mml

Class NaiveBayes

java.lang.Object

la.la.Value

la.la.Function

la.la.Function.Native

mml.UPModel

mml.UPFunctionModel

mml.NaiveBayes

All Implemented Interfaces:

java.lang.Comparable<Value>

public class NaiveBayes
extends UPFunctionModel

The UnParameterised NaiveBayes FunctionModel; the fully parameterised FunctionModel is NaiveBayes.M. For a Discrete Bounded variable, od:O, and arbitrary variable(s), id:I, NaiveBayes I→O is based on a Dependent Model, dpndt:O×I, [Bayes's theorem], and the (naive) assumption that id's components are independent of each other.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
class	NaiveBayes.M The fully parameterised NaiveBayes FunctionModel I→O; the UnParameterised FunctionModel is NaiveBayes.

Nested classes/interfaces inherited from class mml.UPFunctionModel

UPFunctionModel.Est, UPFunctionModel.K

Nested classes/interfaces inherited from class mml.UPModel

UPModel.Transform

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

Modifier and Type	Field and Description
Dependent	dpndt The (UnParameterised) Dependent Model, made of ⟨O, O→I⟩, that we are going to turn around (invert) to get I→O as desired.
Value	lwb lwb and upb, the bounds on the output datum, od.
int	lwb_n lwb_n and upb_n the bounds on the output datum, od, as ints.
Value	upb lwb and upb, the bounds on the output datum, od.
int	upb_n lwb_n and upb_n the bounds on the output datum, od, as ints.

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
NaiveBayes(Value dp) Given definition parameter, dp, being an UnParameterised Dependent Model, dp:O×I, construct an UnParameterised NaiveBayes FunctionModel of I→O.

Method Summary

Modifier and Type	Method and Description
UPFunctionModel.Est	estimator(Value ps) Return the Estimator for NaiveBayes; any parameter(s), ps, is passed to the Dependent's Estimator.
NaiveBayes.M	sp2Model(double msg1, double msg2, Value sps) Given two-part message lengths, and statistical parameters sp, return a fully parameterised model.
Vector	stats(boolean add, Value ss0, Value ss1) Combine stats ss0 and ss1, either additively (add=true) or negatively (add=false).
Vector	stats(Vector ds, int lo, int hi) Given a data-set ds, return statistics ss=ds.[lo,hi) the data itself.

Methods inherited from class mml.UPFunctionModel

apply

Methods inherited from class mml.UPModel

defnParams, main, 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

Field Detail

lwb

public final Value lwb

lwb and upb, the bounds on the output datum, od.

upb

public final Value upb

lwb and upb, the bounds on the output datum, od.

lwb_n

public final int lwb_n

lwb_n and upb_n the bounds on the output datum, od, as ints.

upb_n

public final int upb_n

lwb_n and upb_n the bounds on the output datum, od, as ints.

dpndt

public final Dependent dpndt

The (UnParameterised) Dependent Model, made of ⟨O, O→I⟩, that we are going to turn around (invert) to get I→O as desired. Also see NaiveBayes.M.dpndt_m.

Constructor Detail

NaiveBayes

public NaiveBayes(Value dp)

Given definition parameter, dp, being an UnParameterised Dependent Model, dp:O×I, construct an UnParameterised NaiveBayes FunctionModel of I→O.

Method Detail

stats

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

Given a data-set ds, return statistics ss=ds.[lo,hi) the data itself. (Note M.nlLH(ss) really needs ss to be the raw data.)

Specified by:

stats in class UPModel

stats

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

Combine stats ss0 and ss1, either additively (add=true) or negatively (add=false).

Specified by:

stats in class UPModel

estimator

public UPFunctionModel.Est estimator(Value ps)

Return the Estimator for NaiveBayes; any parameter(s), ps, is passed to the Dependent's Estimator. It seems that msg1 should be smaller than here: The condModels of I got from the O→I need not be so precise because the Is are actually common knowledge?

Overrides:

estimator in class UPModel

sp2Model

public NaiveBayes.M sp2Model(double msg1,
                             double msg2,
                             Value sps)

Given two-part message lengths, and statistical parameters sp, return a fully parameterised model.

Specified by:

sp2Model in class UPFunctionModel