mml

Class Dependent.M

java.lang.Object

la.la.Value

mml.Model

mml.UPModel.M

mml.Multivariate.M

mml.Dependent.M

All Implemented Interfaces:

java.lang.Comparable<Value>

Enclosing class:

Dependent

public class Dependent.M
extends Multivariate.M

A fully parameterised Dependent Model of data pairs, ⟨id, od⟩, made from a Model, im, of the input (independent) datum, id, and from a FunctionModel, fm, of the ouput (dependent) datum, od, conditional upon id. Note that id and/or od may be multivariate. Also see Independent.M.

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
UPFunctionModel.M	fm 'fm', the fully parameterised FunctionModel of the output datum, od, conditional upon the input datum, id.
Model	im 'im', the fully parameterised input Model of the input datum, id.

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 sps) The two-part message lengths are msg1 and msg2, and sps = (iSp, fSp), where iSp is the stat param for the input Model im, and fSp is for the FunctionModel fm.

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 iod) The negative log probability of datum iod = (id, od).
Value.Tuple	random() Return a random (id, od), assuming both im and fm.condModel(.) implement random().
Value.Tuple	stats(boolean add, Value ss0, Value ss1) Calls im's and fm's stats.
Value.Tuple	stats(Vector ds, int lo, int hi) Given a data-set, ds, return statistics, ss = ⟨im's, fm's⟩.

Methods inherited from class mml.Multivariate.M

functionModel, width

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

im

public final Model im

'im', the fully parameterised input Model of the input datum, id.

fm

public final UPFunctionModel.M fm

'fm', the fully parameterised FunctionModel of the output datum, od, conditional upon the input datum, id.

Constructor Detail

M

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

The two-part message lengths are msg1 and msg2, and sps = (iSp, fSp), where iSp is the stat param for the input Model im, and fSp is for the FunctionModel fm.

Method Detail

nlPr

public double nlPr(Value iod)

The negative log probability of datum iod = (id, od).

Specified by:

nlPr in class Model

stats

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

Given a data-set, ds, return statistics, ss = ⟨im's, fm's⟩. ss is a pair. "im's" is the stats from im.stats(.) etc.. Note that im's depends on only the first column of ds, but fm's, being for an UnParameterised FunctionModel, depends on both columns. May or may not equal Dependent.stats(Vector,int,int). More on stats here.

Overrides:

stats in class UPModel.M

stats

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

Calls im's and fm's stats. May or may not equal Dependent.stats(boolean,Value,Value).

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.Tuple random()

Return a random (id, od), assuming both im and fm.condModel(.) implement random().

Overrides:

random in class Model