the method described in.

cdf(double, double) - Method in class umontreal.iro.lecuyer.probdistmulti.ContinuousDistribution2Dim

.

cdf(double, double, double, double) - Method in class umontreal.iro.lecuyer.probdistmulti.ContinuousDistribution2Dim

.

cdf(int[]) - Method in class umontreal.iro.lecuyer.probdistmulti.DiscreteDistributionIntMulti

Computes the cumulative probability function F of the distribution evaluated at x, assuming the lowest values start at 0, i.e.

cdf(int[]) - Method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist

cdf(int, double[], int[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist

Computes the function F of the multinomial distribution with parameters n and (p₁,...,p_d) evaluated at x.

cdf(double, double[], int[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist

Computes the cumulative probability function F of the negative multinomial distribution with parameters γ and (p₁,...,p_k), evaluated at x.

cdf01(double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist

Same as cdf (0, 1, x).

cdf01(double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDistQuick

Same as cdf (0.0, 1.0, x).

cdf2(int, int, double) - Static method in class umontreal.iro.lecuyer.probdist.StudentDist

Returns an approximation of the Student-t distribution function with n degrees of freedom.

changeCapacity(int) - Method in class umontreal.iro.lecuyer.simprocs.Resource

Modifies by diff units (increases if diff > 0, decreases if diff < 0) the capacity (i.e., the number of units) of the resource.

charAt(int) - Method in class umontreal.iro.lecuyer.util.PrintfFormat

chi2(double[], int[], int, int) - Static method in class umontreal.iro.lecuyer.gof.GofStat

Computes and returns the chi-square statistic for the observations o_i in count[smin...smax], for which the corresponding expected values e_i are in nbExp[smin...smax].

chi2(IntArrayList, DiscreteDistributionInt, int, int, double, int[]) - Static method in class umontreal.iro.lecuyer.gof.GofStat

Computes and returns the chi-square statistic for the observations stored in data, assuming that these observations follow the discrete distribution dist.

chi2Equal(double, int[], int, int) - Static method in class umontreal.iro.lecuyer.gof.GofStat

Similar to chi2, except that the expected number of observations per category is assumed to be the same for all categories, and equal to nbExp.

chi2Equal(DoubleArrayList, double) - Static method in class umontreal.iro.lecuyer.gof.GofStat

Computes the chi-square statistic for a continuous distribution.

chi2Equal(DoubleArrayList) - Static method in class umontreal.iro.lecuyer.gof.GofStat

Equivalent to chi2Equal (data, 10).

ChiDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the chi distribution with shape parameter v > 0, where the number of degrees of freedom v is a positive integer.

ChiDist(int) - Constructor for class umontreal.iro.lecuyer.probdist.ChiDist

Constructs a ChiDist object.

ChiGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators for the chi distribution.

ChiGen(RandomStream, int) - Constructor for class umontreal.iro.lecuyer.randvar.ChiGen

Creates a chi random variate generator with ν = nu degrees of freedom, using stream s.

ChiGen(RandomStream, ChiDist) - Constructor for class umontreal.iro.lecuyer.randvar.ChiGen

Create a new generator for the distribution dist, using stream s.

ChiRatioOfUniformsGen - Class in umontreal.iro.lecuyer.randvar

This class implements Chi random variate generators using the ratio of uniforms method with shift.

ChiRatioOfUniformsGen(RandomStream, int) - Constructor for class umontreal.iro.lecuyer.randvar.ChiRatioOfUniformsGen

Creates a chi random variate generator with ν = nu degrees of freedom, using stream s.

ChiRatioOfUniformsGen(RandomStream, ChiDist) - Constructor for class umontreal.iro.lecuyer.randvar.ChiRatioOfUniformsGen

Create a new generator for the distribution dist, using stream s.

ChiSquareDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the chi-square distribution with n degrees of freedom, where n is a positive integer.

ChiSquareDist(int) - Constructor for class umontreal.iro.lecuyer.probdist.ChiSquareDist

Constructs a chi-square distribution with n degrees of freedom.

ChiSquareDistQuick - Class in umontreal.iro.lecuyer.probdist

Provides a variant of ChiSquareDist with faster but less accurate methods.

ChiSquareDistQuick(int) - Constructor for class umontreal.iro.lecuyer.probdist.ChiSquareDistQuick

Constructs a chi-square distribution with n degrees of freedom.

ChiSquareGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators with the chi square distribution with n > 0 degrees of freedom.

ChiSquareGen(RandomStream, int) - Constructor for class umontreal.iro.lecuyer.randvar.ChiSquareGen

Creates a chi square random variate generator with n degrees of freedom, using stream s.

ChiSquareGen(RandomStream, ChiSquareDist) - Constructor for class umontreal.iro.lecuyer.randvar.ChiSquareGen

Create a new generator for the distribution dist and stream s.

ChiSquareNoncentralDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the noncentral chi-square distribution with ν degrees of freedom and noncentrality parameter λ, where ν > 0 and λ > 0.

ChiSquareNoncentralDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist

Constructs a noncentral chi-square distribution with ν = nu degrees of freedom and noncentrality parameter λ = lambda.

ChiSquareNoncentralGamGen - Class in umontreal.iro.lecuyer.randvar

This class implements noncentral chi square random variate generators using the additive property of the noncentral chi square distribution.

ChiSquareNoncentralGamGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralGamGen

Creates a noncentral chi square random variate generator with with ν = nu degrees of freedom and noncentrality parameter λ = lambda using stream stream, as described above.

ChiSquareNoncentralGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators for the noncentral chi square distribution with ν degrees of freedom and noncentrality parameter λ.

ChiSquareNoncentralGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralGen

Creates a noncentral chi square random variate generator with ν > 0 degrees of freedom and noncentrality parameter λ > 0, using stream s.

ChiSquareNoncentralGen(RandomStream, ChiSquareNoncentralDist) - Constructor for class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralGen

Create a new generator for the distribution dist and stream s.

ChiSquareNoncentralPoisGen - Class in umontreal.iro.lecuyer.randvar

This class implements noncentral chi square random variate generators using Poisson and central chi square generators.

ChiSquareNoncentralPoisGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralPoisGen

Creates a noncentral chi square random variate generator with ν = nu degrees of freedom and noncentrality parameter λ = lambda using stream stream, as described above.

CholeskyDecompose(double[][], double[][]) - Static method in class umontreal.iro.lecuyer.util.DMatrix

Given a symmetric positive-definite matrix M, performs the Cholesky decomposition of M and returns the result as a lower triangular matrix L, such that M = LL^T.

CholeskyDecompose(DoubleMatrix2D) - Static method in class umontreal.iro.lecuyer.util.DMatrix

Given a symmetric positive-definite matrix M, performs the Cholesky decomposition of M and returns the result as a lower triangular matrix L, such that M = LL^T.

Chrono - Class in umontreal.iro.lecuyer.util

The Chrono class extends the AbstractChrono class and computes the CPU time for the current thread only.

Chrono() - Constructor for class umontreal.iro.lecuyer.util.Chrono

Constructs a Chrono object and initializes it to zero.

ChronoSingleThread - Class in umontreal.iro.lecuyer.util

Deprecated.

ChronoSingleThread() - Constructor for class umontreal.iro.lecuyer.util.ChronoSingleThread

Deprecated.

Constructs a ChronoSingleThread object and initializes it to zero.

CIRProcess - Class in umontreal.iro.lecuyer.stochprocess

This class represents a CIR (Cox, Ingersoll, Ross) process {X(t) : t >= 0}, sampled at times 0 = t₀ < t₁ < ^...

CIRProcess(double, double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.CIRProcess

Constructs a new CIRProcess with parameters α = alpha, b, σ = sigma and initial value X(t₀) = x0.

CIRProcess(double, double, double, double, ChiSquareNoncentralGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.CIRProcess

The noncentral chi-square variate generator gen is specified directly instead of specifying the stream.

CIRProcessEuler - Class in umontreal.iro.lecuyer.stochprocess

.

CIRProcessEuler(double, double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.CIRProcessEuler

Constructs a new CIRProcessEuler with parameters α = alpha, b, σ = sigma and initial value X(t₀) = x0.

CIRProcessEuler(double, double, double, double, NormalGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.CIRProcessEuler

The normal variate generator gen is specified directly instead of specifying the stream.

ClassFinder - Class in umontreal.iro.lecuyer.util

Utility class used to convert a simple class name to a fully qualified class object.

ClassFinder() - Constructor for class umontreal.iro.lecuyer.util.ClassFinder

Constructs a new class finder with an empty list of import declarations.

clear() - Method in class umontreal.iro.lecuyer.rng.RandomStreamManager

Removes all the streams from the internal list of this random stream manager.

clear() - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree

clear() - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked

clear() - Method in interface umontreal.iro.lecuyer.simevents.eventlist.EventList

Empties the event list, i.e., cancels all events.

clear() - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen

clear() - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree

clear() - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree

clear() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat

clear() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

clear() - Method in class umontreal.iro.lecuyer.util.PrintfFormat

Clears the contents of the buffer.

clear() - Method in class umontreal.iro.lecuyer.util.TransformingList

clearArrayOfObservationListeners() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

Removes all observation listeners from the list of observers of this list of statistical probes.

clearCache() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache

Clears the cached values for this cached generator.

clearCache() - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache

Clears the cached values for this random stream.

clearObservationListeners() - Method in class umontreal.iro.lecuyer.stat.StatProbe

Removes all observation listeners from the list of observers of this statistical probe.

clearRandomShift() - Method in class umontreal.iro.lecuyer.hups.ContainerPointSet

Calls clearRandomShift() of the contained point set.

clearRandomShift() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet

clearRandomShift() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSetBase2

clearRandomShift() - Method in class umontreal.iro.lecuyer.hups.DigitalNet

clearRandomShift() - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2

clearRandomShift() - Method in class umontreal.iro.lecuyer.hups.PointSet

Erases the current random shift, if any.

clearRandomShift() - Method in class umontreal.iro.lecuyer.hups.Rank1Lattice

Clears the random shift.

clone() - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset

Returns a clone of the dataset.

clone() - Method in class umontreal.iro.lecuyer.charts.EmpiricalRenderer

Returns a clone of the renderer.

clone() - Method in class umontreal.iro.lecuyer.functionfit.LeastSquares

clone() - Method in class umontreal.iro.lecuyer.functionfit.PolInterp

clone() - Method in class umontreal.iro.lecuyer.functions.Polynomial

clone() - Method in interface umontreal.iro.lecuyer.rng.CloneableRandomStream

Clones the current object and returns its copy.

clone() - Method in class umontreal.iro.lecuyer.rng.F2NL607

Clones the current generator and return its copy.

clone() - Method in class umontreal.iro.lecuyer.rng.GenF2w32

Clones the current generator and return its copy.

clone() - Method in class umontreal.iro.lecuyer.rng.LFSR113

Clones the current generator and return its copy.

clone() - Method in class umontreal.iro.lecuyer.rng.LFSR258

Clones the current generator and return its copy.

clone() - Method in class umontreal.iro.lecuyer.rng.MRG31k3p

Clones the current generator and return its copy.

clone() - Method in class umontreal.iro.lecuyer.rng.MRG32k3a

Clones the current generator and return its copy.

clone() - Method in class umontreal.iro.lecuyer.rng.MRG32k3aL

clone() - Method in class umontreal.iro.lecuyer.rng.MT19937

Clones the current generator and return its copy.

clone() - Method in class umontreal.iro.lecuyer.rng.RandMrg

Deprecated.

Clones the current generator and return its copy.

clone() - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase

Clones the current generator and return its copy.

clone() - Method in class umontreal.iro.lecuyer.rng.RandRijndael

Clones the current generator and return its copy.

clone() - Method in class umontreal.iro.lecuyer.rng.WELL1024

Clones the current generator and return its copy.

clone() - Method in class umontreal.iro.lecuyer.rng.WELL512

Clones the current generator and return its copy.

clone() - Method in class umontreal.iro.lecuyer.rng.WELL607

Clones the current generator and return its copy.

clone() - Method in class umontreal.iro.lecuyer.simevents.Accumulate

Clone this object.

clone() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

Clones this object.

clone() - Method in class umontreal.iro.lecuyer.stat.list.ListOfTallies

Clones this object.

clone() - Method in class umontreal.iro.lecuyer.stat.list.ListOfTalliesWithCovariance

Clones this object.

clone() - Method in class umontreal.iro.lecuyer.stat.StatProbe

clone() - Method in class umontreal.iro.lecuyer.stat.Tally

Clones this object.

clone() - Method in class umontreal.iro.lecuyer.stat.TallyStore

Clones this object and the array which stores the observations.

clone() - Method in class umontreal.iro.lecuyer.util.BitMatrix

Creates a copy of the BitMatrix.

clone() - Method in class umontreal.iro.lecuyer.util.BitVector

Creates a copy of the BitVector.

clone() - Method in class umontreal.iro.lecuyer.util.ClassFinder

Clones this class finder, and copies its lists of import declarations.

CloneableRandomStream - Interface in umontreal.iro.lecuyer.rng

CloneableRandomStream extends RandomStream and Cloneable.

CM - Static variable in class umontreal.iro.lecuyer.gof.GofFormat

Cramér-von Mises test

combination(int, int) - Static method in class umontreal.iro.lecuyer.util.Num

Returns the number of different combinations of s objects amongst n.

compare(double[], double[]) - Method in class umontreal.iro.lecuyer.util.DoubleArrayComparator

Returns -1, 0, or 1 depending on whether d1[i] is less than, equal to, or greater than d2[i].

compare(T, T) - Method in class umontreal.iro.lecuyer.util.MultiDimComparator

Calls o1.compareTo(o2, i) from class MultiDimComparable.

compareTo(Event) - Method in class umontreal.iro.lecuyer.simevents.Event

Compares this object with the specified object e for order.

compareTo(T, int) - Method in interface umontreal.iro.lecuyer.util.MultiDimComparable

Compares objects of type T in the i-th dimension.

computeDensity(EmpiricalDist, ContinuousDistribution, double, double[]) - Static method in class umontreal.iro.lecuyer.gof.KernelDensity

Given the empirical distribution dist, this method computes the kernel density estimate at each of the m points Y[j], j = 0, 1,…,(m - 1), where m is the length of Y, the kernel is kern.density(x), and the bandwidth is h.

computeDensity(EmpiricalDist, ContinuousDistribution, double[]) - Static method in class umontreal.iro.lecuyer.gof.KernelDensity

Similar to method , but the bandwidth h is obtained from the method KernelDensityGen.getBaseBandwidth(dist) in package randvar.

Condition - Class in umontreal.iro.lecuyer.simprocs

A Condition is a boolean indicator, with a list of processes waiting for the indicator to be true (when it is false).

Condition(boolean) - Constructor for class umontreal.iro.lecuyer.simprocs.Condition

Constructs a new Condition with initial value val, linked with the default simulator.

Condition(ProcessSimulator, boolean) - Constructor for class umontreal.iro.lecuyer.simprocs.Condition

Constructs a new Condition with initial value val, linked with simulator sim.

Condition(boolean, String) - Constructor for class umontreal.iro.lecuyer.simprocs.Condition

Constructs a new Condition with initial value val, identifier name and linked with the default simulator.

Condition(ProcessSimulator, boolean, String) - Constructor for class umontreal.iro.lecuyer.simprocs.Condition

Constructs a new Condition with initial value val, identifier name and linked with simulator sim.

confidenceIntervalNormal(double, double[]) - Method in class umontreal.iro.lecuyer.stat.Tally

Computes a confidence interval on the mean.

confidenceIntervalStudent(double, double[]) - Method in class umontreal.iro.lecuyer.stat.Tally

Computes a confidence interval on the mean.

confidenceIntervalVarianceChi2(double, double[]) - Method in class umontreal.iro.lecuyer.stat.Tally

Computes a confidence interval on the variance.

connectToDatabase(Properties) - Static method in class umontreal.iro.lecuyer.util.JDBCManager

Connects to the database using the properties prop and returns the an object representing the connection.

connectToDatabase(InputStream) - Static method in class umontreal.iro.lecuyer.util.JDBCManager

Returns a connection to the database using the properties read from stream is.

connectToDatabase(URL) - Static method in class umontreal.iro.lecuyer.util.JDBCManager

Equivalent to connectToDatabase (url.openStream()).

connectToDatabase(File) - Static method in class umontreal.iro.lecuyer.util.JDBCManager

Equivalent to connectToDatabase (new FileInputStream (file)).

connectToDatabase(String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager

Equivalent to connectToDatabase (new FileInputStream (fileName)).

connectToDatabaseFromResource(String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager

Uses connectToDatabase with the stream obtained from the resource resource.

ContainerPointSet - Class in umontreal.iro.lecuyer.hups

This acts as a generic base class for all container classes that contain a point set and apply some kind of transformation to the coordinates to define a new point set.

ContainerPointSet() - Constructor for class umontreal.iro.lecuyer.hups.ContainerPointSet

contains(Object) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

containsAll(Collection<?>) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

Continuous - Class in umontreal.iro.lecuyer.simevents

Represents a variable in a continuous-time simulation.

Continuous() - Constructor for class umontreal.iro.lecuyer.simevents.Continuous

Constructs a new continuous-time variable linked to the default simulator, without initializing it.

Continuous(Simulator) - Constructor for class umontreal.iro.lecuyer.simevents.Continuous

Constructs a new continuous-time variable linked to the given simulator, without initializing it.

ContinuousDistChart - Class in umontreal.iro.lecuyer.charts

This class provides tools to plot the density and the cumulative probability of a continuous probability distribution.

ContinuousDistChart(ContinuousDistribution, double, double, int) - Constructor for class umontreal.iro.lecuyer.charts.ContinuousDistChart

Constructor for a new ContinuousDistChart instance.

ContinuousDistribution - Class in umontreal.iro.lecuyer.probdist

Classes implementing continuous distributions should inherit from this base class.

ContinuousDistribution() - Constructor for class umontreal.iro.lecuyer.probdist.ContinuousDistribution

ContinuousDistribution2Dim - Class in umontreal.iro.lecuyer.probdistmulti

Classes implementing 2-dimensional continuous distributions should inherit from this class.

ContinuousDistribution2Dim() - Constructor for class umontreal.iro.lecuyer.probdistmulti.ContinuousDistribution2Dim

ContinuousDistributionMulti - Class in umontreal.iro.lecuyer.probdistmulti

Classes implementing continuous multi-dimensional distributions should inherit from this class.

ContinuousDistributionMulti() - Constructor for class umontreal.iro.lecuyer.probdistmulti.ContinuousDistributionMulti

ContinuousState - Class in umontreal.iro.lecuyer.simevents

Represents the portion of the simulator's state associated with continuous-time simulation.

continuousState() - Method in class umontreal.iro.lecuyer.simevents.Simulator

Returns the current state of continuous variables being integrated during the simulation.

ContinuousState.IntegMethod - Enum in umontreal.iro.lecuyer.simevents

convert(double, TimeUnit, TimeUnit) - Static method in enum umontreal.iro.lecuyer.util.TimeUnit

Converts value expressed in time unit srcUnit to a time duration expressed in dstUnit and returns the result of the conversion.

convertFromInnerType(ListWithStat.Node<E>) - Method in class umontreal.iro.lecuyer.simevents.ListWithStat

convertFromInnerType(IE) - Method in class umontreal.iro.lecuyer.util.TransformingList

Converts an element in the inner list to an element of the outer type.

convertToInnerType(E) - Method in class umontreal.iro.lecuyer.simevents.ListWithStat

convertToInnerType(OE) - Method in class umontreal.iro.lecuyer.util.TransformingList

Converts an element of the outer type to an element for the inner list.

COR - Static variable in class umontreal.iro.lecuyer.gof.GofFormat

Correlation

correlation(int, int) - Method in class umontreal.iro.lecuyer.stat.list.ListOfTallies

Returns the empirical correlation between the observations in tallies with indices i and j.

correlation(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.stat.list.ListOfTallies

Similar to covariance for computing the sample correlation matrix.

covariance(int, int) - Method in class umontreal.iro.lecuyer.stat.list.ListOfTallies

Returns the empirical covariance of the observations in tallies with indices i and j.

covariance(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.stat.list.ListOfTallies

Constructs and returns the sample covariance matrix for the tallies in this list.

covariance(int, int) - Method in class umontreal.iro.lecuyer.stat.list.ListOfTalliesWithCovariance

covariance(TallyStore) - Method in class umontreal.iro.lecuyer.stat.TallyStore

Returns the sample covariance of the observations contained in this tally, and the other tally t2.

cramerVonMises(int, double) - Static method in class umontreal.iro.lecuyer.gof.FBar

Deprecated.

cramerVonMises(int, double) - Static method in class umontreal.iro.lecuyer.gof.FDist

Deprecated.

cramerVonMises(DoubleArrayList) - Static method in class umontreal.iro.lecuyer.gof.GofStat

Computes and returns the Cramér-von Mises statistic W_N².

CramerVonMisesDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the Cramér-von Mises distribution (see).

CramerVonMisesDist(int) - Constructor for class umontreal.iro.lecuyer.probdist.CramerVonMisesDist

Constructs a Cramér-von Mises distribution for a sample of size n.

createApproxBSpline(double[], double[], int, int) - Static method in class umontreal.iro.lecuyer.functionfit.BSpline

Returns a B-spline curve of degree degree smoothing (x_i, y_i), for i = 0,…, n points.

createControlEvent(SimProcess) - Method in class umontreal.iro.lecuyer.simprocs.DSOLProcessSimulator

createControlEvent(SimProcess) - Method in class umontreal.iro.lecuyer.simprocs.ProcessSimulator

Constructs and returns a new Event object used for synchronization.

createControlEvent(SimProcess) - Method in class umontreal.iro.lecuyer.simprocs.ThreadProcessSimulator

createForSingleThread() - Static method in class umontreal.iro.lecuyer.util.Chrono

Creates a Chrono instance adapted for a program using a single thread.

createInterpBSpline(double[], double[], int) - Static method in class umontreal.iro.lecuyer.functionfit.BSpline

Returns a B-spline curve of degree degree interpolating the (x_i, y_i) points.

createWithTally(int) - Static method in class umontreal.iro.lecuyer.stat.list.ListOfTallies

This factory method constructs and returns a list of tallies with size instances of Tally.

createWithTally(int) - Static method in class umontreal.iro.lecuyer.stat.list.ListOfTalliesWithCovariance

This factory method constructs and returns a list of tallies with size instances of Tally.

createWithTallyStore(int) - Static method in class umontreal.iro.lecuyer.stat.list.ListOfTallies

This factory method constructs and returns a list of tallies with size instances of TallyStore.

createWithTallyStore(int) - Static method in class umontreal.iro.lecuyer.stat.list.ListOfTalliesWithCovariance

This factory method constructs and returns a list of tallies with size instances of TallyStore.

currentProcess() - Method in class umontreal.iro.lecuyer.simprocs.ProcessSimulator

Returns the currently active process for this simulator.

CustomHistogramDataset - Class in umontreal.iro.lecuyer.charts

A dataset that can be used for creating histograms.

CustomHistogramDataset() - Constructor for class umontreal.iro.lecuyer.charts.CustomHistogramDataset

Creates a new (empty) dataset with a default type of HistogramType.FREQUENCY.

CycleBasedPointSet - Class in umontreal.iro.lecuyer.hups

This abstract class provides the basic structures for storing and manipulating a highly uniform point set defined by a set of cycles.

CycleBasedPointSet() - Constructor for class umontreal.iro.lecuyer.hups.CycleBasedPointSet

CycleBasedPointSet.CycleBasedPointSetIterator - Class in umontreal.iro.lecuyer.hups

CycleBasedPointSet.CycleBasedPointSetIterator() - Constructor for class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator

CycleBasedPointSetBase2 - Class in umontreal.iro.lecuyer.hups

Similar to CycleBasedPointSet, except that the successive values in the cycles are stored as integers in the range {0,..., 2^k -1}, where 1 <= k <= 31.

CycleBasedPointSetBase2() - Constructor for class umontreal.iro.lecuyer.hups.CycleBasedPointSetBase2

CycleBasedPointSetBase2.CycleBasedPointSetBase2Iterator - Class in umontreal.iro.lecuyer.hups

CycleBasedPointSetBase2.CycleBasedPointSetBase2Iterator() - Constructor for class umontreal.iro.lecuyer.hups.CycleBasedPointSetBase2.CycleBasedPointSetBase2Iterator

D

d(long) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat

Same as d (0, 1, x).

d(int, long) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat

Same as d (fieldwidth, 1, x).

d(int, int, long) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat

Formats the long integer x into a string like %d in the C printf function.

DBL_DIG - Static variable in class umontreal.iro.lecuyer.util.Num

Number of decimal digits of precision in a double.

DBL_EPSILON - Static variable in class umontreal.iro.lecuyer.util.Num

Difference between 1.0 and the smallest double greater than 1.0.

DBL_MAX_10_EXP - Static variable in class umontreal.iro.lecuyer.util.Num

Largest int x such that 10^x is representable (approximately) as a double.

DBL_MAX_EXP - Static variable in class umontreal.iro.lecuyer.util.Num

Largest int x such that 2^x-1 is representable (approximately) as a double.

DBL_MIN - Static variable in class umontreal.iro.lecuyer.util.Num

Smallest normalized positive floating-point double.

DBL_MIN_EXP - Static variable in class umontreal.iro.lecuyer.util.Num

Smallest int x such that 2^x-1 is representable (approximately) as a normalised double.

DEAD - Static variable in class umontreal.iro.lecuyer.simprocs.SimProcess

The process has terminated its execution.

decompPCA(double[][]) - Static method in class umontreal.iro.lecuyer.randvarmulti.MultinormalPCAGen

Computes the decomposition sigma = Σ = VΛV^t.

decompPCA(DoubleMatrix2D) - Static method in class umontreal.iro.lecuyer.randvarmulti.MultinormalPCAGen

Computes the decomposition sigma = Σ = VΛV^t.

decompPCA(double[][]) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCA

decPrec - Variable in class umontreal.iro.lecuyer.probdist.ContinuousDistribution

decPrec - Variable in class umontreal.iro.lecuyer.probdistmulti.ContinuousDistribution2Dim

Defines the target number of decimals of accuracy when approximating a distribution function, but there is no guarantee that this target is always attained.

defaultSimulator - Static variable in class umontreal.iro.lecuyer.simevents.Simulator

Represents the default simulator being used by the class Sim, and the no-argument constructor of Event.

delay(SimProcess, double) - Method in class umontreal.iro.lecuyer.simprocs.DSOLProcessSimulator

delay(SimProcess, double) - Method in class umontreal.iro.lecuyer.simprocs.ProcessSimulator

Suspends the execution of process and schedules it to resume its execution in delay units of simulation time.

delay(double) - Method in class umontreal.iro.lecuyer.simprocs.SimProcess

Suspends the execution of the currently executing process and schedules it to resume its execution in delay units of simulation time.

delay(SimProcess, double) - Method in class umontreal.iro.lecuyer.simprocs.ThreadProcessSimulator

DELAYED - Static variable in class umontreal.iro.lecuyer.simprocs.SimProcess

The process is not executing but has an event in the event list to reactivate it later on.

density(double) - Method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDist

density(int, double) - Static method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDist

Computes the density of the Anderson-Darling distribution with parameter n.

density(double) - Method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDistQuick

density(int, double) - Static method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDistQuick

Computes the density of the distribution with parameter n.

density(double) - Method in class umontreal.iro.lecuyer.probdist.BetaDist

density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist

Same as density (alpha, beta, 0, 1, x).

density(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist

Computes the density function of the beta distribution.

density(double, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist

Returns the density evaluated at x.

density(double) - Method in class umontreal.iro.lecuyer.probdist.CauchyDist

density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.CauchyDist

Computes the density function.

density(double) - Method in class umontreal.iro.lecuyer.probdist.ChiDist

density(int, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiDist

Computes the density function.

density(double) - Method in class umontreal.iro.lecuyer.probdist.ChiSquareDist

density(int, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareDist

Computes the density function for a chi-square distribution with n degrees of freedom.

density(double) - Method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist

density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist

Computes the density function for a noncentral chi-square distribution with ν = nu degrees of freedom and parameter λ = lambda.

density(double) - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution

Returns f (x), the density evaluated at x.

density(double) - Method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist

density(int, double) - Static method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist

Computes the density function for a Cramér-von Mises distribution with parameter n.

density(int, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ErlangDist

Computes the density function.

density(double) - Method in class umontreal.iro.lecuyer.probdist.ExponentialDist

density(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ExponentialDist

Computes the density function.

density(double) - Method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist

Deprecated.

density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist

Deprecated.

Computes the density function.

density(double) - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist

density(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist

Computes the density for the fatigue life distribution with parameters μ, β and γ.

density(double) - Method in class umontreal.iro.lecuyer.probdist.FisherFDist

density(int, int, double) - Static method in class umontreal.iro.lecuyer.probdist.FisherFDist

Computes the density function for a Fisher F distribution with n and m degrees of freedom.

density(double) - Method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist

density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist

Computes the density function of the folded normal distribution.

density(double) - Method in class umontreal.iro.lecuyer.probdist.FrechetDist

density(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FrechetDist

Computes and returns the density function.

density(double) - Method in class umontreal.iro.lecuyer.probdist.GammaDist

density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.GammaDist

Computes the density function at x.

density(double) - Method in class umontreal.iro.lecuyer.probdist.GumbelDist

density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.GumbelDist

Computes and returns the density function.

density(double) - Method in class umontreal.iro.lecuyer.probdist.HalfNormalDist

density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.HalfNormalDist

Computes the density function of the half-normal distribution.

density(double) - Method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist

density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist

Computes the density function for a hyperbolic secant distribution with parameters μ and σ.

density(double) - Method in class umontreal.iro.lecuyer.probdist.InverseDistFromDensity

Computes the probability density at x.

density(double) - Method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist

density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist

Computes the density function for the inverse gaussian distribution with parameters μ and λ, evaluated at x.

density(double) - Method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist

density(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist

Computes the density function.

density(double) - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist

density(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist

Computes the density function f (x).

density(double) - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDist

density(int, double) - Static method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDist

Computes the density for the distribution with parameter n.

density(double) - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDistQuick

density(int, double) - Static method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDistQuick

Computes the density for the distribution with parameter n.

density(double) - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist

density(int, double) - Static method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist

Computes the density of the Kolmogorov-Smirnov+ distribution with parameter n.

density(double) - Method in class umontreal.iro.lecuyer.probdist.LaplaceDist

density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LaplaceDist

Computes the Laplace density function.

density(double) - Method in class umontreal.iro.lecuyer.probdist.LogisticDist

density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LogisticDist

Computes the density function f (x).

density(double) - Method in class umontreal.iro.lecuyer.probdist.LoglogisticDist

density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LoglogisticDist

Computes the density function for a log-logisitic distribution with parameters α and β.

density(double) - Method in class umontreal.iro.lecuyer.probdist.LognormalDist

density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LognormalDist

Computes the lognormal density function f (x).

density(double) - Method in class umontreal.iro.lecuyer.probdist.NakagamiDist

density(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NakagamiDist

Computes the density function of the Nakagami distribution.

density(double) - Method in class umontreal.iro.lecuyer.probdist.NormalDist

density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist

Computes the normal density function.

density(double) - Method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist

density(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist

Computes the density function for the normal inverse gaussian distribution with parameters α, β, μ and δ, evaluated at x.

density(double) - Method in class umontreal.iro.lecuyer.probdist.ParetoDist

density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ParetoDist

Computes the density function.

density(double) - Method in class umontreal.iro.lecuyer.probdist.Pearson5Dist

density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson5Dist

Computes the density function of a Pearson V distribution with shape parameter α and scale parameter β.

density(double) - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist

density(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson6Dist

Computes the density function of a Pearson VI distribution with shape parameters α₁ and α₂, and scale parameter β.

density(double) - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist

density(double) - Method in class umontreal.iro.lecuyer.probdist.PowerDist

density(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.PowerDist

Computes the density function.

density(double) - Method in class umontreal.iro.lecuyer.probdist.RayleighDist

density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist

Computes the density function.

density(double, double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist

Same as density (0, beta, x).

density(double) - Method in class umontreal.iro.lecuyer.probdist.StudentDist

density(int, double) - Static method in class umontreal.iro.lecuyer.probdist.StudentDist

Computes the density function for a Student-t distribution with n degrees of freedom.

density(double) - Method in class umontreal.iro.lecuyer.probdist.TriangularDist

density(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.TriangularDist

Computes the density function.

density(double) - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist

Returns an approximation of the mean obtained using the Simpson 1/3 numerical integration, or throws an UnsupportedOperationException if a or b are infinite.

density(double) - Method in class umontreal.iro.lecuyer.probdist.UniformDist

density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.UniformDist

Computes the uniform density function f (x).

density(double) - Method in class umontreal.iro.lecuyer.probdist.WatsonGDist

density(int, double) - Static method in class umontreal.iro.lecuyer.probdist.WatsonGDist

Computes the density function for a Watson G distribution with parameter n.

density(double) - Method in class umontreal.iro.lecuyer.probdist.WatsonUDist

density(int, double) - Static method in class umontreal.iro.lecuyer.probdist.WatsonUDist

Computes the density of the Watson U distribution with parameter n.

density(double) - Method in class umontreal.iro.lecuyer.probdist.WeibullDist

density(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist

Computes the density function.

density(double, double) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist

Same as density (alpha, 1, 0, x).

density(double, double) - Method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist

density(double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist

Computes the standard binormal density function with μ₁ = μ₂ = 0 and σ₁ = σ₂ = 1.

density(double, double, double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist

Computes the binormal density function with parameters μ₁ = mu1, μ₂ = mu2, σ₁ = sigma1, σ₂ = sigma2 and ρ = rho.

density(double, double) - Method in class umontreal.iro.lecuyer.probdistmulti.BiStudentDist

density(int, double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiStudentDist

Computes the standard bivariate Student's t density function with correlation ρ = rho and ν = nu degrees of freedom.

density(double, double) - Method in class umontreal.iro.lecuyer.probdistmulti.ContinuousDistribution2Dim

Returns f (x, y), the density of (X, Y) evaluated at (x, y).

density(double[]) - Method in class umontreal.iro.lecuyer.probdistmulti.ContinuousDistribution2Dim

Simply calls density (x[0], x[1]).

density(double[]) - Method in class umontreal.iro.lecuyer.probdistmulti.ContinuousDistributionMulti

Returns f (x₁, x₂,…, x_d), the probability density of X evaluated at the point x, where x = {x₁, x₂,…, x_d}.

density(double[]) - Method in class umontreal.iro.lecuyer.probdistmulti.DirichletDist

density(double[], double[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.DirichletDist

Computes the density of the Dirichlet distribution with parameters (α₁,...,α_d).

density(double[]) - Method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist

density(double[], double[][], double[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist

Computes the density of the multinormal distribution with parameters μ = mu and Σ = sigma, evaluated at x.

density01(double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist

Same as density (0, 1, x).

derivative(double) - Method in class umontreal.iro.lecuyer.functionfit.BSpline

derivative(double, int) - Method in class umontreal.iro.lecuyer.functionfit.BSpline

derivative(double) - Method in class umontreal.iro.lecuyer.functionfit.SmoothingCubicSpline

Evaluates and returns the value of the first derivative of the spline at z.

derivative(double, int) - Method in class umontreal.iro.lecuyer.functionfit.SmoothingCubicSpline

Evaluates and returns the value of the n-th derivative of the spline at z.

derivative(double, int) - Method in class umontreal.iro.lecuyer.functions.AverageMathFunction

derivative(double) - Method in class umontreal.iro.lecuyer.functions.AverageMathFunction

derivative(double) - Method in class umontreal.iro.lecuyer.functions.IdentityMathFunction

derivative(double, int) - Method in class umontreal.iro.lecuyer.functions.IdentityMathFunction

derivative(MathFunction, double) - Static method in class umontreal.iro.lecuyer.functions.MathFunctionUtil

Returns the first derivative of the function func evaluated at x.

derivative(MathFunction, double, int) - Static method in class umontreal.iro.lecuyer.functions.MathFunctionUtil

Returns the nth derivative of function func evaluated at x.

derivative(double, int) - Method in interface umontreal.iro.lecuyer.functions.MathFunctionWithDerivative

Computes (or estimates) the nth derivative of the function at point x.

derivative(double) - Method in interface umontreal.iro.lecuyer.functions.MathFunctionWithFirstDerivative

Computes (or estimates) the first derivative of the function at point x.

derivative(double) - Method in class umontreal.iro.lecuyer.functions.Polynomial

derivative(double, int) - Method in class umontreal.iro.lecuyer.functions.Polynomial

derivative(double) - Method in class umontreal.iro.lecuyer.functions.PowerMathFunction

derivative(double) - Method in class umontreal.iro.lecuyer.functions.ShiftedMathFunction

derivative(double, int) - Method in class umontreal.iro.lecuyer.functions.ShiftedMathFunction

derivative(double) - Method in class umontreal.iro.lecuyer.functions.SquareMathFunction

derivative(double) - Method in class umontreal.iro.lecuyer.simevents.Continuous

This method should return the derivative of this variable with respect to time, at time t.

derivativeBSpline() - Method in class umontreal.iro.lecuyer.functionfit.BSpline

Returns the derivative B-spline object of the current variable.

derivativeBSpline(int) - Method in class umontreal.iro.lecuyer.functionfit.BSpline

Returns the ith derivative B-spline object of the current variable; i must be less than the degree of the original B-spline.

derivativePolynomial(int) - Method in class umontreal.iro.lecuyer.functions.Polynomial

Returns a polynomial corresponding to the nth derivative of this polynomial.

diff(IntArrayList, IntArrayList, int, int, int, int) - Static method in class umontreal.iro.lecuyer.gof.GofStat

Assumes that the real-valued observations U₀,..., U_N-1 contained in sortedData are already sorted in increasing order and computes the differences between the successive observations.

diff(DoubleArrayList, DoubleArrayList, int, int, double, double) - Static method in class umontreal.iro.lecuyer.gof.GofStat

Same as method diff(IntArrayList,IntArrayList,int,int,int,int), but for the continuous case.

digamma(double) - Static method in class umontreal.iro.lecuyer.util.Num

Returns the value of the logarithmic derivative of the Gamma function ψ(x) = Γ'(x)/Γ(x).

DigitalNet - Class in umontreal.iro.lecuyer.hups

This class provides the basic structures for storing and manipulating linear digital nets in base b, for an arbitrary base b >= 2.

DigitalNet() - Constructor for class umontreal.iro.lecuyer.hups.DigitalNet

Empty constructor.

DigitalNetBase2 - Class in umontreal.iro.lecuyer.hups

A special case of DigitalNet for the base b = 2.

DigitalNetBase2() - Constructor for class umontreal.iro.lecuyer.hups.DigitalNetBase2

DigitalNetBase2FromFile - Class in umontreal.iro.lecuyer.hups

This class allows us to read the parameters defining a digital net in base 2 either from a file, or from a URL address on the World Wide Web.

DigitalNetBase2FromFile(String, int, int, int) - Constructor for class umontreal.iro.lecuyer.hups.DigitalNetBase2FromFile

Constructs a digital net in base 2 after reading its parameters from file filename.

DigitalNetBase2FromFile(String, int) - Constructor for class umontreal.iro.lecuyer.hups.DigitalNetBase2FromFile

Same as DigitalNetBase2FromFile(filename, r, 31, s1) where s1 is the dimension and r is given in data file filename.

DigitalNetFromFile - Class in umontreal.iro.lecuyer.hups

This class allows us to read the parameters defining a digital net either from a file, or from a URL address on the World Wide Web.

DigitalNetFromFile(String, int, int, int) - Constructor for class umontreal.iro.lecuyer.hups.DigitalNetFromFile

Constructs a digital net after reading its parameters from file filename.

DigitalNetFromFile(String, int) - Constructor for class umontreal.iro.lecuyer.hups.DigitalNetFromFile

Same as DigitalNetFromFile(filename, r, r, s) where s is the dimension and r is given in data file filename.

DigitalSequence - Class in umontreal.iro.lecuyer.hups

This abstract class describes methods specific to digital sequences.

DigitalSequence() - Constructor for class umontreal.iro.lecuyer.hups.DigitalSequence

DigitalSequenceBase2 - Class in umontreal.iro.lecuyer.hups

This abstract class describes methods specific to digital sequences in base 2.

DigitalSequenceBase2() - Constructor for class umontreal.iro.lecuyer.hups.DigitalSequenceBase2

dimension() - Method in class umontreal.iro.lecuyer.util.BatchSort

dimension() - Method in interface umontreal.iro.lecuyer.util.MultiDimComparable

This method returns the number of dimensions for which the method compareTo can be called for this object.

dimension() - Method in interface umontreal.iro.lecuyer.util.MultiDimSort

Returns the greatest dimension used in the sort.

dimension() - Method in class umontreal.iro.lecuyer.util.OneDimSort

Returns the coordinate used in the sort.

dimension() - Method in class umontreal.iro.lecuyer.util.SplitSort

Maximum dimension used for the sort.

DirichletDist - Class in umontreal.iro.lecuyer.probdistmulti

Implements the abstract class ContinuousDistributionMulti for the Dirichlet distribution with parameters (α₁,...,α_d), α_i > 0.

DirichletDist(double[]) - Constructor for class umontreal.iro.lecuyer.probdistmulti.DirichletDist

DirichletGen - Class in umontreal.iro.lecuyer.randvarmulti

Extends RandomMultivariateGen for a Dirichlet distribution.

DirichletGen(RandomStream, double[]) - Constructor for class umontreal.iro.lecuyer.randvarmulti.DirichletGen

Constructs a new Dirichlet generator with parameters α_i+1 = alphas[i], for i = 0,…, k - 1, and the stream stream.

disableAutoCompletion() - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection

Disables auto completion option.

disableCustomLabels() - Method in class umontreal.iro.lecuyer.charts.Axis

Disables the use of custom labels for the current axis.

disableGrid() - Method in class umontreal.iro.lecuyer.charts.CategoryChart

Disables the background grid.

disableGrid() - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart

Disables the background grid.

disableGrid() - Method in class umontreal.iro.lecuyer.charts.XYChart

Disables the background grid.

DiscreteDistIntChart - Class in umontreal.iro.lecuyer.charts

This class provides tools to plot the mass function and the cumulative probability of a discrete probability distribution over the integers.

DiscreteDistIntChart(DiscreteDistributionInt) - Constructor for class umontreal.iro.lecuyer.charts.DiscreteDistIntChart

Constructor for a new DiscreteDistIntChart instance used to plot the probabilities of the discrete distribution dist over the integers.

DiscreteDistIntChart(DiscreteDistributionInt, int, int) - Constructor for class umontreal.iro.lecuyer.charts.DiscreteDistIntChart

Constructor for a new DiscreteDistIntChart instance used to plot the probabilities of the discrete distribution dist over the interval [a, b].

DiscreteDistribution - Class in umontreal.iro.lecuyer.probdist

Classes implementing discrete distributions over a finite set of real numbers should inherit from this class.

DiscreteDistribution(double[], double[], int) - Constructor for class umontreal.iro.lecuyer.probdist.DiscreteDistribution

Constructs a discrete distribution over the n values contained in array obs, with probabilities given in array prob.

DiscreteDistribution(int, double[], double[]) - Constructor for class umontreal.iro.lecuyer.probdist.DiscreteDistribution

Deprecated.

DiscreteDistribution(double[]) - Constructor for class umontreal.iro.lecuyer.probdist.DiscreteDistribution

Constructs a discrete distribution whose parameters are given in a single ordered array: params[0] contains n, the number of values to consider.

DiscreteDistributionInt - Class in umontreal.iro.lecuyer.probdist

Classes implementing discrete distributions over the integers should inherit from this class.

DiscreteDistributionInt() - Constructor for class umontreal.iro.lecuyer.probdist.DiscreteDistributionInt

DiscreteDistributionIntMulti - Class in umontreal.iro.lecuyer.probdistmulti

Classes implementing multi-dimensional discrete distributions over the integers should inherit from this class.

DiscreteDistributionIntMulti() - Constructor for class umontreal.iro.lecuyer.probdistmulti.DiscreteDistributionIntMulti

Distribution - Interface in umontreal.iro.lecuyer.probdist

This interface should be implemented by all classes supporting discrete and continuous distributions.

DistributionFactory - Class in umontreal.iro.lecuyer.probdist

This class implements a string API for the package probdist.

DMatrix - Class in umontreal.iro.lecuyer.util

This class implements a few methods for matrix calculations with double numbers.

DMatrix(int, int) - Constructor for class umontreal.iro.lecuyer.util.DMatrix

Creates a new DMatrix with r rows and c columns.

DMatrix(double[][], int, int) - Constructor for class umontreal.iro.lecuyer.util.DMatrix

Creates a new DMatrix with r rows and c columns using the data in data.

DMatrix(DMatrix) - Constructor for class umontreal.iro.lecuyer.util.DMatrix

Copy constructor.

DoubleArrayComparator - Class in umontreal.iro.lecuyer.util

An implementation of Comparator which compares two double arrays by comparing their i-th element, where i is given in the constructor.

DoubleArrayComparator(int) - Constructor for class umontreal.iro.lecuyer.util.DoubleArrayComparator

Constructs a comparator, where i is the index used for the comparisons.

DoublyLinked - Class in umontreal.iro.lecuyer.simevents.eventlist

An implementation of EventList using a doubly linked linear list.

DoublyLinked() - Constructor for class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked

drawCdf(ContinuousDistribution, double, double, int, String) - Static method in class umontreal.iro.lecuyer.gof.GofFormat

Formats data to plot the graph of the distribution function F over the interval [a, b], and returns the result as a String.

drawDensity(ContinuousDistribution, double, double, int, String) - Static method in class umontreal.iro.lecuyer.gof.GofFormat

Formats data to plot the graph of the density f (x) over the interval [a, b], and returns the result as a String.

drawItem(Graphics2D, XYItemRendererState, Rectangle2D, PlotRenderingInfo, XYPlot, ValueAxis, ValueAxis, XYDataset, int, int, CrosshairState, int) - Method in class umontreal.iro.lecuyer.charts.EmpiricalRenderer

Draws the visual representation of a single data item.

DSOLProcessSimulator - Class in umontreal.iro.lecuyer.simprocs

Represents a simulation process whose actions method is interpreted by the DSOL interpreter, written by Peter Jacobs (http://www.tbm.tudelft.nl/webstaf/peterja/index.htm).

DSOLProcessSimulator() - Constructor for class umontreal.iro.lecuyer.simprocs.DSOLProcessSimulator

Constructs a new DSOLProcessSimulator variable without initialization.

E

E(double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat

Same as E (0, 6, x).

E(int, double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat

Same as E (fieldwidth, 6, x).

E(int, int, double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat

Formats a double-precision number x like %E in C printf.

e(double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat

Same as e (0, 6, x).

e(int, double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat

Same as e (fieldwidth, 6, x).

e(int, int, double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat

The same as E, except that `e' is used as the exponent character instead of `E'.

EBASE - Static variable in class umontreal.iro.lecuyer.util.Num

The constant e.

EmpiricalChart - Class in umontreal.iro.lecuyer.charts

This class provides additional tools to create and manage empirical plots.

EmpiricalChart() - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalChart

Initializes a new EmpiricalChart instance with an empty data set.

EmpiricalChart(String, String, String, double[]...) - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalChart

Initializes a new EmpiricalChart instance with data data.

EmpiricalChart(String, String, String, double[], int) - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalChart

Initializes a new EmpiricalChart instance with a set of points data.

EmpiricalChart(String, String, String, DoubleArrayList...) - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalChart

Similar to the above constructor, but with DoubleArrayList.

EmpiricalChart(String, String, String, XYSeriesCollection) - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalChart

Initializes a new EmpiricalChart instance with data data.

EmpiricalDist - Class in umontreal.iro.lecuyer.probdist

Extends DiscreteDistribution to an empirical distribution function, based on the observations X₍₁₎,..., X_(n) (sorted by increasing order).

EmpiricalDist(double[]) - Constructor for class umontreal.iro.lecuyer.probdist.EmpiricalDist

Constructs a new empirical distribution using all the observations stored in obs, and which are assumed to have been sorted in increasing numerical order.

EmpiricalDist(Reader) - Constructor for class umontreal.iro.lecuyer.probdist.EmpiricalDist

Constructs a new empirical distribution using the observations read from the reader in.

EmpiricalRenderer - Class in umontreal.iro.lecuyer.charts

A renderer that draws horizontal lines between points and/or draws shapes at each data point to provide an empirical style chart.

EmpiricalRenderer() - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalRenderer

Creates a new renderer.

EmpiricalRenderer(XYToolTipGenerator, XYURLGenerator) - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalRenderer

Creates a new renderer with selected tool tip and url generators.

EmpiricalSeriesCollection - Class in umontreal.iro.lecuyer.charts

Stores data used in a EmpiricalChart.

EmpiricalSeriesCollection() - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalSeriesCollection

Creates a new EmpiricalSeriesCollection instance with empty dataset.

EmpiricalSeriesCollection(double[]...) - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalSeriesCollection

Creates a new EmpiricalSeriesCollection instance with default parameters and given data series.

EmpiricalSeriesCollection(double[], int) - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalSeriesCollection

Creates a new EmpiricalSeriesCollection instance with default parameters and a given series data.

EmpiricalSeriesCollection(DoubleArrayList...) - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalSeriesCollection

Creates a new EmpiricalSeriesCollection instance with default parameters and given data.

EmpiricalSeriesCollection(XYSeriesCollection) - Constructor for class umontreal.iro.lecuyer.charts.EmpiricalSeriesCollection

Creates a new EmpiricalSeriesCollection instance with default parameters and given data series.

EmptyRandomization - Class in umontreal.iro.lecuyer.hups

This class implements an empty PointSetRandomization.

EmptyRandomization() - Constructor for class umontreal.iro.lecuyer.hups.EmptyRandomization

enableAutoCompletion() - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection

Enables the auto completion option.

enableCustomLabels() - Method in class umontreal.iro.lecuyer.charts.Axis

Enables the use of custom labels for the current axis.

enableGrid(double, double) - Method in class umontreal.iro.lecuyer.charts.CategoryChart

Puts a grid on the background.

enableGrid(double, double) - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart

Puts grid on the background.

enableGrid(double, double) - Method in class umontreal.iro.lecuyer.charts.XYChart

Puts a grid on the background.

enlarge(int, boolean) - Method in class umontreal.iro.lecuyer.util.BitVector

Resizes the BitVector so that its length is equal to size.

enlarge(int) - Method in class umontreal.iro.lecuyer.util.BitVector

Resizes the BitVector so that its length is equal to size.

EPSILON - Static variable in class umontreal.iro.lecuyer.probdist.DiscreteDistributionInt

Environment variable that determines what probability terms can be considered as negligible when building precomputed tables for distribution and mass functions.

EPSILONAD - Static variable in class umontreal.iro.lecuyer.gof.GofStat

EPSILONP - Static variable in class umontreal.iro.lecuyer.gof.GofFormat

Environment variable used in formatp0 to determine which p-values are too close to 0 or 1 to be printed explicitly.

equals(Object) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset

Tests this dataset for equality with an arbitrary object.

equals(Object) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

equals(BitMatrix) - Method in class umontreal.iro.lecuyer.util.BitMatrix

Verifies that this and that are strictly identical.

equals(BitVector) - Method in class umontreal.iro.lecuyer.util.BitVector

Verifies if two BitVector's have the same length and the same data.

eraseOriginalGeneratorMatrices() - Method in class umontreal.iro.lecuyer.hups.DigitalNet

Erases the original generator matrices and replaces them by the current ones.

ErasePermutations() - Method in class umontreal.iro.lecuyer.hups.HaltonSequence

Erases the Faure permutations: from now on, the digits will not be Faure permuted.

ErasePermutations() - Method in class umontreal.iro.lecuyer.hups.HammersleyPointSet

Erases the Faure permutations: from now on, the digits will not be Faure permuted.

erf(double) - Static method in class umontreal.iro.lecuyer.util.Num

Returns the value of erf(x), the error function.

erfc(double) - Static method in class umontreal.iro.lecuyer.util.Num

Returns the value of erfc(x), the complementary error function.

ErlangConvolutionGen - Class in umontreal.iro.lecuyer.randvar

This class implements Erlang random variate generators using the convolution method.

ErlangConvolutionGen(RandomStream, int, double) - Constructor for class umontreal.iro.lecuyer.randvar.ErlangConvolutionGen

Creates an Erlang random variate generator with parameters k and λ = lambda, using stream s.

ErlangConvolutionGen(RandomStream, int) - Constructor for class umontreal.iro.lecuyer.randvar.ErlangConvolutionGen

Creates an Erlang random variate generator with parameters k and λ = 1, using stream s.

ErlangConvolutionGen(RandomStream, ErlangDist) - Constructor for class umontreal.iro.lecuyer.randvar.ErlangConvolutionGen

Creates a new generator for the distribution dist and stream s.

ErlangDist - Class in umontreal.iro.lecuyer.probdist

Extends the class GammaDist for the special case of the Erlang distribution with shape parameter k > 0 and scale parameter λ > 0.

ErlangDist(int) - Constructor for class umontreal.iro.lecuyer.probdist.ErlangDist

Constructs a ErlangDist object with parameters k = k and λ = 1.

ErlangDist(int, double) - Constructor for class umontreal.iro.lecuyer.probdist.ErlangDist

Constructs a ErlangDist object with parameters k = k and λ = lambda.

ErlangGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators for the Erlang distribution with parameters k > 0 and λ > 0.

ErlangGen(RandomStream, int, double) - Constructor for class umontreal.iro.lecuyer.randvar.ErlangGen

Creates an Erlang random variate generator with parameters k and λ = lambda, using stream s.

ErlangGen(RandomStream, int) - Constructor for class umontreal.iro.lecuyer.randvar.ErlangGen

Creates an Erlang random variate generator with parameters k and λ = 1, using stream s.

ErlangGen(RandomStream, ErlangDist) - Constructor for class umontreal.iro.lecuyer.randvar.ErlangGen

Creates a new generator for the distribution dist and stream s.

