mml

Class Linear1

java.lang.Object

la.la.Value

la.la.Function

la.la.Function.Native

mml.UPModel

mml.UPFunctionModel

mml.Linear1

All Implemented Interfaces:

java.lang.Comparable<Value>

public class Linear1
extends UPFunctionModel

The class of UnParameterised Function Models (regressions) from a Continuous input datum (independent variable), 'x', to a Continuous output datum (dependent variable), 'y', R→R, y=ax+b+N(0,σ). Having the trivial problem defining parameter, we really only need one instance of the UnParameterised Model. Also see the fully parameterised Linear Model.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
class	Linear1.Est An Estimator of a, b and σ in y = a x +b + N(0,σ), where estimator parameter ps = ⟨bmin, bmax, σmin, σmax⟩ .
class	Linear1.M A fully parameterised Linear1 Model, y = a * x + b + N(0, σ) .

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

Modifier and Type	Field and Description
static Linear1	instance Having a trivial problem-defining parameter, we really only need one instance of the UnParameterised Linear1 Model.

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
Linear1(Value dp) The problem defining parameter dp is triv-ial.

Method Summary

Modifier and Type	Method and Description
Linear1.M	apply(Value sp) Return the fully parameterised Linear1 Model having statistical parameters sp.
Linear1.Est	estimator(Value ps) Return the [estimator].
Linear1.M	sp2Model(double m1, double m2, Value sp) Return the fully parameterised Linear1 Model having two-part message lengths m1 and m2 and statistical parameters sp.
Vector	stats(boolean add, Value ss0, Value ss1) Combine sufficient statistics ss0 and ss1 into ss0±ss1, either by addition (add=true) or by subtraction (add=false).
Vector.Doubles	stats(Vector ds, int lo, int hi) Given a data-set, ds, calculate sufficient statistics of ds[lo,hi), that is the quantities 'N' and the sums over all data ⟨x,y⟩* of each of the following, x, x2, xy, y, y2 and y.nlAoM.

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

instance

public static final Linear1 instance

Having a trivial problem-defining parameter, we really only need one instance of the UnParameterised Linear1 Model.

Constructor Detail

Linear1

public Linear1(Value dp)

The problem defining parameter dp is triv-ial.

Method Detail

stats

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

Given a data-set, ds, calculate sufficient statistics of ds[lo,hi), that is the quantities 'N' and the sums over all data ⟨x,y⟩^* of each of the following, x, x², xy, y, y² and y.nlAoM.

Specified by:

stats in class UPModel

stats

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

Combine sufficient statistics ss0 and ss1 into ss0±ss1, either by addition (add=true) or by subtraction (add=false).

Specified by:

stats in class UPModel

apply

public Linear1.M apply(Value sp)

Return the fully parameterised Linear1 Model having statistical parameters sp.

Overrides:

apply in class UPFunctionModel

sp2Model

public Linear1.M sp2Model(double m1,
                          double m2,
                          Value sp)

Return the fully parameterised Linear1 Model having two-part message lengths m1 and m2 and statistical parameters sp.

Specified by:

sp2Model in class UPFunctionModel

estimator

public Linear1.Est estimator(Value ps)

Return the [estimator].

Overrides:

estimator in class UPModel