Index

A B C D E F G H I J K L M N O P Q R S T U V W X Z

A

A() - Method in class graph.Graph: Return the Adjacency Matrix (of {0,1}s) of 'this' Graph.
A - Variable in class mml.ExponentialUPM.M: Statistical parameter 'A', the mean.
a - Variable in class mml.Linear1.M: Statistical parameters of the Model y = a × x + b + N(0, σ).
a - Variable in class mml.LinearD.M: Statistical parameter, 'a[D+1]' of the LinearD Model y=a·x+b+N(0,σ) where 'b' is a[D].
a2m(Alignment, Estimator) - Static method in class la.bioinformatics.Alignment: Estimate a SeriesModel to suit the Alignment al.
Adaptive - Class in mml: The UnParameterised Adapative Model of Bounded Discrete data; also see M.
Adaptive(Value) - Constructor for class mml.Adaptive: Definition parameters dp = (bounds, α) where bounds = (lwb, upb) on 'this' Model's data-space, and α_k > 0 is the "offset" to be added to the frequency of value v_k by nlLH(ss).
Adaptive.M - Class in mml: Model Mdl should be enough for most purposes but here is its class, fully (trivially) parameterised (M).
addMore(Vector.Slice) - Method in class la.maths.Vector: addMore(s,0,s.nElts()).
addMore(Vector, int, int) - Method in class la.maths.Vector: It is impossible, an error, to add more than zero elements (of 'v'==this) to 'this' Vector as they are all already there, but to a Slice is possible.
addMore(Vector, int, int) - Method in class la.maths.Vector.Slice: Return 'this' Slice with elements p.[loP,hiP) prepended or appended as appropriate; it requires (and checks) that p == parent(). It is an error if 'this' and p overlap.
adjacent(int, int) - Method in class graph.Graph: v0 and v1 are adjacent if ⟨v0, v1⟩ is an Edge (isEdge(v0,v1)) or if ⟨v1, v0⟩ is.
adjacent(int, int) - Method in class graph.Undirected: Equivalent to isEdge(v0,v1) for an Undirected Graph (but not for a Directed Graph).
adjacent(int, int) - Method in class graph.Undirected.AsDirected
advance() - Method in class graph.Graph.SubGraphs: Move on to the next subGraph in the Series.
advance() - Method in class la.util.Series: Advance past the current element -- there might or might not be any more; also see Series.hasSome().
advance(int) - Method in class la.util.Series: Advance past 'n' elements (an error if impossible).
advance() - Method in class la.util.Series.Lines
advance() - Method in class la.util.Series.Range
advance() - Method in class la.util.Series.Separator: Advance to the next variable, if any.
advance() - Method in class mml.SeriesModel.Analysis: Advance to the next Model and the next data element.
advanceTo(int) - Method in class la.util.Series: Advance to position 'n' (an error if impossible).
algnmnt(Value.Inc_Or[]) - Static method in class la.bioinformatics.Alignment: One way to create an Alignment, as a given.
Alignment - Class in la.bioinformatics: This is experimental code, not a production application. You are welcome to look, but consider it to be ephemeral.
Alignment() - Constructor for class la.bioinformatics.Alignment
Alignment.UPSame - Class in la.bioinformatics: An UnParameterised SeriesModel of Alignments where both sequences are of the same element Type.
Alignment.UPSame.M - Class in la.bioinformatics: The fully parameterised SeriesModel of Alignments.
alpha() - Method in class mml.Adaptive: The "offsets" to be the initial values of the frequency counters of data Values by nlLH(ss).
alpha - Variable in class mml.BetaUPM.M: Shape parameters 'alpha' and 'beta'.
alpha - Variable in class mml.Dirichlet.M: M's statistical parameter; α_i > 0, 0 ≤ i < D.
alpha - Variable in class mml.Poisson0UPM.M: The mean (and variance) of the Poisson0 distribution.
analyse(String, Vector) - Static method in class eg.Musicians: Fit a Mixture of one or more Normals to data-set 'ds'.
analysis(Value.Scannable) - Method in class la.bioinformatics.Alignment.UPSame.M: Separate out match/indel, Left elements, and Right elements from sv, do analyses of each, combine results.
analysis(Value.Scannable) - Method in class mml.Markov.M: Return the Analysis of a datum Series sv.
analysis(Value.Scannable) - Method in class mml.SeriesModel: Create (start) an Analysis of Scannable sv.
Analysis(Value.Scannable) - Constructor for class mml.SeriesModel.Analysis: Construct (prepare for) the Analysis of a given Scannable Value sv.
analysis(Value.Scannable) - Method in class mml.UPSeriesModel.K.M: The analysis of data Series sv returns the same Model, eltMdl, for every element in sv.
andSy - Static variable in class la.la.Lexical: Integer codes for the various lexical symbols; also see Lexical.Symbol.
AoM() - Method in class la.la.Value: See Value.Cts.AoM().
AoM() - Method in class la.la.Value.Cts: The Accuracy of Measurement (AoM), x()±(AoM()/2), as in 3.14 to the nearest 0.01, say.
AoM() - Method in class la.la.Value.Defer: force(), and return the v.AoM of this Deferred Value.
AoM() - Method in class la.la.Value.Int: Return zero, i.e., Ints are "exact".
AoM() - Method in class la.la.Value.Real: Returns 0.0 because a Real Value is precise.
AoM() - Method in class la.la.Value.Structured: The AoM of 'this' complete Structured Value.
AoM(int) - Method in class la.maths.Vector: Return this.elt(i).AoM() — assuming this is a Vector of Cts.
ap - Variable in class la.la.Value.Defer.App: The Function, 'f', and actual parameter, 'ap', to be applied at a later date, maybe.
aparam - Variable in class la.la.Expression.Application: Represents the application of 'fun' to 'aparam'.
App(Value, Value) - Constructor for class la.la.Value.Defer.App: Construct an unapplied (Function, actual-parameter) pair.
append(Vector) - Method in class la.maths.Vector: Append Vectors 'this' and 'v2', of the same type!
append() - Method in class la.maths.Vector: For 'this' non-empty Vector (data-set?) of Vectors, append them all, that is flatten k dimensions to (k-1) dimensions.
append(Vector) - Method in class la.maths.Vector.Slice: 'this' appended to v2 but be efficient if they have the same parent and abut.
append(Series) - Method in class la.util.Series: Series of elements from 'this' until exhausted, then from 's2'.
appendSB(StringBuffer) - Method in class la.la.Declaration: For printing; also see Expression.appendSB(java.lang.StringBuffer).
appendSB(StringBuffer) - Method in class la.la.Expression: The use of a StringBuffer, sb, gives linear complexity when printing large Values; also see Expression.toString().
appendSB(StringBuffer) - Method in class la.la.Expression.Application
appendSB(StringBuffer) - Method in class la.la.Expression.Binary
appendSB(StringBuffer) - Method in class la.la.Expression.Block
appendSB(StringBuffer) - Method in class la.la.Expression.Const
appendSB(StringBuffer) - Method in class la.la.Expression.Ident
appendSB(StringBuffer) - Method in class la.la.Expression.IfExp
appendSB(StringBuffer) - Method in class la.la.Expression.LambdaExp
appendSB(StringBuffer) - Method in class la.la.Expression.Tuple
appendSB(StringBuffer) - Method in class la.la.Expression.Unary
appendSB(StringBuffer) - Method in class la.la.Type: Also see Expression.appendSB(java.lang.StringBuffer).
APPLICATION - Static variable in class la.la.Expression: A tag; see Expression.n().
Application(Expression, Expression) - Constructor for class la.la.Expression.Application
applicPriority - Static variable in class la.la.Syntax
apply(Value) - Method in class la.la.Function: Apply 'this' Function to parameter 'p'.
apply(Value) - Method in class la.la.Function.Cts2Cts: Calls apply_x(c.x()).
apply(Value) - Method in class la.la.Function.Cts2Cts.Integral: Calls apply_x(lo.x()).
apply(Value) - Method in class la.la.Function.Cts2Cts2Cts: Given 'v', return a Cts2Cts which takes 'w' and calls apply_xx(v,w).
apply(Value) - Method in class la.la.Function.CtsD2CtsD: Given a Vector xs:R^D, return a Vector f(xs):R^D where 'f' is 'this' Function.
apply(Value) - Method in class la.la.Function.Native: apply(v) does whatever you specify to implement 'this' Native Function BUT it must return a Value in at least WHNF; see Value.force().
apply(Value) - Method in class la.la.Function.Native2: Take the parameter 'v0' and return a Function.Native which takes the parameter 'v1' to produce the result (this(v0))(v1).
apply(Value) - Method in class la.la.Function.Native3: Take the given parameter 'v0' and return a Function.Native2 which takes parameters 'v1' and 'v2' and produces a result.
apply(Value) - Method in class la.la.Value: This default throws an exception but see Function.apply(la.la.Value).
apply(Value) - Method in class la.la.Value.Defer: force() the Function and return v.apply(p).
apply(Value) - Method in class la.la.Value.Lambda: Calls code.body.eval(r'), where r' binds the formal parameter to the actual parameter.
Also see Expression.eval(la.la.Environment) on tail recursion.
apply(Value) - Method in class mml.Discretes
apply(Value) - Method in class mml.Estimator: Apply, 'this' Estimator to a data-set, ds, that is, return the Model ds2Model(ds).
apply(Value) - Method in class mml.Linear1: Return the fully parameterised Linear1 Model having statistical parameters sp.
apply(Value) - Method in class mml.NormalMu: apply(σ), return N_μ,σ.
apply(Value) - Method in class mml.NormalUPM: apply((μ, σ)), return a fully parameterised Normal M Model.
apply(Value) - Method in class mml.UPFunctionModel: Return the fully parameterised UPFunctionModel.M with statistical parameters sp.
apply(Value) - Method in class mml.UPModel: Given statistical parameters, sp, return a Model, sp2Model(0, 0, sp), i.e., one that is not estimated, having zero part 1 and part 2 message lengths.
apply(Value) - Method in class mml.UPSeriesModel: Return the fully parameterised SeriesModel with statistical parameters sp.
apply2(Value, Value) - Method in class la.la.Function.Cts2Cts2Cts: apply2 calls apply_xx(x,y).
apply2(Value, Value) - Method in class la.la.Function.Native2: apply2(v0,v1) does whatever you specify, BUT it must return a Value in WHNF – see Value.force().
apply2(Value, Value) - Method in class la.la.Function.Native3: Take the given parameters 'v0' and 'v1' and return a Function.Native which takes 'v2' and produces a result.
apply3(Value, Value, Value) - Method in class la.la.Function.Native3: apply3(v0,v1,v2) does whatever you specify, BUT it must return a Value in WHNF; see Value.force().
apply_nxx(int, double, double) - Method in class la.la.Function.Cts2Cts.Integral: Integrate f from 'lo' to 'x' numerically in 'n' steps using Simpson's rule, called by apply_xx.
apply_Vec(Vector) - Method in class la.la.Function.CtsD2CtsD: Used by apply(xs); ignores issues of the result's AoM.
apply_x(double) - Method in class la.la.Function.Cts2Cts: apply_x(x) is used by apply(c).
apply_x(double) - Method in class la.la.Function.Cts2Cts.Derivative: Calls apply_xx(δ,x) for some "small" finite-difference δ.
apply_x(double) - Method in class la.la.Function.Cts2Cts.Integral: Returns a Cts→Cts which (i) will take hix & call apply_xx(lox,hix), and (ii) has the Derivative f.
apply_x(double) - Method in class la.la.Library.Power: Returns c x^p.
apply_xx(double, double) - Method in class la.la.Function.Cts2Cts.Derivative: Compute the Derivative of f by finite-difference, δ.
apply_xx(double, double) - Method in class la.la.Function.Cts2Cts.Integral: Calls apply_nxx(n,lox,hix) with a small default value of 'n' to integrate f numerically.
apply_xx(double, double) - Method in class la.la.Function.Cts2Cts2Cts: Does the work, roughly this(v)(w)=apply_xx(v,w).
arithOprs - Static variable in class la.la.Syntax
arities - Variable in class la.la.Type.Option: Special case when ids={} and arities={}.
arrayA() - Method in class graph.Graph: Return the Adjacency Matrix as a square int[][].
as_1xn() - Method in class la.maths.Vector: Make a "row-Vector", i.e., a 1×n-Matrix, of 'this' Vector (regardless of the element type).
as_nx1() - Method in class la.maths.Vector: Make a "column Vector", i.e., a n×1-Matrix, of 'this' Vector (regardless of the element type).
asDiagonal(Value) - Method in class la.maths.Vector: Make a square, diagonal Matrix of 'this' Vector, using 'z' for all off-diagonal elements.
asDirected() - Method in class graph.Undirected: Convenience function.
AsDirected() - Constructor for class graph.Undirected.AsDirected
asEstimator(Value) - Method in class mml.Model: Return an Estimator that always "estimates" 'this' Model.
asGiven(double) - Method in class mml.FunctionModel: Calls asGiven(0, msg2).
asGiven(double, double) - Method in class mml.FunctionModel: Return a clone of 'this' FunctionModel but with msg1 and msg2 as specified.
asGiven(double) - Method in class mml.Mixture.M
asGiven(double, double) - Method in class mml.Mixture.M
asGiven(double) - Method in class mml.Model: Return 'this' Model as a "given", with zero first-part message length, and a specified second-part, msg2, asGiven(0,msg2).
asGiven(double, double) - Method in class mml.Model: Enables setting the first- and second-part message lengths, msg1 and msg2, after having estimated the statistical parameter(s) of a Model, say.
asGiven(double) - Method in class mml.SeriesModel: Calls #asGiven(0, msg2).
asGiven(double, double) - Method in class mml.SeriesModel: Return a clone of 'this' SeriesModel but with msg1 and msg2 as specified.
asGiven(double) - Method in class mml.UPFunctionModel.M: Calls asGiven(0,msg2).
asGiven(double, double) - Method in class mml.UPFunctionModel.M: Enables setting the first- and second-part message lengths, msg1 and msg2, after having estimated the statistical parameter(s), say.
asGiven(double) - Method in class mml.UPModel.M: Calls asGiven(0,msg2).
asGiven(double, double) - Method in class mml.UPModel.M: Enables setting the first- and second-part message lengths, msg1 and msg2, after having estimated the statistical parameter(s) of a Model, say.
asGiven(double) - Method in class mml.UPSeriesModel.M: Calls asGiven(0,msg2).
asGiven(double, double) - Method in class mml.UPSeriesModel.M: Enables setting the first- and second-part message lengths, msg1 and msg2, after having estimated the statistical parameter(s), say.
asInt() - Method in class la.util.Series.Discrete: Treat 'this' Series.Discrete as a Series.Int.
asList() - Method in class la.util.Series: Return a List of elements in 'this' Series; beware, do not share the Series because a Series has side-effects but a List does not.
asMatrix() - Method in class la.maths.Matrix: Return 'this'; also see Vector.asMatrix().
asMatrix() - Method in class la.maths.Vector: Provided 'this' is a rectangular Vector of Vectors, return it as the equivalent Matrix; also see Matrix.asMatrix().
asMultivariateM() - Method in class mml.Mixture.M: 'this' Mixture.M is not a Multivariate.M but this.asMultivariateM() is – provided that Mixture.upm is a Multivariate.
asQ() - Method in class la.maths.Q: Return 'this' -- also see Vector.asQ().
asQ() - Method in class la.maths.Vector: Make 'this' 4D Vector of Cts a quaternion in the obvious way.
asQrotn() - Method in class la.maths.Vector: Make 'this' 3D Vector of Cts, (x, y, z), a quaternion (0.0, x, y, z). Note, asQrotn() is used in rotate(q).
asUndirected() - Method in class graph.Directed: Convenience function.
AsUndirected() - Constructor for class graph.Directed.AsUndirected
asUPModel() - Method in class mml.Discretes.Bounded.M: Return a Bounded that always produces 'this' Bounded.M.
asUPModel() - Method in class mml.Estimator: In some context, it might(?) be necessary to treat 'this' Estimator as an UnParameterised Model, upm, where upm.estimator(triv) always returns 'this' Estimator.
asUPModel() - Method in class mml.FunctionModel: Treat 'this' fully parameterised FunctionModel as an UnParameterised one.
asUPModel() - Method in class mml.Model: It might be necessary, in some context, to treat 'this' fully parameterised Model as an UnParameterised Model, having trivial problem-definition parameter, that always produces (by apply, estimator, etc.) 'this' Model asGiven, so to say.
asUPModel() - Method in class mml.SeriesModel: Treat 'this' fully parameterised SeriesModel as an UnParameterised one.
asV3() - Method in class la.maths.Q: For 'this' Quaternion, (a,b,c,d), return the 3D Vector (b,c,d).
Atomic(String) - Constructor for class la.la.Type.Atomic
Atomic() - Constructor for class la.la.Value.Atomic
attributes - Static variable in class eg.Ducks: waddles.zip(quacks).

B

b - Variable in class la.la.Value.Bool: The boolean of 'this' Bool Value.
b - Variable in class mml.LaplaceUPM.M: The median, μ, and the scale, b, of this Model.
b - Variable in class mml.Linear1.M: Statistical parameters of the Model y = a × x + b + N(0, σ).
Bessel_I(double, double) - Static method in class la.maths.Maths: I_α(x), the modified Bessel function of the 1st kind; x ≥ 0, and the order α ≥ 0, are reals here.
Bessel_I2(double, double) - Static method in class la.maths.Maths: Uses Bessel_I_series(,) for "small" x, Bessel_I_asymptotic(,) for "large" x, and both at the cross-over (continuity).
Bessel_I_asymptotic(double, double) - Static method in class la.maths.Maths: For "large" x, uses Hankel's asymptotic expansion.
Bessel_I_series(double, double) - Static method in class la.maths.Maths: For "small" x, uses the series expansion.
BestOf - Class in mml: The UnParameterised BestOf Model – choose the best one out of a given Tuple of alternative UnParameterised Models.
BestOf(Value) - Constructor for class mml.BestOf: The problem definition parameter 'upms' is a Tuple of UnParameterised Models; saved as upms[].
BestOf.M - Class in mml: The fully parameterised Model; BestOf is the UnParameterised Model.
beta - Variable in class mml.BetaUPM.M: Shape parameters 'alpha' and 'beta'.
Beta - Static variable in class mml.MML: The UnParameterised Beta Model.
BetaUPM - Class in mml: (INCOMPLETE, no Estimator yet) The Β (Beta) Model (probability distribution).
BetaUPM(Value) - Constructor for class mml.BetaUPM
BetaUPM.M - Class in mml: The fully parameterised Beta Model (probability distribution).
BINARY - Static variable in class la.la.Expression: A tag; see Expression.n().
Binary(int, Expression, Expression) - Constructor for class la.la.Expression.Binary
bind(String, Value) - Method in class la.la.Environment: Extend 'this' Environment by binding a Variable 'id' to a Value 'v'; typically id is the formal parameter of a Function.
bind(String[], Value[]) - Method in class la.la.Environment: Extend 'this' Environment by binding Variables 'ids' to Values 'vs'.
binOprs - Static variable in class la.la.Syntax
bird_2_walk_N_talk - Static variable in class eg.Ducks: UnParameterised function-model of Species→Bool×Bool.
bird_N_walk_N_talk - Static variable in class eg.Ducks: UnParameterised Model of Species×(Bool×Bool).
birds - Static variable in class eg.Ducks: Also see waddles, quacks and weights.
birdUPM - Static variable in class eg.Ducks: UnParameterised Model over Ducks.Species={coot,duck,swan}.
BLOCK - Static variable in class la.la.Expression: A tag; see Expression.n().
Block(Declaration, Expression) - Constructor for class la.la.Expression.Block
bMax - Variable in class mml.Linear1.Est: Bounds on 'b'; prior, pr(b) is uniform.
bMin - Variable in class mml.Linear1.Est: Bounds on 'b'; prior, pr(b) is uniform.
body - Variable in class la.la.Expression.LambdaExp
book_eg - Static variable in class eg.Ducks: The particular Naive Bayes function-model used as an example in the book.
BOOL - Static variable in class la.la.Type: Also see Value.Bool.
Bool(int) - Constructor for class la.la.Value.Bool: n=0 for false, n=1 for true.
BOOL_N - Static variable in class la.la.Type: Integer codes for various "types" of Type.
boolUPM - Static variable in class eg.Ducks: UnParameterised Model over Bool={Ducks.f,Ducks.t}.
bOp(int, Function) - Method in class la.la.Function: A binary operator on Functions 'this' (f) and 'g', such that (f <op> g)(x) = f(x) <op> g(x).
bOp(int) - Static method in class la.la.Library: Return a curried Function based on the binary operator, 'op' (+, *, etc).
bOp(int, Value) - Method in class la.la.Value.Bool: Apply a binary operator, 'op', that is 'and', 'or' or the comparisons, to Bools 'this' and 'rgt'.
bOp(int, Value) - Method in class la.la.Value: Apply binary Operator 'op' to Values 'this' and 'rgt', if implemented (this default throws an Exception).
bOp(int, Value) - Method in class la.la.Value.Cts: Apply the binary operator 'op' to 'this', and 'rgt', Cts.
bOp(int, Value) - Method in class la.la.Value.Defer: All binary operators (other than 'cons') are strict on 'this', so force() and return v.bOp(op,rgt) for binary operator 'op'.
bOp(int, Value) - Method in class la.la.Value.Discrete: Apply binary operator 'op' to Discrete 'this', and to 'rgt'; this particular 'bOp(,)' takes care of the comparison operators.
bOp(int, Value) - Method in class la.la.Value.Int: Apply binary operator 'op', that is '+', '-', '*', '/' or the comparisons, to 'this' Int, and to 'rgt'.
bOp(int, Value) - Method in class la.la.Value.Tuple: Apply binary operator, 'op', element-wise to Tuples 'this' and 'rgt'.
bOp(int, Value) - Method in class la.maths.Vector: Binary operator, 'op', on 'this' Vector, and 'v', that is apply op element-wise returning a Vector of results.
BOTH - Static variable in class la.la.Value.Inc_Or: LEFT = 0, RIGHT = 1, BOTH = 2.
Both(Value, Value) - Constructor for class la.la.Value.Inc_Or.Both
bounded() - Method in class la.la.Type.Discrete: Does 'this' Discrete have both lower and upper bounds?
Bounded(Value) - Constructor for class mml.Continuous.Bounded
Bounded(Value) - Constructor for class mml.Discretes.Bounded
bounds() - Method in class la.la.Type.Discrete: 'this' Discrete's ⟨lwb, upb⟩ if appropriate, else exception.
bounds() - Method in class mml.Adaptive
bounds() - Method in class mml.Continuous.Bounded: Return (lwb, upb) of the data-space.
bounds() - Method in class mml.Continuous.Bounded.M: Bounded.this.bounds().
bounds() - Method in class mml.Continuous.Uniform
bounds() - Method in class mml.Discretes.Bounded: The bounds, [lwb(), upb()], on 'this' Model's dataspace.
bounds() - Method in class mml.Discretes.Bounded.M
bounds() - Method in class mml.Discretes.Uniform
bounds() - Method in class mml.MultiState
byDegree() - Method in class graph.Graph: Return 'this' Graph Renumbered with Vertices in decreasing order of degree.
byDegree(boolean) - Method in class graph.Graph: Return 'this' Graph Renumbered with Vertices in decreasing or increasing order of degree as 'descending' is true or false.
ByPdf - Class in mml: A class that may be useful in defining (UnParameterised) Models over continuous data-spaces such as, but not limited to, Value.Cts, and as defined by a probability density function pdf(d) and nlPdf(d).
ByPdf(Value) - Constructor for class mml.ByPdf
ByPdf.M - Class in mml: The (abstract) class of fully parameterised ByPdf Models; specify nlPdf(d).

C

C(int) - Constructor for class graph.Directed.C: The number of Vertices in the Graph and in the cycle.
c - Variable in class la.la.Library.Power: 'c' the multiplicative constant, and 'p' the power of 'x', as in c*x^p.
C - Static variable in class la.la.Value
C3 - Static variable in class eg.Graphing: Some possible motifs (subgraphs).
C4 - Static variable in class eg.Graphing: Some possible motifs (subgraphs).
canonical() - Method in class graph.Graph: Return a canonical Renumbering of 'this' Graph, that is the one yielding the largest adjacency matrix when it is read as a binary number, in a "certain order".
Canonical(int[]) - Constructor for class graph.Graph.Canonical: Note, the constructor does not (could not easily) check that 'vs' really is a canonical renumbering.
canonical() - Method in class graph.Graph.Canonical: Returns 'this' – it's canonical, right? Also see @link Graph#canonical() canonical()}.
canonical1(RefInt) - Method in class graph.Graph: Like canonical() but also counting the number of automorphisms.
canonical1R(int, int, BitSet, Graph.Renumbered, Graph.Renumbered, RefInt) - Method in class graph.Graph: Does the hard work of finding a 'bestG' -- 'this' Graph Renumbered -- for canonical1.
canonical2(RefInt) - Method in class graph.Graph: Does the hard work of finding a 'bestG' -- 'this' Graph Renumbered -- for canonical().
canonical2R(int[], int[], int, int, int, BitSet, Graph.Renumbered, Graph.Renumbered, RefInt) - Method in class graph.Graph: Does the hard work of finding a 'bestG' -- 'this' Graph Renumbered -- for canonical2(nAuto).
cartesian2polar - Static variable in class la.la.Library: This Function, R²→R², converts Cartesian coordinates ⟨x,y⟩ to polar coordinates ⟨r,θ⟩.
catalan(int) - Static method in class la.maths.Maths: Return the N^th Catalan number, N ≥ 0.
CD4 - Static variable in class eg.Graphing: Some possible motifs (subgraphs).
CD5 - Static variable in class eg.Graphing: Some possible motifs (subgraphs).
CD6 - Static variable in class eg.Graphing: Some possible motifs (subgraphs).
Cell(Value, Value) - Constructor for class la.la.Value.List.Cell
ch - Variable in class la.la.Value.Char: The char of 'this' Char Value.
CHAR - Static variable in class la.la.Type: Also see Value.Char.
Char(String) - Constructor for class la.la.Type.Char
Char(char) - Constructor for class la.la.Value.Char
CHAR_N - Static variable in class la.la.Type: Integer codes for various "types" of Type.
charLiteral - Static variable in class la.la.Lexical: Integer codes for the various lexical symbols; also see Lexical.Symbol.
CHARS - Static variable in class la.la.Type: Also see Value.Chars.
Chars(String) - Constructor for class la.la.Value.Chars
Chars2 - Static variable in class eg.Graphing: 'CHARS × CHARS' Type, i.e., a pair of CHARS (Strings).
check(int, Graph, int[], Graph, int[]) - Method in class mml.MotifA.M
check(Value) - Method in class mml.Simplex: Perform a validity check on datum v — that it is L₁-normalised.
checkProperties() - Method in class graph.Graph: Check type()'s properties hold for 'this' Graph.
checkProperties(Graph) - Method in class graph.Type: Check that Graph 'g' does satisfy the requirements of 'this' Type.
Child - Interface in graph: A Child Object can return the parent Graph from which it is Derived or to which it is otherwise related.
choice - Variable in class mml.BestOf.M: The index of the best of the upms[] (parameterised).
classEst - Variable in class mml.Mixture.Est: The estimator for an individual class (cluster, component) of the Mixture Model.
clearV(Graph, BitSet, Graph.Induced, BitSet) - Method in class mml.MotifD: ?Obsolete; 'instnc' only uses Vertices in 'free'?
clearV2(Graph, BitSet, Graph.Induced, BitSet) - Method in class mml.MotifD: ?Obsolete; 'instnc' only uses Vertex-pairs (V2) in 'free'?
clone(boolean) - Method in class graph.Type: Return a "cloned" Type but with 'isDirected' set as indicated.
close - Static variable in class la.la.Lexical: Integer codes for the various lexical symbols; also see Lexical.Symbol.
closes() - Method in class la.la.Value.Option: Is ")" if nElts()>0; see Value.Structured.print(java.io.PrintStream).
closes() - Method in class la.la.Value.Structured: Is ")", but can override to change formatting.
closes() - Method in class la.maths.Vector: "]"; see Value.Structured.print(java.io.PrintStream) and Value.Structured.toString().
code - Variable in class la.la.Value.Lambda
col(int) - Method in class la.maths.Vector: Provided 'this' is a Vector of Value.Structured, return the c-th column, as a Vector.
col - Variable in class mml.Tree.DFork: The number of the input column (variable) to be tested; NB.
col() - Method in class mml.Tree.DFork
col() - Method in class mml.Tree.Fork: The number of the column of the input that is to be tested.
col - Variable in class mml.Tree.OFork: The number of the input column (variable) to be tested; NB.
col() - Method in class mml.Tree.OFork
col() - Method in class mml.Tree.Param.Fork
colon - Static variable in class la.la.Lexical: Integer codes for the various lexical symbols; also see Lexical.Symbol.
cols(int[]) - Method in class la.maths.Vector: Provided 'this' is a Vector of Value.Tuple, return the Vector of Tuples made up of the columns specified by cs.
combine(boolean, Vector.Slice) - Method in class la.maths.Vector: Either addMore (add=true) Vector 'v' to this or delete (add=false) v from this Vector.
comma - Static variable in class la.la.Lexical: Integer codes for the various lexical symbols; also see Lexical.Symbol.
comparator - Static variable in class la.la.Value: comparator.compare(v1, v2) returns v1.compareTo(v2).
compare(String, Estimator, Mixture.Est, Vector, Vector) - Static method in class eg.Iris: Compare a no-mixture 'baseEst'-imated Model to a 'mxEst'-imated Mixture model on data-set 'ds4' (the full 'ds5' data-set is only for cross-tabulation).
compareTo(Value) - Method in class la.la.Value: Note, not all Values are actually Comparable.
compareTo(Value) - Method in class la.la.Value.Option: Is 'this' Option < (-1), > (+1), or = (0) rgt? Note, 'this' and 'rgt' must be of the same Option Type.
compareTo(Value) - Method in class la.la.Value.Tuple: Is 'this' Tuple < (-1), > (+1), or equal (0) to 'rgt'? Note, 'this' and rgt must have the same shape.
compareTo(Value) - Method in class la.maths.Vector: Compare 'this' Vector to v, returning -ve (<), 0 (=), or +ve (>).
comparisonOprs - Static variable in class la.la.Syntax
compete(int, int, Model, Estimator, Estimator) - Static method in class mml.Test: Do a number of 'trials', each time using Model 'gen' to generate a data-set of 'N' values, and then fitting Models by estimators 'e1' and 'e2' to that data-set to see which one wins in the minimum message length challenge.
compete(int, int, double, Model, Estimator, Estimator) - Static method in class mml.Test: Version of Test.compete(int, int, mml.Model, mml.Estimator, mml.Estimator) with a specified 'AoM', thus allowing for competition between Continuous.M Models.
condMdls - Variable in class mml.CPT.M: An array of now fully parameterised (conditional) upm-Models, one Model for each possible case (Value) of the input datum.
condModel(Value) - Method in class mml.CPT.M: Return the Model of the ouput datum conditional upon the Value of the input datum (variable), 'id'.
condModel(Value) - Method in class mml.FunctionModel: Return the Model for output datum, od, conditional upon the given input datum, id.
condModel(Value) - Method in class mml.Intervals.M: Return mdls[interval(id)].
condModel(Value) - Method in class mml.Linear1.M: Conditional Model of output datum y given input datum x, i.e., the Normal, N(ax + b, σ) .
condModel(Value) - Method in class mml.LinearD.M: Conditional Model of output datum y given input datum x, i.e., the Normal Model, N(a·x+b,σ).
condModel(Value) - Method in class mml.Multinomial.M: Returns a Trials(n).TM() appropriate to 'n' trials.
condModel(Value) - Method in class mml.NaiveBayes.M: Return a MultiState Model of output datum od conditional on given input datum id.
condModel(Value) - Method in class mml.Tree.DFork: Use column (variable) col of the input datum to choose one of subTrees.
condModel(Value) - Method in class mml.Tree.Leaf: Given input datum 'id', always return mdl of od.
condModel(Value) - Method in class mml.Tree.OFork: Use column (variable) col of the input datum, < v. ≥ split, to choose one of the two subTrees.
condModel(Value) - Method in class mml.UPFunctionModel.K.M: Return mdl; input datum id is ignored in K.
condNl2Pr(Value, Value) - Method in class mml.FunctionModel: Base 2 (bits) version of condNlPr.
condNlPr(Value, Value) - Method in class mml.FunctionModel: Return the negative log probability of the output datum, od, conditional upon the input datum, id.
condPr(Value, Value) - Method in class mml.FunctionModel: Return the probability of the output datum, od, conditional upon the input datum, id.
conjugate() - Method in class la.maths.Q: Return the conjugate, (a,-b,-c,-d), of 'this' Quaternion (a,b,c,d).
cons(Value, Value) - Static method in class la.la.Value: Convenience function returning the List h::t.
consSy - Static variable in class la.la.Lexical: Integer codes for the various lexical symbols; also see Lexical.Symbol.
CONST - Static variable in class la.la.Expression: A tag; see Expression.n().
Const(Value) - Constructor for class la.la.Expression.Const
constAoM() - Method in class la.maths.Vector: By default, return −1 (any negative) indicating the elements' AoMs (if appropriate) may vary from element to element.
constAoM() - Method in class la.maths.Vector.Derived: constAoM() of the original Vector.
constAoM() - Method in class la.maths.Vector.Ints
constAoM() - Method in class la.maths.Vector.Slice
constAoM() - Method in class la.maths.Vector.Weighted: As per parent Vector.
constNlAoM() - Method in class la.maths.Vector: If constAoM() is >0, return its negative log, otherwise an error.
constWt() - Method in class la.maths.Vector: By default, return 1.0 indicating that every element has that weight.
constWt() - Method in class la.maths.Vector.Derived: constWt() of the original Vector.
constWt() - Method in class la.maths.Vector.Slice
constWt() - Method in class la.maths.Vector.Weighted: Default -1, indicating elts' weights may vary from elt to elt.
contains(Type) - Method in class la.la.Type: Does 'this' Type contain (or equal) Type 't'?
contains(Type) - Method in class la.la.Type.Tuple: Does 'this' Tuple Type contain Type 't'?
contains(Type) - Method in class la.la.Type.Tuple.GP: Does 'this' Tuple(t1,t2,...) Type contain Type 't'?
contains(Type) - Method in class la.la.Type.Vector: Does 'this' Vector Type contain Type 't'?
Continuous - Class in mml: Continuous may be used to define (UnParameterised) Models over Cts Values by specifying a (-log) probability density function, Continuous.M.nlPdf_x(double), on a Value's 'x()'.
Continuous(Value) - Constructor for class mml.Continuous
Continuous.Bounded - Class in mml: UnParameterised Bounded Continuous Models, over a range, [lwb, upb], of Cts Values.
Continuous.Bounded.M - Class in mml: Fully parameterised Bounded Continuous Models.
Continuous.M - Class in mml: The (abstract) class of fully parameterised Continuous Models.
Continuous.M.Transform - Class in mml: A Continuous.M.Transform "is a" (extends) Continuous, i.e., is an UnParameterised Continuous model; the fully trivially parameterised Model is Continuous.M.Transform.MM.
Continuous.M.Transform.MM - Class in mml: (Wanted to call this class M, as in Continuous.M.Transform.M, but the compiler (1.8.0_101) objects.) The fully, trivially parameterised...M.
Continuous.Transform - Class in mml: A transformed UnParameterised Continuous model; the parameterised Model is Continuous.Transform.M.
Continuous.Transform.M - Class in mml: The fully parameterised transformed Model; the UnParameterised model is Continuous.Transform.
Continuous.Uniform - Class in mml: The UnParameterised Uniform Continuous Model on the range [lwb, upb].
Continuous.Uniform.M - Class in mml: Continuous.Uniform.Mdl should be sufficient for many purposes.
contraction(int[]) - Method in class graph.Graph: Convenience function.
Contraction(int[]) - Constructor for class graph.Graph.Contraction: vs must be a subset of the parent Graph's {0, ..., vSize()-1}.
coot - Static variable in class eg.Ducks: coot, duck, swan : Ducks.Species.
CPT - Class in mml: CPT -- Conditional Probability Table -- an (UnParameterised) FunctionModel, from an input datum with a working n(), such as a Value.Discrete or a Tuple of Discretes, to a Model of the output datum.
CPT(Value) - Constructor for class mml.CPT: Given problem-definition parameters, dp = (lwb,upb,upm), construct an (UnParameterised) CPT with (upb_n - lwb_n + 1) entries, each entry being (eventually) an fully parameterised upm-Model.
CPT.M - Class in mml: CPT.M, a fully parameterised Function Model, being a conditional probability table.
CR - Static variable in class la.la.Value
csv(boolean, boolean, char, Type.Tuple, int, InputStream) - Static method in class la.maths.Vector: Input a data-set of "comma-"separated values from a given InputStream (often a FileInputStream).
csv(Type.Tuple, InputStream) - Static method in class la.maths.Vector: csv(...) using some common default parameter values.
CTS - Static variable in class la.la.Type: Also see Value.Cts.
Cts(String) - Constructor for class la.la.Type.Cts
cts(double, double) - Static method in class la.la.Value: Return a Cts Value representing x±AoM/2.
