mml

Class R_D.NrmDir.M

java.lang.Object

la.la.Value

mml.Model

mml.UPModel.M

mml.ByPdf.M

mml.R_D.M

mml.R_D.NrmDir.M

All Implemented Interfaces:

java.lang.Comparable<Value>, HasPdf

Enclosing class:

R_D.NrmDir

public class R_D.NrmDir.M
extends R_D.M

A fully parameterised Model of Vectors in R^D, made from a Model of norms (Vector lengths) and a Model of Directions. To be an efficient code the former would assume and make use of v.norm() ≥ 0, for a Vector v.

Nested Class Summary

Nested classes/interfaces inherited from class mml.R_D.M

R_D.M.Transform

Nested classes/interfaces inherited from class mml.Model

Model.Defaults

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
Direction.M	dirnMdl A fully parameterised Model of Directions, that is of RD Vector Directions.
Continuous.M	normMdl A fully parameterised Model of Vector norms (lengths).

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) Two-part message lengths, msg1 and msg2, and statistical parameters sps = (spn, spd) where spn is normMdls's sp and spd is dirnMdls's.

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	nlPdf(Value v) The negative log probability density of a Vector datum, v, in RD.
Vector	random() Generate a random RD-Vector from 'this' Model.
Value	stats(boolean add, Value ss0, Value ss1) Calls upon stats of normMdl and dirnMdl, and may or may not equal R_D.NrmDir.stats(boolean,Value,Value).
Value	stats(Vector ds, int lo, int hi) The statistics, ss = stats(ds), of a data-set, ds.

Methods inherited from class mml.R_D.M

D, random_x, random, transform

Methods inherited from class mml.ByPdf.M

nlPr, nlPr, pdf

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

normMdl

public final Continuous.M normMdl

A fully parameterised Model of Vector norms (lengths). Note that v.norm() ≥ 0, of course.

dirnMdl

public final Direction.M dirnMdl

A fully parameterised Model of Directions, that is of R^D Vector Directions.

Constructor Detail

M

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

Two-part message lengths, msg1 and msg2, and statistical parameters sps = (sp_n, sp_d) where sp_n is normMdls's sp and sp_d is dirnMdls's.

Method Detail

nlPdf

public double nlPdf(Value v)

The negative log probability density of a Vector datum, v, in R^D. Note, clearly v is not normalised in general, so the Direction density is adjusted to a "radius" of v.norm().

Specified by:

nlPdf in interface HasPdf

Specified by:

nlPdf in class ByPdf.M

stats

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

The statistics, ss = stats(ds), of a data-set, ds. ss = (norm stats, dirn stats). Calls upon stats of normMdl and dirnMdl, and may or may not equal R_D.NrmDir.stats(Vector,int,int). Also see M.nlLH(ss). More on stats here.

Overrides:

stats in class UPModel.M

stats

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

Calls upon stats of normMdl and dirnMdl, and may or may not equal R_D.NrmDir.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 Vector random()

Generate a random R^D-Vector from 'this' Model. It needs the sub-Models to implement random.

Overrides:

random in class R_D.M