EULER - Static variable in class umontreal.iro.lecuyer.util.Num

The Euler-Mascheroni constant.

evalCheby(double[], int, double) - Static method in class umontreal.iro.lecuyer.util.Num

Evaluates a series of Chebyshev polynomials T_j at x over the basic interval [- 1, 1].

evalChebyStar(double[], int, double) - Static method in class umontreal.iro.lecuyer.util.Num

Evaluates a series of shifted Chebyshev polynomials T_j^* at x over the basic interval [0, 1].

evalPoly(int, double[], double[], double) - Static method in class umontreal.iro.lecuyer.util.Misc

Given n, X and C as described in interpol(n, X, Y, C), this function returns the value of the interpolating polynomial P(z) evaluated at z (see eq.

evaluate(double) - Method in class umontreal.iro.lecuyer.functionfit.BSpline

evaluate(double) - Method in class umontreal.iro.lecuyer.functionfit.SmoothingCubicSpline

Evaluates and returns the value of the spline at z.

evaluate(double) - Method in class umontreal.iro.lecuyer.functions.AverageMathFunction

evaluate(double) - Method in class umontreal.iro.lecuyer.functions.IdentityMathFunction

evaluate(double) - Method in interface umontreal.iro.lecuyer.functions.MathFunction

Returns the value of the function evaluated at x.

evaluate(double) - Method in class umontreal.iro.lecuyer.functions.PiecewiseConstantFunction

evaluate(double) - Method in class umontreal.iro.lecuyer.functions.Polynomial

evaluate(double) - Method in class umontreal.iro.lecuyer.functions.PowerMathFunction

evaluate(double) - Method in class umontreal.iro.lecuyer.functions.ShiftedMathFunction

evaluate(double) - Method in class umontreal.iro.lecuyer.functions.SqrtMathFunction

evaluate(double) - Method in class umontreal.iro.lecuyer.functions.SquareMathFunction

evaluate(double...) - Method in interface umontreal.iro.lecuyer.util.MultivariateFunction

Computes the function g(x) for the vector x.

evaluate(double...) - Method in class umontreal.iro.lecuyer.util.RatioFunction

evaluateGradient(int, double...) - Method in interface umontreal.iro.lecuyer.util.MultivariateFunction

Computes ∂g(x)/∂x_i, the derivative of g(x) with respect to x_i.

evaluateGradient(int, double...) - Method in class umontreal.iro.lecuyer.util.RatioFunction

evalX(double) - Method in class umontreal.iro.lecuyer.functionfit.BSpline

evalY(double) - Method in class umontreal.iro.lecuyer.functionfit.BSpline

Event - Class in umontreal.iro.lecuyer.simevents

This abstract class provides event scheduling tools.

Event() - Constructor for class umontreal.iro.lecuyer.simevents.Event

Constructs a new event instance, which can be placed afterwards into the event list of the default simulator.

Event(Simulator) - Constructor for class umontreal.iro.lecuyer.simevents.Event

Construct a new event instance associated with the given simulator.

EventList - Interface in umontreal.iro.lecuyer.simevents.eventlist

An interface for implementations of event lists.

EXECUTING - Static variable in class umontreal.iro.lecuyer.simprocs.SimProcess

The process is the one currently executing its actions method.

ExponentialDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the exponential distribution with mean 1/λ where λ > 0.

ExponentialDist() - Constructor for class umontreal.iro.lecuyer.probdist.ExponentialDist

Constructs an ExponentialDist object with parameter λ = 1.

ExponentialDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.ExponentialDist

Constructs an ExponentialDist object with parameter λ = lambda.

ExponentialDistFromMean - Class in umontreal.iro.lecuyer.probdist

Extends the ExponentialDist class with a constructor accepting as argument the mean 1/λ instead of the rate λ.

ExponentialDistFromMean(double) - Constructor for class umontreal.iro.lecuyer.probdist.ExponentialDistFromMean

Constructs a new exponential distribution with mean mean.

ExponentialGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators for the exponential distribution.

ExponentialGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.ExponentialGen

Creates an exponential random variate generator with parameter λ = lambda, using stream s.

ExponentialGen(RandomStream, ExponentialDist) - Constructor for class umontreal.iro.lecuyer.randvar.ExponentialGen

Creates a new generator for the exponential distribution dist and stream s.

extendSequence(int) - Method in class umontreal.iro.lecuyer.hups.DigitalSequence

Increases the number of points to n = b^k from now on.

extendSequence(int) - Method in class umontreal.iro.lecuyer.hups.DigitalSequenceBase2

Increases the number of points to n = 2^k from now on.

extendSequence(int) - Method in class umontreal.iro.lecuyer.hups.FaureSequence

extendSequence(int) - Method in class umontreal.iro.lecuyer.hups.NiedSequenceBase2

extendSequence(int) - Method in class umontreal.iro.lecuyer.hups.NiedXingSequenceBase2

extendSequence(int) - Method in class umontreal.iro.lecuyer.hups.SobolSequence

ExtremeValueDist - Class in umontreal.iro.lecuyer.probdist

Deprecated.

ExtremeValueDist() - Constructor for class umontreal.iro.lecuyer.probdist.ExtremeValueDist

Deprecated.

Constructs a ExtremeValueDist object with parameters α = 0 and λ = 1.

ExtremeValueDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.ExtremeValueDist

Deprecated.

Constructs a ExtremeValueDist object with parameters α = alpha and λ = lambda.

ExtremeValueGen - Class in umontreal.iro.lecuyer.randvar

Deprecated.

ExtremeValueGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.ExtremeValueGen

Deprecated.

Creates an extreme value random variate generator with parameters α = alpha and λ = lambda, using stream s.

ExtremeValueGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.ExtremeValueGen

Deprecated.

Creates an extreme value random variate generator with parameters α = 0 and λ = 1, using stream s.

ExtremeValueGen(RandomStream, ExtremeValueDist) - Constructor for class umontreal.iro.lecuyer.randvar.ExtremeValueGen

Deprecated.

Creates a new generator object for distribution dist and stream s.

F

f(double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat

Same as f (0, 6, x).

f(int, double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat

Same as f (fieldwidth, 6, x).

f(int, int, double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat

Formats the double-precision x into a string like %f in C printf.

F2NL607 - Class in umontreal.iro.lecuyer.rng

Implements the RandomStream interface by using as a backbone generator the combination of the WELL607 proposed in (and implemented in WELL607) with a nonlinear generator.

F2NL607() - Constructor for class umontreal.iro.lecuyer.rng.F2NL607

Constructs a new stream, initializing it at its beginning.

F2NL607(String) - Constructor for class umontreal.iro.lecuyer.rng.F2NL607

Constructs a new stream with the identifier name (used in the toString method).

F2wCycleBasedLFSR - Class in umontreal.iro.lecuyer.hups

This class creates a point set based upon a linear feedback shift register sequence.

F2wCycleBasedLFSR(int, int, int, int, int, int[], int[]) - Constructor for class umontreal.iro.lecuyer.hups.F2wCycleBasedLFSR

Constructs a point set with 2^rw points.

F2wCycleBasedLFSR(String, int) - Constructor for class umontreal.iro.lecuyer.hups.F2wCycleBasedLFSR

Constructs a point set after reading its parameters from file filename; the parameters are located at line numbered no of filename.

F2wCycleBasedPolyLCG - Class in umontreal.iro.lecuyer.hups

This class creates a point set based upon a linear congruential sequence in the finite field F_2^w[z]/P(z).

F2wCycleBasedPolyLCG(int, int, int, int, int, int[], int[]) - Constructor for class umontreal.iro.lecuyer.hups.F2wCycleBasedPolyLCG

Constructs a point set with 2^rw points.

F2wCycleBasedPolyLCG(String, int) - Constructor for class umontreal.iro.lecuyer.hups.F2wCycleBasedPolyLCG

Constructs a point set after reading its parameters from file filename; the parameters are located at line numbered no of filename.

F2wNetLFSR - Class in umontreal.iro.lecuyer.hups

This class implements a digital net in base 2 starting from a linear feedback shift register generator.

F2wNetLFSR(int, int, int, int, int, int[], int[], int) - Constructor for class umontreal.iro.lecuyer.hups.F2wNetLFSR

Constructs a point set with 2^rw points.

F2wNetLFSR(String, int, int) - Constructor for class umontreal.iro.lecuyer.hups.F2wNetLFSR

Constructs a point set after reading its parameters from file filename; the parameters are located at line numbered no of filename.

F2wNetPolyLCG - Class in umontreal.iro.lecuyer.hups

This class implements a digital net in base 2 starting from a polynomial LCG in F_2^w[z]/P(z).

F2wNetPolyLCG(int, int, int, int, int, int, int[], int[], int) - Constructor for class umontreal.iro.lecuyer.hups.F2wNetPolyLCG

Constructs a point set with 2^rw points.

F2wNetPolyLCG(String, int, int) - Constructor for class umontreal.iro.lecuyer.hups.F2wNetPolyLCG

Constructs a point set after reading its parameters from file filename; the parameters are located at line numbered no of filename.

F2wStructure - Class in umontreal.iro.lecuyer.hups

This class implements methods and fields needed by the classes F2wNetLFSR, F2wNetPolyLCG, F2wCycleBasedLFSR and F2wCycleBasedPolyLCG.

factorial(int) - Static method in class umontreal.iro.lecuyer.util.Num

Returns the value of factorial n.

FatigueLifeDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the fatigue life distribution with location parameter μ, scale parameter β and shape parameter γ.

FatigueLifeDist(double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.FatigueLifeDist

Constructs a fatigue life distribution with parameters μ, β and γ.

FatigueLifeGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators for the fatigue life distribution with location parameter μ, scale parameter β and shape parameter γ.

FatigueLifeGen(RandomStream, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.FatigueLifeGen

Creates a fatigue life random variate generator with parameters μ = mu, β = beta and γ = gamma, using stream s.

FatigueLifeGen(RandomStream, FatigueLifeDist) - Constructor for class umontreal.iro.lecuyer.randvar.FatigueLifeGen

Creates a new generator for the distribution dist, using stream s.

FaureSequence - Class in umontreal.iro.lecuyer.hups

This class implements digital nets or digital sequences formed by the first n = b^k points of the Faure sequence in base b.

FaureSequence(int, int, int, int, int) - Constructor for class umontreal.iro.lecuyer.hups.FaureSequence

Constructs a digital net in base b, with n = b^k points and w output digits, in dim dimensions.

FaureSequence(int, int) - Constructor for class umontreal.iro.lecuyer.hups.FaureSequence

Same as FaureSequence(b, k, w, w, dim) with base b equal to the smallest prime larger or equal to dim, and with at least n points.

FBar - Class in umontreal.iro.lecuyer.gof

WARNING: All methods in this class are deprecated, except the method scan.

FDist - Class in umontreal.iro.lecuyer.gof

WARNING: Most methods in this class are deprecated.

findClass(String) - Method in class umontreal.iro.lecuyer.util.ClassFinder

Tries to find the class corresponding to the simple name name.

finiteCenteredDifferenceDerivative(MathFunction, double, double) - Static method in class umontreal.iro.lecuyer.functions.MathFunctionUtil

Returns (f (x + h) - f (x - h))/(2h), an estimate of the first derivative of f (x) using centered differences.

finiteCenteredDifferenceDerivative(MathFunction, double, int, double) - Static method in class umontreal.iro.lecuyer.functions.MathFunctionUtil

Computes and returns an estimate of the nth derivative of the function f (x) using finite centered differences.

finiteDifferenceDerivative(MathFunction, double, int, double) - Static method in class umontreal.iro.lecuyer.functions.MathFunctionUtil

Computes and returns an estimate of the nth derivative of the function f (x).

FisherFDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the Fisher F distribution with n and m degrees of freedom, where n and m are positive integers.

FisherFDist(int, int) - Constructor for class umontreal.iro.lecuyer.probdist.FisherFDist

Constructs a Fisher F distribution with n and m degrees of freedom.

FisherFGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators for the Fisher F distribution with n and m degrees of freedom, where n and m are positive integers.

FisherFGen(RandomStream, int, int) - Constructor for class umontreal.iro.lecuyer.randvar.FisherFGen

Creates a Fisher F random variate generator with n and m degrees of freedom, using stream s.

FisherFGen(RandomStream, FisherFDist) - Constructor for class umontreal.iro.lecuyer.randvar.FisherFGen

Creates a new generator for the distribution dist, using stream s.

FoldedNormalDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the folded normal distribution with parameters μ >= 0 and σ > 0.

FoldedNormalDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.FoldedNormalDist

Constructs a FoldedNormalDist object with parameters μ = mu and σ = sigma.

FoldedNormalGen - Class in umontreal.iro.lecuyer.randvar

This class implements methods for generating random variates from the folded normal distribution with parameters μ >= 0 and σ > 0.

FoldedNormalGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.FoldedNormalGen

Creates a new folded normal generator with parameters μ = mu and σ = sigma, using stream s.

FoldedNormalGen(RandomStream, FoldedNormalDist) - Constructor for class umontreal.iro.lecuyer.randvar.FoldedNormalGen

Creates a new generator for the distribution dist, using stream s.

format() - Method in class umontreal.iro.lecuyer.util.AbstractChrono

Converts the CPU time used by the program since its last call to init for this AbstractChrono to a String in the HH:MM:SS.xx format.

format(double) - Static method in class umontreal.iro.lecuyer.util.AbstractChrono

Converts the time time, given in seconds, to a String in the HH:MM:SS.xx format.

format(long) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat

Same as d (0, 1, x).

format(int, long) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat

Converts a long integer to a String with a minimum length of fieldwidth, the result is right-padded with spaces if necessary but it is not truncated.

format(int, int, int, double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat

Returns a String containing x.

format(Locale, int, int, int, double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat

This method is equivalent to format, except it formats the given value for the locale locale.

format(int[], int, int, int, int) - Static method in class umontreal.iro.lecuyer.util.TableFormat

Formats a String containing the elements n1 to n2 (inclusive) of table V, k elements per line, p positions per element.

format(double[], int, int, int, int, int, int) - Static method in class umontreal.iro.lecuyer.util.TableFormat

Similar to the previous method, but for an array of double's.

format(int[][], int, int, int, int, int, int, int, String) - Static method in class umontreal.iro.lecuyer.util.TableFormat

Formats a submatrix of integers.

format(double[][], int, int, int, int, int, int, int, String) - Static method in class umontreal.iro.lecuyer.util.TableFormat

Formats the submatrix with lines i1 <= i <= i2 and columns j1 <= j <= j2 of the matrix Mat, using the formatting style style.

formatActiveTests(int, double[], double[]) - Static method in class umontreal.iro.lecuyer.gof.GofFormat

Gets the p-values of the active EDF test statistics, which are in activeTests.

formatBase(int, long) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat

Same as formatBase (0, b, x).

formatBase(int, int, long) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat

Converts the integer x to a String representation in base b.

formatBase(int, int, int, double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat

Converts x to a String representation in base b using formatting similar to the f methods.

formatChi2(int, int, double) - Static method in class umontreal.iro.lecuyer.gof.GofFormat

Computes the p-value of the chi-square statistic chi2 for a test with k intervals.

formatCINormal(double, int) - Method in class umontreal.iro.lecuyer.stat.Tally

.

formatCINormal(double) - Method in class umontreal.iro.lecuyer.stat.Tally

Equivalent to formatCINormal (level, 3).

formatCIStudent(double, int) - Method in class umontreal.iro.lecuyer.stat.Tally

.

formatCIStudent(double) - Method in class umontreal.iro.lecuyer.stat.Tally

Equivalent to formatCIStudent (level, 3).

formatCIVarianceChi2(double, int) - Method in class umontreal.iro.lecuyer.stat.Tally

.

formatConfidenceIntervalNormal(double, int) - Method in class umontreal.iro.lecuyer.stat.Tally

Deprecated.

formatConfidenceIntervalNormal(double) - Method in class umontreal.iro.lecuyer.stat.Tally

Deprecated.

formatConfidenceIntervalStudent(double, int) - Method in class umontreal.iro.lecuyer.stat.Tally

Deprecated.

formatConfidenceIntervalStudent(double) - Method in class umontreal.iro.lecuyer.stat.Tally

Deprecated.

formatKS(int, double, double, double) - Static method in class umontreal.iro.lecuyer.gof.GofFormat

Computes the p-values of the three Kolmogorov-Smirnov statistics D_N⁺, D_N^-, and D_N, whose values are in dp, dm, d, respectively, assuming a sample of size n.

formatKS(DoubleArrayList, ContinuousDistribution) - Static method in class umontreal.iro.lecuyer.gof.GofFormat

Computes the KS test statistics to compare the empirical distribution of the observations in data with the theoretical distribution dist and formats the results.

formatKSJumpOne(int, double, double) - Static method in class umontreal.iro.lecuyer.gof.GofFormat

Similar to formatKS, but for the KS statistic D_N⁺(a).

formatKSJumpOne(DoubleArrayList, ContinuousDistribution, double) - Static method in class umontreal.iro.lecuyer.gof.GofFormat

Similar to formatKS, but for D_N⁺(a).

formatp0(double) - Static method in class umontreal.iro.lecuyer.gof.GofFormat

Returns the significance level (or p-value) p of a test, in the format ``1 - p'' if p is close to 1, and p otherwise.

formatp1(double) - Static method in class umontreal.iro.lecuyer.gof.GofFormat

Returns the string ``Significance level of test : '', then calls formatp0 to print p, and adds the marker ``****'' if p is considered suspect (uses the environment variable RSUSPECTP for this).

formatp2(double, double) - Static method in class umontreal.iro.lecuyer.gof.GofFormat

Returns x on a single line, then go to the next line and calls formatp1.

formatp3(String, double, double) - Static method in class umontreal.iro.lecuyer.gof.GofFormat

Formats the test statistic x for a test named testName with p-value p.

formatPoints() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet

formatPoints() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSetBase2

formatPoints() - Method in class umontreal.iro.lecuyer.hups.PointSet

Same as invoking formatPoints(n, d) with n and d equal to the number of points and the dimension of this object, respectively.

formatPoints(int, int) - Method in class umontreal.iro.lecuyer.hups.PointSet

Formats a string that displays the same information as returned by toString, together with the first d coordinates of the first n points.

formatPoints(PointSetIterator) - Method in class umontreal.iro.lecuyer.hups.PointSet

Same as invoking formatPoints(iter, n, d) with n and d equal to the number of points and the dimension, respectively.

formatPoints(PointSetIterator, int, int) - Method in class umontreal.iro.lecuyer.hups.PointSet

Same as invoking formatPoints(n, d), but prints the points by calling iter repeatedly.

formatPointsBase(int) - Method in class umontreal.iro.lecuyer.hups.PointSet

Similar to formatPoints(), but the points coordinates are printed in base b.

formatPointsBase(int, int, int) - Method in class umontreal.iro.lecuyer.hups.PointSet

Similar to formatPoints(n, d), but the points coordinates are printed in base b.

formatPointsBase(PointSetIterator, int) - Method in class umontreal.iro.lecuyer.hups.PointSet

Similar to formatPoints(iter), but the points coordinates are printed in base b.

formatPointsBase(PointSetIterator, int, int, int) - Method in class umontreal.iro.lecuyer.hups.PointSet

Similar to formatPoints(iter, n, d), but the points coordinates are printed in base b.

formatPointsNumbered() - Method in class umontreal.iro.lecuyer.hups.PointSet

Same as invoking formatPointsNumbered(n, d) with n and d equal to the number of points and the dimension, respectively.

formatPointsNumbered(int, int) - Method in class umontreal.iro.lecuyer.hups.PointSet

Same as invoking formatPoints(n,d), except that the points are numbered.

formatState() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator

formatState() - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase

Deprecated.

formatStateFull() - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase

Deprecated.

formatWithError(int, int, int, int, double, double, String[]) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat

Stores a string containing x into res[0], and a string containing error into res[1], both strings being formatted with the same notation.

formatWithError(int, int, int, double, double, String[]) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat

Stores a string containing x into res[0], and a string containing error into res[1], both strings being formatted with the same notation.

formatWithError(Locale, int, int, int, int, double, double, String[]) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat

This method is equivalent to formatWithError, except that it formats the given value and error for the locale locale.

formatWithError(Locale, int, int, int, double, double, String[]) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat

This method is equivalent to formatWithError, except that it formats the given value and error for the locale locale.

FrechetDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the Fréchet distribution, with location parameter δ, scale parameter β > 0, and shape parameter α > 0, where we use the notation z = (x - δ)/β.

FrechetDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.FrechetDist

Constructor for the standard Fréchet distribution with parameters β = 1 and δ = 0.

FrechetDist(double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.FrechetDist

Constructs a FrechetDist object with parameters α = alpha, β = beta and δ = delta.

FrechetGen - Class in umontreal.iro.lecuyer.randvar

This class implements methods for generating random variates from the Fréchet distribution, with location parameter δ, scale parameter β > 0, and shape parameter α > 0, where we use the notation z = (x - δ)/β.

FrechetGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.FrechetGen

Creates a Fréchet random number generator with α = alpha, β = 1 and δ = 0 using stream s.

FrechetGen(RandomStream, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.FrechetGen

Creates a Fréchet random number generator with parameters α = alpha, β = beta and δ = delta using stream s.

FrechetGen(RandomStream, FrechetDist) - Constructor for class umontreal.iro.lecuyer.randvar.FrechetGen

Creates a new generator for the Fréchet distribution dist and stream s.

fromCSV(String) - Static method in class umontreal.iro.lecuyer.charts.PlotFormat

Parses data according to the standard CSV format and stores the extracted values in the returned table.

fromCustomizedFormat(String, String, String) - Static method in class umontreal.iro.lecuyer.charts.PlotFormat

Parses data according to a user defined format and stores the extracted values in the returned table.

fromGNUPlot(String) - Static method in class umontreal.iro.lecuyer.charts.PlotFormat

Parses data according to the standard GNUPlot format and stores the extracted values in the returned table.

G

G(double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat

Same as G (0, 6, x).

G(int, double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat

Same as G (fieldwidth, 6, x).

G(int, int, double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat

Formats the double-precision x into a string like %G in C printf.

g(double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat

Same as g (0, 6, x).

g(int, double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat

Same as g (fieldwidth, 6, x).

g(int, int, double) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat

The same as G, except that `e' is used in the scientific notation.

GammaAcceptanceRejectionGen - Class in umontreal.iro.lecuyer.randvar

This class implements gamma random variate generators using a method that combines acceptance-rejection with acceptance-complement.

GammaAcceptanceRejectionGen(RandomStream, RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.GammaAcceptanceRejectionGen

Creates a gamma random variate generator with parameters α = alpha and λ = lambda, using main stream s and auxiliary stream aux.

GammaAcceptanceRejectionGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.GammaAcceptanceRejectionGen

Creates a gamma random variate generator with parameters α = alpha and λ = lambda, using stream s.

GammaAcceptanceRejectionGen(RandomStream, RandomStream, GammaDist) - Constructor for class umontreal.iro.lecuyer.randvar.GammaAcceptanceRejectionGen

Creates a new generator object for the gamma distribution dist, using main stream s and auxiliary stream aux.

GammaAcceptanceRejectionGen(RandomStream, GammaDist) - Constructor for class umontreal.iro.lecuyer.randvar.GammaAcceptanceRejectionGen

Creates a new generator object for the gamma distribution dist and stream s for both the main and auxiliary stream.

GammaDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the gamma distribution with shape parameter α > 0 and scale parameter λ > 0.

GammaDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.GammaDist

Constructs a GammaDist object with parameters α = alpha and λ = 1.

GammaDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.GammaDist

Constructs a GammaDist object with parameters α = alpha and λ = lambda.

GammaDist(double, double, int) - Constructor for class umontreal.iro.lecuyer.probdist.GammaDist

Constructs a GammaDist object with parameters α = alpha and λ = lambda, and approximations of roughly d decimal digits of precision when computing functions.

GammaDistFromMoments - Class in umontreal.iro.lecuyer.probdist

Extends the GammaDist distribution with constructors accepting the mean μ and variance σ² as arguments instead of a shape parameter α and a scale parameter λ.

GammaDistFromMoments(double, double, int) - Constructor for class umontreal.iro.lecuyer.probdist.GammaDistFromMoments

Constructs a gamma distribution with mean mean, variance var, and d decimal of precision.

GammaDistFromMoments(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.GammaDistFromMoments

Constructs a gamma distribution with mean mean, and variance var.

GammaGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators for the gamma distribution.

GammaGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.GammaGen

Creates a gamma random variate generator with parameters α = alpha and λ = lambda, using stream s.

GammaGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.GammaGen

Creates a gamma random variate generator with parameters α = alpha and λ = 1, using stream s.

GammaGen(RandomStream, GammaDist) - Constructor for class umontreal.iro.lecuyer.randvar.GammaGen

Creates a new generator object for the gamma distribution dist and stream s.

GammaProcess - Class in umontreal.iro.lecuyer.stochprocess

This class represents a gamma process {S(t) = G(t;μ, ν) : t >= 0} with mean parameter μ and variance parameter ν.

GammaProcess(double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.GammaProcess

Constructs a new GammaProcess with parameters μ = mu, ν = nu and initial value S(t₀) = s0.

GammaProcess(double, double, double, GammaGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.GammaProcess

Constructs a new GammaProcess with parameters μ = mu, ν = nu and initial value S(t₀) = s0.

GammaProcessBridge - Class in umontreal.iro.lecuyer.stochprocess

This class represents a gamma process {S(t) = G(t;μ, ν) : t >= 0} with mean parameter μ and variance parameter ν, sampled using the gamma bridge method (see for example).

GammaProcessBridge(double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.GammaProcessBridge

Constructs a new GammaProcessBridge with parameters μ = mu, ν = nu and initial value S(t₀) = s0.

GammaProcessBridge(double, double, double, GammaGen, BetaGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.GammaProcessBridge

Constructs a new GammaProcessBridge.

GammaProcessPCA - Class in umontreal.iro.lecuyer.stochprocess

Represents a gamma process sampled using the principal component analysis (PCA).

GammaProcessPCA(double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.GammaProcessPCA

Constructs a new GammaProcessPCA with parameters μ = mu, ν = nu and initial value S(t₀) = s0.

GammaProcessPCA(double, double, double, GammaGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.GammaProcessPCA

Constructs a new GammaProcessPCA with parameters μ = mu, ν = nu and initial value S(t₀) = s0.

GammaProcessPCABridge - Class in umontreal.iro.lecuyer.stochprocess

Same as GammaProcessPCA, but the generated uniforms correspond to a bridge transformation of the BrownianMotionPCA instead of a sequential transformation.

GammaProcessPCABridge(double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.GammaProcessPCABridge

Constructs a new GammaProcessPCABridge with parameters μ = mu, ν = nu and initial value S(t₀) = s0.

GammaProcessPCASymmetricalBridge - Class in umontreal.iro.lecuyer.stochprocess

Same as GammaProcessPCABridge, but uses the fast inversion method for the symmetrical beta distribution, proposed by L'Ecuyer and Simard, to accelerate the generation of the beta random variables.

GammaProcessPCASymmetricalBridge(double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.GammaProcessPCASymmetricalBridge

Constructs a new GammaProcessPCASymmetricalBridge with parameters μ = mu, ν = nu and initial value S(t₀) = s0.

GammaProcessSymmetricalBridge - Class in umontreal.iro.lecuyer.stochprocess

This class differs from GammaProcessBridge only in that it requires the number of interval of the path to be a power of 2 and of equal size.

GammaProcessSymmetricalBridge(double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.GammaProcessSymmetricalBridge

Constructs a new GammaProcessSymmetricalBridge with parameters μ = mu, ν = nu and initial value S(t₀) = s0.

GammaProcessSymmetricalBridge(double, double, double, GammaGen, BetaSymmetricalGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.GammaProcessSymmetricalBridge

Constructs a new GammaProcessSymmetricalBridge with parameters μ = mu, ν = nu and initial value S(t₀) = s0.

gammaRatioHalf(double) - Static method in class umontreal.iro.lecuyer.util.Num

Returns the value of the ratio Γ(x + 1/2)/Γ(x) of two gamma functions, evaluated in a numerically stable way.

GammaRejectionLoglogisticGen - Class in umontreal.iro.lecuyer.randvar

This class implements gamma random variate generators using a rejection method with loglogistic envelopes,.

GammaRejectionLoglogisticGen(RandomStream, RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.GammaRejectionLoglogisticGen

Creates a gamma random variate generator with parameters α = alpha and λ = lambda, using main stream s and auxiliary stream aux.

GammaRejectionLoglogisticGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.GammaRejectionLoglogisticGen

Creates a gamma random variate generator with parameters α = alpha and λ = lambda, using stream s.

GammaRejectionLoglogisticGen(RandomStream, RandomStream, GammaDist) - Constructor for class umontreal.iro.lecuyer.randvar.GammaRejectionLoglogisticGen

Creates a new generator object for the gamma distribution dist, using main stream s and auxiliary stream aux.

GammaRejectionLoglogisticGen(RandomStream, GammaDist) - Constructor for class umontreal.iro.lecuyer.randvar.GammaRejectionLoglogisticGen

Creates a new generator object for the gamma distribution dist and stream s for both the main and auxiliary stream.

gaussLobatto(MathFunction, double, double, double) - Static method in class umontreal.iro.lecuyer.functions.MathFunctionUtil

Computes and returns a numerical approximation of the integral of f (x) over [a, b], using Gauss-Lobatto adaptive quadrature with 5 nodes, with tolerance tol.

gaussLobatto(MathFunction, double, double, double, double[][]) - Static method in class umontreal.iro.lecuyer.functions.MathFunctionUtil

Similar to method gaussLobatto(MathFunction, double, double, double), but also returns in T[0] the subintervals of integration, and in T[1], the partial values of the integral over the corresponding subintervals.

gcd(int, int) - Static method in class umontreal.iro.lecuyer.util.Num

Returns the greatest common divisor (gcd) of x and y.

gcd(long, long) - Static method in class umontreal.iro.lecuyer.util.Num

Returns the greatest common divisor (gcd) of x and y.

generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotion

generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotion

Same as generatePath(), but a vector of uniform random numbers must be provided to the method.

generatePath(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotion

generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionBridge

generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionBridge

generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCA

generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCA

generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCAEqualSteps

generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCAEqualSteps

generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess

generatePath(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess

generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcessEuler

generatePath(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcessEuler

generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcess

Generates, returns and saves the path {X(t₀), X(t₁),…, X(t_d)}.

generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcess

Generates, returns and saves the path {X(t₀), X(t₁),…, X(t_d)}.

generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessBridge

generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessBridge

generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCA

generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCA

generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCABridge

generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCABridge

generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCASymmetricalBridge

generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCASymmetricalBridge

generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessSymmetricalBridge

generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessSymmetricalBridge

generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion

generatePath(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion

generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricLevyProcess

Generates a path.

generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess

generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess

generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess

generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess

Instead of using the internal stream to generate the path, uses an array of uniforms U[0, 1).

generatePath(double[], double[]) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess

This method does not work for this class, but will be useful for the subclasses that require two streams.

generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessBridge

Generates the path.

generatePath(double[], double[]) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessBridge

Instead of using the internal streams to generate the path, it uses two arrays of uniforms U[0, 1).

generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessMSH

Generates the path.

generatePath(double[], double[]) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessMSH

Instead of using the internal streams to generate the path, uses two arrays of uniforms U[0, 1).

generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessMSH

Not implemented, requires two RandomStream's.

generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessPCA

generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessPCA

Instead of using the internal stream to generate the path, uses an array of uniforms U[0, 1).

generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess

Generates the path.

generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess

generatePath(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess

generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcessEuler

Generates a sample path of the process at all observation times, which are provided in array t.

generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess

Generates, returns, and saves the sample path {X(t₀), X(t₁),…, X(t_d)}.

generatePath(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess

Same as generatePath(), but first resets the stream to stream.

generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess

Generates and returns the path.

generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess

Similar to the usual generatePath(), but here the uniform random numbers used for the simulation must be provided to the method.

generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiff

Generates, returns and saves the path.

generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiff

Similar to the usual generatePath(), but here the uniform random numbers used for the simulation must be provided to the method.

generatePath() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiffPCA

generatePath(double[]) - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiffPCA

GenF2w32 - Class in umontreal.iro.lecuyer.rng

Implements the RandomStream interface via inheritance from RandomStreamBase.

GenF2w32() - Constructor for class umontreal.iro.lecuyer.rng.GenF2w32

Constructs a new stream.

GenF2w32(String) - Constructor for class umontreal.iro.lecuyer.rng.GenF2w32

Constructs a new stream with the identifier name (used in the toString method).

GeometricBrownianMotion - Class in umontreal.iro.lecuyer.stochprocess

.

GeometricBrownianMotion(double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion

Same as GeometricBrownianMotion (s0, mu, sigma, new BrownianMotion (0.0, 0.0, 1.0, stream)).

GeometricBrownianMotion(double, double, double, BrownianMotion) - Constructor for class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion

Constructs a new GeometricBrownianMotion with parameters μ = mu, σ = sigma, and S(t₀) = s0, using bm as the underlying BrownianMotion.

GeometricDist - Class in umontreal.iro.lecuyer.probdist

Extends the class DiscreteDistributionInt for the geometric distribution with parameter p, where 0 < p < 1.

GeometricDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.GeometricDist

Constructs a geometric distribution with parameter p.

GeometricGen - Class in umontreal.iro.lecuyer.randvar

This class implements a random variate generator for the geometric distribution.

GeometricGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.GeometricGen

Creates a geometric random variate generator with parameter p, using stream s.

GeometricGen(RandomStream, GeometricDist) - Constructor for class umontreal.iro.lecuyer.randvar.GeometricGen

Creates a new generator for the distribution dist, using stream s.

GeometricLevyProcess - Class in umontreal.iro.lecuyer.stochprocess

.

GeometricLevyProcess() - Constructor for class umontreal.iro.lecuyer.stochprocess.GeometricLevyProcess

GeometricNormalInverseGaussianProcess - Class in umontreal.iro.lecuyer.stochprocess

.

GeometricNormalInverseGaussianProcess(double, double, double, double, double, double, RandomStream, NormalInverseGaussianProcess) - Constructor for class umontreal.iro.lecuyer.stochprocess.GeometricNormalInverseGaussianProcess

Constructs a new GeometricNormalInverseGaussianProcess.

GeometricNormalInverseGaussianProcess(double, double, double, double, double, double, RandomStream, InverseGaussianProcess) - Constructor for class umontreal.iro.lecuyer.stochprocess.GeometricNormalInverseGaussianProcess

Constructs a new GeometricNormalInverseGaussianProcess.

GeometricNormalInverseGaussianProcess(double, double, double, double, double, double, RandomStream, RandomStream, RandomStream, String) - Constructor for class umontreal.iro.lecuyer.stochprocess.GeometricNormalInverseGaussianProcess

Constructs a new GeometricNormalInverseGaussianProcess.

GeometricNormalInverseGaussianProcess(double, double, double, double, double, double, RandomStream, String) - Constructor for class umontreal.iro.lecuyer.stochprocess.GeometricNormalInverseGaussianProcess

Constructs a new GeometricNormalInverseGaussianProcess.

GeometricVarianceGammaProcess - Class in umontreal.iro.lecuyer.stochprocess

This class represents a geometric variance gamma process S(t) (see).

GeometricVarianceGammaProcess(double, double, double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess

Constructs a new GeometricVarianceGammaProcess with parameters θ = theta, σ = sigma, ν = nu, μ = mu and initial value S(t₀) = s0.

GeometricVarianceGammaProcess(double, double, VarianceGammaProcess) - Constructor for class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess

Constructs a new GeometricVarianceGammaProcess.

get() - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart

Gets the primary dataset.

get(int) - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart

Gets the element at the specified position in the dataset list.

get(int) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

get(int, int) - Method in class umontreal.iro.lecuyer.util.DMatrix

Returns the matrix element in the specified row and column.

get(int) - Method in class umontreal.iro.lecuyer.util.TransformingList

getA() - Method in class umontreal.iro.lecuyer.functions.PowerMathFunction

Returns the value of a.

getA() - Method in class umontreal.iro.lecuyer.functions.SquareMathFunction

Returns the value of a.

getA() - Method in class umontreal.iro.lecuyer.hups.KorobovLattice

Returns the multiplier a of the lattice.

geta() - Method in class umontreal.iro.lecuyer.hups.LCGPointSet

Returns the value of the multiplier a.

getA() - Method in class umontreal.iro.lecuyer.probdist.BetaDist

Returns the parameter a of this object.

getA() - Method in class umontreal.iro.lecuyer.probdist.NakagamiDist

Returns the location parameter a of this object.

getA() - Method in class umontreal.iro.lecuyer.probdist.PowerDist

Returns the parameter a.

getA() - Method in class umontreal.iro.lecuyer.probdist.RayleighDist

Returns the parameter a.

getA() - Method in class umontreal.iro.lecuyer.probdist.TriangularDist

Returns the value of a for this object.

getA() - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist

Returns the value of a.

getA() - Method in class umontreal.iro.lecuyer.probdist.UniformDist

Returns the parameter a.

getA() - Method in class umontreal.iro.lecuyer.randvar.BetaGen

Returns the parameter a of this object.

getA() - Method in class umontreal.iro.lecuyer.randvar.NakagamiGen

Returns the location parameter a of this object.

getA() - Method in class umontreal.iro.lecuyer.randvar.PowerGen

Returns the parameter a.

getA() - Method in class umontreal.iro.lecuyer.randvar.RayleighGen

Returns the parameter a.

getA() - Method in class umontreal.iro.lecuyer.randvar.TriangularGen

Returns the value of a for this object.

getA() - Method in class umontreal.iro.lecuyer.randvar.UniformGen

Returns the value of a for this object.

getAlpha() - Method in class umontreal.iro.lecuyer.probdist.BetaDist

Returns the parameter α of this object.

getAlpha() - Method in class umontreal.iro.lecuyer.probdist.CauchyDist

Returns the value of α for this object.

getAlpha() - Method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist

Deprecated.

Returns the parameter α of this object.

getAlpha() - Method in class umontreal.iro.lecuyer.probdist.FrechetDist

Returns the parameter α of this object.

getAlpha() - Method in class umontreal.iro.lecuyer.probdist.GammaDist

Return the parameter α for this object.

getAlpha() - Method in class umontreal.iro.lecuyer.probdist.LogisticDist

Return the parameter α of this object.

getAlpha() - Method in class umontreal.iro.lecuyer.probdist.LoglogisticDist

Return the parameter α of this object.

getAlpha() - Method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist

Returns the parameter α of this object.

getAlpha() - Method in class umontreal.iro.lecuyer.probdist.ParetoDist

Returns the parameter α.

getAlpha() - Method in class umontreal.iro.lecuyer.probdist.Pearson5Dist

Returns the α parameter of this object.

getAlpha() - Method in class umontreal.iro.lecuyer.probdist.WeibullDist

Returns the parameter α.

getAlpha() - Method in class umontreal.iro.lecuyer.probdistmulti.DirichletDist

Returns the parameters (α₁,...,α_d) of this object.

getAlpha(int) - Method in class umontreal.iro.lecuyer.probdistmulti.DirichletDist

Returns the ith component of the alpha vector.

getAlpha() - Method in class umontreal.iro.lecuyer.randvar.BetaGen

Returns the parameter α of this object.

getAlpha() - Method in class umontreal.iro.lecuyer.randvar.CauchyGen

Returns the parameter α of this object.

getAlpha() - Method in class umontreal.iro.lecuyer.randvar.ExtremeValueGen

Deprecated.

Returns the parameter α of this object.

getAlpha() - Method in class umontreal.iro.lecuyer.randvar.FrechetGen

Returns the parameter α.

getAlpha() - Method in class umontreal.iro.lecuyer.randvar.GammaGen

Returns the parameter α of this object.

getAlpha() - Method in class umontreal.iro.lecuyer.randvar.LogisticGen

Returns the parameter α of this object.

getAlpha() - Method in class umontreal.iro.lecuyer.randvar.LoglogisticGen

Returns the parameter α of this object.

getAlpha() - Method in class umontreal.iro.lecuyer.randvar.NormalInverseGaussianGen

Returns the parameter α of this object.

getAlpha() - Method in class umontreal.iro.lecuyer.randvar.ParetoGen

Returns the parameter α of this object.

getAlpha() - Method in class umontreal.iro.lecuyer.randvar.Pearson5Gen

Returns the parameter α of this object.

getAlpha() - Method in class umontreal.iro.lecuyer.randvar.WeibullGen

Returns the parameter α.

getAlpha(int) - Method in class umontreal.iro.lecuyer.randvarmulti.DirichletGen

Returns the α_i+1 parameter for this Dirichlet generator.

getAlpha() - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess

Returns the value of α.

getAlpha() - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcessEuler

Returns the value of α.

getAlpha() - Method in class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess

Returns alpha.

getAlpha() - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess

Returns the value of α.

getAlpha1() - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist

Returns the α₁ parameter of this object.

getAlpha1() - Method in class umontreal.iro.lecuyer.randvar.Pearson6Gen

Returns the α₁ parameter of this object.

getAlpha2() - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist

Returns the α₂ parameter of this object.

getAlpha2() - Method in class umontreal.iro.lecuyer.randvar.Pearson6Gen

Returns the α₂ parameter of this object.

getAnalyticAverage(double) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess

Returns the analytic average which is δt/γ, with t = time.

getAnalyticAverage(double) - Method in class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess

Returns the analytic average, which is μt + δtβ/γ.

getAnalyticVariance(double) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess

Returns the analytic variance which is (δt)², with t = time.

getAnalyticVariance(double) - Method in class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess

Returns the analytic variance, which is δtα²/γ³.

getArea() - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist

Returns the value of F(b) - F(a), the area under the truncated density function.

getArray() - Method in class umontreal.iro.lecuyer.stat.TallyStore

Returns the observations stored in this probe.

getArrayMappingCounterToIndex() - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess

Returns a reference to an array that maps an integer k to i_k, the index of the observation S(t_ik) corresponding to the k-th observation to be generated for a sample path of this process.

getAs() - Method in class umontreal.iro.lecuyer.hups.Rank1Lattice

Returns the generator a_j of the lattice.

getAuxStream() - Method in class umontreal.iro.lecuyer.randvar.BetaRejectionLoglogisticGen

Returns the auxiliary stream associated with that object.

getAuxStream() - Method in class umontreal.iro.lecuyer.randvar.BetaStratifiedRejectionGen

Returns the auxiliary stream associated with this object.

getAuxStream() - Method in class umontreal.iro.lecuyer.randvar.GammaAcceptanceRejectionGen

Returns the auxiliary stream associated with this object.

getAuxStream() - Method in class umontreal.iro.lecuyer.randvar.GammaRejectionLoglogisticGen

Returns the auxiliary stream associated with this object.

getAuxStream() - Method in class umontreal.iro.lecuyer.randvar.UnuranContinuous

Returns the auxiliary random number stream.

getAuxStream() - Method in class umontreal.iro.lecuyer.randvar.UnuranDiscreteInt

Returns the auxiliary random number stream.

getAuxStream() - Method in class umontreal.iro.lecuyer.randvar.UnuranEmpirical

Returns the auxiliary random number stream.

getAvailable() - Method in class umontreal.iro.lecuyer.simprocs.Bin

Returns the number of available tokens for this bin.

getAvailable() - Method in class umontreal.iro.lecuyer.simprocs.Resource

Returns the number of available units, i.e., the capacity minus the number of units busy.

getB() - Method in class umontreal.iro.lecuyer.functions.PowerMathFunction

Returns the value of b.

getB() - Method in class umontreal.iro.lecuyer.functions.SquareMathFunction

Returns the value of b.

getB() - Method in class umontreal.iro.lecuyer.probdist.BetaDist

Returns the parameter b of this object.

getB() - Method in class umontreal.iro.lecuyer.probdist.PowerDist

Returns the parameter b.

getB() - Method in class umontreal.iro.lecuyer.probdist.TriangularDist

Returns the value of b for this object.

getB() - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist

Returns the value of b.

getB() - Method in class umontreal.iro.lecuyer.probdist.UniformDist

Returns the parameter b.

getB() - Method in class umontreal.iro.lecuyer.randvar.BetaGen

Returns the parameter b of this object.

getB() - Method in class umontreal.iro.lecuyer.randvar.PowerGen

Returns the parameter b.

getB() - Method in class umontreal.iro.lecuyer.randvar.TriangularGen

Returns the value of b for this object.

getB() - Method in class umontreal.iro.lecuyer.randvar.UniformGen

Returns the value of b for this object.

getB() - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess

Returns the value of b.

getB() - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcessEuler

Returns the value of b.

getB() - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess

Returns the value of b.

getBaseBandwidth(EmpiricalDist) - Static method in class umontreal.iro.lecuyer.randvar.KernelDensityGen

Computes and returns the value of h₀ in.

getBeta() - Method in class umontreal.iro.lecuyer.probdist.BetaDist

Returns the parameter β of this object.

getBeta() - Method in class umontreal.iro.lecuyer.probdist.CauchyDist

Returns the value of β for this object.

getBeta() - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist

Returns the parameter β of this object.

getBeta() - Method in class umontreal.iro.lecuyer.probdist.FrechetDist

Returns the parameter β of this object.

getBeta() - Method in class umontreal.iro.lecuyer.probdist.GumbelDist

Returns the parameter β of this object.

getBeta() - Method in class umontreal.iro.lecuyer.probdist.LaplaceDist

Returns the parameter β.

getBeta() - Method in class umontreal.iro.lecuyer.probdist.LoglogisticDist

Returns the parameter β of this object.

getBeta() - Method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist

Returns the parameter β of this object.

getBeta() - Method in class umontreal.iro.lecuyer.probdist.ParetoDist

Returns the parameter β.

getBeta() - Method in class umontreal.iro.lecuyer.probdist.Pearson5Dist

Returns the β parameter of this object.

getBeta() - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist

Returns the β parameter of this object.

getBeta() - Method in class umontreal.iro.lecuyer.randvar.BetaGen

Returns the parameter β of this object.

getBeta() - Method in class umontreal.iro.lecuyer.randvar.CauchyGen

Returns the parameter β of this object.

getBeta() - Method in class umontreal.iro.lecuyer.randvar.FatigueLifeGen

Returns the parameter β of this object.

getBeta() - Method in class umontreal.iro.lecuyer.randvar.FrechetGen

Returns the parameter β.

getBeta() - Method in class umontreal.iro.lecuyer.randvar.GumbelGen

Returns the parameter β.

getBeta() - Method in class umontreal.iro.lecuyer.randvar.LaplaceGen

Returns the parameter β.

getBeta() - Method in class umontreal.iro.lecuyer.randvar.LoglogisticGen

Returns the parameter β of this object.

getBeta() - Method in class umontreal.iro.lecuyer.randvar.NormalInverseGaussianGen

Returns the parameter β of this object.

getBeta() - Method in class umontreal.iro.lecuyer.randvar.ParetoGen

Returns the parameter β of this object.

getBeta() - Method in class umontreal.iro.lecuyer.randvar.Pearson5Gen

Returns the parameter β of this object.

getBeta() - Method in class umontreal.iro.lecuyer.randvar.Pearson6Gen

Returns the β parameter of this object.

getBeta() - Method in class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess

Returns beta.

getBins(int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset

Returns the bins for a series.

getBins(int) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection

Returns the bins for a series.

getBinWidth(int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset

Returns the bin width for a series.

getBMPCA() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCA

Returns the BrownianMotionPCA that is included in the GammaProcessPCA object.

getBMPCA() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCABridge

Returns the inner BrownianMotionPCA.

getBool(int, int) - Method in class umontreal.iro.lecuyer.util.BitMatrix

Returns the value of the bit in the specified row and column.

getBool(int) - Method in class umontreal.iro.lecuyer.util.BitVector

Gives the value of the bit in position pos.

getBrownianMotion() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion

Returns a reference to the BrownianMotion object used to generate the process.

getBrownianMotion() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess

Returns a reference to the inner BrownianMotion.

getBrownianMotionPCA() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessPCA

Returns the BrownianMotionPCA.

getBuffer() - Method in class umontreal.iro.lecuyer.util.PrintfFormat

Returns the StringBuffer associated with that object.

getC() - Method in class umontreal.iro.lecuyer.probdist.NakagamiDist

Returns the shape parameter c of this object.

getC() - Method in class umontreal.iro.lecuyer.probdist.PowerDist

Returns the parameter c.

getC() - Method in class umontreal.iro.lecuyer.randvar.NakagamiGen

Returns the shape parameter c of this object.

getC() - Method in class umontreal.iro.lecuyer.randvar.PowerGen

Returns the parameter c.

getCachedGen() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache

Returns a reference to the random variate generator whose values are cached.

getCachedStream() - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache

Returns a reference to the random stream whose values are cached.

getCachedValues() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache

Returns an array list containing the values cached by this random variate generator.

getCachedValues() - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache

Returns an array list containing the values cached by this random stream.

getCacheIndex() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache

Return the index of the next cached value that will be returned by the generator.

getCacheIndex() - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache

Return the index of the next cached value that will be returned by the stream.

getCapacity() - Method in class umontreal.iro.lecuyer.simprocs.Resource

Returns the current capacity of the resource.

getCategory(int) - Method in class umontreal.iro.lecuyer.charts.SSJCategorySeriesCollection

Returns the category-value in the specified series.

getCdf() - Method in class umontreal.iro.lecuyer.charts.DiscreteDistIntChart

Returns the chart of the cdf.

getChartMargin() - Method in class umontreal.iro.lecuyer.charts.XYChart

Returns the chart margin, which is the fraction by which the chart is enlarged on its borders.

getCholeskyDecompSigma() - Method in class umontreal.iro.lecuyer.randvarmulti.MultinormalCholeskyGen

Returns the lower-triangular matrix A in the Cholesky decomposition of Σ.

getClassFinder() - Method in exception umontreal.iro.lecuyer.util.NameConflictException

Returns the class finder associated with this exception.

getCoefficient(int) - Method in class umontreal.iro.lecuyer.functions.Polynomial

Returns the ith coefficient of the polynomial.

getCoefficients(double[], double[], int) - Static method in class umontreal.iro.lecuyer.functionfit.LeastSquares

Computes and returns the coefficients of the fitting polynomial of degree degree.

getCoefficients(double[], double[]) - Static method in class umontreal.iro.lecuyer.functionfit.PolInterp

Computes and returns the coefficients the polynomial interpolating through the given points (x[0], y[0]), ..., (x[n], y[n]).

getCoefficients() - Method in class umontreal.iro.lecuyer.functions.Polynomial

Returns an array containing the coefficients of the polynomial.

getColor(int) - Method in class umontreal.iro.lecuyer.charts.SSJCategorySeriesCollection

Gets the current plotting color of the selected series.

getColor(int) - Method in class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection

Gets the current plotting color of the selected series.

getConfidenceLevel() - Method in class umontreal.iro.lecuyer.stat.Tally

Returns the level of confidence for the intervals on the mean displayed in reports.

getContinuousDistribution(String) - Static method in class umontreal.iro.lecuyer.probdist.DistributionFactory

Uses the Java Reflection API to construct a ContinuousDistribution object by executing the code contained in the string str.

getContinuousVariables() - Method in class umontreal.iro.lecuyer.simevents.ContinuousState

Returns the list of continuous-time variables currently integrated by the simulator.

getCoordinate(int, int) - Method in class umontreal.iro.lecuyer.hups.AntitheticPointSet

getCoordinate(int, int) - Method in class umontreal.iro.lecuyer.hups.BakerTransformedPointSet

getCoordinate(int, int) - Method in class umontreal.iro.lecuyer.hups.CachedPointSet

getCoordinate(int, int) - Method in class umontreal.iro.lecuyer.hups.ContainerPointSet

getCoordinate(int, int) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet

getCoordinate(int, int) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSetBase2

getCoordinate(int, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNet

getCoordinate(int, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2

getCoordinate(int, int) - Method in class umontreal.iro.lecuyer.hups.HaltonSequence

getCoordinate(int, int) - Method in class umontreal.iro.lecuyer.hups.HammersleyPointSet

getCoordinate(int, int) - Method in class umontreal.iro.lecuyer.hups.KorobovLatticeSequence

getCoordinate(int, int) - Method in class umontreal.iro.lecuyer.hups.PaddedPointSet

getCoordinate(int, int) - Method in class umontreal.iro.lecuyer.hups.PointSet

Returns u_{i, j}, the coordinate j of the point i.

getCoordinate(int, int) - Method in class umontreal.iro.lecuyer.hups.Rank1Lattice

getCoordinate(int, int) - Method in class umontreal.iro.lecuyer.hups.SubsetOfPointSet

getCoordinateNoGray(int, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNet

Returns u_{i, j}, the coordinate j of point i, the points being enumerated in the standard order (no Gray code).

getCoordinateNoGray(int, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2

getCorrelation() - Method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist

getCorrelation(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist

Return the correlation matrix of the binormal distribution.

getCorrelation() - Method in class umontreal.iro.lecuyer.probdistmulti.BiStudentDist

getCorrelation(int, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiStudentDist

Returns the correlation matrix of the bivariate Student's t distribution.

getCorrelation() - Method in class umontreal.iro.lecuyer.probdistmulti.ContinuousDistributionMulti

Returns the correlation matrix of the distribution, defined as ρ_ij = σ_ij/(σ_iiσ_jj)^1/2.

getCorrelation() - Method in class umontreal.iro.lecuyer.probdistmulti.DirichletDist

getCorrelation(double[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.DirichletDist

Computes the correlation matrix of the Dirichlet distribution with parameters (α₁,...,α_d).

getCorrelation() - Method in class umontreal.iro.lecuyer.probdistmulti.DiscreteDistributionIntMulti

Returns the correlation matrix of the distribution, defined as ρ_ij = σ_ij/(σ_iiσ_jj)^1/2.

getCorrelation() - Method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist

getCorrelation(int, double[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist

Computes the correlation matrix of the multinomial distribution with parameters n and (p₁,...,p_d).

getCorrelation() - Method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist

getCorrelation(double[], double[][]) - Static method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist

Computes the correlation matrix of the multinormal distribution with parameters μ and Σ).

getCorrelation() - Method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist

getCorrelation(double, double[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist

Computes the correlation matrix of the negative multinomial distribution with parameters γ and (p₁,...,p_d).

getCovariance() - Method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist

getCovariance(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist

Return the covariance matrix of the binormal distribution.

getCovariance() - Method in class umontreal.iro.lecuyer.probdistmulti.BiStudentDist

getCovariance(int, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiStudentDist

Returns the covariance matrix of the bivariate Student's t distribution.

getCovariance() - Method in class umontreal.iro.lecuyer.probdistmulti.ContinuousDistributionMulti

Returns the variance-covariance matrix of the distribution, defined as
σ_ij = E[(X_i - μ_i)(X_j - μ_j)].

getCovariance() - Method in class umontreal.iro.lecuyer.probdistmulti.DirichletDist

getCovariance(double[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.DirichletDist

Computes the covariance matrix of the Dirichlet distribution with parameters (α₁,...,α_d).

getCovariance() - Method in class umontreal.iro.lecuyer.probdistmulti.DiscreteDistributionIntMulti

Returns the variance-covariance matrix of the distribution, defined as
σ_ij = E[(X_i - μ_i)(X_j - μ_j)].

getCovariance() - Method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist

getCovariance(int, double[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist

Computes the covariance matrix of the multinomial distribution with parameters n and (p₁,...,p_d).

getCovariance() - Method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist

getCovariance(double[], double[][]) - Static method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist

Computes the covariance matrix of the multinormal distribution with parameters μ and Σ.

getCovariance() - Method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist

getCovariance(double, double[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist

Computes the covariance matrix of the negative multinomial distribution with parameters γ and (p₁,...,p_d).

getCurCoordIndex() - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator

Returns the index j of the current coordinate.

getCurPointIndex() - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator

Returns the index i of the current point.

getCurrentObservation() - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess

Returns the value of the last generated observation X(t_j).

getCurrentObservationIndex() - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess

Returns the value of the index j corresponding to the time t_j of the last generated observation.

getCurrentUpperBound() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess

getDashPattern(int) - Method in class umontreal.iro.lecuyer.charts.EmpiricalSeriesCollection

Returns the dash pattern associated with the series-th data series.

getDashPattern(int) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection

Returns the dash pattern associated with the seriesth data series.

getDefaultSimulator() - Static method in class umontreal.iro.lecuyer.simevents.Simulator

Returns the default simulator instance used by the deprecated class Sim.

getDegree() - Method in class umontreal.iro.lecuyer.functions.Polynomial

Returns the degree of this polynomial.

getDelay() - Method in class umontreal.iro.lecuyer.simprocs.SimProcess

If the process is in the DELAYED state, returns the remaining time until the planned occurrence of its activating event.

getDelta() - Method in class umontreal.iro.lecuyer.functions.ShiftedMathFunction

Returns the shift δ = delta.

getDelta() - Method in class umontreal.iro.lecuyer.probdist.FrechetDist

Returns the parameter δ of this object.

getDelta() - Method in class umontreal.iro.lecuyer.probdist.GumbelDist

Returns the parameter δ of this object.

getDelta() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist

Returns the value of δ for this object.

getDelta() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist

Returns the value of δ for this object.

getDelta() - Method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist

Returns the parameter δ of this object.

getDelta() - Method in class umontreal.iro.lecuyer.probdist.WeibullDist

Returns the parameter δ.

getDelta() - Method in class umontreal.iro.lecuyer.randvar.FrechetGen

Returns the parameter δ.

getDelta() - Method in class umontreal.iro.lecuyer.randvar.GumbelGen

Returns the parameter δ.

getDelta() - Method in class umontreal.iro.lecuyer.randvar.JohnsonSBGen

Returns the δ associated with this object.

getDelta() - Method in class umontreal.iro.lecuyer.randvar.JohnsonSUGen

Returns the δ associated with this object.

getDelta() - Method in class umontreal.iro.lecuyer.randvar.NormalInverseGaussianGen

Returns the parameter δ of this object.

getDelta() - Method in class umontreal.iro.lecuyer.randvar.WeibullGen

Returns the parameter δ.

getDelta() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess

Returns δ.

getDelta() - Method in class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess

Returns delta.

getDimension() - Method in class umontreal.iro.lecuyer.hups.ContainerPointSet

Returns the dimension of the contained point set.

getDimension() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet

getDimension() - Method in class umontreal.iro.lecuyer.hups.PointSet

Returns the dimension (number of available coordinates) of the point set.

getDimension() - Method in class umontreal.iro.lecuyer.probdistmulti.ContinuousDistributionMulti

Returns the dimension d of the distribution.

getDimension() - Method in class umontreal.iro.lecuyer.probdistmulti.DiscreteDistributionIntMulti

Returns the dimension d of the distribution.

getDimension() - Method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist

Returns the dimension d of the distribution.

getDimension() - Method in class umontreal.iro.lecuyer.randvarmulti.RandomMultivariateGen

Returns the dimension of this multivariate generator (the dimension of the random points).

getDimension() - Method in interface umontreal.iro.lecuyer.util.MultivariateFunction

Returns d, the dimension of the function computed by this implementation.

getDimension() - Method in class umontreal.iro.lecuyer.util.RatioFunction

getDiscreteDistribution(String) - Static method in class umontreal.iro.lecuyer.probdist.DistributionFactory

Same as getContinuousDistribution, but for discrete distributions over the real numbers.

getDiscreteDistributionInt(String) - Static method in class umontreal.iro.lecuyer.probdist.DistributionFactory

Same as getContinuousDistribution, but for discrete distributions over the integers.

getDistribution(String) - Static method in class umontreal.iro.lecuyer.probdist.DistributionFactory

getDistribution() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGen

Returns the Distribution used by this generator.

getDistribution() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenInt

Returns the DiscreteDistributionInt used by this generator.

getDistribution() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache

getDistribution() - Method in class umontreal.iro.lecuyer.randvar.UnuranContinuous

getDistribution() - Method in class umontreal.iro.lecuyer.randvar.UnuranDiscreteInt

getDistribution() - Method in class umontreal.iro.lecuyer.randvar.UnuranEmpirical

getDistributionMLE(String, double[], int) - Static method in class umontreal.iro.lecuyer.probdist.DistributionFactory

getDistributionMLE(String, int[], int) - Static method in class umontreal.iro.lecuyer.probdist.DistributionFactory

getDistributionMLE(Class<T>, double[], int) - Static method in class umontreal.iro.lecuyer.probdist.DistributionFactory

getDistributionMLE(Class<T>, int[], int) - Static method in class umontreal.iro.lecuyer.probdist.DistributionFactory

getDomainBounds() - Method in class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection

Returns domain (x-coordinates) min and max values.

getDoubleArrayList() - Method in class umontreal.iro.lecuyer.stat.TallyStore

Returns the DoubleArrayList object that contains the observations for this probe.

getElement() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat.Node

Returns the element stored into this node.

getEndX(int, int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset

Returns the end value for a bin.

getEndY(int, int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset

Returns the end y-value for a bin (which is the same as the y-value).

getEpsilon() - Method in class umontreal.iro.lecuyer.probdist.InverseDistFromDensity

Returns the u-resolution eps associated with this object.

getEpsilon() - Method in class umontreal.iro.lecuyer.randvar.InverseFromDensityGen

Returns the u-resolution eps associated with this object.

getEventList() - Static method in class umontreal.iro.lecuyer.simevents.Sim

Gets the currently used event list.

getEventList() - Method in class umontreal.iro.lecuyer.simevents.Simulator

Gets the currently used event list.

getFa() - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist

Returns the value of F(a).

getFaurePermutation(int, int[]) - Static method in class umontreal.iro.lecuyer.hups.RadicalInverse

Computes the Faure permutation σ_b of the set {0,…, b - 1} and puts it in array pi.

getFb() - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist

Returns the value of F(b).

getField(Class<?>, String) - Static method in class umontreal.iro.lecuyer.util.Introspection

This is like getField, except that it can return non-public fields.

getFieldName(Object) - Static method in class umontreal.iro.lecuyer.util.Introspection

Returns the field name corresponding to the value of an enumerated type val.

getFields(Class<?>) - Static method in class umontreal.iro.lecuyer.util.Introspection

Returns all the fields declared and inherited by a class.

getFilled(int) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection

Returns the filled flag associated with the series-th data series.

getFirst() - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree

getFirst() - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked

getFirst() - Method in interface umontreal.iro.lecuyer.simevents.eventlist.EventList

Returns the first event in the event list.

getFirst() - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen

getFirst() - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree

getFirst() - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree

getFirst() - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat

getFirstOfClass(String) - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree

getFirstOfClass(Class<E>) - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree

getFirstOfClass(String) - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked

getFirstOfClass(Class<E>) - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked

getFirstOfClass(String) - Method in interface umontreal.iro.lecuyer.simevents.eventlist.EventList

Returns the first event of the class cl (a subclass of Event) in the event list.

getFirstOfClass(Class<E>) - Method in interface umontreal.iro.lecuyer.simevents.eventlist.EventList

Returns the first event of the class E (a subclass of Event) in the event list.

getFirstOfClass(String) - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen

getFirstOfClass(Class<E>) - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen

getFirstOfClass(String) - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree

getFirstOfClass(Class<E>) - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree

getFirstOfClass(String) - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree

getFirstOfClass(Class<E>) - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree

getFitPolynomialIndex(double) - Method in class umontreal.iro.lecuyer.functionfit.SmoothingCubicSpline

Returns the index of P, the Polynomial instance used to evaluate x, in an ArrayList table instance returned by getSplinePolynomials().

getFunction() - Method in class umontreal.iro.lecuyer.functions.PowerMathFunction

Returns the function f (x).

getFunction() - Method in class umontreal.iro.lecuyer.functions.ShiftedMathFunction

Returns the function f (x).

getFunction() - Method in class umontreal.iro.lecuyer.functions.SqrtMathFunction

Returns the function associated with this object.

getFunction() - Method in class umontreal.iro.lecuyer.functions.SquareMathFunction

Returns the function f (x).

getFunctions() - Method in class umontreal.iro.lecuyer.functions.AverageMathFunction

Returns the functions being averaged.

getGamma() - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist

Returns the parameter γ of this object.

getGamma() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist

Returns the value of γ for this object.

getGamma() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist

Returns the value of γ for this object.

getGamma() - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist

Returns the parameter γ of this object.

getGamma() - Method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist

Returns the parameter γ of this object.

getGamma() - Method in class umontreal.iro.lecuyer.randvar.FatigueLifeGen

Returns the parameter γ of this object.

getGamma() - Method in class umontreal.iro.lecuyer.randvar.JohnsonSBGen

Returns the γ associated with this object.

getGamma() - Method in class umontreal.iro.lecuyer.randvar.JohnsonSUGen

Returns the γ associated with this object.

getGamma() - Method in class umontreal.iro.lecuyer.randvar.NegativeBinomialGen

Returns the parameter γ of this object.

getGamma() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess

Returns γ.

getGamma() - Method in class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess

Returns gamma.

getGammaProcess() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess

Returns a reference to the inner GammaProcess.

getGen() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotion

Returns the normal random variate generator used.

getGen() - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess

Returns the noncentral chi-square random variate generator used.

getGen() - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcessEuler

Returns the normal random variate generator used.

getGen() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion

Returns the NormalGen used.

getGen() - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess

Returns the normal random variate generator used.

getGneg() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiff

Returns a reference to the GammaProcess object gneg used to generate the Γ^- component of the process.

getGpos() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiff

Returns a reference to the GammaProcess object gpos used to generate the Γ⁺ component of the process.

getHours() - Method in class umontreal.iro.lecuyer.util.AbstractChrono

Returns the CPU time in hours used by the program since the last call to init for this AbstractChrono.

getHours() - Method in enum umontreal.iro.lecuyer.util.TimeUnit

Returns this time unit represented in hours.

getI() - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist

Returns the parameter i.

getI() - Method in class umontreal.iro.lecuyer.randvar.UniformIntGen

Returns the parameter i.

getImports() - Method in class umontreal.iro.lecuyer.util.ClassFinder

Returns the current list of import declarations.

getInitTime() - Method in class umontreal.iro.lecuyer.simevents.Accumulate

Returns the initialization time for this object.

getInitTime() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat

Returns the last simulation time initStat was called.

getInnerList() - Method in class umontreal.iro.lecuyer.util.TransformingList

getInsertionTime() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat.Node

Returns the insertion time of the element in this node.

getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist

Creates a new instance of a beta distribution with parameters α and β over the interval [0, 1] estimated using the maximum likelihood method based on the n observations x[i], i = 0, 1,…, n - 1.

getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist

Creates a new instance of a symmetrical beta distribution with parameter α estimated using the maximum likelihood method based on the n observations x[i], i = 0, 1,…, n - 1.

getInstanceFromMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist

Creates a new instance of a binomial distribution with both parameters n and p estimated using the maximum likelihood method, from the m observations x[i], i = 0, 1,…, m - 1.

getInstanceFromMLE(int[], int, int) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist

Creates a new instance of a binomial distribution with given (fixed) parameter n, and with parameter p estimated by the maximum likelihood method based on the m observations x[i], i = 0, 1,…, m - 1.

getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.CauchyDist

Creates a new instance of a Cauchy distribution with parameters α and β estimated using the maximum likelihood method based on the n observations x[i], i = 0, 1,…, n - 1.

getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ChiDist

Creates a new instance of a chi distribution with parameter ν estimated using the maximum likelihood method based on the n observations x[i], i = 0, 1,…, n - 1.

getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareDist

Creates a new instance of a chi-square distribution with parameter n estimated using the maximum likelihood method based on the m observations x[i], i = 0, 1,…, m - 1.

getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ErlangDist

Creates a new instance of an Erlang distribution with parameters k and λ estimated using the maximum likelihood method based on the n observations x[i], i = 0, 1,…, n - 1.

getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ExponentialDist

Creates a new instance of an exponential distribution with parameter λ estimated using the maximum likelihood method based on the n observations x[i], i = 0, 1,…, n - 1.

getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist

Deprecated.

Creates a new instance of an extreme value distribution with parameters α and λ estimated using the maximum likelihood method based on the n observations x[i], i = 0, 1,…, n - 1.

getInstanceFromMLE(double[], int, double) - Static method in class umontreal.iro.lecuyer.probdist.FrechetDist

Given δ = delta, creates a new instance of a Fréchet distribution with parameters α and β estimated using the maximum likelihood method based on the n observations x[i], i = 0, 1,…, n - 1.

getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.GammaDist

Creates a new instance of a gamma distribution with parameters α and λ estimated using the maximum likelihood method based on the n observations x[i], i = 0, 1,…, n - 1.

getInstanceFromMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.GeometricDist

Creates a new instance of a geometric distribution with parameter p estimated using the maximum likelihood method based on the n observations x[i], i = 0, 1,…, n - 1.

getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.GumbelDist

Creates a new instance of an Gumbel distribution with parameters β and δ estimated using the maximum likelihood method based on the n observations x[i], i = 0, 1,…, n - 1, assuming that β > 0.

getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist

Creates a new instance of a hyperbolic secant distribution with parameters μ and σ estimated using the maximum likelihood method based on the n observations x[i], i = 0, 1,…, n - 1.

getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist

Creates a new instance of an inverse gaussian distribution with parameters μ and λ estimated using the maximum likelihood method based on the n observations x[i], i = 0, 1,…, n - 1.

getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.LaplaceDist

Creates a new instance of a Laplace distribution with parameters μ and β estimated using the maximum likelihood method based on the n observations x[i], i = 0, 1,…, n - 1.

getInstanceFromMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.LogarithmicDist

Creates a new instance of a logarithmic distribution with parameter θ estimated using the maximum likelihood method based on the n observations x[i], i = 0, 1,…, n - 1.

getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.LogisticDist

Creates a new instance of a logistic distribution with parameters α and λ estimated using the maximum likelihood method based on the n observations x[i], i = 0, 1,…, n - 1.

getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.LoglogisticDist

Creates a new instance of a log-logistic distribution with parameters α and β estimated using the maximum likelihood method based on the n observations x[i], i = 0, 1,…, n - 1.

getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.LognormalDist

Creates a new instance of a lognormal distribution with parameters μ and σ estimated using the maximum likelihood method based on the n observations x[i], i = 0, 1,…, n - 1.

getInstanceFromMLE(int[], int, double) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist

Creates a new instance of a negative binomial distribution with parameters γ = gamma given and hat(p) estimated using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.

getInstanceFromMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist

Creates a new instance of a negative binomial distribution with parameters γ and p estimated using the maximum likelihood method based on the n observations x[i], i = 0, 1,…, n - 1.

getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist

Creates a new instance of a normal distribution with parameters μ and σ estimated using the maximum likelihood method based on the n observations x[i], i = 0, 1,…, n - 1.

getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist

NOT IMPLEMENTED.

getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ParetoDist

Creates a new instance of a Pareto distribution with parameters α and β estimated using the maximum likelihood method based on the n observations x[i], i = 0, 1,…, n - 1.

getInstanceFromMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.PascalDist

Creates a new instance of a Pascal distribution with parameters n and p estimated using the maximum likelihood method based on the m observations x[i], i = 0, 1,…, m - 1.

getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.Pearson5Dist

Creates a new instance of a Pearson V distribution with parameters α and β estimated using the maximum likelihood method based on the n observations x[i], i = 0, 1,…, n - 1.

getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.Pearson6Dist

Creates a new instance of a Pearson VI distribution with parameters α₁, α₂ and β, estimated using the maximum likelihood method based on the n observations x[i], i = 0, 1,…, n - 1.

getInstanceFromMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.PoissonDist

Creates a new instance of a Poisson distribution with parameter λ estimated using the maximum likelihood method based on the n observations x[i], i = 0, 1,…, n - 1.

getInstanceFromMLE(double[], int, double, double) - Static method in class umontreal.iro.lecuyer.probdist.PowerDist

Creates a new instance of a power distribution with parameters a and b, with c estimated using the maximum likelihood method based on the n observations x[i], i = 0,…, n - 1.

getInstanceFromMLE(double[], int, double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist

Creates a new instance of a Rayleigh distribution with parameters a and hat(β).

getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.StudentDist

Creates a new instance of a Student-t distribution with parameter n estimated using the maximum likelihood method based on the m observations x[i], i = 0, 1,…, m - 1.

getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.UniformDist

Creates a new instance of a uniform distribution with parameters a and b estimated using the maximum likelihood method based on the n observations x[i], i = 0, 1,…, n - 1.

getInstanceFromMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.UniformIntDist

Creates a new instance of a discrete uniform distribution over integers with parameters i and j estimated using the maximum likelihood method based on the n observations x[k], k = 0, 1,…, n - 1.

getInstanceFromMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist

Creates a new instance of a Weibull distribution with parameters α, λ and δ = 0 estimated using the maximum likelihood method based on the n observations x[i], i = 0, 1,…, n - 1.

getInstanceFromMLEmin(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.GumbelDist

Similar to getInstanceFromMLE, but for the case β < 0.

getInt(int) - Method in class umontreal.iro.lecuyer.util.BitVector

Returns an int containing all the bits in the interval [pos×32,pos×32 + 31].

getInterQuartileRange() - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist

Returns the interquartile range of the observations, defined as the difference between the third and first quartiles.

getItemCount(int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset

Returns the number of data items for a series.

getJ() - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist

Returns the parameter j.

getJ() - Method in class umontreal.iro.lecuyer.randvar.UniformIntGen

Returns the parameter j.

getJFreeChart() - Method in class umontreal.iro.lecuyer.charts.CategoryChart

Returns the JFreeChart object associated with this chart.

getJFreeChart() - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart

Returns the JFreeChart variable associated with this chart.

getJFreeChart() - Method in class umontreal.iro.lecuyer.charts.XYChart

Returns the JFreeChart object associated with this chart.

getK() - Method in class umontreal.iro.lecuyer.probdist.ErlangDist

Returns the parameter k for this object.

getK() - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist

Returns the k associated with this object.

getK() - Method in class umontreal.iro.lecuyer.randvar.ErlangGen

Returns the parameter k of this object.

getK() - Method in class umontreal.iro.lecuyer.randvar.HypergeometricGen

Returns the k associated with this object.

getKnots() - Method in class umontreal.iro.lecuyer.functionfit.BSpline

Returns an array containing the knot vector (t₀, t_m-1).

getL() - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist

Returns the l associated with this object.

getL() - Method in class umontreal.iro.lecuyer.randvar.HypergeometricGen

Returns the l associated with this object.

getLabel() - Method in class umontreal.iro.lecuyer.charts.Axis

Returns the axis description.

getLambda() - Method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist

Returns the parameter λ of this object.

getLambda() - Method in class umontreal.iro.lecuyer.probdist.ExponentialDist

Returns the value of λ for this object.

getLambda() - Method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist

Deprecated.

Returns the parameter λ of this object.

getLambda() - Method in class umontreal.iro.lecuyer.probdist.GammaDist

Return the parameter λ for this object.

getLambda() - Method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist

Returns the parameter λ of this object.

getLambda() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist

Returns the value of λ for this object.

getLambda() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist

Returns the value of λ for this object.

getLambda() - Method in class umontreal.iro.lecuyer.probdist.LogisticDist

Returns the parameter λ of this object.

getLambda() - Method in class umontreal.iro.lecuyer.probdist.NakagamiDist

Returns the scale parameter λ of this object.

getLambda() - Method in class umontreal.iro.lecuyer.probdist.PoissonDist

Returns the λ associated with this object.

getLambda() - Method in class umontreal.iro.lecuyer.probdist.WeibullDist

Returns the parameter λ.

getLambda() - Method in class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralGen

Returns the value of λ for this object.

getLambda() - Method in class umontreal.iro.lecuyer.randvar.ExponentialGen

Returns the λ associated with this object.

getLambda() - Method in class umontreal.iro.lecuyer.randvar.ExtremeValueGen

Deprecated.

Returns the parameter λ of this object.

getLambda() - Method in class umontreal.iro.lecuyer.randvar.GammaGen

Returns the parameter λ of this object.

getLambda() - Method in class umontreal.iro.lecuyer.randvar.InverseGaussianGen

Returns the parameter λ of this object.

getLambda() - Method in class umontreal.iro.lecuyer.randvar.JohnsonSBGen

Returns the λ associated with this object.

getLambda() - Method in class umontreal.iro.lecuyer.randvar.JohnsonSUGen

Returns the λ associated with this object.

getLambda() - Method in class umontreal.iro.lecuyer.randvar.LogisticGen

Returns the parameter λ of this object.

getLambda() - Method in class umontreal.iro.lecuyer.randvar.NakagamiGen

Returns the scale parameter λ of this object.

getLambda() - Method in class umontreal.iro.lecuyer.randvar.PoissonGen

Returns the λ associated with this object.

getLambda() - Method in class umontreal.iro.lecuyer.randvar.WeibullGen

Returns the parameter λ.

getLambda(DoubleMatrix2D) - Static method in class umontreal.iro.lecuyer.randvarmulti.MultinormalPCAGen

Computes and returns the eigenvalues of sigma in decreasing order.

getLambda() - Method in class umontreal.iro.lecuyer.randvarmulti.MultinormalPCAGen

Returns the eigenvalues of Σ in decreasing order.

getLast() - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat

getLastTime() - Method in class umontreal.iro.lecuyer.simevents.Accumulate

Returns the last update time for this object.

getLastValue() - Method in class umontreal.iro.lecuyer.simevents.Accumulate

Returns the value passed to this probe by the last call to its update method (or the initial value if update was never called after init).

getLevyProcess() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricLevyProcess

Returns the Lévy process.

getLinearState() - Method in class umontreal.iro.lecuyer.rng.F2NL607

Returns the current state of the linear part of the stream, represented as an array of 19 integers.

getList() - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart

Returns the dataset list.

getLongName() - Method in enum umontreal.iro.lecuyer.util.TimeUnit

Returns the long name of this time unit.

getM() - Method in class umontreal.iro.lecuyer.probdist.FisherFDist

Returns the parameter m of this object.

getM() - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist

Returns the m associated with this object.

getM() - Method in class umontreal.iro.lecuyer.probdist.TriangularDist

Returns the value of m for this object.

getM() - Method in class umontreal.iro.lecuyer.randvar.FisherFGen

Returns the parameter p of this object.

getM() - Method in class umontreal.iro.lecuyer.randvar.HypergeometricGen

Returns the m associated with this object.

getM() - Method in class umontreal.iro.lecuyer.randvar.TriangularGen

Returns the value of m for this object.

getMargin() - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection

Returns the margin which is a percentage amount by which the bars are trimmed.

getMarksType(int) - Method in class umontreal.iro.lecuyer.charts.EmpiricalSeriesCollection

Returns the mark type associated with the series-th data series.

getMarksType(int) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection

Returns the mark type associated with the seriesth data series.

getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist

Deprecated.

getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist

Deprecated.

getMaximumLikelihoodEstimate(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist

Deprecated.

getMaximumLikelihoodEstimate(int[], int, int) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist

Deprecated.

getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.CauchyDist

Deprecated.

getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ChiDist

Deprecated.

getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareDist

Deprecated.

getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ErlangDist

Deprecated.

getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ExponentialDist

Deprecated.

getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist

Deprecated.

getMaximumLikelihoodEstimate(double[], int, double) - Static method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist

Deprecated.

getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.GammaDist

Deprecated.

getMaximumLikelihoodEstimate(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.GeometricDist

Deprecated.

getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist

Deprecated.

getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist

Deprecated.

getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.LaplaceDist

Deprecated.

getMaximumLikelihoodEstimate(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.LogarithmicDist

Deprecated.

getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.LogisticDist

Deprecated.

getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.LoglogisticDist

Deprecated.

getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.LognormalDist

Deprecated.

getMaximumLikelihoodEstimate(int[], int, double) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist

Deprecated.

getMaximumLikelihoodEstimate(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist

Deprecated.

getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist

Deprecated.

getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ParetoDist

Deprecated.

getMaximumLikelihoodEstimate(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.PascalDist

Deprecated.

getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.Pearson5Dist

Deprecated.

getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.Pearson6Dist

Deprecated.

getMaximumLikelihoodEstimate(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.PoissonDist

Deprecated.

getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.StudentDist

Deprecated.

getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.UniformDist

Deprecated.

getMaximumLikelihoodEstimate(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.UniformIntDist

Deprecated.

getMaximumLikelihoodEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist

Deprecated.

getMaximumLikelihoodEstimate(double[][], int, int) - Static method in class umontreal.iro.lecuyer.probdistmulti.DirichletDist

Deprecated.

getMaximumLikelihoodEstimate(int[][], int, int, int) - Static method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist

Deprecated.

getMaximumLikelihoodEstimate(int[][], int, int) - Static method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist

Deprecated.

getMaximumLikelihoodEstimateMu(double[][], int, int) - Static method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist

Deprecated.

getMaximumLikelihoodEstimateSigma(double[][], int, int) - Static method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist

Deprecated.

getMaxKnot() - Method in class umontreal.iro.lecuyer.functionfit.BSpline

Returns the knot maximal value.

getMean() - Method in class umontreal.iro.lecuyer.probdist.BetaDist

getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist

Computes and returns the mean E[X] = α/(α + β) of the beta distribution with parameters α and β, over the interval [0, 1].

getMean(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist

Computes and returns the mean E[X] = (bα + aβ)/(α + β) of the beta distribution with parameters α and β over the interval [a, b].

getMean() - Method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist

getMean(double) - Static method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist

Computes and returns the mean E[X] = 1/2 of the symmetrical beta distribution with parameter α.

getMean() - Method in class umontreal.iro.lecuyer.probdist.BinomialDist

getMean(int, double) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist

Computes the mean E[X] = np of the binomial distribution with parameters n and p.

getMean() - Method in class umontreal.iro.lecuyer.probdist.CauchyDist

getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.CauchyDist

Throws an exception since the mean does not exist.

getMean() - Method in class umontreal.iro.lecuyer.probdist.ChiDist

getMean(int) - Static method in class umontreal.iro.lecuyer.probdist.ChiDist

Computes and returns the mean of the chi distribution with parameter ν.

getMean() - Method in class umontreal.iro.lecuyer.probdist.ChiSquareDist

getMean(int) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareDist

Computes and returns the mean E[X] = n of the chi-square distribution with parameter n.

getMean() - Method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist

getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist

Computes and returns the mean E[X] = ν + λ of the noncentral chi-square distribution with parameters ν = nu and λ = lambda.

getMean() - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution

Returns the mean.

getMean() - Method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist

getMean(int) - Static method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist

Returns the mean of the distribution with parameter n.

getMean() - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistribution

Computes the mean E[X] = ∑_i=1ⁿp_ix_i of the distribution.

getMean() - Method in interface umontreal.iro.lecuyer.probdist.Distribution

Returns the mean of the distribution function.

getMean() - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist

getMean(int, double) - Static method in class umontreal.iro.lecuyer.probdist.ErlangDist

Computes and returns the mean, E[X] = k/λ, of the Erlang distribution with parameters k and λ.

getMean() - Method in class umontreal.iro.lecuyer.probdist.ExponentialDist

getMean(double) - Static method in class umontreal.iro.lecuyer.probdist.ExponentialDist

Computes and returns the mean, E[X] = 1/λ, of the exponential distribution with parameter λ.

getMean() - Method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist

Deprecated.

getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist

Deprecated.

Computes and returns the mean, E[X] = α + γ/λ, of the extreme value distribution with parameters α and λ, where γ = 0.5772156649 is the Euler-Mascheroni constant.

getMean() - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist

getMean(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist

Computes and returns the mean E[X] = μ + β(1 + γ²/2) of the fatigue life distribution with parameters μ, β and γ.

getMean() - Method in class umontreal.iro.lecuyer.probdist.FisherFDist

getMean(int, int) - Static method in class umontreal.iro.lecuyer.probdist.FisherFDist

Computes and returns the mean E[X] = m/(m - 2) of the Fisher F distribution with parameters n and m.

getMean() - Method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist

getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist

.

getMean() - Method in class umontreal.iro.lecuyer.probdist.FrechetDist

getMean(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FrechetDist

Returns the mean of the Fréchet distribution with parameters α, β and δ.

getMean() - Method in class umontreal.iro.lecuyer.probdist.GammaDist

getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.GammaDist

Computes and returns the mean E[X] = α/λ of the gamma distribution with parameters α and λ.

getMean() - Method in class umontreal.iro.lecuyer.probdist.GeometricDist

getMean(double) - Static method in class umontreal.iro.lecuyer.probdist.GeometricDist

Computes and returns the mean E[X] = (1 - p)/p of the geometric distribution with parameter p.

getMean() - Method in class umontreal.iro.lecuyer.probdist.GumbelDist

getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.GumbelDist

Returns the mean, E[X] = δ + γβ, of the Gumbel distribution with parameters β and δ, where γ = 0.5772156649015329 is the Euler-Mascheroni constant.

getMean() - Method in class umontreal.iro.lecuyer.probdist.HalfNormalDist

getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.HalfNormalDist

Computes and returns the mean E[X] = μ + σ(2 / π)^1/2.

getMean() - Method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist

getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist

Computes and returns the mean E[X] = μ of the hyperbolic secant distribution with parameters μ and σ.

getMean() - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist

getMean(int, int, int) - Static method in class umontreal.iro.lecuyer.probdist.HypergeometricDist

Computes and returns the mean E[X] = km/l of the Hypergeometric distribution with parameters m, l and k.

getMean() - Method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist

getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist

Returns the mean E[X] = μ of the inverse gaussian distribution with parameters μ and λ.

getMean() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist

getMean(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist

Computes and returns the mean of the Johnson S_U distribution with parameters γ, δ, ξ and λ.

getMean() - Method in class umontreal.iro.lecuyer.probdist.LaplaceDist

getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LaplaceDist

Computes and returns the mean E[X] = μ of the Laplace distribution with parameters μ and β.

getMean() - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist

getMean(double) - Static method in class umontreal.iro.lecuyer.probdist.LogarithmicDist

Computes and returns the mean of the logarithmic distribution with parameter θ = theta.

getMean() - Method in class umontreal.iro.lecuyer.probdist.LogisticDist

getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LogisticDist

Computes and returns the mean E[X] = α of the logistic distribution with parameters α and λ.

getMean() - Method in class umontreal.iro.lecuyer.probdist.LoglogisticDist

getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LoglogisticDist

Computes and returns the mean of the log-logistic distribution with parameters α and β.

getMean() - Method in class umontreal.iro.lecuyer.probdist.LognormalDist

getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LognormalDist

Computes and returns the mean E[X] = e^μ+σ2/2 of the lognormal distribution with parameters μ and σ.

getMean() - Method in class umontreal.iro.lecuyer.probdist.NakagamiDist

getMean(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NakagamiDist

.

getMean() - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist

getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist

Computes and returns the mean E[X] = γ(1 - p)/p of the negative binomial distribution with parameters γ and p.

getMean() - Method in class umontreal.iro.lecuyer.probdist.NormalDist

getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist

Computes and returns the mean E[X] = μ of the normal distribution with parameters μ and σ.

getMean() - Method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist

getMean(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist

Returns the mean E[X] = μ + δβ/γ of the normal inverse gaussian distribution with parameters α, β, μ and δ.

getMean() - Method in class umontreal.iro.lecuyer.probdist.ParetoDist

getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ParetoDist

Computes and returns the mean E[X] = αβ/(α - 1) of the Pareto distribution with parameters α and β.

getMean() - Method in class umontreal.iro.lecuyer.probdist.Pearson5Dist

getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson5Dist

Computes and returns the mean E[X] = β/(α - 1) of a Pearson V distribution with shape parameter α and scale parameter β.

getMean() - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist

getMean(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson6Dist

Computes and returns the mean E[X] = (βα₁)/(α₂ - 1) of a Pearson VI distribution with shape parameters α₁ and α₂, and scale parameter β.

getMean() - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist

getMean() - Method in class umontreal.iro.lecuyer.probdist.PoissonDist

getMean(double) - Static method in class umontreal.iro.lecuyer.probdist.PoissonDist

Computes and returns the mean E[X] = λ of the Poisson distribution with parameter λ.

getMean() - Method in class umontreal.iro.lecuyer.probdist.PowerDist

getMean(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.PowerDist

Returns the mean a + (b - a)c/(c + 1) of the power distribution with parameters a, b and c.

getMean() - Method in class umontreal.iro.lecuyer.probdist.RayleighDist

getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist

Returns the mean a + β(π/2)^1/2 of the Rayleigh distribution with parameters a and β.

getMean() - Method in class umontreal.iro.lecuyer.probdist.StudentDist

getMean(int) - Static method in class umontreal.iro.lecuyer.probdist.StudentDist

Returns the mean E[X] = 0 of the Student-t distribution with parameter n.

getMean() - Method in class umontreal.iro.lecuyer.probdist.TriangularDist

getMean(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.TriangularDist

Computes and returns the mean E[X] = (a + b + m)/3 of the triangular distribution with parameters a, b, m.

getMean() - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist

getMean() - Method in class umontreal.iro.lecuyer.probdist.UniformDist

getMean(double, double) - Static method in class umontreal.iro.lecuyer.probdist.UniformDist

Computes and returns the mean E[X] = (a + b)/2 of the uniform distribution with parameters a and b.

getMean() - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist

getMean(int, int) - Static method in class umontreal.iro.lecuyer.probdist.UniformIntDist

Computes and returns the mean E[X] = (i + j)/2 of the discrete uniform distribution.

getMean() - Method in class umontreal.iro.lecuyer.probdist.WatsonUDist

getMean(int) - Static method in class umontreal.iro.lecuyer.probdist.WatsonUDist

Returns the mean of the Watson U distribution with parameter n.

getMean() - Method in class umontreal.iro.lecuyer.probdist.WeibullDist

getMean(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist

Computes and returns the mean of the Weibull distribution with parameters α, λ and δ.

getMean() - Method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist

getMean(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist

Return the mean vector E[X] = (μ₁, μ₂) of the binormal distribution.

getMean() - Method in class umontreal.iro.lecuyer.probdistmulti.BiStudentDist

getMean(int, double) - Static method in class umontreal.iro.lecuyer.probdistmulti.BiStudentDist

Returns the mean vector E[X] = (0, 0) of the bivariate Student's t distribution.

getMean() - Method in class umontreal.iro.lecuyer.probdistmulti.ContinuousDistributionMulti

Returns the mean vector of the distribution, defined as μ_i = E[X_i].

getMean() - Method in class umontreal.iro.lecuyer.probdistmulti.DirichletDist

getMean(double[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.DirichletDist

Computes the mean E[X] = α_i/α₀ of the Dirichlet distribution with parameters (α₁,...,α_d), where α₀ = ∑_i=1^dα_i.

getMean() - Method in class umontreal.iro.lecuyer.probdistmulti.DiscreteDistributionIntMulti

Returns the mean vector of the distribution, defined as μ_i = E[X_i].

getMean() - Method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist

getMean(int, double[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist

Computes the mean E[X_i] = np_i of the multinomial distribution with parameters n and (p₁,...,p_d).

getMean() - Method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist

getMean(double[], double[][]) - Static method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist

Returns the mean E[X] = μ of the multinormal distribution with parameters μ and Σ.

getMean() - Method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist

getMean(double, double[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist

Computes the mean E[X] = γp_i/p₀ of the negative multinomial distribution with parameters γ and (p₁,...,p_d).

getMedian() - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist

Returns the n/2^th item of the sorted observations when the number of items is odd, and the mean of the n/2^th and the (n/2 + 1)^th items when the number of items is even.

getMedian(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.EmpiricalDist

Returns the n/2^th item of the array obs when the number of items is odd, and the mean of the n/2^th and the (n/2 + 1)^th items when the number of items is even.

getMethod(Class<?>, String, Class[]) - Static method in class umontreal.iro.lecuyer.util.Introspection

This is like getMethod, except that it can return non-public methods.

getMethods(Class<?>) - Static method in class umontreal.iro.lecuyer.util.Introspection

Returns all the methods declared and inherited by a class.

getMinKnot() - Method in class umontreal.iro.lecuyer.functionfit.BSpline

Returns the knot minimal value.

getMinutes() - Method in class umontreal.iro.lecuyer.util.AbstractChrono

Returns the CPU time in minutes used by the program since the last call to init for this AbstractChrono.

getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist

Estimates the parameters (α, β) of the beta distribution over the interval [0, 1] using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.

getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist

Estimates the parameter α of the symmetrical beta distribution over the interval [0, 1] using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.

getMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist

Estimates the parameters (n, p) of the binomial distribution using the maximum likelihood method, from the m observations x[i], i = 0, 1,…, m - 1.

getMLE(int[], int, int) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist

Estimates the parameter p of the binomial distribution with given (fixed) parameter n, by the maximum likelihood method, from the m observations x[i], i = 0, 1,…, m - 1.

getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.CauchyDist

Estimates the parameters (α, β) of the Cauchy distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.

getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ChiDist

Estimates the parameter ν of the chi distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.

getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareDist

Estimates the parameter n of the chi-square distribution using the maximum likelihood method, from the m observations x[i], i = 0, 1,…, m - 1.

getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ErlangDist

Estimates the parameters (k, λ) of the Erlang distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.

getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ExponentialDist

Estimates the parameter λ of the exponential distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.

getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist

Deprecated.

Estimates the parameters (α, λ) of the extreme value distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.

getMLE(double[], int, double) - Static method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist

Estimates the parameters (μ, β, γ) of the fatigue life distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.

getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist

NOT IMPLEMENTED.

getMLE(double[], int, double) - Static method in class umontreal.iro.lecuyer.probdist.FrechetDist

Given δ = delta, estimates the parameters (α, β) of the Fréchet distribution using the maximum likelihood method with the n observations x[i], i = 0, 1,…, n - 1.

getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.GammaDist

Estimates the parameters (α, λ) of the gamma distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.

getMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.GeometricDist

Estimates the parameter p of the geometric distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.

getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.GumbelDist

Estimates the parameters (β, δ) of the Gumbel distribution, assuming that β > 0, and using the maximum likelihood method with the n observations x[i], i = 0, 1,…, n - 1.

getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.HalfNormalDist

Estimates the parameters μ and σ of the half-normal distribution using the maximum likelihood method from the n observations x[i], i = 0, 1,…, n - 1.

getMLE(double[], int, double) - Static method in class umontreal.iro.lecuyer.probdist.HalfNormalDist

Estimates the parameter σ of the half-normal distribution using the maximum likelihood method from the n observations x[i], i = 0, 1,…, n - 1 and the parameter μ = mu.

getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist

Estimates the parameters (μ, σ) of the hyperbolic secant distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.

getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist

Estimates the parameters (μ, λ) of the inverse gaussian distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.

getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.LaplaceDist

Estimates the parameters (μ, β) of the Laplace distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.

getMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.LogarithmicDist

Estimates the parameter θ of the logarithmic distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.

getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.LogisticDist

Estimates the parameters (α, λ) of the logistic distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.

getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.LoglogisticDist

Estimates the parameters (α, β) of the log-logistic distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.

getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.LognormalDist

Estimates the parameters (μ, σ) of the lognormal distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.

getMLE(int[], int, double) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist

Estimates the parameter p of the negative binomial distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.

getMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist

Estimates the parameter (γ, p) of the negative binomial distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.

getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist

Estimates the parameters (μ, σ) of the normal distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.

getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist

NOT IMPLEMENTED.

getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ParetoDist

Estimates the parameters (α, β) of the Pareto distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.

getMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.PascalDist

Estimates the parameter (n, p) of the Pascal distribution using the maximum likelihood method, from the m observations x[i], i = 0, 1,…, m - 1.

getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.Pearson5Dist

Estimates the parameters (α, β) of the Pearson V distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.

getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.Pearson6Dist

Estimates the parameters (α₁, α₂, β) of the Pearson VI distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.

getMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.PoissonDist

Estimates the parameter λ of the Poisson distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.

getMLE(double[], int, double, double) - Static method in class umontreal.iro.lecuyer.probdist.PowerDist

Estimates the parameter c of the power distribution from the n observations x[i], i = 0, 1,…, n - 1, using the maximum likelihood method and assuming that a and b are known.

getMLE(double[], int, double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist

Estimates the parameter β of the Rayleigh distribution using the maximum likelihood method, assuming that a is known, from the n observations x[i], i = 0, 1,…, n - 1.

getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.StudentDist

Estimates the parameter n of the Student-t distribution using the maximum likelihood method, from the m observations x[i], i = 0, 1,…, m - 1.

getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.UniformDist

Estimates the parameter (a, b) of the uniform distribution using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.

getMLE(int[], int) - Static method in class umontreal.iro.lecuyer.probdist.UniformIntDist

Estimates the parameters (i, j) of the uniform distribution over integers using the maximum likelihood method, from the n observations x[k], k = 0, 1,…, n - 1.

getMLE(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist

Estimates the parameters (α, λ) of the Weibull distribution, assuming that δ = 0, using the maximum likelihood method, from the n observations x[i], i = 0, 1,…, n - 1.

getMLE(double[][], int, int) - Static method in class umontreal.iro.lecuyer.probdistmulti.DirichletDist

Estimates the parameters [ hat(α_1),…, hat(α_d)] of the Dirichlet distribution using the maximum likelihood method.

getMLE(int[][], int, int, int) - Static method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist

Estimates and returns the parameters [hat(p_i),...,hat(p_d)] of the multinomial distribution using the maximum likelihood method.

getMLE(int[][], int, int) - Static method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist

Estimates and returns the parameters [ hat(γ), hat(p_1),..., hat(p_d)] of the negative multinomial distribution using the maximum likelihood method.

getMLEmin(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.GumbelDist

Similar to getMLE, but for the case β < 0.

getMLEMu(double[][], int, int) - Static method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist

Estimates the parameters μ of the multinormal distribution using the maximum likelihood method.

getMLESigma(double[][], int, int) - Static method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist

Estimates the parameters Σ of the multinormal distribution using the maximum likelihood method.

getMomentsEstimate(double[], int) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareDist

Estimates and returns the parameter [hat(n)] of the chi-square distribution using the moments method based on the m observations in table x[i], i = 0, 1,…, m - 1.

getMu() - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist

Returns the parameter μ of this object.

getMu() - Method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist

Returns the parameter μ of this object.

getMu() - Method in class umontreal.iro.lecuyer.probdist.HalfNormalDist

Returns the parameter μ of this object.

getMu() - Method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist

Returns the parameter μ of this object.

getMu() - Method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist

Returns the parameter μ of this object.

getMu() - Method in class umontreal.iro.lecuyer.probdist.LaplaceDist

Returns the parameter μ.

getMu() - Method in class umontreal.iro.lecuyer.probdist.LognormalDist

Returns the parameter μ of this object.

getMu() - Method in class umontreal.iro.lecuyer.probdist.NormalDist

Returns the parameter μ.

getMu() - Method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist

Returns the parameter μ of this object.

getMu() - Method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist

Returns the parameter μ of this object.

getMu(int) - Method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist

Returns the i-th component of the parameter μ of this object.

getMu() - Method in class umontreal.iro.lecuyer.randvar.FatigueLifeGen

Returns the parameter μ of this object.

getMu() - Method in class umontreal.iro.lecuyer.randvar.FoldedNormalGen

Returns the parameter μ of this object.

getMu() - Method in class umontreal.iro.lecuyer.randvar.HalfNormalGen

Returns the parameter μ of this object.

getMu() - Method in class umontreal.iro.lecuyer.randvar.HyperbolicSecantGen

Returns the parameter μ of this object.

getMu() - Method in class umontreal.iro.lecuyer.randvar.InverseGaussianGen

Returns the parameter μ of this object.

getMu() - Method in class umontreal.iro.lecuyer.randvar.LaplaceGen

Returns the parameter μ.

getMu() - Method in class umontreal.iro.lecuyer.randvar.LognormalGen

Returns the parameter μ of this object.

getMu() - Method in class umontreal.iro.lecuyer.randvar.NormalGen

Returns the parameter μ of this object.

getMu() - Method in class umontreal.iro.lecuyer.randvar.NormalInverseGaussianGen

Returns the parameter μ of this object.

getMu() - Method in class umontreal.iro.lecuyer.randvarmulti.MultinormalGen

Returns the mean vector used by this generator.

getMu(int) - Method in class umontreal.iro.lecuyer.randvarmulti.MultinormalGen

Returns the i-th component of the mean vector for this generator.

getMu() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotion

Returns the value of μ.

getMu() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcess

Returns the value of the parameter μ.

getMu() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion

Returns the value of μ.

getMu() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess

Returns the value of the parameter μ.

getMu() - Method in class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess

Returns mu.

getMu1() - Method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist

Returns the parameter μ₁.

getMu2() - Method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist

Returns the parameter μ₂.

getMuGeom() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricLevyProcess

Returns the geometric drift parameter, which is usually the interest rate, r.

getN() - Method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDist

Returns the parameter n of this object.

getN() - Method in class umontreal.iro.lecuyer.probdist.BinomialDist

Returns the parameter n of this object.

getN() - Method in class umontreal.iro.lecuyer.probdist.ChiSquareDist

Returns the parameter n of this object.

getN() - Method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist

Returns the parameter n of this object.

getN() - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist

Returns n, the number of observations.

getN() - Method in class umontreal.iro.lecuyer.probdist.FisherFDist

Returns the parameter n of this object.

getN() - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDist

Returns the parameter n of this object.

getN() - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist

Returns the parameter n of this object.

getN() - Method in class umontreal.iro.lecuyer.probdist.PascalDist

Returns the parameter n of this object.

getN() - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist

Returns n, the number of observations.

getN() - Method in class umontreal.iro.lecuyer.probdist.StudentDist

Returns the parameter n associated with this object.

getN() - Method in class umontreal.iro.lecuyer.probdist.WatsonGDist

Returns the parameter n of this object.

getN() - Method in class umontreal.iro.lecuyer.probdist.WatsonUDist

Returns the parameter n of this object.

getN() - Method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist

Returns the parameter n of this object.

getN() - Method in class umontreal.iro.lecuyer.randvar.BinomialGen

Returns the parameter n of this object.

getN() - Method in class umontreal.iro.lecuyer.randvar.ChiSquareGen

Returns the value of n for this object.

getN() - Method in class umontreal.iro.lecuyer.randvar.FisherFGen

Returns the parameter n of this object.

getN() - Method in class umontreal.iro.lecuyer.randvar.PascalGen

Returns the parameter n of this object.

getN() - Method in class umontreal.iro.lecuyer.randvar.StudentGen

Returns the value of n for this object.

getName(int) - Method in class umontreal.iro.lecuyer.charts.BoxSeriesCollection

Gets the current name of the selected series.

getName(int) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection

Gets the current name of the selected series.

getName() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat

Returns the name associated to this list, or null if no name was assigned.

getName() - Method in class umontreal.iro.lecuyer.simprocs.Condition

Returns the name (or identifier) associated to this condition.

getName() - Method in class umontreal.iro.lecuyer.simprocs.Resource

Returns the name (or identifier) associated to this resource.

getName() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

Returns the global name of this list of statistical probes.

getName() - Method in class umontreal.iro.lecuyer.stat.StatProbe

Returns the name associated with this probe, or null if no name was specified upon construction.

getName() - Method in exception umontreal.iro.lecuyer.util.NameConflictException

Returns the simple name associated with this exception.

getNbObservationTimes() - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess

Returns the number of observation times excluding the time t₀.

getNonLinearData() - Static method in class umontreal.iro.lecuyer.rng.F2NL607

Return the data of all the components of the non-linear part of the random number generator.

getNonLinearState() - Method in class umontreal.iro.lecuyer.rng.F2NL607

Returns the current state of the non-linear part of the stream, represented as an array of n integers, where n is the number of components in the non-linear generator.

getNormalGen() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessMSH

Returns the normal generator.

getNu() - Method in class umontreal.iro.lecuyer.probdist.ChiDist

Returns the value of ν for this object.

getNu() - Method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist

Returns the parameter ν of this object.

getNu() - Method in class umontreal.iro.lecuyer.randvar.ChiGen

Returns the value of ν for this object.

getNu() - Method in class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralGen

Returns the value of ν of this object.

getNu() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcess

Returns the value of the parameter ν.

getNu() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess

Returns the value of the parameter ν.

getNu() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess

Returns the value of the parameter ν.

getNumberOfRandomStreams() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess

Returns the number of random streams of this process.

getNumCachedValues() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache

Returns the total number of values cached by this generator.

getNumCachedValues() - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache

Returns the total number of values cached by this random stream.

getNumPoints() - Method in class umontreal.iro.lecuyer.hups.ContainerPointSet

Returns the number of points of the contained point set.

getNumPoints() - Method in class umontreal.iro.lecuyer.hups.HaltonSequence

getNumPoints() - Method in class umontreal.iro.lecuyer.hups.PointSet

Returns the number of points.

getNumUnits() - Method in class umontreal.iro.lecuyer.simprocs.UserRecord

Returns the number of units requested or used by the associated process.

getObs(int) - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist

Returns the value of X_(i).

getObs(int) - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist

Returns the value of X_(i).

getObservation(int) - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess

Returns X(t_j) from the current sample path.

getObservationTimes() - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess

Returns a reference to the array that contains the observation times (t₀,..., t_d).

getOmega() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricLevyProcess

Returns the risk neutral correction.

getOmega() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess

Returns the value of the quantity ω defined in.

getOrder() - Method in class umontreal.iro.lecuyer.probdist.InverseDistFromDensity

Returns the order associated with this object.

getOrder() - Method in class umontreal.iro.lecuyer.randvar.InverseFromDensityGen

Returns the order associated with this object.

getOtherStream() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessMSH

Returns the otherStream, which is the stream used to choose between the two quadratic roots from the MSH method.

getOutlineWidth(int) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection

Returns the outline width in pt.

getP() - Method in class umontreal.iro.lecuyer.probdist.BinomialDist

Returns the parameter p of this object.

getP() - Method in class umontreal.iro.lecuyer.probdist.GeometricDist

Returns the p associated with this object.

getP() - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist

Returns the parameter p of this object.

getP() - Method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist

Returns the parameters (p₁,...,p_d) of this object.

getP() - Method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist

Returns the parameters (p₁,...,p_d) of this object.

getP() - Method in class umontreal.iro.lecuyer.randvar.BinomialGen

Returns the parameter p of this object.

getP() - Method in class umontreal.iro.lecuyer.randvar.GeometricGen

Returns the parameter p of this object.

getP() - Method in class umontreal.iro.lecuyer.randvar.NegativeBinomialGen

Returns the parameter p of this object.

getP() - Method in class umontreal.iro.lecuyer.randvar.PascalGen

Returns the parameter p of this object.

getParams() - Method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDist

Return an array containing the parameter n of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.BetaDist

Return a table containing parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist

Return a table containing the parameter of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.BinomialDist

Returns a table that contains the parameters (n, p) of the current distribution, in regular order: [n, p].

getParams() - Method in class umontreal.iro.lecuyer.probdist.CauchyDist

Return a table containing parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.ChiDist

Return a table containing parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.ChiSquareDist

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist

Returns a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist

Return an array containing the parameter n of this object.

getParams() - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistribution

Returns a table containing the parameters of the current distribution.

getParams() - Method in interface umontreal.iro.lecuyer.probdist.Distribution

Returns the parameters of the distribution function in the same order as in the constructors.

getParams() - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist

Return a table containing parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.ErlangDist

Return a table containing parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.ExponentialDist

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist

Deprecated.