Cts() - Constructor for class la.la.Value.Cts
Cts2Cts() - Constructor for class la.la.Function.Cts2Cts
Cts2Cts2Cts() - Constructor for class la.la.Function.Cts2Cts2Cts
CTS_N - Static variable in class la.la.Type: Integer codes for various "types" of Type.
CtsD2CtsD() - Constructor for class la.la.Function.CtsD2CtsD
cummulativeCatalans(int) - Static method in class la.maths.Maths: Return ∑_i=0..N catalan(i), N≥0.
curlclose - Static variable in class la.la.Lexical: Integer codes for the various lexical symbols; also see Lexical.Symbol.
curlopen - Static variable in class la.la.Lexical: Integer codes for the various lexical symbols; also see Lexical.Symbol.
currentSecs() - Static method in class la.la.FP: The current running time in seconds (to 0.001).
curry - Static variable in class la.la.Library: curry: ((t, u) → v) → (t → u → v).
cutPts - Variable in class mml.Intervals.M: The N-1 ordered cut-points, N≥1, dividing the data-space of the input datum, id, into N intervals (buckets), {<cp₀, [cp₀,cp₁), ..., ≥cp_N-2}.

D

D(Declaration) - Method in class la.la.Environment: Extend 'this' Environment as specified by Declaration(s) 'dec', semantically D : Decs → Env → Env.
d - Variable in class la.la.Expression.Block: Represents let [rec] d in e
D - Variable in class mml.Direction.Uniform: The dimension of R^D; see D().
D() - Method in class mml.Direction.Uniform: The dimension of R^D; return D.
D() - Method in class mml.LinearD: D(), the dimension of the input variable x:R^D in x→y.
D() - Method in class mml.R_D: D(), the dimension of the data-space R^D.
D - Variable in class mml.R_D.Forest: The number of columns, the dimension, D, of R^D.
D() - Method in class mml.R_D.Forest: The dimension, R_D.Forest.D, of R^D.
D - Variable in class mml.R_D.ForestSearch: A datum is a Vector of 'D' Continuous Values.
D() - Method in class mml.R_D.ForestSearch: Return R_D.ForestSearch.D.
D - Variable in class mml.R_D.Independent: The number of columns of the data.
D() - Method in class mml.R_D.Independent
D() - Method in class mml.R_D.M: R_D.this.D(), the dimension, D, of R^D.
D() - Method in class mml.R_D.M.Transform: The dimension, D(), of R^D is as per the enclosing R_D.M.
D - Variable in class mml.R_D.NrmDir: The dimension, D, of the data-space, R^D, i.e., dirnUPM's D().
D() - Method in class mml.R_D.NrmDir: Return D, the dimension of the data-space, R^D.
D() - Method in class mml.R_D.Transform: The dimension D() of the data-space R^D.
D() - Method in class mml.Simplex: D, the dimension of [0, 1]^D which contains the K-Simplex; D = K()+1.
D() - Method in class mml.vMF: D(), the dimension of R^D.
d2_dx2() - Method in class la.la.Function.Cts2Cts: The second derivative, d²/dx² this, that is d_dx() twice.
d_dx() - Method in class la.la.Function.Cts2Cts: Return the derivative of 'this' function, d/dx this.
Declaration - Class in la.la: Declaration specifies the abstract syntax (parse tree) of [rec] x0=e0, x1=e1, ...
Declaration(boolean, String[], Expression[]) - Constructor for class la.la.Declaration: Create a, possibly recursive (isRec?), Declaration binding identifier ids[0] to Expression es[0], and so on; also see Expression.Block.
Defaults(double, double, Value) - Constructor for class mml.Model.Defaults
defer(Environment) - Method in class la.la.Expression.Const: It is safe to return Value Expression.Const.v, which is known at "compile"-time; also see Expression.defer(la.la.Environment).
defer(Environment) - Method in class la.la.Expression: In general, create a Defer(this,r), a "thunk", to evaluate 'this' Expression later, but also see Expression.Const.defer(la.la.Environment) and Expression.LambdaExp.defer(la.la.Environment).
defer(Environment) - Method in class la.la.Expression.LambdaExp: It is in fact safe to return the eval-uated λ-Function (which contains the given 'r'), after all λ-Function and Value.Defer are very similar.
Defer() - Constructor for class la.la.Value.Defer
defnParams() - Method in class mml.Estimator: Return problem-defining parameter, for example, by UPModel.defnParams().
defnParams() - Method in class mml.NormalMu: Return Real μ, the problem defining parameter.
defnParams() - Method in class mml.UPModel: Return the common-knowledge, problem-defining parameter(s) for the fully parameterised M-Models that 'this' UPModel can and will produce.
defnParams() - Method in class mml.UPModel.Est: Return the enclosing UPModel's problem defining parameters, UPModel.this.defnParams().
degree(int) - Method in class graph.Directed: inDegree(v)+outDegree(v) (a self-loop ⟨v,v⟩ is counted twice).
degree(int) - Method in class graph.Graph: Also see Directed.degree(int) and Undirected.degree(int).
degree(int) - Method in class graph.Graph.Renumbered: The parent's degree(vs[v]).
degree(int) - Method in class graph.Undirected: Also see Directed.degree(int).
degree(int) - Method in class graph.Undirected.Sparse: O(1)-time.
delete(Vector.Slice) - Method in class la.maths.Vector: delete(s.parent(),s.lo,s.hi).
delete(Vector, int, int) - Method in class la.maths.Vector: Delete elements [lo, hi) of 'v' from 'this'; it requires (and checks) that v == this.
delete(Vector, int, int) - Method in class la.maths.Vector.Slice: Return 'this' Slice with elements p.[loP,hiP) deleted; it requires (and checks) that p == parent(). The elements being dropped must in fact be in 'this' Slice.
delta - Variable in class mml.NearInverse.M: The "statistical parameter".
delta_x - Variable in class mml.HeavyTail.Over_x1.M: The double version of δ>1, and the normalising constant 'k'.
dense(Type, Matrix) - Static method in class graph.Directed: Return a Dense Directed Graph having Type 't' and Adjacency Matrix 'A'.
dense(boolean, Matrix) - Static method in class graph.Directed: dense(t,A).
dense(Matrix) - Static method in class graph.Directed: dense(false, A) -- no self-loops.
dense(Type, int[][]) - Static method in class graph.Directed: dense(t,ints(A)).
dense(boolean, int[][]) - Static method in class graph.Directed: dense(t,A).
dense(int[][]) - Static method in class graph.Directed: dense(false,A) -- no self-loops.
Dense(Type, Matrix) - Constructor for class graph.Directed.Dense
dense(Type, Matrix) - Static method in class graph.Graph: Calls Directed.dense(Type,la.maths.Matrix) or Undirected.dense(Type,la.maths.Matrix) according to 't'.
dense(Type, int[][]) - Static method in class graph.Graph: Calls Directed.dense(Type,int[][]) or Undirected.dense(Type,int[][]) according to t.
dense(Type, Matrix) - Static method in class graph.Undirected: Return an Undirected Graph of Type 't' and square symmetric adjacency Matrix 'A'.
dense(boolean, Matrix) - Static method in class graph.Undirected: dense(t,A).
dense(Matrix) - Static method in class graph.Undirected: dense(false, A) -- no self-loops.
dense(Type, int[][]) - Static method in class graph.Undirected: dense(t,ints(.,A,.); A must be upper-right triangular.
dense(boolean, int[][]) - Static method in class graph.Undirected: dense(t,A).
dense(int[][]) - Static method in class graph.Undirected: dense(false,A) -- no self-loops.
Dense(Type, Matrix) - Constructor for class graph.Undirected.Dense
Dependent - Class in mml: An UnParameterised Dependent Model of data pairs, ⟨id, od⟩, made from an UnParameterised Model, upm, of the input (independent) datum, id, and from an UnParameterised FunctionModel, upfm, of the output (dependent) datum, od, conditional on id.
Dependent(Value) - Constructor for class mml.Dependent: Given problem-definition parameters, dp = ⟨Dependent.upm, Dependent.upfm⟩, construct a Dependent UPModel.
Dependent.M - Class in mml: A fully parameterised Dependent Model of data pairs, ⟨id, od⟩, made from a Model, im, of the input (independent) datum, id, and from a FunctionModel, fm, of the ouput (dependent) datum, od, conditional upon id.
Derivative() - Constructor for class la.la.Function.Cts2Cts.Derivative: Construct the Derivative of the Cts2Cts, 'f'.
Derived() - Constructor for class graph.Graph.Derived
Derived() - Constructor for class la.maths.Vector.Derived
DFork(double, double, Value) - Constructor for class mml.Tree.DFork: sp = (col, [sp₀, ..., sp_n-1]) where col is column number and sp_i is the statistical parameter(s) of subTrees_i.
DFork(int, Tree.Param[]) - Constructor for class mml.Tree.Param.DFork
Directed - Class in graph: The class of Directed Graphs.
Directed() - Constructor for class graph.Directed
Directed.AsUndirected - Class in graph: Ignore the directions of the Edges in 'this' Directed Graph to create an Undirected version; in general there is a loss of information.
Directed.C - Class in graph: The class of Cyclic Directed Graphs of the form v0 → v1 → ...
Directed.Dense - Class in graph: A Dense, Directed Graph with Type t and adjacency Matrix A.
Directed.Edge - Class in graph: Also see Undirected.Edge.
Directed.Sparse - Class in graph: The class of Sparse Directed Graphs.
Directed.Sparse.Induced - Class in graph: An induced subgraph of a Sparse Directed Graph is Sparse and Directed.
Directed.Sparse.Renumbered - Class in graph: A Sparse Directed Graph renumbered accpording to vs is Sparse and Directed.
Directed.Vertex - Class in graph: Also see Undirected.Vertex.
Direction - Class in mml: UnParameterised Models of Directions, that is of R^D-Vectors, of known norm (length, size, radius).
Direction(Value) - Constructor for class mml.Direction
Direction.M - Class in mml: (Abstract) a fully parameterised Model of Direction is made by defining nlPdf(v).
Direction.Uniform - Class in mml: The UnParameterised Uniform Model of Directions in R^D.
Direction.Uniform.M - Class in mml: Direction.Uniform.Mdl should be sufficient for many purposes, but here is M, the class of fully parameterised Uniform Direction Models.
directPredecessors(int) - Method in class graph.Directed.Sparse
directPredecessors(int) - Method in class graph.Graph: Only applicable to Directed Graphs.
directSuccessors(int) - Method in class graph.Directed.Sparse
directSuccessors(int) - Method in class graph.Graph: Only applicable to Directed Graphs.
Dirichlet - Class in mml: The UnParameterised Dirichlet Model (probability distribution) for data from a K-Simplex.
Dirichlet(Value) - Constructor for class mml.Dirichlet: K, the degrees of freedom in the data, that is one less than the dimension of the data.
Dirichlet.M - Class in mml: The fully parameterised Dirichlet Model (probability distribution); the UnParameterised Model is here.
dirnMdl - Variable in class mml.R_D.NrmDir.M: A fully parameterised Model of Directions, that is of R^D Vector Directions.
dirnUPM - Variable in class mml.R_D.NrmDir: dirnUPM is the UnParameterised Model of Direction in R^D.
Discrete(String, boolean, int, boolean, int) - Constructor for class la.la.Type.Discrete: Note, this is assumed to be un-ordered.
Discrete(String, boolean, int, boolean, int, boolean) - Constructor for class la.la.Type.Discrete: Construct a Discrete Type, which may or may not have a lower bound, and/or an upper bound, and be ordered or not.
Discrete() - Constructor for class la.la.Value.Discrete
Discrete(Type) - Constructor for class la.util.Series.Discrete
Discretes - Class in mml: The (abstract) sub-class of UnParameterised Model that may be helpful in defining Models of Discrete data-spaces (Values), such as Value.Enum data.
Discretes(Value) - Constructor for class mml.Discretes
Discretes.Bounded - Class in mml: The class of UnParameterised Models over Bounded Discrete data such as [3, 7], or DNA, say.
Discretes.Bounded.M - Class in mml: The (abstract) class of fully parameterised Discrete Bounded Models.
Discretes.M - Class in mml: The (abstract) class of fully parameterised Models of Discrete data-spaces.
Discretes.Shifted - Class in mml: This UnParameterised Model of Discretes shifted by +offset.
Discretes.Uniform - Class in mml: The UnParameterised Uniform Model (distribution) on data in bounds = [lo, hi], pretty much the simplest Discrete Model having only the trivial "statistical parameter." Also see the fully parameterised Discretes.Uniform.M, and Continuous.Uniform.
Discretes.Uniform.M - Class in mml: Discretes.Uniform.Mdl should be sufficient for most purposes.
display(Model, Vector) - Static method in class eg.Iris: Print out a summary of Model 'm', its 1st and 2nd-part message lengths and its performance on data-set 'ds'.
DNA - Static variable in class la.la.Type: DNA, an example Type.Enum Type.
dot - Static variable in class la.la.Lexical: Integer codes for the various lexical symbols; also see Lexical.Symbol.
dot(Vector) - Method in class la.maths.Vector: The dot- (inner-) product of Vectors of Cts, 'this' and 'v'.
double1(double) - Static method in class la.maths.Q: Return the Quaternion (a + 0i +0j + 0k).
doubles(double[][], double) - Static method in class la.maths.Matrix: Convenience function, doubles : double[][] × AoM → Matrix, where every element of the Matrix has the same AoM().
doubles(double[][]) - Static method in class la.maths.Matrix: Convenience function : double[][] → Matrix, where each elt(r,c) is an exact Real.
Doubles() - Constructor for class la.maths.Matrix.Doubles
Doubles(int) - Constructor for class la.maths.Matrix.Doubles
doubles(double[]) - Static method in class la.maths.Q: Given an array of doubles {a,b,c,d}, return the Quaternion (a + bi +cj + dk).
doubles(double, double, double, double) - Static method in class la.maths.Q: Return the Quaternion (a + bi +cj + dk).
doubles(double, double[]) - Static method in class la.maths.Vector: Convenience function : nlAoM × double[] → Vector, where we know the nlAoM of the Vector as a whole, but not of each elt(i).
doubles(double[], double) - Static method in class la.maths.Vector: Convenience function : double[] × AoM → Vector, where every element of the Vector has the same AoM (do not confuse it with Vector.doubles(double,double[]).
doubles(double[]) - Static method in class la.maths.Vector: Convenience function : double[] → Vector; note AoM=0.
Doubles() - Constructor for class la.maths.Vector.Doubles
dp - Variable in class mml.UPModel: The problem-defining parameter(s) of 'this' UnParameterised Model; see UPModel.defnParams().
dpndt - Variable in class mml.NaiveBayes: The (UnParameterised) Dependent Model, made of ⟨O, O→I⟩, that we are going to turn around (invert) to get I→O as desired.
dpndt_m - Variable in class mml.NaiveBayes.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.
drop(int) - Method in class la.maths.Vector: Return all but the first 'm' elements of 'this' Vector; see Vector.take(int) and Vector.slice(int, int).
drop(int, int) - Method in class la.maths.Vector: Drop elements [lo,hi) of 'this' Vector, lo inclusive, hi exclusive.
dropLast(int) - Method in class la.maths.Vector: Return all but the last 'm' elements of 'this' Vector; see Vector.slice(int, int).
ds - Static variable in class eg.Ducks: A toy data-set from which to estimate a function-model.
ds - Variable in class mml.SeriesModel.Analysis: The Series produced by the given data Series.
ds2FunctionModel(Value) - Method in class mml.UPFunctionModel.Est: Synonym for ds2Model(ds).
ds2L1stats(Vector) - Static method in class mml.R_D: Calculate all per-two-column Linear1-stats.
ds2mdlE(Value, Vector) - Method in class mml.Graphs.GERadaptive
ds2mdlE(Value, Vector) - Method in class mml.Graphs.GERfixed
ds2mdlE(Value, Vector) - Method in class mml.Graphs.IndependentEdges: Estimate a Discretes.Bounded.M, of Edge existence (0/1, false/true) from 'ds', a data-set of Graphs.
ds2mdlV(Value, Vector) - Method in class mml.Graphs: Estimate a Model of |V|, from 'ds', a data-set of Graphs.
ds2Model(Vector) - Method in class mml.Estimator: Given a data-set, ds, estimate a Model by mdl = ss2Model(stats(ds)).
ds2Model(Value) - Method in class mml.UPFunctionModel.Est: Given a data-set, ds, return a fully parameterised M.
ds2Model(Value) - Method in class mml.UPSeriesModel.Est: Given a data-set, ds, return a fully parameterised SeriesModel.
ds2ModelSp(Vector) - Method in class mml.Estimator: Given a data-set, ds, return ss2ModelSp(stats(ds)); read ss2ModelSp(ss) carefully!
ds2NorL1stats(Vector, int[]) - Static method in class mml.R_D: Calculate Normal-stats for each "parent-less" column and Linear1-stats for each column that has a 'parent' column.
ds2Nstats(Vector) - Static method in class mml.R_D: Calculate all per-column Normal-stats.
ds2SeriesModel(Value) - Method in class mml.UPSeriesModel.Est: Synonym for ds2Model(ds).
duck - Static variable in class eg.Ducks: coot, duck, swan : Ducks.Species.
Ducks - Class in eg: Ducks is a toy application program to illustrate the "(Naive-) Bayes" function-model in [the book].
Ducks() - Constructor for class eg.Ducks

E e - Variable in class la.la.Expression.Block e - Variable in class la.la.Expression.Unary The subexpression, e.g., 'exp' as in 'not exp'. e - Variable in class la.la.Value.Defer.Exp The Expression to be eval-uated, using Environment r, at some later date, maybe. E - Static variable in class la.la.Value e1 - Variable in class la.la.Expression.IfExp e2 - Variable in class la.la.Expression.IfExp e3 - Variable in class la.la.Expression.IfExp Edge(int, int) - Constructor for class graph.Directed.Edge Edge(int, int) - Constructor for class graph.Graph.Edge If 'this' is Graph.isDirected() ⟨v0,v1⟩, otherwise 'this' is Graph.isUndirected(), (v_s,v_b) where v_s is the smaller and v_b is the larger Vertex. Edge(int, int) - Constructor for class graph.Undirected.Edge edges() - Method in class graph.Graph Return the edges, [..., (e[i][0], e[i]][1]), ...] of 'this' Graph as a E×2 array suitable for Directed.Sparse or Undirected.Sparse, say. edgesCorrespond(Graph) - Method in class graph.Graph Assumes this.vSize() = g.vSize() and gets on with checking Edge correspondence, this.v_i : g.v_i. eg - package eg Package 'eg'; see eg's README. eigen() - Method in class la.maths.Matrix Return the Eigen-Values and Eigen-Vectors, (eVals, eVecs), of 'this' square, symmetric Matrix using the Matrix.Jacobi algorithm. eigenValues() - Method in class la.maths.Matrix Return the Eigen-Values of 'this' square, symmetric Matrix. eigenVectors() - Method in class la.maths.Matrix Return the Eigen-Vectors of 'this' square, symmetric Matrix. eight - Static variable in class la.la.Value eLabel(int, int) - Method in class graph.Directed.AsUndirected eLabel(int, int) - Method in class graph.Directed.Sparse.Induced eLabel(int, int) - Method in class graph.Directed.Sparse.Renumbered eLabel(int, int) - Method in class graph.Graph.Contraction Edge labels, if any, as per v2pv and the parent Graph. eLabel(int, int) - Method in class graph.Graph UnsupportedOperation, the default assumption is no Edge labels. eLabel(int, int) - Method in class graph.Graph.Induced Edge labels, if any, as per vs and the parent() Graph. eLabel(int, int) - Method in class graph.Graph.Renumbered The parent's eLabel(vs[v0],isEdge[v1]). eLabel(int, int) - Method in class graph.Graph.ToDirected eLabel(int, int) - Method in class graph.Graph.ToUndirected eLabel(int, int) - Method in class graph.Undirected.AsDirected eLabel(int, int) - Method in class graph.Undirected.Sparse.Induced eLabel(int, int) - Method in class graph.Undirected.Sparse.Renumbered eLabelled() - Method in class graph.Graph Are the Edges labelled? Also see eLabel(v0,v1). eLabels() - Method in class graph.Graph Return all Edge labels, or null if unlabelled. elseSy - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. elt() - Method in class graph.Graph.SubGraphs Return the current subGraph in 'this' Series if hasSome(). elt(int) - Method in class graph.Type 0:isDirected, 1:selfLoops, 2:vType, 3:eType. elt(int) - Method in class la.bioinformatics.Alignment elt(i) must be an Value.Inc_Or. elt(int) - Method in class la.la.Type This default throws an error, inapplicable. elt(int) - Method in class la.la.Type.Function elt(0) returns the input Type, elt(1) the output, if known! elt(int) - Method in class la.la.Type.Model elt(0) returns the dataspace (Type), if known! elt(int) - Method in class la.la.Type.Tuple.GP Return the i-th component Type. elt(int) - Method in class la.la.Type.Vector elt(int) - Method in class la.la.Value.Chars elt(int) - Method in class la.la.Value.Defer force(), and return the v.elt(i) of this Deferred Value. elt(int) - Method in class la.la.Value See Value.Structured.elt(int). elt(int) - Method in class la.la.Value.Inc_Or.Both elt(int) - Method in class la.la.Value.Inc_Or.Left elt(int) - Method in class la.la.Value.Inc_Or.Right elt(int) - Method in class la.la.Value.List Generally use Value.List.hd() or Value.List.tl() instead. elt(int) - Method in class la.la.Value.Maybe.Just elt(int) - Method in class la.la.Value.Option.GP Return 'this' Option's i-th element, elt(i), in WHNF. elt(int) - Method in class la.la.Value.Structured The i-th element (component, field) of 'this' Structured Value, counting from zero. elt(int) - Method in class la.la.Value.Tuple Returns the i-th elt (forced). elt(int) - Method in class la.la.Value.Tuple.GP elt(int) - Method in class la.maths.Matrix Row 'r' (top-level element r) of 'this' Matrix; a sub-class of Matrix might be able to provide a more efficient version than this default. elt(int, int) - Method in class la.maths.Matrix The Matrix element at position c of elt(r), that is at column c of row r of 'this' Matrix. elt(int) - Method in class la.maths.Matrix.GP2 elt(int, int) - Method in class la.maths.Matrix.GP2 Return the (r,c)-th of elts, forced. elt(int) - Method in class la.maths.Vector.Derived elt(i) of the original Vector. elt(int) - Method in class la.maths.Vector Element 'i' of 'this' Vector. elt(int, int) - Method in class la.maths.Vector Provided 'this' is a Vector of Value.Structured, return the element at row 'r', column 'c'. elt(int) - Method in class la.maths.Vector.GP Return the i-th element (forced). elt(int) - Method in class la.maths.Vector.Slice parent().elt(lo+i). elt(int) - Method in class la.maths.Vector.Weighted elt() - Method in class la.util.Series.Discrete See elt_n(). elt() - Method in class la.util.Series Return the current element, if there is one, but do not Series.advance(). elt() - Method in class la.util.Series.Lines The current line (a Chars Value). elt() - Method in class la.util.Series.Range elt() - Method in class la.util.Series.Separator elt() - Method in class mml.SeriesModel.Analysis Return a pair being the Model for the current data element and that data element, that is ⟨eltM(), eltD()⟩. elt(int) - Method in class mml.Tree.Param.DFork Return col if i==0, Tree.Param.Fork.subTs() if i==1. elt(int) - Method in class mml.Tree.Param.Leaf Return sp, providing i==0. elt(int) - Method in class mml.Tree.Param.OFork Return col if i==0, split if i==1, or Tree.Param.Fork.subTs() if i==2. elt_n() - Method in class la.util.Series.Discrete The current element's int "code". elt_n() - Method in class la.util.Series.Range eltD() - Method in class mml.SeriesModel.Analysis Return the current element of the data Series; also see SeriesModel.Analysis.elt(). eltM() - Method in class mml.SeriesModel.Analysis Return the Model of the current data element; also see SeriesModel.Analysis.elt() and SeriesModel.Analysis.finalModel(). eltMdl - Variable in class mml.Sequences.K.M The (parameterised) Model to be used as the Model of every element of the Sequence. eltMdl - Variable in class mml.UPSeriesModel.K.M Fully parameterised Model, eltMdl, of elements. elts - Variable in class la.la.Type.Tuple.GP The component Types (fields) of 'this' Tuple. elts - Variable in class la.la.Value.Option.GP The elements (components, fields) 'elts', if any. elts() - Method in class la.la.Value.Option.GP elts() - Method in class la.la.Value.Structured All the elements (components, fields) as an array; this default implementation may well be bettered in a sub-class. elts(int[]) - Method in class la.la.Value.Tuple Return a sub-Tuple of 'this', made of just the elements at the selected positions, 'ps'. elts - Variable in class la.la.Value.Tuple.GP The elements of 'this' Tuple.GP. elts(int[], int[]) - Method in class la.maths.Matrix Return the sub-Matrix with rows, rs, and columns, cs. elts - Variable in class la.maths.Matrix.GP2 The [][] of Values held in 'this' GP2. elts(int[]) - Method in class la.maths.Vector Return a sub-Vector of 'this', being made up of the positions, ps. elts - Variable in class la.maths.Vector.GP The array of Values held in 'this' GP. elts() - Method in class la.maths.Vector.GP eltType - Variable in class la.la.Type.Vector The element Type of a Vector (if known). eltType() - Method in class la.la.Value.Chars The element Type is Type.CHAR. eltType() - Method in class la.maths.Matrix.Doubles eltType() - Method in class la.maths.Matrix The element Type of 'this' Matrix must be VECTOR. eltType() - Method in class la.maths.Matrix.Ints eltType() - Method in class la.maths.Vector.Derived The element Type of the original Vector. eltType() - Method in class la.maths.Vector.Doubles eltType() - Method in class la.maths.Vector The type of all(!) elements of 'this' Vector. eltType() - Method in class la.maths.Vector.Ints eltType() - Method in class la.maths.Vector.Slice eltType() - Method in class la.maths.Vector.Strings eltType() - Method in class la.maths.Vector.Weighted eltUPM - Variable in class mml.Sequences.K The (UnParameterised) Model to be used as the Model of every element of every Sequence datum. eltUPM - Variable in class mml.UPSeriesModel.K eltUPM, the UnParameterised Model of elements; also see UPSeriesModel.K.lenUPM. EM(Vector, Vector, Estimator) - Static method in class la.bioinformatics.Alignment EM(Mixture.M, double[][], Vector) - Method in class mml.Mixture.Est The core Expectation Maximization algorithm. EM(double[][], Vector) - Method in class mml.Mixture.Est Find a good Mixture Model starting from the given memberships. EM(Mixture.M, Vector) - Method in class mml.Mixture.Est Find a good Mixture Model starting from the given Model, mx. empty - Static variable in class la.la.Environment A (the) empty Environment -- might be useful for Functions that are complete unto themselves? empty - Static variable in class la.maths.Vector The empty Vector with no elements at all. EMPTY - Static variable in class la.util.Series The EMPTY Series always hasNone(). Enum(String, String[]) - Constructor for class la.la.Type.Enum An Enum Type created thus is assumed to be un-ordered. Enum(String, String[], boolean) - Constructor for class la.la.Type.Enum E.g., DNA = A | C | G | T. Enum(int) - Constructor for class la.la.Value.Enum ENUM_N - Static variable in class la.la.Type Integer codes for various "types" of Type. env - Static variable in class la.la.Library An Environment binding names (Strings) to standard Functions, for example, "id"→id, "fst"→fst, etc. Environment - Class in la.la Semantically, Environment = Identifier → Value, an Environment, r : Environment ('r' is close to the Greek ρ), is a function (in Java a class) mapping identifers onto the Values bound to them. Environment() - Constructor for class la.la.Environment Create an empty Environment (with no sub-Environment). Environment(Environment) - Constructor for class la.la.Environment Create an empty Environment linked to next. eofSy - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. eoi() - Method in class la.la.Lexical Is this Lexical at its 'end of input', or not? epsilon - Static variable in class la.maths.Maths A tiny amount, epsilon, to allow for rounding error where necessary. eq - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. errMsg(String) - Method in class la.la.Value It has all gone pear-shaped; identify 'this' and 'msg'. error(String) - Method in class la.la.Expression Throw a RuntimeException. error(String) - Method in class la.la.Lexical Print an error message, msg, and stop. error(String) - Method in class la.la.Syntax Print an error message, msg, and stop. error(String) - Method in class la.la.Value Throws RTE(msg). error(String) - Method in class la.util.Series Throw a RuntimeException. error(String) - Method in class la.util.Timer Throw a RuntimeException. error(String) - Static method in class la.util.Util Throw a RuntimeException, RTE(msg). es - Variable in class graph.Directed.Sparse The Edges of 'this' Sparse, as an int[E][2] array. es - Variable in class graph.Undirected.Sparse The Edges, as int[|E|][2], defining 'this' Undirected Sparse Graph. es - Variable in class la.la.Declaration The Expressions (Values) bound to the identifiers, 'ids'. eSize() - Method in class graph.Directed.Sparse eSize() - Method in class graph.Graph The number of Edges in 'this' Graph, |E|≥0. eSize() - Method in class graph.Undirected.Sparse Est(Value) - Constructor for class mml.Linear1.Est Parameter ps = ⟨bMin, bMax, sigmaMin, sigmaMax⟩. Est(Value) - Constructor for class mml.LinearD.Est The estimators parameter 'ps' is the bounds on σ. Est(Value) - Constructor for class mml.Mixture.Est Note that classEst = upm.estimator(ps). Est(Value) - Constructor for class mml.NormalMu.Est ps = (siMin, siMax). Est(Value) - Constructor for class mml.NormalUPM.Est ps = ⟨ μ_min, μ_max, σ_min, σ_max ⟩, uniform prior on μ and 1/σ on σ, in bounds. Est(Value) - Constructor for class mml.Tree.Est Note, ps is passed to leafUPM.estimator(ps) to make leafEst. Est(Value) - Constructor for class mml.UPFunctionModel.Est Est(Value) - Constructor for class mml.UPModel.Est Given parameter 'ps', possibly trivial, construct an Estimator of fully parameterised M-Models. Est(Value) - Constructor for class mml.UPSeriesModel.Est Parameter(s) 'ps' may, for example, control a prior on a Model's statistical parameters. eStats() - Method in class graph.Graph Return Edge statistics, [#non_Edges, #Edges], as doubles. estDF(int, boolean[], Vector, int, Tree.Est.Sel, Vector, int) - Method in class mml.Tree.Est Estimate a DFork on the Discrete Bounded column 'col', numbered 'cN', of the input (indep.) datum. estimated - Static variable in class eg.Ducks A Naive Bayes function-model estimated from data-set Ducks.ds. estimator(Value) - Method in class la.bioinformatics.Alignment.UPSame Return the Estimator. estimator(Value) - Method in class mml.Adaptive Return the trivial Estimator, Mdl.asEstimator(t), of Mdl. estimator(Value) - Method in class mml.BestOf Return an Estimator of a fully parameterised BestOf.M Model (essentially pick the best of the upms[]). estimator(Value) - Method in class mml.BetaUPM The Estimator is not yet implemented. estimator(Value) - Method in class mml.Continuous.M.Transform Trival estimator, always "estimates" Mdl. estimator(Value) - Method in class mml.Continuous.Transform Uses the enclosing Continuous.this's estimator but statistics take the transforming function 'f' into account. estimator(Value) - Method in class mml.Continuous.Uniform The trivial Estimator that is a Uniform's. estimator(Value) - Method in class mml.CPT Return an Estimator of fully parameterised CPTs -- of CPT.M. estimator(Value) - Method in class mml.Dependent The Estimator's parameter is a pair, ps = ⟨upm's, upfm's⟩. estimator(Value) - Method in class mml.Direction.Uniform The trivial Estimator that is a Uniform Direction's. estimator(Value) - Method in class mml.Discretes.Shifted The shifted Estimator estimates a shifted Model. estimator(Value) - Method in class mml.Discretes.Uniform The trivial Estimator that is a Uniform's. Estimator - Class in mml An Estimator estimates (fits) a fully parameterised Model to a data-set, thus solving the inference problem posed by some UnParameterised Model. Estimator(Value) - Constructor for class mml.Estimator An Estimator may have parameters, notably parameters of a prior. estimator(Value) - Method in class mml.ExponentialUPM The prior is 1/A over [lwb, upb] and, with this prior, the MML estimate is the sample mean. estimator(Value) - Method in class mml.GammaUPM TODO, Gamma's Estimator is not yet implemented. estimator(Value) - Method in class mml.Geometric0UPM The Estimator has a parameter, 'AA', the parameter of the Exponential prior on the mean of the Geometric distribution. estimator(Value) - Method in class mml.Graphs.GERadaptive Graphs.IndependentEdges.estimator(la.la.Value)(pair(ps,triv)), the triv for Adaptive's estimator. estimator(Value) - Method in class mml.Graphs.GERfixed Graphs.IndependentEdges.estimator(la.la.Value)(pair(ps,triv)), the triv for MultiState's estimator. estimator(Value) - Method in class mml.Graphs.IndependentEdges estimator(Value) - Method in class mml.Graphs.Skewed Only Graphs.M.mdlV is actually estimated. estimator(Value) - Method in class mml.Independent Given a Tuple ps, where ps.elt(i) is the parameter of sub-Model Estimator upms[i].estimator(ps.elt(i)), return an Estimator for the Independent Model. estimator(Value) - Method in class mml.Intervals Return an Estimator for M. estimator(Value) - Method in class mml.LaplaceUPM Parameters ps = (μ_min, μ_max, b_min, b_max). estimator(Value) - Method in class mml.Linear1 Return the [estimator]. estimator(Value) - Method in class mml.LinearD Return an Estimator of a fully parameterised LinearD.M Model. estimator(Value) - Method in class mml.LogStar0UPM Return the "trivial" Estimator of logStar0. estimator(Value) - Method in class mml.Markov Return the Markov Model Estimator. estimator(Value) - Method in class mml.Missing Return an Estimator of a fully parameterised Missing.M-Model. estimator(Value) - Method in class mml.Mixture Return an estimator for 'this' Mixture; note, its parameter will be upm's parameter. estimator(Value) - Method in class mml.Model.Transform Trivial estimator, always "estimates" Mdl. estimator(Value) - Method in class mml.MotifA Return an Estimator for a Motif Model. estimator(Value) - Method in class mml.MotifD Return an Estimator for a MotifD Model. estimator(Value) - Method in class mml.Multinomial Return an Estimator of a Multinomial.M; parameter 'ps' is 'triv', '( )'. estimator(Value) - Method in class mml.MultiState Return an Estimator for a MultiState Model; this version has no non-trivial parameters, but another version could -- if it used a non-uniform prior, say. estimator(Value) - Method in class mml.NaiveBayes Return the Estimator for NaiveBayes; any parameter(s), ps, is passed to the Dependent's Estimator. estimator(Value) - Method in class mml.NormalMu Return an Estimator of a fully parameterised Normal Model. estimator(Value) - Method in class mml.NormalUPM Return an Estimator for the Normal distribution where parameter ps = (μ_min, μ_max, σ_min, σ_max) is for the Uniform prior on μ, and 1/σ on σ, within the bounds. estimator(Value) - Method in class mml.Permutation.Uniform The trivial Estimator that is a Uniform Model's. estimator(Value) - Method in class mml.Poisson0UPM The Estimator has a parameter, AA, the parameter and mean of the Exponential prior. estimator(Value) - Method in class mml.R_D.Forest Estimator for a fully parameterised Forest model. estimator(Value) - Method in class mml.R_D.ForestSearch Parameter 'ps' is bounds on μ, σ, and b. estimator(Value) - Method in class mml.R_D.Independent Use upm's estimator to estimate message lengths and statistical parameters for each column of the data. estimator(Value) - Method in class mml.R_D.M.Transform Trival estimator, always "estimates" Mdl. estimator(Value) - Method in class mml.R_D.NrmDir Return an Estimator; its parameter ps = (ps_n, ps_d) where ps_n is for normUPM's estimator and ps_d is for dirnUPM's. estimator(Value) - Method in class mml.R_D.Transform Uses the enclosing R_D.this's estimator but statistics take the transforming function 'f' into account. estimator(Value) - Method in class mml.Sequences.K Return the Estimator of a K.M. estimator(Value) - Method in class mml.Simplex.Uniform The trivial Estimator that is a Uniform's. estimator(Value) - Method in class mml.Tree Return an Estimator for a Tree FunctionModel, M:id→od. estimator(Value) - Method in class mml.UPFunctionModel.K The Estimator gets the Model for the (every) output datum, od, from UPFunctionModel.K.upm.estimator(ps). estimator(Value) - Method in class mml.UPModel Given parameter(s), 'ps' (possibly parameters of a prior), return an Estimator for 'this' UPModel. estimator(Value) - Method in class mml.UPModel.Transform The Estimator uses UPModel.this's estimator(ps) but its ss2Model(ss) is given statistics transformed by Function f. estimator(Value) - Method in class mml.UPSeriesModel.K ps = (ps for lenUPM's, ps for eltUPM's). estimator(Value) - Method in class mml.vMF Calls the MML estimator. estimator(Value) - Method in class mml.WallaceInt0UPM Return the trivial Estimator of WallaceInt0. estimatorB(Value) - Method in class mml.NormalMu Return an Estimator of a fully parameterised Normal Model; note, t = triv! The Estimator calls NormalUPM.estSigma(la.la.Value, double, double, double) to estimate σ. estimatorB(Value) - Method in class mml.NormalUPM Return an Estimator for the Normal distribution where parameter bounds = (μ_min, μ_max) is for the Uniform prior on μ; σ ~ nearly 1/σ and is unbounded. estimatorMaxLH(Value) - Method in class mml.vMF The (rough and ready) maximum likelhood estimator for μ and κ; see the MML estimator. estimatorMML(Value) - Method in class mml.vMF The MML Estimator for μ and κ. estLeaf(Vector) - Method in class mml.Tree.Est Estimate a Leaf, i.e., Model of the output (dependent) column ignoring the input (indep.) column(s). estOF(int, boolean[], Vector, int, Tree.Est.Sel, Vector, int) - Method in class mml.Tree.Est Estimate an OFork on the input column 'col', numbered 'cN'. estSigma(Value, double, double, double) - Static method in class mml.NormalUPM estSigma is called upon by NormalUPM.estimatorB and also by NormalMu.estimatorB to estimate σ. eType - Variable in class graph.Type Does the Graph have Vertex- and/or Edge- labels, and if so, of what Type(s)? eval(Environment) - Method in class la.la.Expression.Binary Apply 'this' binary operator, opr, to lft and rgt sub-Expressions in the given Environment 'r'. eval(Environment) - Method in class la.la.Expression.Const Note, a Const was evaluated, to Expression.Const.v, at "compile"-time. eval(Environment) - Method in class la.la.Expression EVALuate 'this' Expression, using Environment 'r', to produce a Value. eval(Environment) - Method in class la.la.Expression.Ident lookup 'this' Ident's Value in 'r'. eval(Environment) - Method in class la.la.Expression.LambdaExp Note, evaluate 'this' Lambda Expression to a Function, not call a function. eval(Environment) - Method in class la.la.Expression.Tuple A Tuple Expression evaluates to a Tuple Value. eval(Environment) - Method in class la.la.Expression.Unary Apply 'this' unary operator, opr, to the sub-Expression in the given Environment 'r'. eVals - Variable in class la.maths.Matrix.Jacobi The Eigen-values, as double[]. eVecs - Variable in class la.maths.Matrix.Jacobi The Eigen-vectors, as double[][], one E-vector per row. exactIntegral(Function.Cts2Cts) - Method in class la.la.Function.Cts2Cts Creates a definite integral from an indefinite one, F, if such a closed form is known. exercise(String, Estimator, Vector) - Static method in class eg.Iris Using Estimator 'e', fit a Model to data-set 'ds' and show some results. exercise(Estimator, Vector) - Static method in class mml.Test Use Estimator 'est' to fit a Model 'm' to data 'ds', then exercise(m,ds). exercise(Model, Vector) - Static method in class mml.Test Run Model 'm' through a few simple tests on data-set 'ds'. exhaust() - Method in class la.util.Series Advance until hasNone, presumably for side-effects. exp - Static variable in class la.la.Library exp is a Cts2Cts with an Inverse-Function, i.e., log. exp() - Method in class la.la.Syntax Exp(Expression, Environment) - Constructor for class la.la.Value.Defer.Exp Construct an un-eval-uated (Expression, Environment) pair. Exponential - Static variable in class mml.MML The UnParameterised Exponential Model. ExponentialUPM - Class in mml The class of UnParameterised (negative-) Exponential Model(s). ExponentialUPM(Value) - Constructor for class mml.ExponentialUPM ExponentialUPM.M - Class in mml The fully parameterised (negative-) Exponential Model, has statistical parameter A, its mean. Expression - Class in la.la Class Expression defines the abstract-syntax (i.e., parse tree) of Expressions in the language; also see Value. Expression() - Constructor for class la.la.Expression Expression.Application - Class in la.la A Function application (e1 e2), where e1 must evaluate to a Function. Expression.Binary - Class in la.la (e1 op e2); yes it's just a special kind of Expression.Application. Expression.Block - Class in la.la let <Decs> in e. Expression.Const - Class in la.la Various Constants (literals) of the language; can be eval-uated at "compile" time; see defer. Expression.Ident - Class in la.la An Identifier, 'id', as (it should have been) declared in let id = e in e, or λid.e . Expression.IfExp - Class in la.la (if e1 then e2 else e3); yes it's just a special kind of Expression.Application -- of cond = \x1.