mml

Class UPFunctionModel.K.M

java.lang.Object

la.la.Value

mml.Model

mml.FunctionModel

mml.UPFunctionModel.M

mml.UPFunctionModel.K.M

All Implemented Interfaces:

java.lang.Comparable<Value>

Enclosing class:

UPFunctionModel.K

public class UPFunctionModel.K.M
extends UPFunctionModel.M

The fully parameterised Konstant function-model. It models the output datum with mdl, regardless of the input data value. Also see the unparameterised K.

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
Model	mdl The Model of the output datum, od (for every input, id).

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) Statistical parameter sp is for mdl = upm.apply(sp).

Method Summary

Modifier and Type	Method and Description
Model	condModel(Value id) Return mdl; input datum id is ignored in K.
double	nlLH(Value ss) Negative log likelihood; note, statistics are ss = stats(ds).
Value	stats(boolean add, Value ss0, Value ss1) mdl.stats(add,.,.).
Value	stats(Vector ds, int lo, int hi) Given a data-set, 'ds', statistics, ss = mdl.stats(ds.col(1),lo,hi), on the output datum only, are as per mdl.

Methods inherited from class mml.UPFunctionModel.M

asGiven, asGiven, 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

mdl

public final Model mdl

The Model of the output datum, od (for every input, id). It is upm, parameterised.

Constructor Detail

M

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

Statistical parameter sp is for mdl = upm.apply(sp).

Method Detail

condModel

public Model condModel(Value id)

Return mdl; input datum id is ignored in K.

Specified by:

condModel in class FunctionModel

stats

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

Given a data-set, 'ds', statistics, ss = mdl.stats(ds.col(1),lo,hi), on the output datum only, are as per mdl. More on stats here.

Overrides:

stats in class UPFunctionModel.M

stats

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

mdl.stats(add,.,.).

Overrides:

stats in class UPFunctionModel.M

nlLH

public double nlLH(Value ss)

Negative log likelihood; note, statistics are ss = stats(ds).

Specified by:

nlLH in class Model