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.FisherFDist

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.FrechetDist

Return an array containing the parameters of the current object in regular order: [α, β, δ].

getParams() - Method in class umontreal.iro.lecuyer.probdist.GammaDist

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.GeometricDist

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.GumbelDist

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.HalfNormalDist

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.InverseDistFromDensity

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDist

Returns an array containing the parameter n of this object.

getParams() - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist

Returns an array containing the parameter n of this object.

getParams() - Method in class umontreal.iro.lecuyer.probdist.LaplaceDist

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.LogisticDist

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.LoglogisticDist

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.LognormalDist

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.NakagamiDist

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.NormalDist

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist

Returns a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.ParetoDist

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.PascalDist

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.Pearson5Dist

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist

Return a table containing parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.PoissonDist

Return a table containing the parameter of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.PowerDist

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.RayleighDist

Return an array containing the parameters of the current distribution in the order: [a, β].

getParams() - Method in class umontreal.iro.lecuyer.probdist.StudentDist

Return a table containing the parameter of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.TriangularDist

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.UniformDist

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist

Return a table containing the parameters of the current distribution.

getParams() - Method in class umontreal.iro.lecuyer.probdist.WatsonGDist

Return an array containing the parameter n of this object.

getParams() - Method in class umontreal.iro.lecuyer.probdist.WatsonUDist

Return an array containing the parameter n of this object.

getParams() - Method in class umontreal.iro.lecuyer.probdist.WeibullDist

Return a table containing the parameters of the current distribution.

getPath() - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess

Returns a reference to the last generated sample path {X(t₀),..., X(t_d)}.

getPCADecompSigma() - Method in class umontreal.iro.lecuyer.randvarmulti.MultinormalPCAGen

Returns the matrix A = V()^1/2 of this object.

getPhi() - Method in class umontreal.iro.lecuyer.probdist.LaplaceDist

Deprecated.

getPlotStyle(int) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection

Gets the current plot style for the selected series.

getPower() - Method in class umontreal.iro.lecuyer.functions.PowerMathFunction

Returns the power p.

getPrimes(int) - Static method in class umontreal.iro.lecuyer.hups.RadicalInverse

Provides an elementary method for obtaining the first n prime numbers larger than 1.

getProb() - Method in class umontreal.iro.lecuyer.charts.DiscreteDistIntChart

Returns the chart of the probability.

getProcess() - Method in class umontreal.iro.lecuyer.simprocs.UserRecord

Returns the process object associated with this record.

getRandomStreamClass() - Method in class umontreal.iro.lecuyer.rng.BasicRandomStreamFactory

Returns the random stream class associated with this object.

getRandomStreamFactory() - Method in exception umontreal.iro.lecuyer.rng.RandomStreamInstantiationException

Returns the random stream factory concerned by this exception.

getRangeBounds() - Method in class umontreal.iro.lecuyer.charts.BoxSeriesCollection

Returns the range (y-coordinates) min and max values.

getRangeBounds() - Method in class umontreal.iro.lecuyer.charts.SSJCategorySeriesCollection

Returns range (y-coordinates) min and max values.

getRangeBounds() - Method in class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection

Returns range (y-coordinates) min and max values.

getRenderer() - Method in class umontreal.iro.lecuyer.charts.SSJCategorySeriesCollection

Returns the CategoryItemRenderer object associated with the current object.

getRenderer() - Method in class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection

Returns the XYItemRenderer object associated with the current object.

getRequestTime() - Method in class umontreal.iro.lecuyer.simprocs.UserRecord

Returns the time of creation of this record.

getRho() - Method in class umontreal.iro.lecuyer.functionfit.SmoothingCubicSpline

Returns the smoothing factor used to construct the spline.

getSampleMean() - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist

Returns the sample mean of the observations.

getSampleMean() - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist

Returns the sample mean of the observations.

getSampleStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist

Returns the sample standard deviation of the observations.

getSampleStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist

Returns the sample standard deviation of the observations.

getSampleVariance() - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist

Returns the sample variance of the observations.

getSampleVariance() - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist

Returns the sample variance of the observations.

getSeconds() - Method in class umontreal.iro.lecuyer.util.AbstractChrono

Returns the CPU time in seconds used by the program since the last call to init for this AbstractChrono.

getSeriesCollection() - Method in class umontreal.iro.lecuyer.charts.BoxChart

Returns the chart's dataset.

getSeriesCollection() - Method in class umontreal.iro.lecuyer.charts.EmpiricalChart

Returns the chart's dataset.

getSeriesCollection() - Method in class umontreal.iro.lecuyer.charts.HistogramChart

Returns the chart's dataset.

getSeriesCollection() - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection

Returns the CustomHistogramDataset object associated with the current variable.

getSeriesCollection() - Method in class umontreal.iro.lecuyer.charts.ScatterChart

Returns the chart's dataset.

getSeriesCollection() - Method in class umontreal.iro.lecuyer.charts.SSJCategorySeriesCollection

Returns the CategoryDataset object associated with the current object.

getSeriesCollection() - Method in class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection

Returns the XYDataset object associated with the current object.

getSeriesCollection() - Method in class umontreal.iro.lecuyer.charts.XYLineChart

Returns the chart's dataset.

getSeriesCount() - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset

Returns the number of series in the dataset.

getSeriesKey(int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset

Returns the key for a series.

getShiftDimension() - Method in class umontreal.iro.lecuyer.hups.RandShiftedPointSet

Returns the number of dimensions of the current random shift.

getShortName() - Method in enum umontreal.iro.lecuyer.util.TimeUnit

Returns the short name representing this unit in a string specifying a time duration.

getSigma() - Method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist

Returns the parameter σ of this object.

getSigma() - Method in class umontreal.iro.lecuyer.probdist.HalfNormalDist

Returns the parameter σ of this object.

getSigma() - Method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist

Returns the parameter σ of this object.

getSigma() - Method in class umontreal.iro.lecuyer.probdist.LognormalDist

Returns the parameter σ of this object.

getSigma() - Method in class umontreal.iro.lecuyer.probdist.NormalDist

Returns the parameter σ.

getSigma() - Method in class umontreal.iro.lecuyer.probdist.RayleighDist

Returns the parameter β.

getSigma() - Method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist

Returns the parameter Σ of this object.

getSigma() - Method in class umontreal.iro.lecuyer.randvar.FoldedNormalGen

Returns the parameter σ of this object.

getSigma() - Method in class umontreal.iro.lecuyer.randvar.HalfNormalGen

Returns the parameter σ of this object.

getSigma() - Method in class umontreal.iro.lecuyer.randvar.HyperbolicSecantGen

Returns the parameter σ of this object.

getSigma() - Method in class umontreal.iro.lecuyer.randvar.LognormalGen

Returns the parameter σ of this object.

getSigma() - Method in class umontreal.iro.lecuyer.randvar.NormalGen

Returns the parameter σ of this object.

getSigma() - Method in class umontreal.iro.lecuyer.randvar.RayleighGen

Returns the parameter β.

getSigma() - Method in class umontreal.iro.lecuyer.randvarmulti.MultinormalGen

Returns the covariance matrix Σ used by this generator.

getSigma() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotion

Returns the value of σ.

getSigma() - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess

Returns the value of σ.

getSigma() - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcessEuler

Returns the value of σ.

getSigma() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion

Returns the value of σ.

getSigma() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess

Returns the value of the parameter σ.

getSigma() - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess

Returns the value of σ.

getSigma() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess

Returns the value of the parameter σ.

getSigma1() - Method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist

Returns the parameter σ₁.

getSigma2() - Method in class umontreal.iro.lecuyer.probdistmulti.BiNormalDist

Returns the parameter σ₂.

getSimpleName(Class<?>) - Method in class umontreal.iro.lecuyer.util.ClassFinder

Returns the simple name of the class cls that can be used when the imports contained in this class finder are used.

getSortedEigenvalues() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCA

Returns the sorted eigenvalues obtained in the PCA decomposition.

getSortedEigenvalues() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCAEqualSteps

getSplinePolynomials() - Method in class umontreal.iro.lecuyer.functionfit.SmoothingCubicSpline

Returns a table containing all fitting polynomials.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.BetaDist

getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist

Computes the standard deviation of the beta distribution with parameters α and β, over the interval [0, 1].

getStandardDeviation(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist

Computes the standard deviation of the beta distribution with parameters α and β, over the interval [a, b].

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist

getStandardDeviation(double) - Static method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist

Computes and returns the standard deviation of the symmetrical beta distribution with parameter α.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.BinomialDist

getStandardDeviation(int, double) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist

Computes the standard deviation of the Binomial distribution with parameters n and p.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.CauchyDist

getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.CauchyDist

Returns ∞ since the standard deviation does not exist.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.ChiDist

getStandardDeviation(int) - Static method in class umontreal.iro.lecuyer.probdist.ChiDist

Computes and returns the standard deviation of the chi distribution with parameter ν.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.ChiSquareDist

getStandardDeviation(int) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareDist

Returns the standard deviation of the chi-square distribution with parameter n.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist

getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist

Computes and returns the standard deviation of the noncentral chi-square distribution with parameters ν = nu and λ = lambda.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution

Returns the standard deviation.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist

getStandardDeviation(int) - Static method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist

Returns the standard deviation of the distribution with parameter n.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistribution

Computes the standard deviation of the distribution.

getStandardDeviation() - Method in interface umontreal.iro.lecuyer.probdist.Distribution

Returns the standard deviation of the distribution function.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist

getStandardDeviation(int, double) - Static method in class umontreal.iro.lecuyer.probdist.ErlangDist

Computes and returns the standard deviation of the Erlang distribution with parameters k and λ.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.ExponentialDist

getStandardDeviation(double) - Static method in class umontreal.iro.lecuyer.probdist.ExponentialDist

Computes and returns the standard deviation of the exponential distribution with parameter λ.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist

Deprecated.

getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist

Deprecated.

Computes and returns the standard deviation of the extreme value distribution with parameters α and λ.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist

getStandardDeviation(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist

Computes and returns the standard deviation of the fatigue life distribution with parameters μ, β and γ.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.FisherFDist

getStandardDeviation(int, int) - Static method in class umontreal.iro.lecuyer.probdist.FisherFDist

Computes and returns the standard deviation of the Fisher F distribution with parameters n and m.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist

getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist

Computes the standard deviation of the folded normal distribution with parameters μ and σ.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.FrechetDist

getStandardDeviation(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FrechetDist

Returns the standard deviation of the Fréchet distribution with parameters α, β and δ.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.GammaDist

getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.GammaDist

Computes and returns the standard deviation of the gamma distribution with parameters α and λ.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.GeometricDist

getStandardDeviation(double) - Static method in class umontreal.iro.lecuyer.probdist.GeometricDist

Computes and returns the standard deviation of the geometric distribution with parameter p.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.GumbelDist

getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.GumbelDist

Returns the standard deviation of the Gumbel distribution with parameters β and δ.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.HalfNormalDist

getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.HalfNormalDist

Computes the standard deviation of the half-normal distribution with parameters μ and σ.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist

getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist

Computes and returns the standard deviation of the hyperbolic secant distribution with parameters μ and σ.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist

getStandardDeviation(int, int, int) - Static method in class umontreal.iro.lecuyer.probdist.HypergeometricDist

Computes and returns the standard deviation of the hypergeometric distribution with parameters m, l and k.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist

getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist

Computes and returns the standard deviation of the inverse gaussian distribution with parameters μ and λ.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist

getStandardDeviation(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist

Computes and returns the standard deviation of the Johnson S_U distribution with parameters γ, δ, ξ and λ.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.LaplaceDist

getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LaplaceDist

Computes and returns the standard deviation of the Laplace distribution with parameters μ and β.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist

getStandardDeviation(double) - Static method in class umontreal.iro.lecuyer.probdist.LogarithmicDist

Computes and returns the standard deviation of the logarithmic distribution with parameter θ = theta.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.LogisticDist

getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LogisticDist

Computes and returns the standard deviation of the logistic distribution with parameters α and λ.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.LoglogisticDist

getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LoglogisticDist

Computes and returns the standard deviation of the log-logistic distribution with parameters α and β.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.LognormalDist

getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LognormalDist

Computes and returns the standard deviation of the lognormal distribution with parameters μ and σ.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.NakagamiDist

getStandardDeviation(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NakagamiDist

Computes the standard deviation of the Nakagami distribution with parameters a, λ and c.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist

getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist

Computes and returns the standard deviation of the negative binomial distribution with parameters γ and p.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.NormalDist

getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist

Computes and returns the standard deviation σ of the normal distribution with parameters μ and σ.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist

getStandardDeviation(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist

Computes and returns the standard deviation of the normal inverse gaussian distribution with parameters α, β, μ and δ.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.ParetoDist

getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ParetoDist

Computes and returns the standard deviation of the Pareto distribution with parameters α and β.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.Pearson5Dist

getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson5Dist

Computes and returns the standard deviation of a Pearson V distribution with shape parameter α and scale parameter β.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist

getStandardDeviation(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson6Dist

Computes and returns the standard deviation of a Pearson VI distribution with shape parameters α₁ and α₂, and scale parameter β.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.PoissonDist

getStandardDeviation(double) - Static method in class umontreal.iro.lecuyer.probdist.PoissonDist

Computes and returns the standard deviation of the Poisson distribution with parameter λ.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.PowerDist

getStandardDeviation(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.PowerDist

Computes and returns the standard deviation of the power distribution with parameters a, b and c.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.RayleighDist

getStandardDeviation(double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist

Returns the standard deviation β(2 - π/2)^1/2 of the Rayleigh distribution with parameter β.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.StudentDist

getStandardDeviation(int) - Static method in class umontreal.iro.lecuyer.probdist.StudentDist

Computes and returns the standard deviation of the Student-t distribution with parameter n.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.TriangularDist

getStandardDeviation(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.TriangularDist

Computes and returns the standard deviation of the triangular distribution with parameters a, b, m.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.UniformDist

getStandardDeviation(double, double) - Static method in class umontreal.iro.lecuyer.probdist.UniformDist

Computes and returns the standard deviation of the uniform distribution with parameters a and b.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist

getStandardDeviation(int, int) - Static method in class umontreal.iro.lecuyer.probdist.UniformIntDist

Computes and returns the standard deviation of the discrete uniform distribution.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.WatsonUDist

getStandardDeviation(int) - Static method in class umontreal.iro.lecuyer.probdist.WatsonUDist

Returns the standard deviation of the Watson U distribution with parameter n.

getStandardDeviation() - Method in class umontreal.iro.lecuyer.probdist.WeibullDist

getStandardDeviation(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist

Computes and returns the standard deviation of the Weibull distribution with parameters α, λ and δ.

getStartX(int, int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset

Returns the start value for a bin.

getStartY(int, int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset

Returns the start y-value for a bin (which is the same as the y-value).

getStatCollecting() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat

Returns true if the list collects statistics about its size and sojourn times of elements, and false otherwise.

getState() - Method in class umontreal.iro.lecuyer.rng.GenF2w32

Returns the current state of the stream, represented as an array of 25 integers.

getState() - Method in class umontreal.iro.lecuyer.rng.LFSR113

Returns the current state of the stream, represented as an array of four integers.

getState() - Method in class umontreal.iro.lecuyer.rng.LFSR258

Returns the current state of the stream, represented as an array of five integers.

getState() - Method in class umontreal.iro.lecuyer.rng.MRG31k3p

Returns the current state C_g of this stream.

getState() - Method in class umontreal.iro.lecuyer.rng.MRG32k3a

Returns the current state C_g of this stream.

getState() - Method in class umontreal.iro.lecuyer.rng.MRG32k3aL

getState() - Method in class umontreal.iro.lecuyer.rng.RandMrg

Deprecated.

Returns the current state C_g of this stream.

getState() - Method in class umontreal.iro.lecuyer.rng.RandRijndael

Returns the current state of the stream, represented as an array of four integers.

getState() - Method in class umontreal.iro.lecuyer.rng.WELL1024

Returns the current state of the stream, represented as an array of 32 integers.

getState() - Method in class umontreal.iro.lecuyer.rng.WELL512

Returns the current state of the stream, represented as an array of 16 integers.

getState() - Method in class umontreal.iro.lecuyer.rng.WELL607

Returns the current state of the stream, represented as an array of 19 integers.

getState() - Method in class umontreal.iro.lecuyer.simprocs.SimProcess

Returns the state of the process.

getStream() - Method in class umontreal.iro.lecuyer.hups.EmptyRandomization

Returns the internal RandomStream.

getStream() - Method in class umontreal.iro.lecuyer.hups.PointSet

Returns the random stream used to generate random shifts.

getStream() - Method in interface umontreal.iro.lecuyer.hups.PointSetRandomization

Returns the internal RandomStream.

getStream() - Method in class umontreal.iro.lecuyer.hups.RandomShift

Returns the internal RandomStream.

getStream() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGen

Returns the RandomStream used by this generator.

getStream() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache

getStream() - Method in class umontreal.iro.lecuyer.randvar.UnuranContinuous

getStream() - Method in class umontreal.iro.lecuyer.randvar.UnuranDiscreteInt

getStream() - Method in class umontreal.iro.lecuyer.randvar.UnuranEmpirical

getStream() - Method in class umontreal.iro.lecuyer.randvarmulti.RandomMultivariateGen

Returns the RandomStream used by this object.

getStream() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotion

Returns the random stream of the normal generator.

getStream() - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess

Returns the random stream of the noncentral chi-square generator.

getStream() - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcessEuler

Returns the random stream of the normal generator.

getStream() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcess

Returns the RandomStream stream.

getStream() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion

Returns the RandomStream for the underlying Brownian motion.

getStream() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricLevyProcess

Returns the stream from the underlying Lévy process.

getStream() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess

getStream() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess

getStream() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessBridge

Only returns a stream if both inner streams are the same.

getStream() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessMSH

Only returns a stream if both inner RandomStream's are the same.

getStream() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessPCA

getStream() - Method in class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess

Only returns the stream if all streams are equal, including the stream(s) in the underlying InverseGaussianProcess.

getStream() - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess

Returns the random stream of the normal generator.

getStream() - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess

Returns the random stream of the underlying generator.

getStream() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess

Returns the random stream of the BrownianMotion process, which should be the same as for the GammaProcess.

getStream2() - Method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalBestGen

Returns stream s2 associated with this object.

getStream2() - Method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalPolarGen

Returns stream s2 associated with this object.

getStream3() - Method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalBestGen

Returns stream s3 associated with this object.

getStreams() - Method in class umontreal.iro.lecuyer.rng.RandomStreamManager

Returns an unmodifiable list containing all the random streams in this random stream manager.

getSubpath(double[], int[]) - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess

Returns in subpath the values of the process at a subset of the observation times, specified as the times t_j whose indices j are in the array pathIndices.

getTheta() - Method in class umontreal.iro.lecuyer.probdist.LaplaceDist

Deprecated.

getTheta() - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist

Returns the θ associated with this object.

getTheta() - Method in class umontreal.iro.lecuyer.randvar.LogarithmicGen

Returns the θ associated with this object.

getTheta() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess

Returns the value of the parameter θ.

getTheta() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess

Returns the value of the parameter θ.

getTheta0() - Method in class umontreal.iro.lecuyer.randvar.LogarithmicGen

Returns the θ₀ associated with this object.

getTimeInterval(double[], int, int, double) - Static method in class umontreal.iro.lecuyer.util.Misc

Returns the index of the time interval corresponding to time t.

getTitle() - Method in class umontreal.iro.lecuyer.charts.CategoryChart

Gets the current chart title.

getTitle() - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart

Gets the current chart title.

getTitle() - Method in class umontreal.iro.lecuyer.charts.XYChart

Gets the current chart title.

getTotal(int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset

Returns the total number of observations for a series.

getTwinAxisPosition() - Method in class umontreal.iro.lecuyer.charts.Axis

Returns the drawing position parameter (default equals 0).

getType() - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset

Returns the histogram type.

getValue(int, int) - Method in class umontreal.iro.lecuyer.charts.SSJCategorySeriesCollection

Returns the y-value at the specified index in the specified series.

getValues(int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset

Returns the values for a series.

getValues(int) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection

Returns the values for a series.

getValuesList(int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset

Returns the values for a series.

getValuesList(int) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection

Returns the values for a series.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.BetaDist

getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist

.

getVariance(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist

.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist

getVariance(double) - Static method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist

Computes and returns the variance, Var[X] = 1/(8α + 4), of the symmetrical beta distribution with parameter α.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.BinomialDist

getVariance(int, double) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist

Computes the variance Var[X] = np(1 - p) of the binomial distribution with parameters n and p.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.CauchyDist

getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.CauchyDist

Returns ∞ since the variance does not exist.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.ChiDist

getVariance(int) - Static method in class umontreal.iro.lecuyer.probdist.ChiDist

Computes and returns the variance of the chi distribution with parameter ν.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.ChiSquareDist

getVariance(int) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareDist

Returns the variance Var[X] = 2n of the chi-square distribution with parameter n.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist

getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist

Computes and returns the variance Var[X] = 2(ν +2λ) of the noncentral chi-square distribution with parameters ν = nu and λ = lambda.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution

Returns the variance.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist

getVariance(int) - Static method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist

Returns the variance of the distribution with parameter n.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistribution

Computes the variance Var[X] = ∑_i=1ⁿp_i(x_i - E[X])² of the distribution.

getVariance() - Method in interface umontreal.iro.lecuyer.probdist.Distribution

Returns the variance of the distribution function.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist

getVariance(int, double) - Static method in class umontreal.iro.lecuyer.probdist.ErlangDist

Computes and returns the variance, Var[X] = k/λ², of the Erlang distribution with parameters k and λ.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.ExponentialDist

getVariance(double) - Static method in class umontreal.iro.lecuyer.probdist.ExponentialDist

Computes and returns the variance, Var[X] = 1/λ², of the exponential distribution with parameter λ.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist

Deprecated.

getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist

Deprecated.

Computes and returns the variance, Var[X] = π²/(6λ²), of the extreme value distribution with parameters α and λ.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist

getVariance(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist

Computes and returns the variance Var[X] = β²γ²(1 + 5γ²/4) of the fatigue life distribution with parameters μ, β and γ.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.FisherFDist

getVariance(int, int) - Static method in class umontreal.iro.lecuyer.probdist.FisherFDist

Computes and returns the variance of the Fisher F distribution with parameters n and m.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist

getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist

.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.FrechetDist

getVariance(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FrechetDist

Returns the variance of the Fréchet distribution with parameters α, β and δ.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.GammaDist

getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.GammaDist

Computes and returns the variance Var[X] = α/λ² of the gamma distribution with parameters α and λ.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.GeometricDist

getVariance(double) - Static method in class umontreal.iro.lecuyer.probdist.GeometricDist

Computes and returns the variance Var[X] = (1 - p)/p² of the geometric distribution with parameter p.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.GumbelDist

getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.GumbelDist

Returns the variance Var[X] = π²β²/6 of the Gumbel distribution with parameters β and δ.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.HalfNormalDist

getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.HalfNormalDist

Computes and returns the variance Var[X] = (1 - 2/π)σ².

getVariance() - Method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist

getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist

Computes and returns the variance Var[X] = σ² of the hyperbolic secant distribution with parameters μ and σ.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist

getVariance(int, int, int) - Static method in class umontreal.iro.lecuyer.probdist.HypergeometricDist

Computes and returns the variance of the hypergeometric distribution with parameters m, l and k.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist

getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist

Computes and returns the variance Var[X] = μ³/λ of the inverse gaussian distribution with parameters μ and λ.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist

getVariance(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist

Computes and returns the variance of the Johnson S_U distribution with parameters γ, δ, ξ and λ.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.LaplaceDist

getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LaplaceDist

Computes and returns the variance Var[X] = 2β² of the Laplace distribution with parameters μ and β.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist

getVariance(double) - Static method in class umontreal.iro.lecuyer.probdist.LogarithmicDist

Computes and returns the variance of the logarithmic distribution with parameter θ = theta.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.LogisticDist

getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LogisticDist

Computes and returns the variance Var[X] = π²/(3λ²) of the logistic distribution with parameters α and λ.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.LoglogisticDist

getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LoglogisticDist

Computes and returns the variance of the log-logistic distribution with parameters α and β.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.LognormalDist

getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LognormalDist

Computes and returns the variance Var[X] = e^2μ+σ2(e^σ2 - 1) of the lognormal distribution with parameters μ and σ.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.NakagamiDist

getVariance(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NakagamiDist

.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist

getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist

Computes and returns the variance Var[X] = γ(1 - p)/p² of the negative binomial distribution with parameters γ and p.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.NormalDist

getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist

Computes and returns the variance Var[X] = σ² of the normal distribution with parameters μ and σ.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist

getVariance(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist

Computes and returns the variance Var[X] = δα²/γ³ of the normal inverse gaussian distribution with parameters α, β, μ and δ.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.ParetoDist

getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ParetoDist

Computes and returns the variance of the Pareto distribution with parameters α and β.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.Pearson5Dist

getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson5Dist

Computes and returns the variance Var[X] = β²/((α -1)²(α - 2) of a Pearson V distribution with shape parameter α and scale parameter β.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist

getVariance(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson6Dist

Computes and returns the variance Var[X] = [β²α₁(α₁ + α₂ -1)]/[(α₂ -1)²(α₂ - 2)] of a Pearson VI distribution with shape parameters α₁ and α₂, and scale parameter β.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist

getVariance() - Method in class umontreal.iro.lecuyer.probdist.PoissonDist

getVariance(double) - Static method in class umontreal.iro.lecuyer.probdist.PoissonDist

Computes and returns the variance = λ of the Poisson distribution with parameter λ.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.PowerDist

getVariance(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.PowerDist

Computes and returns the variance (b - a)²c/[(c + 1)²(c + 2)] of the power distribution with parameters a, b and c.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.RayleighDist

getVariance(double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist

Returns the variance of the Rayleigh distribution with parameter β.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.StudentDist

getVariance(int) - Static method in class umontreal.iro.lecuyer.probdist.StudentDist

Computes and returns the variance Var[X] = n/(n - 2) of the Student-t distribution with parameter n.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.TriangularDist

getVariance(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.TriangularDist

Computes and returns the variance Var[X] = (a² + b² + m² - ab - am - bm)/18 of the triangular distribution with parameters a, b, m.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist

Returns an approximation of the variance obtained using the Simpson 1/3 numerical integration, or throws an UnsupportedOperationException if a or b are infinite.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.UniformDist

getVariance(double, double) - Static method in class umontreal.iro.lecuyer.probdist.UniformDist

Computes and returns the variance Var[X] = (b - a)²/12 of the uniform distribution with parameters a and b.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist

getVariance(int, int) - Static method in class umontreal.iro.lecuyer.probdist.UniformIntDist

Computes and returns the variance Var[X] = [(j - i + 1)² -1]/12 of the discrete uniform distribution.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.WatsonUDist

getVariance(int) - Static method in class umontreal.iro.lecuyer.probdist.WatsonUDist

Returns the variance of the Watson U distribution with parameter n.

getVariance() - Method in class umontreal.iro.lecuyer.probdist.WeibullDist

getVariance(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist

Computes and returns the variance of the Weibull distribution with parameters α, λ and δ.

getVarianceGammaProcess() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess

Returns a reference to the variance gamma process X defined in the constructor.

getWeights() - Method in class umontreal.iro.lecuyer.functionfit.SmoothingCubicSpline

Returns the weights of the points.

getX(int, int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset

Returns the X value for a bin.

getX(int, int) - Method in class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection

Returns the x-value at the specified index in the specified series.

getX() - Method in class umontreal.iro.lecuyer.functionfit.BSpline

Returns the X_i coordinates for this spline.

getX() - Method in class umontreal.iro.lecuyer.functionfit.LeastSquares

Returns the x coordinates of the fitted points.

getX() - Method in class umontreal.iro.lecuyer.functionfit.PolInterp

Returns the x coordinates of the interpolated points.

getX() - Method in class umontreal.iro.lecuyer.functionfit.SmoothingCubicSpline

Returns the x_i coordinates for this spline.

getX() - Method in class umontreal.iro.lecuyer.functions.PiecewiseConstantFunction

Returns the X coordinates of the function.

getX0() - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess

Returns the initial value X(t₀) for this process.

getXAxis() - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart

Returns the chart's domain axis (x-axis) object.

getXAxis() - Method in class umontreal.iro.lecuyer.charts.XYChart

Returns the chart's domain axis (x-axis) object.

getXc() - Method in class umontreal.iro.lecuyer.probdist.InverseDistFromDensity

Returns the xc given in the constructor.

getXc() - Method in class umontreal.iro.lecuyer.randvar.InverseFromDensityGen

Returns the xc given in the constructor.

getXi() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist

Returns the value of ξ for this object.

getXi() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist

Returns the value of ξ for this object.

getXi() - Method in class umontreal.iro.lecuyer.randvar.JohnsonSBGen

Returns the ξ associated with this object.

getXi() - Method in class umontreal.iro.lecuyer.randvar.JohnsonSUGen

Returns the ξ associated with this object.

getXinf() - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution

Returns x_a such that the probability density is 0 everywhere outside the interval [x_a, x_b].

getXinf() - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistribution

Returns the lower limit x_a of the support of the distribution.

getXinf() - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistributionInt

Returns the lower limit x_a of the support of the probability mass function.

getXsup() - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution

Returns x_b such that the probability density is 0 everywhere outside the interval [x_a, x_b].

getXsup() - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistribution

Returns the upper limit x_b of the support of the distribution.

getXsup() - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistributionInt

Returns the upper limit x_b of the support of the probability mass function.

getY(int, int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset

Returns the y-value for a bin (calculated to take into account the histogram type).

getY(int, int) - Method in class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection

Returns the y-value at the specified index in the specified series.

getY() - Method in class umontreal.iro.lecuyer.functionfit.BSpline

Returns the Y_i coordinates for this spline.

getY() - Method in class umontreal.iro.lecuyer.functionfit.LeastSquares

Returns the y coordinates of the fitted points.

getY() - Method in class umontreal.iro.lecuyer.functionfit.PolInterp

Returns the y coordinates of the interpolated points.

getY() - Method in class umontreal.iro.lecuyer.functionfit.SmoothingCubicSpline

Returns the y_i coordinates for this spline.

getY() - Method in class umontreal.iro.lecuyer.functions.PiecewiseConstantFunction

Returns the Y coordinates of the function.

getYAxis() - Method in class umontreal.iro.lecuyer.charts.CategoryChart

Returns the chart's range axis (y-axis) object.

getYAxis() - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart

Returns the chart's range axis (y-axis) object.

getYAxis() - Method in class umontreal.iro.lecuyer.charts.XYChart

Returns the chart's range axis (y-axis) object.

getZeroOverZeroValue() - Method in class umontreal.iro.lecuyer.util.RatioFunction

Returns the value returned by evaluate in the case where the 0/0 function is calculated.

GlobalCPUTimeChrono - Class in umontreal.iro.lecuyer.util

Extends the AbstractChrono class to compute the global CPU time used by the Java Virtual Machine.

GlobalCPUTimeChrono() - Constructor for class umontreal.iro.lecuyer.util.GlobalCPUTimeChrono

Constructs a Chrono object and initializes it to zero.

GNUPLOT - Static variable in class umontreal.iro.lecuyer.gof.GofFormat

Data file format used for plotting functions with Gnuplot.

GofFormat - Class in umontreal.iro.lecuyer.gof

This class contains methods used to format results of GOF test statistics, or to apply a series of tests simultaneously and format the results.

GofStat - Class in umontreal.iro.lecuyer.gof

This class provides methods to compute several types of EDF goodness-of-fit test statistics and to apply certain transformations to a set of observations.

GofStat.OutcomeCategoriesChi2 - Class in umontreal.iro.lecuyer.gof

This class helps managing the partitions of possible outcomes into categories for applying chi-square tests.

GofStat.OutcomeCategoriesChi2(double[]) - Constructor for class umontreal.iro.lecuyer.gof.GofStat.OutcomeCategoriesChi2

Constructs an OutcomeCategoriesChi2 object using the array nbExp for the number of expected observations in each category.

GofStat.OutcomeCategoriesChi2(double[], int, int) - Constructor for class umontreal.iro.lecuyer.gof.GofStat.OutcomeCategoriesChi2

Constructs an OutcomeCategoriesChi2 object using the given nbExp expected observations array.

GofStat.OutcomeCategoriesChi2(double[], int[], int, int, int) - Constructor for class umontreal.iro.lecuyer.gof.GofStat.OutcomeCategoriesChi2

Constructs an OutcomeCategoriesChi2 object.

graphDistUnif(DoubleArrayList, String) - Static method in class umontreal.iro.lecuyer.gof.GofFormat

Formats data to plot the empirical distribution of U₍₁₎,..., U_(N), which are assumed to be in data[0...N-1], and to compare it with the uniform distribution.

graphFunc(ContinuousDistribution, double, double, int, int, String) - Static method in class umontreal.iro.lecuyer.gof.GofFormat

Deprecated.

graphSoft - Static variable in class umontreal.iro.lecuyer.gof.GofFormat

Environment variable that selects the type of software to be used for plotting the graphs of functions.

GumbelDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the Gumbel distribution, with location parameter δ and scale parameter β≠ 0.

GumbelDist() - Constructor for class umontreal.iro.lecuyer.probdist.GumbelDist

Constructor for the standard Gumbel distribution with parameters β = 1 and δ = 0.

GumbelDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.GumbelDist

Constructs a GumbelDist object with parameters β = beta and δ = delta.

GumbelGen - Class in umontreal.iro.lecuyer.randvar

This class implements methods for generating random variates from the Gumbel distribution.

GumbelGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.GumbelGen

Creates a Gumbel random number generator with β = 1 and δ = 0 using stream s.

GumbelGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.GumbelGen

Creates a Gumbel random number generator with parameters β = beta and δ = delta using stream s.

GumbelGen(RandomStream, GumbelDist) - Constructor for class umontreal.iro.lecuyer.randvar.GumbelGen

Creates a new generator for the Gumbel distribution dist and stream s.

H

H - Static variable in class umontreal.iro.lecuyer.functions.MathFunctionUtil

Step length in x to compute derivatives.

HalfNormalDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the half-normal distribution with parameters μ and σ > 0.

HalfNormalDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.HalfNormalDist

Constructs a HalfNormalDist object with parameters μ = mu and σ = sigma.

HalfNormalGen - Class in umontreal.iro.lecuyer.randvar

This class implements methods for generating random variates from the half-normal distribution with parameters μ and σ > 0.

HalfNormalGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.HalfNormalGen

Creates a new half-normal generator with parameters μ = mu and σ = sigma, using stream s.

HalfNormalGen(RandomStream, HalfNormalDist) - Constructor for class umontreal.iro.lecuyer.randvar.HalfNormalGen

Creates a new generator for the distribution dist, using stream s.

HaltonSequence - Class in umontreal.iro.lecuyer.hups

This class implements the sequence of Halton, which is essentially a modification of Hammersley nets for producing an infinite sequence of points having low discrepancy.

HaltonSequence(int) - Constructor for class umontreal.iro.lecuyer.hups.HaltonSequence

Constructs a new Halton sequence in dim dimensions.

HammersleyPointSet - Class in umontreal.iro.lecuyer.hups

This class implements Hammersley point sets, which are defined as follows.

HammersleyPointSet(int, int) - Constructor for class umontreal.iro.lecuyer.hups.HammersleyPointSet

Constructs a new Hammersley point set with n points in dim dimensions.

harmonic(long) - Static method in class umontreal.iro.lecuyer.util.Num

Computes the n-th harmonic number H_n = ∑_j=1ⁿ1/j.

harmonic2(long) - Static method in class umontreal.iro.lecuyer.util.Num

.

hashCode() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

hasNextCoordinate() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator

hasNextCoordinate() - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator

Returns true if the current point has another coordinate.

hasNextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess

Returns true if j < d, where j is the number of observations of the current sample path generated since the last call to resetStartProcess.

hasNextPoint() - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator

Returns true if there is a next point.

Henriksen - Class in umontreal.iro.lecuyer.simevents.eventlist

An implementation of EventList using the doubly-linked indexed list of Henriksen (see also).

Henriksen() - Constructor for class umontreal.iro.lecuyer.simevents.eventlist.Henriksen

HistogramChart - Class in umontreal.iro.lecuyer.charts

This class provides tools to create and manage histograms.

HistogramChart() - Constructor for class umontreal.iro.lecuyer.charts.HistogramChart

Initializes a new HistogramChart instance with an empty data set.

HistogramChart(String, String, String, double[]...) - Constructor for class umontreal.iro.lecuyer.charts.HistogramChart

Initializes a new HistogramChart instance with input data.

HistogramChart(String, String, String, double[], int) - Constructor for class umontreal.iro.lecuyer.charts.HistogramChart

Initializes a new HistogramChart instance with input data.

HistogramChart(String, String, String, DoubleArrayList...) - Constructor for class umontreal.iro.lecuyer.charts.HistogramChart

Initializes a new HistogramChart instance with data data.

HistogramChart(String, String, String, CustomHistogramDataset) - Constructor for class umontreal.iro.lecuyer.charts.HistogramChart

Initializes a new HistogramChart instance with data data.

HistogramChart(String, String, String, int[], double[]) - Constructor for class umontreal.iro.lecuyer.charts.HistogramChart

Initializes a new HistogramChart instance with data count and bound.

HistogramSeriesCollection - Class in umontreal.iro.lecuyer.charts

Stores data used in a HistogramChart.

HistogramSeriesCollection() - Constructor for class umontreal.iro.lecuyer.charts.HistogramSeriesCollection

Creates a new HistogramSeriesCollection instance with empty dataset.

HistogramSeriesCollection(double[]...) - Constructor for class umontreal.iro.lecuyer.charts.HistogramSeriesCollection

Creates a new HistogramSeriesCollection instance with given data series.

HistogramSeriesCollection(double[], int) - Constructor for class umontreal.iro.lecuyer.charts.HistogramSeriesCollection

Creates a new HistogramSeriesCollection instance with the given data series data.

HistogramSeriesCollection(DoubleArrayList...) - Constructor for class umontreal.iro.lecuyer.charts.HistogramSeriesCollection

Creates a new HistogramSeriesCollection.

HistogramSeriesCollection(CustomHistogramDataset) - Constructor for class umontreal.iro.lecuyer.charts.HistogramSeriesCollection

Creates a new HistogramSeriesCollection instance.

HyperbolicSecantDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the Hyperbolic Secant distribution with location parameter μ and scale parameter σ > 0.

HyperbolicSecantDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist

Constructs a hyperbolic secant distribution with parameters μ and σ.

HyperbolicSecantGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators for the hyperbolic secant distribution with location parameter μ and scale parameter σ.

HyperbolicSecantGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.HyperbolicSecantGen

Creates a hyperbolic secant random variate generator with parameters μ = mu and σ = sigma, using stream s.

HyperbolicSecantGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.HyperbolicSecantGen

Creates a hyperbolic secant random variate generator with parameters μ = 0 and σ = 1, using stream s.

HyperbolicSecantGen(RandomStream, HyperbolicSecantDist) - Constructor for class umontreal.iro.lecuyer.randvar.HyperbolicSecantGen

Creates a new generator for the distribution dist, using stream s.

HypergeometricDist - Class in umontreal.iro.lecuyer.probdist

Extends the class DiscreteDistributionInt for the hypergeometric distribution with k elements chosen among l, m being of one type, and l - m of the other.

HypergeometricDist(int, int, int) - Constructor for class umontreal.iro.lecuyer.probdist.HypergeometricDist

Constructs an hypergeometric distribution with parameters m, l and k.

HypergeometricGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators for the hypergeometric distribution.

HypergeometricGen(RandomStream, int, int, int) - Constructor for class umontreal.iro.lecuyer.randvar.HypergeometricGen

Creates a hypergeometric generator with parameters m = m, l = l and k = k, using stream s.

HypergeometricGen(RandomStream, HypergeometricDist) - Constructor for class umontreal.iro.lecuyer.randvar.HypergeometricGen

Creates a new generator for distribution dist, using stream s.

I

iBinomialMatrixScramble(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNet

Applies the i-binomial matrix scramble proposed by Tezuka .

iBinomialMatrixScramble(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2

iBinomialMatrixScrambleFaurePermut(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNet

Similar to iBinomialMatrixScramble except that the diagonal elements of each matrix M_j are chosen as in leftMatrixScrambleFaurePermut.

iBinomialMatrixScrambleFaurePermut(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2

iBinomialMatrixScrambleFaurePermutAll(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNet

Similar to iBinomialMatrixScrambleFaurePermut except that the elements under the diagonal are also chosen from the same restricted set as the diagonal elements.

iBinomialMatrixScrambleFaurePermutAll(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2

iBinomialMatrixScrambleFaurePermutDiag(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNet

Similar to iBinomialMatrixScrambleFaurePermut except that all the off-diagonal elements are 0.

iBinomialMatrixScrambleFaurePermutDiag(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2

IdentityMathFunction - Class in umontreal.iro.lecuyer.functions

Represents the identity function f (x) = x.

IdentityMathFunction() - Constructor for class umontreal.iro.lecuyer.functions.IdentityMathFunction

IIDMultivariateGen - Class in umontreal.iro.lecuyer.randvarmulti

Extends RandomVariateGen for a vector of independent identically distributed (i.i.d.) random variables.

IIDMultivariateGen(RandomVariateGen, int) - Constructor for class umontreal.iro.lecuyer.randvarmulti.IIDMultivariateGen

Constructs a generator for a d-dimensional vector of i.i.d.

ILN2 - Static variable in class umontreal.iro.lecuyer.util.Num

The values of 1/ln 2.

increasedPrecis(boolean) - Method in class umontreal.iro.lecuyer.rng.RandMrg

Deprecated.

After calling this method with incp = true, each call to the generator (direct or indirect) for this stream will return a uniform random number with (roughly) 53 bits of resolution instead of 32 bits, and will advance the state of the stream by 2 steps instead of 1.

increasedPrecision(boolean) - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase

After calling this method with incp = true, each call to the RNG (direct or indirect) for this stream will return a uniform random number with more bits of precision than what is returned by nextValue, and will advance the state of the stream by 2 steps instead of 1 (i.e., nextValue will be called twice for each random number).

indexOf(Object) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

init(double[]) - Method in class umontreal.iro.lecuyer.hups.HaltonSequence

Initializes the Halton sequence starting at point x0.

init(byte[], int) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation

Initializes array with the first n positive integers in natural order as array[i - 1] = i, for i = 1,..., n.

init(short[], int) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation

Similar to init(byte[], int).

init(int[], int) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation

Similar to init(byte[], int).

init(long[], int) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation

Similar to init(byte[], int).

init(float[], int) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation

Similar to init(byte[], int).

init(double[], int) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation

Similar to init(byte[], int).

init() - Method in class umontreal.iro.lecuyer.simevents.Accumulate

Initializes the statistical collector and puts the current value of the corresponding variable to 0.

init(double) - Method in class umontreal.iro.lecuyer.simevents.Accumulate

Same as init followed by update(x).

init(double) - Method in class umontreal.iro.lecuyer.simevents.Continuous

Initializes or reinitializes the continuous-time variable to val.

init() - Static method in class umontreal.iro.lecuyer.simevents.Sim

Reinitializes the simulation executive by clearing up the event list, and resetting the simulation clock to zero.

init(EventList) - Static method in class umontreal.iro.lecuyer.simevents.Sim

Same as init, but also chooses evlist as the event list to be used.

init() - Method in class umontreal.iro.lecuyer.simevents.Simulator

Reinitializes the simulation executive by clearing up the event list, and resetting the simulation clock to zero.

init(EventList) - Method in class umontreal.iro.lecuyer.simevents.Simulator

Same as init, but also chooses evlist as the event list to be used.

init() - Method in class umontreal.iro.lecuyer.simprocs.Bin

Reinitializes this bin by clearing up its pile of tokens and its waiting list.

init(boolean) - Method in class umontreal.iro.lecuyer.simprocs.Condition

Reinitializes this Condition by clearing up its waiting list and resetting its state to val.

init() - Method in class umontreal.iro.lecuyer.simprocs.DSOLProcessSimulator

Initializes the process-driven simulation.

init(EventList) - Method in class umontreal.iro.lecuyer.simprocs.DSOLProcessSimulator

Initializes the simulation and sets the given event list evlist to be used by the simulation executive.

init() - Method in class umontreal.iro.lecuyer.simprocs.Resource

Reinitializes this resource by clearing up its waiting list and service list.

init() - Static method in class umontreal.iro.lecuyer.simprocs.SimProcess

This method calls ProcessSimulator.initDefault(), which initializes the default simulator to use processes.

init() - Method in class umontreal.iro.lecuyer.simprocs.ThreadProcessSimulator

Initializes the thread process-driven simulation using SplayTree algorithm as EventList.

init(EventList) - Method in class umontreal.iro.lecuyer.simprocs.ThreadProcessSimulator

Initializes the thread process-driven simulation using evlist variable as EventList.

init() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

Initializes this list of statistical probes by calling init on each element.

init() - Method in class umontreal.iro.lecuyer.stat.list.ListOfTalliesWithCovariance

init() - Method in class umontreal.iro.lecuyer.stat.StatProbe

Initializes the statistical collector.

init() - Method in class umontreal.iro.lecuyer.stat.Tally

init() - Method in class umontreal.iro.lecuyer.stat.TallyStore

init() - Method in class umontreal.iro.lecuyer.util.AbstractChrono

Initializes this AbstractChrono to zero.

initCache() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache

Resets this generator to recover values from the cache.

initCache() - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache

Resets this random stream to recover values from the cache.

initDefault() - Static method in class umontreal.iro.lecuyer.simprocs.ProcessSimulator

Initializes the default simulator to use processes.

INITIAL - Static variable in class umontreal.iro.lecuyer.simprocs.SimProcess

The process has been created but not yet scheduled.

initStat() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat

Reinitializes the two statistical probes created by setStatCollecting (true) and makes an update for the probe on the list size.

initStat() - Method in class umontreal.iro.lecuyer.simprocs.Bin

Reinitializes all the statistical collectors for this bin.

initStat() - Method in class umontreal.iro.lecuyer.simprocs.Resource

Reinitializes all the statistical collectors for this resource.

integerRadicalInverse(int, int) - Static method in class umontreal.iro.lecuyer.hups.RadicalInverse

Computes the integer radical inverse of i in base b, equal to b^kψ_b(i) if i has k b-ary digits.

integMethod() - Method in class umontreal.iro.lecuyer.simevents.ContinuousState

Return an integer that represent the integration method in use.

integral(double, double) - Method in class umontreal.iro.lecuyer.functionfit.BSpline

integral(double, double) - Method in class umontreal.iro.lecuyer.functionfit.SmoothingCubicSpline

Evaluates and returns the value of the integral of the spline from a to b.

integral(double, double) - Method in class umontreal.iro.lecuyer.functions.AverageMathFunction

integral(double, double) - Method in class umontreal.iro.lecuyer.functions.IdentityMathFunction

integral(MathFunction, double, double) - Static method in class umontreal.iro.lecuyer.functions.MathFunctionUtil

Returns the integral of the function func over [a, b].

integral(double, double) - Method in interface umontreal.iro.lecuyer.functions.MathFunctionWithIntegral

Computes (or estimates) the integral of the function over the interval [a, b].

integral(double, double) - Method in class umontreal.iro.lecuyer.functions.Polynomial

integral(double, double) - Method in class umontreal.iro.lecuyer.functions.ShiftedMathFunction

integralPolynomial(double) - Method in class umontreal.iro.lecuyer.functions.Polynomial

Returns a polynomial representing the integral of this polynomial.

interpol(int, double[], double[], double[]) - Static method in class umontreal.iro.lecuyer.util.Misc

Computes the Newton interpolating polynomial.

Introspection - Class in umontreal.iro.lecuyer.util

Provides utility methods for introspection using Java Reflection API.

inverseBisection(double) - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution

Computes and returns the inverse distribution function x = F^-1(u), using bisection.

inverseBrent(double, double, double, double) - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution

Computes the inverse distribution function x = F^-1(u), using the Brent-Dekker method.

InverseDistFromDensity - Class in umontreal.iro.lecuyer.probdist

Implements a method for computing the inverse of an arbitrary continuous distribution function when only the probability density is known.

InverseDistFromDensity(ContinuousDistribution, double, double, int) - Constructor for class umontreal.iro.lecuyer.probdist.InverseDistFromDensity

Given a continuous distribution dist with a well-defined density method, this class will compute tables for the numerical inverse of the distribution.

InverseDistFromDensity(MathFunction, double, double, int, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.InverseDistFromDensity

Given a continuous probability density dens, this class will compute tables for the numerical inverse of the distribution.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDist

inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDist

Computes the inverse x = F_n^-1(u) of the Anderson-Darling distribution with parameter n.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDistQuick

inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDistQuick

Computes the inverse x = F_n^-1(u) of the distribution with parameter n.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.BetaDist

inverseF(double, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist

Same as inverseF (alpha, beta, 0, 1, d, u).

inverseF(double, double, double, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaDist

Returns the inverse beta distribution function using the algorithm implemented in the Cephes math library.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist

inverseF(double, double) - Static method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist

Returns the inverse distribution function evaluated at u, for the symmetrical beta distribution over the interval [0, 1], with shape parameters 0 < α = β = alpha.

inverseF(int, double, double) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist

Computes the inverse of the binomial distribution, x = F^-1(u), using a linear search starting at the mode if n is small.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.CauchyDist

inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.CauchyDist

Computes the inverse of the distribution.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.ChiDist

inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiDist

Returns the inverse distribution function computed using the gamma inversion.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.ChiSquareDist

inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareDist

Computes an approximation of F^-1(u), where F is the chi-square distribution with n degrees of freedom.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.ChiSquareDistQuick

inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareDistQuick

Computes a quick-and-dirty approximation of F^-1(u), where F is the chi-square distribution with n degrees of freedom.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist

inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist

Computes the inverse of the noncentral chi-square distribution with ν = nu degrees of freedom and parameter λ = lambda.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution

Returns the inverse distribution function x = F^-1(u).

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist

inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist

Computes x = F_n^-1(u), where F_n is the Cramér-von Mises distribution with parameter n.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistribution

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistributionInt

Returns the inverse distribution function F^-1(u), where 0 <= u <= 1.

inverseF(double) - Method in interface umontreal.iro.lecuyer.probdist.Distribution

Returns the inverse distribution function F^-1(u), defined in.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist

inverseF(int, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.ErlangDist

Returns the inverse distribution function.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.ExponentialDist

inverseF(double, double) - Static method in class umontreal.iro.lecuyer.probdist.ExponentialDist

Computes the inverse distribution function.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist

Deprecated.

inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist

Deprecated.

Computes the inverse distribution function.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist

inverseF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist

Computes the inverse of the fatigue life distribution with parameters μ, β and γ.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.FisherFDist

inverseF(int, int, int, double) - Static method in class umontreal.iro.lecuyer.probdist.FisherFDist

Computes the inverse of the Fisher F distribution with parameters n and m, evaluated at x, with roughly d decimal digits of precision.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist

inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist

Computes the inverse of the distribution function.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.FrechetDist

inverseF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.FrechetDist

Computes and returns the inverse distribution function.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.GammaDist

inverseF(double, double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.GammaDist

Computes the inverse distribution function using the algorithm implemented in the Cephes Math Library.

inverseF(double, int, double) - Static method in class umontreal.iro.lecuyer.probdist.GammaDist

Same as inverseF (alpha, 1, d, u).

inverseF(double, double) - Static method in class umontreal.iro.lecuyer.probdist.GeometricDist

Computes the inverse of the geometric distribution.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.GumbelDist

inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.GumbelDist

Computes and returns the inverse distribution function.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.HalfNormalDist

inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.HalfNormalDist

Computes the inverse of the distribution function.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist

inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist

Computes the inverse of the hyperbolic secant distribution with parameters μ and σ.

inverseF(int, int, int, double) - Static method in class umontreal.iro.lecuyer.probdist.HypergeometricDist

Computes F^-1(u) for the hypergeometric distribution without using precomputed tables.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.InverseDistFromDensity

Computes the inverse distribution function at u.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist

inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist

Computes the inverse of the inverse gaussian distribution with parameters μ and λ.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist

inverseF(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist

Computes the inverse of the distribution.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist

inverseF(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist

Computes the inverse distribution function F^-1(u).

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDist

inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDist

Computes the inverse x = F^-1(u) of the distribution F(x) with parameter n.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDistQuick

inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDistQuick

Computes the inverse x = F^-1(u) of the distribution F(x) with parameter n.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist

inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist

Computes the inverse x = F^-1(u) of the distribution with parameter n.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.LaplaceDist

inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LaplaceDist

Computes the inverse Laplace distribution function.

inverseF(double, double) - Static method in class umontreal.iro.lecuyer.probdist.LogarithmicDist

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.LogisticDist

inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LogisticDist

Computes the inverse distribution function F^-1(u).

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.LoglogisticDist

inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LoglogisticDist

Computes the inverse of the log-logistic distribution with parameters α and β.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.LognormalDist

inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.LognormalDist

Computes the inverse of the lognormal distribution function, using NormalDist.inverseF01.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.NakagamiDist

inverseF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NakagamiDist

Computes the inverse of the distribution function.

inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist

Computes the inverse function without precomputing tables.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.NormalDist

inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist

Computes the inverse normal distribution function with mean μ and variance σ².

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.NormalDistQuick

inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDistQuick

Returns an approximation of Φ^-1(u), where Φ is the standard normal distribution function, with mean 0 and variance 1.

inverseF(double, double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist

NOT IMPLEMENTED.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.ParetoDist

inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.ParetoDist

Computes the inverse of the distribution function.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.Pearson5Dist

inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson5Dist

Computes the inverse distribution function of a Pearson V distribution with shape parameter α and scale parameter β.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist

inverseF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.Pearson6Dist

Computes the inverse distribution function of a Pearson VI distribution with shape parameters α₁ and α₂, and scale parameter β.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist

inverseF(double, double) - Static method in class umontreal.iro.lecuyer.probdist.PoissonDist

Performs a linear search to get the inverse function without precomputed tables.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.PowerDist

inverseF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.PowerDist

Computes the inverse of the distribution function.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.RayleighDist

inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist

Computes the inverse of the distribution function.

inverseF(double, double) - Static method in class umontreal.iro.lecuyer.probdist.RayleighDist

Same as inverseF (0, beta, u).

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.StudentDist

inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.StudentDist

Returns an approximation of F^-1(u), where F is the Student-t distribution function with n degrees of freedom.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.TriangularDist

inverseF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.TriangularDist

Computes the inverse distribution function.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.UniformDist

inverseF(double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.UniformDist

Computes the inverse of the uniform distribution function.

inverseF(int, int, double) - Static method in class umontreal.iro.lecuyer.probdist.UniformIntDist

Computes the inverse of the discrete uniform distribution function.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.WatsonGDist

inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.WatsonGDist

Computes x = F_n^-1(u), where F_n is the Watson G distribution with parameter n.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.WatsonUDist

inverseF(int, double) - Static method in class umontreal.iro.lecuyer.probdist.WatsonUDist

Computes x = F_n^-1(u), where F_n is the Watson U distribution with parameter n.

inverseF(double) - Method in class umontreal.iro.lecuyer.probdist.WeibullDist

inverseF(double, double, double, double) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist

Computes the inverse of the distribution function.

inverseF(double, double) - Static method in class umontreal.iro.lecuyer.probdist.WeibullDist

Same as inverseF (alpha, 1, 0, x).

inverseF01(double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDist

Same as inverseF (0, 1, u).

inverseF01(double) - Static method in class umontreal.iro.lecuyer.probdist.NormalDistQuick

Same as inverseF (0.0, 1.0, u).

inverseFInt(double) - Method in class umontreal.iro.lecuyer.probdist.BinomialDist

inverseFInt(double) - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistributionInt

Returns the inverse distribution function F^-1(u), where 0 <= u <= 1.

inverseFInt(double) - Method in class umontreal.iro.lecuyer.probdist.GeometricDist

inverseFInt(double) - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist

inverseFInt(double) - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist

inverseFInt(double) - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist

inverseFInt(double) - Method in class umontreal.iro.lecuyer.probdist.PoissonDist

inverseFInt(double) - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist

InverseFromDensityGen - Class in umontreal.iro.lecuyer.randvar

Implements a method for generating random variates by numerical inversion of an arbitrary continuous distribution when only the probability density is known.

InverseFromDensityGen(RandomStream, ContinuousDistribution, double, double, int) - Constructor for class umontreal.iro.lecuyer.randvar.InverseFromDensityGen

Creates a new generator for the continuous distribution dis, using stream s.

InverseFromDensityGen(RandomStream, MathFunction, double, double, int, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.InverseFromDensityGen

Creates a new generator from the continuous probability density dens.

InverseGaussianDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the inverse Gaussian distribution with location parameter μ > 0 and scale parameter λ > 0.

InverseGaussianDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.InverseGaussianDist

Constructs the inverse Gaussian distribution with parameters μ and λ.

InverseGaussianGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators for the inverse Gaussian distribution with location parameter μ > 0 and scale parameter λ > 0.

InverseGaussianGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.InverseGaussianGen

Creates an inverse Gaussian random variate generator with parameters μ = mu and λ = lambda, using stream s.

InverseGaussianGen(RandomStream, InverseGaussianDist) - Constructor for class umontreal.iro.lecuyer.randvar.InverseGaussianGen

Creates a new generator for the distribution dist, using stream s.

InverseGaussianMSHGen - Class in umontreal.iro.lecuyer.randvar

This class implements inverse gaussian random variate generators using the many-to-one transformation method of Michael, Schucany and Haas (MHS).

InverseGaussianMSHGen(RandomStream, NormalGen, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.InverseGaussianMSHGen

Creates an inverse gaussian random variate generator with parameters μ = mu and λ = lambda, using streams s and sn.

InverseGaussianMSHGen(RandomStream, NormalGen, InverseGaussianDist) - Constructor for class umontreal.iro.lecuyer.randvar.InverseGaussianMSHGen

Creates a new generator for the distribution dist using streams s and sn.

InverseGaussianProcess - Class in umontreal.iro.lecuyer.stochprocess

The inverse Gaussian process is a non-decreasing process where the increments are additive and are given by the inverse gaussian distribution, InverseGaussianDist.

InverseGaussianProcess(double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess

Constructs a new InverseGaussianProcess.

InverseGaussianProcessBridge - Class in umontreal.iro.lecuyer.stochprocess

Samples the path by bridge sampling: first finding the process value at the final time and then the middle time, etc.

InverseGaussianProcessBridge(double, double, double, RandomStream, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessBridge

Constructs a new InverseGaussianProcessBridge.

InverseGaussianProcessMSH - Class in umontreal.iro.lecuyer.stochprocess

Uses a faster generating method (MSH) than the simple inversion of the distribution function used by InverseGaussianProcess.

InverseGaussianProcessMSH(double, double, double, RandomStream, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessMSH

Constructs a new InverseGaussianProcessMSH.

InverseGaussianProcessPCA - Class in umontreal.iro.lecuyer.stochprocess

Approximates a principal component analysis (PCA) decomposition of the InverseGaussianProcess.

InverseGaussianProcessPCA(double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessPCA

Constructs a new InverseGaussianProcessPCA.

IRAC2 - Static variable in class umontreal.iro.lecuyer.util.Num

The value of 1/(2)^1/2.

isAlive() - Method in class umontreal.iro.lecuyer.simprocs.SimProcess

Returns true if the process is alive, otherwise false.

isBroadcasting() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

Determines if this list of statistical probes is broadcasting observations to registered observers.

isBroadcasting() - Method in class umontreal.iro.lecuyer.stat.StatProbe

Determines if this statistical probe is broadcasting observations to registered observers.

isCaching() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache

Determines if the random variate generator is caching values, default being true.

isCaching() - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache

Determines if the random stream is caching values, default being true.

isCollecting() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

Determines if this list of statistical probes is collecting values.

isCollecting() - Method in class umontreal.iro.lecuyer.stat.StatProbe

Determines if this statistical probe is collecting values.

isEmpty() - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree

isEmpty() - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked

isEmpty() - Method in interface umontreal.iro.lecuyer.simevents.eventlist.EventList

Returns true if and only if the event list is empty (no event is scheduled).

isEmpty() - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen

isEmpty() - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree

isEmpty() - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree

isEmpty() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

isModifiable() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

Determines if this list of statistical probes is modifiable, i.e., if probes can be added or removed.

isSimulating() - Method in class umontreal.iro.lecuyer.simevents.Simulator

Determines if this simulator is currently running, i.e., executing scheduled events.

isStopped() - Method in class umontreal.iro.lecuyer.simevents.Simulator

Determines if this simulator was stopped by an event.

iterateSpacings(DoubleArrayList, DoubleArrayList) - Static method in class umontreal.iro.lecuyer.gof.GofStat

Applies one iteration of the iterated spacings transformation.

iterator() - Method in class umontreal.iro.lecuyer.hups.AntitheticPointSet

iterator() - Method in class umontreal.iro.lecuyer.hups.BakerTransformedPointSet

iterator() - Method in class umontreal.iro.lecuyer.hups.ContainerPointSet

iterator() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet

iterator() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSetBase2

iterator() - Method in class umontreal.iro.lecuyer.hups.DigitalNet

iterator() - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2

iterator() - Method in class umontreal.iro.lecuyer.hups.PaddedPointSet

iterator() - Method in class umontreal.iro.lecuyer.hups.PointSet

Constructs and returns a point set iterator.

iterator() - Method in class umontreal.iro.lecuyer.hups.RandShiftedPointSet

iterator() - Method in class umontreal.iro.lecuyer.hups.Rank1Lattice

iterator() - Method in class umontreal.iro.lecuyer.hups.SubsetOfPointSet

iterator() - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree

iterator() - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked

iterator() - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen

iterator() - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree

iterator() - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree

iterator() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat

iterator() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

iterator() - Method in class umontreal.iro.lecuyer.util.TransformingList

iteratorNoGray() - Method in class umontreal.iro.lecuyer.hups.DigitalNet

This iterator does not use the Gray code.

iteratorNoGray() - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2

This iterator does not use the Gray code.

iteratorShift() - Method in class umontreal.iro.lecuyer.hups.DigitalSequence

Similar to iterator, except that the first coordinate of the points is i/n, the second coordinate is obtained via the generating matrix C₀, the next one via C₁, and so on.

iteratorShift() - Method in class umontreal.iro.lecuyer.hups.DigitalSequenceBase2

Similar to iterator, except that the first coordinate of the points is i/n, the second coordinate is obtained via the generating matrix C₀, the next one via C₁, and so on.

iteratorShiftNoGray() - Method in class umontreal.iro.lecuyer.hups.DigitalSequence

This iterator shifts all coordinates of each point one position to the right and sets the first coordinate of point i to i/n, so that the points enumerated with this iterator have one more dimension.

iteratorShiftNoGray() - Method in class umontreal.iro.lecuyer.hups.DigitalSequenceBase2

This iterator shifts all coordinates of each point one position to the right and sets the first coordinate of point i to i/n, so that the points enumerated with this iterator have one more dimension.

iterPowRatioTests(DoubleArrayList, int, boolean, boolean, PrintWriter) - Static method in class umontreal.iro.lecuyer.gof.GofFormat

Similar to iterSpacingsTests, but with the GofStat.powerRatios transformation.

iterSpacingsTests(DoubleArrayList, int, boolean, boolean, PrintWriter) - Static method in class umontreal.iro.lecuyer.gof.GofFormat

Repeats the following k times: Applies the GofStat.iterateSpacings transformation to the U₍₀₎,..., U_(N-1), assuming that these observations are in sortedData, then computes the EDF test statistics and calls activeTests after each transformation.

J

JDBCManager - Class in umontreal.iro.lecuyer.util

This class provides some facilities to connect to a SQL database and to retrieve data stored in it.

JDBCManager() - Constructor for class umontreal.iro.lecuyer.util.JDBCManager

JohnsonSBDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the Johnson S_B distribution with shape parameters γ and δ > 0, location parameter ξ, and scale parameter λ > 0.

JohnsonSBDist(double, double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.JohnsonSBDist

Constructs a JohnsonSBDist object with shape parameters γ and δ, location parameter ξ and scale parameter λ.

JohnsonSBGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators for the Johnson S_B distribution.

JohnsonSBGen(RandomStream, double, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.JohnsonSBGen

Creates a JohnsonSB random variate generator.

JohnsonSBGen(RandomStream, JohnsonSBDist) - Constructor for class umontreal.iro.lecuyer.randvar.JohnsonSBGen

Creates a new generator for the JohnsonSB distribution dist, using stream s.

JohnsonSUDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the Johnson S_U distribution.

JohnsonSUDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.JohnsonSUDist

Same as JohnsonSUDist (gamma, delta, 0.0, 1.0).

JohnsonSUDist(double, double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.JohnsonSUDist

Constructs a JohnsonSUDist object with shape parameters γ and δ, location parameter ξ, and scale parameter λ.

JohnsonSUGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators for the Johnson S_U distribution.

JohnsonSUGen(RandomStream, double, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.JohnsonSUGen

Creates a JohnsonSU random variate generator.

JohnsonSUGen(RandomStream, JohnsonSUDist) - Constructor for class umontreal.iro.lecuyer.randvar.JohnsonSUGen

Creates a new generator for the JohnsonSU distribution dist, using stream s.

K

KernelDensity - Class in umontreal.iro.lecuyer.gof

This class provides methods to compute a kernel density estimator from a set of n individual observations x₀,…, x_n-1, and returns its value at m selected points.

KernelDensity() - Constructor for class umontreal.iro.lecuyer.gof.KernelDensity

KernelDensityGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators for distributions obtained via kernel density estimation methods from a set of n individual observations x₁,..., x_n.

KernelDensityGen(RandomStream, EmpiricalDist, RandomVariateGen, double) - Constructor for class umontreal.iro.lecuyer.randvar.KernelDensityGen

Creates a new generator for a kernel density estimated from the observations given by the empirical distribution dist, using stream s to select the observations, generator kGen to generate the added noise from the kernel density, and bandwidth h.

KernelDensityGen(RandomStream, EmpiricalDist, NormalGen) - Constructor for class umontreal.iro.lecuyer.randvar.KernelDensityGen

This constructor uses a gaussian kernel and the default bandwidth h = α_kh₀ with the α_k suggested in Table for the gaussian distribution.

KernelDensityVarCorrectGen - Class in umontreal.iro.lecuyer.randvar

This class is a variant of KernelDensityGen, but with a rescaling of the empirical distribution so that the variance of the density used to generate the random variates is equal to the empirical variance, as suggested by Silverman.

KernelDensityVarCorrectGen(RandomStream, EmpiricalDist, RandomVariateGen, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.KernelDensityVarCorrectGen

Creates a new generator for a kernel density estimated from the observations given by the empirical distribution dist, using stream s to select the observations, generator kGen to generate the added noise from the kernel density, bandwidth h, and σ_k² = sigmak2 used for the variance correction.

KernelDensityVarCorrectGen(RandomStream, EmpiricalDist, NormalGen) - Constructor for class umontreal.iro.lecuyer.randvar.KernelDensityVarCorrectGen

This constructor uses a gaussian kernel and the default bandwidth suggested in Table for the gaussian distribution.

kill(SimProcess) - Method in class umontreal.iro.lecuyer.simprocs.DSOLProcessSimulator

kill(SimProcess) - Method in class umontreal.iro.lecuyer.simprocs.ProcessSimulator

Terminates the life of process and sets its state to DEAD, after canceling its control event if there is one.

kill() - Method in class umontreal.iro.lecuyer.simprocs.SimProcess

Terminates the life of this process and sets its state to DEAD, after canceling its activating event if there is one.

kill(SimProcess) - Method in class umontreal.iro.lecuyer.simprocs.ThreadProcessSimulator

killAll() - Method in class umontreal.iro.lecuyer.simprocs.DSOLProcessSimulator

killAll() - Method in class umontreal.iro.lecuyer.simprocs.ProcessSimulator

Kills all currently living (active, delayed, or suspended) processes managed by this simulator.

killAll() - Method in class umontreal.iro.lecuyer.simprocs.ThreadProcessSimulator

Kills all threads linked to the current variable.

kolmogorovSmirnov(int, double) - Static method in class umontreal.iro.lecuyer.gof.FBar

Deprecated.

kolmogorovSmirnov(int, double) - Static method in class umontreal.iro.lecuyer.gof.FDist

Deprecated.

kolmogorovSmirnov(DoubleArrayList) - Static method in class umontreal.iro.lecuyer.gof.GofStat

Computes the Kolmogorov-Smirnov (KS) test statistics D_N⁺, D_N^-, and D_N.

KolmogorovSmirnovDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the Kolmogorov-Smirnov distribution with parameter n.

KolmogorovSmirnovDist(int) - Constructor for class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDist

Constructs a distribution with parameter n.

KolmogorovSmirnovDistQuick - Class in umontreal.iro.lecuyer.probdist

Extends the class KolmogorovSmirnovDist for the distribution.

KolmogorovSmirnovDistQuick(int) - Constructor for class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDistQuick

Constructs a distribution with parameter n.

kolmogorovSmirnovJumpOne(DoubleArrayList, double) - Static method in class umontreal.iro.lecuyer.gof.GofStat

Compute the KS statistics D_N⁺(a) and D_N^-(a) defined in the description of the method FDist.kolmogorovSmirnovPlusJumpOne, assuming that F is the uniform distribution over [0, 1] and that U₍₁₎,..., U_(N) are in sortedData.

kolmogorovSmirnovPlus(int, double) - Static method in class umontreal.iro.lecuyer.gof.FBar

Deprecated.

kolmogorovSmirnovPlus(int, double) - Static method in class umontreal.iro.lecuyer.gof.FDist

Deprecated.

KolmogorovSmirnovPlusDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the Kolmogorov-Smirnov+ distribution (see).

KolmogorovSmirnovPlusDist(int) - Constructor for class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist

Constructs an Kolmogorov-Smirnov+ distribution for a sample of size n.

kolmogorovSmirnovPlusJumpOne(int, double, double) - Static method in class umontreal.iro.lecuyer.gof.FDist

Similar to kolmogorovSmirnovPlus but for the case where the distribution function F has a jump of size a at a given point x₀, is zero at the left of x₀, and is continuous at the right of x₀.

KorobovLattice - Class in umontreal.iro.lecuyer.hups

This class implements Korobov lattices, which are the same point sets as in class LCGPointSet, but implemented differently.

KorobovLattice(int, int, int) - Constructor for class umontreal.iro.lecuyer.hups.KorobovLattice

Instantiates a Korobov lattice point set with modulus n and multiplier a in dimension s.

KorobovLattice(int, int, int, int) - Constructor for class umontreal.iro.lecuyer.hups.KorobovLattice

Instantiates a shifted Korobov lattice point set with modulus n and multiplier a in dimension s.

KorobovLatticeSequence - Class in umontreal.iro.lecuyer.hups

This class implements Korobov lattice sequences, defined as follows.

KorobovLatticeSequence(int, int) - Constructor for class umontreal.iro.lecuyer.hups.KorobovLatticeSequence

Constructs a new lattice sequence with base b and generator = a.

KS - Static variable in class umontreal.iro.lecuyer.gof.GofFormat

Kolmogorov-Smirnov test

KSM - Static variable in class umontreal.iro.lecuyer.gof.GofFormat

Kolmogorov-Smirnov- test

KSP - Static variable in class umontreal.iro.lecuyer.gof.GofFormat

Kolmogorov-Smirnov+ test

L

LaplaceDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the Laplace distribution.

LaplaceDist() - Constructor for class umontreal.iro.lecuyer.probdist.LaplaceDist

Constructs a LaplaceDist object with default parameters μ = 0 and β = 1.

LaplaceDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.LaplaceDist

Constructs a LaplaceDist object with parameters μ = mu and β = beta.

LaplaceGen - Class in umontreal.iro.lecuyer.randvar

This class implements methods for generating random variates from the Laplace distribution.

LaplaceGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.LaplaceGen

Creates a Laplace random variate generator with parameters μ = mu and β = beta, using stream s.

LaplaceGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.LaplaceGen

Creates a Laplace random variate generator with parameters μ = 0 and β = 1, using stream s.

LaplaceGen(RandomStream, LaplaceDist) - Constructor for class umontreal.iro.lecuyer.randvar.LaplaceGen

Creates a new generator for the Laplace distribution dist and stream s.

lastIndexOf(Object) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

LCGPointSet - Class in umontreal.iro.lecuyer.hups

Implements a recurrence-based point set defined via a linear congruential recurrence of the form x_i = ax_i-1mod n and u_i = x_i/n.

LCGPointSet(int, int) - Constructor for class umontreal.iro.lecuyer.hups.LCGPointSet

Constructs and stores the set of cycles for an LCG with modulus n and multiplier a.

LCGPointSet(int, int, int, int) - Constructor for class umontreal.iro.lecuyer.hups.LCGPointSet

Constructs and stores the set of cycles for an LCG with modulus n = b^e + c and multiplier a.

LeastSquares - Class in umontreal.iro.lecuyer.functionfit

Represents a polynomial obtained by the least squares method on a set of points.

LeastSquares(double[], double[], int) - Constructor for class umontreal.iro.lecuyer.functionfit.LeastSquares

Constructs a new least squares polynomial with points (x[0], y[0]),..., (x[n], y[n]).

leftMatrixScramble(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNet

Applies a linear scramble by multiplying each C_j on the left by a w×w nonsingular lower-triangular matrix M_j, as suggested by Matoušek and implemented by Hong and Hickernell.

leftMatrixScramble(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2

leftMatrixScrambleDiag(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNet

Similar to leftMatrixScramble except that all the off-diagonal elements of the M_j are 0.

leftMatrixScrambleDiag(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2

leftMatrixScrambleFaurePermut(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNet

Similar to leftMatrixScramble except that the diagonal elements of each matrix M_j are chosen from a restricted set of the best integers as calculated by Faure.

leftMatrixScrambleFaurePermut(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2

leftMatrixScrambleFaurePermutAll(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNet

Similar to leftMatrixScrambleFaurePermut except that the elements under the diagonal are also chosen from the same restricted set as the diagonal elements.

leftMatrixScrambleFaurePermutAll(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2

leftMatrixScrambleFaurePermutDiag(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNet

Similar to leftMatrixScrambleFaurePermut except that all off-diagonal elements are 0.

leftMatrixScrambleFaurePermutDiag(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2

length() - Method in class umontreal.iro.lecuyer.util.PrintfFormat

LFSR113 - Class in umontreal.iro.lecuyer.rng

Extends RandomStreamBase using a composite linear feedback shift register (LFSR) (or Tausworthe) RNG as defined in.

LFSR113() - Constructor for class umontreal.iro.lecuyer.rng.LFSR113

Constructs a new stream.

LFSR113(String) - Constructor for class umontreal.iro.lecuyer.rng.LFSR113

Constructs a new stream with the identifier name.

LFSR258 - Class in umontreal.iro.lecuyer.rng

Extends RandomStreamBase using a 64-bit composite linear feedback shift register (LFSR) (or Tausworthe) RNG as defined in.

LFSR258() - Constructor for class umontreal.iro.lecuyer.rng.LFSR258

Constructs a new stream.

LFSR258(String) - Constructor for class umontreal.iro.lecuyer.rng.LFSR258

Constructs a new stream with the identifier name.

LINE_SEPARATOR - Static variable in class umontreal.iro.lecuyer.util.PrintfFormat

Deprecated.

LinkedListStat<E> - Class in umontreal.iro.lecuyer.simevents

This class extends ListWithStat, and uses a linked list as the internal data structure.

LinkedListStat() - Constructor for class umontreal.iro.lecuyer.simevents.LinkedListStat

Constructs a new list, initially empty.

LinkedListStat(Simulator) - Constructor for class umontreal.iro.lecuyer.simevents.LinkedListStat

Constructs a new list, initially empty, and using the default simulator.

LinkedListStat(Collection<? extends E>) - Constructor for class umontreal.iro.lecuyer.simevents.LinkedListStat

Constructs a list containing the elements of the specified collection, using the default simulator.

LinkedListStat(Simulator, Collection<? extends E>) - Constructor for class umontreal.iro.lecuyer.simevents.LinkedListStat

Constructs a list containing the elements of the specified collection.

LinkedListStat(String) - Constructor for class umontreal.iro.lecuyer.simevents.LinkedListStat

Constructs a new list with name name, using the default simulator.

LinkedListStat(Simulator, String) - Constructor for class umontreal.iro.lecuyer.simevents.LinkedListStat

Constructs a new list with name name.

LinkedListStat(Collection<? extends E>, String) - Constructor for class umontreal.iro.lecuyer.simevents.LinkedListStat

Constructs a new list containing the elements of the specified collection c and with name name, using the default simulator.

LinkedListStat(Simulator, Collection<? extends E>, String) - Constructor for class umontreal.iro.lecuyer.simevents.LinkedListStat

Constructs a new list containing the elements of the specified collection c and with name name.

listDir(String) - Static method in class umontreal.iro.lecuyer.hups.DigitalNetBase2FromFile

Lists all files (or directories) in directory dirname.

listDir(String) - Static method in class umontreal.iro.lecuyer.hups.DigitalNetFromFile

Lists all files (or directories) in directory dirname.

listDirHTML(String, String) - Static method in class umontreal.iro.lecuyer.hups.DigitalNetFromFile

Creates a list of all data files in directory dirname and writes that list in format HTML in output file filename.

listIterator() - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree

listIterator() - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked

listIterator() - Method in interface umontreal.iro.lecuyer.simevents.eventlist.EventList

Returns a list iterator over the elements of the class Event in this list.

listIterator() - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen

listIterator() - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree

listIterator() - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree

listIterator() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat

listIterator(int) - Method in class umontreal.iro.lecuyer.simevents.ListWithStat

listIterator() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

listIterator(int) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

listIterator() - Method in class umontreal.iro.lecuyer.util.TransformingList

listIterator(int) - Method in class umontreal.iro.lecuyer.util.TransformingList

ListOfStatProbes<E extends StatProbe> - Class in umontreal.iro.lecuyer.stat.list

Represents a list of statistical probes that can be managed simultaneously.

ListOfStatProbes() - Constructor for class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

Constructs an empty list of statistical probes.

ListOfStatProbes(String) - Constructor for class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

Constructs an empty list of statistical probes with name name.

ListOfTallies<E extends Tally> - Class in umontreal.iro.lecuyer.stat.list

Represents a list of tally statistical collectors.

ListOfTallies() - Constructor for class umontreal.iro.lecuyer.stat.list.ListOfTallies

Constructs a new empty list of tallies.

ListOfTallies(String) - Constructor for class umontreal.iro.lecuyer.stat.list.ListOfTallies

Constructs a new empty list of tallies with name name.

ListOfTalliesWithCovariance<E extends Tally> - Class in umontreal.iro.lecuyer.stat.list

Extends ListOfTallies to add support for the computation of the sample covariance between each pair of elements in a list, without storing all observations.

ListOfTalliesWithCovariance() - Constructor for class umontreal.iro.lecuyer.stat.list.ListOfTalliesWithCovariance

Creates an empty list of tallies with covariance support.

ListOfTalliesWithCovariance(String) - Constructor for class umontreal.iro.lecuyer.stat.list.ListOfTalliesWithCovariance

Creates an empty list of tallies with covariance support and name name.

ListWithStat<E> - Class in umontreal.iro.lecuyer.simevents

Implements a list with integrated statistical probes to provide automatic collection of statistics on the sojourn times of objects in the list and on the size of the list as a function of time given by a simulator.

ListWithStat(List<ListWithStat.Node<E>>) - Constructor for class umontreal.iro.lecuyer.simevents.ListWithStat

Constructs a new list with internal data structure using the default simulator and implemented by nodeList.

ListWithStat(Simulator, List<ListWithStat.Node<E>>) - Constructor for class umontreal.iro.lecuyer.simevents.ListWithStat

Constructs a new list with internal data structure implemented by nodeList.

ListWithStat(List<ListWithStat.Node<E>>, Collection<? extends E>) - Constructor for class umontreal.iro.lecuyer.simevents.ListWithStat

Constructs a list containing the elements of the specified collection, whose elements are stored into nodeList and using the default simulator.

ListWithStat(Simulator, List<ListWithStat.Node<E>>, Collection<? extends E>) - Constructor for class umontreal.iro.lecuyer.simevents.ListWithStat

Constructs a list containing the elements of the specified collection, whose elements are stored into nodeList.

ListWithStat(List<ListWithStat.Node<E>>, String) - Constructor for class umontreal.iro.lecuyer.simevents.ListWithStat

Constructs a new list with name name, internal list nodeList, and using the default simulator.

ListWithStat(Simulator, List<ListWithStat.Node<E>>, String) - Constructor for class umontreal.iro.lecuyer.simevents.ListWithStat

Constructs a new list with name name, and internal list nodeList.

ListWithStat(List<ListWithStat.Node<E>>, Collection<? extends E>, String) - Constructor for class umontreal.iro.lecuyer.simevents.ListWithStat

Constructs a new list containing the elements of the specified collection c, with name name, internal list nodeList, and using the default simulator.

ListWithStat(Simulator, List<ListWithStat.Node<E>>, Collection<? extends E>, String) - Constructor for class umontreal.iro.lecuyer.simevents.ListWithStat

Constructs a new list containing the elements of the specified collection c, with name name, and internal list nodeList.

ListWithStat.Node<E> - Class in umontreal.iro.lecuyer.simevents

Represents a node that can be part of a list with statistical collecting.

ListWithStat.Node(E, double) - Constructor for class umontreal.iro.lecuyer.simevents.ListWithStat.Node

Constructs a new node containing element element inserted into the list at time insertionTime.

LMScrambleShift - Class in umontreal.iro.lecuyer.hups

This class implements a PointSetRandomization that performs a left matrix scrambling and adds a random digital shift.

LMScrambleShift(RandomStream) - Constructor for class umontreal.iro.lecuyer.hups.LMScrambleShift

Sets internal variable stream to the given stream.

LN2 - Static variable in class umontreal.iro.lecuyer.util.Num

The values of ln 2.

LN_DBL_MIN - Static variable in class umontreal.iro.lecuyer.util.Num

Natural logarithm of DBL_MIN.

lnFactorial(int) - Static method in class umontreal.iro.lecuyer.util.Num

Returns the value of the natural logarithm of factorial n.

lnGamma(double) - Static method in class umontreal.iro.lecuyer.util.Num

Returns the natural logarithm of the gamma function Γ(x) evaluated at x.

loc - Variable in class umontreal.iro.lecuyer.gof.GofStat.OutcomeCategoriesChi2

loc[i] gives the relocation of the category i in the nbExp array.

log1p(double) - Static method in class umontreal.iro.lecuyer.util.Num

Deprecated.

log2(double) - Static method in class umontreal.iro.lecuyer.util.Num

Returns log₂(x).

LogarithmicDist - Class in umontreal.iro.lecuyer.probdist

Extends the class DiscreteDistributionInt for the logarithmic distribution.

LogarithmicDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.LogarithmicDist

Constructs a logarithmic distribution with parameter θ = theta.

LogarithmicGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators for the (discrete) logarithmic distribution.

LogarithmicGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.LogarithmicGen

Creates a logarithmic random variate generator with parameters θ = theta and default value θ₀ = 0.96, using stream s.

LogarithmicGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.LogarithmicGen

Creates a logarithmic random variate generator with parameters θ = theta and θ₀ = theta0, using stream s.

LogarithmicGen(RandomStream, LogarithmicDist) - Constructor for class umontreal.iro.lecuyer.randvar.LogarithmicGen

Creates a new generator with distribution dist and stream s, with default value θ₀ = 0.96.

LogarithmicGen(RandomStream, LogarithmicDist, double) - Constructor for class umontreal.iro.lecuyer.randvar.LogarithmicGen

Creates a new generator with distribution dist and stream s, with θ₀ = theta0.

LogisticDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the logistic distribution.

LogisticDist() - Constructor for class umontreal.iro.lecuyer.probdist.LogisticDist

Constructs a LogisticDist object with default parameters α = 0 and λ = 1.

LogisticDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.LogisticDist

Constructs a LogisticDist object with parameters α = alpha and λ = lambda.

LogisticGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators for the logistic distribution.

LogisticGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.LogisticGen

Creates a logistic random variate generator with parameters α = alpha and λ = lambda, using stream s.

LogisticGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.LogisticGen

Creates a logistic random variate generator with parameters α = 0 and λ = 1, using stream s.

LogisticGen(RandomStream, LogisticDist) - Constructor for class umontreal.iro.lecuyer.randvar.LogisticGen

Creates a new generator for the logistic distribution dist and stream s.

LoglogisticDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the Log-Logistic distribution with shape parameter α > 0 and scale parameter β > 0.

LoglogisticDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.LoglogisticDist

Constructs a log-logistic distribution with parameters α and β.

LoglogisticGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators for the log-logistic distribution with shape parameter α > 0 and scale parameter β > 0.

LoglogisticGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.LoglogisticGen

Creates a log-logistic random variate generator with parameters α = alpha and β = beta, using stream s.

LoglogisticGen(RandomStream, LoglogisticDist) - Constructor for class umontreal.iro.lecuyer.randvar.LoglogisticGen

Creates a new generator for the distribution dist, using stream s.

LognormalDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the lognormal distribution.

LognormalDist() - Constructor for class umontreal.iro.lecuyer.probdist.LognormalDist

Constructs a LognormalDist object with default parameters μ = 0 and σ = 1.

LognormalDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.LognormalDist

Constructs a LognormalDist object with parameters μ = mu and σ = sigma.

LognormalDistFromMoments - Class in umontreal.iro.lecuyer.probdist

Extends the LognormalDist class with a constructor accepting the mean m and the variance v of the distribution as arguments.

LognormalDistFromMoments(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.LognormalDistFromMoments

LognormalGen - Class in umontreal.iro.lecuyer.randvar

This class implements methods for generating random variates from the lognormal distribution.

LognormalGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.LognormalGen

Creates a lognormal random variate generator with parameters μ = mu and σ = sigma, using stream s.

LognormalGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.LognormalGen

Creates a lognormal random variate generator with parameters μ = 0 and σ = 1, using stream s.

LognormalGen(RandomStream, LognormalDist) - Constructor for class umontreal.iro.lecuyer.randvar.LognormalGen

Create a random variate generator for the lognormal distribution dist and stream s.

LognormalSpecialGen - Class in umontreal.iro.lecuyer.randvar

Implements methods for generating random variates from the lognormal distribution using an arbitrary normal random variate generator.

LognormalSpecialGen(NormalGen) - Constructor for class umontreal.iro.lecuyer.randvar.LognormalSpecialGen

Create a lognormal random variate generator using the normal generator g and with the same parameters.

M

main(String[]) - Static method in class umontreal.iro.lecuyer.rng.GenF2w32

This method is only meant to be used during the compilation process.

main(String[]) - Static method in class umontreal.iro.lecuyer.rng.WELL1024

main(String[]) - Static method in class umontreal.iro.lecuyer.rng.WELL512

This method is only meant to be used during the compilation process.

main(String[]) - Static method in class umontreal.iro.lecuyer.rng.WELL607

MATHEMATICA - Static variable in class umontreal.iro.lecuyer.gof.GofFormat

Data file format used for creating graphics with Mathematica.

MATHEMATICA - Static variable in class umontreal.iro.lecuyer.util.TableFormat

Mathematica matrix printing style

MathFunction - Interface in umontreal.iro.lecuyer.functions

This interface should be implemented by classes which represent univariate mathematical functions.

MathFunctionUtil - Class in umontreal.iro.lecuyer.functions

Provides utility methods for computing derivatives and integrals of functions.

MathFunctionWithDerivative - Interface in umontreal.iro.lecuyer.functions

Represents a mathematical function whose nth derivative can be computed using derivative.

MathFunctionWithFirstDerivative - Interface in umontreal.iro.lecuyer.functions

Represents a mathematical function whose derivative can be computed using derivative.

MathFunctionWithIntegral - Interface in umontreal.iro.lecuyer.functions

Represents a mathematical function whose integral can be computed by the integral method.

MATLAB - Static variable in class umontreal.iro.lecuyer.util.TableFormat

Matlab matrix printing style

matMatModM(double[][], double[][], double[][], double) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod

Computes A×B mod m and puts the result in C.

matMatModM(int[][], int[][], int[][], int) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod

Exactly like matMatModM using double, but with int instead of double.

matMatModM(long[][], long[][], long[][], long) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod

Exactly like matMatModM using double, but with long instead of double.

matPowModM(double[][], double[][], double, int) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod

Computes A^c mod m and puts the result in B.

matPowModM(int[][], int[][], int, int) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod

Exactly like matPowModM using double, but with int instead of double.

matPowModM(long[][], long[][], long, int) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod

Exactly like matPowModM using double, but with long instead of double.

matTwoPowModM(double[][], double[][], double, int) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod

Computes A^2e mod m and puts the result in B.

matTwoPowModM(int[][], int[][], int, int) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod

Exactly like matTwoPowModM using double, but with int instead of double.

matTwoPowModM(long[][], long[][], long, int) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod

Exactly like matTwoPowModM using double, but with long instead of double.

matVecModM(double[][], double[], double[], double) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod

Computes the result of A×s mod m and puts the result in v.

matVecModM(int[][], int[], int[], int) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod

Exactly like matVecModM using double, but with int instead of double.

matVecModM(long[][], long[], long[], long) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod

Exactly like matVecModM using double, but with long instead of double.

max() - Method in class umontreal.iro.lecuyer.stat.StatProbe

Returns the largest value taken by the variable since the last initialization of this probe.

MAXINTDOUBLE - Static variable in class umontreal.iro.lecuyer.util.Num

Largest integer n₀ = 2⁵³ such that any integer n <= n₀ is represented exactly as a double.

MAXLAMBDA - Static variable in class umontreal.iro.lecuyer.probdist.PoissonDist

MAXN - Static variable in class umontreal.iro.lecuyer.probdist.BinomialDist

MAXN - Static variable in class umontreal.iro.lecuyer.probdist.HypergeometricDist

MAXN - Static variable in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist

MAXTWOEXP - Static variable in class umontreal.iro.lecuyer.util.Num

Powers of 2 up to MAXTWOEXP are stored exactly in the array TWOEXP.

MEAN - Static variable in class umontreal.iro.lecuyer.gof.GofFormat

Mean

min() - Method in class umontreal.iro.lecuyer.stat.StatProbe

Returns the smallest value taken by the variable since the last initialization of this probe.

Misc - Class in umontreal.iro.lecuyer.util

This class provides miscellaneous functions that are hard to classify.

MRG31k3p - Class in umontreal.iro.lecuyer.rng

Extends the abstract class RandomStreamBase, thus implementing the RandomStream interface indirectly.

MRG31k3p() - Constructor for class umontreal.iro.lecuyer.rng.MRG31k3p

Constructs a new stream, initialized at its beginning.

MRG31k3p(String) - Constructor for class umontreal.iro.lecuyer.rng.MRG31k3p

Constructs a new stream with the identifier name (used when formatting the stream state).

MRG32k3a - Class in umontreal.iro.lecuyer.rng

Extends the abstract class RandomStreamBase by using as a backbone (or main) generator the combined multiple recursive generator (CMRG) MRG32k3a proposed by L'Ecuyer, implemented in 64-bit floating-point arithmetic.

MRG32k3a() - Constructor for class umontreal.iro.lecuyer.rng.MRG32k3a

Constructs a new stream, initializes its seed I_g, sets B_g and C_g equal to I_g, and sets its antithetic switch to false.

MRG32k3a(String) - Constructor for class umontreal.iro.lecuyer.rng.MRG32k3a

Constructs a new stream with an identifier name (used when printing the stream state).

MRG32k3aL - Class in umontreal.iro.lecuyer.rng

The same generator as MRG32k3a, except here it is implemented with type long instead of double.

MRG32k3aL() - Constructor for class umontreal.iro.lecuyer.rng.MRG32k3aL

MRG32k3aL(String) - Constructor for class umontreal.iro.lecuyer.rng.MRG32k3aL

MT19937 - Class in umontreal.iro.lecuyer.rng

Implements the RandomStream interface via inheritance from RandomStreamBase.

MT19937(CloneableRandomStream) - Constructor for class umontreal.iro.lecuyer.rng.MT19937

Constructs a new stream, using rng to fill its initial state.

MT19937(CloneableRandomStream, String) - Constructor for class umontreal.iro.lecuyer.rng.MT19937

Constructs a new stream with the identifier name (used in the toString method).

MultiDimComparable<T> - Interface in umontreal.iro.lecuyer.util

This interface represents an object which can be compared in many dimensions or in many ways.

MultiDimComparator<T extends MultiDimComparable<? super T>> - Class in umontreal.iro.lecuyer.util

This class defines a bridge between our interface MultiDimComparable and the classic Comparator in Java.

MultiDimComparator(int) - Constructor for class umontreal.iro.lecuyer.util.MultiDimComparator

Constructs a comparator, where i is the dimension used in the comparisons.

MultiDimSort - Interface in umontreal.iro.lecuyer.util

This interface represents a sort on multidimensional arrays.

MultinomialDist - Class in umontreal.iro.lecuyer.probdistmulti

Implements the abstract class DiscreteDistributionIntMulti for the multinomial distribution with parameters n and (p₁, ...,p_d).

MultinomialDist(int, double[]) - Constructor for class umontreal.iro.lecuyer.probdistmulti.MultinomialDist

Creates a MultinomialDist object with parameters n and (p₁,...,p_d) such that ∑_i=1^dp_i = 1.

MultinormalCholeskyGen - Class in umontreal.iro.lecuyer.randvarmulti

Extends MultinormalGen for a multivariate normal distribution, generated via a Cholesky decomposition of the covariance matrix.

MultinormalCholeskyGen(NormalGen, double[], double[][]) - Constructor for class umontreal.iro.lecuyer.randvarmulti.MultinormalCholeskyGen

Equivalent to MultinormalCholeskyGen(gen1, mu, new DenseDoubleMatrix2D(sigma)).

MultinormalCholeskyGen(NormalGen, double[], DoubleMatrix2D) - Constructor for class umontreal.iro.lecuyer.randvarmulti.MultinormalCholeskyGen

Constructs a multinormal generator with mean vector mu and covariance matrix sigma.

MultiNormalDist - Class in umontreal.iro.lecuyer.probdistmulti

Implements the abstract class ContinuousDistributionMulti for the multinormal distribution with mean vector μ and covariance matrix Σ.

MultiNormalDist(double[], double[][]) - Constructor for class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist

MultinormalGen - Class in umontreal.iro.lecuyer.randvarmulti

Extends RandomMultivariateGen for a multivariate normal (or multinormal) distribution.

MultinormalGen(NormalGen, int) - Constructor for class umontreal.iro.lecuyer.randvarmulti.MultinormalGen

Constructs a generator with the standard multinormal distribution (with μ = 0 and Σ = I) in d dimensions.

MultinormalPCAGen - Class in umontreal.iro.lecuyer.randvarmulti

Extends MultinormalGen for a multivariate normal distribution, generated via the method of principal components analysis (PCA) of the covariance matrix.

MultinormalPCAGen(NormalGen, double[], double[][]) - Constructor for class umontreal.iro.lecuyer.randvarmulti.MultinormalPCAGen

Equivalent to MultinormalPCAGen(gen1, mu, new DenseDoubleMatrix2D(sigma)).

MultinormalPCAGen(NormalGen, double[], DoubleMatrix2D) - Constructor for class umontreal.iro.lecuyer.randvarmulti.MultinormalPCAGen

Constructs a multinormal generator with mean vector mu and covariance matrix sigma.

MultipleDatasetChart - Class in umontreal.iro.lecuyer.charts

Provides tools to plot many datasets on the same chart.

MultipleDatasetChart() - Constructor for class umontreal.iro.lecuyer.charts.MultipleDatasetChart

Initializes a new MultipleDatasetChart.

MultipleDatasetChart(String, String, String) - Constructor for class umontreal.iro.lecuyer.charts.MultipleDatasetChart

Initializes a new MultipleDatasetChart instance.

multiply(BitVector) - Method in class umontreal.iro.lecuyer.util.BitMatrix

Multiplies the column BitVector by a BitMatrix and returns the result.

multiply(int) - Method in class umontreal.iro.lecuyer.util.BitMatrix

Multiplies vect, seen as a column BitVector, by a BitMatrix.

multiply(BitMatrix) - Method in class umontreal.iro.lecuyer.util.BitMatrix

Multiplies two BitMatrix's together.

MultivariateFunction - Interface in umontreal.iro.lecuyer.util

Represents a function of multiple variables.

multMod(int, int, int, int) - Static method in class umontreal.iro.lecuyer.util.Num

Deprecated.

multMod(long, long, long, long) - Static method in class umontreal.iro.lecuyer.util.Num

Deprecated.

multMod(double, double, double, double) - Static method in class umontreal.iro.lecuyer.util.Num

Deprecated.

multModM(double, double, double, double) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod

Computes (a×s + c) mod m.

multModM(int, int, int, int) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod

Computes (a×s + c) mod m.

multModM(long, long, long, long) - Static method in class umontreal.iro.lecuyer.util.ArithmeticMod

Computes (a×s + c) mod m.

N

NakagamiDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the Nakagami distribution with location parameter a, scale parameter λ > 0 and shape parameter c > 0.

NakagamiDist(double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.NakagamiDist

Constructs a NakagamiDist object with parameters a = a, λ = lambda and c = c.

NakagamiGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators for the Nakagami distribution.

NakagamiGen(RandomStream, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.NakagamiGen

Creates a new Nakagami generator with parameters a = a, λ = lambda and c = c, using stream s.

NakagamiGen(RandomStream, NakagamiDist) - Constructor for class umontreal.iro.lecuyer.randvar.NakagamiGen

Creates a new generator for the distribution dist, using stream s.

NameConflictException - Exception in umontreal.iro.lecuyer.util

This exception is thrown by a ClassFinder when two or more fully qualified class names can be associated with a simple class name.

NameConflictException() - Constructor for exception umontreal.iro.lecuyer.util.NameConflictException

Constructs a new name conflict exception.

NameConflictException(String) - Constructor for exception umontreal.iro.lecuyer.util.NameConflictException

Constructs a new name conflict exception with message message.

NameConflictException(ClassFinder, String, String) - Constructor for exception umontreal.iro.lecuyer.util.NameConflictException

Constructs a new name conflict exception with class finder finder, simple name name, and message message.

nbCategories - Variable in class umontreal.iro.lecuyer.gof.GofStat.OutcomeCategoriesChi2

Total number of categories.

nbExp - Variable in class umontreal.iro.lecuyer.gof.GofStat.OutcomeCategoriesChi2

Expected number of observations for each category.

NegativeBinomialDist - Class in umontreal.iro.lecuyer.probdist

Extends the class DiscreteDistributionInt for the negative binomial distribution with real parameters γ and p, where γ > 0 and 0 <= p <= 1.

NegativeBinomialDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.NegativeBinomialDist

Creates an object that contains the probability terms and the distribution function for the negative binomial distribution with parameters γ and p.

NegativeBinomialGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators having the negative binomial distribution.

NegativeBinomialGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.NegativeBinomialGen

Creates a negative binomial random variate generator with parameters γ = gamma and p, using stream s.

NegativeBinomialGen(RandomStream, NegativeBinomialDist) - Constructor for class umontreal.iro.lecuyer.randvar.NegativeBinomialGen

Creates a new generator for the distribution dist, using stream s.

NegativeMultinomialDist - Class in umontreal.iro.lecuyer.probdistmulti

Implements the abstract class DiscreteDistributionIntMulti for the negative multinomial distribution with parameters γ > 0 and ( p₁,…, p_d), such that all 0 < p_i < 1 and ∑_i=1^dp_i < 1.

NegativeMultinomialDist(double, double[]) - Constructor for class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist

Creates a NegativeMultinomialDist object with parameters γ = gamma and (p₁,...,p_d) such that ∑_i=1^dp_i < 1, as described above.

newArrayOfObservations(ListOfStatProbes<?>, double[]) - Method in interface umontreal.iro.lecuyer.stat.list.ArrayOfObservationListener

Receives the new array of observations x broadcast by the list of statistical probes listOfProbes.

newInstance() - Method in class umontreal.iro.lecuyer.rng.BasicRandomStreamFactory

newInstance() - Method in interface umontreal.iro.lecuyer.rng.RandomStreamFactory

Constructs and returns a new random stream.

newInstance() - Static method in class umontreal.iro.lecuyer.simprocs.ProcessSimulator

Constructs and returns a new process-oriented simulator.

NEWLINE - Static variable in class umontreal.iro.lecuyer.util.PrintfFormat

End-of-line symbol or line separator.

newObservation(StatProbe, double) - Method in interface umontreal.iro.lecuyer.stat.ObservationListener

Receives the new observation x broadcast by probe.

nextArrayOfDouble(double[], int, int) - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGen

Generates n random numbers from the continuous distribution contained in this object.

nextArrayOfDouble(double[], int, int) - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache

nextArrayOfDouble(double[], int, int) - Method in class umontreal.iro.lecuyer.randvar.UnuranContinuous

nextArrayOfDouble(double[], int, int) - Method in class umontreal.iro.lecuyer.randvar.UnuranEmpirical

nextArrayOfDouble(double[], int, int) - Method in class umontreal.iro.lecuyer.rng.AntitheticStream

Calls nextArrayOfDouble (u, start, n) for the base stream, then replaces each u[i] by 1.0 - u[i].

nextArrayOfDouble(double[], int, int) - Method in class umontreal.iro.lecuyer.rng.BakerTransformedStream

Calls nextArrayOfDouble (u, start, n) for the base stream, then applies the baker transformation.

nextArrayOfDouble(double[], int, int) - Method in class umontreal.iro.lecuyer.rng.RandMrg

Deprecated.

nextArrayOfDouble(double[], int, int) - Method in interface umontreal.iro.lecuyer.rng.RandomStream

Generates n (pseudo)random numbers from the uniform distribution and stores them into the array u starting at index start.

nextArrayOfDouble(double[], int, int) - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase

Calls nextDouble n times to fill the array u.

nextArrayOfDouble(double[], int, int) - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache

nextArrayOfDouble(double[], int, int) - Method in class umontreal.iro.lecuyer.rng.TruncatedRandomStream

nextArrayOfInt(int[], int, int) - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenInt

Generates n random numbers from the discrete distribution contained in this object.

nextArrayOfInt(int[], int, int) - Method in class umontreal.iro.lecuyer.randvar.UnuranDiscreteInt

nextArrayOfInt(int, int, int[], int, int) - Method in class umontreal.iro.lecuyer.rng.AntitheticStream

Calls nextArrayOfInt (i, j, u, start, n) for the base stream, then replaces each u[i] by j - i - u[i].

nextArrayOfInt(int, int, int[], int, int) - Method in class umontreal.iro.lecuyer.rng.BakerTransformedStream

Fills up the array by calling nextInt (i, j).

nextArrayOfInt(int, int, int[], int, int) - Method in class umontreal.iro.lecuyer.rng.RandMrg

Deprecated.

nextArrayOfInt(int, int, int[], int, int) - Method in interface umontreal.iro.lecuyer.rng.RandomStream

Generates n (pseudo)random numbers from the discrete uniform distribution over the integers {i, i + 1,..., j}, using this stream and stores the result in the array u starting at index start.

nextArrayOfInt(int, int, int[], int, int) - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase

Calls nextInt n times to fill the array u.

nextArrayOfInt(int, int, int[], int, int) - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache

nextArrayOfInt(int, int, int[], int, int) - Method in class umontreal.iro.lecuyer.rng.TruncatedRandomStream

nextArrayOfPoints(double[][], int, int) - Method in class umontreal.iro.lecuyer.randvarmulti.RandomMultivariateGen

Generates n random points.

nextCoordinate() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator

nextCoordinate() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSetBase2.CycleBasedPointSetBase2Iterator

nextCoordinate() - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator

Returns the current coordinate u_{i, j} and advances to the next one.

nextCoordinates(double[], int) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator

nextCoordinates(double[], int) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSetBase2.CycleBasedPointSetBase2Iterator

nextCoordinates(double[], int) - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator

Returns the next d coordinates of the current point in p and advances the current coordinate index by d.

nextDouble() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator

nextDouble(RandomStream, double, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.BetaGen

Generates a variate from the beta distribution with parameters α = alpha, β = beta, over the interval (a, b), using stream s.

nextDouble() - Method in class umontreal.iro.lecuyer.randvar.BetaRejectionLoglogisticGen

nextDouble() - Method in class umontreal.iro.lecuyer.randvar.BetaStratifiedRejectionGen

nextDouble(RandomStream, double, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.BetaStratifiedRejectionGen

nextDouble(RandomStream, RandomStream, RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalBestGen

Generates a random number using Devroye's one-liner method.

nextDouble(RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalBestGen

Generates a random number using Devroye's one-liner method with only one stream s.

nextDouble() - Method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalBestGen

nextDouble(RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalGen

nextDouble(RandomStream, RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalPolarGen

Generates a random number using Ulrich's polar method.

nextDouble(RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalPolarGen

Generates a random number by Ulrich's polar method using stream s.

nextDouble() - Method in class umontreal.iro.lecuyer.randvar.BetaSymmetricalPolarGen

nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.CauchyGen

Generates a new variate from the Cauchy distribution with parameters α = alpha and β = beta, using stream s.

nextDouble(RandomStream, int) - Static method in class umontreal.iro.lecuyer.randvar.ChiGen

Generates a random variate from the chi distribution with ν = nu degrees of freedom, using stream s.

nextDouble() - Method in class umontreal.iro.lecuyer.randvar.ChiRatioOfUniformsGen

nextDouble(RandomStream, int) - Static method in class umontreal.iro.lecuyer.randvar.ChiRatioOfUniformsGen

nextDouble(RandomStream, int) - Static method in class umontreal.iro.lecuyer.randvar.ChiSquareGen

Generates a new variate from the chi square distribution with n degrees of freedom, using stream s.

nextDouble() - Method in class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralGamGen

nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralGamGen

Generates a variate from the noncentral chi square distribution with parameters ν = nu and λ = lambda using stream stream, as described above.

nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralGen

Generates a new variate from the noncentral chi square distribution with ν degrees of freedom and noncentrality parameter λ, using stream s.

nextDouble() - Method in class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralPoisGen

nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.ChiSquareNoncentralPoisGen

Generates a variate from the noncentral chi square distribution with parameters ν = nu and λ = lambda using stream stream, as described above.

nextDouble() - Method in class umontreal.iro.lecuyer.randvar.ErlangConvolutionGen

nextDouble(RandomStream, int, double) - Static method in class umontreal.iro.lecuyer.randvar.ErlangConvolutionGen

nextDouble(RandomStream, int, double) - Static method in class umontreal.iro.lecuyer.randvar.ErlangGen

Generates a new variate from the Erlang distribution with parameters k = k and λ = lambda, using stream s.

nextDouble(RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.ExponentialGen

Uses inversion to generate a new exponential variate with parameter λ = lambda, using stream s.

nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.ExtremeValueGen

Deprecated.

Uses inversion to generate a new variate from the extreme value distribution with parameters α = alpha and λ = lambda, using stream s.

nextDouble(RandomStream, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.FatigueLifeGen

Generates a variate from the fatigue life distribution with location parameter μ, scale parameter β and shape parameter γ.

nextDouble(RandomStream, int, int) - Static method in class umontreal.iro.lecuyer.randvar.FisherFGen

Generates a variate from the Fisher F distribution with n and m degrees of freedom, using stream s.

nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.FoldedNormalGen

Generates a variate from the folded normal distribution with parameters μ = mu and σ = sigma, using stream s.

nextDouble(RandomStream, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.FrechetGen

Generates a new variate from the Fréchet distribution with parameters α = alpha, β = beta and δ = delta using stream s.

nextDouble(RandomStream, RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.GammaAcceptanceRejectionGen

Generates a new gamma variate with parameters α = alpha and λ = lambda, using main stream s and auxiliary stream aux.

nextDouble() - Method in class umontreal.iro.lecuyer.randvar.GammaAcceptanceRejectionGen

nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.GammaAcceptanceRejectionGen

Same as nextDouble (s, s, alpha, lambda).

nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.GammaGen

Generates a new gamma random variate with parameters α = alpha and λ = lambda, using stream s.

nextDouble() - Method in class umontreal.iro.lecuyer.randvar.GammaRejectionLoglogisticGen

nextDouble(RandomStream, RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.GammaRejectionLoglogisticGen

Generates a new gamma variate with parameters α = alpha and λ = lambda, using main stream s and auxiliary stream aux.

nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.GammaRejectionLoglogisticGen

Same as nextDouble (s, s, alpha, lambda).

nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.GumbelGen

Generates a new variate from the Gumbel distribution with parameters β = beta and δ = delta using stream s.

nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.HalfNormalGen

Generates a variate from the half-normal distribution with parameters μ = mu and σ = sigma, using stream s.

nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.HyperbolicSecantGen

Generates a variate from the hyperbolic secant distribution with location parameter μ and scale parameter σ.

nextDouble() - Method in class umontreal.iro.lecuyer.randvar.InverseFromDensityGen

Generates a new random variate.

nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.InverseGaussianGen

Generates a variate from the inverse gaussian distribution with location parameter μ > 0 and scale parameter λ > 0.

nextDouble(RandomStream, NormalGen, double, double) - Static method in class umontreal.iro.lecuyer.randvar.InverseGaussianMSHGen

Generates a new variate from the inverse gaussian distribution with parameters μ = mu and λ = lambda, using streams s and sn.

nextDouble() - Method in class umontreal.iro.lecuyer.randvar.InverseGaussianMSHGen

nextDouble(RandomStream, double, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.JohnsonSBGen

Uses inversion to generate a new JohnsonSB variate, using stream s.

nextDouble(RandomStream, double, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.JohnsonSUGen

Uses inversion to generate a new JohnsonSU variate, using stream s.

nextDouble() - Method in class umontreal.iro.lecuyer.randvar.KernelDensityGen

nextDouble() - Method in class umontreal.iro.lecuyer.randvar.KernelDensityVarCorrectGen

nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.LaplaceGen

Generates a new variate from the Laplace distribution with parameters μ = mu and β = beta, using stream s.

nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.LogisticGen

Generates a new variate from the logistic distribution with parameters α = alpha and λ = lambda, using stream s.

nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.LoglogisticGen

Generates a variate from the log-logistic distribution with shape parameter α > 0 and scale parameter β > 0.

nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.LognormalGen

Generates a new variate from the lognormal distribution with parameters μ = mu and σ = sigma, using stream s.

nextDouble() - Method in class umontreal.iro.lecuyer.randvar.LognormalSpecialGen

nextDouble(RandomStream, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.NakagamiGen

Generates a variate from the Nakagami distribution with parameters a = a, λ = lambda and c = c, using stream s.

nextDouble() - Method in class umontreal.iro.lecuyer.randvar.NormalACRGen

nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.NormalACRGen

nextDouble() - Method in class umontreal.iro.lecuyer.randvar.NormalBoxMullerGen

nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.NormalBoxMullerGen

nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.NormalGen

Generates a variate from the normal distribution with parameters μ = mu and σ = sigma, using stream s.

nextDouble(RandomStream, double, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.NormalInverseGaussianGen

NOT IMPLEMENTED.

nextDouble(InverseGaussianGen, NormalGen, double, double) - Static method in class umontreal.iro.lecuyer.randvar.NormalInverseGaussianIGGen

Generates a new variate from the distribution with parameters α, β = beta, μ = mu and δ, using generators ig and ng, as described in eq..

nextDouble() - Method in class umontreal.iro.lecuyer.randvar.NormalInverseGaussianIGGen

nextDouble() - Method in class umontreal.iro.lecuyer.randvar.NormalKindermannRamageGen

nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.NormalKindermannRamageGen

nextDouble() - Method in class umontreal.iro.lecuyer.randvar.NormalPolarGen

nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.NormalPolarGen

nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.ParetoGen

Generates a new variate from the Pareto distribution with parameters α = alpha and β = beta, using stream s.

nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.Pearson5Gen

Generates a variate from the Pearson V distribution with shape parameter α > 0 and scale parameter β > 0.

nextDouble(RandomStream, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.Pearson6Gen

Generates a variate from the Pearson VI distribution with shape parameters α₁ > 0 and α₂ > 0, and scale parameter β > 0.

nextDouble(RandomStream, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.PowerGen

Uses inversion to generate a new variate from the power distribution with parameters a = a, b = b, and c = c, using stream s.

nextDouble() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGen

Generates a random number from the continuous distribution contained in this object.

nextDouble() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache

nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.RayleighGen

Uses inversion to generate a new variate from the Rayleigh distribution with parameters a = a and β = beta, using stream s.

nextDouble(RandomStream, int) - Static method in class umontreal.iro.lecuyer.randvar.StudentGen

Generates a new variate from the Student distribution with n = n degrees of freedom, using stream s.

nextDouble() - Method in class umontreal.iro.lecuyer.randvar.StudentPolarGen

nextDouble(RandomStream, int) - Static method in class umontreal.iro.lecuyer.randvar.StudentPolarGen

nextDouble(RandomStream, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.TriangularGen

Generates a new variate from the triangular distribution with parameters a = a, b = b and m = m and stream s, using inversion.

nextDouble(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.UniformGen

Generates a uniform random variate over the interval (a, b) by inversion, using stream s.

nextDouble() - Method in class umontreal.iro.lecuyer.randvar.UnuranContinuous

nextDouble() - Method in class umontreal.iro.lecuyer.randvar.UnuranEmpirical

nextDouble(RandomStream, double, double, double) - Static method in class umontreal.iro.lecuyer.randvar.WeibullGen

Uses inversion to generate a new variate from the Weibull distribution with parameters α = alpha, λ = lambda, and δ = delta, using stream s.

nextDouble() - Method in class umontreal.iro.lecuyer.rng.AntitheticStream

Returns 1.0 - s.nextDouble() where s is the base stream.

nextDouble() - Method in class umontreal.iro.lecuyer.rng.BakerTransformedStream

Returns the baker transformation of s.nextDouble() where s is the base stream.

nextDouble() - Method in class umontreal.iro.lecuyer.rng.RandMrg

Deprecated.