\x2.\x3.x1 x2 x3 Expression.LambdaExp - Class in la.la λ x.e denotes a Function. Expression.Tuple - Class in la.la (e0, e1, ...); also see Value.Tuple. Expression.Unary - Class in la.la (op e); yes it's just a special kind of Expression.Application. F f - Static variable in class eg.Ducks 't' and 'f', short for Value.ttrue and Value.ttrue. f - Variable in class la.la.Function.Cts2Cts.Derivative "f" is a synonym for Cts2Cts.this, the Cts2Cts of which "this" is the Derivative, f'. f - Variable in class la.la.Function.Cts2Cts.Integral 'f' is a synonym for 'Cts2Cts.this' and 'this' Integral is the (definite) Integral of 'f'. f - Variable in class la.la.Value.Defer.App The Function, 'f', and actual parameter, 'ap', to be applied at a later date, maybe. f - Variable in class mml.Continuous.M.Transform The Function, f:Cts→Cts, doing the transforming of data. f - Variable in class mml.Continuous.Transform The continuous Function, f:Cts→Cts, doing the transforming of data. f(double) - Method in class mml.HeavyTail.Over_x1.M f(x) = 1/(1+x)^δ, where δ>1. f - Variable in class mml.Model.Transform 'f' the function doing the transforming (of data). f - Variable in class mml.R_D.M.Transform The Function, f:Cts^D→Cts^D, doing the transforming (of data). f - Variable in class mml.R_D.Transform The Function, f:CtsD2CtsD, R^D→R^D doing the transforming of data. f - Variable in class mml.UPModel.Transform 'f' is the Function doing the transforming (of data). falseSy - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. ffalse - Static variable in class la.la.Expression Expression.Constant false; see Value.ffalse. ffalse - Static variable in class la.la.Value FFL - Static variable in class eg.Graphing Some possible motifs (subgraphs). FFL4 - Static variable in class eg.Graphing Some possible motifs (subgraphs). field(Value) - Method in class mml.Tree.Fork Select column (col()≥0), or all (col()<0), of the input datum. finalModel() - Method in class mml.SeriesModel.Analysis If and when the data Series is exhausted, return the Model of a future, hypothetical data element. five - Static variable in class la.la.Value fiveR - Static variable in class la.la.Value flags() - Method in class la.bioinformatics.Alignment The Scannable Int(n())s of this Alignment. flags(Value.Scannable) - Static method in class la.bioinformatics.Alignment The Scannable Int(n())s of a Scannable of Inc_OR. flatten() - Method in class graph.Graph Return a Graph of equivalent structure but with any trail of inducing, renumbering, etc., fixed and the trail lost. fm - Variable in class mml.Dependent.M 'fm', the fully parameterised FunctionModel of the output datum, od, conditional upon the input datum, id. foldl(Function, Value) - Method in class la.maths.Vector foldl (fold-left, reduce) 'this' Vector with curried Function 'fc' and zero (identity) 'z', in effect ((((z fc e₀) fc e₁) ... ) fc e_n-1). foldl(Function) - Method in class la.maths.Vector drop(1) .foldl(fc,elt(0)) foldr(Function, Value) - Method in class la.maths.Vector foldr (fold-right, reduce) 'this' Vector with curried Function 'fc' and zero (identity) 'z', in effect (e₀ fc ( ... (e_n-2 fc (e_n-1 fc z)))). foldr(Function) - Method in class la.maths.Vector take(n_1) .foldr(fc,elt(n_1)), where n_1 = nElts() - 1. force() - Method in class la.la.Value.Defer.App Cause Function 'Value.Defer.App.f' to be apply-ed to actual parameter 'Value.Defer.App.ap' and the result cached in 'v'. force() - Method in class la.la.Value.Defer.Exp Cause 'this' Deferred Expression Value.Defer.Exp.e to be eval-uated to WHNF and cached in 'v'. force() - Method in class la.la.Value.Defer Cause 'this' lazy, Deferred Value to be computed to at last WHNF and cached in 'v'. force() - Method in class la.la.Value This default implementation returns 'this' Value, but the real interest is in Value.Defer.force(). Forest(Value) - Constructor for class mml.R_D.Forest Problem definition parameter 'dp' gives parent[], must specify a forest. ForestSearch(Value) - Constructor for class mml.R_D.ForestSearch 'dp' is the dimension, "width", R_D.ForestSearch.D. forFile(File) - Static method in class la.la.Lexical Return a Lexical analyser of a source File, 'file'. forFile(String) - Static method in class la.la.Lexical Return a Lexical analyser of a source File named 'fn'. Fork(double, double, Value) - Constructor for class mml.Tree.Fork Fork(int, Tree.Param[]) - Constructor for class mml.Tree.Param.Fork forString(String) - Static method in class la.la.Lexical Return a Lexical analyser of a source String, 'str'. four - Static variable in class la.la.Value fourR - Static variable in class la.la.Value FP - Class in la.la Wrapper for the interpreter, bringing Syntax and Semantics together. FP() - Constructor for class la.la.FP FPapplet - Class in la.la FPapplet() - Constructor for class la.la.FPapplet fparam - Variable in class la.la.Expression.LambdaExp freqs(Vector, int, int) - Method in class mml.Discretes.Bounded For probable use by stats(ds,lo,hi), return the frequency counts of elements [lo, hi) of data-set 'ds'. freqs(Vector) - Method in class mml.Discretes.Bounded freqs(boolean, Value, Value) - Method in class mml.Discretes.Bounded For frequency counts 'ss0' and 'ss1', either combine ss0 and ss1 (add=true), or remove ss1 from ss0 (add=false). from(Value) - Method in class mml.Tree.Est.Sel fromBop(int) - Static method in class la.la.Function Return a Curried Function based on the binary operator, 'op' (+, *, etc.). fromFunction(Function) - Static method in class la.la.Function.Cts2Cts (Static) fromFunction(f) creates a Cts2Cts from a Function, 'f', where f is not an instance of class Cts2Cts but f does in fact accept, and return, Cts values. fromInts(int[]) - Static method in class la.util.Series fromLexical(Lexical) - Static method in class la.maths.Matrix Read a Matrix of Cts from Lexical analyser, 'lex', such as a Lexical.forFile(fname), say. fromScannable(Value.Scannable) - Static method in class la.maths.Vector Make a Vector from the given Scannable Value sv. fromUop(int) - Static method in class la.la.Function Return a Function, of one parameter, based on the unary operator 'op' (not, -, hd, etc.). fromVector(Vector) - Static method in class la.util.Series frth - Static variable in class la.la.Library Return the fourth element of a Value.Tuple. frth() - Method in class la.la.Value.Structured frth(), short for elt(3). fst - Static variable in class la.la.Library Return the first element of a Value.Tuple. fst() - Method in class la.la.Value.Structured fst(), short for elt(0). fst - Variable in class la.util.Series.Range fun - Variable in class la.la.Expression.Application Represents the application of 'fun' to 'aparam'. Function - Class in la.la The class of Functions; note that a Function is a Value. Function() - Constructor for class la.la.Function FUNCTION - Static variable in class la.la.Type Also see Function. Function() - Constructor for class la.la.Type.Function Function(String) - Constructor for class la.la.Type.Function Function(String, Type, Type) - Constructor for class la.la.Type.Function Function.Cts2Cts - Class in la.la The class of Cts → Cts Functions. Function.Cts2Cts.Derivative - Class in la.la This default Derivative of a given function 'f' uses finite-differences – see apply_x(x) and apply_xx(δ,x). Function.Cts2Cts.Integral - Class in la.la Integral: Cts→Cts→Cts (±) integrates f from 'lo' to 'hi'. Function.Cts2Cts.WithInverse - Class in la.la This class exists so that one can create an anonymous Cts2Cts which (implements) HasInverse. Function.Cts2Cts2Cts - Class in la.la Curried continuous functions, R→R→R or more correctly Cts→Cts→Cts. Function.CtsD2CtsD - Class in la.la Functions of Vectors of D Continuous Values, R^D→R^D. Function.CtsD2CtsD.WithInverse - Class in la.la Class CtsD2CtsD.WithInverse exists so that one can create an anonymous CtsD2CtsD which implements HasInverse. Function.HasInverse - Interface in la.la A Function, f, might have an inverse() Function. Function.Native - Class in la.la Native, Functions specified by arbitrary Java code in apply(v). Function.Native.WithInverse - Class in la.la This class exists so that one can create an anonymous Function.Native which (implements) HasInverse. Function.Native2 - Class in la.la Curried native Functions specified by arbitrary Java code in apply2(v0,v1). Function.Native3 - Class in la.la 3-Curried native Functions specified by arbitrary Java code in apply3(v0,v1,v2). Function.WithInverse - Class in la.la This class exists so that one can create an anonymous Function which (implements) HasInverse. FUNCTION_N - Static variable in class la.la.Type Integer codes for various "types" of Type. FunctionModel - Class in mml The fully parameterised Function Model (aka regression) with input datum (independent variable), 'id', and output datum (dependent variable), 'od'. FunctionModel(double, double, Value) - Constructor for class mml.FunctionModel Construct a fully parameterised FunctionModel with 2-part message length msg1+msg2, and statistical parameter(s), sp. functionModel(int, Type) - Method in class mml.Multivariate.M Return a FunctionModel of Cs→col where col is of Bounded Discrete colType and is one of 'this' MultiVariate's columns (variables), and Cs is a Tuple of Values for all the other columns (variables). G Gamma(double) - Static method in class la.maths.Maths Math.exp(logGamma(x)), for real x. Gamma - Static variable in class mml.MML The UnParameterised Gamma Model. GammaUPM - Class in mml For most purposes MML.Gamma and GammaUPM.M should be enough, with little need to call upon GammaUPM directly. GammaUPM(Value) - Constructor for class mml.GammaUPM GammaUPM.M - Class in mml The fully parameterised γ Model (probability distribution). ge - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. Geometric0 - Static variable in class mml.MML The UnParameterised Geometric Model (distribution) over integers in [0, ∞), capable of estimating a fully parameterised Geometric Model. Geometric0UPM - Class in mml The UnParameterised Geometric0 Model. Geometric0UPM(Value) - Constructor for class mml.Geometric0UPM Geometric0UPM.M - Class in mml The fully parameterised Geometric Model (probability distribution) on integers in [0, ∞); the mean, μ, is its one statistical-parameter. GERadaptive(Value) - Constructor for class mml.Graphs.GERadaptive Problem defining parameters dp = ⟨gType, upmV(), [α₀, α₁]⟩. GERfixed(Value) - Constructor for class mml.Graphs.GERfixed Problem defining parameters dp = ⟨gType, upmV()⟩. getch() - Method in class la.la.Lexical Note, getch() changes the state variables, including 'ch', as a side-effect. getData(String) - Static method in class eg.Graphing Get the raw String data (two columns: v₀ v₁) defining a Graph from the named file using Vector.csv(boolean, boolean, char, la.la.Type.Tuple, int, java.io.InputStream). getData(String) - Static method in class eg.Iris Get the Iris data-set from the named file using Vector.csv(boolean, boolean, char, la.la.Type.Tuple, int, java.io.InputStream). getData(String) - Static method in class eg.Musicians Get the data from the named file; return a Vector of ages weighted by frequency. getGraph(String) - Static method in class eg.Graphing Get the graph from 'filename'. GJ_fp() - Method in class la.maths.Matrix GJ_fp, the Gauss-Jordan (GJ) elimination algorithm with full row and column pivoting (fp); also see Matrix.inverse(). GP(String, Type[]) - Constructor for class la.la.Type.Tuple.GP GP(Type, int) - Constructor for class la.la.Value.Enum.GP 't' is the Enum Value's type, and 'n' is the Enum Value's int code. GP(Type, int) - Constructor for class la.la.Value.Option.GP Construct an Option Value of Type t, number n, zero elements. GP(Type, int, Value) - Constructor for class la.la.Value.Option.GP Construct an Option Value of Type t, number n, one element v. GP(Type, int, Value[]) - Constructor for class la.la.Value.Option.GP Construct an Option Value of Type t, number n, elements elts. GP(Value[]) - Constructor for class la.la.Value.Tuple.GP Construct a Tuple.GP from several elements 'elts'. GP(Value[]) - Constructor for class la.maths.Vector.GP Given an array [] of Values, create a Vector. GP2(Value[][]) - Constructor for class la.maths.Matrix.GP2 Given a 2D Java array, [] [], of Values, construct a GP2, a Matrix. graph - package graph Package 'graph'; see graph's README. Graph - Class in graph Core Graph operations and useful constants etc.. Graph() - Constructor for class graph.Graph Graph.Canonical - Class in graph Canonical is mostly just class Graph.Renumbered except that it (i) declares 'this' to already be in a canonical vertex-numbering, and thus (ii) Canonical.canonical() returns 'this'. Graph.Contraction - Class in graph A Vertex-Contraction of 'this' Graph, collapsing (identifying) Vertices [vs[0], vs[1], ...] onto the single Vertex vs[0]. Graph.Dense - Interface in graph See Graph.Sparse, Directed.Dense and Undirected.Dense. Graph.Derived - Class in graph this.new Derived() is just like 'this' Graph, but a Function on Graphs may return a sub-class of Derived — extend it — with one or two methods overridden. Graph.Edge - Class in graph An Edge ⟨v0, v1⟩ (Directed), or (v0, v1) (Undirected, v0≤v1), of 'this' Graph. Graph.Induced - Class in graph A SubGraph of 'this' Graph induced by selecting Vertices [vs[0], vs[1], ...], and the Edges between them. Graph.Renumbered - Class in graph A version of 'this' Graph with the Vertices renumbered according to vs[]. Graph.Sparse - Interface in graph See Graph.Dense, Directed.Sparse and Undirected.Sparse. Graph.SubGraph - Interface in graph A SubGraph of a Graph is a Child and must also provide a mapping from its Vertices to those of the parent Graph. Graph.SubGraphs - Class in graph A Series of lo- to hi-sized, (weakly-) connected, Induced subGraphs of 'this' Graph. Graph.ToDirected - Class in graph Provided 'this' Graph is in fact Directed, be an instance of that class. Graph.ToUndirected - Class in graph Provided 'this' Graph is in fact Undirected, be an instance of that class. Graph.Vertex - Class in graph A Vertex of 'this' Graph. Graphing - Class in eg Using various MML Models of Graphs, analyse a given Graph defined in a file. Graphing() - Constructor for class eg.Graphing Graphs - Class in mml The general abstract UnParameterised Model of the structure of Graphs having explicit gType and upmV where the former is the Type (population) of Graphs and the latter is the Model of the number of Vertices, |V|. Graphs(Value) - Constructor for class mml.Graphs Constructor only for internal use; it does not set gType or upmV. Graphs.GERadaptive - Class in mml Special case of the Graphs.IndependentEdges Model where upmE() is Adaptive over [0, 1]. Graphs.GERadaptive.M - Class in mml The fully parameterised Gilbert, Erdos-Renyi Adaptive Model of Graphs. Graphs.GERfixed - Class in mml Common special case of the Graphs.IndependentEdges Model where upmE() is MultiState over [0, 1]. Graphs.GERfixed.M - Class in mml The fully parameterised Gilbert, Erdos-Renyi Model of Graphs. Graphs.IndependentEdges - Class in mml Extends Graphs to Models where there is also an explicit Model upmE on the existence of Edges. Graphs.IndependentEdges.M - Class in mml The general abstract fully parameterised Graph Model having both explicit mdlV and mdlE. Graphs.M - Class in mml Fully parameterised Graphs Models; also see UnParameterised Graphs. Graphs.Motifs - Class in mml UnParameterised Models based on the notion of frequent sub-graphs (motifs, patterns) and that can calculate the message-length contribution of a set of motifs. Graphs.Motifs.M - Class in mml Fully parameterised Models based around the notion of frequent sub-graphs (patterns motifs). Graphs.Skewed - Class in mml An UnParameterised Model of Graphs where the degree distribution is skewed, few Vertices having high degree and many having low degree. Graphs.Skewed.M - Class in mml The fully parameterised Model of Skewed Graphs. greedy(Graphs.Motifs, Graph[], Value.Cts, Graph[]) - Static method in class eg.Graphing For UnParameterised Motifs Model of Graphs 'UMM', perform a greedy(,,) search to select zero or more of several given candidate 'motifs' (subGraphs) to form a fully-parameterised Model. greedy(Graphs.Motifs[], Graph[], Value.Cts, Graph[]) - Static method in class eg.Graphing For each of several UnParameterised Motifs Models of Graphs UMMs[0], UMMs[1], ..., and data-set of Graphs 'Gs' do greedy(,,,Gs). greedy(Graphs.Motifs[], Graph[], Value.Cts, Graph) - Static method in class eg.Graphing For each of several UnParameterised Motifs Models of Graphs UMMs[0], UMMs[1], ..., and one Graph 'G' do greedy(,,,[G]). greedy(Graphs.Motifs, Graph[], Value.Cts, Graph) - Static method in class eg.Graphing For UnParameterised Motifs Model of Graphs 'UMM' and one Graph 'G' do greedy(,,,[G]). greedy(Vector, Discretes.M, Value) - Method in class mml.Graphs.Motifs Greedy search for a subset of some given 'motifs' that minimises the message length under this Motif Model of Graphs. greedy(Value, Vector, Value) - Method in class mml.Graphs.Motifs Given candidate 'motifs' and ss=stats(Gs), estimate mdlV and then return greedy(motifs, mdlV, ss). gt - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. Gtype - Static variable in class eg.Graphing The Type is Directed.unlabelled in this case. gType - Variable in class mml.Graphs Public face is gType(). gType() - Method in class mml.Graphs The Graph-Type being modelled, particularly Directed versus Undirected Graphs. H half - Static variable in class la.la.Value hashCode() - Method in class graph.Graph Return some kind of hash-code invariant under Vertex-renumbering. hasLwb() - Method in class la.la.Type.Discrete Does 'this' Discrete have a Type.Discrete.lwb? hasNone() - Method in class la.util.Series For convenience -- return not hasSome(). HasPdf - Interface in mml Implemented by a (fully parameterised) Model class that has a probability density function HasPdf.pdf(la.la.Value), and a HasPdf.nlPdf(la.la.Value). hasSome() - Method in class graph.Graph.SubGraphs Is there at least a current Graph remaining in the Series? hasSome() - Method in class la.util.Series Is there at least a current element, (Series.elt())? Also see Series.hasNone(). hasSome() - Method in class la.util.Series.Lines hasSome() - Method in class la.util.Series.Range hasSome() - Method in class la.util.Series.Separator hasSome() - Method in class mml.SeriesModel.Analysis Return ds.hasSome() where ds is the given data Series. hasUpb() - Method in class la.la.Type.Discrete Does 'this' Discrete have a Type.Discrete.upb? hd - Variable in class la.la.Value.List.Cell The head, hd, and tail, tl, of 'this' List Cell. hd() - Method in class la.la.Value.List.Cell The head of the List. hd() - Method in class la.la.Value.List The head (1st element) of the List. hdSy - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. HeavyTail - Class in mml Experimental, may change or disappear: Heavy-tailed continuous probability distribution(s). HeavyTail() - Constructor for class mml.HeavyTail HeavyTail.Over_x1 - Class in mml A heavy-tailed, log-symmetric, everywhere differentiable, probability distribution for continuous (real-valued) X>0. HeavyTail.Over_x1.M - Class in mml The fully parameterised Over_x1 Model; see pdf_x(x). hex(int) - Static method in class la.util.Test Return 'n' in hexadecimal as a String. hi - Variable in class graph.Graph.SubGraphs subGraphs of Vertex-size lo to hi inclusive. hi - Variable in class la.maths.Vector.Slice The range [lo, hi), lo inclusive to hi exclusive, of the parent()'s elements. I i() - Method in class la.maths.Q The i-part, elt(1).x(). id - Variable in class la.la.Expression.Ident id - Static variable in class la.la.Library The identity Function, id x = x. id(int) - Static method in class la.maths.Matrix Return the N×N identity Matrix of Ints; note, is static. IDENT - Static variable in class la.la.Expression A tag; see Expression.n(). Ident(String) - Constructor for class la.la.Expression.Ident ids - Variable in class la.la.Declaration The identifiers bound to Expressions 'es'. ids - Variable in class la.la.Type.Enum String ids[i] denotes Value vals[i], i=0.. . ids - Variable in class la.la.Type.Option For example, {"emptyT", "fork"}. IFEXP - Static variable in class la.la.Expression A tag; see Expression.n(). IfExp(Expression, Expression, Expression) - Constructor for class la.la.Expression.IfExp ifSy - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. im - Variable in class mml.Dependent.M 'im', the fully parameterised input Model of the input datum, id. improve(Mixture.Est, Mixture.M, Vector) - Static method in class eg.Musicians Starting with mixture model 'mx1', use est.EM(mx1,ds) to improve it. INC_OR - Static variable in class la.la.Type Inclusive Or, as in Inc_Or Int Char, say. Inc_Or() - Constructor for class la.la.Value.Inc_Or inDegree(int) - Method in class graph.Directed.Sparse O(1)-time. inDegree(int) - Method in class graph.Graph Only applicable to Directed Graphs. Independent - Class in mml A Tuple of UnParameterised Models makes an UnParameterised Model of Tuples, i.e., of Multivariate data, and a Tuple of statistical parameters make the statistical parameter of the Model, etc.. Independent(Value) - Constructor for class mml.Independent Given a Tuple of UnParameterised Models, 'upms', construct an UnParameterised Model of Tuples — of multivariate data. Independent(Value) - Constructor for class mml.R_D.Independent Problem defining parameters are dp=⟨@link #upm upm},D⟩. Independent.M - Class in mml A fully parameterised Model of Tuples made from a Tuple of independent Models by the UnParameterised Independent Model. IndependentEdges(Value) - Constructor for class mml.Graphs.IndependentEdges Constructor only for use by Graphs.GERfixed and the like; it does not set gType, upmV or upmE. induced(int[]) - Method in class graph.Directed.Sparse An induced subgraph of a Sparse Directed Graph is Sparse and Directed. Induced(int[]) - Constructor for class graph.Directed.Sparse.Induced induced(int[]) - Method in class graph.Graph Convenience function. Induced(int[]) - Constructor for class graph.Graph.Induced vs must be ascending and a subset of the parent Graph's {0, ..., vSize()-1}. induced(int[]) - Method in class graph.Undirected.Sparse An induced SubGraph of a Sparse Undirected Graph is Sparse and Undirected. Induced(int[]) - Constructor for class graph.Undirected.Sparse.Induced informativeIncrement(int) - Static method in class mml.MML See p.180 Wallace (2005), the informative explanation (D parameters estimated) versus the uninformative explanation, I1 - I0 = (D/2)(1 + log(2 π k[D])), nits. init() - Method in class la.la.FPapplet initial - Static variable in class la.la.Environment The initial Environment (standard Library). inp - Variable in class la.la.Lexical inp - Variable in class la.util.Series.Lines The InputStream that 'this' Lines is based on. instance - Static variable in class mml.Linear1 Having a trivial problem-defining parameter, we really only need one instance of the UnParameterised Linear1 Model. inSy - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. insymbol() - Method in class la.la.Lexical Get the next symbol, from the InputStream 'inp'. INT - Static variable in class la.la.Type Also see Value.Int. Int(String) - Constructor for class la.la.Type.Int Construct "the" Int Type. Int(String, boolean, int, boolean, int) - Constructor for class la.la.Type.Int Construct an Int Type possibly bounded above, or below, or both. Int(int) - Constructor for class la.la.Value.Int Int() - Constructor for class la.util.Series.Int Int1 - Class in mml A simple model of positive integers, >0, where pr(n) = 1/(n(n+1)). Int1(Value) - Constructor for class mml.Int1 dp must be triv. Int1.M - Class in mml A (trivially) fully parameterised model of integers >0 where pr(n) = 1/(n(n+1)). INT2 - Static variable in class eg.Musicians Each row of the input is: INT2 = int x int, being age & frequency. int2sy(int) - Static method in class la.la.Lexical Return the n-th Lexical Symbol as a String, if possible. int2value(int) - Method in class la.la.Type.Char int2value(int) - Method in class la.la.Type.Discrete Convert an int, n, into a Value of 'this' Discrete Type. int2value(int) - Method in class la.la.Type.Enum Return the n-th Value, vals[n], of 'this' Enum. int2value(int) - Method in class la.la.Type.Int Return (a new) Value.Int(n). int2value(int) - Method in class la.la.Type.Triv INT_N - Static variable in class la.la.Type Integer codes for various "types" of Type. integral() - Method in class la.la.Function.Cts2Cts Return the integral of 'this' function, ∫ this(x) dx. Integral() - Constructor for class la.la.Function.Cts2Cts.Integral Construct the definite Integral of Cts2Cts f. interleave16(int, int) - Static method in class la.util.Util Return the lower 16 bits of 'm' and of 'n' interleaved. interval(Value) - Method in class mml.Intervals.M Return 'i' where input datum 'id' falls into interval 'i', 0 ≤ i < N. Intervals - Class in mml Intervals, the UnParameterised FunctionModel. Intervals(Value) - Constructor for class mml.Intervals The problem defining parameter, upm, is an UnParameterised Model suitable for the ouput datum, od. Intervals.M - Class in mml A fully parameterised FunctionModel in which the data-space of the input datum, id, is cut into intervals, and for each interval there is a (conditional) Model of the output datum, od. ints(int[][]) - Static method in class la.maths.Matrix Convenience function, ints : int[][] → Matrix. Ints() - Constructor for class la.maths.Matrix.Ints Ints(int) - Constructor for class la.maths.Matrix.Ints ints(int[]) - Static method in class la.maths.Vector Convenience function, ints : int[] → Vector. ints(Type.Discrete, int[]) - Static method in class la.maths.Vector Convenience function to make a Vector with elements of Discrete Type et, and integer codes, ns. Ints() - Constructor for class la.maths.Vector.Ints ints_UR(boolean, int[][], int) - Static method in class la.maths.Matrix Make a symmetric Int Matrix from 'ns' which is an upper-right triangular array of array of int. inv - Static variable in class la.la.Library The function, inv(x)=1/x. inverse() - Method in interface la.la.Function.HasInverse inverse() - Method in class la.maths.Matrix Return the inverse of a (square, non-singular) Matrix by Matrix.GJ_fp(). inverse() - Method in class la.maths.Q Return the inverse of 'this' Quaternion, that is, its conjugate divided by its norm². invLog2 - Static variable in class mml.MML 1 / log_e(2) Iris - Class in eg Iris, a simple example of an application program that runs some MML-analysis of "Iris.csv". Iris() - Constructor for class eg.Iris isDirected() - Method in class graph.Graph isDirected - Variable in class graph.Type Is the Graph Directed, and are self-loops allowed? isEdge(int, int) - Method in class graph.Directed.AsUndirected isEdge(int, int) - Method in class graph.Directed.Dense isEdge(int, int) - Method in class graph.Directed.Sparse isEdge(int, int) - Method in class graph.Graph.Contraction If v0 and/or v1 is vs[0] to which >1 vertices of the parent Graph are contracted, was there an Edge from/to any of the latter in the parent? isEdge(int, int) - Method in class graph.Graph.Derived The parent's isEdge(v0,v1) (is very likely to be changed, overridden). isEdge(int, int) - Method in class graph.Graph.Induced parent().isEdge(vs[v0], vs[v1]). isEdge(int, int) - Method in class graph.Graph Is ⟨v0, v1⟩ an Edge in 'this' Graph? Also see adjacent(v0,v1). isEdge(int, int) - Method in class graph.Graph.Renumbered The parent's isEdge(vs[v0],isEdge[v1]). isEdge(int, int) - Method in class graph.Graph.ToDirected isEdge(int, int) - Method in class graph.Graph.ToUndirected isEdge(int, int) - Method in class graph.Undirected.AsDirected isEdge(int, int) - Method in class graph.Undirected.Dense isEdge(int, int) - Method in class graph.Undirected.Sparse isNil() - Method in class la.la.Value.List.Cell ffalse isNil() - Method in class la.la.Value.List Is 'this' List 'nil' (empty), or not? isomorphic(Graph) - Method in class graph.Graph Is 'this' Graph isomorphic to 'g' (in terms of the existence of Edges)? It makes use of canonical() and edgesCorrespond(g). isRec - Variable in class la.la.Declaration Are these Declarations recursive, or not? isRectangular() - Method in class la.maths.Matrix True, every Matrix is rectangular by definition. isRectangular() - Method in class la.maths.Vector Provided 'this' is a Vector of Vectors, is it rectangular? That is, is it non-"jagged"? Also see Vector.isSquare(), and Matrix. isSquare() - Method in class la.maths.Matrix ?nElts()==nCols()? isSquare() - Method in class la.maths.Vector Provided 'this' is a Vector of Vectors, is it square? Also see Vector.isRectangular(). isTuple(int) - Method in class la.la.Value Check that 'this' Value really is a k-Tuple and, if it is, return it as a Tuple. isUndirected() - Method in class graph.Graph ¬isDirected(). iType - Variable in class mml.Tree The Type of the input datum (variable), id, in id→od. J J(Vector) - Method in class la.la.Function.CtsD2CtsD The Jacobian is a matrix, J, where J_i,j=∂f_i/∂x_j, and 'f' is 'this' Function. j() - Method in class la.maths.Q The j-part, elt(2).x(). Jacobi() - Constructor for class la.maths.Matrix.Jacobi The "constructor" carries out the computation of the Eigen-Values and Eigen-Vectors. join(Mixture.M, int, int, Vector) - Method in class mml.Mixture.Est Merge classes c1 and c2 into one class, and adjust. joined - Variable in class graph.Undirected.Sparse Given an Edge (v0, v1), v1 is in (ascending) joined[v0], and v0 is in joined[v1]. joinedTo(int) - Method in class graph.Directed.Sparse joinedTo(int) - Method in class graph.Graph An increasing Series of those Vertices 'w' adjacent to v, that is (v,w) is an Edge or (w,v) is (includes v, once, if there is a loop (v,v)). joinedTo(int) - Method in class graph.Undirected.Sparse just(Value) - Static method in class la.la.Value Convenience function, returns new Maybe.Just(v). JUST - Static variable in class la.la.Value.Maybe NONE = 0, JUST = 1. Just(Value) - Constructor for class la.la.Value.Maybe.Just K K(int) - Constructor for class graph.Undirected.K Construct the complete Undirected Graph on 'n' Vertices. k - Variable in class la.la.Type.Tuple The number of componets (fields) of 'this' Typle Type. k() - Method in class la.maths.Q The k-part, elt(3).x(). K() - Method in class mml.Direction Degrees of freedom (surface dimension), K = D - 1, on the unit-radius K-Sphere in R^D. K() - Method in class mml.Dirichlet The degrees of freedom of the K-Simplex sub-space ⊆ [0, 1]^K+1. k - Variable in class mml.GammaUPM.M Shape parameter 'k', and scale parameter θ (theta). k - Variable in class mml.HeavyTail.Over_x1.M The double version of δ>1, and the normalising constant 'k'. k - Variable in class mml.Multinomial The number of categories 'k'. K(Value) - Constructor for class mml.Sequences.K dp = ⟨Sequences.lengthUPM, Sequences.K.eltUPM⟩. K() - Method in class mml.Simplex K, the dimension (degrees of freedom) of the data-space which is a sub-space of [0, 1]^K+1. K() - Method in class mml.Simplex.Uniform The dimension (degrees of freedom) of the sub-space (in [0, 1]^K+1). K(Value) - Constructor for class mml.UPFunctionModel.K The problem-defining parameter 'upm' is used to set UPFunctionModel.K.upm. K(Value) - Constructor for class mml.UPSeriesModel.K Problem defining parameter dp = ⟨lenUPM, eltUPM⟩, for lengths and elements respectively. K22 - Static variable in class eg.Graphing Some possible motifs (subgraphs). K23 - Static variable in class eg.Graphing Some possible motifs (subgraphs). k3 - Variable in class mml.Linear1.Est latticeConstant(3). K33 - Static variable in class eg.Graphing Some possible motifs (subgraphs). K5 - Static variable in class eg.Graphing Some possible motifs (subgraphs). kappa - Variable in class mml.vMF.M The concentration parameter, κ. kappa() - Method in class mml.vMF.M κ ≥ 0, the concentration parameter. kD - Variable in class mml.LinearD.Est latticeConstant(D). KD23 - Static variable in class eg.Graphing Some possible motifs (subgraphs). KD33 - Static variable in class eg.Graphing Some possible motifs (subgraphs). kill(Mixture.M, int, Vector) - Method in class mml.Mixture.Est Remove class 'c', redistribute its members, and adjust. Known - Static variable in class mml.MML A Model of given, known, common knowledge. KnownClass - Class in mml See Known Model. KnownClass(double, double, Value) - Constructor for class mml.KnownClass KnownUPM - Static variable in class mml.MML The unParameterised Model version of MML.Known. L la - package la Package 'la'; see la's README. la.bioinformatics - package la.bioinformatics Package 'la.bioinformatics'; see la.bioinformatics's README. la.la - package la.la Package 'la.la'; see la.la's README. la.maths - package la.maths Package 'la.maths'; see la.maths's README. la.util - package la.util Package 'la.util'; see la.util's README. label() - Method in class graph.Graph.Edge The Edge label, if any. label() - Method in class graph.Graph.Vertex labelled() - Method in class graph.Graph.Edge Are (all) Edges