API
VarNames
AbstractPPL.VarName — TypeVarName{sym}(optic=identity)A variable identifier for a symbol sym and optic optic.
The Julia variable in the model corresponding to sym can refer to a single value or to a hierarchical array structure of univariate, multivariate or matrix variables. The field lens stores the indices requires to access the random variable from the Julia variable indicated by sym as a tuple of tuples. Each element of the tuple thereby contains the indices of one optic operation.
VarNames can be manually constructed using the VarName{sym}(optic) constructor, or from an optic expression through the @varname convenience macro.
Examples
julia> vn = VarName{:x}(Accessors.IndexLens((Colon(), 1)) ⨟ Accessors.IndexLens((2, )))
+API · AbstractPPL API
VarNames
AbstractPPL.VarName — TypeVarName{sym}(optic=identity)
A variable identifier for a symbol sym and optic optic.
The Julia variable in the model corresponding to sym can refer to a single value or to a hierarchical array structure of univariate, multivariate or matrix variables. The field lens stores the indices requires to access the random variable from the Julia variable indicated by sym as a tuple of tuples. Each element of the tuple thereby contains the indices of one optic operation.
VarNames can be manually constructed using the VarName{sym}(optic) constructor, or from an optic expression through the @varname convenience macro.
Examples
julia> vn = VarName{:x}(Accessors.IndexLens((Colon(), 1)) ⨟ Accessors.IndexLens((2, )))
x[:, 1][2]
julia> getoptic(vn)
(@o _[Colon(), 1][2])
julia> @varname x[:, 1][1+1]
-x[:, 1][2]
sourceAbstractPPL.getsym — Functiongetsym(vn::VarName)
Return the symbol of the Julia variable used to generate vn.
Examples
julia> getsym(@varname(x[1][2:3]))
+x[:, 1][2]
sourceAbstractPPL.getsym — Functiongetsym(vn::VarName)
Return the symbol of the Julia variable used to generate vn.
Examples
julia> getsym(@varname(x[1][2:3]))
:x
julia> getsym(@varname(y))
-:y
sourceAbstractPPL.getoptic — Functiongetoptic(vn::VarName)
Return the optic of the Julia variable used to generate vn.
Examples
julia> getoptic(@varname(x[1][2:3]))
+:y
sourceAbstractPPL.getoptic — Functiongetoptic(vn::VarName)
Return the optic of the Julia variable used to generate vn.
Examples
julia> getoptic(@varname(x[1][2:3]))
(@o _[1][2:3])
julia> getoptic(@varname(y))
-identity (generic function with 1 method)
sourceAbstractPPL.inspace — Functioninspace(vn::Union{VarName, Symbol}, space::Tuple)
Check whether vn's variable symbol is in space. The empty tuple counts as the "universal space" containing all variables. Subsumption (see subsumes) is respected.
Examples
julia> inspace(@varname(x[1][2:3]), ())
+identity (generic function with 1 method)
sourceAbstractPPL.inspace — Functioninspace(vn::Union{VarName, Symbol}, space::Tuple)
Check whether vn's variable symbol is in space. The empty tuple counts as the "universal space" containing all variables. Subsumption (see subsumes) is respected.
Examples
julia> inspace(@varname(x[1][2:3]), ())
true
julia> inspace(@varname(x[1][2:3]), (:x,))
@@ -488,7 +30,7 @@
true
julia> inspace(@varname(x[1][2:3]), (@varname(x[1:10]),))
-true
sourceAbstractPPL.subsumes — Functionsubsumes(u::VarName, v::VarName)
Check whether the variable name v describes a sub-range of the variable u. Supported indexing:
- Scalar:
```jldoctest julia> subsumes(@varname(x), @varname(x[1, 2])) true
julia> subsumes(@varname(x[1, 2]), @varname(x[1, 2][3])) true ```
- Array of scalar: basically everything that fulfills
issubset.
