mml

Class Missing.M

java.lang.Object

la.la.Value

mml.Model

mml.UPModel.M

mml.Missing.M

All Implemented Interfaces:

java.lang.Comparable<Value>

Enclosing class:

Missing

public class Missing.M
extends UPModel.M

The fully parameterised Model; Missing is the UnParameterised Model.

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

Modifier and Type	Field and Description
MultiState.M	presentMdl The fully parameterised Model of whether a Value is present (known) or missing (unknown).
Model	valueMdl The fully parameterised Model all present (known) Values.

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) Two part message lengths, msg1 and msg2, and statistical parameter(s) 'sp'.

Method Summary

Modifier and Type	Method and Description
double	nlLH(Value ss) Given sufficient statistics, ss=stats(ds), of a data-set 'ds', return the negative log LikeliHood of 'ds'.
double	nlPr(Value d) Return the negative log probability of datum 'd'.
Value	random() Return a random Value from 'this' Model – provided that valueMdl can do random().
Value.Tuple	stats(boolean add, Value ss0, Value ss1) Use presentMdl.stats(...) and valueMdl.stats(...).
Value.Tuple	stats(Vector ds, int lo, int hi) Use presentMdl.stats(...) and valueMdl.stats(...) to calculate the statistics of ds[lo,hi).

Methods inherited from class mml.UPModel.M

asGiven, asGiven, toString

Methods inherited from class mml.Model

asEstimator, asUPModel, m1m2sp, msg, msg1, msg1bits, msg2, msg2bits, msgBits, nl2LH, nl2Pr, pr, 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

Field Detail

presentMdl

public final MultiState.M presentMdl

The fully parameterised Model of whether a Value is present (known) or missing (unknown). Also see Missing.presentUPM.

valueMdl

public final Model valueMdl

The fully parameterised Model all present (known) Values. Also see Missing.valueUPM.

Constructor Detail

M

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

Two part message lengths, msg1 and msg2, and statistical parameter(s) 'sp'. 'sp' is a pair of parameters — one for presentUPM and one for valueUPM.

Method Detail

nlPr

public double nlPr(Value d)

Return the negative log probability of datum 'd'. This uses presentMdl for None, and both presentMdl and valueMdl for Just(v).

Specified by:

nlPr in class Model

stats

public Value.Tuple stats(Vector ds,
                         int lo,
                         int hi)

Use presentMdl.stats(...) and valueMdl.stats(...) to calculate the statistics of ds[lo,hi). Note this might be the same as Missing.stats(la.maths.Vector,int,int), or not!

Overrides:

stats in class UPModel.M

stats

public Value.Tuple stats(boolean add,
                         Value ss0,
                         Value ss1)

Use presentMdl.stats(...) and valueMdl.stats(...). Combine statistics ss0 and ss1, either ss0 "+" ss1, or ss0 "-" ss1, depending on 'add' being true or false. Note this might be the same as Missing.stats(boolean,Value,Value), or not!

Overrides:

stats in class UPModel.M

nlLH

public double nlLH(Value ss)

Given sufficient statistics, ss=stats(ds), of a data-set 'ds', return the negative log LikeliHood of 'ds'.

Specified by:

nlLH in class Model

random

public Value random()

Return a random Value from 'this' Model – provided that valueMdl can do random().

Overrides:

random in class Model