mml

Class Discretes.M

java.lang.Object

la.la.Value

mml.Model

mml.UPModel.M

mml.Discretes.M

All Implemented Interfaces:

java.lang.Comparable<Value>

Direct Known Subclasses:

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

Enclosing class:

Discretes

public abstract class Discretes.M
extends UPModel.M

The (abstract) class of fully parameterised Models of Discrete data-spaces. You need to specify nlPr_n(n).

Nested Class Summary

Nested classes/interfaces inherited from class mml.Model

Model.Defaults, Model.Transform

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.Model

msg1, msg2, sp

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
M(double msg1, double msg2, Value sp)

Method Summary

Modifier and Type	Method and Description
abstract double	nlPr_n(int n) Back-room works for negative log probability of datum int n, nlPr(la.la.Value); it must be implemented in an instance.
double	nlPr(Value d) Get nlPr(d) from nlPr_n(d.n()).
double	pr_n(int n) Back-room works for pr(la.la.Value), returns Math.exp( - nlPr_n(n).
double	pr(Value d) Get pr(d) from pr_n(d.n()).
abstract int	random_n() Back-room works for Model.random().
Discretes.M	shifted(int offset) Shift 'this' fully parameterised Model of Discretes by +offset (~shift data by -offset).

Methods inherited from class mml.UPModel.M

asGiven, asGiven, stats, stats, toString

Methods inherited from class mml.Model

asEstimator, asUPModel, m1m2sp, msg, msg1, msg1bits, msg2, msg2bits, msgBits, nl2LH, nl2Pr, nlLH, random, random, randomSeries, statParams, stats, stats, sumNlPr, transform, type, zeroTriv

Methods inherited from class la.la.Value

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

Methods inherited from class java.lang.Object

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

Constructor Detail

M

public M(double msg1,
         double msg2,
         Value sp)

Method Detail

pr_n

public double pr_n(int n)

Back-room works for pr(la.la.Value), returns Math.exp( - nlPr_n(n).

pr

public double pr(Value d)

Get pr(d) from pr_n(d.n()).

Overrides:

pr in class Model

nlPr

public double nlPr(Value d)

Get nlPr(d) from nlPr_n(d.n()).

Specified by:

nlPr in class Model

nlPr_n

public abstract double nlPr_n(int n)

Back-room works for negative log probability of datum int n, nlPr(la.la.Value); it must be implemented in an instance.

random_n

public abstract int random_n()

Back-room works for Model.random(). There is a tricky semantic problem about turning it into a Value through random() -- e.g., does '1' denote C:DNA, or true:boolean, and so on (we first need to know the Type of the data-space).

shifted

public Discretes.M shifted(int offset)

Shift 'this' fully parameterised Model of Discretes by +offset (~shift data by -offset). Its nlPr(d) and stats(ds,lo,hi) subtract offset from data before handing it to the unshifted Model. Note that shifting a Bounded Model may reduce the data-space. Also see Discretes.shifted(int).