mml

Class NaiveBayes.M

java.lang.Object

la.la.Value

mml.Model

mml.FunctionModel

mml.UPFunctionModel.M

mml.NaiveBayes.M

All Implemented Interfaces:

java.lang.Comparable<Value>

Enclosing class:

NaiveBayes

public class NaiveBayes.M
extends UPFunctionModel.M

The fully parameterised NaiveBayes FunctionModel I→O; the UnParameterised FunctionModel is NaiveBayes. pr(od|id) = pr(id&od)/pr(id) = pr(od).pr(id|od)/pr(id), so pr(od|id) ∝ pr(od).pr(id|od); calculate for each case of od and normalise.

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
Dependent.M	dpndt_m The "backwards" Dependent Model of (O×I), that we are turning around, I→O; it is made of ⟨O, O→I⟩ and we want I→O.
FunctionModel	O2I dpndt_m's "backwards" FunctionModel of O→I.
Discretes.Bounded.M	Omdl dpndt_m's Model of O.

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) sp is for dpndt_m = dpndt.apply(sp).

Method Summary

Modifier and Type	Method and Description
Model	condModel(Value id) Return a MultiState Model of output datum od conditional on given input datum id.
double	nlLH(Value ss) Assumes statistics 'ss' are the data-set itself.

Methods inherited from class mml.UPFunctionModel.M

asGiven, asGiven, stats, stats, toString

Methods inherited from class mml.FunctionModel

asUPModel, condNl2Pr, condNlPr, condPr, nlPr, pr, random, random

Methods inherited from class mml.Model

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

dpndt_m

public final Dependent.M dpndt_m

The "backwards" Dependent Model of (O×I), that we are turning around, I→O; it is made of ⟨O, O→I⟩ and we want I→O.

Omdl

public final Discretes.Bounded.M Omdl

dpndt_m's Model of O.

O2I

public final FunctionModel O2I

dpndt_m's "backwards" FunctionModel of O→I.

Constructor Detail

M

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

sp is for dpndt_m = dpndt.apply(sp).

Method Detail

nlLH

public double nlLH(Value ss)

Assumes statistics 'ss' are the data-set itself. It all hinges on condModel.

Specified by:

nlLH in class Model

condModel

public Model condModel(Value id)

Return a MultiState Model of output datum od conditional on given input datum id. Try every possible value, v, for od, calculate joint probability pr(v&id), normalise these probabilities, which yields pr(v|id).

Specified by:

condModel in class FunctionModel