Returns a (pseudo)random number from the uniform distribution over the interval (0, 1), using this stream, after advancing its state by one step.

nextDouble() - Method in interface umontreal.iro.lecuyer.rng.RandomStream

Returns a (pseudo)random number from the uniform distribution over the interval (0, 1), using this stream, after advancing its state by one step.

nextDouble() - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase

Returns a uniform random number between 0 and 1 from the stream.

nextDouble() - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache

nextDouble() - Method in class umontreal.iro.lecuyer.rng.TruncatedRandomStream

nextInt() - Method in class umontreal.iro.lecuyer.randvar.BinomialConvolutionGen

nextInt(RandomStream, int, double) - Static method in class umontreal.iro.lecuyer.randvar.BinomialConvolutionGen

nextInt(RandomStream, int, double) - Static method in class umontreal.iro.lecuyer.randvar.BinomialGen

Generates a new integer from the binomial distribution with parameters n = n and p = p, using the given stream s.

nextInt() - Method in class umontreal.iro.lecuyer.randvar.GeometricGen

nextInt(RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.GeometricGen

Generates a geometric random variate with parameter p = p, using stream s, by inversion.

nextInt(RandomStream, int, int, int) - Static method in class umontreal.iro.lecuyer.randvar.HypergeometricGen

Generates a new variate from the hypergeometric distribution with parameters m = m, l = l and k = k, using stream s.

nextInt() - Method in class umontreal.iro.lecuyer.randvar.LogarithmicGen

nextInt(RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.LogarithmicGen

Uses stream s to generate a new variate from the logarithmic distribution with parameter θ = theta.

nextInt(RandomStream, double, double) - Static method in class umontreal.iro.lecuyer.randvar.NegativeBinomialGen

Generates a new variate from the negative binomial distribution, with parameters γ = gamma and p = p, using stream s.

nextInt() - Method in class umontreal.iro.lecuyer.randvar.PascalConvolutionGen

nextInt(RandomStream, int, double) - Static method in class umontreal.iro.lecuyer.randvar.PascalConvolutionGen

nextInt(RandomStream, int, double) - Static method in class umontreal.iro.lecuyer.randvar.PascalGen

Generates a new variate from the Pascal distribution, with parameters n = n and p = p, using stream s.

nextInt(RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.PoissonGen

A static method for generating a random variate from a Poisson distribution with parameter λ = lambda.

nextInt() - Method in class umontreal.iro.lecuyer.randvar.PoissonTIACGen

nextInt(RandomStream, double) - Static method in class umontreal.iro.lecuyer.randvar.PoissonTIACGen

nextInt() - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenInt

Generates a random number (an integer) from the discrete distribution contained in this object.

nextInt(RandomStream, int, int) - Static method in class umontreal.iro.lecuyer.randvar.UniformIntGen

Generates a new uniform random variate over the interval [i, j], using stream s, by inversion.

nextInt() - Method in class umontreal.iro.lecuyer.randvar.UnuranDiscreteInt

nextInt(int, int) - Method in class umontreal.iro.lecuyer.rng.AntitheticStream

Returns j - i - s.nextInt(i, j) where s is the base stream.

nextInt(int, int) - Method in class umontreal.iro.lecuyer.rng.BakerTransformedStream

Generates a random integer in {i,..., j} via nextDouble (in which the baker transformation is applied).

nextInt(int, int) - Method in class umontreal.iro.lecuyer.rng.LFSR113

nextInt(int, int) - Method in class umontreal.iro.lecuyer.rng.LFSR258

nextInt(int, int) - Method in class umontreal.iro.lecuyer.rng.RandMrg

Deprecated.

nextInt(int, int) - Method in interface umontreal.iro.lecuyer.rng.RandomStream

Returns a (pseudo)random number from the discrete uniform distribution over the integers {i, i + 1,..., j}, using this stream.

nextInt(int, int) - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase

Calls nextDouble once to create one integer between i and j.

nextInt(int, int) - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache

nextInt(int, int) - Method in class umontreal.iro.lecuyer.rng.TruncatedRandomStream

nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotion

nextObservation(double) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotion

Generates and returns the next observation at time t_j+1 = nextTime.

nextObservation(double, double) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotion

Generates an observation of the process in dt time units, assuming that the process has value x at the current time.

nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionBridge

nextObservation(double) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionBridge

nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCA

nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCAEqualSteps

nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess

nextObservation(double) - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess

Generates and returns the next observation at time t_j+1 = nextTime, using the previous observation time t_j defined earlier (either by this method or by setObservationTimes), as well as the value of the previous observation X(t_j).

nextObservation(double, double) - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess

Generates an observation of the process in dt time units, assuming that the process has value x at the current time.

nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcessEuler

nextObservation(double) - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcessEuler

Generates and returns the next observation at time t_j+1 = nextTime, using the previous observation time t_j defined earlier (either by this method or by setObservationTimes), as well as the value of the previous observation X(t_j).

nextObservation(double, double) - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcessEuler

Generates an observation of the process in dt time units, assuming that the process has value x at the current time.

nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcess

nextObservation(double) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcess

Generates and returns the next observation at time t_j+1 = nextTime, using the previous observation time t_j defined earlier (either by this method or by setObservationTimes), as well as the value of the previous observation X(t_j).

nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessBridge

nextObservation(double) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessBridge

nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCA

This method is not implemented in this class since the path cannot be generated sequentially.

nextObservation(double) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCA

This method is not implemented in this class since the path cannot be generated sequentially.

nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessSymmetricalBridge

nextObservation(double) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessSymmetricalBridge

nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion

nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricLevyProcess

Returns the next observation.

nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess

nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess

nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessBridge

Returns the next observation in the bridge order, not the sequential order.

nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessMSH

nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessPCA

Not implementable for PCA.

nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess

Returns the value of the process for the next time step.

nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess

nextObservation(double) - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess

Generates and returns the next observation at time t_j+1 = nextTime, using the previous observation time t_j defined earlier (either by this method or by setObservationTimes), as well as the value of the previous observation X(t_j).

nextObservation(double, double) - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess

Generates an observation of the process in dt time units, assuming that the process has value x at the current time.

nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcessEuler

Generates and returns the next observation at time t_j+1 = nextTime.

nextObservation(double) - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcessEuler

nextObservation(double, double) - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcessEuler

Generates and returns an observation of the process in dt time units, assuming that the process has value x at the current time.

nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess

Generates and returns the next observation X(t_j) of the stochastic process.

nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess

Generates the observation for the next time.

nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiff

nextObservation() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiffPCA

This method is not implemented is this class since the path cannot be generated sequentially.

nextPoint(double[], int) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator

nextPoint(double[], int) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSetBase2.CycleBasedPointSetBase2Iterator

nextPoint(double[], int) - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator

Returns the first d coordinates of the current point in p, advances to the next point, and returns the index of the new current point.

nextPoint(RandomStream, double[], double[]) - Static method in class umontreal.iro.lecuyer.randvarmulti.DirichletGen

Generates a new point from the Dirichlet distribution with parameters alphas, using the stream stream.

nextPoint(double[]) - Method in class umontreal.iro.lecuyer.randvarmulti.DirichletGen

Generates a point from the Dirichlet distribution.

nextPoint(double[]) - Method in class umontreal.iro.lecuyer.randvarmulti.IIDMultivariateGen

Generates a vector of i.i.d.

nextPoint(NormalGen, double[], double[][], double[]) - Static method in class umontreal.iro.lecuyer.randvarmulti.MultinormalCholeskyGen

Equivalent to nextPoint(gen1, mu, new DenseDoubleMatrix2D(sigma), p).

nextPoint(NormalGen, double[], DoubleMatrix2D, double[]) - Static method in class umontreal.iro.lecuyer.randvarmulti.MultinormalCholeskyGen

Generates a d-dimensional vector from the multinormal distribution with mean vector mu and covariance matrix sigma, using the one-dimensional normal generator gen1 to generate the coordinates of Z, and using the Cholesky decomposition of Σ.

nextPoint(double[]) - Method in class umontreal.iro.lecuyer.randvarmulti.MultinormalCholeskyGen

Generates a point from this multinormal distribution.

nextPoint(double[]) - Method in class umontreal.iro.lecuyer.randvarmulti.MultinormalGen

Generates a point from this multinormal distribution.

nextPoint(NormalGen, double[], DoubleMatrix2D, double[]) - Static method in class umontreal.iro.lecuyer.randvarmulti.MultinormalPCAGen

Generates a d-dimensional vector from the multinormal distribution with mean vector mu and covariance matrix sigma, using the one-dimensional normal generator gen1 to generate the coordinates of Z, and using the PCA decomposition of Σ.

nextPoint(double[]) - Method in class umontreal.iro.lecuyer.randvarmulti.MultinormalPCAGen

Generates a point from this multinormal distribution.

nextPoint(double[]) - Method in class umontreal.iro.lecuyer.randvarmulti.RandomMultivariateGen

Generates a random point p using the the stream contained in this object.

nextRadicalInverse(double, double) - Static method in class umontreal.iro.lecuyer.hups.RadicalInverse

A fast method that incrementally computes the radical inverse x_i+1 in base b from x_i = x = ψ_b(i), using addition with rigthward carry.

nextRadicalInverse() - Method in class umontreal.iro.lecuyer.hups.RadicalInverse

A fast method that incrementally computes the radical inverse x_i+1 in base b from x_i = ψ_b(i), using addition with rigthward carry as described in Wang and Hickernell.

nextRadicalInverseDigits(int, int, int[]) - Static method in class umontreal.iro.lecuyer.hups.RadicalInverse

Given the k digits of the integer radical inverse of i in bdigits, in base b, this method replaces them by the digits of the integer radical inverse of i + 1 and returns their number.

NiedSequenceBase2 - Class in umontreal.iro.lecuyer.hups

This class implements digital sequences constructed from the Niederreiter sequence in base 2.

NiedSequenceBase2(int, int, int) - Constructor for class umontreal.iro.lecuyer.hups.NiedSequenceBase2

Constructs a new digital sequence in base 2 from the first n = 2^k points of the Niederreiter sequence, with w output digits, in dim dimensions.

NiedXingSequenceBase2 - Class in umontreal.iro.lecuyer.hups

This class implements digital sequences based on the Niederreiter-Xing sequence in base 2.

NiedXingSequenceBase2(int, int, int) - Constructor for class umontreal.iro.lecuyer.hups.NiedXingSequenceBase2

Constructs a new Niederreiter-Xing digital sequence in base 2 with n = 2^k points and w output digits, in dim dimensions.

NormalACRGen - Class in umontreal.iro.lecuyer.randvar

This class implements normal random variate generators using the acceptance-complement ratio method.

NormalACRGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.NormalACRGen

Creates a normal random variate generator with mean mu and standard deviation sigma, using stream s.

NormalACRGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.NormalACRGen

Creates a standard normal random variate generator with mean 0 and standard deviation 1, using stream s.

NormalACRGen(RandomStream, NormalDist) - Constructor for class umontreal.iro.lecuyer.randvar.NormalACRGen

Creates a random variate generator for the normal distribution dist and stream s.

NormalBoxMullerGen - Class in umontreal.iro.lecuyer.randvar

This class implements normal random variate generators using the Box-Muller method.

NormalBoxMullerGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.NormalBoxMullerGen

Creates a normal random variate generator with mean mu and standard deviation sigma, using stream s.

NormalBoxMullerGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.NormalBoxMullerGen

Creates a standard normal random variate generator with mean 0 and standard deviation 1, using stream s.

NormalBoxMullerGen(RandomStream, NormalDist) - Constructor for class umontreal.iro.lecuyer.randvar.NormalBoxMullerGen

Creates a random variate generator for the normal distribution dist and stream s.

NormalDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the normal distribution (e.g.,).

NormalDist() - Constructor for class umontreal.iro.lecuyer.probdist.NormalDist

Constructs a NormalDist object with default parameters μ = 0 and σ = 1.

NormalDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.NormalDist

Constructs a NormalDist object with mean μ = mu and standard deviation σ = sigma.

NormalDistQuick - Class in umontreal.iro.lecuyer.probdist

A variant of the class NormalDist (for the normal distribution with mean μ and variance σ²).

NormalDistQuick() - Constructor for class umontreal.iro.lecuyer.probdist.NormalDistQuick

Constructs a NormalDistQuick object with default parameters μ = 0 and σ = 1.

NormalDistQuick(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.NormalDistQuick

Constructs a NormalDistQuick object with mean μ = mu and standard deviation σ = sigma.

NormalGen - Class in umontreal.iro.lecuyer.randvar

This class implements methods for generating random variates from the normal distribution N(μ, σ).

NormalGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.NormalGen

Creates a normal random variate generator with mean mu and standard deviation sigma, using stream s.

NormalGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.NormalGen

Creates a standard normal random variate generator with mean 0 and standard deviation 1, using stream s.

NormalGen(RandomStream, NormalDist) - Constructor for class umontreal.iro.lecuyer.randvar.NormalGen

Creates a random variate generator for the normal distribution dist and stream s.

NormalInverseGaussianDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the normal inverse gaussian distribution with location parameter μ, scale parameter δ > 0, tail heavyness α > 0, and asymmetry parameter β such that 0 <= | β| < α.

NormalInverseGaussianDist(double, double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist

Constructor for a normal inverse gaussian distribution with parameters α = alpha, β = beta, μ = mu and δ = delta.

NormalInverseGaussianGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators for the normal inverse gaussian (NIG) distribution.

NormalInverseGaussianGen(RandomStream, double, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.NormalInverseGaussianGen

Creates an normal inverse gaussian random variate generator with parameters α = alpha, β = beta, μ = mu and δ = delta, using stream s.

NormalInverseGaussianGen(RandomStream, NormalInverseGaussianDist) - Constructor for class umontreal.iro.lecuyer.randvar.NormalInverseGaussianGen

Creates a new generator for the distribution dist, using stream s.

NormalInverseGaussianIGGen - Class in umontreal.iro.lecuyer.randvar

.

NormalInverseGaussianIGGen(InverseGaussianGen, NormalGen, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.NormalInverseGaussianIGGen

Creates a random variate generator with parameters α, β = beta, μ = mu and δ, using generators ig and ng, as described above.

NormalInverseGaussianProcess - Class in umontreal.iro.lecuyer.stochprocess

This class represents a normal inverse gaussian process (NIG).

NormalInverseGaussianProcess(double, double, double, double, double, RandomStream, InverseGaussianProcess) - Constructor for class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess

Given an InverseGaussianProcess igP, constructs a new NormalInverseGaussianProcess.

NormalInverseGaussianProcess(double, double, double, double, double, RandomStream, RandomStream, RandomStream, String) - Constructor for class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess

Constructs a new NormalInverseGaussianProcess.

NormalInverseGaussianProcess(double, double, double, double, double, RandomStream, String) - Constructor for class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess

Same as above, but all RandomStream's are set to the same stream, streamAll.

NormalKindermannRamageGen - Class in umontreal.iro.lecuyer.randvar

This class implements normal random variate generators using the Kindermann-Ramage method.

NormalKindermannRamageGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.NormalKindermannRamageGen

Creates a normal random variate generator with mean mu and standard deviation sigma, using stream s.

NormalKindermannRamageGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.NormalKindermannRamageGen

Creates a standard normal random variate generator with mean 0 and standard deviation 1, using stream s.

NormalKindermannRamageGen(RandomStream, NormalDist) - Constructor for class umontreal.iro.lecuyer.randvar.NormalKindermannRamageGen

Creates a random variate generator for the normal distribution dist and stream s.

NormalPolarGen - Class in umontreal.iro.lecuyer.randvar

This class implements normal random variate generators using the polar method with rejection.

NormalPolarGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.NormalPolarGen

Creates a normal random variate generator with mean mu and standard deviation sigma, using stream s.

NormalPolarGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.NormalPolarGen

Creates a standard normal random variate generator with μ = 0 and σ = 1, using stream s.

NormalPolarGen(RandomStream, NormalDist) - Constructor for class umontreal.iro.lecuyer.randvar.NormalPolarGen

Creates a random variate generator for the normal distribution dist and stream s.

not() - Method in class umontreal.iro.lecuyer.util.BitMatrix

Returns the BitMatrix resulting from the application of the not operator on the original BitMatrix.

not() - Method in class umontreal.iro.lecuyer.util.BitVector

Returns a BitVector which is the result of the not operator on the current BitVector.

notifyListeners(double[]) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

Notifies the observation x to all registered observers if broadcasting is ON.

notifyListeners(double) - Method in class umontreal.iro.lecuyer.stat.StatProbe

Notifies the observation x to all registered observers if broadcasting is ON.

NTESTTYPES - Static variable in class umontreal.iro.lecuyer.gof.GofFormat

Total number of test types

Num - Class in umontreal.iro.lecuyer.util

This class provides a few constants and some methods to compute numerical quantities such as factorials, combinations, gamma functions, and so on.

numberObs() - Method in class umontreal.iro.lecuyer.stat.list.ListOfTallies

Assuming that each tally in this list contains the same number of observations, returns the number of observations in tally 0, or 0 if this list is empty.

numberObs() - Method in class umontreal.iro.lecuyer.stat.Tally

Returns the number of observations given to this probe since its last initialization.

numColumns() - Method in class umontreal.iro.lecuyer.util.BitMatrix

Returns the number of columns of the BitMatrix.

numColumns() - Method in class umontreal.iro.lecuyer.util.DMatrix

Returns the number of columns of the DMatrix.

NUMINTERVALS - Static variable in class umontreal.iro.lecuyer.functions.MathFunctionUtil

Default number of intervals for Simpson's integral.

NUMINTERVALS - Static variable in class umontreal.iro.lecuyer.probdist.TruncatedDist

numRows() - Method in class umontreal.iro.lecuyer.util.BitMatrix

Returns the number of rows of the BitMatrix.

numRows() - Method in class umontreal.iro.lecuyer.util.DMatrix

Returns the number of rows of the DMatrix.

O

ObservationListener - Interface in umontreal.iro.lecuyer.stat

Represents an object that can listen to observations broadcast by statistical probes.

OneDimSort - Class in umontreal.iro.lecuyer.util

This class implements a MultiDimSort that simply sorts the arrays according to one dimension specified in the constructor.

OneDimSort(int) - Constructor for class umontreal.iro.lecuyer.util.OneDimSort

Constructs a OneDimSort that will sort according to coordinate i.

or(BitMatrix) - Method in class umontreal.iro.lecuyer.util.BitMatrix

Returns the BitMatrix resulting from the application of the or operator on the original BitMatrix and that.

or(BitVector) - Method in class umontreal.iro.lecuyer.util.BitVector

Returns a BitVector which is the result of the or operator with both the this and that BitVector's.

ORIENTATION_HORIZONTAL - Static variable in class umontreal.iro.lecuyer.charts.Axis

ORIENTATION_VERTICAL - Static variable in class umontreal.iro.lecuyer.charts.Axis

OrnsteinUhlenbeckProcess - Class in umontreal.iro.lecuyer.stochprocess

This class represents an Ornstein-Uhlenbeck process {X(t) : t >= 0}, sampled at times 0 = t₀ < t₁ < ^...

OrnsteinUhlenbeckProcess(double, double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess

Constructs a new OrnsteinUhlenbeckProcess with parameters α = alpha, b, σ = sigma and initial value X(t₀) = x0.

OrnsteinUhlenbeckProcess(double, double, double, double, NormalGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess

Here, the normal variate generator is specified directly instead of specifying the stream.

OrnsteinUhlenbeckProcessEuler - Class in umontreal.iro.lecuyer.stochprocess

.

OrnsteinUhlenbeckProcessEuler(double, double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcessEuler

Constructor with parameters α = alpha, b, σ = sigma and initial value X(t₀) = x0.

OrnsteinUhlenbeckProcessEuler(double, double, double, double, NormalGen) - Constructor for class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcessEuler

Here, the normal variate generator is specified directly instead of specifying the stream.

P

PaddedPointSet - Class in umontreal.iro.lecuyer.hups

This container class realizes padded point sets, constructed by taking some coordinates from a point set P₁, other coordinates from a point set P₂, and so on.

PaddedPointSet(int) - Constructor for class umontreal.iro.lecuyer.hups.PaddedPointSet

Constructs a structure for padding at most maxPointSets point sets.

padPointSet(PointSet) - Method in class umontreal.iro.lecuyer.hups.PaddedPointSet

Pads the point set P to the present structure.

padPointSetPermute(PointSet) - Method in class umontreal.iro.lecuyer.hups.PaddedPointSet

Pads the point set P, which is assumed to be finite.

ParetoDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for a distribution from the Pareto family, with shape parameter α > 0 and location parameter β > 0.

ParetoDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.ParetoDist

Constructs a ParetoDist object with parameters α = alpha and β = 1.

ParetoDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.ParetoDist

Constructs a ParetoDist object with parameters α = alpha and β = beta.

ParetoGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators for one of the Pareto distributions, with parameters α > 0 and β > 0.

ParetoGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.ParetoGen

Creates a Pareto random variate generator with parameters α = alpha and β = beta, using stream s.

ParetoGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.ParetoGen

Creates a Pareto random variate generator with parameters α = alpha and β = 1, using stream s.

ParetoGen(RandomStream, ParetoDist) - Constructor for class umontreal.iro.lecuyer.randvar.ParetoGen

Creates a new generator for the Pareto distribution dist and stream s.

PascalConvolutionGen - Class in umontreal.iro.lecuyer.randvar

Implements Pascal random variate generators by the convolution method.

PascalConvolutionGen(RandomStream, int, double) - Constructor for class umontreal.iro.lecuyer.randvar.PascalConvolutionGen

Creates a Pascal random variate generator with parameters n and p, using stream s.

PascalConvolutionGen(RandomStream, PascalDist) - Constructor for class umontreal.iro.lecuyer.randvar.PascalConvolutionGen

Creates a new generator for the distribution dist, using stream s.

PascalDist - Class in umontreal.iro.lecuyer.probdist

The Pascal distribution is a special case of the negative binomial distribution with parameters n and p, where n is a positive integer and 0 <= p <= 1.

PascalDist(int, double) - Constructor for class umontreal.iro.lecuyer.probdist.PascalDist

Creates an object that contains the probability terms and the distribution function for the Pascal distribution with parameter n and p.

PascalGen - Class in umontreal.iro.lecuyer.randvar

Implements Pascal random variate generators, which is a special case of the negative binomial generator with parameter γ equal to a positive integer.

PascalGen(RandomStream, int, double) - Constructor for class umontreal.iro.lecuyer.randvar.PascalGen

Creates a Pascal random variate generator with parameters n and p, using stream s.

PascalGen(RandomStream, PascalDist) - Constructor for class umontreal.iro.lecuyer.randvar.PascalGen

Creates a new generator for the distribution dist, using stream s.

path - Variable in class umontreal.iro.lecuyer.stochprocess.StochasticProcess

PCADecompose(double[][], double[][], double[]) - Static method in class umontreal.iro.lecuyer.util.DMatrix

Computes the principal components decomposition M = UΛU^t by using the singular value decomposition of matrix M.

PCADecompose(DoubleMatrix2D, double[]) - Static method in class umontreal.iro.lecuyer.util.DMatrix

Computes the principal components decomposition M = UΛU^t by using the singular value decomposition of matrix M.

pDisc(double, double) - Static method in class umontreal.iro.lecuyer.gof.GofStat

Computes a variant of the p-value p whenever a test statistic has a discrete probability distribution.

Pearson5Dist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the Pearson type V distribution with shape parameter α > 0 and scale parameter β > 0.

Pearson5Dist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.Pearson5Dist

Constructs a Pearson5Dist object with parameters α = alpha and β = beta.

Pearson5Gen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators for the Pearson type V distribution with shape parameter α > 0 and scale parameter β > 0.

Pearson5Gen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.Pearson5Gen

Creates a Pearson5 random variate generator with parameters α = alpha and β = beta, using stream s.

Pearson5Gen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.Pearson5Gen

Creates a Pearson5 random variate generator with parameters α = alpha and β = 1, using stream s.

Pearson5Gen(RandomStream, Pearson5Dist) - Constructor for class umontreal.iro.lecuyer.randvar.Pearson5Gen

Creates a new generator for the distribution dist, using stream s.

Pearson6Dist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the Pearson type VI distribution with shape parameters α₁ > 0 and α₂ > 0, and scale parameter β > 0.

Pearson6Dist(double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.Pearson6Dist

Constructs a Pearson6Dist object with parameters α₁ = alpha1, α₂ = alpha2 and β = beta.

Pearson6Gen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators for the Pearson type VI distribution with shape parameters α₁ > 0 and α₂ > 0, and scale parameter β > 0.

Pearson6Gen(RandomStream, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.Pearson6Gen

Creates a Pearson6 random variate generator with parameters α₁ = alpha1, α₂ = alpha2 and β = beta, using stream s.

Pearson6Gen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.Pearson6Gen

Creates a Pearson6 random variate generator with parameters α₁ = alpha1, α₂ = alpha2 and β = 1, using stream s.

Pearson6Gen(RandomStream, Pearson6Dist) - Constructor for class umontreal.iro.lecuyer.randvar.Pearson6Gen

Creates a new generator for the distribution dist, using stream s.

permutedRadicalInverse(int, int[], int) - Static method in class umontreal.iro.lecuyer.hups.RadicalInverse

Computes the radical inverse of i in base b, where the digits are permuted using the permutation π.

PiecewiseConstantFunction - Class in umontreal.iro.lecuyer.functions

Represents a piecewise-constant function.

PiecewiseConstantFunction(double[], double[]) - Constructor for class umontreal.iro.lecuyer.functions.PiecewiseConstantFunction

Constructs a new piecewise-constant function with X and Y coordinates given by x and y.

PiecewiseLinearEmpiricalDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for a piecewise-linear approximation of the empirical distribution function, based on the observations X₍₁₎,..., X_(n) (sorted by increasing order), and defined as follows (e.g.,).

PiecewiseLinearEmpiricalDist(double[]) - Constructor for class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist

Constructs a new piecewise-linear distribution using all the observations stored in obs.

PiecewiseLinearEmpiricalDist(Reader) - Constructor for class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist

Constructs a new empirical distribution using the observations read from the reader in.

PLAIN - Static variable in class umontreal.iro.lecuyer.util.TableFormat

Plain text matrix printing style

PlotFormat - Class in umontreal.iro.lecuyer.charts

Provide tools to import and export data set tables to and from Gnuplot, MATLAB and Mathematica compatible formats, or customized format.

PointSet - Class in umontreal.iro.lecuyer.hups

This abstract class defines the basic methods for accessing and manipulating point sets.

PointSet() - Constructor for class umontreal.iro.lecuyer.hups.PointSet

PointSetIterator - Interface in umontreal.iro.lecuyer.hups

Objects of classes that implement this interface are iterators that permit one to enumerate (or observe) the successive points of a point set and the successive coordinates of these points.

PointSetRandomization - Interface in umontreal.iro.lecuyer.hups

This interface is used to randomize a PointSet.

PoissonDist - Class in umontreal.iro.lecuyer.probdist

Extends the class DiscreteDistributionInt for the Poisson distribution with mean λ >= 0.

PoissonDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.PoissonDist

Creates an object that contains the probability and distribution functions, for the Poisson distribution with parameter lambda, which are computed and stored in dynamic arrays inside that object.

PoissonGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators having the Poisson distribution.

PoissonGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.PoissonGen

Creates a Poisson random variate generator with parameter λ = lambda, using stream s.

PoissonGen(RandomStream, PoissonDist) - Constructor for class umontreal.iro.lecuyer.randvar.PoissonGen

Creates a new random variate generator using the Poisson distribution dist and stream s.

PoissonTIACGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators having the Poisson distribution (see PoissonGen).

PoissonTIACGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.PoissonTIACGen

Creates a Poisson random variate generator with parameter λ = lambda, using stream s.

PoissonTIACGen(RandomStream, PoissonDist) - Constructor for class umontreal.iro.lecuyer.randvar.PoissonTIACGen

Creates a new random variate generator using the Poisson distribution dist and stream s.

PolInterp - Class in umontreal.iro.lecuyer.functionfit

Represents a polynomial that interpolates through a set of points.

PolInterp(double[], double[]) - Constructor for class umontreal.iro.lecuyer.functionfit.PolInterp

Constructs a new polynomial interpolating through the given points (x[0], y[0]), ..., (x[n], y[n]).

Polynomial - Class in umontreal.iro.lecuyer.functions

Represents a polynomial of degree n in power form.

Polynomial(double...) - Constructor for class umontreal.iro.lecuyer.functions.Polynomial

Constructs a new polynomial with coefficients coeff.

power(long) - Method in class umontreal.iro.lecuyer.util.BitMatrix

Raises the BitMatrix to the power p.

power2e(int) - Method in class umontreal.iro.lecuyer.util.BitMatrix

Raises the BitMatrix to power 2^e.

PowerDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the power distribution with shape parameter c > 0, over the interval [a, b], where a < b.

PowerDist(double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.PowerDist

Constructs a PowerDist object with parameters a = a, b = b and c = c.

PowerDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.PowerDist

Constructs a PowerDist object with parameters a = 0, b = b and c = c.

PowerDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.PowerDist

Constructs a PowerDist object with parameters a = 0, b = 1 and c = c.

PowerGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators for the power distribution with shape parameter c > 0, over the interval [a, b].

PowerGen(RandomStream, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.PowerGen

Creates a Power random variate generator with parameters a = a, b = b and c = c, using stream s.

PowerGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.PowerGen

Creates a Power random variate generator with parameters a = 0, b = 1 and c = c, using stream s.

PowerGen(RandomStream, PowerDist) - Constructor for class umontreal.iro.lecuyer.randvar.PowerGen

Creates a new generator for the power distribution dist and stream s.

PowerMathFunction - Class in umontreal.iro.lecuyer.functions

Represents a function computing (af (x) + b)^p for a user-defined function f (x) and power p.

PowerMathFunction(MathFunction, double) - Constructor for class umontreal.iro.lecuyer.functions.PowerMathFunction

Constructs a new power function for function func and power power.

PowerMathFunction(MathFunction, double, double, double) - Constructor for class umontreal.iro.lecuyer.functions.PowerMathFunction

Constructs a new power function for function func, power power, and constants a and b.

powerRatios(DoubleArrayList) - Static method in class umontreal.iro.lecuyer.gof.GofStat

Applies the power ratios transformation W.

print(String) - Static method in class umontreal.iro.lecuyer.hups.F2wStructure

Prints the content of file filename.

printData() - Method in class umontreal.iro.lecuyer.util.BitMatrix

Creates a String containing all the data of the BitMatrix.

PrintfFormat - Class in umontreal.iro.lecuyer.util

This class acts like a StringBuffer which defines new types of append methods.

PrintfFormat() - Constructor for class umontreal.iro.lecuyer.util.PrintfFormat

Constructs a new buffer object containing an empty string.

PrintfFormat(int) - Constructor for class umontreal.iro.lecuyer.util.PrintfFormat

Constructs a new buffer object with an initial capacity of length.

PrintfFormat(String) - Constructor for class umontreal.iro.lecuyer.util.PrintfFormat

Constructs a new buffer object containing the initial string str.

printGeneratorMatrices(int) - Method in class umontreal.iro.lecuyer.hups.DigitalNet

Prints the generator matrices in standard form for dimensions 1 to s.

printGeneratorMatrices(int) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2

Prints the generator matrices as bit matrices in standard form for dimensions 1 to s.

printGeneratorMatricesTrans(int) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2

Prints the generator matrices transposed in the form of integers for dimensions 1 to s.

priority() - Method in class umontreal.iro.lecuyer.simevents.Event

Returns the priority of this event.

priority() - Method in class umontreal.iro.lecuyer.simprocs.SimProcess

Returns the priority of the current variable.

prob(int) - Method in class umontreal.iro.lecuyer.probdist.BinomialDist

prob(int, double, int) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist

Computes and returns the binomial probability p(x) in eq..

prob(int, double, double, int) - Static method in class umontreal.iro.lecuyer.probdist.BinomialDist

A generalization of the previous method.

prob(int) - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistribution

Returns p_k, the probability of the k-th observation, for 0 <= k < n.

prob(int) - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistributionInt

Returns p(x), the probability of x, which should be a real number in the interval [0, 1].

prob(int) - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist

prob(int) - Method in class umontreal.iro.lecuyer.probdist.GeometricDist

prob(double, int) - Static method in class umontreal.iro.lecuyer.probdist.GeometricDist

Computes the geometric probability p(x).

prob(int) - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist

prob(int, int, int, int) - Static method in class umontreal.iro.lecuyer.probdist.HypergeometricDist

Computes the hypergeometric probability p(x).

prob(int) - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist

prob(double, int) - Static method in class umontreal.iro.lecuyer.probdist.LogarithmicDist

Computes the logarithmic probability p(x).

prob(int) - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist

prob(double, double, int) - Static method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist

Computes the probability p(x).

prob(int) - Method in class umontreal.iro.lecuyer.probdist.PoissonDist

prob(double, int) - Static method in class umontreal.iro.lecuyer.probdist.PoissonDist

Computes and returns the Poisson probability p(x) for λ = lambda..

prob(int) - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist

prob(int, int, int) - Static method in class umontreal.iro.lecuyer.probdist.UniformIntDist

Computes the discrete uniform probability p(x).

prob(int[]) - Method in class umontreal.iro.lecuyer.probdistmulti.DiscreteDistributionIntMulti

Returns the probability mass function p(x₁, x₂,…, x_d), which should be a real number in [0, 1].

prob(int[]) - Method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist

prob(int, double[], int[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist

Computes the probability mass function of the multinomial distribution with parameters n and (p₁,...,p_d) evaluated at x.

prob(int[]) - Method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist

prob(double, double[], int[]) - Static method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist

Computes the probability mass function of the negative multinomial distribution with parameters γ and (p₁,...,p_d), evaluated at x.

ProcessSimulator - Class in umontreal.iro.lecuyer.simprocs

Defines a special type of simulator capable of managing processes.

ProcessSimulator() - Constructor for class umontreal.iro.lecuyer.simprocs.ProcessSimulator

put(int) - Method in class umontreal.iro.lecuyer.simprocs.Bin

Adds n tokens to this bin.

Q

quickSelect(double[], int, int) - Static method in class umontreal.iro.lecuyer.util.Misc

Returns the k^th smallest item of the array t of size n.

quickSelect(int[], int, int) - Static method in class umontreal.iro.lecuyer.util.Misc

Returns the k^th smallest item of the array t of size n.

quickSort() - Method in class umontreal.iro.lecuyer.stat.TallyStore

Sorts the elements of this probe using the quicksort from Colt.

R

RAC2 - Static variable in class umontreal.iro.lecuyer.util.Num

The value of (2)^1/2.

RadicalInverse - Class in umontreal.iro.lecuyer.hups

This class implements basic methods for working with radical inverses of integers in an arbitrary basis b.

RadicalInverse(int, double) - Constructor for class umontreal.iro.lecuyer.hups.RadicalInverse

Initializes the base of this object to b and its first value of x to x0.

radicalInverse(int, long) - Static method in class umontreal.iro.lecuyer.hups.RadicalInverse

Computes the radical inverse of i in base b.

RandMrg - Class in umontreal.iro.lecuyer.rng

Deprecated.

RandMrg() - Constructor for class umontreal.iro.lecuyer.rng.RandMrg

Deprecated.

Constructs a new stream, initializes its seed I_g, sets B_g and C_g equal to I_g, and sets its antithetic switch to false.

RandMrg(String) - Constructor for class umontreal.iro.lecuyer.rng.RandMrg

Deprecated.

Constructs a new stream with an identifier name (can be used when printing the stream state, in error messages, etc.).

Randomization - Interface in umontreal.iro.lecuyer.hups

Deprecated.

randomize(PointSetRandomization) - Method in class umontreal.iro.lecuyer.hups.CachedPointSet

Randomizes the underlying point set using rand and recaches the points.

randomize(PointSetRandomization) - Method in class umontreal.iro.lecuyer.hups.ContainerPointSet

Randomizes the contained point set using rand.

randomize(PointSet) - Method in class umontreal.iro.lecuyer.hups.EmptyRandomization

This method does nothing.

randomize(PointSet) - Method in class umontreal.iro.lecuyer.hups.LMScrambleShift

This method calls leftMatrixScramble, then addRandomShift.

randomize(RandomStream) - Method in class umontreal.iro.lecuyer.hups.PaddedPointSet

randomize(PointSetRandomization) - Method in class umontreal.iro.lecuyer.hups.PointSet

Randomizes the point set using the given rand.

randomize(int, int, RandomStream) - Method in class umontreal.iro.lecuyer.hups.PointSet

By default, this method simply calls addRandomShift(d1, d2, stream).

randomize(RandomStream) - Method in class umontreal.iro.lecuyer.hups.PointSet

By default, this method simply calls addRandomShift(stream).

randomize(int, int) - Method in class umontreal.iro.lecuyer.hups.PointSet

Deprecated.

randomize() - Method in class umontreal.iro.lecuyer.hups.PointSet

Deprecated.

randomize(PointSet) - Method in interface umontreal.iro.lecuyer.hups.PointSetRandomization

This method must randomize p.

randomize(PointSet) - Method in class umontreal.iro.lecuyer.hups.RandomShift

This method calls addRandomShift (stream).

randomize(PointSet) - Method in class umontreal.iro.lecuyer.hups.SMScrambleShift

This method calls stripedMatrixScramble, then addRandomShift.

RandomMultivariateGen - Class in umontreal.iro.lecuyer.randvarmulti

This class is the multivariate counterpart of RandomVariateGen.

RandomMultivariateGen() - Constructor for class umontreal.iro.lecuyer.randvarmulti.RandomMultivariateGen

RandomPermutation - Class in umontreal.iro.lecuyer.rng

Provides methods to randomly shuffle arrays or lists using a random stream.

RandomPermutation() - Constructor for class umontreal.iro.lecuyer.rng.RandomPermutation

RandomShift - Class in umontreal.iro.lecuyer.hups

This class implements a PointSetRandomization.

RandomShift(RandomStream) - Constructor for class umontreal.iro.lecuyer.hups.RandomShift

Sets the internal RandomStream to stream.

RandomStream - Interface in umontreal.iro.lecuyer.rng

This interface defines the basic structures to handle multiple streams of uniform (pseudo)random numbers and convenient tools to move around within and across these streams.

RandomStreamBase - Class in umontreal.iro.lecuyer.rng

This class provides a convenient foundation on which RNGs can be built.

RandomStreamBase() - Constructor for class umontreal.iro.lecuyer.rng.RandomStreamBase

RandomStreamFactory - Interface in umontreal.iro.lecuyer.rng

Represents a random stream factory capable of constructing instances of a given type of random stream by invoking the newInstance method each time a new random stream is needed, instead of invoking directly the specific constructor of the desired type.

RandomStreamInstantiationException - Exception in umontreal.iro.lecuyer.rng

This exception is thrown when a random stream factory cannot instantiate a stream on a call to its newInstance method.

RandomStreamInstantiationException(RandomStreamFactory) - Constructor for exception umontreal.iro.lecuyer.rng.RandomStreamInstantiationException

Constructs a new random stream instantiation exception with no message, no cause, and thrown by the given factory.

RandomStreamInstantiationException(RandomStreamFactory, String) - Constructor for exception umontreal.iro.lecuyer.rng.RandomStreamInstantiationException

Constructs a new random stream instantiation exception with the given message, no cause, and concerning factory.

RandomStreamInstantiationException(RandomStreamFactory, Throwable) - Constructor for exception umontreal.iro.lecuyer.rng.RandomStreamInstantiationException

Constructs a new random stream instantiation exception with no message, the given cause, and concerning factory.

RandomStreamInstantiationException(RandomStreamFactory, String, Throwable) - Constructor for exception umontreal.iro.lecuyer.rng.RandomStreamInstantiationException

Constructs a new random stream instantiation exception with the given message, the supplied cause, and concerning factory.

RandomStreamManager - Class in umontreal.iro.lecuyer.rng

Manages a list of random streams for more convenient synchronization.

RandomStreamManager() - Constructor for class umontreal.iro.lecuyer.rng.RandomStreamManager

RandomStreamWithCache - Class in umontreal.iro.lecuyer.rng

This class represents a random stream whose uniforms are cached for more efficiency when using common random numbers.

RandomStreamWithCache(RandomStream) - Constructor for class umontreal.iro.lecuyer.rng.RandomStreamWithCache

Constructs a new cached random stream with internal stream stream.

RandomStreamWithCache(RandomStream, int) - Constructor for class umontreal.iro.lecuyer.rng.RandomStreamWithCache

Constructs a new cached random stream with internal stream stream.

RandomVariateGen - Class in umontreal.iro.lecuyer.randvar

This is the base class for all random variate generators over the real line.

RandomVariateGen(RandomStream, Distribution) - Constructor for class umontreal.iro.lecuyer.randvar.RandomVariateGen

Creates a new random variate generator from the distribution dist, using stream s.

RandomVariateGenInt - Class in umontreal.iro.lecuyer.randvar

This is the base class for all generators of discrete random variates over the set of integers.

RandomVariateGenInt(RandomStream, DiscreteDistributionInt) - Constructor for class umontreal.iro.lecuyer.randvar.RandomVariateGenInt

Creates a new random variate generator for the discrete distribution dist, using stream s.

RandomVariateGenWithCache - Class in umontreal.iro.lecuyer.randvar

This class represents a random variate generator whose values are cached for more efficiency when using common random numbers.

RandomVariateGenWithCache(RandomVariateGen) - Constructor for class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache

Constructs a new cached random variate generator with internal generator rvg.

RandomVariateGenWithCache(RandomVariateGen, int) - Constructor for class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache

Constructs a new cached random variate generator with internal generator rvg.

RandRijndael - Class in umontreal.iro.lecuyer.rng

Implements a RNG using the Rijndael block cipher algorithm (AES) with key and block lengths of 128 bits.

RandRijndael() - Constructor for class umontreal.iro.lecuyer.rng.RandRijndael

Constructs a new stream.

RandRijndael(String) - Constructor for class umontreal.iro.lecuyer.rng.RandRijndael

Constructs a new stream with the identifier name (used in the toString method).

RandShiftedPointSet - Class in umontreal.iro.lecuyer.hups

This container class embodies a point set to which a random shift modulo 1 is applied (i.e., a single uniform random point is added to all points, modulo 1, to randomize the inner point set).

RandShiftedPointSet(PointSet, int, RandomStream) - Constructor for class umontreal.iro.lecuyer.hups.RandShiftedPointSet

Constructs a structure to contain a randomly shifted version of P.

Rank1Lattice - Class in umontreal.iro.lecuyer.hups

This class implements point sets defined by integration lattices of rank 1, defined as follows.

Rank1Lattice(int, int[], int) - Constructor for class umontreal.iro.lecuyer.hups.Rank1Lattice

Instantiates a Rank1Lattice with n points and lattice vector a of dimension s.

RatioFunction - Class in umontreal.iro.lecuyer.util

Represents a function computing a ratio of two values.

RatioFunction() - Constructor for class umontreal.iro.lecuyer.util.RatioFunction

Constructs a new ratio function.

RatioFunction(double) - Constructor for class umontreal.iro.lecuyer.util.RatioFunction

Constructs a new ratio function that returns zeroOverZero for the special case of 0/0.

RayleighDist - Class in umontreal.iro.lecuyer.probdist

This class extends the class ContinuousDistribution for the Rayleigh distribution with location parameter a, and scale parameter β > 0.

RayleighDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.RayleighDist

Constructs a RayleighDist object with parameters a = 0 and β = beta.

RayleighDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.RayleighDist

Constructs a RayleighDist object with parameters a = a, and β = beta.

RayleighGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators for the Rayleigh distribution.

RayleighGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.RayleighGen

Creates a Rayleigh random variate generator with parameters a = a and β = beta, using stream s.

RayleighGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.RayleighGen

Creates a Rayleigh random variate generator with parameters a = 0 and β = beta, using stream s.

RayleighGen(RandomStream, RayleighDist) - Constructor for class umontreal.iro.lecuyer.randvar.RayleighGen

Creates a new generator for the Rayleigh distribution dist and stream s.

readCSVData(Reader, char, char) - Static method in class umontreal.iro.lecuyer.util.TextDataReader

Reads comma-separated values (CSV) from reader input, and returns a 2D array of strings corresponding to the read data.

readCSVData(URL, char, char) - Static method in class umontreal.iro.lecuyer.util.TextDataReader

Connects to the URL referred to by the URL object url, and calls readCSVData to obtain a matrix of strings from the resource.

readCSVData(File, char, char) - Static method in class umontreal.iro.lecuyer.util.TextDataReader

This is equivalent to readDoubleData2D, for reading strings.

readCSVData(String, char, char) - Static method in class umontreal.iro.lecuyer.util.TextDataReader

This is equivalent to readDoubleData2D, for reading strings.

readDoubleData(Statement, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager

Copies the result of the SQL query query into an array of double-precision values.

readDoubleData(Connection, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager

Copies the result of the SQL query query into an array of double-precision values.

readDoubleData(Statement, String, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager

Returns the values of the column column of the table table.

readDoubleData(Connection, String, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager

Returns the values of the column column of the table table.

readDoubleData(Reader) - Static method in class umontreal.iro.lecuyer.util.TextDataReader

Reads an array of double-precision values from the reader input.

readDoubleData(URL) - Static method in class umontreal.iro.lecuyer.util.TextDataReader

Connects to the URL referred to by the URL object url, and calls readDoubleData to obtain an array of double-precision values from the resource.

readDoubleData(File) - Static method in class umontreal.iro.lecuyer.util.TextDataReader

Opens the file referred to by the file object file, and calls readDoubleData to obtain an array of double-precision values from the file.

readDoubleData(String) - Static method in class umontreal.iro.lecuyer.util.TextDataReader

Opens the file with name file, and calls readDoubleData to obtain an array of double-precision values from the file.

readDoubleData2D(Statement, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager

Copies the result of the SQL query query into a rectangular 2D array of double-precision values.

readDoubleData2D(Connection, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager

Copies the result of the SQL query query into a rectangular 2D array of double-precision values.

readDoubleData2D(Reader) - Static method in class umontreal.iro.lecuyer.util.TextDataReader

Uses the reader input to obtain a 2-dimensional array of double-precision values.

readDoubleData2D(URL) - Static method in class umontreal.iro.lecuyer.util.TextDataReader

Connects to the URL referred to by the URL object url, and calls readDoubleData2D to obtain a matrix of double-precision values from the resource.

readDoubleData2D(File) - Static method in class umontreal.iro.lecuyer.util.TextDataReader

Opens the file referred to by the file object file, and calls readDoubleData2D to obtain a matrix of double-precision values from the file.

readDoubleData2D(String) - Static method in class umontreal.iro.lecuyer.util.TextDataReader

Opens the file with name file, and calls readDoubleData2D to obtain a matrix of double-precision values from the file.

readDoubleData2DTable(Statement, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager

Returns the values of the columns of the table table.

readDoubleData2DTable(Connection, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager

Returns the values of the columns of the table table.

readIntData(Statement, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager

Copies the result of the SQL query query into an array of integers.

readIntData(Connection, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager

Copies the result of the SQL query query into an array of integers.

readIntData(Statement, String, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager

Returns the values of the column column of the table table.

readIntData(Connection, String, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager

Returns the values of the column column of the table table.

readIntData(Reader) - Static method in class umontreal.iro.lecuyer.util.TextDataReader

This is equivalent to readDoubleData, for reading integers.

readIntData(URL) - Static method in class umontreal.iro.lecuyer.util.TextDataReader

Connects to the URL referred to by the URL object url, and calls readIntData to obtain an array of integers from the resource.

readIntData(File) - Static method in class umontreal.iro.lecuyer.util.TextDataReader

This is equivalent to readDoubleData, for reading integers.

readIntData(String) - Static method in class umontreal.iro.lecuyer.util.TextDataReader

This is equivalent to readDoubleData, for reading integers.

readIntData2D(Statement, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager

Copies the result of the SQL query query into a rectangular 2D array of integers.

readIntData2D(Connection, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager

Copies the result of the SQL query query into a rectangular 2D array of integers.

readIntData2D(Reader) - Static method in class umontreal.iro.lecuyer.util.TextDataReader

This is equivalent to readDoubleData2D, for reading integers.

readIntData2D(URL) - Static method in class umontreal.iro.lecuyer.util.TextDataReader

Connects to the URL referred to by the URL object url, and calls readDoubleData to obtain a matrix of integers from the resource.

readIntData2D(File) - Static method in class umontreal.iro.lecuyer.util.TextDataReader

This is equivalent to readDoubleData2D, for reading integers.

readIntData2D(String) - Static method in class umontreal.iro.lecuyer.util.TextDataReader

This is equivalent to readDoubleData2D, for reading integers.

readIntData2DTable(Statement, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager

Returns the values of the columns of the table table.

readIntData2DTable(Connection, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager

Returns the values of the columns of the table table.

readObjectData(Statement, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager

Copies the result of the SQL query query into an array of objects.

readObjectData(Connection, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager

Copies the result of the SQL query query into an array of objects.

readObjectData(Statement, String, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager

Returns the values of the column column of the table table.

readObjectData(Connection, String, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager

Returns the values of the column column of the table table.

readObjectData2D(Statement, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager

Copies the result of the SQL query query into a rectangular 2D array of objects.

readObjectData2D(Connection, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager

Copies the result of the SQL query query into a rectangular 2D array of integers.

readObjectData2DTable(Statement, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager

Returns the values of the columns of the table table.

readObjectData2DTable(Connection, String) - Static method in class umontreal.iro.lecuyer.util.JDBCManager

Returns the values of the columns of the table table.

readStringData(Reader) - Static method in class umontreal.iro.lecuyer.util.TextDataReader

Reads an array of strings from the reader input.

readStringData(URL) - Static method in class umontreal.iro.lecuyer.util.TextDataReader

Connects to the URL referred to by the URL object url, and calls readStringData to obtain an array of integers from the resource.

readStringData(File) - Static method in class umontreal.iro.lecuyer.util.TextDataReader

This is equivalent to readDoubleData, for reading strings.

readStringData(String) - Static method in class umontreal.iro.lecuyer.util.TextDataReader

This is equivalent to readDoubleData, for reading strings.

RedblackTree - Class in umontreal.iro.lecuyer.simevents.eventlist

An implementation of EventList using a red black tree, which is similar to a binary search tree except that every node is colored red or black.