labelled in 'this' Graph? labelled() - Method in class graph.Graph.Vertex Lambda(Expression.LambdaExp, Environment) - Constructor for class la.la.Value.Lambda LAMBDAEXP - Static variable in class la.la.Expression A tag; see Expression.n(). LambdaExp(Expression, Expression) - Constructor for class la.la.Expression.LambdaExp lambdaSy - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. Laplace - Static variable in class mml.MML The UnParameterised Laplace probability distribution. LaplaceUPM - Class in mml The UnParameterised Laplace. LaplaceUPM(Value) - Constructor for class mml.LaplaceUPM Requires definition parameters dp = triv. LaplaceUPM.M - Class in mml The fully parameterised Laplace probability distribution. lastTime() - Method in class la.util.Timer Time since the last start() in milliseconds to the subsequent stop(), if any, otherwise to 'now'. latticeConstant(int) - Static method in class mml.MML latticeConstant(D) (aka κ(D)), the lattice constant for D dimensions, D ≥ 1, i.e., D parameters, [www]. le - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. Leaf(double, double, Value) - Constructor for class mml.Tree.Leaf leafUPM parameterised by 'sp' gives mdl. Leaf(Value) - Constructor for class mml.Tree.Param.Leaf leafEst - Variable in class mml.Tree.Est leafEst estimates a Model of the output datum, col=1. leafUPM - Variable in class mml.Tree The UnParameterised Model of the output datum, od, that is to be parameterised in each Leaf of the fully parameterised Tree. LEFT - Static variable in class la.la.Value.Inc_Or LEFT = 0, RIGHT = 1, BOTH = 2. Left(Value) - Constructor for class la.la.Value.Inc_Or.Left Length(Value) - Constructor for class mml.UPSeriesModel.Length 'dp' is the problem-defining parameter(s), possibly trivial. lengthMdl - Variable in class mml.Sequences.M The parameterised Model of the lengths of Sequences. lengthStats(Vector, int, int) - Method in class mml.Sequences Get Sequences.lengthUPM's statistics on lengths out of 'ds[lo,hi)'. lengthStats(boolean, Value, Value) - Method in class mml.Sequences Get Sequences.lengthUPM's stats(add,ss0,ss1) on lengths. lengthStats(Vector, int, int) - Method in class mml.Sequences.M Get Sequences.M.lengthMdl's statistics on lengths out of 'ds[lo,hi)'. lengthUPM - Variable in class mml.Sequences The UnParameterised Model of the lengths of Sequences. lenMdl() - Method in class la.bioinformatics.Alignment.UPSame.M sm3.lenMdl() provides the Model of lengths. lenMdl() - Method in class mml.Markov.M lenMdl() - Method in class mml.UPSeriesModel.K.M lenMdl() - Method in class mml.UPSeriesModel.Length.M Return the explicit, fully parameterised Model of lengths. lenStats(Vector, int, int) - Method in class mml.UPSeriesModel.Length Return UPSeriesModel.Length.lenUPM().stats(seqs,lo.hi) statistics of the lengths of seqs, a data-set of Vectors. lenStats(Vector, int, int) - Method in class mml.UPSeriesModel.Length.M Return UPSeriesModel.Length.M.lenMdl().stats(seqs,lo,hi) statistics on the lengths of seqs, a data-set of Vectors. lenUPM() - Method in class la.bioinformatics.Alignment.UPSame UPsm3.lenUPM() provides the UPModel of lengths. lenUPM() - Method in class mml.Markov lenUPM() - Method in class mml.UPSeriesModel.K Return the UnParameterised Model of Lengths, lenUPM. lenUPM() - Method in class mml.UPSeriesModel.Length Return the explicit, UnParameterised Model of Series lengths. letSy - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. levels - Variable in class la.la.Expression.Ident Set by Environment.lookup(la.la.Expression.Ident). Lexical - Class in la.la A stateful Lexical analyser, also see Syntax. Lexical(InputStream) - Constructor for class la.la.Lexical Construct a Lexical analyser of a source InputStream, 'inp'. lft - Variable in class la.la.Expression.Binary lft and rgt, the left and right sub-Expressions. Library - Class in la.la The standard library of predefined Functions and other Values; also see Environment. Library() - Constructor for class la.la.Library Library.Power - Class in la.la EXPERIMENTAL, The class of Cts2Cts Functions, Power(c,p)(x)=c.x^p. Lin1 - Variable in class mml.R_D.Forest.M There is one Linear1 model n Lin1[] per parent-ed column, in order. Lin1 - Variable in class mml.R_D.ForestSearch.M The models for the with-parent variables. Linear1 - Class in mml 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). Linear1(Value) - Constructor for class mml.Linear1 The problem defining parameter dp is triv-ial. Linear1 - Static variable in class mml.MML The instance of the UnParameterised Linear1 FunctionModel; (Cts→Cts, linear regression). Linear1.Est - Class in mml An Estimator of a, b and σ in y = a x +b + N(0,σ), where estimator parameter ps = ⟨b_min, b_max, σ_min, σ_max⟩ . Linear1.M - Class in mml A fully parameterised Linear1 Model, y = a * x + b + N(0, σ) . LinearD - Class in mml The UnParameterised LinearD function-model; also see the fully parameterised LinearD function-model. LinearD(Value) - Constructor for class mml.LinearD The problem definition parameter gives LinearD.D()>1. LinearD.Est - Class in mml Estimator for a fully parameterised LinearD.M Linear-Model. LinearD.M - Class in mml The fully parameterised function-model; LinearD is the UnParameterised function-model. Lines(InputStream) - Constructor for class la.util.Series.Lines lines1stSy() - Method in class la.la.Lexical Is the current input symbol, the first one on the line? LIST - Static variable in class la.la.Type Also see Value.List. List() - Constructor for class la.la.Value.List lo - Variable in class graph.Graph.SubGraphs subGraphs of Vertex-size lo to hi inclusive. lo - Variable in class la.maths.Vector.Slice The range [lo, hi), lo inclusive to hi exclusive, of the parent()'s elements. localHash(int) - Method in class graph.Graph Return an int that is "likely" to differ for different Vertices v and v', and that can be computed "quickly". location(int[], int) - Static method in class la.util.Util Binary search 'ns[]' for 'n'; return n's location or else -1. log - Static variable in class la.la.Library log is a Cts2Cts with an Inverse-Function, i.e., exp. log2 - Static variable in class mml.MML log_e(2) log2 - Static variable in class mml.Tree log_e(2), i.e., one bit. log2PI - Static variable in class mml.MML log_e(2π). log_fact_N - Variable in class mml.Permutation.Uniform log(N!), the cost of stating a Permutation of {0, ..., N-1} uniformly. log_nCk(int, int) - Static method in class la.maths.Maths log_e(ⁿC_k). logArea() - Method in class mml.Direction Return the log of the surface-area of the unit-radius K-Sphere, the surface of the unit-radius D-Ball, where K = D - 1. logArea() - Method in class mml.Simplex The area of the standard K-Simplex is (√(K+1))/K!, so log of that. logB - Variable in class mml.Dirichlet.M The (log(B)) normalising constant. logBeta(double, double) - Static method in class la.maths.Maths The log Beta (β) function, log(Β(x,y)), returns logΓ(x)+logΓ(y)-logΓ(x+y). logCD(double) - Method in class mml.vMF The Model's log normalisation constant. logCD - Variable in class mml.vMF.M The concentration parameter, κ. logDeterminant() - Method in class la.maths.Matrix Return the log of the determinant of 'this' square, symmetric Matrix. logF(double, double) - Method in class mml.ExponentialUPM F = N / A². logF(double, double, double, double) - Method in class mml.Linear1.Est The log of the Fisher information from N, σ, mean(x_i²), and mean(x_i). logF(double, double, Matrix) - Method in class mml.LinearD.Est Log Fisher. logF(double, double) - Method in class mml.vMF log(F(κ)), log Fisher. logFactorial(int) - Static method in class la.maths.Maths Implemented as logGamma(n+1). logFactorial(double) - Static method in class la.maths.Maths Implemented as logGamma(x+1). logGamma(double) - Static method in class la.maths.Maths log(Γ(x)), real x>0; note, Γ n = (n-1)!, for int n≥1. logicOprs - Static variable in class la.la.Syntax logIntegral - Variable in class mml.NearInverse.M The log of the normalising constant. logNormal - Static variable in class mml.MML The UnParameterised Transformed Model that is the log–Normal Model. logPI - Static variable in class mml.MML log_e(π) logStar0 - Static variable in class mml.MML The fully (trivially) parameterised log^* Model, or "universal" probability distribution, for integers n ≥ 0. LogStar0UPM - Class in mml Model MML.logStar0 should be enough for most purposes, but here are the classes, UnParameterised (LogStar0UPM) and fully (trivially) parameterised (M). LogStar0UPM(Value) - Constructor for class mml.LogStar0UPM logStar0upm - Static variable in class mml.MML The UnParameterised log^* Model for integers n ≥ 0. LogStar0UPM.M - Class in mml Model logStar0 should be enough for most purposes, but here are the classes, fully (trivially) parameterised (M) and UnParameterised (LogStar0UPM). logSum(double[]) - Static method in class la.maths.Maths 'logSum', aka 'logPlus', logSum_i( - log(p[i])) = - log( ∑_i exp( log(p[i]) ) = - log( ∑_i p[i] ). logSum(double, double) - Static method in class la.maths.Maths Equivalent to logSum({nlX, nlY}), but a little quicker. lookup(Expression.Ident) - Method in class la.la.Environment Return the Value bound to Variable 'EId'. lookup(Expression.Ident, int) - Method in class la.la.Environment Note, nothing is declared initially. lt - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. LtoR(Value) - Constructor for class mml.Sequences.LtoR lwb() - Method in class la.la.Type.Discrete 'this' Discrete's lower bound if any otherwise an Exception; also see Type.Discrete.lwb_n(). lwb() - Method in class mml.Continuous.Bounded The lower bound, bounds().fst(). lwb() - Method in class mml.Continuous.Bounded.M Bounded.this.lwb(). lwb - Variable in class mml.CPT lwb, upb, the bounds on the input datum. lwb() - Method in class mml.Discretes.Bounded The lower bound on 'this' Model's dataspace. lwb() - Method in class mml.Discretes.Bounded.M Return Bounded.this.lwb(). lwb - Variable in class mml.Markov The Markov Models are of a given 'order', over Series of data, [lwb, upb]^* (bounds as ints). lwb - Variable in class mml.NaiveBayes lwb and upb, the bounds on the output datum, od. lwb_n() - Method in class la.la.Type.Discrete 'this' Discrete's int lower bound - if any, else exception, alse see Type.Discrete.lwb. lwb_n - Variable in class mml.CPT lwb_n, upb_n, the bounds on the input datum, as ints. lwb_n() - Method in class mml.Discretes.Bounded Discretes.Bounded.lwb().n() lwb_n() - Method in class mml.Discretes.Bounded.M Discretes.Bounded.M.lwb().n() lwb_n - Variable in class mml.NaiveBayes lwb_n and upb_n the bounds on the output datum, od, as ints. lwb_x() - Method in class mml.Continuous.Bounded The lower bound of the data-space as a double. lwb_x() - Method in class mml.Continuous.Bounded.M Bounded.this.lwb_x(). lwb_x() - Method in class mml.Continuous.Uniform M M(double, double, Value) - Constructor for class la.bioinformatics.Alignment.UPSame.M Statistical parameters, sp, are for (UPsm3, UPsmE, UPx2y). M(double, double, Value) - Constructor for class mml.Adaptive.M Requires msg1=0, sp=triv; msg2 may be > 0 for fair competition on training data. M(double, double, Value) - Constructor for class mml.BestOf.M Two part message lengths, msg1 and msg2, and statistical parameter(s) sp=⟨i,sp_i⟩. M(double, double, Value) - Constructor for class mml.BetaUPM.M Statistical parameters, sp = ⟨alpha, beta⟩. M(double, double, Value) - Constructor for class mml.ByPdf.M M(double, double, Value) - Constructor for class mml.Continuous.Bounded.M M(double, double, Value) - Constructor for class mml.Continuous.M M(double, double, Value) - Constructor for class mml.Continuous.Transform.M Statistical parameter(s) 'sp' are as per Continuous.M and are used to create 'm'. m - Variable in class mml.Continuous.Transform.M The Continuous.M Model 'm' doing the work behind the scenes. M(double, double, Value) - Constructor for class mml.Continuous.Uniform.M requires msg1=0, sp=triv. M(double, double, Vector) - Constructor for class mml.CPT.M Given (m1,m2,sps), where sps is a Vector of statistical parameters, one for each entry of the CPT, construct the fully parameterised CPT. M(double, double, Value) - Constructor for class mml.Dependent.M The two-part message lengths are msg1 and msg2, and sps = (iSp, fSp), where iSp is the stat param for the input Model im, and fSp is for the FunctionModel fm. M(double, double, Value) - Constructor for class mml.Direction.M M(double, double, Value) - Constructor for class mml.Direction.Uniform.M Requires msg1=0, sp=triv. M(double, double, Value) - Constructor for class mml.Dirichlet.M Note, the length of the statistical parameter α is D = K + 1. M(double, double, Value) - Constructor for class mml.Discretes.Bounded.M M(double, double, Value) - Constructor for class mml.Discretes.M M(double, double, Value) - Constructor for class mml.Discretes.Uniform.M Requires msg1=0, sp=triv. M(double, double, Value) - Constructor for class mml.ExponentialUPM.M Note that A is the mean (and standard deviation). M(double, double, Value) - Constructor for class mml.GammaUPM.M Statistical parameters, sp = ⟨k, θ⟩. M(double, double, Value) - Constructor for class mml.Geometric0UPM.M M(double, double, Value) - Constructor for class mml.Graphs.GERadaptive.M Statistical parameter(s) 'sp' is for mdlV = Graphs.upmV(sp). M(double, double, Value) - Constructor for class mml.Graphs.GERfixed.M M(double, double, Value) - Constructor for class mml.Graphs.IndependentEdges.M Constructor only for use by Graphs.GERfixed.M and the like. M(double, double, Value) - Constructor for class mml.Graphs.M M(double, double, Value) - Constructor for class mml.Graphs.Motifs.M M(double, double, Value) - Constructor for class mml.Graphs.Skewed.M Statistical parameters sp = mdlV_sp. M(double, double, Value) - Constructor for class mml.HeavyTail.Over_x1.M Real-valued δ>1. M(double, double, Value) - Constructor for class mml.Independent.M Given two-part message lengths, msg1 and msg2, and a Tuple of statistical parameters, sps, construct a Model of Tuples from the upms. M(double, double, Value) - Constructor for class mml.Int1.M msg1 must be zero and sp must be triv. M(double, double, Value) - Constructor for class mml.Intervals.M M's statistical parameters, sp = (cutPts,sps), where sps are the statistical parameters for instances of upm. M(double, double, Value) - Constructor for class mml.LaplaceUPM.M M(double, double, Value) - Constructor for class mml.Linear1.M Two-part message lengths msg1 and msg2, and statistical parameters sps = ⟨a, b, σ⟩. M(double, double, Value) - Constructor for class mml.LinearD.M Two part message lengths, msg1 and msg2, and statistical parameters sp. M(double, double, Value) - Constructor for class mml.LogStar0UPM.M Note, requires msg1=0 and sp=triv. M(double, double, Value) - Constructor for class mml.Markov.M sp = (lenMdl()'s, mdls'). M(double, double, Value) - Constructor for class mml.Missing.M Two part message lengths, msg1 and msg2, and statistical parameter(s) 'sp'. M(double, double, Value) - Constructor for class mml.Mixture.M Given statistical parameter sp = (wts, sps), where 'wts' is the relative abundances and 'sps' the classes' statistical parameters, construct a Mixture Model. M(double, double, Value) - Constructor for class mml.Model.Transform.M Note, msg1=0 and statistical parameter sp=triv, checked. M(double, double, Value) - Constructor for class mml.MotifA.M Statistical parameters sp = ⟨sp_mdlV, motifs⟩. M(double, double, Value) - Constructor for class mml.MotifD.M sp = ⟨sp_mdlV, motifs⟩. M(double, double, Value) - Constructor for class mml.Multinomial.M 'prs' is the probabilities of the 'k' categories. M(double, double, Value) - Constructor for class mml.MultiState.M The statistical parameters, prs, are the probabilities. M(double, double, Value) - Constructor for class mml.Multivariate.M Given two-part message lengths, msg1 and msg2, and a Tuple of statistical parameters, sps, construct a Model of Tuples from the upms. M(double, double, Value) - Constructor for class mml.NaiveBayes.M sp is for dpndt_m = dpndt.apply(sp). M(double, double, Value) - Constructor for class mml.NearInverse.M The standard constructor for NearInverse Model; statistical parameter delta must be a small Value.Cts in (0, 1) such as 0.1. M(double) - Constructor for class mml.NearInverse.M A possible use is for σ's prior in MML.Normal. M(double, double, Value) - Constructor for class mml.NormalMu.M Given 1st and 2nd part message lengths, msg1 and msg2, and statistical parameter σ (μ is the problem-defining parameter), construct a Normal Model, N_μ,σ, of Cts data. M(double, double, Value) - Constructor for class mml.NormalUPM.M Given the two-part message lengths, and statistical parameter (μ σ), construct N_μσ. M(double, double, double, Value) - Constructor for class mml.NormalUPM.M A constructor with one statistical parameter, σ, for use by NormalMu.M. M(double, double, Value) - Constructor for class mml.Permutation.M M(double, double, Value) - Constructor for class mml.Permutation.Uniform.M Requires msg1=0 and sp=triv (trivial "statistical" parameters); also see Mdl. M(double, double, Value) - Constructor for class mml.Poisson0UPM.M M(double, double, Value) - Constructor for class mml.R_D.Forest.M 'sp' is a pair, a Vector of parameters for Normal- and a Vector of parameters for Linear1-models. M(double, double, Value) - Constructor for class mml.R_D.ForestSearch.M 'sp' contains [p0, p2, ..., p(D-1)] where p_i is the parent of 'i', a negative value indicates that p_i has "no parent", parameters ⟨μ,σ⟩ of the various parent-less N_(μ,σ), and ⟨a,b,s⟩ of the parented Linear1_(a,b,s). M(double, double, Value) - Constructor for class mml.R_D.Independent.M Statistical parameters, sps, is a Vector of statistical parameters, one per mdls[i]. M(double, double, Value) - Constructor for class mml.R_D.M M(double, double, Value) - Constructor for class mml.R_D.NrmDir.M Two-part message lengths, msg1 and msg2, and statistical parameters sps = (sp_n, sp_d) where sp_n is normMdls's sp and sp_d is dirnMdls's. M(double, double, Value) - Constructor for class mml.R_D.Transform.M Statistical parameter(s) 'sp' are as per the underlying R_D.M Model and are used to create 'm'. m - Variable in class mml.R_D.Transform.M The R_D.M Model 'm' doing the work behind the scenes. M(double, double, Value) - Constructor for class mml.Sequences.K.M sp = ⟨for_Sequences.M.lengthMdl, for_Sequences.K.M.eltMdl⟩. M(double, double, Value) - Constructor for class mml.Sequences.LtoR.M M(double, double, Value) - Constructor for class mml.Sequences.M Two part message lengths, msg1 and msg2, and statistical parameter(s) sp. M(double, double, Value) - Constructor for class mml.Simplex.Uniform.M NB. M(double, double, Value) - Constructor for class mml.Tree.M M(double, double, Value) - Constructor for class mml.UPFunctionModel.K.M Statistical parameter sp is for mdl = upm.apply(sp). M(double, double, Value) - Constructor for class mml.UPFunctionModel.M M(double, double, Value) - Constructor for class mml.UPModel.M Given two-part message lengths, msg1 and msg2, and statistical parameter(s), sp, construct an M-Model. M(double, double, Value) - Constructor for class mml.UPModel.Transform.M Statistical parameter(s) 'sp', as per the enclosing UPModel.M, is (are) used to create m. m - Variable in class mml.UPModel.Transform.M 'm', the UPModel.M doing the work behind the scenes. M(double, double, Value) - Constructor for class mml.UPSeriesModel.K.M Statistical parameters, sps = ⟨lenMdl's sp, eltMdl's sp⟩. M(double, double, Value) - Constructor for class mml.UPSeriesModel.Length.M M(double, double, Value) - Constructor for class mml.UPSeriesModel.M Two-part message length, msg1 and msg2, and statistical parameter(s), construct a fully parameterised Series Model. M(double, double, Value) - Constructor for class mml.vMF.M The vMF's statistical parameters sp = (μ, κ). M(double, double, Value) - Constructor for class mml.WallaceInt0UPM.M Requires msg1=0, sp=triv. m1m2sp() - Method in class mml.Model Return the triple(msg1(), msg2(), statParams()). M5 - Static variable in class eg.Graphing Some possible motifs (subgraphs). main(String[]) - Static method in class eg.Ducks Walk the walk and talk the talk. main(String[]) - Static method in class eg.Graphing Analyse the Graph defined in the nominated file. main(String[]) - Static method in class eg.Iris Run MML-analysis of the Iris data (Iris.csv), including a Mixture-Model and a (decision-) classification-Tree. main(String[]) - Static method in class eg.Musicians Simple analysis of a data-set, [(ageAtDeath, frequency)], from a given file or from file ../data/musicians-age-freq. main(String[]) - Static method in class graph.Directed See Test.main(java.lang.String[]). main(String[]) - Static method in class graph.Graph Calls Test.main(java.lang.String[]). main(String[]) - Static method in class graph.Test Run a few, very(!) simple tests. main(String[]) - Static method in class graph.Undirected See Test.main(java.lang.String[]). main(String[]) - Static method in class la.bioinformatics.Alignment A very rudimentary test. main(String[]) - Static method in class la.la.FP main(String[]) - Static method in class la.la.Function main() allows class Function to be very slightly tested, in isolation. main(String[]) - Static method in class la.la.Lexical main() allows Lexical to be tested, a little, in isolation, for example java ...Lexical < inputfile main(String[]) - Static method in class la.la.Library Run a few simple tests on Library. main(String[]) - Static method in class la.la.Syntax 'main' allows Syntax to be tested on its own, for example, java ...Syntax < someFPsourceFile main(String[]) - Static method in class la.la.Type 'main' allows Type to be tested (v.slightly) on its own e.g., java Type. main(String[]) - Static method in class la.la.Value main() allows Value to be (very slightly) tested in isolation. main(String[]) - Static method in class la.maths.Maths main(String[]) - Static method in class la.maths.Matrix main() allows Vector to be (slightly) tested, in isolation. main(String[]) - Static method in class la.maths.Q main() allows Q to be (slightly) tested, in isolation. main(String[]) - Static method in class la.maths.Test Run some simple tests on package la.maths, that is on Vector, Matrix and Q. main(String[]) - Static method in class la.maths.Vector main() allows Vector to be (slightly) tested, in isolation. main(String[]) - Static method in class la.util.Series main(String[]) - Static method in class la.util.Test Run a few, very simple tests. main(String[]) - Static method in class la.util.Util main(String[]) - Static method in class mml.Adaptive See Test. main(String[]) - Static method in class mml.BetaUPM Trival tests of the Β Model (probability distribution). main(String[]) - Static method in class mml.Dirichlet A very few, very rudimentary tests; also see Test. main(String[]) - Static method in class mml.LinearD One very little test. main(String[]) - Static method in class mml.MML Runs Test.main(argv). main(String[]) - Static method in class mml.MotifD Developer's debugging tests; main() will "go away" one day. main(String[]) - Static method in class mml.Test Run a few, very(!) simple tests. main(String[]) - Static method in class mml.Tree main() allows Tree to be (slightly) tested, in isolation. main(String[]) - Static method in class mml.UPModel Nothing new here; see Test. main(String[]) - Static method in class mml.vMF Run a few, very(!) simple tests. make_d_dx() - Method in class la.la.Function.Cts2Cts.Derivative The Derivative of 'this' derivative is the second Derivative of f, by finite differences. make_d_dx() - Method in class la.la.Function.Cts2Cts Slave worker for d_dx(). make_d_dx() - Method in class la.la.Library.Power The derivative of c.x^p is c.p.x^p-1. make_integral() - Method in class la.la.Function.Cts2Cts.Derivative The indefinite Integral of 'this' Derivative is f, of course. So, _lo∫^x this(x) dx = f(x)−f(lo). make_integral() - Method in class la.la.Function.Cts2Cts Slave worker for integral(). make_integral() - Method in class la.la.Library.Power The integral of c.x^p is (c/(p+1))x^p+1 unless p=−1 in which case the integral is c.log(x). makeNclasses(int, Vector) - Method in class mml.Mixture.Est Try to Find a good Mixture Model with 'nClass' classes (clusters). map(Function) - Method in class la.maths.Vector Apply Function 'f' to each element of 'this' Vector, returning a Vector of results. Markov - Class in mml A Markov Model of a given order — the UnParameterised Series Model. Markov(Value) - Constructor for class mml.Markov The problem defining parameter(s) dp = (lenUPM, order, lwb, upb). Markov.M - Class in mml Markov.M, the fully parameterised Markov Model, of a given order, for data [lwb, upb]^*. markTime() - Method in class la.util.Timer stop(), lastTime() (is returned), start(). match(Vector) - Method in class la.maths.Vector Find the rotation (as a quaternion), by some angle about some axis through the origin, that minimises the sum of squared errors between the Vectors of 3D Vectors of Cts, 'this' and 't'. Maths - Class in la.maths Useful mathematical constants and functions. Maths() - Constructor for class la.maths.Maths Matrix - Class in la.maths (Abstract) Matrix, for two-dimensional, rectangular, Vectors of Vectors; the new, principal operations are • nCols(), and • elt(r,c), plus as for all Vectors. Matrix(int) - Constructor for class la.maths.Matrix Constructor for use when the number of columns, nC, is known before calling the constructor, and the Matrix is known to be proper (rectangular). Matrix() - Constructor for class la.maths.Matrix The trivial constructor for use when the number of columns is not known before calling a Matrix constructor. Matrix.Doubles - Class in la.maths Matrices of Reals (Cts, doubles). Matrix.GP2 - Class in la.maths A simple, general-purpose implementation of a Matrix. Matrix.Ints - Class in la.maths Matrices of Ints (ints). Matrix.Jacobi - Class in la.maths The Jacobi algorithm finds the Eigen-Values and Eigen-Vectors of 'this' square, symmetric Matrix of Cts; it is usually invoked via Matrix.eigen(), Matrix.eigenValues(), or Matrix.eigenVectors(). MATRIX_CTS - Static variable in class la.la.Type MATRIX_INT - Static variable in class la.la.Type maxEdges() - Method in class graph.Graph The maximum possible number of Edges in a Graph with 'this' Graph's type and vSize. maxEdges(int) - Method in class graph.Type What is the maximum number of Edges for a Graph of this Type with 'vSize' Vertices? (If allowed, a self-loop counts as one.) maxV(int[][]) - Static method in class graph.Directed.Sparse Return the largest Vertex number mentioned in es, which may or may not be the largest one in the Graph. MAYBE - Static variable in class la.la.Type Maybe T, for optional Values, missing data, etc. Maybe() - Constructor for class la.la.Value.Maybe maybe_J(Vector) - Static method in class mml.Missing For a Vector 'ds' of Value.Maybe, return a Vector of those 'v' that are present in ds. Mdl - Variable in class mml.Adaptive Adaptive fully (trivially) parameterised. mdl - Variable in class mml.BestOf.M The fully parameterised upms[choice]-Model, the best choice out of the upms[]. Mdl - Variable in class mml.Continuous.M.Transform The fully (trivially) parameterised Continuous.M.Transform.MM Model. Mdl - Variable in class mml.Continuous.Uniform The fully parameterised Uniform continuous Model on [lwb, upb]. Mdl - Variable in class mml.Direction.Uniform The (trivially) fully parameterised Uniform Model of Directions. Mdl - Variable in class mml.Discretes.Uniform The Uniform Model on [lwb, upb]. Mdl - Variable in class mml.Int1 "The" Int1.M. Mdl - Variable in class mml.LogStar0UPM Note, logStar0 is Mdl. Mdl - Variable in class mml.Model.Transform The instance of M, 'Mdl'. Mdl - Variable in class mml.Permutation.Uniform The (trivially) fully parameterised Uniform Model of Permutations; also see its class, M. Mdl - Variable in class mml.R_D.M.Transform The fully (trivially) parameterised R_D.M.Transform.MM Model. Mdl - Variable in class mml.Simplex.Uniform The "given" fully parameterised Simplex.Uniform Model. mdl - Variable in class mml.Tree.Leaf 'mdl', the Model of the output datum, 'od'. mdl - Variable in class mml.UPFunctionModel.K.M The Model of the output datum, od (for every input, id). Mdl - Variable in class mml.WallaceInt0UPM Note, WallaceInt0 is Mdl. mdlE - Variable in class mml.Graphs.IndependentEdges.M The Model of Edge existence. mdlE() - Method in class mml.Graphs.IndependentEdges.M The fully parameterised Model of the existence of Edges. mdlE - Variable in class mml.MotifA.M The fully parameterised (Adaptive) Model of that part of the Adjacency Matrix not covered by instances of motifs[.]. mdlE - Variable in class mml.MotifD.M The fully parameterised (Adaptive) Model of that part of the Adjacency Matrix not covered by instances of motifs[.]. mdlMotif - Variable in class mml.Graphs.Motifs The Model used for each individual motifs (subGraph, pattern) in a Graphs.Motifs.M-Model. mdlNmotifs - Variable in class mml.Graphs.Motifs The (prior) Model of the number of motifs (motifs, patterns) in a Graphs.Motifs.M Model. mdls - Variable in class mml.Intervals.M The N Models of the output datum, od, one for each interval of id, N≥1. mdls - Variable in class mml.Markov.M mdls, MultiState Models, one per context. mdls - Variable in class mml.R_D.Independent.M One parameterised upm-Model, mdls[i], per column of the data. mdlV - Variable in class mml.Graphs.M Public face of 'mdlV' is mdlV(). mdlV() - Method in class mml.Graphs.M The fully parameterised Model of the number of Vertices, |V|. meanTime() - Method in class la.util.Timer Mean time in milliseconds of all periods of 'running'. merge(int, int, int[], int[], Comparator<Value>) - Method in class la.maths.Vector MergeSort tgt[lo,hi) on the basis of cmp(elt(tgt[.]), elt(tgt[.])),, possibly using src[lo,mid) and src[mid,hi) where mid = (lo + hi)÷2. merge(Series) - Method in class la.util.Series.Discrete merge(boolean, Series) - Method in class la.util.Series.Discrete merge(Series) - Method in class la.util.Series.Int merge(boolean, Series) - Method in class la.util.Series.Int merge(Series) - Method in class la.util.Series merge(true,s2), i.e., keep elements shared by 'this' and 's2'. merge(boolean, Series) - Method in class la.util.Series Merge the outputs of 'this' and 's2' in ascending order. minus - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. Missing - Class in mml The UnParameterised Missing Model of "missing data" – data that may be known (present) or may be missing (absent, unknown). Missing(Value) - Constructor for class mml.Missing Problem definition parameter 'dp' sets valueUPM. Missing.M - Class in mml The fully parameterised Model; Missing is the UnParameterised Model. mixer() - Method in class mml.Mixture.M Return the MultiState that weights the class Models. Mixture - Class in mml An UnParameterised Mixture Model capable of producing a fully parameterised Mixture Model; in this case, the problem-defining parameter is a UPModel, Mixture.upm, and the statistical-parameters are the weights, plus the statParams, of the clusters — the classes in the Snob sense of "class". Mixture(UPModel) - Constructor for class mml.Mixture Mixture.Est - Class in mml The Unparameterised Mixture's Estimator, capable of estimating a fully parameterised Mixture Model. Mixture.M - Class in mml A fully parameterised (non-abstract) Mixture Model with statistical-parameters being the weights and parameters of the classes (clusters, components). mLwb - Static variable in class eg.Musicians Bounds on mean(s) and standard deviation(s). MM(double, double, Value) - Constructor for class mml.Continuous.M.Transform.MM Note, msg1=0 and statistical parameter sp=triv, checked. MM(double, double, Value) - Constructor for class mml.R_D.M.Transform.MM Note, msg1=0 and statistical parameter sp=triv, checked. mml - package mml Package 'mml'; see mml's README. MML - Class in mml The class of Minimum Message Length [MML] tools for statistical and inductive inference, and machine learning. MML() - Constructor for class mml.MML MODEL - Static variable in class la.la.Type For statistical Models and mml. Model() - Constructor for class la.la.Type.Model Model(String) - Constructor for class la.la.Type.Model Model(String, Type) - Constructor for class la.la.Type.Model Model - Class in mml The abstract class of fully parameterised statistical Models. Model(double, double, Value) - Constructor for class mml.Model msg1 and msg2 are the lengths, in nits, of transmitting (i) the Model's statistical parameter(s), sp, and (ii) training data-set, ds|sp, where D was the training-data used to estimate sp. Model.Defaults - Class in mml A subclass of Model that sets default methods stats(ds,lo,hi), stats(add,ss0,ss1) and nlLH(ss), even if they are slow. Model.Transform - Class in mml Transform (the data for) Model.this by Function f (which is the problem defining parameter). Model.Transform.M - Class in mml The fully parameterised transformed Model has a trivial statistical parameter, sp. Model2mshp(Mixture.M, double[][], Vector) - Method in class mml.Mixture.Est Given a Mixture Model, mx, return the (fractional) class memberships, mshp, of the data. Model2mshp(Mixture.M, Vector) - Method in class mml.Mixture.Est See Mixture.Est.Model2mshp(mml.Mixture.M, double[][], la.maths.Vector). MODEL_N - Static variable in class la.la.Type Integer codes for various "types" of Type. MotifA - Class in mml 'MotifA', the UnParameterised adaptive Motif Model of' Graphs. MotifA(Value) - Constructor for class mml.MotifA Problem definition parameters dp = ⟨gType, upmV, ⟨sgMinV, sgMaxV⟩⟩. MotifA.M - Class in mml The fully parameterised MotifA Model of Graphs. MotifD - Class in mml The UnParameterised 'MotifD' Model of Graphs. MotifD(Value) - Constructor for class mml.MotifD Problem definition parameters, dp = ⟨gType, upmV, ⟨sgMinV, sgMaxV⟩⟩. MotifD.M - Class in mml The fully parameterised MotifD Model of Graphs. Motifs(Value) - Constructor for class mml.Graphs.Motifs Constructor only for use by subclasses of this class; it does not set gType or upmV. motifs() - Method in class mml.Graphs.Motifs.M Return the Motifs on which this Model is based. motifs - Variable in class mml.MotifA.M The motifs (patterns, templates), sorted (down) on |V|, that can be used to compress a given Graph. motifs() - Method in class mml.MotifA.M Returns motifs[]; see Graphs.Motifs.M.motifs(). motifs - Variable in class mml.MotifD.M The motifs (patterns, templates), sorted on |V|, that can be used to compress a given Graph. motifs() - Method in class mml.MotifD.M Returns motifs[]; see Graphs.Motifs.M.motifs(). ms - Variable in class mml.Independent.M The sub-Models making 'this' Independent.M Model. ms() - Method in class mml.Mixture.M Return the sub-Models, one per class (cluster). msg() - Method in class mml.Model The length of a two-part MML message, 'M; (ds|M)', where ds was the training-data used when estimating 'this' Model 'M'. msg1 - Variable in class mml.Model Where appropriate, the lengths of the first (msg1), and second (msg2) parts of an [MML] message transmitting (i) a Model (parameter estimate) θ, and (ii) training data-set ds|θ. msg1() - Method in class mml.Model Length of the 1st part, 'M', of a two-part MML message 'M; (ds|M)', where ds was the training data. msg1(double, double) - Method in class mml.vMF Length of the first part of the message. msg1bits() - Method in class mml.Model Model.msg1() in bits. msg2 - Variable in class mml.Model Where appropriate, the lengths of the first (msg1), and second (msg2) parts of an [MML] message transmitting (i) a Model (parameter estimate) θ, and (ii) training data-set ds|θ. msg2() - Method in class mml.Model Length of the 2nd part, '(ds|M)', of a two-part MML message 'M; (ds|M)', where D was the training-data. msg2(Vector, double, Value) - Method in class mml.vMF Length of the second part of the message. msg2bits() - Method in class mml.Model Model.msg2() in bits. msgBits() - Method in class mml.Model Model.msg() in bits. msgMotifs(Graph[]) - Method in class mml.Graphs.Motifs.M Motifs.msgMotifs(ms). msgMotifs(Vector) - Method in class mml.Graphs.Motifs.M Motifs.msgMotifs(vm). msgMotifs(Graph[]) - Method in class mml.Graphs.Motifs Return the cost, in nits, of stating 'N' motifs m[0], ..., m[N-1] (including stating N) as part of some fully parameterised Model. msgMotifs(Vector) - Method in class mml.Graphs.Motifs Return the cost, in nits, of stating 'N' motifs m₀, ..., m_N-1 (including stating N). mshp2abndc(double[][], Vector) - Method in class mml.Mixture.Est Calculate class abundances from class memberships, mshp. mshp2Model(double[][], Vector) - Method in class mml.Mixture.Est Given (possibly fractional) class memberships, mshp, (re-)estimate the Mixture Model (but note that msg2() is zero). mu - Variable in class mml.Geometric0UPM.M Statistical parameter μ, the