mml

Class R_D

java.lang.Object

la.la.Value

la.la.Function

la.la.Function.Native

mml.UPModel

mml.ByPdf

mml.R_D

All Implemented Interfaces:

java.lang.Comparable<Value>

Direct Known Subclasses:

Direction, R_D.Forest, R_D.ForestSearch, R_D.Independent, R_D.M.Transform, R_D.NrmDir, R_D.Transform, Simplex

public abstract class R_D
extends ByPdf

R_D, the (abstract) class of UnParameterised Models of D-dimensional Vectors of Cts, that is of R^D. Fully parameterised R_D Models start at R_D.M.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	R_D.Forest A (UnParameterised) model in which a column may be linearly dependent on a single parent column.
static class	R_D.ForestSearch A generalisation of R_D.Forest that is able to estimate the model's structure – the child-parent relations – as well as the parameters of the component distributions.
static class	R_D.Independent A Vector of Models is a Model of Vectors RD.
class	R_D.M (abstract) M, fully parameterised Models of RD.
static class	R_D.NrmDir An UnParameterised Model of Vectors in RD made from UnParameterised Models of norms (lengths) and of Directions.
class	R_D.Transform The UnParameterised R_D.Transform model; the parameterised Model is R_D.Transform.M. NB. the preferred way to transform an R_D is with function R_D.transform(f).

Nested classes/interfaces inherited from class mml.UPModel

UPModel.Est

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
R_D(Value dp) Given problem-defining parameter(s), dp, construct an UnParameterised Model of Vectors in RD.

Method Summary

Modifier and Type	Method and Description
abstract int	D() D(), the dimension of the data-space RD.
static Matrix	ds2L1stats(Vector ds) Calculate all per-two-column Linear1-stats.
static Value.Tuple	ds2NorL1stats(Vector ds, int[] parent) Calculate Normal-stats for each "parent-less" column and Linear1-stats for each column that has a 'parent' column.
static Vector	ds2Nstats(Vector ds) Calculate all per-column Normal-stats.
abstract R_D.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.
R_D	transform(Function.CtsD2CtsD f) transform(f) is the preferred way to transform an R_D with a Function f.

Methods inherited from class mml.UPModel

apply, 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

R_D

public R_D(Value dp)

Given problem-defining parameter(s), dp, construct an UnParameterised Model of Vectors in R^D.

Method Detail

D

public abstract int D()

D(), the dimension of the data-space R^D. R_D Models are of fixed-length Vectors of Cts, nElts()==D(). D() is logically part of the type of 'this'. You cannot count on learning D() from a data-set - it might be empty.

ds2Nstats

public static Vector ds2Nstats(Vector ds)

Calculate all per-column Normal-stats. This may be of use in various R_D models including generalisations of, and variations on, R_D.Forest. NB. static. Also see ds2L1stats(la.maths.Vector) and ds2NorL1stats(la.maths.Vector, int[]).

ds2L1stats

public static Matrix ds2L1stats(Vector ds)

Calculate all per-two-column Linear1-stats. This may be of use in various Linear models including generalisations of, and variations on, R_D.Forest. NB. static. Also see ds2Nstats(la.maths.Vector) and ds2NorL1stats(la.maths.Vector, int[]).

ds2NorL1stats

public static Value.Tuple ds2NorL1stats(Vector ds,
                                        int[] parent)

Calculate Normal-stats for each "parent-less" column and Linear1-stats for each column that has a 'parent' column. If parent[i]<0 then column 'i' has no parent. Result is a pair, a Vector for the Normals and a Vector for the Linear1s. Used by R_D.Forest and might be useful in other Linear models. NB. static. Also see ds2Nstats(la.maths.Vector) and ds2L1stats(la.maths.Vector).

sp2Model

public abstract R_D.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

transform

public R_D transform(Function.CtsD2CtsD f)

transform(f) is the preferred way to transform an R_D with a Function f. (Returns an R_D, in fact an R_D.Transform.) Also see the related but quite different R_D.M.transform(la.la.Function.CtsD2CtsD).