RedblackTree() - Constructor for class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree

regroupCategories(double) - Method in class umontreal.iro.lecuyer.gof.GofStat.OutcomeCategoriesChi2

Regroup categories as explained earlier, so that the expected number of observations in each category is at least minExp.

release(int) - Method in class umontreal.iro.lecuyer.simprocs.Resource

The executing process that invokes this method releases n units of the resource.

remove(RandomStream) - Method in class umontreal.iro.lecuyer.rng.RandomStreamManager

Removes the given stream from the internal list of this random stream manager.

remove(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree

remove(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked

remove(Event) - Method in interface umontreal.iro.lecuyer.simevents.eventlist.EventList

Removes the event ev from the event list (cancels this event).

remove(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen

remove(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree

remove(Event) - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree

remove(int) - Method in class umontreal.iro.lecuyer.simevents.ListWithStat

remove(int) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

remove(Object) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

remove(int) - Method in class umontreal.iro.lecuyer.util.TransformingList

removeAll(Collection<?>) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

removeArrayOfObservationListener(ArrayOfObservationListener) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

Removes the observation listener l from the list of observers of this list of statistical probes.

removeFirst() - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree

removeFirst() - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked

removeFirst() - Method in interface umontreal.iro.lecuyer.simevents.eventlist.EventList

Removes the first event from the event list (to cancel or execute this event).

removeFirst() - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen

removeFirst() - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree

removeFirst() - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree

removeFirst() - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat

removeLast() - Method in class umontreal.iro.lecuyer.simevents.LinkedListStat

removeNaNs(double[], double[]) - Static method in class umontreal.iro.lecuyer.functions.MathFunctionUtil

Removes any point (NaN, y) or (x, NaN) from x and y, and returns a 2D array containing the filtered points.

removeObservationListener(ObservationListener) - Method in class umontreal.iro.lecuyer.stat.StatProbe

Removes the observation listener l from the list of observers of this statistical probe.

report() - Method in class umontreal.iro.lecuyer.simevents.Accumulate

report() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat

Returns a string containing a statistical report on the list, provided that setStatCollecting (true) has been called before for this list.

report() - Method in class umontreal.iro.lecuyer.simprocs.Bin

Returns a string containing a complete statistical report on this bin.

report() - Method in class umontreal.iro.lecuyer.simprocs.Resource

Returns a string containing a complete statistical report on this resource.

report() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

Formats a report for each probe in the list of statistical probes.

report() - Method in class umontreal.iro.lecuyer.stat.StatProbe

Returns a string containing a report for this statistical collector.

report(String, StatProbe[]) - Static method in class umontreal.iro.lecuyer.stat.StatProbe

Formats short reports for each statistical probe in the array probes while aligning the probes' names.

report(String, Iterable<? extends StatProbe>) - Static method in class umontreal.iro.lecuyer.stat.StatProbe

Equivalent to report, except that probes is an Iterable object instead of an array.

report() - Method in class umontreal.iro.lecuyer.stat.Tally

Returns a formatted string that contains a report on this probe.

report(double, int) - Method in class umontreal.iro.lecuyer.stat.Tally

Returns a formatted string that contains a report on this probe with a confidence interval level level using d fractional decimal digits.

reportAndCIStudent(double, int) - Method in class umontreal.iro.lecuyer.stat.Tally

Returns a formatted string that contains a report on this probe (as in report), followed by a confidence interval (as in formatCIStudent), using d fractional decimal digits.

reportAndCIStudent(double) - Method in class umontreal.iro.lecuyer.stat.Tally

Same as reportAndCIStudent (level, 3).

reportAndConfidenceIntervalStudent(double, int) - Method in class umontreal.iro.lecuyer.stat.Tally

Deprecated.

reportAndConfidenceIntervalStudent(double) - Method in class umontreal.iro.lecuyer.stat.Tally

Deprecated.

request(int) - Method in class umontreal.iro.lecuyer.simprocs.Resource

The executing process invoking this method requests for n units of this resource.

reschedule(double) - Method in class umontreal.iro.lecuyer.simevents.Event

Cancels this event and reschedules it to happen in delay time units.

reschedule(double) - Method in class umontreal.iro.lecuyer.simprocs.SimProcess

If the process is in the DELAYED state, removes it from the event list and reschedules it in delay units of time.

resetCurCoordIndex() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator

resetCurCoordIndex() - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator

Equivalent to setCurCoordIndex (0).

resetCurCycle(int) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator

resetCurCycle(int) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSetBase2.CycleBasedPointSetBase2Iterator

resetCurPointIndex() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator

resetCurPointIndex() - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator

Equivalent to setCurPointIndex (0).

resetGeneratorMatrices() - Method in class umontreal.iro.lecuyer.hups.DigitalNet

Restores the original generator matrices.

resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.AntitheticStream

resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.BakerTransformedStream

resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.F2NL607

resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.GenF2w32

resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.LFSR113

resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.LFSR258

resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.MRG31k3p

resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.MRG32k3a

resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.MRG32k3aL

resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.MT19937

resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.RandMrg

Deprecated.

resetNextSubstream() - Method in interface umontreal.iro.lecuyer.rng.RandomStream

Reinitializes the stream to the beginning of its next substream: N_g is computed, and C_g and B_g are set to N_g.

resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase

resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.RandomStreamManager

Forwards to the resetNextSubstream methods of all streams in the list.

resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache

resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.RandRijndael

resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.TruncatedRandomStream

resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.WELL1024

resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.WELL512

resetNextSubstream() - Method in class umontreal.iro.lecuyer.rng.WELL607

resetRiskNeutralCorrection(double) - Method in class umontreal.iro.lecuyer.stochprocess.GeometricLevyProcess

Changes the value of ω_RN.

resetStartProcess() - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionBridge

resetStartProcess() - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessBridge

resetStartProcess() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion

Same as in StochasticProcess, but also invokes resetStartProcess for the underlying BrownianMotion object.

resetStartProcess() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricLevyProcess

Resets the step counter of the geometric process and the underlying Lévy process to the start value.

resetStartProcess() - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess

Resets the GeometricaVarianceGammaProcess, but also applies the resetStartProcess method to the VarianceGammaProcess object used to generate this process.

resetStartProcess() - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessBridge

resetStartProcess() - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess

Resets the observation counter to its initial value j = 0, so that the current observation X(t_j) becomes X(t₀).

resetStartProcess() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess

Resets the observation index and counter to 0 and applies the resetStartProcess method to the BrownianMotion and the GammaProcess objects used to generate this process.

resetStartProcess() - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiff

Sets the observation times on the VarianceGammaProcessDiff as usual, but also applies the resetStartProcess method to the two GammaProcess objects used to generate this process.

resetStartStream() - Method in class umontreal.iro.lecuyer.rng.AntitheticStream

resetStartStream() - Method in class umontreal.iro.lecuyer.rng.BakerTransformedStream

resetStartStream() - Method in class umontreal.iro.lecuyer.rng.F2NL607

resetStartStream() - Method in class umontreal.iro.lecuyer.rng.GenF2w32

resetStartStream() - Method in class umontreal.iro.lecuyer.rng.LFSR113

resetStartStream() - Method in class umontreal.iro.lecuyer.rng.LFSR258

resetStartStream() - Method in class umontreal.iro.lecuyer.rng.MRG31k3p

resetStartStream() - Method in class umontreal.iro.lecuyer.rng.MRG32k3a

resetStartStream() - Method in class umontreal.iro.lecuyer.rng.MRG32k3aL

resetStartStream() - Method in class umontreal.iro.lecuyer.rng.MT19937

resetStartStream() - Method in class umontreal.iro.lecuyer.rng.RandMrg

Deprecated.

resetStartStream() - Method in interface umontreal.iro.lecuyer.rng.RandomStream

Reinitializes the stream to its initial state I_g: C_g and B_g are set to I_g.

resetStartStream() - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase

resetStartStream() - Method in class umontreal.iro.lecuyer.rng.RandomStreamManager

Forwards to the resetStartStream methods of all streams in the list.

resetStartStream() - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache

resetStartStream() - Method in class umontreal.iro.lecuyer.rng.RandRijndael

resetStartStream() - Method in class umontreal.iro.lecuyer.rng.TruncatedRandomStream

resetStartStream() - Method in class umontreal.iro.lecuyer.rng.WELL1024

resetStartStream() - Method in class umontreal.iro.lecuyer.rng.WELL512

resetStartStream() - Method in class umontreal.iro.lecuyer.rng.WELL607

resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.AntitheticStream

resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.BakerTransformedStream

resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.F2NL607

resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.GenF2w32

resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.LFSR113

resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.LFSR258

resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.MRG31k3p

resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.MRG32k3a

resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.MRG32k3aL

resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.MT19937

resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.RandMrg

Deprecated.

resetStartSubstream() - Method in interface umontreal.iro.lecuyer.rng.RandomStream

Reinitializes the stream to the beginning of its current substream: C_g is set to B_g.

resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase

resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.RandomStreamManager

Forwards to the resetStartSubstream methods of all streams in the list.

resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache

resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.RandRijndael

resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.TruncatedRandomStream

resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.WELL1024

resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.WELL512

resetStartSubstream() - Method in class umontreal.iro.lecuyer.rng.WELL607

resetToNextPoint() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator

resetToNextPoint() - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator

Increases the current point index by 1 and returns its new value.

Resource - Class in umontreal.iro.lecuyer.simprocs

Objects of this class are resources having limited capacity, and which can be requested and released by Process objects.

Resource(int) - Constructor for class umontreal.iro.lecuyer.simprocs.Resource

Constructs a new resource linked with the default simulator, with initial capacity capacity, and service policy FIFO.

Resource(ProcessSimulator, int) - Constructor for class umontreal.iro.lecuyer.simprocs.Resource

Constructs a new resource linked with the simulator sim, with initial capacity capacity, and service policy FIFO.

Resource(int, String) - Constructor for class umontreal.iro.lecuyer.simprocs.Resource

Constructs a new resource linked with the default simulator, with initial capacity capacity, service policy FIFO, and identifier name.

Resource(ProcessSimulator, int, String) - Constructor for class umontreal.iro.lecuyer.simprocs.Resource

Constructs a new resource linked with the simulator sim, with initial capacity capacity, service policy FIFO, and identifier (or name) name.

restoreImports() - Method in class umontreal.iro.lecuyer.util.ClassFinder

Restores the list of import declarations.

resume() - Method in class umontreal.iro.lecuyer.simprocs.SimProcess

Places this process at the beginning of the event list to resume its execution.

retainAll(Collection<?>) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

reverseDigits(int, int[], int[]) - Static method in class umontreal.iro.lecuyer.hups.RadicalInverse

Given the k b-ary digits of i in bdigits, returns the k digits of the integer radical inverse of i in idigits.

rightMatrixScramble(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNet

Applies a linear scramble by multiplying each C_j on the right by a single k×k nonsingular upper-triangular matrix M, as suggested by Faure and Tezuka.

rightMatrixScramble(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2

RootFinder - Class in umontreal.iro.lecuyer.util

This class provides methods to solve non-linear equations.

S

s(String) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat

Same as s (0, str).

s(int, String) - Static method in class umontreal.iro.lecuyer.util.PrintfFormat

Formats the string str like the %s in the C printf function.

sameSignature(Method, Method) - Static method in class umontreal.iro.lecuyer.util.Introspection

Determines if two methods m1 and m2 share the same signature.

saveImports() - Method in class umontreal.iro.lecuyer.util.ClassFinder

Saves the current import list on the import stack.

scalarProduct(BitVector) - Method in class umontreal.iro.lecuyer.util.BitVector

Returns the scalar product of two BitVector's modulo 2.

scan(int, double, int) - Static method in class umontreal.iro.lecuyer.gof.FBar

Return P[S_N(d ) >= m], where S_N(d ) is the scan statistic.

scan(int, double, int) - Static method in class umontreal.iro.lecuyer.gof.FDist

Returns F(m), the distribution function of the scan statistic with parameters N and d, evaluated at m.

scan(DoubleArrayList, double) - Static method in class umontreal.iro.lecuyer.gof.GofStat

Computes and returns the scan statistic S_N(d ), defined in FBar.scan.

ScatterChart - Class in umontreal.iro.lecuyer.charts

This class provides tools to create and manage scatter plots.

ScatterChart() - Constructor for class umontreal.iro.lecuyer.charts.ScatterChart

Initializes a new ScatterChart instance with an empty data set.

ScatterChart(String, String, String, double[][]...) - Constructor for class umontreal.iro.lecuyer.charts.ScatterChart

Initializes a new ScatterChart instance with data data.

ScatterChart(String, String, String, double[][], int) - Constructor for class umontreal.iro.lecuyer.charts.ScatterChart

Initializes a new ScatterChart instance with sets of points data.

ScatterChart(String, String, String, double[][], int, int) - Constructor for class umontreal.iro.lecuyer.charts.ScatterChart

Initializes a new ScatterChart instance using subsets of data.

ScatterChart(String, String, String, DoubleArrayList...) - Constructor for class umontreal.iro.lecuyer.charts.ScatterChart

Initializes a new ScatterChart instance with data data.

ScatterChart(String, String, String, XYSeriesCollection) - Constructor for class umontreal.iro.lecuyer.charts.ScatterChart

Initializes a new ScatterChart instance with data data.

schedule(double) - Method in class umontreal.iro.lecuyer.simevents.Event

Schedules this event to happen in delay time units, i.e., at time sim.time() + delay, by inserting it in the event list.

schedule(double) - Method in class umontreal.iro.lecuyer.simprocs.SimProcess

Schedules the process to start in delay time units.

scheduleAfter(Event) - Method in class umontreal.iro.lecuyer.simevents.Event

Schedules this event to happen just after, and at the same time, as the event other.

scheduleBefore(Event) - Method in class umontreal.iro.lecuyer.simevents.Event

Schedules this event to happen just before, and at the same time, as the event other.

scheduledEvent() - Method in class umontreal.iro.lecuyer.simprocs.SimProcess

Returns the Event associated with the current variable.

scheduleNext() - Method in class umontreal.iro.lecuyer.simevents.Event

Schedules this event as the first event in the event list, to be executed at the current time (as the next event).

scheduleNext() - Method in class umontreal.iro.lecuyer.simprocs.SimProcess

Schedules this process to start at the current time, by placing it at the beginning of the event list.

selectCoordinates(int[], int) - Method in class umontreal.iro.lecuyer.hups.SubsetOfPointSet

Selects the numCoord coordinates whose numbers are provided in the array coordIndices.

selectCoordinatesRange(int, int) - Method in class umontreal.iro.lecuyer.hups.SubsetOfPointSet

Selects the coordinates from ``from'' to ``to - 1'' from the original point set.

selectEuler(double) - Static method in class umontreal.iro.lecuyer.simevents.Continuous

Selects the Euler method as the integration method, with the integration step size h, in time units, for the default simulator.

selectEuler(double) - Method in class umontreal.iro.lecuyer.simevents.ContinuousState

Selects the Euler method as the integration method, with the integration step size h, in time units.

selectPoints(int[], int) - Method in class umontreal.iro.lecuyer.hups.SubsetOfPointSet

Selects the numPoints points whose numbers are provided in the array pointIndices.

selectPointsRange(int, int) - Method in class umontreal.iro.lecuyer.hups.SubsetOfPointSet

Selects the points numbered from ``from'' to ``to - 1'' from the original point set.

selectRungeKutta2(double) - Static method in class umontreal.iro.lecuyer.simevents.Continuous

Selects a Runge-Kutta method of order 2 as the integration method to be used, with step size h.

selectRungeKutta2(double) - Method in class umontreal.iro.lecuyer.simevents.ContinuousState

Selects a Runge-Kutta method of order 2 as the integration method to be used, with step size h.

selectRungeKutta4(double) - Static method in class umontreal.iro.lecuyer.simevents.Continuous

Selects a Runge-Kutta method of order 4 as the integration method to be used, with step size h.

selectRungeKutta4(double) - Method in class umontreal.iro.lecuyer.simevents.ContinuousState

Selects a Runge-Kutta method of order 4 as the integration method to be used, with step size h.

selfAnd(BitVector) - Method in class umontreal.iro.lecuyer.util.BitVector

Applies the and operator on this with that.

selfNot() - Method in class umontreal.iro.lecuyer.util.BitVector

Applies the not operator on the current BitVector and returns it.

selfOr(BitVector) - Method in class umontreal.iro.lecuyer.util.BitVector

Applies the or operator on this with that.

selfShift(int) - Method in class umontreal.iro.lecuyer.util.BitVector

Shift all the bits of the current BitVector j positions to the right if j is positive, and j positions to the left if j is negative.

selfXor(BitVector) - Method in class umontreal.iro.lecuyer.util.BitVector

Applies the xor operator on this with that.

servList() - Method in class umontreal.iro.lecuyer.simprocs.Resource

Returns the list that contains the UserRecord objects for the processes in the service list for this resource.

set(SSJXYSeriesCollection) - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart

Sets the primary dataset for the plot, replacing the existing dataset if there is one.

set(int, SSJXYSeriesCollection) - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart

Replaces the element at the specified position in the dataset list with the specified element.

set(int, E) - Method in class umontreal.iro.lecuyer.simevents.ListWithStat

set(boolean) - Method in class umontreal.iro.lecuyer.simprocs.Condition

Sets the condition to val.

set(int, E) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

set(int, int, double) - Method in class umontreal.iro.lecuyer.util.DMatrix

Sets the value of the element in the specified row and column.

set(int, OE) - Method in class umontreal.iro.lecuyer.util.TransformingList

setAntithetic(boolean) - Method in class umontreal.iro.lecuyer.rng.RandMrg

Deprecated.

setAutoRange() - Method in class umontreal.iro.lecuyer.charts.CategoryChart

Sets chart y range to automatic values.

setAutoRange(boolean, boolean) - Method in class umontreal.iro.lecuyer.charts.HistogramChart

setAutoRange() - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart

Sets chart range to automatic values.

setAutoRange() - Method in class umontreal.iro.lecuyer.charts.XYChart

The x and the y ranges of the chart are set automatically.

setAutoRange(boolean, boolean) - Method in class umontreal.iro.lecuyer.charts.XYChart

The x and the y ranges of the chart are set automatically.

setAutoRange00(boolean, boolean) - Method in class umontreal.iro.lecuyer.charts.XYChart

The x and the y ranges of the chart are set automatically.

setBandwidth(double) - Method in class umontreal.iro.lecuyer.randvar.KernelDensityGen

Sets the bandwidth to h.

setBandwidth(double) - Method in class umontreal.iro.lecuyer.randvar.KernelDensityVarCorrectGen

setBins(int, int) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset

Sets the bins for a series.

setBins(int, int, double, double) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset

Sets the bins for a series.

setBins(int, HistogramBin[]) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset

Sets the bins for a series.

setBins(int, int) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection

Sets bins periodic bins from the observation minimum values to the observation maximum value for a series.

setBins(int, int, double, double) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection

Sets bins periodic bins from minimum to maximum for a series.

setBins(int, HistogramBin[]) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection

Links bins given by table binsTable to a series.

setBool(int, int, boolean) - Method in class umontreal.iro.lecuyer.util.BitMatrix

Changes the value of the bit in the specified row and column.

setBool(int, boolean) - Method in class umontreal.iro.lecuyer.util.BitVector

Sets the value of the bit in position pos.

setBroadcasting(boolean) - Method in class umontreal.iro.lecuyer.simprocs.Condition

Instructs the condition to start or stop observation broadcasting.

setBroadcasting(boolean) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

Sets the status of the observation broadcasting mechanism to b.

setBroadcasting(boolean) - Method in class umontreal.iro.lecuyer.stat.StatProbe

Instructs the probe to turn its broadcasting ON or OFF.

setBrownianMotionPCA(BrownianMotionPCA) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessPCA

Sets the brownian motion PCA.

setCachedGen(RandomVariateGen) - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache

Sets the random variate generator whose values are cached to rvg.

setCachedStream(RandomStream) - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache

Sets the random stream whose values are cached to stream.

setCachedValues(DoubleArrayList) - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache

Sets the array list containing the cached values to values.

setCachedValues(DoubleArrayList) - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache

Sets the array list containing the cached values to values.

setCacheIndex(int) - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache

Sets the index, in the cache, of the next value returned by nextDouble.

setCacheIndex(int) - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache

Sets the index, in the cache, of the next value returned by nextDouble.

setCaching(boolean) - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGenWithCache

Sets the caching indicator to caching.

setCaching(boolean) - Method in class umontreal.iro.lecuyer.rng.RandomStreamWithCache

Sets the caching indicator to caching.

setCapacity(int) - Method in class umontreal.iro.lecuyer.simprocs.Resource

Sets the capacity to newcap.

setChartMargin(double) - Method in class umontreal.iro.lecuyer.charts.XYChart

Sets the chart margin to margin.

setCoefficients(double...) - Method in class umontreal.iro.lecuyer.functions.Polynomial

Sets the array of coefficients of this polynomial to coeff.

setCollecting(boolean) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

Sets the status of the statistical collecting mechanism to c.

setCollecting(boolean) - Method in class umontreal.iro.lecuyer.stat.StatProbe

Turns ON or OFF the collection of statistical observations.

setColor(int, Color) - Method in class umontreal.iro.lecuyer.charts.SSJCategorySeriesCollection

Sets a new plotting color to the series series.

setColor(int, Color) - Method in class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection

Sets a new plotting color to the series series.

setConfidenceIntervalNone() - Method in class umontreal.iro.lecuyer.stat.Tally

Indicates that no confidence interval needs to be printed in reports formatted by report, and shortReport.

setConfidenceIntervalNormal() - Method in class umontreal.iro.lecuyer.stat.Tally

Indicates that a confidence interval on the true mean, based on the central limit theorem, needs to be included in reports formatted by report and shortReport.

setConfidenceIntervalStudent() - Method in class umontreal.iro.lecuyer.stat.Tally

Indicates that a confidence interval on the true mean, based on the normality assumption, needs to be included in reports formatted by report and shortReport.

setConfidenceLevel(double) - Method in class umontreal.iro.lecuyer.stat.Tally

Sets the level of confidence for the intervals on the mean displayed in reports.

setCurCoordIndex(int) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator

setCurCoordIndex(int) - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator

Sets the current coordinate index to j, so that the next calls to nextCoordinate or nextCoordinates will return the values u_{i, j}, u_{i, j+1},..., where i is the index of the current point.

setCurPointIndex(int) - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet.CycleBasedPointSetIterator

setCurPointIndex(int) - Method in interface umontreal.iro.lecuyer.hups.PointSetIterator

Resets the current point index to i and the current coordinate index to zero.

setDashPattern(int, String) - Method in class umontreal.iro.lecuyer.charts.EmpiricalSeriesCollection

Selects dash pattern for a data series.

setDashPattern(int, String) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection

Selects dash pattern for a data series.

setDimension(int) - Method in class umontreal.iro.lecuyer.randvarmulti.IIDMultivariateGen

Changes the dimension of the vector to d.

setFillBox(boolean) - Method in class umontreal.iro.lecuyer.charts.BoxChart

Sets fill to true, if the boxes are to be filled.

setFilled(int, boolean) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection

Sets the filled flag.

setLabel(String) - Method in class umontreal.iro.lecuyer.charts.Axis

Sets the axis description.

setLabels(double) - Method in class umontreal.iro.lecuyer.charts.Axis

Sets a periodic label display.

setLabels(double[]) - Method in class umontreal.iro.lecuyer.charts.Axis

Sets the position of each label on this axis.

setLabels(double[], String[]) - Method in class umontreal.iro.lecuyer.charts.Axis

Assigns custom labels to user-defined positions on the axis.

setLabelsAuto() - Method in class umontreal.iro.lecuyer.charts.Axis

Calculates and sets an automatic tick unit.

setLambda(double) - Method in class umontreal.iro.lecuyer.probdist.ExponentialDist

Sets the value of λ for this object.

setLambda(double) - Method in class umontreal.iro.lecuyer.probdist.PoissonDist

Sets the λ associated with this object.

setLatexDocFlag(boolean) - Method in class umontreal.iro.lecuyer.charts.CategoryChart

Same as in XYChart.

setLatexDocFlag(boolean) - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart

Same as in XYChart.

setLatexDocFlag(boolean) - Method in class umontreal.iro.lecuyer.charts.XYChart

Flag to remove the \documentclass (and other) commands in the created L^ATEX files.

setLinearSeed(int[]) - Method in class umontreal.iro.lecuyer.rng.F2NL607

This method is discouraged for normal use.

setManualRange(double[]) - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart

Sets new x-axis and y-axis bounds, with format: axisRange = [xmin, xmax, ymin, ymax].

setManualRange(double[]) - Method in class umontreal.iro.lecuyer.charts.XYChart

Sets the x and y ranges of the chart using the format: range = [xmin, xmax, ymin, ymax].

setManualRange(double[], boolean, boolean) - Method in class umontreal.iro.lecuyer.charts.XYChart

Sets the x and y ranges of the chart using the format: range = [xmin, xmax, ymin, ymax].

setManualRange00(double[], boolean, boolean) - Method in class umontreal.iro.lecuyer.charts.XYChart

Sets the x and y ranges of the chart using the format: range = [xmin, xmax, ymin, ymax].

setManuelRange(double[], boolean, boolean) - Method in class umontreal.iro.lecuyer.charts.HistogramChart

setMargin(double) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection

Sets the margin which is a percentage amount by which the bars are trimmed for all series.

setMarksType(int, String) - Method in class umontreal.iro.lecuyer.charts.EmpiricalSeriesCollection

Adds marks on points to a data series.

setMarksType(int, String) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection

Adds marks on points to a data series.

setMean(double) - Method in class umontreal.iro.lecuyer.probdist.ExponentialDistFromMean

Calls setLambda with argument 1/mean to change the mean of this distribution.

setMu(double[]) - Method in class umontreal.iro.lecuyer.randvarmulti.MultinormalGen

Sets the mean vector to mu.

setMu(int, double) - Method in class umontreal.iro.lecuyer.randvarmulti.MultinormalGen

Sets the i-th component of the mean vector to mui.

setMuGeom(double) - Method in class umontreal.iro.lecuyer.stochprocess.GeometricLevyProcess

Sets the drift parameter (interest rate) of the geometric term.

setN(int) - Method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDist

Sets the parameter n of this object.

setN(int) - Method in class umontreal.iro.lecuyer.probdist.ChiSquareDist

Sets the parameter n of this object.

setN(int) - Method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist

Sets the parameter n of this object.

setN(int) - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDist

Sets the parameter n of this object.

setN(int) - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist

Sets the parameter n of this object.

setN(int) - Method in class umontreal.iro.lecuyer.probdist.StudentDist

Sets the parameter n associated with this object.

setN(int) - Method in class umontreal.iro.lecuyer.probdist.WatsonGDist

Sets the parameter n of this object.

setN(int) - Method in class umontreal.iro.lecuyer.probdist.WatsonUDist

Sets the parameter n of this object.

setName(int, String) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection

Sets the name of the selected series.

setName(String) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

Sets the global name of this list to name.

setName(String) - Method in class umontreal.iro.lecuyer.stat.StatProbe

Sets the name of this statistical collector to name.

setNonLinearData(int[][]) - Static method in class umontreal.iro.lecuyer.rng.F2NL607

Selects new data for the components of the non-linear generator.

setNonLinearSeed(int[]) - Method in class umontreal.iro.lecuyer.rng.F2NL607

This method is discouraged for normal use.

setNormalGen(NormalGen) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessMSH

Sets the normal generator.

setNu(int) - Method in class umontreal.iro.lecuyer.probdist.ChiDist

Sets the value of ν for this object.

setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCAEqualSteps

setObservationTimes(double, int) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCAEqualSteps

setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCA

Sets the observation times of the GammaProcessPCA and the BrownianMotionPCA.

setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCABridge

setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion

setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.GeometricLevyProcess

Sets the observation times on the geometric process and the underlying Lévy process.

setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessPCA

Sets the observation times of both the InverseGaussianProcessPCA and the inner
BrownianMotionPCA.

setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess

Sets the observation times on the NIG process as usual, but also sets the observation times of the underlying InverseGaussianProcess.

setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess

Sets the observation times of the process to a copy of T, with t₀ = T[0] and t_d = T[d].

setObservationTimes(double, int) - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess

Sets equidistant observation times at t_j = jδ, for j = 0,..., d, and delta = δ.

setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess

Sets the observation times on the VarianceGammaProcess as usual, but also sets the observation times of the underlying GammaProcess.

setObservationTimes(double[], int) - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiff

Sets the observation times on the VarianceGammaProcesDiff as usual, but also sets the observation times of the underlying GammaProcess'es.

setOtherStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessMSH

Sets the otherStream, which is the stream used to choose between the two roots in the MSH method.

setOutlineWidth(int, double) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection

Sets the outline width in pt.

setP(double) - Method in class umontreal.iro.lecuyer.probdist.GeometricDist

Resets the value of p associated with this object.

setPackageLinearSeed(int[]) - Static method in class umontreal.iro.lecuyer.rng.F2NL607

Sets the initial seed of the linear part of the class F2NL607 to the 19 integers of the vector seed[0..18].

setPackageNonLinearSeed(int[]) - Static method in class umontreal.iro.lecuyer.rng.F2NL607

Sets the non-linear part of the initial seed of the class F2NL607 to the n integers of the vector seed[0..n-1], where n is the number of components of the non-linear part.

setPackageSeed(int[]) - Static method in class umontreal.iro.lecuyer.rng.GenF2w32

Sets the initial seed of the class GenF2w2r32 to the 25 integers of the vector seed[0..24].

setPackageSeed(int[]) - Static method in class umontreal.iro.lecuyer.rng.LFSR113

Sets the initial seed for the class LFSR113 to the four integers of the vector seed[0..3].

setPackageSeed(long[]) - Static method in class umontreal.iro.lecuyer.rng.LFSR258

Sets the initial seed for the class LFSR258 to the five integers of array seed[0..4].

setPackageSeed(int[]) - Static method in class umontreal.iro.lecuyer.rng.MRG31k3p

Sets the initial seed for the class MRG31k3p to the six integers of the vector seed[0..5].

setPackageSeed(long[]) - Static method in class umontreal.iro.lecuyer.rng.MRG32k3a

Sets the initial seed for the class MRG32k3a to the six integers in the vector seed[0..5].

setPackageSeed(long[]) - Static method in class umontreal.iro.lecuyer.rng.MRG32k3aL

setPackageSeed(long[]) - Static method in class umontreal.iro.lecuyer.rng.RandMrg

Deprecated.

Sets the initial seed for the class RandMrg to the six integers in the vector seed[0..5].

setPackageSeed(byte[]) - Static method in class umontreal.iro.lecuyer.rng.RandRijndael

Sets the initial seed for the class RandRijndael to the 16 bytes of the vector seed[0..15].

setPackageSeed(int[]) - Static method in class umontreal.iro.lecuyer.rng.WELL1024

Sets the initial seed of this class to the 32 integers of array seed[0..31].

setPackageSeed(int[]) - Static method in class umontreal.iro.lecuyer.rng.WELL512

Sets the initial seed of the class WELL512 to the 16 integers of the vector seed[0..15].

setPackageSeed(int[]) - Static method in class umontreal.iro.lecuyer.rng.WELL607

Sets the initial seed of the class WELL607 to the 19 integers of the vector seed[0..18].

setParam(double, double, int) - Method in class umontreal.iro.lecuyer.charts.ContinuousDistChart

Sets the parameters a, b and m for this object.

setParam(int, int) - Method in class umontreal.iro.lecuyer.charts.DiscreteDistIntChart

Sets the parameters a and b for this object.

setParam(double, double) - Method in class umontreal.iro.lecuyer.probdist.Pearson5Dist

Sets the parameters α and β of this object.

setParam(double, double, double) - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist

Sets the parameters α₁, α₂ and β of this object.

setParams(double, double, double, double, int) - Method in class umontreal.iro.lecuyer.probdist.BetaDist

setParams(double, double, double, double, int) - Method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist

setParams(int, double) - Method in class umontreal.iro.lecuyer.probdist.BinomialDist

Resets the parameters to these new values and recomputes everything as in the constructor.

setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.CauchyDist

Sets the value of the parameters α and β for this object.

setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist

Sets the parameters ν = nu and λ = lambda of this object.

setParams(int, double, int) - Method in class umontreal.iro.lecuyer.probdist.ErlangDist

Sets the parameters k and λ of the distribution for this object.

setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist

Deprecated.

Sets the parameters α and λ of this object.

setParams(double, double, double) - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist

Sets the parameters μ, β and γ of this object.

setParams(int, int) - Method in class umontreal.iro.lecuyer.probdist.FisherFDist

Sets the parameters n and m of this object.

setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist

Sets the parameters μ and σ for this object.

setParams(double, double, double) - Method in class umontreal.iro.lecuyer.probdist.FrechetDist

Sets the parameters α, β and δ of this object.

setParams(double, double, int) - Method in class umontreal.iro.lecuyer.probdist.GammaDist

setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.GumbelDist

Sets the parameters β and δ of this object.

setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.HalfNormalDist

Sets the parameters μ and σ.

setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist

Sets the parameters μ and σ of this object.

setParams(int, int, int) - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist

Resets the parameters of this object to m, l and k.

setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist

Sets the parameters μ and λ of this object.

setParams(double, double, double, double) - Method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist

Sets the value of the parameters γ, δ, ξ and λ for this object.

setParams(double, double, double, double) - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist

Sets the value of the parameters γ, δ, ξ and λ for this object.

setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.LogisticDist

Sets the parameters α and λ of this object.

setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.LoglogisticDist

Sets the parameters α and β of this object.

setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.LognormalDist

Sets the parameters μ and σ of this object.

setParams(double, double, double) - Method in class umontreal.iro.lecuyer.probdist.NakagamiDist

Sets the parameters a, λ and c of this object.

setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist

Sets the parameter γ and p of this object.

setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.NormalDist

Sets the parameters μ and σ of this object.

setParams(double, double, double, double) - Method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist

Sets the parameters α, β, μ and δ of this object.

setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.ParetoDist

Sets the parameter α and β for this object.

setParams(int, double) - Method in class umontreal.iro.lecuyer.probdist.PascalDist

Sets the parameter n and p of this object.

setParams(double, double, double) - Method in class umontreal.iro.lecuyer.probdist.PowerDist

Sets the parameters a, b and c for this object.

setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.RayleighDist

Sets the parameters a and β for this object.

setParams(double, double, double) - Method in class umontreal.iro.lecuyer.probdist.TriangularDist

Sets the value of the parameters a, b and m for this object.

setParams(ContinuousDistribution, double, double) - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist

Sets the parameters dist, a and b for this object.

setParams(double, double) - Method in class umontreal.iro.lecuyer.probdist.UniformDist

Sets the parameters a and b for this object.

setParams(int, int) - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist

Sets the parameters i and j for this object.

setParams(double, double, double) - Method in class umontreal.iro.lecuyer.probdist.WeibullDist

Sets the parameters α, λ and δ for this object.

setParams(double[]) - Method in class umontreal.iro.lecuyer.probdistmulti.DirichletDist

Sets the parameters (α₁,...,α_d) of this object.

setParams(int, double[]) - Method in class umontreal.iro.lecuyer.probdistmulti.MultinomialDist

Sets the parameters n and (p₁,...,p_d) of this object.

setParams(double[], double[][]) - Method in class umontreal.iro.lecuyer.probdistmulti.MultiNormalDist

Sets the parameters μ and Σ of this object.

setParams(double, double[]) - Method in class umontreal.iro.lecuyer.probdistmulti.NegativeMultinomialDist

Sets the parameters γ and (p₁,...,p_d) of this object.

setParams(double, double, double, double) - Method in class umontreal.iro.lecuyer.randvar.NormalInverseGaussianGen

Sets the parameters α, β, μ and δ of this object.

setParams(double, double, double) - Method in class umontreal.iro.lecuyer.randvar.Pearson6Gen

Sets the parameters α₁, α₂ and β of this object.

setParams(double, double, double) - Method in class umontreal.iro.lecuyer.randvar.PowerGen

Sets the parameters a, b and c for this object.

setParams(double, double) - Method in class umontreal.iro.lecuyer.randvar.RayleighGen

Sets the parameters a = a and β = beta for this object.

setParams(double, double, double) - Method in class umontreal.iro.lecuyer.randvar.WeibullGen

Sets the parameters α, λ and δ for this object.

setParams(double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotion

Resets the parameters X(t₀) = x0, μ = mu and σ = sigma of the process.

setParams(double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotionPCA

setParams(double, double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess

Resets the parameters X(t₀) = x0, α = alpha, b = b and σ = sigma of the process.

setParams(double, double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcessEuler

Resets the parameters X(t₀) = x0, α = alpha, b = b and σ = sigma of the process.

setParams(double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcess

Sets the parameters S(t₀) = s0, μ = mu and ν = nu of the process.

setParams(double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCA

Sets the parameters s0, μ and ν to new values, and sets the variance parameters of the BrownianMotionPCA to ν.

setParams(double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCABridge

setParams(double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion

Sets the parameters S(t₀) = s0, μ = mu and σ = sigma of the process.

setParams(double, double, double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess

Sets the parameters S(t₀) = s0, θ = theta, σ = sigma, ν = nu and μ = mu of the process.

setParams(double, double) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess

Sets the parameters.

setParams(double, double, double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess

Sets the parameters.

setParams(double, double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess

Resets the parameters X(t₀) = x0, α = alpha, b = b and σ = sigma of the process.

setParams(double, double, double, double) - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess

Sets the parameters S(t₀) = s0, θ = theta, σ = sigma and ν = nu of the process.

setPlotStyle(int, String) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection

Selects the plot style for a given series.

setPolicyFIFO() - Method in class umontreal.iro.lecuyer.simprocs.Bin

Sets the service policy for ordering processes waiting for tokens on the bin to FIFO (first in, first out): the processes are placed in the list (and served) according to their order of arrival.

setPolicyFIFO() - Method in class umontreal.iro.lecuyer.simprocs.Resource

Set the service policy to FIFO (first in, first out): the processes are placed in the list (and served) according to their order of arrival.

setPolicyLIFO() - Method in class umontreal.iro.lecuyer.simprocs.Bin

Sets the service policy for ordering processes waiting for tokens on the bin to LIFO (last in, first out): the processes are placed in the list (and served) according to their inverse order of arrival: the last arrived are served first.

setPolicyLIFO() - Method in class umontreal.iro.lecuyer.simprocs.Resource

Set the service policy to LIFO (last in, first out): the processes are placed in the list (and served) according to their inverse order of arrival, the last arrived are served first.

setPositiveReflection(boolean) - Method in class umontreal.iro.lecuyer.randvar.KernelDensityGen

After this method is called with true, the generator will produce only positive values, by using the reflection method: replace all negative values by their absolute values.

setPriority(double) - Method in class umontreal.iro.lecuyer.simevents.Event

Sets the priority of this event to inPriority.

setPriority(double) - Method in class umontreal.iro.lecuyer.simprocs.SimProcess

Sets the priority assigned to the current variable in the simulation.

setprobFlag(boolean) - Method in class umontreal.iro.lecuyer.charts.XYChart

Must be set true when plotting probabilities, false otherwise.

setRandomStreamClass(Class) - Method in class umontreal.iro.lecuyer.rng.BasicRandomStreamFactory

Sets the associated random stream class to rsClass.

setRenderer(CategoryItemRenderer) - Method in class umontreal.iro.lecuyer.charts.SSJCategorySeriesCollection

Sets the CategoryItemRenderer object associated with the current variable.

setRenderer(XYItemRenderer) - Method in class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection

Sets the XYItemRenderer object associated with the current variable.

setScheduledEvent(Event) - Method in class umontreal.iro.lecuyer.simprocs.SimProcess

Sets the event associated to the current variable.

setScrambleData(RandomStream, int, int[]) - Static method in class umontreal.iro.lecuyer.rng.F2NL607

Selects new data for the components of the non-linear generator.

setSeed(int[]) - Method in class umontreal.iro.lecuyer.rng.GenF2w32

This method is discouraged for normal use.

setSeed(int[]) - Method in class umontreal.iro.lecuyer.rng.LFSR113

This method is discouraged for normal use.

setSeed(long[]) - Method in class umontreal.iro.lecuyer.rng.LFSR258

This method is discouraged for normal use.

setSeed(int[]) - Method in class umontreal.iro.lecuyer.rng.MRG31k3p

Use of this method is strongly discouraged.

setSeed(long[]) - Method in class umontreal.iro.lecuyer.rng.MRG32k3a

Sets the initial seed I_g of this stream to the vector seed[0..5].

setSeed(long[]) - Method in class umontreal.iro.lecuyer.rng.MRG32k3aL

setSeed(long[]) - Method in class umontreal.iro.lecuyer.rng.RandMrg

Deprecated.

Sets the initial seed I_g of this stream to the vector seed[0..5].

setSeed(byte[]) - Method in class umontreal.iro.lecuyer.rng.RandRijndael

This method is discouraged for normal use.

setSeed(int[]) - Method in class umontreal.iro.lecuyer.rng.WELL1024

This method is discouraged for normal use.

setSeed(int[]) - Method in class umontreal.iro.lecuyer.rng.WELL512

This method is discouraged for normal use.

setSeed(int[]) - Method in class umontreal.iro.lecuyer.rng.WELL607

This method is discouraged for normal use.

setSeriesCollection(BoxSeriesCollection) - Method in class umontreal.iro.lecuyer.charts.BoxChart

Links a new dataset to the current chart.

setSeriesCollection(EmpiricalSeriesCollection) - Method in class umontreal.iro.lecuyer.charts.EmpiricalChart

Links a new dataset to the current chart.

setSeriesCollection(HistogramSeriesCollection) - Method in class umontreal.iro.lecuyer.charts.HistogramChart

Links a new dataset to the current chart.

setSeriesCollection(XYListSeriesCollection) - Method in class umontreal.iro.lecuyer.charts.ScatterChart

Links a new dataset to the current chart.

setSeriesCollection(XYListSeriesCollection) - Method in class umontreal.iro.lecuyer.charts.XYLineChart

Links a new dataset to the current chart.

setShowNumberObs(boolean) - Method in class umontreal.iro.lecuyer.stat.Tally

Determines if the number of observations must be displayed in reports.

setSigma(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.randvarmulti.MultinormalCholeskyGen

Sets the covariance matrix Σ of this multinormal generator to sigma (and recomputes A).

setSigma(DoubleMatrix2D) - Method in class umontreal.iro.lecuyer.randvarmulti.MultinormalPCAGen

Sets the covariance matrix Σ of this multinormal generator to sigma (and recomputes A).

setSimulator(Simulator) - Method in class umontreal.iro.lecuyer.simevents.Accumulate

Sets the simulator associated with this probe to sim.

setSimulator(Simulator) - Method in class umontreal.iro.lecuyer.simevents.Continuous

Sets the simulator linked to this continuous-time variable.

setSimulator(Simulator) - Method in class umontreal.iro.lecuyer.simevents.Event

Sets the simulator associated with this event to sim.

setSimulator(Simulator) - Method in class umontreal.iro.lecuyer.simevents.ListWithStat

Sets the simulator associated with this list.

setSimulator(ProcessSimulator) - Method in class umontreal.iro.lecuyer.simprocs.Bin

Set the current simulator of this continuous-time variable.

setStatCollecting(boolean) - Method in class umontreal.iro.lecuyer.simevents.ListWithStat

Starts or stops collecting statistics on this list.

setStatCollecting(boolean) - Method in class umontreal.iro.lecuyer.simprocs.Bin

Starts or stops collecting statistics on the list returned by waitList for this bin.

setStatCollecting(boolean) - Method in class umontreal.iro.lecuyer.simprocs.Resource

Starts or stops collecting statistics on the lists returned by waitList and servList for this resource.

setStream(RandomStream) - Method in class umontreal.iro.lecuyer.hups.EmptyRandomization

Sets the internal RandomStream to stream.

setStream(RandomStream) - Method in class umontreal.iro.lecuyer.hups.PointSet

Sets the random stream used to generate random shifts to stream.

setStream(RandomStream) - Method in interface umontreal.iro.lecuyer.hups.PointSetRandomization

Sets the internal RandomStream to stream.

setStream(RandomStream) - Method in class umontreal.iro.lecuyer.hups.RandomShift

Sets the internal RandomStream to stream.

setStream(RandomStream) - Method in class umontreal.iro.lecuyer.randvar.RandomVariateGen

Sets the RandomStream used by this generator to stream.

setStream(RandomStream) - Method in class umontreal.iro.lecuyer.randvarmulti.RandomMultivariateGen

Sets the RandomStream used by this object to stream.

setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.BrownianMotion

Resets the random stream of the normal generator to stream.

setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcess

Resets the random stream of the noncentral chi-square generator to stream.

setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.CIRProcessEuler

Resets the random stream of the normal generator to stream.

setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcess

Resets the RandomStream of the GammaGen to stream.

setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessBridge

Resets the RandomStream of the GammaGen and the BetaGen to stream.

setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.GammaProcessPCA

Resets the RandomStream of the gamma generator and the RandomStream of the inner BrownianMotionPCA to stream.

setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.GeometricBrownianMotion

Resets the RandomStream for the underlying Brownian motion to stream.

setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.GeometricLevyProcess

Resets the stream in the underlying Lévy process.

setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.GeometricVarianceGammaProcess

setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcess

setStream(RandomStream, RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessBridge

Sets the streams.

setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessBridge

Sets both inner streams to the same stream.

setStream(RandomStream, RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessMSH

Sets the streams.

setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessMSH

Sets both inner streams to stream.

setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.InverseGaussianProcessPCA

setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.NormalInverseGaussianProcess

Sets all internal streams to stream, including the stream(s) of the underlying InverseGaussianProcess.

setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.OrnsteinUhlenbeckProcess

Resets the random stream of the normal generator to stream.

setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess

Resets the random stream of the underlying generator to stream.

setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess

Resets the RandomStream's.

setStream(RandomStream) - Method in class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiff

Sets the RandomStream of the two GammaProcess'es to the same stream.

setTheta(double) - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist

Sets the θ associated with this object.

setTicksSynchro(int) - Method in class umontreal.iro.lecuyer.charts.EmpiricalChart

Synchronizes x-axis ticks to the s-th series x-values.

setTicksSynchro(int) - Method in class umontreal.iro.lecuyer.charts.HistogramChart

Synchronizes x-axis ticks to the s-th histogram bins.

setTicksSynchro(int) - Method in class umontreal.iro.lecuyer.charts.ScatterChart

Synchronizes X-axis ticks to the s-th series x-values.

setTicksSynchro(int) - Method in class umontreal.iro.lecuyer.charts.XYChart

Synchronizes x-axis ticks to the s-th series x-values.

setTicksSynchro(int) - Method in class umontreal.iro.lecuyer.charts.XYLineChart

Synchronizes X-axis ticks to the s-th series x-values.

setTime(double) - Method in class umontreal.iro.lecuyer.simevents.Event

Sets the (planned) time of occurence of this event to time.

setTitle(String) - Method in class umontreal.iro.lecuyer.charts.CategoryChart

Sets a title to this chart.

setTitle(String) - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart

Sets a title to the chart.

setTitle(String) - Method in class umontreal.iro.lecuyer.charts.XYChart

Sets a title to this chart.

setTwinAxisPosition(double) - Method in class umontreal.iro.lecuyer.charts.Axis

Defines where the opposite axis must be drawn on the current axis, where it should appear, and on which label.

setType(HistogramType) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset

Sets the histogram type and sends a DatasetChangeEvent to all registered listeners.

setUnmodifiable() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

Forbids any future modification to this list of statistical probes.

setValues(int, List) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset

Sets the values for a series.

setValues(int, double[]) - Method in class umontreal.iro.lecuyer.charts.CustomHistogramDataset

Sets the values for a series.

setValues(int, List) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection

Sets a new values set to a series from a List variable.

setValues(int, double[]) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection

Sets a new values set to a series from a table.

setX0(double) - Method in class umontreal.iro.lecuyer.stochprocess.StochasticProcess

Sets the initial value X(t₀) for this process to s0, and reinitializes.

setXinf(double) - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution

Sets the value x_a = xa, such that the probability density is 0 everywhere outside the interval [x_a, x_b].

setXsup(double) - Method in class umontreal.iro.lecuyer.probdist.ContinuousDistribution

Sets the value x_b = xb, such that the probability density is 0 everywhere outside the interval [x_a, x_b].

setZeroOverZeroValue(double) - Method in class umontreal.iro.lecuyer.util.RatioFunction

Sets the value returned by evaluate for the undefined function 0/0 to zeroOverZero.

shift(int) - Method in class umontreal.iro.lecuyer.util.BitVector

Returns a BitVector equal to the original with all the bits shifted j positions to the right if j is positive, and shifted j positions to the left if j is negative.

ShiftedMathFunction - Class in umontreal.iro.lecuyer.functions

Represents a function computing f (x) - δ for a user-defined function f (x) and shift δ.

ShiftedMathFunction(MathFunction, double) - Constructor for class umontreal.iro.lecuyer.functions.ShiftedMathFunction

Constructs a new function shifting the function func by a shift delta.

shortReport() - Method in class umontreal.iro.lecuyer.simevents.Accumulate

shortReport() - Method in class umontreal.iro.lecuyer.stat.StatProbe

Formats and returns a short, one-line report about this statistical probe.

shortReport() - Method in class umontreal.iro.lecuyer.stat.Tally

Formats and returns a short statistical report for this tally.

shortReportHeader() - Method in class umontreal.iro.lecuyer.simevents.Accumulate

shortReportHeader() - Method in class umontreal.iro.lecuyer.stat.StatProbe

Returns a string containing the name of the values returned in the report strings.

shortReportHeader() - Method in class umontreal.iro.lecuyer.stat.Tally

shouldBeInterpreted(Method) - Method in class umontreal.iro.lecuyer.simprocs.SSJInterpretationOracle

shuffle(List<?>, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation

shuffle(Object[], RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation

Randomly permutes array using stream.

shuffle(byte[], RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation

Randomly permutes array using stream.

shuffle(short[], RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation

Similar to shuffle(byte[], RandomStream).

shuffle(int[], RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation

Similar to shuffle(byte[], RandomStream).

shuffle(long[], RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation

Similar to shuffle(byte[], RandomStream).

shuffle(char[], RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation

Similar to shuffle(byte[], RandomStream).

shuffle(boolean[], RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation

Similar to shuffle(byte[], RandomStream).

shuffle(float[], RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation

Similar to shuffle(byte[], RandomStream).

shuffle(double[], RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation

Similar to shuffle(byte[], RandomStream).

shuffle(List<?>, int, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation

shuffle(Object[], int, int, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation

Partially permutes array as follows using stream: draws the new k elements, array[0] to array[k-1], randomly among the old n elements, array[0] to array[n-1], assuming that k <= n <= array.length.

shuffle(byte[], int, int, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation

Similar to shuffle(Object[], n, k, RandomStream).

shuffle(short[], int, int, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation

Similar to shuffle(Object[], n, k, RandomStream).

shuffle(int[], int, int, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation

Similar to shuffle(Object[], n, k, RandomStream).

shuffle(long[], int, int, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation

Similar to shuffle(Object[], n, k, RandomStream).

shuffle(char[], int, int, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation

Similar to shuffle(Object[], n, k, RandomStream).

shuffle(boolean[], int, int, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation

Similar to shuffle(Object[], n, k, RandomStream).

shuffle(float[], int, int, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation

Similar to shuffle(Object[], n, k, RandomStream).

shuffle(double[], int, int, RandomStream) - Static method in class umontreal.iro.lecuyer.rng.RandomPermutation

Similar to shuffle(Object[], n, k, RandomStream).

Sim - Class in umontreal.iro.lecuyer.simevents

This static class contains the executive of a discrete-event simulation.

SimProcess - Class in umontreal.iro.lecuyer.simprocs

This abstract class provides process scheduling tools.

SimProcess() - Constructor for class umontreal.iro.lecuyer.simprocs.SimProcess

Constructs a new process without scheduling it and associates this new process with the default simulator; one can get additional knowledge with Simulator static methods.

SimProcess(ProcessSimulator) - Constructor for class umontreal.iro.lecuyer.simprocs.SimProcess

Constructs a new process associated with sim without scheduling it.

simpsonIntegral(MathFunction, double, double, int) - Static method in class umontreal.iro.lecuyer.functions.MathFunctionUtil

Computes and returns an approximation of the integral of func over [a, b], using the Simpson's 1/3 method with numIntervals intervals.

simulator() - Method in class umontreal.iro.lecuyer.simevents.Accumulate

Returns the simulator associated with this statistical probe.

simulator() - Method in class umontreal.iro.lecuyer.simevents.Continuous

Returns the simulator linked to this continuous-time variable.

simulator() - Method in class umontreal.iro.lecuyer.simevents.Event

Returns the simulator linked to this event.

simulator() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat

Returns the simulator associated with this list.

Simulator - Class in umontreal.iro.lecuyer.simevents

Represents the executive of a discrete-event simulator.

Simulator() - Constructor for class umontreal.iro.lecuyer.simevents.Simulator

Constructs a new simulator using a splay tree for the event list.

Simulator(EventList) - Constructor for class umontreal.iro.lecuyer.simevents.Simulator

Constructs a new simulator using eventList for the event list.

simulator() - Method in class umontreal.iro.lecuyer.simprocs.Bin

Returns the current simulator of this continuous-time variable.

size() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

size() - Method in class umontreal.iro.lecuyer.util.BitVector

Returns the length of the BitVector.

size() - Method in class umontreal.iro.lecuyer.util.TransformingList

smax - Variable in class umontreal.iro.lecuyer.gof.GofStat.OutcomeCategoriesChi2

Maximum index for valid expected numbers in the array nbExp.

smin - Variable in class umontreal.iro.lecuyer.gof.GofStat.OutcomeCategoriesChi2

Minimum index for valid expected numbers in the array nbExp.

SmoothingCubicSpline - Class in umontreal.iro.lecuyer.functionfit

Represents a cubic spline with nodes at (x_i, y_i) computed with the smoothing cubic spline algorithm of Schoenberg.

SmoothingCubicSpline(double[], double[], double[], double) - Constructor for class umontreal.iro.lecuyer.functionfit.SmoothingCubicSpline

Constructs a spline with nodes at (x_i, y_i), with weights w_i and smoothing factor ρ = rho.

SmoothingCubicSpline(double[], double[], double) - Constructor for class umontreal.iro.lecuyer.functionfit.SmoothingCubicSpline

Constructs a spline with nodes at (x_i, y_i), with weights = 1 and smoothing factor ρ = rho.

SMScrambleShift - Class in umontreal.iro.lecuyer.hups

This class implements a PointSetRandomization that performs a striped matrix scrambling and adds a random digital shift.

SMScrambleShift(RandomStream) - Constructor for class umontreal.iro.lecuyer.hups.SMScrambleShift

Sets internal variable stream to the given stream.

SobolSequence - Class in umontreal.iro.lecuyer.hups

This class implements digital nets or digital sequences in base 2 formed by the first n = 2^k points of a Sobol' sequence.

SobolSequence(int, int, int) - Constructor for class umontreal.iro.lecuyer.hups.SobolSequence

Constructs a new digital net with n = 2^k points and w output digits, in dimension dim, formed by taking the first n points of the Sobol' sequence.

SobolSequence(int, int) - Constructor for class umontreal.iro.lecuyer.hups.SobolSequence

Constructs a Sobol point set with at least n points and 31 output digits, in dimension dim.

SobolSequence(String, int, int, int) - Constructor for class umontreal.iro.lecuyer.hups.SobolSequence

Constructs a new digital net using the direction numbers provided in file filename.

sort(T[], int, int) - Method in class umontreal.iro.lecuyer.util.BatchSort

sort(T[]) - Method in class umontreal.iro.lecuyer.util.BatchSort

sort(double[][], int, int) - Method in class umontreal.iro.lecuyer.util.BatchSort

sort(double[][]) - Method in class umontreal.iro.lecuyer.util.BatchSort

sort(T[], int, int) - Method in interface umontreal.iro.lecuyer.util.MultiDimSort

Sorts the subarray of a made of the elements with indices from iMin to iMax-1.

sort(T[]) - Method in interface umontreal.iro.lecuyer.util.MultiDimSort

Sorts the entire array.

sort(double[][], int, int) - Method in interface umontreal.iro.lecuyer.util.MultiDimSort

Sorts the subarray of a made of the elements with indices from iMin to iMax-1.

sort(double[][]) - Method in interface umontreal.iro.lecuyer.util.MultiDimSort

Sorts the entire array.

sort(T[], int, int) - Method in class umontreal.iro.lecuyer.util.OneDimSort

sort(T[]) - Method in class umontreal.iro.lecuyer.util.OneDimSort

sort(double[][], int, int) - Method in class umontreal.iro.lecuyer.util.OneDimSort

sort(double[][]) - Method in class umontreal.iro.lecuyer.util.OneDimSort

sort(T[], int, int) - Method in class umontreal.iro.lecuyer.util.SplitSort

sort(T[]) - Method in class umontreal.iro.lecuyer.util.SplitSort

sort(double[][], int, int) - Method in class umontreal.iro.lecuyer.util.SplitSort

sort(double[][]) - Method in class umontreal.iro.lecuyer.util.SplitSort

SplayTree - Class in umontreal.iro.lecuyer.simevents.eventlist

An implementation of EventList using a splay tree.