mean, as a double. mu - Variable in class mml.LaplaceUPM.M The median, μ, and the scale, b, of this Model. mu - Variable in class mml.NormalMu The given, problem-defining parameter, μ, as a double. mu - Variable in class mml.NormalUPM.M The mean, μ, and standard deviation, σ. mu - Variable in class mml.vMF.M The mean Direction, μ. mu() - Method in class mml.vMF.M μ, the mean, a Direction in R^D. mu_x - Variable in class mml.vMF.M The mean Direction mu as double[]. muC - Variable in class mml.NormalMu The given, problem-defining parameter, μ, as a Cts. Multinomial - Class in mml An UnParameterised FunctionModel, given a number of categories 'k', from a number of trials 'n' to a Vector of 'k' frequencies that sum to 'n'. Multinomial(Value) - Constructor for class mml.Multinomial Problem definition parameter 'k' > 0 is the number of categories. Multinomial.M - Class in mml The fully parameterised FunctionModel from a number of trials 'n' to a Vector of 'k' frequencies that sum to 'n'. Multinomial.M.Trials - Class in mml An UnParameterised Model, given 'n' trials over 'k' categories, of Vectors of 'k' frequencies f_i, i=0..k-1, that sum to 'n'. Multinomial.M.Trials.TM - Class in mml A fully (trivially) parameterised Model of Vectors of 'k' frequencies that sum to 'n'. MultiState - Class in mml The UnParameterised MultiState Model (MultiState distribution) on data in bounds = [lo, hi], capable of estimating a fully parameterised MultiState.M Model. MultiState(Value) - Constructor for class mml.MultiState bounds = [lwb, upb], on 'this' Model's data-space. MultiState.M - Class in mml A fully parameterised MultiState (Multinomial) Model; also see the UnParameterised MultiState Model. Multivariate - Class in mml The (abstract) class of UnParameterised Models over Multivariate data (Tuples). Multivariate(Value) - Constructor for class mml.Multivariate Multivariate.M - Class in mml The (abstract) class of fully parameterised Models over Multivariate data (i.e., Tuples). mUpb - Static variable in class eg.Musicians Bounds on mean(s) and standard deviation(s). Musicians - Class in eg Is the 27 Club a thing or is it an urban myth? Simple analysis of the age at death of musicians. Musicians() - Constructor for class eg.Musicians N n() - Method in class graph.Type n - Variable in class graph.Undirected.K The number of Vertices. n() - Method in class la.la.Expression.Application n() - Method in class la.la.Expression.Binary n() - Method in class la.la.Expression.Block n() - Method in class la.la.Expression.Const n() - Method in class la.la.Expression.Ident n() - Method in class la.la.Expression.IfExp n() - Method in class la.la.Expression.LambdaExp n() - Method in class la.la.Expression n(), the numerical code (tag) of a subclass of Expression, for example, for switch-ing. n() - Method in class la.la.Expression.Tuple n() - Method in class la.la.Expression.Unary n() - Method in class la.la.Type.Char n() - Method in class la.la.Type.Cts n() - Method in class la.la.Type.Enum n() - Method in class la.la.Type.Function n() - Method in class la.la.Type.Int Return INT_N. n() - Method in class la.la.Type.Model n() - Method in class la.la.Type.Option n() - Method in class la.la.Type.Triv n() - Method in class la.la.Type.Tuple n() - Method in class la.la.Type.TYPE n() - Method in class la.la.Type.Vector n() - Method in class la.la.Value.Char Return the Char's int code. n() - Method in class la.la.Value.Defer force(), and return the v.n() of this Deferred Value. n() - Method in class la.la.Value.Discrete The int value or tag corresponding to 'this' Discrete Value. n() - Method in class la.la.Value.Enum Return the Enum Value's int "code". n() - Method in class la.la.Value.Inc_Or.Both n() - Method in class la.la.Value.Inc_Or.Left n() - Method in class la.la.Value.Inc_Or.Right n - Variable in class la.la.Value.Int The int of 'this' Int Value. n() - Method in class la.la.Value.Int The Java int, 'n', itself. n() - Method in class la.la.Value.List.Cell A Cell is Option number 1 of List (NIL is number 0). n() - Method in class la.la.Value.Maybe.Just n() - Method in class la.la.Value n() corresponds to an Int.n() or an Option.n() etc.; this default throws a RuntimeException but see Bool.n(), say. n - Variable in class la.la.Value.Option.GP The particular Option number, 'n', within Type 't'. n() - Method in class la.la.Value.Option.GP Return the Option number, Value.Option.GP.n. n() - Method in class la.la.Value.Option Return this Option Value's number within its Option Type. n() - Method in class la.la.Value.Triv Returns zero, 0, that is the only case of Triv. n() - Method in class la.la.Value.Tuple For a Tuple consisting entirely of bounded Discrete Values, return the obvious integer encoding, hoping it doesn't overflow. n(int) - Method in class la.maths.Vector Return this.elt(i).n() — assuming this is a Vector of Int. n - Variable in class la.util.RefInt The int value itself. n - Variable in class mml.Multinomial.M.Trials The number of trials, 'n', of the 'k' categories. N() - Method in class mml.Permutation Permutations of {0, ..., N()-1}. N - Variable in class mml.Permutation.Uniform The problem-defining parameter is 'N' (here as an int) for Permutations of {0, ..., N-1}. N() - Method in class mml.Permutation.Uniform n() - Method in class mml.Tree.Param.DFork n() - Method in class mml.Tree.Param.Leaf n() - Method in class mml.Tree.Param.OFork N01 - Static variable in class mml.MML The fully parameterised Normal Model, N_{⟨μ=0,σ=1⟩}; it might be useful. NaiveBayes - Class in mml The UnParameterised NaiveBayes FunctionModel; the fully parameterised FunctionModel is NaiveBayes.M. NaiveBayes(Value) - Constructor for class mml.NaiveBayes Given definition parameter, dp, being an UnParameterised Dependent Model, dp:O×I, construct an UnParameterised NaiveBayes FunctionModel of I→O. NaiveBayes.M - Class in mml The fully parameterised NaiveBayes FunctionModel I→O; the UnParameterised FunctionModel is NaiveBayes. name - Variable in class la.la.Type The (optional) name of 'this' Type. NandSum(Vector) - Static method in class mml.Discretes Some Discrete Models, such as Geometric0UPM.M, have (N,sum) as sufficients statistics, ss = stats(ds), of a data-set ds, in which case they may implement Model.stats(la.maths.Vector) by calling NandSum. NandSum(Vector, int, int) - Static method in class mml.Discretes NandSum for elements [lo, hi). Native() - Constructor for class la.la.Function.Native Native2() - Constructor for class la.la.Function.Native2 Native3() - Constructor for class la.la.Function.Native3 nAutomorphisms() - Method in class graph.Graph The number of automorphisms of 'this' Graph. nChildren - Variable in class mml.R_D.Forest The number of columns that have a parent. nChildren - Variable in class mml.R_D.ForestSearch.M The number of variables that have a parent. nCols() - Method in class la.maths.Matrix The number of colums; note every Matrix is rectangular. nCols() - Method in class la.maths.Vector Provided 'this' is a rectangular Vector of Structured, which property is checked within, return its number of columns, otherwise throw an error. ne - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. NearInverse - Class in mml The UnParameterised NearInverse Model of positive reals, (0, ∞). NearInverse(Value) - Constructor for class mml.NearInverse NearInverse.M - Class in mml The fully parameterised NearInverse Model of positive reals. nearlyEqual(double, double) - Static method in class la.maths.Maths Is |x-y| ≤ a standard 'epsilon'-fraction of max(|x|, |y|) ? nearlyEqual(double, double, double) - Static method in class la.maths.Maths Is |x-y| ≤ an epsilon-fraction of max(|x|, |y|) ? nearlySymmetric(double) - Method in class la.maths.Vector Provided 'this' is a Vector of Vectors of Cts, is it square && (nearly-)symmetric? That is, are x[r][c] and x[c][r] nearlyEqual for all r and c? nearlySymmetric() - Method in class la.maths.Vector Provided 'this' is a Vector of Vectors of Cts, return nearlySymmetric(Maths.epsilon). negCR - Static variable in class la.la.Value negOne - Static variable in class la.la.Value negOneR - Static variable in class la.la.Value negTenR - Static variable in class la.la.Value nElts() - Method in class graph.Type Four, see elt(i). nElts() - Method in class la.la.Type.Function Two elements (if known), the input and output Types. nElts() - Method in class la.la.Type.Model One element (if known), the dataspace (Type). nElts() - Method in class la.la.Type This default returns 0; override it if necessary. nElts() - Method in class la.la.Type.Tuple nElts() - Method in class la.la.Type.Vector nElts() - Method in class la.la.Value.Chars nElts() - Method in class la.la.Value.Defer force(), and return the v.nElts() of this Deferred Value. nElts() - Method in class la.la.Value.Inc_Or.Both nElts() - Method in class la.la.Value.Inc_Or.Left nElts() - Method in class la.la.Value.Inc_Or.Right nElts() - Method in class la.la.Value.List.Cell 2 elements nElts() - Method in class la.la.Value.Maybe.Just nElts() - Method in class la.la.Value This default throws an exception but see Value.Structured.nElts(). nElts() - Method in class la.la.Value.Option Return this Option's number of elements (fields). nElts() - Method in class la.la.Value.Structured The number of elements (components, fields) in 'this' Structured Value. nElts() - Method in class la.la.Value.Tuple.GP nElts() - Method in class la.la.Value.Tuple nElts() - Method in class la.maths.Matrix.GP2 nElts() - Method in class la.maths.Matrix The number of rows (top-level elements) in 'this' Matrix. nElts(int) - Method in class la.maths.Matrix Return Matrix.nCols(); note that every Matrix is rectangular. nElts() - Method in class la.maths.Q Return 4. nElts() - Method in class la.maths.Vector.Derived The number of elements in original Vector. nElts() - Method in class la.maths.Vector.GP nElts() - Method in class la.maths.Vector The number of elements (rows) in 'this' Vector. nElts(int) - Method in class la.maths.Vector Provided 'this' is a Vector of Value.Structured, return nElts() of elt(row). nElts() - Method in class la.maths.Vector.Slice hi - lo. nElts() - Method in class la.maths.Vector.Weighted nEltsRaw(int) - Method in class la.maths.Matrix.GP2 nEltsRaw(int) - Method in class la.maths.Matrix Called in the rectangularity-check within Matrix.setNcols(int) if the constructor Matrix() was used; it need not be implemented (overridden) if Matrix(nC) was used. NewtonRaphson(double) - Method in class la.la.Function.Cts2Cts Use Newton-Raphson to solve f(x) = 0, where 'f' is 'this' Cts2Cts, given an initial guess, x0. next - Variable in class la.la.Environment next links 'this' to the previous- (sub-) Environment, if any. nextFM - Variable in class mml.Sequences.LtoR.M The FunctionModel of the next element given the context. nextUPFM - Variable in class mml.Sequences.LtoR The UnParameterised Function Model for the "next" element. ni(Value) - Method in class graph.Type Does Graph g belong to (satisfy) 'this' Graph Type? ni(Value) - Method in class la.la.Type ¿Is Value v's in this Type; does Value 'v' have exactly 'this' Type? (--???or_Inclusion???) ni(Value) - Method in class la.la.Type.Tuple.GP Does Value v structurally match 'this' Type? ni(Value) - Method in class la.la.Type.Tuple Is Value v's Type in 'this' Tuple Type? ni(Value) - Method in class la.la.Type.Vector Is Value v's type in 'this' Vector Type? NIL - Static variable in class la.la.Value.List The empty list, NIL; also see Value.List.Cell. nilCon - Static variable in class la.la.Expression Expression.Constant nil; see Value.nilVal. nilSy - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. nilVal - Static variable in class la.la.Value nine - Static variable in class la.la.Value nl2LH(Value) - Method in class mml.Model The nlLH(ss) of (sufficient stats, ss, of) a data-set, ds, but in bits, instead of nits. nl2Pr(Value) - Method in class mml.Model Return the negative log₂ probability, nl2Pr, of datum 'd', in bits. nl2Pr() - Method in class mml.SeriesModel.Analysis The neg log (base 2) probability of the current data element. nlAoM() - Method in class la.la.Value.Cts The negative log AoM() of 'this' Cts. nlAoM() - Method in class la.la.Value.Defer force(), and return the v.nlAoM of this Deferred Value. nlAoM() - Method in class la.la.Value See Value.Cts.nlAoM(). nlAoM() - Method in class la.la.Value.Real Throws an exception because a Real Value is precise. nlAoM() - Method in class la.la.Value.Structured The total nlAoM of 'this' complete Structured Value. nlAoM() - Method in class la.maths.Vector The total -ve log AoM of the whole Vector (if appropriate); it looks at constAoM() and acts accordingly. nlAoM(int, int) - Method in class la.maths.Vector Return the sum nlAoM of elements [lo, hi). nlAoM(int) - Method in class la.maths.Vector Return this.elt(i).nlAoM() — assuming this is a Vector of Cts. nlJ(Vector) - Method in class la.la.Function.CtsD2CtsD Given a Vector xs:R^D, return the negative log of the determinant of the Jacobian matrix (must be square). nlLH(Value) - Method in class la.bioinformatics.Alignment.UPSame.M nlLH(Value) - Method in class mml.Adaptive.M The negative log likelihood of data-set 'ds', where statistics ss = stats(ds). nlLH(Value) - Method in class mml.BestOf.M Given sufficient statistics, ss=stats(ds), of a data-set ds, return mdl's negative log LikeliHood of ds. nlLH(Value) - Method in class mml.BetaUPM.M Assumes that statistics are the data-set itself. nlLH(Value) - Method in class mml.Continuous.M.Transform.MM Use the enclosing Continuous.M.this's nlLH(ss) but statistics 'ss' come from Continuous.M.Transform.stats(la.maths.Vector, int, int). nlLH(Value) - Method in class mml.Continuous.Transform.M m.nlLH(ss), but note that statistics 'ss' comes from stats. nlLH(Value) - Method in class mml.Continuous.Uniform.M Return Continuous.Uniform.nlLH(ss). nlLH(Value) - Method in class mml.Continuous.Uniform Given sufficient statistics, ss = stats(ds), of a data-set, ds, return the negative log likelihood of ds. nlLH(Value) - Method in class mml.CPT.M Given sufficient statistics, ss = stats(ds), of a data-set, ds, return the negative log likelihood of ds. nlLH(Value) - Method in class mml.Dependent.M Given sufficient statistics, ss = stats(ds) of a data-set, ds, return the negative log likelihood of ds. nlLH(Value) - Method in class mml.Direction.Uniform.M Assumes that statistics are the data-set itself. nlLH(Value) - Method in class mml.Dirichlet.M Assumes that statistics are the data-set itself. nlLH(Value) - Method in class mml.Discretes.Uniform.M nlLH(ss), see Discretes.Uniform.nlLH(la.la.Value). nlLH(Value) - Method in class mml.Discretes.Uniform Given sufficient statistics, ss = stats(ds), of a data-set, ds, return the negative log LikeliHood, nlLH(ss), that is the neg log prob of ds under a Uniform Model. nlLH(Value) - Method in class mml.ExponentialUPM.M See stats(...) of a data-set ds; for N data, {x_i}, nlLH = N.log A + (1/A) ∑ x_i. nlLH(Value) - Method in class mml.GammaUPM.M Assumes that statistics are the data-set itself. nlLH(Value) - Method in class mml.Geometric0UPM.M Given sufficient statistics, ss = stats(ds), of a data-set ds, return the negative log LikeliHood of ds. nlLH(Value) - Method in class mml.Graphs.M Sum of nlPrs — assumes that statistics 'ss' is the data-set of Graphs itself. nlLH(Value) - Method in class mml.HeavyTail.Over_x1.M The negative log likelihood, nlLH(ss), of a data-set ds having statistics ss=stats(ds) (= ds itself). nlLH(Value) - Method in class mml.Independent.M Given sufficient statistics, ss = stats(ds), of a data-set, ds, return the negative log likelihood of ds. nlLH(Value) - Method in class mml.Int1.M Negative log likelihood of data-set ds[lo,hi) where ss=stats(ds,lo,hi). nlLH(Value) - Method in class mml.Intervals.M Assumes that statistics 'ss' are the data-set itself (actually sorted). nlLH(Value) - Method in class mml.KnownClass For every data-set, ds, nlLH(ss=stats(ds)) = 0. nlLH(Value) - Method in class mml.LaplaceUPM.M Assumes that statistics are ~the data-set itself. nlLH(Value) - Method in class mml.Linear1.M Given a data-set, ds = zip xs ys, having sufficient statistics ss = stats(ds), return the negative log likelihood of the ys given the xs. nlLH(Value) - Method in class mml.LinearD.M Given sufficient statistics, ss=stats(ds), of a data-set ds, return the negative log LikeliHood of ds. nlLH(Value) - Method in class mml.LogStar0UPM.M Assumes that statistics are the data-set itself. nlLH(Value) - Method in class mml.Markov.M Given statistics, ss = stats(ds), of a data-set, ds, return the negative log likelihood of ds. nlLH(Value) - Method in class mml.Missing.M Given sufficient statistics, ss=stats(ds), of a data-set 'ds', return the negative log LikeliHood of 'ds'. nlLH(Value) - Method in class mml.Mixture.M Assumes that statistics are the data-set itself. nlLH(Value) - Method in class mml.Model.Defaults This default implementation assumes that statistics are the data themselves and uses sumNlPr but many Models can do better. nlLH(Value) - Method in class mml.Model Given sufficient statistics, ss = stats(ds), of a data-set, ds, return the negative log LikeliHood, nlLH(ss), of ds. nlLH(Value) - Method in class mml.Model.Transform.M The enclosing Model.this's nlLH applied to statistics 'ss'. nlLH(Value) - Method in class mml.MotifA.M ss = stats(ds,lo,hi), requires that ss is ds, the Vector (data-set) of Graphs itself. nlLH(Value) - Method in class mml.MotifD.M ss = stats(ds,lo,hi), requires that ss is ds, the Vector (data-set) of Graphs itself. nlLH(Value) - Method in class mml.Multinomial.M nlLH(Value) - Method in class mml.Multinomial.M.Trials.TM Not implemented, throw an RTE! nlLH(Value) - Method in class mml.MultiState.M Given sufficient statistics, ss = stats(ds), of a data-set, ds, return nlLH(ss), the negative log LikeliHood (negative log probability) of ds. nlLH(Value) - Method in class mml.NaiveBayes.M Assumes statistics 'ss' are the data-set itself. nlLH(Value) - Method in class mml.NearInverse.M Assumes that statistics are the data-set itself. nlLH(Value) - Method in class mml.NormalUPM.M Given ss = stats(ds), of a data-set, ds, return nlLH(ss), that is the negative log likelihood of ds. nlLH(Value) - Method in class mml.Permutation.Uniform.M The negative log likelhood of Permutations data-set 'ds' where ss=stats(ds). nlLH(Value) - Method in class mml.Poisson0UPM.M Given sufficient statistics, ss = stats(ds), of a data-set ds, return the negative log LikeliHood of ds. nlLH(Value) - Method in class mml.R_D.Forest.M Negative log likelihood for data-set ds where ss=stats(ds). nlLH(Value) - Method in class mml.R_D.ForestSearch.M Negative log likelihood for data-set ds where ss=stats(ds). nlLH(Value) - Method in class mml.R_D.Independent.M Given sufficient statistics, ss = stats(ds), of a data-set, ds, return the negative log likelihood of ds. nlLH(Value) - Method in class mml.R_D.M.Transform.MM Use the enclosing R_D.M.this's nlLH(ss) but statistics 'ss' come from R_D.M.Transform.stats(la.maths.Vector, int, int). nlLH(Value) - Method in class mml.R_D.NrmDir.M Given sufficient statistics, ss = stats(ds), of a data-set, ds, return the negative log likelihood of ds. nlLH(Value) - Method in class mml.R_D.Transform.M m.nlLH(ss), but note that statistics 'ss' comes from stats. nlLH(Value) - Method in class mml.Sequences.K.M The negative log likelihood of data-set 'ds' where ss=stats(ds). nlLH(Value) - Method in class mml.Simplex.Uniform.M Assumes that statistics are the data-set itself. nlLH(Value) - Method in class mml.Tree.Fork Assumes statistics 'ss' are the data-set itself. nlLH(Value) - Method in class mml.Tree.Leaf Assumes statistics 'ss' are the data-set itself. nlLH(Value) - Method in class mml.UPFunctionModel.K.M Negative log likelihood; note, statistics are ss = stats(ds). nlLH(Value) - Method in class mml.UPModel.Transform.M Use m on transformed data, m.nlLH(ss), but note that statistics ss comes from stats. nlLH(Value) - Method in class mml.UPSeriesModel.K.M Given statistics, ss = stats(ds), of a data-set, ds, return the negative log likelihood of ds. nlLH(Value) - Method in class mml.vMF.M Given statistics, ss = stats(ds) of a data-set, ds, return the negative log likelihood of ds. nlLH(Vector, double, Value) - Method in class mml.vMF Given μ, κ, and statistics, ss = stats(ds) of a data-set, ds, return the negative log likelihood of ds. nlLH(Value) - Method in class mml.WallaceInt0UPM.M Assumes that statistics are the data-set itself. nlPdf(Value) - Method in class mml.ByPdf.M The negative log pdf, nlPdf(d), of a datum d, must be specified to define a ByPdf. nlPdf(Value) - Method in class mml.Continuous.M Calls nlPdf_x(d.x()). nlPdf(Value) - Method in class mml.Direction.M The negative log probability density of datum Vector v must be defined on the unit-radius K-sphere, the surface of a D-ball in R^D, D=K+1. nlPdf(Value) - Method in class mml.Direction.Uniform.M The negative log probability density, that is logArea. nlPdf(Value) - Method in class mml.Dirichlet.M pdf(d) = (∏_i d_i^α_i-1) / B(α) where datum 'd' is a Vector in [0,1]^D such that ||d||₁ = 1. nlPdf(Value) - Method in interface mml.HasPdf nlPdf, the negative log pdf(d). nlPdf(Value) - Method in class mml.Linear1.M Negative log pdf of 'y' given 'x'. nlPdf(Value) - Method in class mml.LinearD.M Negative log pdf of 'y' given 'x'. nlPdf(Value) - Method in class mml.R_D.Forest.M Negative log probability density of a D-element datum v. nlPdf(Value) - Method in class mml.R_D.ForestSearch.M nlPdf(Value) - Method in class mml.R_D.Independent.M The negative log pdf(d) where d is a datum, a member of R^D. nlPdf(Value) - Method in class mml.R_D.M.Transform.MM R_D.M.this.nlPdf(f.apply(v)) + f.nlJ(v). nlPdf(Value) - Method in class mml.R_D.NrmDir.M The negative log probability density of a Vector datum, v, in R^D. nlPdf(Value) - Method in class mml.R_D.Transform.M Negative log probability density as per m but "adjusted" by f's nlJ(xs). nlPdf(Value) - Method in class mml.Simplex.Uniform.M The negative log probability density of datum d (L₁-normalised which is checked(!)) under the Uniform model is +logArea(). nlPdf(Value) - Method in class mml.vMF.M The negative log pdf(v) for datum Direction v, return - κ μ.v - log C_D(κ). nlPdf_x(double) - Method in class mml.BetaUPM.M pdf(x) = {1 / Β(alpha, beta)} x^alpha-1 (1-x)^beta-1. nlPdf_x(double) - Method in class mml.Continuous.M The negative log probability density of x; also see Continuous.M.nlPdf(la.la.Value). nlPdf_x(double) - Method in class mml.Continuous.M.Transform.MM M.nlPdf_x(f(x)) − log(|f.d_dx()(x)|), the second term to "correct" the datum's nlAoM. nlPdf_x(double) - Method in class mml.Continuous.Transform.M Negative log probability density as per m but corrected by f's derivative. nlPdf_x(double) - Method in class mml.Continuous.Uniform.M nlPdf_x(double) - Method in class mml.ExponentialUPM.M The pdf is ¹/_A e^-x/A, for x≥0. nlPdf_x(double) - Method in class mml.GammaUPM.M pdf(x) = {1 / (Γ(k) θ^k)} x^k-1 e^-x/θ. nlPdf_x(double) - Method in class mml.HeavyTail.Over_x1.M The negative log of pdf_x(x). nlPdf_x(double) - Method in class mml.LaplaceUPM.M The pdf is ¹/_2b e^-|x-μ|/b, its negative log being |x-μ|/b + log(2b). nlPdf_x(double) - Method in class mml.NearInverse.M Nearly - log(1/x), that is + log(x), but not quite, corresponding to a pdf(x) of ~1/x, but not quite (as always, x > 0, of course). nlPdf_x(double) - Method in class mml.NormalUPM.M The negative log pdf of a datum, x (double), for use by nlPdf. nlPr(Value) - Method in class mml.Adaptive.M The negative log probability of Discrete datum 'd', but pay careful attention to the remarks on nlLH(ss). nlPr(Value) - Method in class mml.BestOf.M Return mdl's negative log probability of data 'd'. nlPr(double, Value) - Method in class mml.ByPdf.M A convenience function using a negative log AoM, 'nlAoM', other than that set in the datum, d, itself. nlPr(Value) - Method in class mml.ByPdf.M The negative log probability of datum d; returns nlPdf(d) + d.nlAoM(). nlPr(Value) - Method in class mml.Dependent.M The negative log probability of datum iod = (id, od). nlPr(Value) - Method in class mml.Direction.M The negative log probability of datum Vector v; note that v need not be normalised but it is taken to be a Direction with v.norm() being "common knowledge". nlPr(Value) - Method in class mml.Discretes.M Get nlPr(d) from nlPr_n(d.n()). nlPr(Value) - Method in class mml.FunctionModel Given iod = ⟨id, od⟩, return -log pr(od|id). nlPr(Value) - Method in class mml.Graphs.IndependentEdges.M The negative log probability of a Graph datum 'g'; it costs (i) |V| and (ii) the adjacency matrix. nlPr(Value) - Method in class mml.Graphs.Skewed.M The negative log probability of Graph 'g'. nlPr(Value) - Method in class mml.Independent.M The negative log probability of a Tuple-datum, 'd', where the elements (fields, components, columns) of d are modelled independently. nlPr(Value) - Method in class mml.KnownClass For all data, d, pr(d)=1, nlPr(d)=0. nlPr(Value) - Method in class mml.Missing.M Return the negative log probability of datum 'd'. nlPr(Value) - Method in class mml.Mixture.M The negative log probability of datum d under 'this' Mixture is log( ∑{ mixer().pr(i) × ms()[i].pr(d) } ) = logSum{ mixer().nlPr(i) + ms()[i].nlPr(d) } . nlPr(int, Value) - Method in class mml.Mixture.M The negative log probability of datum 'd' according to class 'i' alone. nlPr(Value) - Method in class mml.Model The negative log_e probability of a datum, 'd', in nits; nlPr must be defined when implementing a Model. nlPr(Value) - Method in class mml.Model.Transform.M The enclosing Model.this's nlPr(f(d)). nlPr(Value) - Method in class mml.MotifA.M The negative log probability of datum (Graph) G. nlPr(Value) - Method in class mml.MotifD.M The negative log probability of datum (Graph) G. nlPr(Value) - Method in class mml.Multinomial.M.Trials.TM Negative log probability of 'f', frequencies of 'k' categories in 'n' trials. pr(f) = (n! / (∏ f[i]!)) (∏ pr[i]^f[i]) The 'pr[i]' , actually the nlPrs[i], come from the Multinomial.M. nlPr(Value) - Method in class mml.Permutation.Uniform.M The negative log probability of Permutation p, that is log N! nlPr(Value) - Method in class mml.Sequences.K.M The negative log probability of Sequence (Vector) datum 'd'. nlPr(Value) - Method in class mml.Sequences.M Return the negative log probability of Sequence (Vector) datum 'd'. nlPr() - Method in class mml.SeriesModel.Analysis The negative log probability of the current data element. nlPr(Value) - Method in class mml.SeriesModel The negative log probability of datum Vector vec. nlPr(Value) - Method in class mml.UPModel.Transform.M Use m on transformed data, m.nlPr(f(d)). nlPr(Value) - Method in class mml.UPSeriesModel.Length.M The negative log probability of datum Series sv is the sum of the nlPr of sv's length, and of sv's elements. nlPr_n(int) - Method in class mml.Adaptive.M The negative log probability of datum int 'n', but pay careful attention to the remarks on nlLH(ss). nlPr_n(int) - Method in class mml.Discretes.M Back-room works for negative log probability of datum int n, Discretes.M.nlPr(la.la.Value); it must be implemented in an instance. nlPr_n(int) - Method in class mml.Discretes.Uniform.M Back-room works for Discretes.M.nlPr(la.la.Value). nlPr_n(int) - Method in class mml.Geometric0UPM.M The negative log probability of n; note, n≥0. nlPr_n(int) - Method in class mml.Int1.M pr(n) = 1/(n(n+1)) so -log pr(n) equals log(n) + log(n+1). nlPr_n(int) - Method in class mml.LogStar0UPM.M Negative log probability of datum int, n ≥ 0. nlPr_n(int) - Method in class mml.MultiState.M Return MultiState.M.nlPrs[dn-lwb_n()]. nlPr_n(int) - Method in class mml.Poisson0UPM.M The negative log of pr(n|α) = (e^-α).(αⁿ) / n!, integer n ≥ 0. nlPr_n(int) - Method in class mml.WallaceInt0UPM.M The negative log probability of int n≥0 depends on cummulativeCatalans(i). nlPrior(double, double, double) - Method in class mml.Linear1.Est The negative log prior on ⟨a, b, σ⟩. nlPrior(Vector, double) - Method in class mml.LinearD.Est Negative log prior (probability density). nlPrs - Variable in class mml.Multinomial.M The negative log probabilities of the 'k' categories. nlPrs2odds(double[]) - Static method in class la.maths.Maths Convert an array, nlPr, of message lengths (negative log probabilities) into a normalised array of probabilities. nlPrs2odds(double[], double[]) - Static method in class la.maths.Maths Convert an array, nlPr, of negative log probabilities into a normalised array of probabilities, pr. NONE - Static variable in class la.la.Value.Maybe NONE = 0, JUST = 1. None - Static variable in class la.la.Value.Maybe None, where the optional Value is missing, absent, late,... None - Static variable in class la.la.Value Equal to Maybe.None norm() - Method in class la.maths.Vector The Euclidean norm, ||v||=√(v.v), of 'this' Vector of Cts, that is, √sumSq(). norm_Cts() - Method in class la.maths.Vector 'This' Vector's Euclidean norm as a Value.Cts with nlAoM() being the Vector's divided by nElts(). Normal - Static variable in class mml.MML The UnParameterised Normal Model (Gaussian distribution) capable of estimating both μ and σ together to give a fully parameterised Normal M Model. normalised() - Method in class la.maths.Vector Return 'this' Vector of Cts normalised to 1.0, which has implications for Vector.AoM(int) and Vector.nlAoM(). normalised1() - Method in class la.maths.Vector Return 'this' Vector of Cts normalised by making a Vector of new (scaled) elements. normalised2() - Method in class la.maths.Vector Return 'this' Vector of Cts normalised but without copying the (scaled) elements. NormalMu - Class in mml An UnParameterised Normal Model for cases where μ is the problem-defining parameter (common knowledge, given) and σ alone is the statistical parameter of the fully parameterised NormalMu.M to be estimated. NormalMu(Value) - Constructor for class mml.NormalMu μ is a given, the single problem-defining parameter. NormalMu.Est - Class in mml The standard Estimator for a Normal Model with specified μ. NormalMu.M - Class in mml M, a fully parameterised Normal Model with problem-defining parameter μ, and statistical parameter σ, as produced by the UnParameterised NormalMu. NormalMu0 - Static variable in class mml.MML The UnParameterised NormalMu Model with μ = 0, given, and σ unset. NormalUPM - Class in mml The class of UnParameterised Normal Models (Gaussian distributions). NormalUPM(Value) - Constructor for class mml.NormalUPM Constructor for UnParameterised Normal Models; note t=triv! There is little need to call it -- use the previously prepared Normal. NormalUPM() - Constructor for class mml.NormalUPM A constructor for any subclass of NormalUPM that deals with its own problem-defining parameter(s), e.g., NormalMu. NormalUPM.Est - Class in mml The standard Estimator for a Normal Model with unknown μ and σ. NormalUPM.M - Class in mml NormalUPM.M, a fully parameterised Normal Model (Gaussian probability distribution), N_μ,σ. normMdl - Variable in class mml.R_D.NrmDir.M A fully parameterised Model of Vector norms (lengths). normUPM - Variable in class mml.R_D.NrmDir normUPM is the UnParameterised Continuous Model of norm (length, magnitude, ...). notSy - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. NrmDir(Value) - Constructor for class mml.R_D.NrmDir Given dp = (normUPM, dirnUPM), construct the UnParameterised Model. Nrml - Variable in class mml.R_D.Forest.M There is one Normal model in Nrml[] per parent-less column, in order. Nrml - Variable in class mml.R_D.ForestSearch.M The models for the parent-less variables. nullSy - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. numeral - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. numeralR - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. O O2I - Variable in class mml.NaiveBayes.M dpndt_m's "backwards" FunctionModel of O→I. offset - Variable in class la.la.Expression.Ident Set by Environment.lookup(la.la.Expression.Ident). offset - Variable in class mml.Discretes.Shifted The offset adjustment, as an int. OFork(double, double, Value) - Constructor for class mml.Tree.OFork sp = (col, split, [sp₀, sp₁]). OFork(int, Value, Tree.Param[]) - Constructor for class mml.Tree.Param.OFork Omdl - Variable in class mml.NaiveBayes.M dpndt_m's Model of O. one - Static variable in class la.la.Value oneR - Static variable in class la.la.Value open - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. opens() - Method in class la.la.Value.Option Is ["("]+id where id is the name of 'this' Option Value; see Value.Structured.print(java.io.PrintStream). opens() - Method in class la.la.Value.Structured Is "(", but can override to change formatting. opens() - Method in class la.maths.Vector "["; see Value.Structured.print(java.io.PrintStream) and Value.Structured.toString(). opr - Variable in class la.la.Expression.Binary The binary operator, e.g., '+', etc.. opr - Variable in class la.la.Expression.Unary The unary operator, e.g., notSy, etc.. oprPriority - Static variable in class la.la.Syntax Option(String) - Constructor for class la.la.Type.Option Special case when ids={} and arities={}. Option(String, String[], int[]) - Constructor for class la.la.Type.Option Construct a Type.Option with name 'name' etc. Option() - Constructor for class la.la.Value.Option OPTION_N - Static variable in class la.la.Type Integer codes for various "types" of Type. order - Variable in class mml.Markov The Markov Models are of a given 'order', over Series of data, [lwb, upb]^* (bounds as ints). ordered() - Method in class la.la.Type.Atomic This default return true; i.e., the Type is assumed to be ordered. ordered() - Method in class la.la.Type.Discrete Is 'this' Type ordered, or not? orSy - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. outDegree(int) - Method in class graph.Directed.Sparse O(1)-time. outDegree(int) - Method in class graph.Graph Only applicable to Directed Graphs. over - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. Over_x1(Value) - Constructor for class mml.HeavyTail.Over_x1 No non-trivial problem-defining parameters; t=triv. ovr - Variable in class la.util.Series.Range P p - Variable in class la.la.Library.Power 'c' the multiplicative constant, and 'p' the power of 'x', as in c*x^p. PAIR - Static variable in class la.la.Type Also see Value.Tuple. pair(Value, Value) - Static method in class la.la.Value pair, a convenience function to make a 2-Tuple. Param() - Constructor for class mml.Tree.Param params - Variable in class mml.Estimator The Estimator's parameters, notably the parameters of a prior on the estimated-Model's statistical parameters. parent() - Method in interface graph.Child Return the parent Graph of which 'this' is a Child. parent() - Method in class graph.Directed.AsUndirected parent() - Method in class graph.Directed.Edge parent() - Method in class graph.Directed.Sparse.Induced parent() - Method in class graph.Directed.Sparse.Renumbered parent() - Method in class graph.Directed.Vertex parent() - Method in class graph.Graph.Derived Returns 'Graph.this', that is, returns the Graph of which 'this' is a Derivation. parent() - Method in class graph.Graph.Edge parent() - Method in class graph.Graph.ToDirected parent() - Method in class graph.Graph.ToUndirected parent() - Method in class graph.Graph.Vertex parent() - Method in class graph.Undirected.AsDirected parent() - Method in class graph.Undirected.Edge parent() - Method in class graph.Undirected.Sparse.Induced parent() - Method in class graph.Undirected.Sparse.Renumbered parent() - Method in class graph.Undirected.Vertex parent() - Method in class la.maths.Vector.Derived The Vector from which 'this' is Derived. parent() - Method in class la.maths.Vector.Slice The Vector of which 'this' Slice is a Slice. parent - Variable in class mml.R_D.Forest If parent[i] ≥ 0 then column 'i' has that parent column. parent - Variable in class mml.R_D.ForestSearch.M If parent[i]≥0 then variable i has that parent. pdf(Value) - Method in class mml.ByPdf.M Probability density function, pdf(d), of a datum d; returns exp(-nlPdf(d)). pdf(Value) - Method in interface mml.HasPdf pdf, the probability density function, as applied to a datum, d. pdf(Value) - Method in class mml.Linear1.M pdf of 'y' given 'x'. pdf(Value) - Method in class mml.LinearD.M pdf of 'y' given 'x'. pdf_x(double) - Method in class mml.HeavyTail.Over_x1.M A log-symmetric probability density function. Permutation - Class in mml The abstract class of UnParameterised Models over Permutations {0, ..., N-1}. Permutation(Value) - Constructor for class mml.Permutation Permutation.M - Class in mml Permutation.Uniform - Class in mml The UnParameterised Uniform Model of Permutations. Permutation.Uniform.M - Class in mml Mdl should be sufficient for most purposes, but here is the class of "fully parameterised" Uniform Models of Permutations. PI - Static variable in class la.la.Value PIby2 - Static variable in class la.la.Value plus - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. point01 - Static variable in class la.la.Value point1 - Static variable in class la.la.Value point5 - Static variable in class la.la.Value point9 - Static variable in class la.la.Value Poisson0 - Static variable in class mml.MML The UnParameterised Poisson Model (distribution) over integers in [0, ∞), capable of estimating a fully parameterised Poisson Model. Poisson0UPM - Class in mml The UnParameterised Poisson0 Model. Poisson0UPM(Value) - Constructor for class mml.Poisson0UPM Poisson0UPM.M - Class in mml The fully parameterised Poisson0 Model (probability distribution) on integers in [0, ∞); its one statistical parameter, α, is both the mean and variance. Poisson1 - Static variable in class eg.Graphing Poisson1 = Poisson0.shifted(1). polar2cartesian - Static variable in class la.la.Library This Function, R²→R², converts polar coordinates ⟨r,θ⟩ to Cartesian ⟨x,y⟩. position() - Method in class graph.Graph.SubGraphs Return the position, p, of the current subGraph in the Series, or how many were generated if hasNone(). position() - Method in class la.util.Series.Lines The current line number, starting at zero. position() - Method in class la.util.Series What position in the Series are we at (starting at zero)? If Series.hasNone(), the series was finite and position() is now its length. position() - Method in class la.util.Series.Range position() - Method in class la.util.Series.Separator position() - Method in class mml.SeriesModel.Analysis Return ds.position() where ds is in the given data Series. Power(Value, Value) - Constructor for class la.la.Library.Power Power(Value) - Constructor for class la.la.Library.Power Power(double) - Constructor for class la.la.Library.Power Power(double, double) - Constructor for class la.la.Library.Power pr(Value) - Method in class mml.Discretes.M Get pr(d) from pr_n(d.n()). pr(Value) - Method in class mml.FunctionModel Given iod = ⟨id, od⟩, return pr(od|id). pr(Value) - Method in class mml.Model The probability of a datum, 'd', given 'this' Model; also see nlPr(d). pr(Value) - Method in class mml.Model.Transform.M The enclosing Model.this's pr(f(d)). pr() - Method in class mml.SeriesModel.Analysis The probability of the current data element. pr(Value) - Method in class mml.UPModel.Transform.M Use m on transformed data, m.pr(f(d)). pr_n(int) - Method in class mml.Discretes.M Back-room works for Discretes.M.pr(la.la.Value), returns Math.exp( - nlPr_n(n). pr_n(int) - Method in class mml.Discretes.Uniform.M Back-room works for Discretes.M.pr(la.la.Value). pr_n(int) - Method in class mml.MultiState.M Return MultiState.M.prs[dn-lwb_n()]. pred - Variable in class graph.Directed.Sparse Given an Edge ⟨v0, v1⟩, v1 is in (ascending) succ[v0], and v0 is in (ascending) pred[v1]. present(Vector) - Static method in class mml.Missing For a Vector 'ds' of Value.Maybe, return a Vector of Bool. presentMdl - Variable in class mml.Missing.M The fully parameterised Model of whether a Value is present (known) or missing (unknown). presentUPM - Variable in class mml.Missing The UnParameterised 2-state Model of whether a data value is known (present, true) or missing (absent, false). print(PrintStream) - Method in class la.la.Value.Defer Note, exp.defer(env).print(ps) is what causes evaluation to happen by force().print()-ing the program. print(PrintStream) - Method in class la.la.Value.List Note, the tail of a List is printed iteratively. print(PrintStream) - Method in class la.la.Value Note, evaluation is print driven; this default print(ps) calls ps.print(Value.toString()) and is only used by small, atomic values; other sub-classes must override it. print(PrintStream) - Method in class la.la.Value.Structured This default print(ps) outputs all elements, (elt(0), elt(1), ...), to 'ps'. prior_k(double) - Method in class mml.vMF Maybe change this(?)