mml

Class LinearD

java.lang.Object

la.la.Value

la.la.Function

la.la.Function.Native

mml.UPModel

mml.UPFunctionModel

mml.LinearD

All Implemented Interfaces:

java.lang.Comparable<Value>

public class LinearD
extends UPFunctionModel

The UnParameterised LinearD function-model; also see the fully parameterised LinearD function-model. The function-model of R^D→R is y=∑_j=0..D-1{a_jx_j + b + N_0,σ}. Matrix X=[x_i,j], i=0..N-1, j=0..D-1, and vector Y =[y_i], i=0..N-1. As is customary, define X₊ to be X augmented to the right with an extra column of 1s, and 'b' becomes a_D. Also see Linear1, R→R.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
class	LinearD.Est Estimator for a fully parameterised LinearD.M Linear-Model.
class	LinearD.M The fully parameterised function-model; LinearD is the UnParameterised function-model.

Nested classes/interfaces inherited from class mml.UPFunctionModel

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 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
LinearD(Value D) The problem definition parameter gives D()>1.

Method Summary

Modifier and Type	Method and Description
int	D() D(), the dimension of the input variable x:RD in x→y.
LinearD.Est	estimator(Value ps) Return an Estimator of a fully parameterised LinearD.M Model.
static void	main(java.lang.String[] argv) One very little test.
LinearD.M	sp2Model(double msg1, double msg2, Value sp) Given first- and second-part message lengths, msg1 and msg2, and statistical parameter(s), sp, return a fully parameterised LinearD.M Model.
Value.Tuple	stats(boolean add, Value ss0, Value ss1) Combine statistics ss0 and ss1, either ss0 "+" ss1, or ss0 "-" ss1, depending on 'add' being true or false.
Value.Tuple	stats(Vector ds, int lo, int hi) Calculate the sufficient statistics of ds[lo,hi).

Methods inherited from class mml.UPFunctionModel

apply

Methods inherited from class mml.UPModel

defnParams, 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

LinearD

public LinearD(Value D)

The problem definition parameter gives D()>1.

Method Detail

D

public int D()

D(), the dimension of the input variable x:R^D in x→y.

stats

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

Calculate the sufficient statistics of ds[lo,hi). That is ⟨N, X₊^T X₊, Y^T X₊, Y.Y, nlAoM⟩. Note that X₊ is the matrix of x-values augmented with a column of '1's to the right, and that nlAoM is the nlAoM of the y-values (the xs being common knowledge).

Specified by:

stats in class UPModel

stats

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

Combine statistics ss0 and ss1, either ss0 "+" ss1, or ss0 "-" ss1, depending on 'add' being true or false.

Specified by:

stats in class UPModel

sp2Model

public LinearD.M sp2Model(double msg1,
                          double msg2,
                          Value sp)

Given first- and second-part message lengths, msg1 and msg2, and statistical parameter(s), sp, return a fully parameterised LinearD.M Model.

Specified by:

sp2Model in class UPFunctionModel

estimator

public LinearD.Est estimator(Value ps)

Return an Estimator of a fully parameterised LinearD.M Model.

Overrides:

estimator in class UPModel

main

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

One very little test.