SplayTree() - Constructor for class umontreal.iro.lecuyer.simevents.eventlist.SplayTree

SplitSort - Class in umontreal.iro.lecuyer.util

This class implements a MultiDimSort that performs a split sort on the arrays.

SplitSort(int) - Constructor for class umontreal.iro.lecuyer.util.SplitSort

Constructs a SplitSort that will use the first d dimensions to sort.

SqrtMathFunction - Class in umontreal.iro.lecuyer.functions

Represents a function computing the square root of another function f (x).

SqrtMathFunction(MathFunction) - Constructor for class umontreal.iro.lecuyer.functions.SqrtMathFunction

Computes and returns the square root of the function func.

SquareMathFunction - Class in umontreal.iro.lecuyer.functions

Represents a function computing (af (x) + b)² for a user-defined function f (x).

SquareMathFunction(MathFunction) - Constructor for class umontreal.iro.lecuyer.functions.SquareMathFunction

Constructs a new square function for function func.

SquareMathFunction(MathFunction, double, double) - Constructor for class umontreal.iro.lecuyer.functions.SquareMathFunction

Constructs a new power function for function func, and constants a and b.

SSJCategorySeriesCollection - Class in umontreal.iro.lecuyer.charts

Stores data used in a CategoryChart.

SSJCategorySeriesCollection() - Constructor for class umontreal.iro.lecuyer.charts.SSJCategorySeriesCollection

SSJInterpretationOracle - Class in umontreal.iro.lecuyer.simprocs

Determines which classes should be interpreted by the DSOL interpreter during process simulation.

SSJInterpretationOracle() - Constructor for class umontreal.iro.lecuyer.simprocs.SSJInterpretationOracle

SSJXYSeriesCollection - Class in umontreal.iro.lecuyer.charts

Stores data used in a XYChart.

SSJXYSeriesCollection() - Constructor for class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection

standardDeviation(double[]) - Method in class umontreal.iro.lecuyer.stat.list.ListOfTallies

For each tally in this list, computes the sample standard deviation, and stores the standard deviations into the array std.

standardDeviation() - Method in class umontreal.iro.lecuyer.stat.Tally

Returns the sample standard deviation of the observations since the last initialization.

start() - Static method in class umontreal.iro.lecuyer.simevents.Sim

Starts the simulation executive.

start() - Method in class umontreal.iro.lecuyer.simevents.Simulator

Starts the simulation executive.

STARTING - Static variable in class umontreal.iro.lecuyer.simprocs.SimProcess

startInteg() - Method in class umontreal.iro.lecuyer.simevents.Continuous

Starts the integration process that will change the state of this variable at each integration step.

startInteg(double) - Method in class umontreal.iro.lecuyer.simevents.Continuous

Same as startInteg, after initializing the variable to val.

state() - Method in class umontreal.iro.lecuyer.simprocs.Condition

Returns the state (true or false) of the condition.

statOnAvail() - Method in class umontreal.iro.lecuyer.simprocs.Bin

Returns the statistical collector for the available tokens on the bin as a function of time.

statOnCapacity() - Method in class umontreal.iro.lecuyer.simprocs.Resource

Returns the statistical collector that measures the evolution of the capacity of the resource as a function of time.

statOnSojourn() - Method in class umontreal.iro.lecuyer.simprocs.Resource

Returns the statistical collector for the sojourn times of the UserRecord for this resource.

statOnUtil() - Method in class umontreal.iro.lecuyer.simprocs.Resource

Returns the statistical collector for the utilization of the resource (number of units busy) as a function of time.

StatProbe - Class in umontreal.iro.lecuyer.stat

The objects of this class are statistical probes or collectors, which are elementary devices for collecting statistics.

StatProbe() - Constructor for class umontreal.iro.lecuyer.stat.StatProbe

statSize() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat

Returns the statistical probe on the evolution of the size of the list as a function of the simulation time.

statSojourn() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat

Returns the statistical probe on the sojourn times of the objects in the list.

StochasticProcess - Class in umontreal.iro.lecuyer.stochprocess

Abstract base class for a stochastic process {X(t) : t >= 0} sampled (or observed) at a finite number of time points, 0 = t₀ < t₁ < ^...

StochasticProcess() - Constructor for class umontreal.iro.lecuyer.stochprocess.StochasticProcess

stop() - Static method in class umontreal.iro.lecuyer.simevents.Sim

Tells the simulation executive to stop as soon as it takes control, and to return control to the program that called start.

stop() - Method in class umontreal.iro.lecuyer.simevents.Simulator

Tells the simulation executive to stop as soon as it takes control, and to return control to the program that called start.

stopInteg() - Method in class umontreal.iro.lecuyer.simevents.Continuous

Stops the integration process for this continuous variable.

stripedMatrixScramble(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNet

Applies the striped matrix scramble proposed by Owen.

stripedMatrixScramble(RandomStream) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2

stripedMatrixScrambleFaurePermutAll(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNet

Similar to stripedMatrixScramble except that the elements on and under the diagonal of each matrix M_j are chosen as in leftMatrixScrambleFaurePermut.

stripedMatrixScrambleFaurePermutAll(RandomStream, int) - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2

StudentDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the Student-t distribution with n degrees of freedom, where n is a positive integer.

StudentDist(int) - Constructor for class umontreal.iro.lecuyer.probdist.StudentDist

Constructs a StudentDist object with n degrees of freedom.

StudentGen - Class in umontreal.iro.lecuyer.randvar

This class implements methods for generating random variates from the Student distribution with n > 0 degrees of freedom.

StudentGen(RandomStream, int) - Constructor for class umontreal.iro.lecuyer.randvar.StudentGen

Creates a Student random variate generator with n degrees of freedom, using stream s.

StudentGen(RandomStream, StudentDist) - Constructor for class umontreal.iro.lecuyer.randvar.StudentGen

Creates a new generator for the Student distribution dist and stream s.

StudentPolarGen - Class in umontreal.iro.lecuyer.randvar

This class implements Student random variate generators using the polar method of.

StudentPolarGen(RandomStream, int) - Constructor for class umontreal.iro.lecuyer.randvar.StudentPolarGen

Creates a Student random variate generator with n degrees of freedom, using stream s.

StudentPolarGen(RandomStream, StudentDist) - Constructor for class umontreal.iro.lecuyer.randvar.StudentPolarGen

Creates a new generator for the Student distribution dist and stream s.

subList(int, int) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

subSequence(int, int) - Method in class umontreal.iro.lecuyer.util.PrintfFormat

SubsetOfPointSet - Class in umontreal.iro.lecuyer.hups

This container class permits one to select a subset of a point set.

SubsetOfPointSet(PointSet) - Constructor for class umontreal.iro.lecuyer.hups.SubsetOfPointSet

Constructs a new PointSet object, initially identical to P, and from which a subset of the points and/or a subset of the coordinates is to be extracted.

sum() - Method in class umontreal.iro.lecuyer.simevents.Accumulate

sum(double[]) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

For each probe in the list, computes the sum by calling sum, and stores the results into the array s.

sum() - Method in class umontreal.iro.lecuyer.stat.StatProbe

Returns the sum cumulated so far for this probe.

SUSPECTP - Static variable in class umontreal.iro.lecuyer.gof.GofFormat

Environment variable used in formatp1 to determine which p-values should be marked as suspect when printing test results.

suspend(SimProcess) - Method in class umontreal.iro.lecuyer.simprocs.DSOLProcessSimulator

suspend(SimProcess) - Method in class umontreal.iro.lecuyer.simprocs.ProcessSimulator

Suspends process.

suspend() - Method in class umontreal.iro.lecuyer.simprocs.SimProcess

This method can only be invoked for the EXECUTING or a DELAYED process.

suspend(SimProcess) - Method in class umontreal.iro.lecuyer.simprocs.ThreadProcessSimulator

SUSPENDED - Static variable in class umontreal.iro.lecuyer.simprocs.SimProcess

The process is not executing and will have to be reactivated by another process or event later on.

SystemTimeChrono - Class in umontreal.iro.lecuyer.util

Extends the AbstractChrono class to compute the total system time using Java's builtin System.nanoTime.

SystemTimeChrono() - Constructor for class umontreal.iro.lecuyer.util.SystemTimeChrono

Constructs a new chrono object and initializes it to zero.

T

TableFormat - Class in umontreal.iro.lecuyer.util

This class provides methods to format arrays and matrices into Strings in different styles.

take(int) - Method in class umontreal.iro.lecuyer.simprocs.Bin

The executing process invoking this method requests n tokens from this bin.

Tally - Class in umontreal.iro.lecuyer.stat

This type of statistical collector takes a sequence of real-valued observations and can return the average, the variance, a confidence interval for the theoretical mean, etc.

Tally() - Constructor for class umontreal.iro.lecuyer.stat.Tally

Constructs a new unnamed Tally statistical probe.

Tally(String) - Constructor for class umontreal.iro.lecuyer.stat.Tally

Constructs a new Tally statistical probe with name name.

TallyStore - Class in umontreal.iro.lecuyer.stat

This class is a variant of Tally for which the individual observations are stored in a list implemented as a DoubleArrayList.

TallyStore() - Constructor for class umontreal.iro.lecuyer.stat.TallyStore

Constructs a new TallyStore statistical probe.

TallyStore(String) - Constructor for class umontreal.iro.lecuyer.stat.TallyStore

Constructs a new TallyStore statistical probe with name name.

TallyStore(int) - Constructor for class umontreal.iro.lecuyer.stat.TallyStore

Constructs a new TallyStore statistical probe with given initial capacity capacity for its associated array.

TallyStore(String, int) - Constructor for class umontreal.iro.lecuyer.stat.TallyStore

Constructs a new TallyStore statistical probe with name name and given initial capacity capacity for its associated array.

TallyStore(DoubleArrayList) - Constructor for class umontreal.iro.lecuyer.stat.TallyStore

Constructs a new TallyStore statistical probe with given associated array.

TEN_NEG_POW - Static variable in class umontreal.iro.lecuyer.util.Num

Contains the precomputed negative powers of 10.

TESTNAMES - Static variable in class umontreal.iro.lecuyer.gof.GofFormat

Name of each testType test.

tests(DoubleArrayList, double[]) - Static method in class umontreal.iro.lecuyer.gof.GofFormat

Computes all EDF test statistics to compare the empirical distribution of U₍₀₎,..., U_(N-1) with the uniform distribution, assuming that these sorted observations are in sortedData.

tests(DoubleArrayList, ContinuousDistribution, double[]) - Static method in class umontreal.iro.lecuyer.gof.GofFormat

The observations V are in data, not necessarily sorted, and their empirical distribution is compared with the continuous distribution dist.

tetragamma(double) - Static method in class umontreal.iro.lecuyer.util.Num

Returns the value of the tetragamma function d²ψ(x)/d²x, the second derivative of the digamma function, evaluated at x.

TextDataReader - Class in umontreal.iro.lecuyer.util

Provides static methods to read data from text files.

TextDataReader() - Constructor for class umontreal.iro.lecuyer.util.TextDataReader

ThreadCPUTimeChrono - Class in umontreal.iro.lecuyer.util

Extends the AbstractChrono class to compute the CPU time for a single thread.

ThreadCPUTimeChrono() - Constructor for class umontreal.iro.lecuyer.util.ThreadCPUTimeChrono

Constructs a ThreadCPUTimeChrono object associated with current thread and initializes it to zero.

ThreadCPUTimeChrono(Thread) - Constructor for class umontreal.iro.lecuyer.util.ThreadCPUTimeChrono

Constructs a ThreadCPUTimeChrono object associated with the given Thread variable and initializes it to zero.

ThreadProcessSimulator - Class in umontreal.iro.lecuyer.simprocs

Represents a process simulator using Java threads for process synchronization.

ThreadProcessSimulator() - Constructor for class umontreal.iro.lecuyer.simprocs.ThreadProcessSimulator

Creates a new ThreadProcessSimulator variable.

time() - Method in class umontreal.iro.lecuyer.simevents.Event

Returns the (planned) time of occurence of this event.

time() - Static method in class umontreal.iro.lecuyer.simevents.Sim

Returns the current value of the simulation clock.

time() - Method in class umontreal.iro.lecuyer.simevents.Simulator

Returns the current value of the simulation clock.

TimeUnit - Enum in umontreal.iro.lecuyer.util

toArray() - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

toArray(T[]) - Method in class umontreal.iro.lecuyer.stat.list.ListOfStatProbes

toCSV(double[]...) - Static method in class umontreal.iro.lecuyer.charts.PlotFormat

Stores data tables data into a String with format CSV (comma-separated value tabular data).

toCSV(XYSeriesCollection) - Static method in class umontreal.iro.lecuyer.charts.PlotFormat

Stores series collection data into a String with format CSV (comma-separated value tabular data).

toCustomizedFormat(String, String, String, String, int, double[]...) - Static method in class umontreal.iro.lecuyer.charts.PlotFormat

Stores data tables data into a String with customized format.

toCustomizedFormat(String, String, String, String, int, XYSeriesCollection) - Static method in class umontreal.iro.lecuyer.charts.PlotFormat

Stores data tables data into a String with customized format from an XYSeriesCollection variable.

toGNUPlot(double[]...) - Static method in class umontreal.iro.lecuyer.charts.PlotFormat

Stores data tables data into a String, with format understandable by GNUPlot.

toGNUPlot(XYSeriesCollection) - Static method in class umontreal.iro.lecuyer.charts.PlotFormat

Stores series collection data into a String, with format understandable by GNUPlot.

toLatex(double) - Method in class umontreal.iro.lecuyer.charts.Axis

Formats and returns a string containing a L^ATEX-compatible source code which represents this axis and its parameters.

toLatex(double, double) - Method in class umontreal.iro.lecuyer.charts.BoxChart

NOT IMPLEMENTED.

toLatex(double, double, double, double) - Method in class umontreal.iro.lecuyer.charts.BoxSeriesCollection

NOT IMPLEMENTED: To do.

toLatex(double, double) - Method in class umontreal.iro.lecuyer.charts.CategoryChart

Transforms the chart into L^ATEX form and returns it as a String.

toLatex(double, double) - Method in class umontreal.iro.lecuyer.charts.EmpiricalChart

toLatex(double, double, double, double, double, double, double, double) - Method in class umontreal.iro.lecuyer.charts.EmpiricalSeriesCollection

toLatex(double, double) - Method in class umontreal.iro.lecuyer.charts.HistogramChart

toLatex(double, double, double, double, double, double, double, double) - Method in class umontreal.iro.lecuyer.charts.HistogramSeriesCollection

toLatex(double, double) - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart

Same as in XYChart.

toLatex(double, double) - Method in class umontreal.iro.lecuyer.charts.ScatterChart

toLatex(double, double, double, double) - Method in class umontreal.iro.lecuyer.charts.SSJCategorySeriesCollection

Formats and returns a string containing a L^ATEX-compatible source code which represents this data series collection.

toLatex(double, double, double, double, double, double, double, double) - Method in class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection

Formats and returns a string containing a L^ATEX-compatible source code which represents this data series collection.

toLatex(double, double) - Method in class umontreal.iro.lecuyer.charts.XYChart

Exports the chart to a L^ATEX source code using PGF/TikZ.

toLatex(double, double) - Method in class umontreal.iro.lecuyer.charts.XYLineChart

toLatex(double, double, double, double, double, double, double, double) - Method in class umontreal.iro.lecuyer.charts.XYListSeriesCollection

toLatexCdf(int, int) - Method in class umontreal.iro.lecuyer.charts.ContinuousDistChart

Exports a chart of the cdf to a L^ATEX source code using PGF/TikZ.

toLatexCdf(int, int) - Method in class umontreal.iro.lecuyer.charts.DiscreteDistIntChart

Exports a chart of the cumulative probability to a L^ATEX source code using PGF/TikZ.

toLatexDensity(int, int) - Method in class umontreal.iro.lecuyer.charts.ContinuousDistChart

Similar to toLatexCdf, but for the probability density instead of the cdf.

toLatexFile(String, double, double) - Method in class umontreal.iro.lecuyer.charts.XYChart

Transforms the chart to L^ATEX form and writes it in file fileName.

toLatexProb(int, int) - Method in class umontreal.iro.lecuyer.charts.DiscreteDistIntChart

Similar to toLatexCdf, but for the probability instead of the cdf.

toNet() - Method in class umontreal.iro.lecuyer.hups.DigitalSequence

Transforms this digital sequence into a digital net without changing the coordinates of the points.

toNet() - Method in class umontreal.iro.lecuyer.hups.DigitalSequenceBase2

Transforms this digital sequence into a digital net without changing the coordinates of the points.

toNetShiftCj() - Method in class umontreal.iro.lecuyer.hups.DigitalSequence

Transforms this digital sequence into a digital net by adding one dimension and shifting all coordinates by one position.

toNetShiftCj() - Method in class umontreal.iro.lecuyer.hups.DigitalSequenceBase2

Transforms this digital sequence into a digital net by adding one dimension and shifting all coordinates by one position.

toString() - Method in class umontreal.iro.lecuyer.charts.BoxSeriesCollection

Returns in a String all data contained in the current object.

toString() - Method in class umontreal.iro.lecuyer.charts.SSJCategorySeriesCollection

Returns in a String all data contained in the current object.

toString() - Method in class umontreal.iro.lecuyer.charts.SSJXYSeriesCollection

Returns in a String all data contained in the current object.

toString() - Method in class umontreal.iro.lecuyer.functionfit.LeastSquares

Calls toString with the associated points.

toString(double[], double[]) - Static method in class umontreal.iro.lecuyer.functionfit.PolInterp

Makes a string representation of a set of points.

toString() - Method in class umontreal.iro.lecuyer.functionfit.PolInterp

toString() - Method in class umontreal.iro.lecuyer.functions.Polynomial

toString() - Method in class umontreal.iro.lecuyer.gof.GofStat.OutcomeCategoriesChi2

Provides a report on the categories.

toString() - Method in class umontreal.iro.lecuyer.hups.AntitheticPointSet

toString() - Method in class umontreal.iro.lecuyer.hups.BakerTransformedPointSet

toString() - Method in class umontreal.iro.lecuyer.hups.CachedPointSet

toString() - Method in class umontreal.iro.lecuyer.hups.ContainerPointSet

toString() - Method in class umontreal.iro.lecuyer.hups.CycleBasedPointSet

toString() - Method in class umontreal.iro.lecuyer.hups.DigitalNet

toString() - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2

toString() - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2FromFile

toString() - Method in class umontreal.iro.lecuyer.hups.DigitalNetFromFile

toString() - Method in class umontreal.iro.lecuyer.hups.F2wCycleBasedLFSR

toString() - Method in class umontreal.iro.lecuyer.hups.F2wCycleBasedPolyLCG

toString() - Method in class umontreal.iro.lecuyer.hups.F2wNetLFSR

toString() - Method in class umontreal.iro.lecuyer.hups.F2wNetPolyLCG

toString() - Method in class umontreal.iro.lecuyer.hups.F2wStructure

This method returns a string containing the polynomial P(z) and the stepping parameter.

toString() - Method in class umontreal.iro.lecuyer.hups.FaureSequence

toString() - Method in class umontreal.iro.lecuyer.hups.KorobovLattice

toString() - Method in class umontreal.iro.lecuyer.hups.LCGPointSet

toString() - Method in class umontreal.iro.lecuyer.hups.NiedSequenceBase2

toString() - Method in class umontreal.iro.lecuyer.hups.NiedXingSequenceBase2

toString() - Method in class umontreal.iro.lecuyer.hups.PaddedPointSet

toString() - Method in class umontreal.iro.lecuyer.hups.PointSet

Formats a string that contains information about the point set.

toString() - Method in class umontreal.iro.lecuyer.hups.RandShiftedPointSet

toString() - Method in class umontreal.iro.lecuyer.hups.Rank1Lattice

toString() - Method in class umontreal.iro.lecuyer.hups.SobolSequence

toString() - Method in class umontreal.iro.lecuyer.hups.SubsetOfPointSet

toString() - Method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDist

toString() - Method in class umontreal.iro.lecuyer.probdist.AndersonDarlingDistQuick

toString() - Method in class umontreal.iro.lecuyer.probdist.BetaDist

toString() - Method in class umontreal.iro.lecuyer.probdist.BetaSymmetricalDist

toString() - Method in class umontreal.iro.lecuyer.probdist.BinomialDist

toString() - Method in class umontreal.iro.lecuyer.probdist.CauchyDist

toString() - Method in class umontreal.iro.lecuyer.probdist.ChiDist

toString() - Method in class umontreal.iro.lecuyer.probdist.ChiSquareDist

toString() - Method in class umontreal.iro.lecuyer.probdist.ChiSquareNoncentralDist

toString() - Method in class umontreal.iro.lecuyer.probdist.CramerVonMisesDist

toString() - Method in class umontreal.iro.lecuyer.probdist.DiscreteDistribution

Returns a String containing information about the current distribution.

toString() - Method in class umontreal.iro.lecuyer.probdist.EmpiricalDist

Returns a String containing information about the current distribution.

toString() - Method in class umontreal.iro.lecuyer.probdist.ErlangDist

toString() - Method in class umontreal.iro.lecuyer.probdist.ExponentialDist

toString() - Method in class umontreal.iro.lecuyer.probdist.ExtremeValueDist

Deprecated.

toString() - Method in class umontreal.iro.lecuyer.probdist.FatigueLifeDist

toString() - Method in class umontreal.iro.lecuyer.probdist.FisherFDist

toString() - Method in class umontreal.iro.lecuyer.probdist.FoldedNormalDist

Returns a String containing information about the current distribution.

toString() - Method in class umontreal.iro.lecuyer.probdist.FrechetDist

toString() - Method in class umontreal.iro.lecuyer.probdist.GammaDist

toString() - Method in class umontreal.iro.lecuyer.probdist.GeometricDist

toString() - Method in class umontreal.iro.lecuyer.probdist.GumbelDist

toString() - Method in class umontreal.iro.lecuyer.probdist.HalfNormalDist

Returns a String containing information about the current distribution.

toString() - Method in class umontreal.iro.lecuyer.probdist.HyperbolicSecantDist

toString() - Method in class umontreal.iro.lecuyer.probdist.HypergeometricDist

toString() - Method in class umontreal.iro.lecuyer.probdist.InverseDistFromDensity

Returns a String containing information about the current distribution.

toString() - Method in class umontreal.iro.lecuyer.probdist.InverseGaussianDist

toString() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSBDist

toString() - Method in class umontreal.iro.lecuyer.probdist.JohnsonSUDist

toString() - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovDist

toString() - Method in class umontreal.iro.lecuyer.probdist.KolmogorovSmirnovPlusDist

toString() - Method in class umontreal.iro.lecuyer.probdist.LaplaceDist

toString() - Method in class umontreal.iro.lecuyer.probdist.LogarithmicDist

toString() - Method in class umontreal.iro.lecuyer.probdist.LogisticDist

toString() - Method in class umontreal.iro.lecuyer.probdist.LoglogisticDist

toString() - Method in class umontreal.iro.lecuyer.probdist.LognormalDist

toString() - Method in class umontreal.iro.lecuyer.probdist.NakagamiDist

Returns a String containing information about the current distribution.

toString() - Method in class umontreal.iro.lecuyer.probdist.NegativeBinomialDist

toString() - Method in class umontreal.iro.lecuyer.probdist.NormalDist

toString() - Method in class umontreal.iro.lecuyer.probdist.NormalInverseGaussianDist

toString() - Method in class umontreal.iro.lecuyer.probdist.ParetoDist

toString() - Method in class umontreal.iro.lecuyer.probdist.PascalDist

toString() - Method in class umontreal.iro.lecuyer.probdist.Pearson5Dist

toString() - Method in class umontreal.iro.lecuyer.probdist.Pearson6Dist

toString() - Method in class umontreal.iro.lecuyer.probdist.PiecewiseLinearEmpiricalDist

Returns a String containing information about the current distribution.

toString() - Method in class umontreal.iro.lecuyer.probdist.PoissonDist

toString() - Method in class umontreal.iro.lecuyer.probdist.PowerDist

toString() - Method in class umontreal.iro.lecuyer.probdist.RayleighDist

toString() - Method in class umontreal.iro.lecuyer.probdist.StudentDist

toString() - Method in class umontreal.iro.lecuyer.probdist.TriangularDist

Returns a String containing information about the current distribution.

toString() - Method in class umontreal.iro.lecuyer.probdist.TruncatedDist

Returns a String containing information about the current distribution.

toString() - Method in class umontreal.iro.lecuyer.probdist.UniformDist

toString() - Method in class umontreal.iro.lecuyer.probdist.UniformIntDist

toString() - Method in class umontreal.iro.lecuyer.probdist.WatsonGDist

toString() - Method in class umontreal.iro.lecuyer.probdist.WatsonUDist

toString() - Method in class umontreal.iro.lecuyer.probdist.WeibullDist

toString() - Method in class umontreal.iro.lecuyer.randvarmulti.IIDMultivariateGen

Returns a string representation of the generator.

toString() - Method in class umontreal.iro.lecuyer.rng.AntitheticStream

Returns a string starting with "Antithetic of " and finishing with the result of the call to the toString method of the generator.

toString() - Method in class umontreal.iro.lecuyer.rng.BakerTransformedStream

Returns a string starting with "Baker transformation of " and finishing with the result of the call to the toString method of the generator.

toString() - Method in class umontreal.iro.lecuyer.rng.BasicRandomStreamFactory

toString() - Method in class umontreal.iro.lecuyer.rng.F2NL607

toString() - Method in class umontreal.iro.lecuyer.rng.GenF2w32

toString() - Method in class umontreal.iro.lecuyer.rng.LFSR113

toString() - Method in class umontreal.iro.lecuyer.rng.LFSR258

toString() - Method in class umontreal.iro.lecuyer.rng.MRG31k3p

toString() - Method in class umontreal.iro.lecuyer.rng.MRG32k3a

Returns a string containing the name and the current state C_g of this stream.

toString() - Method in class umontreal.iro.lecuyer.rng.MRG32k3aL

toString() - Method in class umontreal.iro.lecuyer.rng.MT19937

toString() - Method in class umontreal.iro.lecuyer.rng.RandMrg

Deprecated.

toString() - Method in interface umontreal.iro.lecuyer.rng.RandomStream

Returns a string containing the current state of this stream.

toString() - Method in class umontreal.iro.lecuyer.rng.RandomStreamBase

toString() - Method in exception umontreal.iro.lecuyer.rng.RandomStreamInstantiationException

Returns a short description of the exception.

toString() - Method in class umontreal.iro.lecuyer.rng.RandomStreamManager

toString() - Method in class umontreal.iro.lecuyer.rng.RandRijndael

toString() - Method in class umontreal.iro.lecuyer.rng.WELL1024

toString() - Method in class umontreal.iro.lecuyer.rng.WELL512

toString() - Method in class umontreal.iro.lecuyer.rng.WELL607

toString() - Method in class umontreal.iro.lecuyer.simevents.eventlist.BinaryTree

toString() - Method in class umontreal.iro.lecuyer.simevents.eventlist.DoublyLinked

toString() - Method in class umontreal.iro.lecuyer.simevents.eventlist.Henriksen

toString() - Method in class umontreal.iro.lecuyer.simevents.eventlist.RedblackTree

toString() - Method in class umontreal.iro.lecuyer.simevents.eventlist.SplayTree

toString() - Method in class umontreal.iro.lecuyer.simevents.ListWithStat.Node

toString() - Method in class umontreal.iro.lecuyer.stat.TallyStore

Returns the observations stored in this object as a String.

toString() - Method in class umontreal.iro.lecuyer.util.BitMatrix

Creates a String containing all the data of the BitMatrix.

toString() - Method in class umontreal.iro.lecuyer.util.BitVector

Returns a string containing all the bits of the BitVector, starting with the highest order bit and finishing with the lowest order bit.

toString(double[][]) - Static method in class umontreal.iro.lecuyer.util.DMatrix

Returns matrix M as a string.

toString() - Method in class umontreal.iro.lecuyer.util.DMatrix

Creates a String containing all the data of the DMatrix.

toString() - Method in class umontreal.iro.lecuyer.util.PrintfFormat

Converts the buffer into a String.

toString() - Method in enum umontreal.iro.lecuyer.util.TimeUnit

Calls getLongName.

toStringDetailed() - Method in class umontreal.iro.lecuyer.hups.DigitalNetBase2FromFile

Writes the parameters and the generating matrices of this digital net to a string.

toStringDetailed() - Method in class umontreal.iro.lecuyer.hups.DigitalNetFromFile

Writes the parameters and the generating matrices of this digital net to a string.

toStringFull() - Method in class umontreal.iro.lecuyer.rng.MRG32k3a

Returns a string containing the name of this stream and the values of all its internal variables.

toStringFull() - Method in class umontreal.iro.lecuyer.rng.MRG32k3aL

toStringFull() - Method in class umontreal.iro.lecuyer.rng.RandMrg

Deprecated.

Returns a string containing the name of this stream and the values of all its internal variables.

TransformingList<OE,IE> - Class in umontreal.iro.lecuyer.util

Represents a list that dynamically transforms the elements of another list.

TransformingList(List<IE>) - Constructor for class umontreal.iro.lecuyer.util.TransformingList

Creates a new transforming list wrapping the inner list fromList.

transpose() - Method in class umontreal.iro.lecuyer.util.BitMatrix

Returns the transposed matrix.

transpose() - Method in class umontreal.iro.lecuyer.util.DMatrix

Returns the transposed matrix.

TriangularDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the triangular distribution with domain [a, b] and mode (or shape parameter) m, where a <= m <= b.

TriangularDist() - Constructor for class umontreal.iro.lecuyer.probdist.TriangularDist

Constructs a TriangularDist object with default parameters a = 0, b = 1, and m = 0.5.

TriangularDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.TriangularDist

Constructs a TriangularDist object with parameters a = 0 , b = 1 and m = m.

TriangularDist(double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.TriangularDist

Constructs a TriangularDist object with parameters a, b and m.

TriangularGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators for the triangular distribution.

TriangularGen(RandomStream, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.TriangularGen

Creates a triangular random variate generator over the interval (a, b), with parameter m, using stream s.

TriangularGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.TriangularGen

Creates a triangular random variate generator over the interval (0, 1), with parameter m, using stream s.

TriangularGen(RandomStream, TriangularDist) - Constructor for class umontreal.iro.lecuyer.randvar.TriangularGen

Creates a new generator for the triangular distribution dist and stream s.

trigamma(double) - Static method in class umontreal.iro.lecuyer.util.Num

Returns the value of the trigamma function dψ(x)/dx, the derivative of the digamma function, evaluated at x.

TruncatedDist - Class in umontreal.iro.lecuyer.probdist

This container class takes an arbitrary continuous distribution and truncates it to an interval [a, b], where a and b can be finite or infinite.

TruncatedDist(ContinuousDistribution, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.TruncatedDist

Constructs a new distribution by truncating distribution dist to the interval [a, b].

TruncatedRandomStream - Class in umontreal.iro.lecuyer.rng

Represents a container random stream generating numbers in an interval (a, b) instead of in (0, 1), where 0 <= a < b <= 1, by using the contained stream.

TruncatedRandomStream(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.rng.TruncatedRandomStream

TWOEXP - Static variable in class umontreal.iro.lecuyer.util.Num

Contains the precomputed positive powers of 2.

type - Variable in class umontreal.iro.lecuyer.charts.CustomHistogramDataset

The histogram type.

U

umontreal.iro.lecuyer.charts - package umontreal.iro.lecuyer.charts

This package contains classes to produce charts used in the Java software developed in the simulation laboratory of the DIRO, at the Université de Montréal.

umontreal.iro.lecuyer.functionfit - package umontreal.iro.lecuyer.functionfit

umontreal.iro.lecuyer.functions - package umontreal.iro.lecuyer.functions

umontreal.iro.lecuyer.gof - package umontreal.iro.lecuyer.gof

This package contains tools for performing univariate goodness-of-fit (GOF) statistical tests.

umontreal.iro.lecuyer.hups - package umontreal.iro.lecuyer.hups

Monte Carlo and quasi-Monte Carlo

umontreal.iro.lecuyer.probdist - package umontreal.iro.lecuyer.probdist

This package contains a set of Java classes providing methods to compute mass, density, distribution, complementary distribution, and inverse distribution functions for some discrete and continuous probability distributions.

umontreal.iro.lecuyer.probdistmulti - package umontreal.iro.lecuyer.probdistmulti

This package contains Java classes providing methods to compute mass, density, distribution and complementary distribution functions for some multi-dimensional discrete and continuous probability distributions.

umontreal.iro.lecuyer.randvar - package umontreal.iro.lecuyer.randvar

This package provides a collection of classes for non-uniform random variate generation, primarily from standard distributions.

umontreal.iro.lecuyer.randvarmulti - package umontreal.iro.lecuyer.randvarmulti

This package provides a collection of classes for non-uniform random variate generation, very similar to randvar, but for multivariate distributions.

umontreal.iro.lecuyer.rng - package umontreal.iro.lecuyer.rng

This package offers the basic facilities for generating uniform random numbers.

umontreal.iro.lecuyer.simevents - package umontreal.iro.lecuyer.simevents

This package provides the simulation clock and tools to manage the future events list.

umontreal.iro.lecuyer.simevents.eventlist - package umontreal.iro.lecuyer.simevents.eventlist

This package provides different kinds of event list implementations.

umontreal.iro.lecuyer.simprocs - package umontreal.iro.lecuyer.simprocs

Process-oriented simulation is managed through this package.

umontreal.iro.lecuyer.stat - package umontreal.iro.lecuyer.stat

This package provides elementary tools for collecting statistics and computing confidence intervals.

umontreal.iro.lecuyer.stat.list - package umontreal.iro.lecuyer.stat.list

Provides support for lists of statistical probes.

umontreal.iro.lecuyer.stochprocess - package umontreal.iro.lecuyer.stochprocess

This package provides classes to define stochastic processes {X(t), t≥0}, and to simulate their sample paths at a finite number of (discrete) observation times t₀≤t₁≤ ^...

umontreal.iro.lecuyer.util - package umontreal.iro.lecuyer.util

This package contains utility classes used in the Java software developed in the simulation laboratory of the DIRO, at the Université de Montréal.

UniformDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the uniform distribution over the interval [a, b].

UniformDist() - Constructor for class umontreal.iro.lecuyer.probdist.UniformDist

Constructs a uniform distribution over the interval (a, b) = (0, 1).

UniformDist(double, double) - Constructor for class umontreal.iro.lecuyer.probdist.UniformDist

Constructs a uniform distribution over the interval (a, b).

UniformGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators for the (continuous) uniform distribution over the interval (a, b), where a and b are real numbers with a < b.

UniformGen(RandomStream, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.UniformGen

Creates a uniform random variate generator over the interval (a, b), using stream s.

UniformGen(RandomStream) - Constructor for class umontreal.iro.lecuyer.randvar.UniformGen

Creates a uniform random variate generator over the interval (0, 1), using stream s.

UniformGen(RandomStream, UniformDist) - Constructor for class umontreal.iro.lecuyer.randvar.UniformGen

Creates a new generator for the uniform distribution dist and stream s.

UniformIntDist - Class in umontreal.iro.lecuyer.probdist

Extends the class DiscreteDistributionInt for the discrete uniform distribution over the range [i, j].

UniformIntDist(int, int) - Constructor for class umontreal.iro.lecuyer.probdist.UniformIntDist

Constructs a discrete uniform distribution over the interval [i, j].

UniformIntGen - Class in umontreal.iro.lecuyer.randvar

This class implements a random variate generator for the uniform distribution over integers, over the interval [i, j].

UniformIntGen(RandomStream, int, int) - Constructor for class umontreal.iro.lecuyer.randvar.UniformIntGen

Creates a uniform random variate generator over the integers in the closed interval [i, j], using stream s.

UniformIntGen(RandomStream, UniformIntDist) - Constructor for class umontreal.iro.lecuyer.randvar.UniformIntGen

Creates a new generator for the distribution dist, using stream s.

unifTransform(DoubleArrayList, ContinuousDistribution) - Static method in class umontreal.iro.lecuyer.gof.GofStat

Applies the transformation U_i = F(V_i) for i = 0, 1,…, N - 1, where F is a continuous distribution function, and returns the result as an array of length N.

unifTransform(DoubleArrayList, DiscreteDistribution) - Static method in class umontreal.iro.lecuyer.gof.GofStat

Applies the transformation U_i = F(V_i) for i = 0, 1,…, N - 1, where F is a discrete distribution function, and returns the result as an array of length N.

unrandomize() - Method in class umontreal.iro.lecuyer.hups.DigitalNet

unrandomize() - Method in class umontreal.iro.lecuyer.hups.PaddedPointSet

unrandomize() - Method in class umontreal.iro.lecuyer.hups.PointSet

By default, this method simply calls clearRandomShift().

UnuranContinuous - Class in umontreal.iro.lecuyer.randvar

This class permits one to create continuous univariate distribution using UNURAN via its string API.

UnuranContinuous(RandomStream, String) - Constructor for class umontreal.iro.lecuyer.randvar.UnuranContinuous

Same as UnuranContinuous(s, s, genStr).

UnuranContinuous(RandomStream, RandomStream, String) - Constructor for class umontreal.iro.lecuyer.randvar.UnuranContinuous

Constructs a new continuous random number generator using the UNURAN generator specification string genStr, main stream s, and auxiliary stream aux.

UnuranDiscreteInt - Class in umontreal.iro.lecuyer.randvar

This class permits one to create a discrete univariate distribution using UNURAN via its string API.

UnuranDiscreteInt(RandomStream, String) - Constructor for class umontreal.iro.lecuyer.randvar.UnuranDiscreteInt

Same as UnuranDiscreteInt (s, s, genStr).

UnuranDiscreteInt(RandomStream, RandomStream, String) - Constructor for class umontreal.iro.lecuyer.randvar.UnuranDiscreteInt

Constructs a new discrete random number generator using the UNURAN generator specification string genStr, main stream s, and auxiliary stream aux.

UnuranEmpirical - Class in umontreal.iro.lecuyer.randvar

This class permits one to create generators for empirical and quasi-empirical univariate distributions using UNURAN via its string interface.

UnuranEmpirical(RandomStream, String) - Constructor for class umontreal.iro.lecuyer.randvar.UnuranEmpirical

Constructs a new empirical univariate generator using the specification string genStr and stream s.

UnuranEmpirical(RandomStream, RandomStream, String) - Constructor for class umontreal.iro.lecuyer.randvar.UnuranEmpirical

Constructs a new empirical univariate generator using the specification string genStr, with main stream s and auxiliary stream aux.

UnuranEmpirical(RandomStream, PiecewiseLinearEmpiricalDist, String) - Constructor for class umontreal.iro.lecuyer.randvar.UnuranEmpirical

Same as UnuranEmpirical(s, s, dist, genStr).

UnuranEmpirical(RandomStream, RandomStream, PiecewiseLinearEmpiricalDist, String) - Constructor for class umontreal.iro.lecuyer.randvar.UnuranEmpirical

Same as UnuranEmpirical(s, aux, genStr), but reading the observations from the empirical distribution dist.

UnuranException - Exception in umontreal.iro.lecuyer.randvar

This type of unchecked exception is thrown when an error occurs inside the UNURAN package.

UnuranException() - Constructor for exception umontreal.iro.lecuyer.randvar.UnuranException

Constructs a new generic UNURAN exception.

UnuranException(String) - Constructor for exception umontreal.iro.lecuyer.randvar.UnuranException

Constructs a UNURAN exception with the error message message

update() - Method in class umontreal.iro.lecuyer.simevents.Accumulate

Updates the accumulator using the last value passed to update.

update(double) - Method in class umontreal.iro.lecuyer.simevents.Accumulate

Gives a new observation x to the statistical collector.

UserRecord - Class in umontreal.iro.lecuyer.simprocs

This class represents a record object to store information related to the request of a process for a Resource or for Bin tokens, or when a process waits for a Condition.

V

value() - Method in class umontreal.iro.lecuyer.simevents.Continuous

Returns the current value of this continuous-time variable.

valueOf(String) - Static method in enum umontreal.iro.lecuyer.simevents.ContinuousState.IntegMethod

Returns the enum constant of this type with the specified name.

valueOf(Class<T>, String) - Static method in class umontreal.iro.lecuyer.util.Introspection

Returns the field of class cls corresponding to the name name.

valueOf(String) - Static method in enum umontreal.iro.lecuyer.util.TimeUnit

Returns the enum constant of this type with the specified name.

valueOfIgnoreCase(Class<T>, String) - Static method in class umontreal.iro.lecuyer.util.Introspection

Similar to valueOf (cls, name), with case insensitive field name look-up.

values() - Static method in enum umontreal.iro.lecuyer.simevents.ContinuousState.IntegMethod

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum umontreal.iro.lecuyer.util.TimeUnit

Returns an array containing the constants of this enum type, in the order they are declared.

variance(double[]) - Method in class umontreal.iro.lecuyer.stat.list.ListOfTallies

For each tally in this list, computes the sample variance, and stores the variances into the array v.

variance() - Method in class umontreal.iro.lecuyer.stat.Tally

Returns the sample variance of the observations since the last initialization.

VarianceGammaProcess - Class in umontreal.iro.lecuyer.stochprocess

This class represents a variance gamma (VG) process {S(t) = X(t;θ, σ, ν) : t >= 0}.

VarianceGammaProcess() - Constructor for class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess

VarianceGammaProcess(double, double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess

Constructs a new VarianceGammaProcess with parameters θ = theta, σ = sigma, ν = nu and initial value S(t₀) = s0.

VarianceGammaProcess(double, BrownianMotion, GammaProcess) - Constructor for class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcess

Constructs a new VarianceGammaProcess.

VarianceGammaProcessDiff - Class in umontreal.iro.lecuyer.stochprocess

This class represents a variance gamma (VG) process {S(t) = X(t;θ, σ, ν) : t >= 0}.

VarianceGammaProcessDiff(double, double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiff

Constructs a new VarianceGammaProcessDiff with parameters θ = theta, σ = sigma, ν = nu and initial value S(t₀) = s0.

VarianceGammaProcessDiff(double, double, double, double, GammaProcess, GammaProcess) - Constructor for class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiff

Same as above.

VarianceGammaProcessDiffPCA - Class in umontreal.iro.lecuyer.stochprocess

Same as VarianceGammaProcessDiff, but the two inner GammaProcess'es are of PCA type.

VarianceGammaProcessDiffPCA(double, double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiffPCA

Constructs a new VarianceGammaProcessDiffPCA with parameters θ = theta, σ = sigma, ν = nu and initial value S(t₀) = s0.

VarianceGammaProcessDiffPCA(double, double, double, double, GammaProcessPCA, GammaProcessPCA) - Constructor for class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiffPCA

Constructs a new VarianceGammaProcessDiffPCA with parameters θ = theta, σ = sigma, ν = nu and initial value S(t₀) = s0.

VarianceGammaProcessDiffPCABridge - Class in umontreal.iro.lecuyer.stochprocess

Same as VarianceGammaProcessDiff, but the two inner GammaProcess'es are of the type PCABridge.

VarianceGammaProcessDiffPCABridge(double, double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiffPCABridge

Constructs a new VarianceGammaProcessDiffPCABridge with parameters θ = theta, σ = sigma, ν = nu and initial value S(t₀) = s0.

VarianceGammaProcessDiffPCASymmetricalBridge - Class in umontreal.iro.lecuyer.stochprocess

Same as VarianceGammaProcessDiff, but the two inner GammaProcess'es are of the PCASymmetricalBridge type.

VarianceGammaProcessDiffPCASymmetricalBridge(double, double, double, double, RandomStream) - Constructor for class umontreal.iro.lecuyer.stochprocess.VarianceGammaProcessDiffPCASymmetricalBridge

Constructs a new VarianceGammaProcessDiffPCASymmetricalBridge with parameters θ = theta, σ = sigma, ν = nu and initial value S(t₀) = s0.

view(int, int) - Method in class umontreal.iro.lecuyer.charts.BoxChart

Displays chart on the screen using Swing.

view(int, int) - Method in class umontreal.iro.lecuyer.charts.CategoryChart

view(int, int) - Method in class umontreal.iro.lecuyer.charts.EmpiricalChart

Displays chart on the screen using Swing.

view(int, int) - Method in class umontreal.iro.lecuyer.charts.HistogramChart

Displays chart on the screen using Swing.

view(int, int) - Method in class umontreal.iro.lecuyer.charts.MultipleDatasetChart

Displays chart on the screen using Swing.

view(int, int) - Method in class umontreal.iro.lecuyer.charts.ScatterChart

Displays chart on the screen using Swing.

view(int, int) - Method in class umontreal.iro.lecuyer.charts.XYChart

view(int, int) - Method in class umontreal.iro.lecuyer.charts.XYLineChart

Displays chart on the screen using Swing.

viewBar(int, int) - Method in class umontreal.iro.lecuyer.charts.XYLineChart

Displays bar chart on the screen using Swing.

viewCdf(int, int) - Method in class umontreal.iro.lecuyer.charts.ContinuousDistChart

Displays a chart of the cumulative distribution function (cdf) on the screen using Swing.

viewCdf(int, int, int, int) - Method in class umontreal.iro.lecuyer.charts.DiscreteDistIntChart

Displays a chart of the cumulative distribution function (cdf) over the interval [a, b] on the screen using Swing.

viewCdf(int, int) - Method in class umontreal.iro.lecuyer.charts.DiscreteDistIntChart

Similar to method viewCdf above.

viewDensity(int, int) - Method in class umontreal.iro.lecuyer.charts.ContinuousDistChart

Similar to viewCdf, but for the probability density instead of the cdf.

viewProb(int, int, int, int) - Method in class umontreal.iro.lecuyer.charts.DiscreteDistIntChart

Displays a chart of the probability mass function over the interval [a, b] on the screen using Swing.

viewProb(int, int) - Method in class umontreal.iro.lecuyer.charts.DiscreteDistIntChart

Similar to method viewProb above.

volumeSphere(double, int) - Static method in class umontreal.iro.lecuyer.util.Num

Returns the volume V of a sphere of radius 1 in t dimensions using the norm L_p.

W

waitFor() - Method in class umontreal.iro.lecuyer.simprocs.Condition

The executing process invoking this method must wait for this condition to be true.

WAITING - Static variable in class umontreal.iro.lecuyer.simprocs.SimProcess

waitList() - Method in class umontreal.iro.lecuyer.simprocs.Bin

Returns the list of UserRecord for the processes waiting for tokens from this bin.

waitList() - Method in class umontreal.iro.lecuyer.simprocs.Condition

Returns the list of UserRecord for the processes waiting for this condition.

waitList() - Method in class umontreal.iro.lecuyer.simprocs.Resource

Returns the list that contains the UserRecord objects for the processes in the waiting list for this resource.

watsonG(int, double) - Static method in class umontreal.iro.lecuyer.gof.FBar

Deprecated.

watsonG(int, double) - Static method in class umontreal.iro.lecuyer.gof.FDist

Deprecated.

watsonG(DoubleArrayList) - Static method in class umontreal.iro.lecuyer.gof.GofStat

Computes and returns the Watson statistic G_N.

WatsonGDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the Watson G distribution (see).

WatsonGDist(int) - Constructor for class umontreal.iro.lecuyer.probdist.WatsonGDist

Constructs a Watson distribution for a sample of size n.

watsonU(int, double) - Static method in class umontreal.iro.lecuyer.gof.FBar

Deprecated.

watsonU(int, double) - Static method in class umontreal.iro.lecuyer.gof.FDist

Deprecated.

watsonU(DoubleArrayList) - Static method in class umontreal.iro.lecuyer.gof.GofStat

Computes and returns the Watson statistic U_N².

WatsonUDist - Class in umontreal.iro.lecuyer.probdist

Extends the class ContinuousDistribution for the Watson U distribution (see).

WatsonUDist(int) - Constructor for class umontreal.iro.lecuyer.probdist.WatsonUDist

Constructs a Watson U distribution for a sample of size n.

WeibullDist - Class in umontreal.iro.lecuyer.probdist

This class extends the class ContinuousDistribution for the Weibull distribution with shape parameter α > 0, location parameter δ, and scale parameter λ > 0.

WeibullDist(double) - Constructor for class umontreal.iro.lecuyer.probdist.WeibullDist

Constructs a WeibullDist object with parameters α = alpha, λ = 1, and δ = 0.

WeibullDist(double, double, double) - Constructor for class umontreal.iro.lecuyer.probdist.WeibullDist

Constructs a WeibullDist object with parameters α = alpha, λ = lambda, and δ = delta.

WeibullGen - Class in umontreal.iro.lecuyer.randvar

This class implements random variate generators for the Weibull distribution.

WeibullGen(RandomStream, double, double, double) - Constructor for class umontreal.iro.lecuyer.randvar.WeibullGen

Creates a Weibull random variate generator with parameters α = alpha, λ = lambda and δ = delta, using stream s.

WeibullGen(RandomStream, double) - Constructor for class umontreal.iro.lecuyer.randvar.WeibullGen

Creates a Weibull random variate generator with parameters α = alpha, λ = 1 and δ = 0, using stream s.

WeibullGen(RandomStream, WeibullDist) - Constructor for class umontreal.iro.lecuyer.randvar.WeibullGen

Creates a new generator for the Weibull distribution dist and stream s.

WELL1024 - Class in umontreal.iro.lecuyer.rng

Implements the RandomStream interface via inheritance from RandomStreamBase.

WELL1024() - Constructor for class umontreal.iro.lecuyer.rng.WELL1024

Constructs a new stream.

WELL1024(String) - Constructor for class umontreal.iro.lecuyer.rng.WELL1024

Constructs a new stream with the identifier name (used in the toString method).

WELL512 - Class in umontreal.iro.lecuyer.rng

This class implements the RandomStream interface via inheritance from RandomStreamBase.

WELL512() - Constructor for class umontreal.iro.lecuyer.rng.WELL512

Constructs a new stream.

WELL512(String) - Constructor for class umontreal.iro.lecuyer.rng.WELL512

Constructs a new stream with the identifier name (used in the toString method).

WELL607 - Class in umontreal.iro.lecuyer.rng

This class implements the RandomStream interface via inheritance from RandomStreamBase.

WELL607() - Constructor for class umontreal.iro.lecuyer.rng.WELL607

Constructs a new stream.

WELL607(String) - Constructor for class umontreal.iro.lecuyer.rng.WELL607

Constructs a new stream with the identifier name (used in the toString method).

WG - Static variable in class umontreal.iro.lecuyer.gof.GofFormat

Watson G test

WU - Static variable in class umontreal.iro.lecuyer.gof.GofFormat

Watson U test

X

xor(BitMatrix) - Method in class umontreal.iro.lecuyer.util.BitMatrix

Returns the BitMatrix resulting from the application of the xor operator on the original BitMatrix and that.

xor(BitVector) - Method in class umontreal.iro.lecuyer.util.BitVector

Returns a BitVector which is the result of the xor operator applied on this and that.

XYChart - Class in umontreal.iro.lecuyer.charts

This class provides tools to create charts from data in a simple way.

XYChart() - Constructor for class umontreal.iro.lecuyer.charts.XYChart

XYLineChart - Class in umontreal.iro.lecuyer.charts

This class provides tools to create and manage curve plots.

XYLineChart() - Constructor for class umontreal.iro.lecuyer.charts.XYLineChart

Initializes a new XYLineChart instance with an empty data set.

XYLineChart(String, String, String, double[][]...) - Constructor for class umontreal.iro.lecuyer.charts.XYLineChart

Initializes a new XYLineChart instance with sets of points data.

XYLineChart(String, String, String, double[][], int) - Constructor for class umontreal.iro.lecuyer.charts.XYLineChart

Initializes a new XYLineChart instance with sets of points data.

XYLineChart(String, String, String, double[][], int, int) - Constructor for class umontreal.iro.lecuyer.charts.XYLineChart

Initializes a new XYLineChart instance using subsets of data.

XYLineChart(String, String, String, DoubleArrayList...) - Constructor for class umontreal.iro.lecuyer.charts.XYLineChart

Initializes a new XYLineChart instance with data data.

XYLineChart(String, String, String, XYSeriesCollection) - Constructor for class umontreal.iro.lecuyer.charts.XYLineChart

Initializes a new XYLineChart instance with data data.

XYListSeriesCollection - Class in umontreal.iro.lecuyer.charts

This class extends SSJXYSeriesCollection.

XYListSeriesCollection() - Constructor for class umontreal.iro.lecuyer.charts.XYListSeriesCollection

Creates a new XYListSeriesCollection instance with an empty dataset.

XYListSeriesCollection(double[][]...) - Constructor for class umontreal.iro.lecuyer.charts.XYListSeriesCollection

Creates a new XYListSeriesCollection instance with default parameters and given data series.

XYListSeriesCollection(double[][], int) - Constructor for class umontreal.iro.lecuyer.charts.XYListSeriesCollection

Creates a new XYListSeriesCollection instance with default parameters and given points data.

XYListSeriesCollection(DoubleArrayList...) - Constructor for class umontreal.iro.lecuyer.charts.XYListSeriesCollection

Creates a new XYListSeriesCollection instance with default parameters and given data.

XYListSeriesCollection(XYSeriesCollection) - Constructor for class umontreal.iro.lecuyer.charts.XYListSeriesCollection

Creates a new XYListSeriesCollection instance with default parameters and given data series.

Y

YListChart - Class in umontreal.iro.lecuyer.charts

This class extends the class XYLineChart.

YListChart(String, String, String, double[]...) - Constructor for class umontreal.iro.lecuyer.charts.YListChart

Initializes a new YListChart instance with set of points data.

YListChart(String, String, String, double[], int) - Constructor for class umontreal.iro.lecuyer.charts.YListChart

Initializes a new YListChart instance with a set of points data.

YListChart(String, String, String, double[][], int) - Constructor for class umontreal.iro.lecuyer.charts.YListChart

Initializes a new YListChart instance with set of points data.

YListSeriesCollection - Class in umontreal.iro.lecuyer.charts

This class extends XYListSeriesCollection.

YListSeriesCollection(double[]...) - Constructor for class umontreal.iro.lecuyer.charts.YListSeriesCollection

Creates a new YListSeriesCollection instance with default parameters and given data series.

YListSeriesCollection(double[], int) - Constructor for class umontreal.iro.lecuyer.charts.YListSeriesCollection

Creates a new YListSeriesCollection instance with default parameters and one data series.

YListSeriesCollection(double[][], int) - Constructor for class umontreal.iro.lecuyer.charts.YListSeriesCollection

Creates a new YListSeriesCollection instance with default parameters and given data series.

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