-- the prior's pdf on κ is (1+κ)^-2, κ>0. Q Q - Class in la.maths Q, the (abstract) class of quaternions. Q() - Constructor for class la.maths.Q quacks - Static variable in class eg.Ducks Also see birds, waddles and weights. quad(Value, Value, Value, Value) - Static method in class la.la.Value quad, a convenience function to make a 4-Tuple. quarter - Static variable in class la.la.Value R r - Variable in class la.la.Value.Defer.Exp The Environment to be used with Expression e, at some later date, maybe. r - Variable in class la.la.Value.Lambda R4 - Static variable in class eg.Iris Type 'R4' is 4 CTS, i.e., R⁴, that is sepal and petal, length and width. R4Species - Static variable in class eg.Iris Type 'R4Species' is a 5-tuple of dimensions and Species. R_b - Variable in class mml.Linear1.Est bMax-bMin and log(sigmaMax)-Math.log(sigmaMin) resp. R_D - Class in mml R_D, the (abstract) class of UnParameterised Models of D-dimensional Vectors of Cts, that is of R^D. R_D(Value) - Constructor for class mml.R_D Given problem-defining parameter(s), dp, construct an UnParameterised Model of Vectors in R^D. R_D.Forest - Class in mml A (UnParameterised) model in which a column may be linearly dependent on a single parent column. R_D.Forest.M - Class in mml Fully parameterised R_D.Forest-model. R_D.ForestSearch - Class in mml A generalisation of R_D.Forest that is able to estimate the model's structure – the child-parent relations – as well as the parameters of the component distributions. R_D.ForestSearch.M - Class in mml The fully parameterised R_D.ForestSearch-model. R_D.Independent - Class in mml A Vector of Models is a Model of Vectors R^D. R_D.Independent.M - Class in mml A Vector of parameterised Continuous Models makes a parameterised Model of Continuous Vectors R^D. R_D.M - Class in mml (abstract) M, fully parameterised Models of R^D. R_D.M.Transform - Class in mml The UnParameterised R_D.M.Transform Model; the (trivially) parameterised Model is R_D.M.Transform.MM. NB. the preferred way to transform an already parameterised R_D.M is to use function R_D.M.transform(f). Also see the related but different R_D.Transform. R_D.M.Transform.MM - Class in mml (Wanted to call this class M, as in R_D.M.Transform.M, but the compiler (1.8.0_101) objects.) The fully, trivially parameterised...M. R_D.NrmDir - Class in mml An UnParameterised Model of Vectors in R^D made from UnParameterised Models of norms (lengths) and of Directions. R_D.NrmDir.M - Class in mml A fully parameterised Model of Vectors in R^D, made from a Model of norms (Vector lengths) and a Model of Directions. R_D.Transform - Class in mml The UnParameterised R_D.Transform model; the parameterised Model is R_D.Transform.M. NB. the preferred way to transform an R_D is with function R_D.transform(f). R_D.Transform.M - Class in mml The parameterised R_D.Transform.M Model, the UnParameterised model is R_D.Transform. R_s - Variable in class mml.Linear1.Est bMax-bMin and log(sigmaMax)-Math.log(sigmaMin) resp. R_s - Variable in class mml.LinearD.Est 1/σ "range", log(sigmaMax)-log(sigmaMin). random(int) - Method in class mml.Adaptive.M Uses randomSeries() to sample 'n' random homogeneous Values. random() - Method in class mml.BestOf.M Return a Value from mdl. random() - Method in class mml.Continuous.M Call Continuous.M.random_x(), if implemented, to return an exact random Value.Real; it might be necessary to do something different if, for example, you want accuracy (AoM) to depend on the result's x(). random(double) - Method in class mml.Continuous.M Return a random {#link la.la.Value#cts Cts}(random_x(),AoM). random(int, double) - Method in class mml.Continuous.M Return n random Cts each with the specified AoM. random() - Method in class mml.Dependent.M Return a random (id, od), assuming both Dependent.M.im and Dependent.M.fm.condModel(.) implement random(). random(double[]) - Method in class mml.Direction.Uniform.M Generate a random Direction, a random unit Vector in R^D. random() - Method in class mml.Dirichlet.M Generate a Dirichlet-distributed random variate, a Vector of D elements. random() - Method in class mml.Discretes.Bounded.M Return a random Value in the range of 'this' Model. random(int) - Method in class mml.Discretes.Bounded.M Calls Discretes.Bounded.M.random_n() n times. random() - Method in class mml.FunctionModel random() is inappropriate and throws an Exception but see random(id). random(Value) - Method in class mml.FunctionModel condModel(id).random(). random() - Method in class mml.Geometric0UPM.M Return a random number from 'this' Geometric distribution; calls Geometric0UPM.M.random_n(). random() - Method in class mml.Graphs.IndependentEdges.M Generate a random (unlabelled) Graph, Directed or Undirected according to gType. random() - Method in class mml.Graphs.M Not implemented for Graphs.M; a subclass may do so. random() - Method in class mml.Graphs.Skewed.M PROTOTYPE, Generate a random (unlabelled) Graph, Directed or Undirected according to gType. random() - Method in class mml.Independent.M Return a random Tuple, assuming each of the sub-Models can play its part. random() - Method in class mml.Missing.M Return a random Value from 'this' Model – provided that valueMdl can do random(). random() - Method in class mml.Mixture.M Produce (sample) a random Value from 'this' Mixture Model. random() - Method in class mml.Model Return a random Value from the modelled population, if possible. random(int) - Method in class mml.Model Return 'n' random() Values, if possible. random() - Method in class mml.Model.Transform.M f^-1 (Model.this.random()). random() - Method in class mml.MotifD.M Generate a random Graph according to 'this' Model. random() - Method in class mml.Permutation.M Generate a Uniform random Permutation of {0, ..., N-1}. random(int[]) - Method in class mml.Permutation.M Fill array p[] with a Uniform random Permutation of {0, ..., N-1}. random(int[]) - Static method in class mml.Permutation Fill array p[] with a Uniform random Permutation of {0, 1, ..., |p|-1}. random(int[]) - Method in class mml.Permutation.Uniform.M Fill array p[] with a Uniform random Permutation of {0, 1, ..., N-1}. random() - Method in class mml.Poisson0UPM.M Return a random number from 'this' Poisson distribution; calls Poisson0UPM.M.random_n(). random(double[]) - Method in class mml.R_D.Forest.M TODO random(xs) not yet tested??? random(double[]) - Method in class mml.R_D.ForestSearch.M TODO – not tested! random() - Method in class mml.R_D.Independent.M Return a random (exact) Vector in R^D, one element per mdls[i]. random() - Method in class mml.R_D.M Return a random Vector in R^D() (calls R_D.M.random_x()). random(double[]) - Method in class mml.R_D.M Optional - this default throws an Exception, may be overridden. random() - Method in class mml.R_D.NrmDir.M Generate a random R^D-Vector from 'this' Model. random() - Method in class mml.Sequences.K.M Return a random Sequence (Vector). random(double[]) - Method in class mml.Simplex.Uniform.M Put in array 'x' the components of a Vector uniformly at random in the K-Simplex. random() - Method in class mml.UPModel.Transform.M Get a random() from m and apply f⁻¹ to it if f has an inverse implemented. random() - Method in class mml.UPSeriesModel.Length.M Generate a random Series, that is a Vector of a random length, with random contents, from the Model. random(double[]) - Method in class mml.vMF.M Generate a random Direction, a random unit Vector in R^D. random_n() - Method in class mml.Adaptive.M Not implemented, arguably it is not possible (do not think that using random(1) truly gets around this). random_n() - Method in class mml.Discretes.Bounded.M Return a random int in the range of 'this' Model; this default is rather inefficient; is called by Discretes.Bounded.M.random(). random_n() - Method in class mml.Discretes.M Back-room works for Model.random(). random_n() - Method in class mml.Discretes.Uniform.M Generate a Uniform random int in [lwb, upb]. random_n() - Method in class mml.Geometric0UPM.M ???random_n() could be made much(!) more efficient!!! This implementation is just for completeness! random_n() - Method in class mml.Int1.M Not yet implemented (lazy). random_n() - Method in class mml.LogStar0UPM.M random_n() is not supported. random_n() - Method in class mml.Poisson0UPM.M ??? Poisson0.random_n() could be made much(!) more efficient!! This implementation is just for completeness. random_n() - Method in class mml.WallaceInt0UPM.M random_n() is not supported. random_x() - Method in class mml.BetaUPM.M If X ~ Gamma(alpha, theta) and Y ~ Gamma(beta, theta) independently then (X/(X+Y)) ~ B(alpha, beta) -- van der Waerden, Mathematical Statistics, Springer, 1969, cited in wikip. random_x(int) - Method in class mml.Continuous.M Return an array double[n] of random_x(). random_x() - Method in class mml.Continuous.M Called by Continuous.M.random(); this default is not implemented (throws an Exception) but see Continuous.Uniform.M.random_x() and NormalUPM.M.random_x(), say. random_x() - Method in class mml.Continuous.M.Transform.MM Requires 'f' to have an implemented inverse to work. random_x() - Method in class mml.Continuous.Transform.M Sample from m and apply f's inverse (provided it is defined). random_x() - Method in class mml.Continuous.Uniform.M Called by Continuous.M.random(); note, the generated Value.Cts is exact, its AoM=0. random_x() - Method in class mml.ExponentialUPM.M Return random double from this Model, that is A*times;log(x), where x is uniform in [0,1). random_x() - Method in class mml.GammaUPM.M See Marsaglia & Tsang, A simple method for generating Gamma variables, ACM Trans on Math Software, 26(3), 363-372, 2000. random_x() - Method in class mml.LaplaceUPM.M Generate a random (double) from 'this' Model. random_x() - Method in class mml.NormalUPM.M Generate a double from N_μ,σ for use in random(). random_x() - Method in class mml.R_D.M Return the components of a random Vector (calls R_D.M.random(double[])). random_x() - Method in class mml.R_D.M.Transform.MM Requires 'f' to have an implemented inverse to work. random_x() - Method in class mml.R_D.Transform.M Sample from m and apply f's inverse (provided it is defined). randomised() - Method in class graph.Graph Return a randomised Graph with the same (in- and out-) degree distribution(s) as 'this' Graph. randomMshp(int, int) - Method in class mml.Mixture.Est Return a matrix of random class memberships. randomSeries() - Method in class mml.Adaptive.M Return a Series.Discrete which is random and homogeneous: Sample probabilities 'prs[]' from Dirichlet.M(alpha) and then repeatedly sample Ints from MultiState.M(prs), i.e., the Series is random and homogeneous. randomSeries() - Method in class mml.Discretes.Bounded.M Return a Series.Discrete that repeatedly samples a random Value, provided 'this' Model can. randomSeries() - Method in class mml.Model Return a Series which repeatedly returns a random() Value from 'this' Model, provided the Model can do so. Range(Value.Int, Value.Int) - Constructor for class la.util.Series.Range Range(Value.Int, Value.Int, Value.Int) - Constructor for class la.util.Series.Range Range(int, int) - Constructor for class la.util.Series.Range Range(int, int, int) - Constructor for class la.util.Series.Range The principal constructor for [fst, fst+step, fst+2*step, ...]. README - Class in eg About package 'eg' — example application programs. README() - Constructor for class eg.README README - Class in graph About package 'graph' -- tools for Graphs (networks) README() - Constructor for class graph.README README - Class in la.bioinformatics About package 'la.bioinformatics' — experimental! README() - Constructor for class la.bioinformatics.README README - Class in la.la About package 'la.la' — LA's implementation of the λ-calculus. README() - Constructor for class la.la.README README - Class in la.maths About package 'la.maths' README() - Constructor for class la.maths.README README - Class in la About package 'la' README() - Constructor for class la.README README - Class in la.util About package 'la.util' README() - Constructor for class la.util.README README - Class in mml About package 'mml' — tools for Minimum Message Length inference. README() - Constructor for class mml.README real(double) - Static method in class la.la.Value Return an exact Real Value corresponding to the double x. Real() - Constructor for class la.la.Value.Real rec(int, Graph, int[], int[], Graph, int[], BitSet) - Method in class mml.MotifA.M Try to match vertex mvs[depth] of m to a Vertex of 'sent' that is joined to tvs[parent[depth]] of 'sent'. recSearch(int, boolean[], Vector, int) - Method in class mml.Tree.Est Coordinate the recursive search for a good (best?) Tree Model. recSy - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. RefInt - Class in la.util Class for a "by reference", "variable", or "result" int parameter. RefInt() - Constructor for class la.util.RefInt The value is not yet set. RefInt(int) - Constructor for class la.util.RefInt The value is set to 'n'. remainingNlPr() - Method in class mml.SeriesModel.Analysis The total nlPr() of any remaining elements of the Series, including the current element if there is one. renumbered(int[]) - Method in class graph.Directed.Sparse A renumbered Sparse Directed Graph is Sparse and Directed. Renumbered(int[]) - Constructor for class graph.Directed.Sparse.Renumbered renumbered(int[]) - Method in class graph.Graph Convenience function. Renumbered(int[]) - Constructor for class graph.Graph.Renumbered vs must be a permutation of [0, ..., vSize()-1]. renumbered(int[]) - Method in class graph.Undirected.Sparse A renumbered Sparse Undirected Graph is Sparse and Undirected. Renumbered(int[]) - Constructor for class graph.Undirected.Sparse.Renumbered repeat(Value, int) - Static method in class la.maths.Vector The Vector {v, v, ..., v}, n times. results(Model, Vector) - Static method in class eg.Musicians Display Model 'm', its two-part message length, and the negative log likelihood of data-set 'ds'. rgt - Variable in class la.la.Expression.Binary lft and rgt, the left and right sub-Expressions. RIGHT - Static variable in class la.la.Value.Inc_Or LEFT = 0, RIGHT = 1, BOTH = 2. Right(Value) - Constructor for class la.la.Value.Inc_Or.Right rightAssoc - Static variable in class la.la.Syntax RNG - Static variable in class mml.MML Standard practice is for those Models that do implement Model.random() to use RNG so that a run can be repeated by setting RNG's seed to a known value. root(double) - Method in class la.la.Function.Cts2Cts Solve f(x) = 0, where 'f' is 'this' Cts2Cts, given an initial guess, x0. rotate(double, Vector) - Method in class la.maths.Vector Given an angle, 't', in radians, and an axis, 'n', rotate 'this' 3D Vector by t about n. rotate(Q) - Method in class la.maths.Vector As specified by the quaternion, 'q', rotate 'this' 3D Vector by an angle about an axis. rowLength() - Method in class la.maths.Matrix Every row of 'this' Matrix has nCols() elements. rowLength() - Method in class la.maths.Vector Provided this is a Vector of Vectors, if it is rectangular return its row length, else a negative int. RTE(String) - Method in class la.la.Value Short for RuntimeException(errMsg(msg)) RTE(String) - Static method in class la.util.Util Convenience function for new RuntimeException(msg). S s - Variable in class la.la.Value.Chars The String of 'this' Chars. s - Variable in class la.util.Series.Separator The String being scanned for variables. scaled(double) - Method in class la.maths.Vector Return 'this' Vector scaled by a factor, s. search(int[], int) - Static method in class la.util.Util Binary search 'ns[]' for 'n'. search(Vector, int) - Method in class mml.Tree.Est Given a data-set, ds=(id,od)^*, estimate a fully parameterised M:id→od; calls recSearch. searchMV(int, boolean[], Vector, int, Tree.M) - Method in class mml.Tree.Est Search for a column of a Multivariate input datum on which to split. sel(int) - Method in class mml.Tree.Est Selector of input column (variable) 'col' of the input datum id, where iod=(id,od). Sel() - Constructor for class mml.Tree.Est.Sel sel0 - Variable in class mml.Tree.Est Selects all of the input datum id, where iod=(id,od). selfLoops() - Method in class graph.Graph Are self-loops ⟨v, v⟩ allowed? Not, does this Graph actually have any self-loops, but rather could it have a self-loop? selfLoops - Variable in class graph.Type Is the Graph Directed, and are self-loops allowed? separator() - Method in class la.la.Value.Option Is space, " "; see Value.Structured.print(java.io.PrintStream). separator() - Method in class la.la.Value.Structured Is ", " but can override to change formatting. Separator(String) - Constructor for class la.util.Series.Separator Construct a Separator with the most common choice, separator = ','. Separator(char, String) - Constructor for class la.util.Series.Separator Construct a Separator with a given separator character. Separator(boolean, boolean, char, String) - Constructor for class la.util.Series.Separator Note, if separator =' ', trimming makes no sense. separator - Variable in class la.util.Series.Separator The separator between variables, often but not always ','. Sequences - Class in mml The UnParameterised Sequences Model; the fully parameterised Model is Sequences.M. Sequences(Value) - Constructor for class mml.Sequences Problem definition parameter(s) 'dp'. Sequences.K - Class in mml Sequence.K uses the same Model, Sequences.K.eltUPM (parameterised), for every element of every Sequence datum. Sequences.K.M - Class in mml K.M uses the same Model, Sequences.K.M.eltMdl, for every element of a Sequence. Sequences.LtoR - Class in mml The UnParameterised Left-to-Right Model of Sequences. Sequences.LtoR.M - Class in mml The fully parameterised Left-to-Right Model of Sequenves. Sequences.M - Class in mml The fully parameterised Model of Sequences; Sequences is the UnParameterised Model. Series - Class in la.util Series, of Values, is like the standard Java Enumeration and Iterator but with simpler semantics. Series() - Constructor for class la.util.Series Series.Discrete - Class in la.util Series producing Discrete Values. Series.Int - Class in la.util Series producing Int Values. Series.Lines - Class in la.util Series of lines (Value.Chars, strings) from an input stream of byte such as a FileInputStream, say. Series.Range - Class in la.util Series of Ints from 'fst' inclusive, to 'ovr' exclusive, with an optional 'step' (default is 1) which may be negative. Series.Separator - Class in la.util A Series of "comma"-separated variables (CSV) as Chars (strings) out of a String, s. SeriesModel - Class in mml Fully parameterised (Time-) Series Models of data Series (Vectors). SeriesModel(double, double, Value) - Constructor for class mml.SeriesModel SeriesModel.Analysis - Class in mml An Analysis of a Scannable Value (Series). setNcols(int) - Method in class la.maths.Matrix If the trivial constructor, Matrix(), has been used, a sub-class of Matrix must override Matrix.nEltsRaw(int) and call setNcols(nC) before nCols() (or nElts(r)) is used. seven - Static variable in class la.la.Value sgMaxV - Variable in class mml.MotifA.M |V| for the smallest and largest of the motifs[.]. sgMaxV - Variable in class mml.MotifA Bounds [sgMinV, sgMaxV] on |V| for the motifs (patterns) that the estimator will search for (but not necessarily find). sgMaxV - Variable in class mml.MotifD.M |V| for the smallest and largest of the motifs[.]. sgMaxV - Variable in class mml.MotifD Bounds [sgMinV, sgMaxV] on |V| for the motifs (patterns) that the estimator will search for (but not necessarily find). sgMinV - Variable in class mml.MotifA.M |V| for the smallest and largest of the motifs[.]. sgMinV - Variable in class mml.MotifA Bounds [sgMinV, sgMaxV] on |V| for the motifs (patterns) that the estimator will search for (but not necessarily find). sgMinV - Variable in class mml.MotifD.M |V| for the smallest and largest of the motifs[.]. sgMinV - Variable in class mml.MotifD Bounds [sgMinV, sgMaxV] on |V| for the motifs (patterns) that the estimator will search for (but not necessarily find). shape() - Method in class la.maths.Vector For a Vector of Vector of Vec..., return the "nElts()" of the leading (hyper-)rectangular dimensions, e.g., a simple Vector would give [m], an m×n Matrix [m,n], etc., and a jagged Vector of Vectors [m]. shifted(int) - Method in class mml.Discretes.M Shift 'this' fully parameterised Model of Discretes by +offset (~shift data by -offset). shifted(int) - Method in class mml.Discretes Convenience function for new Shifted(new Int(offset)), for example, Poisson0.shifted(1) is the unparameterised Poisson distribution for integers n≥1. shifted(Value.Int) - Method in class mml.Discretes Convenience function for new Shifted(offset), for example, Poisson0.shifted(1) is the unparameterised Poisson distribution for integers n≥1. Shifted(Value) - Constructor for class mml.Discretes.Shifted The problem defining parameter, dp, is the offset Int. show(Value) - Static method in class la.la.Type Show a Value 'v' -- which must not be infinite, or even "big". show(Value[]) - Static method in class la.la.Type Type.show(Value) several Values. side(int) - Method in class la.bioinformatics.Alignment sel = LEFT | RIGHT | BOTH. side(Value.Scannable, int) - Static method in class la.bioinformatics.Alignment sel = LEFT | RIGHT | BOTH. sigma - Variable in class mml.Linear1.M Statistical parameters of the Model y = a × x + b + N(0, σ). sigma - Variable in class mml.LinearD.M Statistical parameter of the Model y=a·x+b+N(0,σ). sigma - Variable in class mml.NormalUPM.M The mean, μ, and standard deviation, σ. sigmaMax - Variable in class mml.Linear1.Est Bounds on σ; prior, pr(σ) ~ 1/σ . sigmaMax - Variable in class mml.LinearD.Est Bounds on σ; prior, pr(σ)~1/σ. sigmaMin - Variable in class mml.Linear1.Est Bounds on σ; prior, pr(σ) ~ 1/σ . sigmaMin - Variable in class mml.LinearD.Est Bounds on σ; prior, pr(σ)~1/σ. simple(Vector, Vector, Alignment.UPSame.M) - Static method in class la.bioinformatics.Alignment Given two sequences, s1 and s2, and a SeriesModel of Alignments, m, return an optimal global Alignment of s1 and s2. Simplex - Class in mml For Models of data s : K_Simplex; a standard K_Simplex is the space of (K+1)-Vectors, {[s₀, ..., s_K]} (NB. 0..K) where 0 ≤ s_i ≤ 1 and ∑s_i = 1. Simplex(Value) - Constructor for class mml.Simplex Simplex.Uniform - Class in mml The UnParameterised Uniform Model over a K_Simplex data-space (Mdl is fully parameterised). Simplex.Uniform.M - Class in mml Simplex.Uniform.Mdl should be sufficient for most purposes, but here is M, the class of fully parameterised Uniform Simplex Model(s). singleton(Value) - Static method in class la.maths.Vector Return a Vector consisting of a single element, 'e'. singleton(Value) - Static method in class la.util.Series The Series of just one element. six - Static variable in class la.la.Value Skewed(Value) - Constructor for class mml.Graphs.Skewed Problem definition parameters dp = ⟨Graphs.gType, Graphs.upmV⟩. skipRest() - Method in class la.la.Lexical Skip, and return, up to a "few" remaining input lines and characters. skipRest(int, int) - Method in class la.la.Lexical Skip, and return, the rest of the input up to the limits 'maxLines' lines and 'maxChars' characters. slice(int, int) - Method in class la.maths.Vector Return a Slice (section), [lo, hi), of 'this' Vector, that is, the elements between lo inclusive, and hi exclusive. Slice(int, int) - Constructor for class la.maths.Vector.Slice Note, [lo, hi), lo inclusive to hi exclusive. slice(int, int) - Method in class la.maths.Vector.Slice A Slice of a Slice is just a Slice, parent().slice(lo+lo2,lo+hi2). slowDeterminant() - Method in class la.maths.Matrix Calculate the determinant of 'this' Matrix by the slow, recursive (naive) method. sLwb - Static variable in class eg.Musicians Bounds on mean(s) and standard deviation(s). sm3 - Variable in class la.bioinformatics.Alignment.UPSame.M sm3, the SeriesModel of (LEFT | RIGHT | BOTH)^*. smE - Variable in class la.bioinformatics.Alignment.UPSame.M smE, the SeriesModel of elements. snd - Static variable in class la.la.Library Return the second element of a Value.Tuple. snd() - Method in class la.la.Value.Structured snd(), short for elt(1). sorted() - Method in class la.maths.Vector Return 'this' Vector, sorted on Value.comparator. sorted(int) - Method in class la.maths.Vector Return 'this' Vector sorted on column 'col'. sorted(Comparator<Value>) - Method in class la.maths.Vector Return 'this' Vector sorted on 'cmp'. sp - Variable in class mml.Model Holds the statistical parameter(s), if any, of 'this' Model; is returned by Model.statParams(). sp2Model(double, double, Value) - Method in class la.bioinformatics.Alignment.UPSame Given two part message lengths, msg1 and msg2, and statistical parameters, sp, return a M. sp2Model(double, double, Value) - Method in class mml.Adaptive Return a Model with 2-part message lengths, msg1=0 and msg2, and statistical parameter sp=(), triv. sp2Model(double, double, Value) - Method in class mml.BestOf Given first- and second-part message lengths, msg1 and msg2, and statistical parameter(s), sp, return a fully parameterised BestOf.M Model. sp2Model(double, double, Value) - Method in class mml.BetaUPM Given two-part message lengths, m1 & m2, and sp = ⟨α, β⟩, return a fully parameterised Beta Model. sp2Model(double, double, Value) - Method in class mml.Continuous.M.Transform sp2Model(double, double, Value) - Method in class mml.Continuous sp2Model(double, double, Value) - Method in class mml.Continuous.Transform Return a fully parameterised Continuous.Transform.M. sp2Model(double, double, Value) - Method in class mml.Continuous.Uniform Return a fully (trivially) parameterised Uniform Continuous Model; note that the statistical parameter, t, is trivial. sp2Model(double, double, Value) - Method in class mml.CPT Given sps, a Vector of statistical parameters, one for each input Value (that is, one for each entry of the CPT), return a fully parameterised CPT Model. sp2Model(double, double, Value) - Method in class mml.Dependent Given 2-part message lengths, m1 & m2, and statistical parameters sps = ⟨upm's, upfm's⟩, return a fully parameterised M Model. sp2Model(double, double, Value) - Method in class mml.Direction.Uniform sp2Model(0, m2, ()) returns a M Model. sp2Model(double, double, Value) - Method in class mml.Dirichlet Return a "fully parameterised" Dirichlet Model. sp2Model(double, double, Value) - Method in class mml.Discretes.Shifted sp2Model(double, double, Value) - Method in class mml.Discretes sp2Model(double, double, Value) - Method in class mml.Discretes.Uniform sp2Model(0, msg2, ()) -- Uniform has no true stat params. sp2Model(double, double, Value) - Method in class mml.Estimator Given part 1 and part 2 message lengths, m1 and m2, and statistical parameter(s), sp, return a fully parameterised Model. sp2Model(double, double, Value) - Method in class mml.ExponentialUPM sp2Model(double, double, Value) - Method in class mml.GammaUPM Given two-part message lengths m1 and m2, and statistical parameters sp, return a fully parameterised Model. sp2Model(double, double, Value) - Method in class mml.Geometric0UPM Given m1, m2 and μ return a fully parameterised Geometric Model. sp2Model(double, double, Value) - Method in class mml.Graphs.GERadaptive sp2Model(double, double, Value) - Method in class mml.Graphs.GERfixed sp2Model(double, double, Value) - Method in class mml.Graphs.IndependentEdges sp2Model(double, double, Value) - Method in class mml.Graphs.Motifs sp2Model(double, double, Value) - Method in class mml.Graphs.Skewed Return a fully parameterised Graphs.Skewed.M. sp2Model(double, double, Value) - Method in class mml.HeavyTail.Over_x1 sp2Model(double, double, Value) - Method in class mml.Independent Statistical parameter sps = ⟨upms[0]'s, ... etc.⟩. sp2Model(double, double, Value) - Method in class mml.Int1 sp2Model(double, double, Value) - Method in class mml.Intervals Given two-part message lengths, msg1 and msg2, and statistical parameters, sp, return a fully parameterised M. sp2Model(double, double, Value) - Method in class mml.LaplaceUPM Given two part message lengths, msg1 and msg2, and statistical parameters, sp, return an M. sp2Model(double, double, Value) - Method in class mml.Linear1 Return the fully parameterised Linear1 Model having two-part message lengths m1 and m2 and statistical parameters sp. sp2Model(double, double, Value) - Method in class mml.LinearD Given first- and second-part message lengths, msg1 and msg2, and statistical parameter(s), sp, return a fully parameterised LinearD.M Model. sp2Model(double, double, Value) - Method in class mml.LogStar0UPM Return logStar0 essentially, with msg2 set. sp2Model(double, double, Value) - Method in class mml.Markov Given two part message lengths, msg1 and msg2, and statistical parameters, sp, return a fully parameterised Markov Model. sp2Model(double, double, Value) - Method in class mml.Missing Given first- and second-part message lengths, msg1 and msg2, and statistical parameter(s), sp, return a fully parameterised Missing.M-Model. sp2Model(double, double, Value) - Method in class mml.Mixture sp2Model(msg1, msg2, (wts,sps)) where wts is the weights of the classes (clusters) and sps is the statParams of the classes, to be given to upm. sp2Model(double, double, Value) - Method in class mml.Model.Transform sp2Model(double, double, Value) - Method in class mml.MotifA Return a fully parameterised MotifA Model. sp2Model(double, double, Value) - Method in class mml.MotifD Return a fully parameterised MotifD Model. sp2Model(double, double, Value) - Method in class mml.Multinomial.M.Trials sp2Model(double, double, Value) - Method in class mml.Multinomial sp2Model(double, double, Value) - Method in class mml.MultiState sp2Model(msg1, msg2, sp), where sp is the statistical parameters, that is the probabilities, return a fully parameterised MultiState Model. sp2Model(double, double, Value) - Method in class mml.NaiveBayes Given two-part message lengths, and statistical parameters sp, return a fully parameterised model. sp2Model(double, double, Value) - Method in class mml.NearInverse sp2Model(double, double, Value) - Method in class mml.NormalMu Given msg1, msg2 and the (one) statistical parameter, σ, return a fully parameterised Normal Model. sp2Model(double, double, Value) - Method in class mml.NormalUPM Given (msg1, msg2, (μ, σ)) return a fully parameterised Normal M Model. sp2Model(double, double, Value) - Method in class mml.Permutation.Uniform sp2Model(0, m2, ()) returns a Model. sp2Model(double, double, Value) - Method in class mml.Poisson0UPM Given m1, m2 and α, return a fully parameterised Poisson0. sp2Model(double, double, Value) - Method in class mml.R_D.Forest sp2Model(double, double, Value) - Method in class mml.R_D.ForestSearch Return a fully parameterised model. sp2Model(double, double, Value) - Method in class mml.R_D.Independent sp2Model(double, double, Value) - Method in class mml.R_D.M.Transform sp2Model(double, double, Value) - Method in class mml.R_D.NrmDir Given 1st & 2nd part message lengths, m1 & m2, and statistical parameters, sp = (norm's, dirn's), return a fully parameterised Model. sp2Model(double, double, Value) - Method in class mml.R_D sp2Model(double, double, Value) - Method in class mml.R_D.Transform Return a fully parameterised R_D.Transform.M. sp2Model(double, double, Value) - Method in class mml.Sequences.K Return a fully parameterised K.M Model. sp2Model(double, double, Value) - Method in class mml.Simplex.Uniform Return a "fully parameterised" Uniform Simplex Model; the statistical parameter, t, is trivial. sp2Model(double, double, Value) - Method in class mml.Tree Return a fully parameterised Tree FunctionModel. sp2Model(double, double, Value) - Method in class mml.UPFunctionModel.K Two-part message lengths m1 and m2, and statistical parameter 'sp', for fully parameterised K.M. sp2Model(double, double, Value) - Method in class mml.UPFunctionModel Given two-part message lengths, msg1 and msg2, and statistical parameter(s), sp, return a fully parameterised Function Model. sp2Model(double, double, Value) - Method in class mml.UPModel.Est Given part 1 and part 2 message lengths, m1 and m2, and statistical parameter(s), sp, return the fully parameterised Model UPModel.this.sp2Model(m1,m2,sp). sp2Model(double, double, Value) - Method in class mml.UPModel Given two-part message lengths msg1 & msg2, and statistical parameter sp, return a fully parameterised M-Model. sp2Model(double, double, Value) - Method in class mml.UPModel.Transform sp2Model(double, double, Value) - Method in class mml.UPSeriesModel.K Statistical parameter sp = (lenMdl's sp, eltMdl's sp), return a fully parameterised K.M Series Model. sp2Model(double, double, Value) - Method in class mml.UPSeriesModel Two part message lengths, msg1 and msg2, and statistical parameters, sp, return a fully parameterised UPSeriesModel.M. sp2Model(double, double, Value) - Method in class mml.vMF Given two-part message lengths, and statistical parameters sp = (μ, κ), return a fully parameterised von Mises - Fisher Model. sp2Model(double, double, Value) - Method in class mml.WallaceInt0UPM Return WallaceInt0 essentially, with msg2 set. sparse(Type, int, int[][]) - Static method in class graph.Directed Convenience function. Sparse(int[][]) - Constructor for class graph.Directed.Sparse Assume that the max Vertex mentioned in es is the last Vertex. Sparse(Type, int, int[][]) - Constructor for class graph.Directed.Sparse Directed Edges es = {{v00, v01}, {v10, v11}, ...