```jldoctest julia> subsumes(@varname(x[[1, 2], 3]), @varname(x[1, 3])) true
julia> subsumes(@varname(x[1:3]), @varname(x[2][1])) true ```
- Slices:
jldoctest julia> subsumes(@varname(x[2, :]), @varname(x[2, 10][1])) true
Currently not supported are:
- Boolean indexing, literal
CartesianIndex (these could be added, though) - Linear indexing of multidimensional arrays:
x[4] does not subsume x[2, 2] for a matrix x - Trailing ones:
x[2, 1] does not subsume x[2] for a vector x
sourceAbstractPPL.subsumedby — Functionsubsumedby(t, u)
True if t is subsumed by u, i.e., if subsumes(u, t) is true.
sourceAbstractPPL.vsym — Functionvsym(expr)
Return name part of the @varname-compatible expression expr as a symbol for input of the VarName constructor.
sourceAbstractPPL.@varname — Macro@varname(expr, concretize=false)
A macro that returns an instance of VarName given a symbol or indexing expression expr.
If concretize is true, the resulting expression will be wrapped in a concretize() call.
Note that expressions involving dynamic indexing, i.e. begin and/or end, will always need to be concretized as VarName only supports non-dynamic indexing as determined by is_static_optic. See examples below.
Examples
Dynamic indexing
julia> x = (a = [1.0 2.0; 3.0 4.0; 5.0 6.0], );
+true
sourceAbstractPPL.subsumes — Functionsubsumes(u::VarName, v::VarName)
Check whether the variable name v describes a sub-range of the variable u. Supported indexing:
- Scalar:
```jldoctest julia> subsumes(@varname(x), @varname(x[1, 2])) true
julia> subsumes(@varname(x[1, 2]), @varname(x[1, 2][3])) true ```
- Array of scalar: basically everything that fulfills
issubset.
```jldoctest julia> subsumes(@varname(x[[1, 2], 3]), @varname(x[1, 3])) true
julia> subsumes(@varname(x[1:3]), @varname(x[2][1])) true ```
- Slices:
jldoctest julia> subsumes(@varname(x[2, :]), @varname(x[2, 10][1])) true
Currently not supported are:
- Boolean indexing, literal
CartesianIndex (these could be added, though) - Linear indexing of multidimensional arrays:
x[4] does not subsume x[2, 2] for a matrix x - Trailing ones:
x[2, 1] does not subsume x[2] for a vector x
sourceAbstractPPL.subsumedby — Functionsubsumedby(t, u)
True if t is subsumed by u, i.e., if subsumes(u, t) is true.
sourceAbstractPPL.vsym — Functionvsym(expr)
Return name part of the @varname-compatible expression expr as a symbol for input of the VarName constructor.
sourceAbstractPPL.@varname — Macro@varname(expr, concretize=false)
A macro that returns an instance of VarName given a symbol or indexing expression expr.
If concretize is true, the resulting expression will be wrapped in a concretize() call.
Note that expressions involving dynamic indexing, i.e. begin and/or end, will always need to be concretized as VarName only supports non-dynamic indexing as determined by is_static_optic. See examples below.
Examples
Dynamic indexing
julia> x = (a = [1.0 2.0; 3.0 4.0; 5.0 6.0], );
julia> @varname(x.a[1:end, end][:], true)
x.a[1:3, 2][:]
@@ -542,14 +84,14 @@
a.x[1]
julia> @varname(b.$name.x[1])
-b.a.x[1]
sourceAbstractPPL.@vsym — Macro@vsym(expr)
A macro that returns the variable symbol given the input variable expression expr. For example, @vsym x[1] returns :x.
Examples
julia> @vsym x
+b.a.x[1]
sourceAbstractPPL.@vsym — Macro@vsym(expr)
A macro that returns the variable symbol given the input variable expression expr. For example, @vsym x[1] returns :x.
