mml

Class Discretes

java.lang.Object

la.la.Value

la.la.Function

la.la.Function.Native

mml.UPModel

mml.Discretes

All Implemented Interfaces:

java.lang.Comparable<Value>

Direct Known Subclasses:

Discretes.Bounded, Discretes.Shifted, Geometric0UPM, Int1, LogStar0UPM, Poisson0UPM, WallaceInt0UPM

public abstract class Discretes
extends UPModel

The (abstract) sub-class of UnParameterised Model that may be helpful in defining Models of Discrete data-spaces (Values), such as Value.Enum data. The fully parameterised model is Discretes.M. Particularly see Discretes.M.nlPr_n(int) and Discretes.M.random_n().

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	Discretes.Bounded The class of UnParameterised Models over Bounded Discrete data such as [3, 7], or DNA, say.
class	Discretes.M The (abstract) class of fully parameterised Models of Discrete data-spaces.
class	Discretes.Shifted This UnParameterised Model of Discretes shifted by +offset.
static class	Discretes.Uniform The UnParameterised Uniform Model (distribution) on data in bounds = [lo, hi], pretty much the simplest Discrete Model having only the trivial "statistical parameter." Also see the fully parameterised Discretes.Uniform.M, and Continuous.Uniform.

Nested classes/interfaces inherited from class mml.UPModel

UPModel.Est, UPModel.Transform

Nested classes/interfaces inherited from class la.la.Function.Native

Function.Native.WithInverse

Nested classes/interfaces inherited from class la.la.Function

Function.Cts2Cts, Function.Cts2Cts2Cts, Function.CtsD2CtsD, Function.HasInverse, Function.Native, Function.Native2, Function.Native3

Nested classes/interfaces inherited from class la.la.Value

Value.Atomic, Value.Bool, Value.Char, Value.Chars, Value.Cts, Value.Defer, Value.Discrete, Value.Enum, Value.Inc_Or, Value.Int, Value.Lambda, Value.List, Value.Maybe, Value.Option, Value.Real, Value.Scannable, Value.Structured, Value.Triv, Value.Tuple

Field Summary

Fields inherited from class mml.UPModel

dp

Fields inherited from class la.la.Value

C, comparator, CR, E, eight, ffalse, five, fiveR, four, fourR, half, negCR, negOne, negOneR, negTenR, nilVal, nine, None, one, oneR, PI, PIby2, point01, point1, point5, point9, quarter, seven, six, ten, tenR, three, threeR, triv, ttrue, two, twoR, zero, zeroR

Constructor Summary

Constructors

Constructor and Description
Discretes(Value sp)

Method Summary

Modifier and Type	Method and Description
Discretes.M	apply(Value sp) Given statistical parameters, sp, return a Model, sp2Model(0, 0, sp), i.e., one that is not estimated, having zero part 1 and part 2 message lengths.
static Vector	NandSum(Vector ds) Some Discrete Models, such as Geometric0UPM.M, have (N,sum) as sufficients statistics, ss = stats(ds), of a data-set ds, in which case they may implement Model.stats(la.maths.Vector) by calling NandSum.
static Vector	NandSum(Vector ds, int lo, int hi) NandSum for elements [lo, hi).
Discretes	shifted(int offset) Convenience function for new Shifted(new Int(offset)), for example, Poisson0.shifted(1) is the unparameterised Poisson distribution for integers n≥1.
Discretes	shifted(Value.Int offset) Convenience function for new Shifted(offset), for example, Poisson0.shifted(1) is the unparameterised Poisson distribution for integers n≥1.
abstract Discretes.M	sp2Model(double msg1, double msg2, Value sp) Given two-part message lengths msg1 & msg2, and statistical parameter sp, return a fully parameterised M-Model.

Methods inherited from class mml.UPModel

defnParams, estimator, main, stats, stats, stats, stats, toString, transform

Methods inherited from class la.la.Function

bOp, fromBop, fromUop, type, uOp

Methods inherited from class la.la.Value

AoM, bOp, compareTo, cons, cts, elt, errMsg, error, force, isTuple, just, n, nElts, nlAoM, pair, print, quad, real, RTE, triple, tuple, UOE, x

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

Discretes

public Discretes(Value sp)

Method Detail

apply

public Discretes.M apply(Value sp)

Description copied from class: UPModel

Given statistical parameters, sp, return a Model, sp2Model(0, 0, sp), i.e., one that is not estimated, having zero part 1 and part 2 message lengths. (Also see Function.apply(la.la.Value).)

Overrides:

apply in class UPModel

sp2Model

public abstract Discretes.M sp2Model(double msg1,
                                     double msg2,
                                     Value sp)

Description copied from class: UPModel

Given two-part message lengths msg1 & msg2, and statistical parameter sp, return a fully parameterised M-Model. If 'this' UPModel produces a Model m, then sp2Model(m.msg1(), m.msg2(), m.statParams()) must be equivalent to m. Also see apply(sp).

Specified by:

sp2Model in class UPModel

NandSum

public static Vector NandSum(Vector ds)

Some Discrete Models, such as Geometric0UPM.M, have (N,sum) as sufficients statistics, ss = stats(ds), of a data-set ds, in which case they may implement Model.stats(la.maths.Vector) by calling NandSum. Note the Values of N and sum are Cts because we may have fractionally weighted data, say in a Mixture.

NandSum

public static Vector NandSum(Vector ds,
                             int lo,
                             int hi)

NandSum for elements [lo, hi).

shifted

public Discretes shifted(int offset)

Convenience function for new Shifted(new Int(offset)), for example, Poisson0.shifted(1) is the unparameterised Poisson distribution for integers n≥1. Also see Discretes.M.shifted(int).

shifted

public Discretes shifted(Value.Int offset)

Convenience function for new Shifted(offset), for example, Poisson0.shifted(1) is the unparameterised Poisson distribution for integers n≥1. Also see Discretes.M.shifted(int).