}. sparse(Type, int, int[][]) - Static method in class graph.Undirected Convenience function. Sparse(int[][]) - Constructor for class graph.Undirected.Sparse Assume that the max Vertex mentioned in es is the last Vertex. Sparse(Type, int, int[][]) - Constructor for class graph.Undirected.Sparse Undirected Edges es = {{v00, v01}, {v10, v11}, ...}. Species - Static variable in class eg.Ducks Species = coot | duck | swan. Species - Static variable in class eg.Iris The Enum Type 'Species' is 'Iris-setosa | Iris-versicolor | Iris-virginica'. split(Mixture.M, int, Vector) - Method in class mml.Mixture.Est Split class 'c', replace it with two sub-classes, and adjust. split - Variable in class mml.Tree.OFork Select subTree '0', or '1', as the input datum is '<' or '≥' split respectively. split() - Method in class mml.Tree.Param.OFork sqclose - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. sqopen - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. ss(Vector, int, int) - Static method in class mml.NormalUPM Given a data-set, ds, return its sufficient statistics, ss, that is [# of items, sum, sum of squares, nlAoM}]. ss2FunctionModel(Value) - Method in class mml.UPFunctionModel.Est Synonym for ss2Model(ds). ss2Model(Value) - Method in class mml.Estimator Given sufficient statistics, ss=stats(ds), of a data-set, ds, estimate a fully parameterised Model, ss2Model(ss). ss2Model(Value) - Method in class mml.Linear1.Est Given sufficient statistics ss = stats(ds) of a data-set ds, estimate a fully parameterised Model. ss2Model(Value) - Method in class mml.LinearD.Est Given sufficient statistics ss=stats(ds) of a data-set 'ds', estimate a fully parameterised Linear Model. ss2Model(Value) - Method in class mml.Mixture.Est Given ss (=ds), estimate a Mixture Model. ss2Model(Value) - Method in class mml.NormalMu.Est Given ss = stats(ds) for a data-set, ds, estimate a Model, i.e., σ. ss2Model(Value) - Method in class mml.NormalUPM.Est Given statistics, ss = stats(ds), of a data-set, ds, estimate an M, i.e., μ and σ. ss2Model(Value) - Method in class mml.Tree.Est Given statistics ss (= ds) of a data-set, ds, estimate a fully parameterised M:id→od; calls search(ss, 1). ss2Model(Value) - Method in class mml.UPFunctionModel.Est Given statistics ss = stats(ds) of a data-set, ds, return a fully parameterised M. ss2Model(Value) - Method in class mml.UPSeriesModel.Est Given statistics ss = stats(ds) of a data-set, ds, return a fully parameterised SeriesModel. ss2ModelSp(Value) - Method in class mml.Estimator Given stats, ss, estimate a Model and parameters; this default is (mdl, sp), where mdl=ss2Model(ss) and sp=mdl.statParams() (usually). ss2SeriesModel(Value) - Method in class mml.UPSeriesModel.Est Synonym for ss2Model(ds). STAR3 - Static variable in class eg.Graphing Some possible motifs (subgraphs). STAR4 - Static variable in class eg.Graphing Some possible motifs (subgraphs). STAR5 - Static variable in class eg.Graphing Some possible motifs (subgraphs). STAR6 - Static variable in class eg.Graphing Some possible motifs (subgraphs). start() - Method in class la.util.Timer Start the clock. startsExp - Static variable in class la.la.Syntax statParams() - Method in class mml.Model The statistical parameters (possibly estimated), if any, of 'this' Model as stored in sp. stats(Vector, int, int) - Method in class la.bioinformatics.Alignment.UPSame In this case, statistics ss is the data-set vs. stats(boolean, Value, Value) - Method in class la.bioinformatics.Alignment.UPSame stats(Vector, int, int) - Method in class mml.Adaptive Return sufficient statistics, that is frequency counts, for elements [lo, hi) of data-set 'ds'. stats(boolean, Value, Value) - Method in class mml.Adaptive For sufficient statisticses 'ss0' and 'ss1', either combine ss0 and ss1 (add=true), or remove ss1 from ss0 (add=false). stats(Vector, int, int) - Method in class mml.BestOf.M Return mdl's stats for ds[lo,hi). stats(boolean, Value, Value) - Method in class mml.BestOf.M Return mdl's stats ss0±ss1, "+" if add is true otherwise "-". stats(Vector, int, int) - Method in class mml.BestOf Calculate the sufficient statistics of ds[lo,hi) for all possible alternatives upms[.]. stats(boolean, Value, Value) - Method in class mml.BestOf Combine statistics ss0 and ss1, either ss0 "+" ss1, or ss0 "-" ss1, depending on 'add' being true or false. stats(Vector, int, int) - Method in class mml.BetaUPM Return elements [lo, hi) of data-set ds, itself; TODO note that ⟨∑ log X_i, ∑ log(1-X_i)⟩ would be better. stats(boolean, Value, Value) - Method in class mml.BetaUPM stats(Vector, int, int) - Method in class mml.Continuous.M.Transform As per the enclosing Continuous.M.this's stats(...) on the transformed data-set, ds.map(f). stats(boolean, Value, Value) - Method in class mml.Continuous.M.Transform Combine statistics ss0 and ss1 as as per the enclosing Continuous.M.this does. stats(Vector, int, int) - Method in class mml.Continuous.Transform.M m.stats(ds.map(f),...), i.e., transformed data. stats(boolean, Value, Value) - Method in class mml.Continuous.Transform.M Combine statistics 'ss0' and 'ss1' as 'm' does. stats(Vector, int, int) - Method in class mml.Continuous.Transform The enclosing Continuous.this's stats(ds.map(f)), on a transformed data-set. stats(boolean, Value, Value) - Method in class mml.Continuous.Transform Combine statistics ss0 and ss1 as per the enclosing Continuous.this. stats(Vector, int, int) - Method in class mml.Continuous.Uniform Given a data-set, ds, return sufficient statistics, ss, that is the (weighted) number of elements and their nlAoM. stats(boolean, Value, Value) - Method in class mml.Continuous.Uniform stats(Vector, int, int) - Method in class mml.CPT.M Given a data-set, ds, calculate statistics (of the output datum) for each case (Value) of the input datum. stats(boolean, Value, Value) - Method in class mml.CPT.M Calls upon condMdls[.].stats(add,.,.). stats(Vector, int, int) - Method in class mml.CPT Given a data-set, ds, calculate sufficient statistics (of the output datum) for each possible case (Value) of the input datum. stats(boolean, Value, Value) - Method in class mml.CPT Calls upon upm.stats(add,.,.). stats(Vector, int, int) - Method in class mml.Dependent.M Given a data-set, ds, return statistics, ss = ⟨im's, fm's⟩. stats(boolean, Value, Value) - Method in class mml.Dependent.M Calls Dependent.M.im's and Dependent.M.fm's stats. stats(Vector, int, int) - Method in class mml.Dependent Given a data-set, ds, return sufficient statistics, ss = ⟨upm's, upfm's⟩. stats(boolean, Value, Value) - Method in class mml.Dependent Calls Dependent.upm's and Dependent.upfm's stats. stats(Vector, int, int) - Method in class mml.Direction.Uniform The default sufficient statistics ss = stats(ds) of a data-set ds; ss = ds itself. stats(boolean, Value, Value) - Method in class mml.Direction.Uniform stats(Vector, int, int) - Method in class mml.Dirichlet The default sufficient statistics ss = stats(ds) = ds; maybe we can do better in the future? (???TODO: Looks like maybe [∑ log d_i,0, ...] would be the go, but beware any datum d=[0,...]???) More on stats here. stats(boolean, Value, Value) - Method in class mml.Dirichlet stats(Vector, int, int) - Method in class mml.Discretes.Shifted Return sufficient statistics, ss=stats(ds,lo,hi), for a shifted (-offset) data-set ds[lo,hi). stats(boolean, Value, Value) - Method in class mml.Discretes.Shifted stats(Vector, int, int) - Method in class mml.Discretes.Uniform Given a data-set, ds, return sufficient statistics, ss, that is the (weighted) number of elements in ds, for use in Estimator.ss2Model(la.la.Value) and Discretes.Uniform.M.nlLH(la.la.Value). stats(boolean, Value, Value) - Method in class mml.Discretes.Uniform stats(Vector) - Method in class mml.Estimator stats(0,ds.nElts()). stats(Vector, int, int) - Method in class mml.Estimator Given a data-set 'ds', return sufficient statistics for elements [lo, hi), lo inclusive to hi exclusive, ss = stats(ds,lo,hi). stats(boolean, Value, Value) - Method in class mml.Estimator Combine statisticses ss0 and ss1, '+' if add=true otherwise '-'. stats(boolean, Value, Vector, int, int) - Method in class mml.Estimator Add (remove) statistics for ds.[lo,hi) to (from) statistics ss0. stats(Vector, int, int) - Method in class mml.ExponentialUPM The sufficient statistics ss = stats(ds) of elements [lo, hi) of data-set ds; are [N, ∑ds(i), ∑nlAoM]. stats(boolean, Value, Value) - Method in class mml.ExponentialUPM stats(Vector, int, int) - Method in class mml.GammaUPM Return the data-set, ds.[lo,hi). stats(boolean, Value, Value) - Method in class mml.GammaUPM stats(Vector, int, int) - Method in class mml.Geometric0UPM Returns NandSum(ds,lo,hi). stats(boolean, Value, Value) - Method in class mml.Geometric0UPM stats(Vector, int, int) - Method in class mml.Graphs.Skewed Sufficient statistics are elements [lo, hi) of the data-set 'ds' itself. stats(boolean, Value, Value) - Method in class mml.Graphs.Skewed stats(Vector, int, int) - Method in class mml.Graphs Statistics are the data-set ds[lo,hi) itself. stats(boolean, Value, Value) - Method in class mml.Graphs stats(Vector, int, int) - Method in class mml.HeavyTail.Over_x1 Sufficient statistics, ss = stats(ds,lo,hi), of elements [lo,hi) of a data-set ds; here ss = ds.[lo,hi). stats(boolean, Value, Value) - Method in class mml.HeavyTail.Over_x1 stats(Vector, int, int) - Method in class mml.Independent.M Given a multivariate data-set, ds, return statistics, ss, a Tuple of course, one element per sub-ms. stats(boolean, Value, Value) - Method in class mml.Independent.M Calls upon the stats(,,) of the ms[]. stats(Vector, int, int) - Method in class mml.Independent Given a multivariate data-set, ds, return sufficient statistics, ss, a Tuple of course, one element per sub-upms. stats(boolean, Value, Value) - Method in class mml.Independent Calls upon the stats(,,) of the upms[]. stats(Vector, int, int) - Method in class mml.Int1 Sufficient statistics 'ss' are the data-set itself. stats(boolean, Value, Value) - Method in class mml.Int1 stats(Vector, int, int) - Method in class mml.Intervals Sufficient statistics, ss = stats(ds), for a data-set, ds, are ds itself, sorted on the input data. stats(boolean, Value, Value) - Method in class mml.Intervals stats(Vector, int, int) - Method in class mml.KnownClass Return triv = stats(ds). stats(boolean, Value, Value) - Method in class mml.KnownClass stats(Vector, int, int) - Method in class mml.LaplaceUPM Given a data-set, ds, return statistics, ss = stats(ds,lo,hi), that is ds.slice[lo,hi).sorted(), a Sorted Slice. stats(boolean, Value, Value) - Method in class mml.LaplaceUPM Given sufficient statisticses 'ss0' and 'ss1', either 'add' them or remove (add=false) ss1 from ss0. stats(Vector, int, int) - Method in class mml.Linear1 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. stats(boolean, Value, Value) - Method in class mml.Linear1 Combine sufficient statistics ss0 and ss1 into ss0±ss1, either by addition (add=true) or by subtraction (add=false). stats(Vector, int, int) - Method in class mml.LinearD Calculate the sufficient statistics of ds[lo,hi). stats(boolean, Value, Value) - Method in class mml.LinearD Combine statistics ss0 and ss1, either ss0 "+" ss1, or ss0 "-" ss1, depending on 'add' being true or false. stats(Vector, int, int) - Method in class mml.LogStar0UPM Return ss = ds.[lo, hi) that is the data itself as sufficient statistics of ds. stats(boolean, Value, Value) - Method in class mml.LogStar0UPM stats(Vector, int, int) - Method in class mml.Markov.M Collect statistics, ss = stats(seqs,lo,hi), of a data-set, that is of a Vector of Vectors, seqs. stats(boolean, Value, Value) - Method in class mml.Markov.M Calls upon stats(add,.,) of Markov.M.lenMdl. stats(Vector, int, int) - Method in class mml.Markov Collect statistics, ss = stats(seqs,lo,hi), of a data-set, that is of a Vector of Vectors, seqs. stats(boolean, Value, Value) - Method in class mml.Markov Calls upon stats(add,.,) of Markov.lenUPM. stats(Vector, int, int) - Method in class mml.Missing.M Use presentMdl.stats(...) and valueMdl.stats(...) to calculate the statistics of ds[lo,hi). stats(boolean, Value, Value) - Method in class mml.Missing.M Use presentMdl.stats(...) and valueMdl.stats(...). stats(Vector, int, int) - Method in class mml.Missing Use presentUPM.stats(...) and valueUPM.stats(...) to calculate the sufficient statistics of ds[lo,hi). stats(boolean, Value, Value) - Method in class mml.Missing Use presentUPM.stats(...) and valueUPM.stats(...). stats(Vector, int, int) - Method in class mml.Mixture Mixture sufficient statistics, ss = ds.[lo,hi) itself. stats(boolean, Value, Value) - Method in class mml.Mixture stats(Vector, int, int) - Method in class mml.Model.Defaults This default returns the data itself, ds.slice(lo,hi), as statistics but many Models can do much better. stats(boolean, Value, Value) - Method in class mml.Model.Defaults statistics ss0 ± ss1, '+' if 'add' is true othewise '−'. stats(Vector) - Method in class mml.Model Return sufficient statistics, stats(ds,0,ds.nElts()), of a data-set 'ds'. stats(Vector, int, int) - Method in class mml.Model For 'this' Model, calculate sufficient statistics, 'ss', of elements [lo, hi) of 'ds', e.g., for use in nlLH(ss). stats(boolean, Value, Value) - Method in class mml.Model Combine statisticses, ss0 ± ss1. stats(boolean, Value, Vector, int, int) - Method in class mml.Model Combine statisticses ss0 and stats(ds,lo,hi). stats(Vector, int, int) - Method in class mml.Model.Transform The enclosing Model.this's stats(...) applied to the data-set transformed by map(f). stats(boolean, Value, Value) - Method in class mml.Model.Transform Combine statistics 'ss0' and 'ss1' in the way that Model.this does. stats(Vector, int, int) - Method in class mml.MotifA Sufficient statistics are elements [lo, hi) of the data-set ds itself. stats(boolean, Value, Value) - Method in class mml.MotifA stats(Vector, int, int) - Method in class mml.MotifD Sufficient statistics are elements [lo, hi) of the data-set ds itself. stats(boolean, Value, Value) - Method in class mml.MotifD stats(Vector, int, int) - Method in class mml.Multinomial.M.Trials Not implemented, throw an RTE! stats(boolean, Value, Value) - Method in class mml.Multinomial.M.Trials Not implemented, throw an RTE! stats(Vector, int, int) - Method in class mml.Multinomial Stats 'ss' is just the data-set ds[lo,hi). stats(boolean, Value, Value) - Method in class mml.Multinomial stats(Vector, int, int) - Method in class mml.MultiState Return sufficient statistics, that is frequency counts, for elements [lo, hi) of data-set 'ds'. stats(boolean, Value, Value) - Method in class mml.MultiState For sufficient statisticses 'ss0' and 'ss1', either combine ss0 and ss1 (add=true), or remove ss1 from ss0 (add=false). stats(Vector, int, int) - Method in class mml.NaiveBayes Given a data-set ds, return statistics ss=ds.[lo,hi) the data itself. stats(boolean, Value, Value) - Method in class mml.NaiveBayes Combine stats ss0 and ss1, either additively (add=true) or negatively (add=false). stats(Vector, int, int) - Method in class mml.NearInverse Sufficient statistics, ss = stats(ds,lo,hi), of elements [lo,hi) of a data-set ds; here ss = ds.[lo,hi). stats(boolean, Value, Value) - Method in class mml.NearInverse stats(Vector, int, int) - Method in class mml.NormalUPM Return the sufficient statistics of data-set ds; calls NormalUPM.ss(la.maths.Vector, int, int)(ds). stats(boolean, Value, Value) - Method in class mml.NormalUPM stats(Vector, int, int) - Method in class mml.Permutation.Uniform Given a data-set ds[lo,hi) of Permutations return sufficient statistics ss=ds.wts[lo,hi). stats(boolean, Value, Value) - Method in class mml.Permutation.Uniform Combine sufficient statisticses 'ss0' and 'ss1'. stats(Vector, int, int) - Method in class mml.Poisson0UPM Calculate the sufficient statistics, [N, sum, sum log factorials], of a data-set ds. stats(boolean, Value, Value) - Method in class mml.Poisson0UPM stats(Vector, int, int) - Method in class mml.R_D.Forest Statistical parameters, a pair – a Vector for the Normals and another for the Linear1s. stats(boolean, Value, Value) - Method in class mml.R_D.Forest stats(Vector, int, int) - Method in class mml.R_D.ForestSearch.M Note that these M.stats(...) differs from R_D.ForestSearch.stats(Vector,int,int) because parent[] is known in M. stats(boolean, Value, Value) - Method in class mml.R_D.ForestSearch.M Combine (+/-) two statistics, ss0 and ss1. stats(Vector, int, int) - Method in class mml.R_D.ForestSearch Sufficient statistics 'ss' are ⟨D× stats for Normals, D×(D-1) stats for Linear1s⟩. stats(boolean, Value, Value) - Method in class mml.R_D.ForestSearch Combine statistics ss0 and ss1, either positively (add=true) or negatively (add=false). stats(Vector, int, int) - Method in class mml.R_D.Independent.M Return a Vector of statisticses, one element per parameterised sub-model mdls[.]. stats(boolean, Value, Value) - Method in class mml.R_D.Independent.M Combine each i^th of the collections of D statistics, ss0 and ss1, as mdls[.] does. stats(Vector, int, int) - Method in class mml.R_D.Independent Return a Vector of statistics, one element for each (yet to be) parameterised instance of upm. stats(boolean, Value, Value) - Method in class mml.R_D.Independent Combine each i^th of the collections of D statistics, ss0 and ss1, as upm does. stats(Vector, int, int) - Method in class mml.R_D.M.Transform As per the enclosing R_D.M.this's stats(...) but on the transformed data-set, ds.map(f). stats(boolean, Value, Value) - Method in class mml.R_D.M.Transform Combine statistics ss0 and ss1 as as per the enclosing R_D.M.this does. stats(Vector, int, int) - Method in class mml.R_D.NrmDir.M The statistics, ss = stats(ds), of a data-set, ds. stats(boolean, Value, Value) - Method in class mml.R_D.NrmDir.M Calls upon stats of R_D.NrmDir.M.normMdl and R_D.NrmDir.M.dirnMdl, and may or may not equal R_D.NrmDir.stats(boolean,Value,Value). stats(Vector, int, int) - Method in class mml.R_D.NrmDir The sufficient statistics, ss = stats(ds), of a data-set, ds. stats(boolean, Value, Value) - Method in class mml.R_D.NrmDir Calls upon stats of R_D.NrmDir.normUPM and R_D.NrmDir.dirnUPM, and may or may not equal R_D.NrmDir.M.stats(boolean,Value,Value). stats(Vector, int, int) - Method in class mml.R_D.Transform.M m.stats(ds.map(f),...), i.e., transformed data. stats(boolean, Value, Value) - Method in class mml.R_D.Transform.M Combine statistics 'ss0' and 'ss1' as 'm' does. stats(Vector, int, int) - Method in class mml.R_D.Transform The enclosing R_D.this's stats(ds.map(f)) but on a transformed data-set, ds.map(f). stats(boolean, Value, Value) - Method in class mml.R_D.Transform Combine statistics ss0 and ss1 as per the enclosing R_D.this. stats(Vector, int, int) - Method in class mml.Sequences.K.M Return ⟨lengthStats, eltStats⟩. stats(boolean, Value, Value) - Method in class mml.Sequences.K.M Return ⟨lengthStats, eltStats⟩. stats(Vector, int, int) - Method in class mml.Sequences.K Return ⟨lengthStats, eltStats⟩. stats(boolean, Value, Value) - Method in class mml.Sequences.K Return ⟨lengthStats, eltStats⟩. stats(Vector, int, int) - Method in class mml.Simplex.Uniform The default sufficient statistics ss = stats(ds) of a data-set ds; ss = ds itself. stats(boolean, Value, Value) - Method in class mml.Simplex.Uniform stats(Vector, int, int) - Method in class mml.Tree Statistics ss = stats(ds), of data-set ds, is ds itself. stats(boolean, Value, Value) - Method in class mml.Tree stats(Vector, int, int) - Method in class mml.UPFunctionModel.K.M Given a data-set, 'ds', statistics, ss = mdl.stats(ds.col(1),lo,hi), on the output datum only, are as per mdl. stats(boolean, Value, Value) - Method in class mml.UPFunctionModel.K.M mdl.stats(add,.,.). stats(Vector, int, int) - Method in class mml.UPFunctionModel.K Given a data-set, 'ds', sufficient statistics, ss = upm.stats(ds.col(1),lo,hi), on the output datum only, are as per upm. stats(boolean, Value, Value) - Method in class mml.UPFunctionModel.K upm.stats(add,.,.). stats(Vector, int, int) - Method in class mml.UPFunctionModel.M Return sufficient statistics, ss = stats(ds,lo,hi), of elements [lo,hi) of a data-set ds as per the enclosing UPFunctionModel's stats(ds,lo,hi). stats(boolean, Value, Value) - Method in class mml.UPFunctionModel.M Use the enclosing UPFunctionModel's stats(add,ss0,ss1) to combine statisticses ss0 and ss1. stats(Vector, int, int) - Method in class mml.UPModel.Est Given a data-set ds and element bounds [lo, hi), return sufficient statistics by calling the enclosing UPModel's stats(ds,lo,hi), for use in ss2Model(ss), say. stats(boolean, Value, Value) - Method in class mml.UPModel.Est Return UPModel's stats(add,ss0,ss1). stats(Vector, int, int) - Method in class mml.UPModel.M Return sufficient statistics ss=stats(ds,lo,hi) of elements [lo, hi), lo inclusive to hi exclusive, of data-set 'ds' using the stats(ds,lo,hi) of the enclosing UPModel. stats(boolean, Value, Value) - Method in class mml.UPModel.M By default, combine statisticses ss0 and ss1 using the stats(add,ss0,ss1) of the the enclosing UPModel. stats(Vector) - Method in class mml.UPModel Return stats(ds,0,ds.nElts()) (there is more on stats at the link). stats(Vector, int, int) - Method in class mml.UPModel Return sufficient statistics 'ss' of elements [lo, hi), lo inclusive to hi exclusive, of data-set ds; also see M.stats(...). stats(boolean, Value, Value) - Method in class mml.UPModel Combine sufficient statisticses 'ss0' and 'ss1' additively (add=true), or remove ss1 from ss0 (add=false). stats(boolean, Value, Vector, int, int) - Method in class mml.UPModel Combine statisticses ss0 and stats(ds,lo,hi). stats(Vector, int, int) - Method in class mml.UPModel.Transform.M m.stats(ds.map(f),...), i.e., m's stats on transformed data. stats(boolean, Value, Value) - Method in class mml.UPModel.Transform.M Combine statistics 'ss0' and 'ss1' as m does. stats(Vector, int, int) - Method in class mml.UPModel.Transform Statistics are UPModel.this.stats(ds.map(f),...). stats(boolean, Value, Value) - Method in class mml.UPModel.Transform Combine statistics 'ss0' and 'ss1' as UPModel.this does. stats(Vector, int, int) - Method in class mml.UPSeriesModel.K.M Given a data-set, seqs, that is a Vector of Vectors, compute statistics, ss = (lenMdl.stats(...), eltMdl.stats(...)). stats(boolean, Value, Value) - Method in class mml.UPSeriesModel.K.M Uses the stats(add,.,.) of UPSeriesModel.K.M.lenMdl and UPSeriesModel.K.M.eltMdl. stats(Vector, int, int) - Method in class mml.UPSeriesModel.K Given a data-set, seqs, that is a Vector of Vectors, compute sufficient statistics, ss = (lenUPM.stats(...), eltUPM.stats(...)). stats(boolean, Value, Value) - Method in class mml.UPSeriesModel.K Uses the stats(add,.,.) of UPSeriesModel.K.lenUPM and UPSeriesModel.K.eltUPM. stats(Vector, int, int) - Method in class mml.UPSeriesModel.M Use the enclosing UPSeriesModel's stats(ds,lo,hi) to return statistics of elements [lo, hi) of data-set 'ds'. stats(boolean, Value, Value) - Method in class mml.UPSeriesModel.M Use the enclosing UPSeriesModel's stats(add,ss0,ss1) to combine statisticses ss0 and ss1. stats(Vector, int, int) - Method in class mml.vMF Statistics, ss = stats(ds) = (N, R, nlAoM), of a data-set, ds. stats(boolean, Value, Value) - Method in class mml.vMF stats(Vector, int, int) - Method in class mml.WallaceInt0UPM Return ss = ds, that is the data-set itself. stats(boolean, Value, Value) - Method in class mml.WallaceInt0UPM step - Variable in class la.util.Series.Range stop() - Method in class la.util.Timer Stop (pause) 'this' running Timer and add the time since its last start() to its total. string2n(String) - Method in class la.la.Type.Char String s must be a single character; return it's int code. string2n(String) - Method in class la.la.Type.Discrete Return the int "code" for a constant denoted by String 's'. string2n(String) - Method in class la.la.Type.Enum Return the int code corresponding to 'str'. string2n(String) - Method in class la.la.Type.Int Return an int from a String such as "123", "-456" or even " + 789". string2value(String) - Method in class la.la.Type.Discrete Return int2value(string2n(s)). string2value(String) - Method in class la.la.Type.Enum stringLiteral - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. strings(String[]) - Static method in class la.maths.Vector Convenience function : String[] → Vector. Strings() - Constructor for class la.maths.Vector.Strings structurallyIdentical(Graph) - Method in class graph.Graph Is 'this' Graph structurally identical to 'g', this.v_i : g.v_i, in terms of the existence of Edges? Checks vSize() of 'this' and 'g' match, then calls edgesCorrespond(g). Structured(String) - Constructor for class la.la.Type.Structured Structured() - Constructor for class la.la.Value.Structured subExps - Variable in class la.la.Expression.Tuple subGiso(Graph, Graph, int, int, boolean) - Method in class mml.MotifA.M mv:tv, and mw:mustUse (where mv--mw), and then the rest. subGraphs(int) - Method in class graph.Graph subGraphs(1, hi). subGraphs(int, int) - Method in class graph.Graph Return a Series of all Vertex-size lo to hi, (weakly-) connected, Vertex-induced subgraphs of 'this' Graph. SubGraphs(int, int) - Constructor for class graph.Graph.SubGraphs lo and hi inclusive are bounds on the vertex-size of the subGraphs in the Series. subT(int) - Method in class mml.Tree.Param.Fork subTrees - Variable in class mml.Tree.DFork The sub-Models, one for each possible Value of the (Discrete) input column. subTrees - Variable in class mml.Tree.OFork The two sub-Models for <split or ≥split respectively. subTs() - Method in class mml.Tree.Param.Fork succ - Variable in class graph.Directed.Sparse Given an Edge ⟨v0, v1⟩, v1 is in (ascending) succ[v0], and v0 is in (ascending) pred[v1]. sumNlPr(Vector) - Method in class mml.Model ∑ negative log probability over all data elements in data-set ds; you might want nlLH(ss) and stats(ds) instead? sumNlPr(ds) and nlLH(ss) should be equal but the latter is often quicker (where ss=stats(ds)). sumSq() - Method in class la.maths.Vector Provided 'this' is a Vector of Cts, return the sum of squares, ∑_i elt(i).x()². sUpb - Static variable in class eg.Musicians Bounds on mean(s) and standard deviation(s). swan - Static variable in class eg.Ducks coot, duck, swan : Ducks.Species. sy() - Method in class la.la.Lexical Return the kind of the current input symbol. syInfo() - Method in class la.la.Lexical Return a String representation of the current symbol, e.g., for debugging or tracing purposes. Symbol - Static variable in class la.la.Lexical String representations of the various lexical symbols. Syntax - Class in la.la The parser, also see Lexical and Expression. Syntax(Lexical) - Constructor for class la.la.Syntax Construct a Syntax analyser of a given Lexical source. T t - Static variable in class eg.Ducks 't' and 'f', short for Value.ttrue and Value.ttrue. t - Variable in class la.la.Value.Option.GP The Type of 'this' Option. take(int) - Method in class la.maths.Vector Return the first 'm' elements of 'this' Vector; see Vector.drop(int) and Vector.slice(int, int). takeLast(int) - Method in class la.maths.Vector Return the last 'm' elements of 'this' Vector; see Vector.slice(int, int). ten - Static variable in class la.la.Value tenR - Static variable in class la.la.Value Test - Class in graph Run a few(!), simple(!) tests on Graphs. Test() - Constructor for class graph.Test Test - Class in la.maths Test.main() runs some simple(!) tests on package la.maths. Test() - Constructor for class la.maths.Test Test - Class in la.util Test.main(java.lang.String[]) runs a few simple tests of Util and Series in package la.util. Test() - Constructor for class la.util.Test Test - Class in mml Test.main(java.lang.String[]) runs a few simple tests on mml. Test() - Constructor for class mml.Test tests(int) - Static method in class eg.Graphing Perform some elementary tests of Graph Models on random Graphs of 'V' vertices. theAcc() - Method in class la.la.Lexical Return the supposed precision of the real-valued numeral, if any. theDbl() - Method in class la.la.Lexical Return the current real-valued numeral, if any. theInt() - Method in class la.la.Lexical Return the current integer numeral, if any. thenSy - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. theta - Variable in class mml.GammaUPM.M Shape parameter 'k', and scale parameter θ (theta). theWord() - Method in class la.la.Lexical Return the current word (identifier or keyword), if any. thrd - Static variable in class la.la.Library Return the third element of a Value.Tuple. thrd() - Method in class la.la.Value.Structured thrd(), short for elt(2). three - Static variable in class la.la.Value threeR - Static variable in class la.la.Value Timer - Class in la.util A simple timer that can start(), stop() (i.e., pause), and (re-)start(). Timer() - Constructor for class la.util.Timer Anonymous, not running (timing). Timer(String) - Constructor for class la.util.Timer Named, not running (timing). Timer(boolean) - Constructor for class la.util.Timer Anonymous, running as specified. Timer(String, boolean) - Constructor for class la.util.Timer Named, running as specified. times - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. times(Vector) - Method in class la.maths.Matrix 'this' Matrix, M, times the Vector of Cts, v, as a column-Vector, as in M v. Also see Matrix.times(Matrix). times(Matrix) - Method in class la.maths.Matrix Matrix-multiplication of two matrices of Cts, 'this' and M. times(Q) - Method in class la.maths.Q Multiply Quaternions 'this' and 'q'. times(Matrix) - Method in class la.maths.Vector 'this' Vector of Cts, v, as a row-Vector, times Matrix M, as in v M. tl - Variable in class la.la.Value.List.Cell The head, hd, and tail, tl, of 'this' List Cell. tl() - Method in class la.la.Value.List.Cell The tail (rest) of the List. tl() - Method in class la.la.Value.List The tail (all but the Value.List.hd()) of the List. tlSy - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. TM(double, double, Value) - Constructor for class mml.Multinomial.M.Trials.TM Note, 'sp' is 'triv', '( )'. toDirected() - Method in class graph.Directed Identity function; see Graph.toDirected(). toDirected() - Method in class graph.Graph Provided 'this' Graph is in fact Directed, return it as an instance of that class. ToDirected() - Constructor for class graph.Graph.ToDirected toSeries() - Method in interface la.la.Value.Scannable Return a Series of Values. toSeries() - Method in class la.maths.Vector Make a Series, the elements of 'this' Vector, one at a time. toString() - Method in class eg.README toString() - Method in class graph.Directed.AsUndirected toString() - Method in class graph.Directed.C toString() - Method in class graph.Directed.Dense toString() - Method in class graph.Directed.Edge toString() - Method in class graph.Directed.Sparse.Induced toString() - Method in class graph.Directed.Sparse.Renumbered toString() - Method in class graph.Directed.Sparse toString() - Method in class graph.Graph.Canonical toString() - Method in class graph.Graph.Contraction toString() - Method in class graph.Graph.Edge toString() - Method in class graph.Graph.Induced toString() - Method in class graph.Graph.Renumbered toString() - Method in class graph.Graph.ToDirected toString() - Method in class graph.Graph.ToUndirected toString() - Method in class graph.Graph.Vertex toString() - Method in class graph.README toString() - Method in class graph.Undirected.AsDirected toString() - Method in class graph.Undirected.Dense toString() - Method in class graph.Undirected.Edge toString() - Method in class graph.Undirected.K toString() - Method in class graph.Undirected.Sparse.Induced toString() - Method in class graph.Undirected.Sparse.Renumbered toString() - Method in class graph.Undirected.Sparse toString() - Method in class la.bioinformatics.README toString() - Method in class la.la.Environment For debugging. toString() - Method in class la.la.Expression The use of Expression.appendSB(java.lang.StringBuffer), and hence a StringBuffer, gives linear rather than quadratic complexity. toString() - Method in class la.la.Function Return the String "Function". toString() - Method in class la.la.README toString() - Method in class la.la.Type Return a String representation of 'this' Type. toString() - Method in class la.la.Value.Char Return the 'char' of this Char. toString() - Method in class la.la.Value.Chars Return the "string" of this Chars. toString() - Method in class la.la.Value.Cts Return a String representation of 'this' Cts Value. toString() - Method in class la.la.Value.Defer.App Note, may be used in error messages, and for debugging, only; you probably want Value.Defer.print(java.io.PrintStream) instead! toString() - Method in class la.la.Value.Defer.Exp Note, may be used in error messages, and for debugging, only; you probably want print(ps) instead! toString() - Method in class la.la.Value.Enum Return the String representation of 'this' Enum Value. toString() - Method in class la.la.Value.Int Return the String (numeral) of 'this' Int. toString() - Method in class la.la.Value.Structured toString() may be acceptable for small Structured Values or you may want print(ps) instead? Also see Value.Structured.opens(), Value.Structured.separator(), Value.Structured.closes(), and Value.toString(). toString() - Method in class la.la.Value Used, for example, in printing of constants, and in calls to Value.error(java.lang.String). toString() - Method in class la.la.Value.Triv Return the String representation of Triv. toString() - Method in class la.maths.Q Show 'this' Quaternion as "(a±bi±cj±dk)". toString() - Method in class la.maths.README toString() - Method in class la.README toString() - Method in class la.util.README toString() - Method in class la.util.RefInt Return RefInt.n — as a String. toString() - Method in class la.util.Series.Lines toString() - Method in class la.util.Series.Range toString() - Method in class la.util.Series.Separator toString() - Method in class la.util.Series Return a short String description of 'this' Series, say for use in debugging. toString() - Method in class la.util.Timer toString() - Method in class mml.BetaUPM toString() - Method in class mml.Continuous.M.Transform.MM toString() - Method in class mml.Continuous.Transform toString() - Method in class mml.Discretes.Shifted toString() - Method in class mml.Estimator Return a String representation of 'this' Estimator. toString() - Method in class mml.ExponentialUPM toString() - Method in class mml.GammaUPM toString() - Method in class mml.Geometric0UPM toString() - Method in class mml.LaplaceUPM toString() - Method in class mml.LogStar0UPM.M toString() - Method in class mml.LogStar0UPM toString() - Method in class mml.Mixture.M Return a short description of the Mixture Model. toString() - Method in class mml.Model Show the details of 'this' Model. toString() - Method in class mml.Model.Transform.M toString() - Method in class mml.NormalUPM toString() - Method in class mml.Poisson0UPM toString() - Method in class mml.R_D.M.Transform.MM toString() - Method in class mml.R_D.Transform toString() - Method in class mml.README toString() - Method in class mml.UPFunctionModel.M Return a String representation of 'this' FunctionModel. toString() - Method in class mml.UPModel.Est Return a String representation of 'this' Estimator. toString() - Method in class mml.UPModel.M Return a String representation of 'this' fully parameterised Model. toString() - Method in class mml.UPModel Return a String representation of 'this' UnParameterised Model, including its problem-defining parameters. toString() - Method in class mml.UPModel.Transform.M toString() - Method in class mml.UPSeriesModel.M Return a String representation of 'this' SeriesModel. toString() - Method in class mml.WallaceInt0UPM.M toString() - Method in class mml.WallaceInt0UPM totalTime() - Method in class la.util.Timer Total time in milliseconds between all start() - stop() pairs, plus current elapsed if started and not yet stopped. toUndirected() - Method in class graph.Graph Provided 'this' Graph is in fact Undirected, return it as an instance of that class. ToUndirected() - Constructor for class graph.Graph.ToUndirected toUndirected() - Method in class graph.Undirected Identity function; see Graph.toUndirected(). transform(Function.Cts2Cts) - Method in class mml.Continuous.M This transform(f) is like Model.transform(f) except that, instead of "just" returning a Model, this transform returns a (trivially) parameterised Continuous.M (actually a Continuous.M.Transform.MM). Also see the related but different Continuous.transform(f). Transform(Value) - Constructor for class mml.Continuous.M.Transform The "problem defining" parameter 'f' is a Cts2Cts and is saved in 'f'. transform(Function.Cts2Cts) - Method in class mml.Continuous This transform(f) is like UPModel.transform(f) except that, instead of "just" returning a UPModel, this transform returns a (UnParameterised) Continuous (actually a Continuous.Transform). Also see the related but different Continuous.M.transform(f). Transform(Value) - Constructor for class mml.Continuous.Transform Problem defining parameter 'f' must be a Function.Cts2Cts. transform(Function) - Method in class mml.Model Transform 'this' already parameterised Model by Function 'f', roughly transform: (a→b)→Model a→Model b. Transform(Value) - Constructor for class mml.Model.Transform Note, Function 'f' is the problem defining parameter. transform(Function.CtsD2CtsD) - Method in class mml.R_D.M Transform an already parameterised R_D.M with Function f. Transform(Value) - Constructor for class mml.R_D.M.Transform The "problem defining" parameter 'f' is a CtsD2CtsD, a R^D→R^D, and is saved in 'f'. transform(Function.CtsD2CtsD) - Method in class mml.R_D transform(f) is the preferred way to transform an R_D with a Function f. Transform(Value) - Constructor for class mml.R_D.Transform Problem defining parameter 'f'' must be a Function.CtsD2CtsD. transform(Function) - Method in class mml.UPModel transform: (a→b)→UPModel a→UPModel b, convenience function for 'new Transform(f)'. Transform(Value) - Constructor for class mml.UPModel.Transform The problem-defining parameter is Function 'f'. transpose() - Method in class la.maths.Matrix Return the transpose of 'this' Matrix. Tree - Class in mml The class of UnParameterised (Decision | Classification | Regression)- Tree FunctionModels. Tree(Value) - Constructor for class mml.Tree Problem defining parameter dp is (iType, leafUPM). Tree.DFork - Class in mml A fully parameterised Tree FunctionModel that tests a Discrete Bounded column (variable) of the input datum to select a sub-Model into which to descend. Tree.Est - Class in mml The class of Estimators for a Tree FunctionModel. Tree.Est.Sel - Class in mml Class of variable selector of the input datum id, where iod=(id,od). Tree.Fork - Class in mml Superclass of fully parameterised non-Leaf FunctionModels; see Tree.DFork and Tree.OFork. Tree.Leaf - Class in mml A fully parameterised Tree FunctionModel consisting of a single Leaf; it contains a Model over the output (dependent) datum, 'od'. Tree.M - Class in mml The superclass of fully parameterised (Decision | Classification | Regression) -Trees; see Tree.Leaf, Tree.DFork and Tree.OFork. Tree.OFork - Class in mml A fully parameterised Tree FunctionModel that compares an ordered field (variable) of the input datum to the split to select a sub-Model into which to descend. Tree.Param - Class in mml The root class for the statistical parameter of a fully parameterised Tree FunctionModel. Tree.Param.DFork - Class in mml The statistical parameter of a Tree.DFork, i.e., an input-column number, and a Vector of parameters for sub-Trees. Tree.Param.Fork - Class in mml The superclass of Tree.Param.DFork and Tree.Param.OFork. Tree.Param.Leaf - Class in mml The statistical parameter of a Tree.Leaf, i.e., a statistical parameter to make a Tree.Leaf from Tree.leafUPM. Tree.Param.OFork - Class in mml The statistical parameter of a Tree.OFork, i.e., an input-column number, a splitting Value (< v. TreeParam - Static variable in class mml.Tree The Type of the Tree.Param - statistical parameter - of a fully parameterised Tree.M FunctionModel. Trials(Value) - Constructor for class mml.Multinomial.M.Trials 'n' is the number of trials. trimHead - Variable in class la.util.Series.Separator To trim white space from heads and tails, or not? trimTail - Variable in class la.util.Series.Separator To trim white space from heads and tails, or not? TRIPLE - Static variable in class la.la.Type Also see Value.Tuple. triple(Value, Value, Value) - Static method in class la.la.Value triple, a convenience function to make a 3-Tuples. triv - Static variable in class la.la.Expression Expression.Constant triv; see Value.triv. triv - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. TRIV - Static variable in class la.la.Type Also see Value.Triv. Triv(String) - Constructor for class la.la.Type.Triv triv - Static variable in class la.la.Value Triv() - Constructor for class la.la.Value.Triv TRIV_N - Static variable in class la.la.Type Integer codes for various "types" of Type. trueSy - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. ttrue - Static variable in class la.la.Expression Expression.Constant true; see Value.ttrue. ttrue - Static variable in class la.la.Value TUPLE - Static variable in class la.la.Expression A tag; see Expression.n(). Tuple(Expression[]) - Constructor for class la.la.Expression.Tuple tuple(int) - Static method in class la.la.Type [null, null, PAIR, TRIPLE, 4_Tuple, ...]