Examples
julia> @vsym x
:x
julia> @vsym x[1,1][2,3]
:x
julia> @vsym x[end]
-:x
sourceAbstractPPL.vn_to_string — Functionvn_to_string(vn::VarName)
Convert a VarName as a string, via an intermediate named tuple. This differs from string(vn) in that concretised slices are faithfully represented (rather than being pretty-printed as colons).
julia> vn_to_string(@varname(x))
+:x
sourceAbstractPPL.vn_to_string — Functionvn_to_string(vn::VarName)
Convert a VarName as a string, via an intermediate named tuple. This differs from string(vn) in that concretised slices are faithfully represented (rather than being pretty-printed as colons).
julia> vn_to_string(@varname(x))
"(sym = \"x\", optic = (type = \"identity\",))"
julia> vn_to_string(@varname(x.a))
@@ -559,5 +101,4 @@
"(sym = \"y\", optic = (type = \"index\", indices = \"(:,)\"))"
julia> y = ones(2); vn_to_string(@varname(y[:], true))
-"(sym = \"y\", optic = (type = \"index\", indices = \"(ConcretizedSlice(Base.OneTo(2)),)\"))"
sourceAbstractPPL.vn_from_string — Functionvn_from_string(str)
Convert a string representation of a VarName back to a VarName. The string should have been generated by vn_to_string.
Warning This function should only be used with trusted input, as it uses eval
and Meta.parse to parse the string.
sourceAbstract model functions
AbstractPPL.AbstractProbabilisticProgram — TypeAbstractProbabilisticProgram
Common base type for models expressed as probabilistic programs.
sourceAbstractPPL.condition — Functioncondition(model, observations)
Condition the generative model model on some observed data, creating a new model of the (possibly unnormalized) posterior distribution over them.
observations can be of any supported internal trace type, or a fixed probability expression.
The invariant
m = decondition(condition(m, obs))
should hold for generative models m and arbitrary obs.
sourceAbstractPPL.decondition — Functiondecondition(conditioned_model)
Remove the conditioning (i.e., observation data) from conditioned_model, turning it into a generative model over prior and observed variables.
The invariant
m == condition(decondition(m), obs)
should hold for models m with conditioned variables obs.
sourceDensityInterface.logdensityof — Functionlogdensityof(model, trace)
Evaluate the (possibly unnormalized) density of the model specified by the probabilistic program in model, at specific values for the random variables given through trace.
trace can be of any supported internal trace type, or a fixed probability expression.
logdensityof should interact with conditioning and deconditioning in the way required by probability theory.
sourceAbstractPPL.AbstractContext — TypeAbstractContext
Common base type for evaluation contexts.
sourceAbstractPPL.evaluate!! — Functionevaluate!!
General API for model operations, e.g. prior evaluation, log density, log joint etc.
sourceAbstract traces
AbstractPPL.AbstractModelTrace — TypeAbstractModelTrace
Common base class for various trace or "variable info" types.
sourceSettings
This document was generated with Documenter.jl version 1.7.0 on Thursday 26 September 2024. Using Julia version 1.10.5.
AbstractPPL.vn_from_string — Functionvn_from_string(str)Convert a string representation of a VarName back to a VarName. The string should have been generated by vn_to_string.
This function should only be used with trusted input, as it uses eval
and Meta.parse to parse the string.
Abstract model functions
AbstractPPL.AbstractProbabilisticProgram — TypeAbstractProbabilisticProgramCommon base type for models expressed as probabilistic programs.
AbstractPPL.condition — Functioncondition(model, observations)Condition the generative model model on some observed data, creating a new model of the (possibly unnormalized) posterior distribution over them.
observations can be of any supported internal trace type, or a fixed probability expression.
The invariant
m = decondition(condition(m, obs))should hold for generative models m and arbitrary obs.
AbstractPPL.decondition — Functiondecondition(conditioned_model)Remove the conditioning (i.e., observation data) from conditioned_model, turning it into a generative model over prior and observed variables.
The invariant
m == condition(decondition(m), obs)should hold for models m with conditioned variables obs.
DensityInterface.logdensityof — Functionlogdensityof(model, trace)Evaluate the (possibly unnormalized) density of the model specified by the probabilistic program in model, at specific values for the random variables given through trace.
trace can be of any supported internal trace type, or a fixed probability expression.
logdensityof should interact with conditioning and deconditioning in the way required by probability theory.
AbstractPPL.AbstractContext — TypeAbstractContextCommon base type for evaluation contexts.
AbstractPPL.evaluate!! — Functionevaluate!!
General API for model operations, e.g. prior evaluation, log density, log joint etc.
Abstract traces
AbstractPPL.AbstractModelTrace — TypeAbstractModelTraceCommon base class for various trace or "variable info" types.