mml

Class Dirichlet

java.lang.Object

la.la.Value

la.la.Function

la.la.Function.Native

mml.UPModel

mml.ByPdf

mml.R_D

mml.Simplex

mml.Dirichlet

All Implemented Interfaces:

java.lang.Comparable<Value>

public class Dirichlet
extends Simplex

The UnParameterised Dirichlet Model (probability distribution) for data from a K-Simplex. It is often applied to the proportions (relative abundancies) of categories. (An estimator is not yet implemented.)

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
class	Dirichlet.M The fully parameterised Dirichlet Model (probability distribution); the UnParameterised Model is here.

Nested classes/interfaces inherited from class mml.Simplex

Simplex.Uniform

Nested classes/interfaces inherited from class mml.R_D

R_D.Forest, R_D.ForestSearch, R_D.Independent, R_D.NrmDir, R_D.Transform

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
Dirichlet(Value K) K, the degrees of freedom in the data, that is one less than the dimension of the data.

Method Summary

Modifier and Type	Method and Description
int	K() The degrees of freedom of the K-Simplex sub-space ⊆ [0, 1]K+1.
static void	main(java.lang.String[] argv) A very few, very rudimentary tests; also see Test.
Dirichlet.M	sp2Model(double m1, double m2, Value alpha) Return a "fully parameterised" Dirichlet Model.
Vector	stats(boolean add, Value ss0, Value ss1) Combine sufficient statisticses 'ss0' and 'ss1' additively (add=true), or remove ss1 from ss0 (add=false).
Vector	stats(Vector ds, int lo, int hi) The default sufficient statistics ss = stats(ds) = ds; maybe we can do better in the future? (???TODO: Looks like maybe [∑ log di,0, ...] would be the go, but beware any datum d=[0,...]???) More on stats here.

Methods inherited from class mml.Simplex

check, D, logArea

Methods inherited from class mml.R_D

ds2L1stats, ds2NorL1stats, ds2Nstats, transform

Methods inherited from class mml.UPModel

apply, defnParams, estimator, 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

Dirichlet

public Dirichlet(Value K)

K, the degrees of freedom in the data, that is one less than the dimension of the data.

Method Detail

K

public int K()

The degrees of freedom of the K-Simplex sub-space ⊆ [0, 1]^K+1. Also see Simplex.D().

Specified by:

K in class Simplex

sp2Model

public Dirichlet.M sp2Model(double m1,
                            double m2,
                            Value alpha)

Return a "fully parameterised" Dirichlet Model.

Specified by:

sp2Model in class R_D

stats

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

The default sufficient statistics ss = stats(ds) = ds; maybe we can do better in the future? (???TODO: Looks like maybe [∑ log d_i,0, ...] would be the go, but beware any datum d=[0,...]???) More on stats here.

Specified by:

stats in class UPModel

stats

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

Description copied from class: UPModel

Combine sufficient statisticses 'ss0' and 'ss1' additively (add=true), or remove ss1 from ss0 (add=false). Also see stats(ds,lo,hi).

Specified by:

stats in class UPModel

main

public static void main(java.lang.String[] argv)

A very few, very rudimentary tests; also see Test.