. Tuple(String, int) - Constructor for class la.la.Type.Tuple tuple(Value[]) - Static method in class la.la.Value tuple, a convenience function to make a k-Tuple. Tuple() - Constructor for class la.la.Value.Tuple TUPLE_N - Static variable in class la.la.Type Integer codes for various "types" of Type. turning(double) - Method in class la.la.Function.Cts2Cts Find a turning point of 'this' Cts2Cts, 'f', that is find x such that f'(x)=0, given an initial guess, x0. two - Static variable in class la.la.Value twoPI - Static variable in class la.maths.Maths twoPI = 2π. twoR - Static variable in class la.la.Value type() - Method in class graph.Directed.AsUndirected type() - Method in class graph.Directed.Dense type() - Method in class graph.Directed.Sparse type() - Method in class graph.Graph.Derived The parent's type() (often the same). type() - Method in class graph.Graph.ToDirected type() - Method in class graph.Graph.ToUndirected type() - Method in class graph.Graph Note, type(), Graph.adjacent(int, int), Graph.vLabelled(), Graph.vLabel(int), Graph.eLabelled(), and Graph.eLabel(int, int) must be consistent. Type - Class in graph The class of Graph Types. Type(boolean) - Constructor for class graph.Type Construct a Graph Type with no Vertex or Edge label Types, that is unlabelled. Type(String, boolean) - Constructor for class graph.Type Assumes no self-loops, and not Vertex- or Edge- labelled. Type(String, boolean, boolean) - Constructor for class graph.Type Assumes not Vertex- or Edge- labelled. Type(String, boolean, boolean, Type, Type) - Constructor for class graph.Type Construct a Graph Type with specified isDirected(?), selfLoops(?), Vertex label Type 'vType', and Edge label Type 'eType. type() - Method in class graph.Undirected.AsDirected type() - Method in class graph.Undirected.Dense type() - Method in class graph.Undirected.Sparse type() - Method in class la.la.Function Return Type.FUNCTION. Type - Class in la.la The Types of Values; it is for a very simple dynamic type system; Type.Enum and Type.Option are interesting. Type(String) - Constructor for class la.la.Type Special case when ids={} and arities={}. TYPE - Static variable in class la.la.Type The Type of a Type is TYPE. type() - Method in class la.la.Type Return TYPE. TYPE() - Constructor for class la.la.Type.TYPE type() - Method in class la.la.Value.Bool The Type is Type.BOOL. type() - Method in class la.la.Value.Char The Type is Type.CHAR. type() - Method in class la.la.Value.Chars The Type is Type.CHARS. type() - Method in class la.la.Value.Cts type() - Method in class la.la.Value.Defer force(), and return the v.type() of this Deferred Value. type() - Method in class la.la.Value.Enum.GP type() - Method in class la.la.Value.Enum type() - Method in class la.la.Value.Inc_Or Its Type is Type.INC_OR. type() - Method in class la.la.Value.Int The Type is Type.INT. type() - Method in class la.la.Value.List The LIST Type, of course. type() - Method in class la.la.Value.Maybe Its Type is Type.MAYBE. type() - Method in class la.la.Value.Option.GP type() - Method in class la.la.Value.Triv type() - Method in class la.la.Value.Tuple.GP type() - Method in class la.la.Value.Tuple type() - Method in class la.la.Value Return the Type of 'this' Value. type() - Method in class la.maths.Matrix.Doubles type() - Method in class la.maths.Matrix.Ints type() - Method in class la.maths.Vector.Derived The Type of the original Vector. type() - Method in class la.maths.Vector.Doubles type() - Method in class la.maths.Vector.Ints type() - Method in class la.maths.Vector.Slice type() - Method in class la.maths.Vector.Strings type() - Method in class la.maths.Vector Type.VECTOR. type() - Method in class la.maths.Vector.Weighted ??? The following are copied from 'class Derived' to ??? work around Apple Java 1.6 (Feb 2013). type() - Method in class mml.Discretes.Bounded.M This Model's Type is Model of the bounds' Type. type() - Method in class mml.Model Returns Type.MODEL. type() - Method in class mml.Tree.Param Returns Tree.TreeParam. Type.Atomic - Class in la.la The superclass of Atomic Types, notably of Discrete and CTS. Type.Char - Class in la.la The class of the CHAR Type. Type.Cts - Class in la.la The class of the CTS Type. Type.Discrete - Class in la.la The superclass of Discrete Types. Type.Enum - Class in la.la Enum Types, e.g., DNA={A,C,G,T}, types of Value.Enum. Type.Function - Class in la.la Function Types. Type.Int - Class in la.la The class of the INT Type. Type.Model - Class in la.la Model Types. Type.Option - Class in la.la Option Types, e.g., Value.List : Type.LIST, or Tree where Tree e = emptyT | fork (Tree e) e (Tree e). Also see Type.Enum. Type.Structured - Class in la.la Superclass of Type.Vector etc; also see Type.Atomic. Type.Triv - Class in la.la The class of the TRIV Type. Type.Tuple - Class in la.la The Types of k-Tuples; there is a different type for each value of k. Type.Tuple.GP - Class in la.la A Tuple Type with specific field Types, Type.Tuple.GP.elts. Type.TYPE - Class in la.la The class of the Type of a Type; see TYPE. Type.Vector - Class in la.la TYPE_N - Static variable in class la.la.Type Integer codes for various "types" of Type. U UNARY - Static variable in class la.la.Expression A tag; see Expression.n(). Unary(int, Expression) - Constructor for class la.la.Expression.Unary uncurry - Static variable in class la.la.Library uncurry: (t → u → v) → ((t, u) → v). Undirected - Class in graph The class of Undirected Graphs. Undirected() - Constructor for class graph.Undirected Undirected.AsDirected - Class in graph Treat every Undirected Edge of 'this' Undirected Graph as two Directed Edges (a self-loop remains a single self-loop, now Directed). Undirected.Dense - Class in graph A Dense, Undirected Graph with Type t and adjacency Matrix A. Undirected.Edge - Class in graph Also see Directed.Edge. Undirected.K - Class in graph The class of complete Undirected (unlabelled) Graphs, K_n. Undirected.Sparse - Class in graph The class of Sparse Undirected Graphs. Undirected.Sparse.Induced - Class in graph An induced SubGraph of a Sparse Undirected Graph is Sparse and Undirected. Undirected.Sparse.Renumbered - Class in graph A Sparse Undirected Graph, renumbered according to vs, is Sparse and Undirected. Undirected.Vertex - Class in graph Also see Directed.Vertex. Uniform(Value) - Constructor for class mml.Continuous.Uniform Construct an UnParameterised Uniform Model (distribution) over the bounds=[lwb,upb]. Uniform(Value) - Constructor for class mml.Direction.Uniform Given dimension, D, construct a Uniform Direction UPModel. Uniform(Value) - Constructor for class mml.Discretes.Uniform Construct an "UnParameterised" Uniform Discrete Model over bounds = [lwb, upb]. Uniform(Value) - Constructor for class mml.Permutation.Uniform See N. Uniform(Value) - Constructor for class mml.Simplex.Uniform Uniform K-simplex in [0, 1]^D, where D=K+1. uniqueElts() - Method in class la.maths.Vector Return an array containing the unique elements of 'this' Vector in ascending order. unlabelled - Static variable in class graph.Directed The standard type of Directed (directed, no self-loop) Graphs. unlabelled - Static variable in class graph.Undirected The standard type of Undirected, unlabelled, no self-loops Graphs. unlabelled_O - Static variable in class graph.Directed Type of Directed, unlabelled, self-loops (O) allowed Graphs. unlabelled_O - Static variable in class graph.Undirected Type of Undirected, unlabelled, self-loops (O) allowed Graphs. unOprs - Static variable in class la.la.Syntax unOrdered() - Method in class la.la.Type.Discrete Return not ordered(). unzip() - Method in class la.maths.Vector unzip 'this' Vector of Tuples into a Tuple of Vectors. unzip(int) - Method in class la.maths.Vector unzip 'this' Vector of Tuples each of (must be) nCol elements. UOE(String) - Method in class la.la.Value Short for UnsupportedOperationException(errMsg(msg)) uOp(int) - Method in class la.la.Function.Cts2Cts For example, sin.uOp(minus) satisfies (sin.uOp(minus))(x) = −sin(x). uOp(int) - Method in class la.la.Function A unary operator on 'this' Function f, such that (<op> f)(x) = <op> (f(x)). That is, f.uOp(op).apply(x) = f.apply(x).uOp(op). uOp(int) - Static method in class la.la.Library Return a Function based on the unary operator, 'op' (not, -, hd, etc.). uOp(int) - Method in class la.la.Value.Bool Apply unary operator, op, that is 'not', on 'this' Bool. uOp(int) - Method in class la.la.Value.Cts Apply the unary operator, 'op', i.e., '+' or '-', to 'this' Cts. uOp(int) - Method in class la.la.Value.Defer All unary operators are strict, so force() 'this' and return v.uOp(op). uOp(int) - Method in class la.la.Value.Int Apply unary operator op, that is '+' or '-', on 'this' Int. uOp(int) - Method in class la.la.Value.List.Cell Unary operator, op, i.e., null, hd, or tl, on Cells. uOp(int) - Method in class la.la.Value.Tuple Apply unary operator, 'op', element-wise to 'this' Tuple. uOp(int) - Method in class la.la.Value Apply unary Operator 'op' to 'this' Value, if implemented (this default throws an Exception). uOp(int) - Method in class la.maths.Vector Unary operator, 'op', on 'this' Vector, that is apply op element-wise, returning a new Vector of results. upb() - Method in class la.la.Type.Discrete 'this' Discrete's upper bound if any otherwise an Exception; also see Type.Discrete.upb_n(). upb() - Method in class mml.Continuous.Bounded.M Bounded.this.upb(). upb() - Method in class mml.Continuous.Bounded The upper bound, bounds().snd(). upb - Variable in class mml.CPT lwb, upb, the bounds on the input datum. upb() - Method in class mml.Discretes.Bounded.M Return Bounded.this.upb(). upb() - Method in class mml.Discretes.Bounded The upper bound on 'this' Model's dataspace. upb - Variable in class mml.Markov The Markov Models are of a given 'order', over Series of data, [lwb, upb]^* (bounds as ints). upb - Variable in class mml.NaiveBayes lwb and upb, the bounds on the output datum, od. upb_n() - Method in class la.la.Type.Discrete 'this' Discrete's int upper bound - if any, else exception, alse see Type.Discrete.upb. upb_n - Variable in class mml.CPT lwb_n, upb_n, the bounds on the input datum, as ints. upb_n() - Method in class mml.Discretes.Bounded.M Discretes.Bounded.M.upb().n() upb_n() - Method in class mml.Discretes.Bounded Discretes.Bounded.upb().n() upb_n - Variable in class mml.NaiveBayes lwb_n and upb_n the bounds on the output datum, od, as ints. upb_x() - Method in class mml.Continuous.Bounded.M Bounded.this.upb_x(). upb_x() - Method in class mml.Continuous.Bounded The upper bound of the data-space as a double. upb_x() - Method in class mml.Continuous.Uniform upfm - Variable in class mml.Dependent UnParameterised FunctionModel of id→od. UPFunctionModel - Class in mml UnParameterised (abstract) Function Model (aka regression) with input datum (independent variable), 'id', and output datum (dependent variable), 'od'. UPFunctionModel(Value) - Constructor for class mml.UPFunctionModel 'dp' is given, problem-defing parameters. UPFunctionModel.Est - Class in mml A class of Estimator of FunctionModel, that uses information from its enclosing UPFunctionModel. UPFunctionModel.K - Class in mml A very simple UnParameterised Function Model that uses a single Model for every output datum, od, regardless of the input datum, id. UPFunctionModel.K.M - Class in mml The fully parameterised Konstant function-model. UPFunctionModel.M - Class in mml M, the (abstract) fully parameterised Function Model class of an UnParameterised Function Model. upm - Variable in class mml.CPT The UnParameterised Model to be parameterised for each case (Value) of the input datum. upm - Variable in class mml.Dependent UnParameterised input Model of input datum id. upm - Variable in class mml.Intervals upm, the UnParameterised Model for the output (dependent) datum, od. upm - Variable in class mml.Mixture The UPModel of a class- (component-) sub-Model, upm : statParams → Model, that is to be "mixed" is the single UPModel.defnParams(). upm - Variable in class mml.R_D.Independent The UnParameterised Model, upm, that will be used, in different parameterisations, to model each column of the data. upm - Variable in class mml.UPFunctionModel.K upm, the UnParameterised Model, to be fully parameterised and then applied to the output datum in every input "case" by K.M. upmE - Variable in class mml.Graphs.IndependentEdges Public face is upmE(). upmE() - Method in class mml.Graphs.IndependentEdges The UnParameterised 2-state Model over the existence (0/1, false/true) of possible Edges. upmE - Variable in class mml.MotifA The UnParameterised (Adaptive) background Model over the existence of possible Edges outside instances of motifs. upmE - Variable in class mml.MotifD The UnParameterised (Adaptive) Model over the probability of existence of possible Edges outside instances of motifs. UPModel - Class in mml UPModel, the abstract class of UnParameterised statistical Models. UPModel(Value) - Constructor for class mml.UPModel Given common-knowledge problem-defining parameter(s), dp, create an UnParameterised Model. UPModel.Est - Class in mml Est, an Estimator that uses information from its enclosing UnParameterised Model. UPModel.M - Class in mml A fully parameterised Model, M, nested within an UnParameterised UPModel. UPModel.Transform - Class in mml Transform 'this' UPModel with Function f. UPModel.Transform.M - Class in mml The fully parameterised Transform-ed Model. upms - Variable in class mml.BestOf The alternative UnParameterised Models from which 'this' BestOf is to choose just one in estimating BestOf.M. upms - Variable in class mml.Independent The sub-UPModels making 'this' Independent. upmV - Variable in class mml.Graphs Public face of 'upmV' is upmV(). upmV() - Method in class mml.Graphs The UnParameterised Model of |V|. upperRightA() - Method in class graph.Undirected Return the upper-right triangular part of the adjacency (array) Matrix of this Undirected, and hence symmetric, Graph. UPSame(Value) - Constructor for class la.bioinformatics.Alignment.UPSame UPSeriesModel - Class in mml The (abstract) class of UnParameterised Models of Series. UPSeriesModel(Value) - Constructor for class mml.UPSeriesModel Given problem-defining parameter(s) 'dp', construct a SeriesModel. UPSeriesModel.Est - Class in mml A class of Estimator of SeriesModel, that uses information from its enclosing UPSeriesModel. UPSeriesModel.K - Class in mml A very simple, UnParameterised SeriesModel that uses a given UnParameterised Model of length ≥ 0, and a given UnParameterised Model of (every) element. UPSeriesModel.K.M - Class in mml K's fully parameterised SeriesModel. UPSeriesModel.Length - Class in mml A subclass of UPSeriesModels where Length.M has an explicit model of lengths (as opposed to a terminating Value/symbol). UPSeriesModel.Length.M - Class in mml Fully parameterised Model of Series having an explicit Model of lengths (as opposed to a terminating Value/symbol). UPSeriesModel.M - Class in mml The (abstract) fully parameterised Series Model. UPsm3 - Variable in class la.bioinformatics.Alignment.UPSame UPsm3, the UnParameterised SeriesModel of the "flags", (LEFT | RIGHT | BOTH)^*. UPsmE - Variable in class la.bioinformatics.Alignment.UPSame UPsmE, the UnParameterised SeriesModel of elements. UPx2y - Variable in class la.bioinformatics.Alignment.UPSame UPx2y, the UnParameterised FunctionModel of l→r and r→l. Util - Class in la.util A few general purpose constants and utility functions/methods. Util() - Constructor for class la.util.Util V v - Variable in class graph.Graph.Vertex The number of this Vertex (not its label, if any). v - Variable in class la.la.Expression.Const The Value denoted by 'this' Const (literal). v - Variable in class la.la.Value.Defer v holds the once Deferred Value when (if) it is eventually computed, by Value.Defer.force(), to at least WHNF. v - Variable in class la.la.Value.Maybe.Just The Value, v, in Just v is present. v0 - Variable in class graph.Graph.Edge v0 and v1, the Vertices of 'this' Edge. v0 - Variable in class la.la.Value.Inc_Or.Both The Values, v0, v1, in Both v0 v1. v0 - Variable in class la.la.Value.Inc_Or.Left The Value, v0, in Left v0. v1 - Variable in class graph.Graph.Edge v0 and v1, the Vertices of 'this' Edge. v1 - Variable in class la.la.Value.Inc_Or.Both The Values, v0, v1, in Both v0 v1. v1 - Variable in class la.la.Value.Inc_Or.Right The Value, v1, in Right v1. v2pv(int) - Method in class graph.Directed.AsUndirected v2pv(int) - Method in class graph.Directed.Sparse.Induced v2pv(int) - Method in class graph.Directed.Sparse.Renumbered v2pv(int) - Method in class graph.Graph.Contraction The Vertex of the parent that 'v' corresponds to. v2pv(int) - Method in class graph.Graph.Induced Vertex 'v' of 'this' (Vertex-)Induced sub-graph is Vertex vs[v] of the parent() Graph. v2pv(int) - Method in class graph.Graph.Renumbered Vertex 'v' in 'this' Renumbered is Vertex vs[v] of the parent() Graph. v2pv(int) - Method in interface graph.Graph.SubGraph Vertex 'v' of 'this' Graph corresponds to v2pv(v) of the parent Graph. v2pv(int) - Method in class graph.Undirected.AsDirected v2pv(int) - Method in class graph.Undirected.Sparse.Induced v2pv(int) - Method in class graph.Undirected.Sparse.Renumbered val(int, int) - Method in class la.la.Environment Descend 'levels' of sub-Environments, and there return the Value bound to the 'offset'^th Variable. val(int) - Method in class la.la.Environment Return the Value bound to the 'offset'^th Variable. Value - Class in la.la Value = int + bool + char + triv + ... Value() - Constructor for class la.la.Value Value.Atomic - Class in la.la The abstract class of atomic, non-structured Values, notably Discrete and Cts. Value.Bool - Class in la.la Bool = false | true; also see Type.BOOL. Value.Char - Class in la.la A Char Value; also see Type.CHAR. Value.Chars - Class in la.la A Chars (String) Value; also see Type.CHARS. Value.Cts - Class in la.la Continuous Values, x()+/-(AoM()/2); see Type.CTS and Value.Real. Value.Defer - Class in la.la The class of lazy, that is, not yet computed, Deferred Values, Value.Defer.Exp and Value.Defer.App. Value.Defer.App - Class in la.la A lazy, un-apply-ed, Deferred Function-application, a (Function, actual-parameter) pair. Value.Defer.Exp - Class in la.la A lazy unevaluated, Deferred Expression is a closure, (e, r), a (Expression, Environment). Value.Discrete - Class in la.la Discrete Values, such as Value.Bool, are subclasses of Discrete. Value.Enum - Class in la.la Enum Values, for example, A:DNA; also see Type.Enum. Value.Enum.GP - Class in la.la A basic "general purpose" (GP) implementation of an Enum Value having a given Type.Enum t. Value.Inc_Or - Class in la.la Inc_Or t0 t1 = Left t0 | Right t1 | Both t0 t1, for where one, or both, of v0:t0 and v1:t1 can be present. Value.Inc_Or.Both - Class in la.la Both v0 v1. Value.Inc_Or.Left - Class in la.la Left v0, the first (Left) Option alone. Value.Inc_Or.Right - Class in la.la Right v1, the second (Right) Option alone. Value.Int - Class in la.la Also see Type.INT. Value.Lambda - Class in la.la Function produced from a Expression.LambdaExp and an Environment. Value.List - Class in la.la Linked Lists, a special implementation of the abstract Value.Option. Value.List.Cell - Class in la.la A List Cell; also see Value.List.NIL. Value.Maybe - Class in la.la Maybe t = None | Just t, for where a Value may be missing (None), or present (Just v). Value.Maybe.Just - Class in la.la The Value is present, Just v. Value.Option - Class in la.la An Option Value has a "tag", Value.Option.n(), and zero or more sub-Values, Value.Structured.elt(int) depending on the tag. Value.Option.GP - Class in la.la A general purpose (GP) implementation of an Value.Option Value; also see Type.Option, and Value.List. Value.Real - Class in la.la A class of exact Value.Cts Values -- those having Value.Real.AoM() = 0 . Value.Scannable - Interface in la.la A class that implements Scannable can produce (by toSeries()) a Series of Values. Value.Structured - Class in la.la The super-class of Values, such as Tuples and Vectors, having zero or more elements (components, fields), elt(i). Value.Triv - Class in la.la () : TRIV . Value.Tuple - Class in la.la The class of heterogeneous k-Tuples, that is pairs, triples, and so on; also see Type.Tuple and Vector. Value.Tuple.GP - Class in la.la A simple general purpose (GP) implementation of a Tuple Value. valueMdl - Variable in class mml.Missing.M The fully parameterised Model all present (known) Values. values(Value[][]) - Static method in class la.maths.Matrix Convenience function, values : Value[][] → GP2. values(Value[]) - Static method in class la.maths.Vector Convenience function, values : Value[] → GP. values(Value[]) - Static method in class la.util.Series Series of elements from an array of Values, one at a time. valueUPM - Variable in class mml.Missing The UnParameterised Model of those data that are known (present). variance() - Method in class mml.NormalUPM.M Return ν = sigma². VECTOR - Static variable in class la.la.Type Vector() - Constructor for class la.la.Type.Vector Vector(Type) - Constructor for class la.la.Type.Vector Vector(String, Type) - Constructor for class la.la.Type.Vector Vector - Class in la.maths The class of homogeneous Vectors of arbitrary length. Vector() - Constructor for class la.maths.Vector Vector.Derived - Class in la.maths Class Derived can be useful when making a modified Vector. Vector.Doubles - Class in la.maths A Vector containing doubles (Cts Values) can probably arrange efficient storage and operations. Vector.GP - Class in la.maths A simple, general purpose (GP) implementation of class Vector. Vector.Ints - Class in la.maths A Vector containing Int Values can probably arrange efficient storage and operations. Vector.Slice - Class in la.maths Vector.Slice, a subVector of 'this' Vector; see slice. Vector.Strings - Class in la.maths A Vector containing Strings, that is, having Chars elements. Vector.Weighted - Class in la.maths 'This' Vector (data-set?) of elements, each element explicitly weighted, somehow. VECTOR_CHARS - Static variable in class la.la.Type VECTOR_CTS - Static variable in class la.la.Type VECTOR_INT - Static variable in class la.la.Type VECTOR_N - Static variable in class la.la.Type Integer codes for various "types" of Type. verbosity - Variable in class mml.Graphs.Motifs Whether (verbosity>0) or not (verbosity==0) to Print tracing information. Vertex(int) - Constructor for class graph.Directed.Vertex Vertex(int) - Constructor for class graph.Graph.Vertex Vertex(int) - Constructor for class graph.Undirected.Vertex vertices() - Method in class graph.Graph.SubGraphs Return the set of Vertices (of 'this' Graph) that are in the current subGraph of 'this' Series. vLabel(int) - Method in class graph.Directed.AsUndirected vLabel(int) - Method in class graph.Directed.Sparse.Induced vLabel(int) - Method in class graph.Directed.Sparse.Renumbered vLabel(int) - Method in class graph.Graph.Contraction Vertex labels, if any, as per v2pv and the parent Graph. vLabel(int) - Method in class graph.Graph.Induced Vertex labels, if any, as per vs and the parent() Graph. vLabel(int) - Method in class graph.Graph.Renumbered The parent's vLabel(vs[v]), if any. vLabel(int) - Method in class graph.Graph.ToDirected vLabel(int) - Method in class graph.Graph.ToUndirected vLabel(int) - Method in class graph.Graph UnsupportedOperation, the default assumption is no Vertex labels. vLabel(int) - Method in class graph.Undirected.AsDirected vLabel(int) - Method in class graph.Undirected.Sparse.Induced vLabel(int) - Method in class graph.Undirected.Sparse.Renumbered vLabelled() - Method in class graph.Graph Are the Vertices labelled? Also see vLabel(v). vLabels() - Method in class graph.Graph Return all Vertex labels, or null if unlabelled. vMF - Class in mml The UnParameterised von Mises - Fisher Model of Directions in R^D. vMF(Value) - Constructor for class mml.vMF D, the dimension of R^D. vMF.M - Class in mml The fully parameterised von Mises - Fisher Model of Directions in R^D. vMF3 - Static variable in class mml.MML The UnParameterised von Mises - Fisher Model (distribution) of Directions in R³. vPair2n(int, int) - Method in class graph.Graph Given Vertices v0 and v1 that could form a legitimate Edge, return a unique integer that is ≥0 and <maxEdges(). vs - Variable in class graph.Directed.Sparse.Induced vs - Variable in class graph.Directed.Sparse.Renumbered Vertex 'v' of 'this' is Vertex vs[v] of the parent Graph. vs - Variable in class graph.Graph.Contraction The contracted vertices of the parent Graph. vs - Variable in class graph.Graph.Induced Vertex 'v' of 'this' Induced sub-graph is Vertex vs[v] of the parent() Graph. vs - Variable in class graph.Graph.Renumbered Vertex 'v' of 'this' is Vertex vs[v] of the parent() Graph. vs - Variable in class graph.Undirected.Sparse.Induced vs - Variable in class graph.Undirected.Sparse.Renumbered Vertex 'v' of 'this' is Vertex vs[v] of the parent Graph. vSize() - Method in class graph.Directed.AsUndirected vSize() - Method in class graph.Directed.Dense vSize() - Method in class graph.Directed.Sparse vSize() - Method in class graph.Graph.Contraction The number of Vertices in 'this' Contracted Graph. vSize() - Method in class graph.Graph.Derived The parent's vSize() (sometimes the same). vSize() - Method in class graph.Graph.Induced Return |vs|. vSize() - Method in class graph.Graph.ToDirected vSize() - Method in class graph.Graph.ToUndirected vSize() - Method in class graph.Graph The number of Vertices, |V|≥1, V = {v₀, ..., v_(vSize()-1)}, of 'this' Graph. vSize() - Method in class graph.Undirected.AsDirected vSize() - Method in class graph.Undirected.Dense vSize() - Method in class graph.Undirected.Sparse vStats(Vector, int, int) - Method in class mml.Graphs Return Graphs.upmV's stats on |V| for ds[lo,hi). vType - Variable in class graph.Type Does the Graph have Vertex- and/or Edge- labels, and if so, of what Type(s)? W W5 - Static variable in class eg.Graphing Some possible motifs (subgraphs). waddles - Static variable in class eg.Ducks Also see birds, quacks and weights. walk_N_talk - Static variable in class eg.Ducks UnParameterised Independent Model over Bool×Bool. walk_N_talk_2_bird - Static variable in class eg.Ducks new NaiveBayes(Ducks.bird_N_walk_N_talk). WallaceInt0 - Static variable in class mml.MML The fully (trivially) parameterised Wallace Model for integers n ≥ 0. WallaceInt0upm - Static variable in class mml.MML The UnParameterised Wallace Model for integers n ≥ 0. WallaceInt0UPM - Class in mml Model WallaceInt0 should be enough for most purposes but here are the classes, UnParameterised (WallaceInt0UPM) and fully (trivially) parameterised (M). WallaceInt0UPM(Value) - Constructor for class mml.WallaceInt0UPM dp=triv -- the problem defining parameter is trivial. WallaceInt0UPM.M - Class in mml Model WallaceInt0 should be enough for most purposes but here are the classes, fully (trivially) parameterised (M) and UnParameterised (WallaceInt0UPM). weight(double[]) - Method in class la.maths.Vector Return a Vector, based on the data in 'this' Vector, but with weights adjusted × wts[i]. weight(double) - Method in class la.maths.Vector Return a Vector, based on the data in 'this' Vector, but with weights adjusted × 'wt' ≥ 0. weight(Vector) - Method in class la.maths.Vector Re-weight 'this' Vector adjusting weights by × v.elt(i).x(). Weighted() - Constructor for class la.maths.Vector.Weighted weightLike(double) - Method in class la.maths.Vector Re-weight 'this' Vector setting all weights equal to w ≥ 0. weightLike(Vector) - Method in class la.maths.Vector Re-weight 'this' Vector setting weights, wt(i), equal to v.wt(i). weights - Static variable in class eg.Ducks 'weights' are frequencies of the observations; also see birds, waddles and quacks. whnf - Static variable in class la.la.Library whnf p0 p1 can be used to make a parameter (p0) strict, say: force p0 to weak head normal form (WHNF), but return p1 (also in WHNF). width() - Method in class mml.Dependent Two columns, width = 2. width() - Method in class mml.Independent The number of variables (columns) and of upms[]. width() - Method in class mml.Multivariate.M Return the enclosing Multivariate's width(). width() - Method in class mml.Multivariate Return Multivariate.width(), the number of variables (columns). WithInverse() - Constructor for class la.la.Function.Cts2Cts.WithInverse WithInverse() - Constructor for class la.la.Function.CtsD2CtsD.WithInverse WithInverse() - Constructor for class la.la.Function.Native.WithInverse WithInverse() - Constructor for class la.la.Function.WithInverse word - Static variable in class la.la.Lexical Integer codes for the various lexical symbols; also see Lexical.Symbol. wt(int) - Method in class la.maths.Q Error: wt(i) is not a Quaternion operation. wt(int) - Method in class la.maths.Vector.Derived wt(i) of the original Vector. wt(int) - Method in class la.maths.Vector.Slice parent().wt(lo+i). wt(int) - Method in class la.maths.Vector.Weighted The weight of elt(i) in this Vector (data-set). wt(int) - Method in class la.maths.Vector wt(i), the weight, of elt(i) in 'this' data-set (Vector); this default is 1.0. wt() - Method in class la.util.Series Return the weight of the current element; this default gives 1.0. wtMedian() - Method in class la.maths.Vector Return the index, m, of the median element on the basis of weights. wtNlAoM() - Method in class la.maths.Vector The total -ve log AoM of the whole Vector (if appropriate), taking into account element weights. wtNlAoM(int, int) - Method in class la.maths.Vector Return the total weighted nlAoM of elements [lo, hi). wts() - Method in class la.maths.Q Error: wts() is not a Quaternion operation. wts() - Method in class la.maths.Vector Return ∑_i wt(i). It looks at constWt() and acts accordingly. wts(int, int) - Method in class la.maths.Vector Return the total weights of elements [lo, hi). X x() - Method in class la.la.Value.Cts Return the double corresponding to 'this' Cts. x() - Method in class la.la.Value.Defer force(), and return the v.x() of this Deferred Value. x() - Method in class la.la.Value.Int Float the Value.Int.n() to a double. x() - Method in class la.la.Value x() returns a Cts.x(), and also see Int.x(), but this default throws a RuntimeException (see Value.n()). x() - Method in class la.maths.Q The "real" part, elt(0).x(). x(int) - Method in class la.maths.Vector Return this.elt(i).x() — assuming this is a Vector of Cts. x2S(double) - Static method in class eg.Iris Return just a few significant digits of x; uses Util.x2String(x). x2S(double) - Static method in class eg.Musicians A double compactly to at least 4 significant figures. x2String(double) - Static method in class la.util.Util Render a double, x, compactly and to at least three significant digits; also see Util.x2String(double,int). x2String(double, int) - Static method in class la.util.Util Render a double, x, as compactly as possibly while guaranteeing 'sigDigits' significant digits. x2y - Variable in class la.bioinformatics.Alignment.UPSame.M x2y, the Function Model of l→r and r→l. Z zero - Static variable in class la.la.Value zeroR - Static variable in class la.la.Value zeroTriv(double, Value) - Method in class mml.Model Check msg1 = 0 and sp = (), that both are trivial. zip() - Method in class la.la.Value.Tuple From 'this' Tuple of Vectors (which is checked), return a zipped Vector of Tuples. zip(Vector) - Method in class la.maths.Vector zip 'this' Vector with another Vector 'v', of the same length, to produce a Vector of pairs as in, zip(new Vector [] {this, v}. zip(Vector[]) - Static method in class la.maths.Vector zip several Vectors to make a Vector of Tuples. A B C D E F G H I J K L M N O P Q R S T U V W X Z