Develop delta fn eus fix indexed call rule

Project.toml

@@ -1,7 +1,7 @@
 name = "ReactiveMP"
 uuid = "a194aa59-28ba-4574-a09c-4a745416d6e3"
 authors = ["Dmitry Bagaev <[email protected]>", "Albert Podusenko <[email protected]>", "Bart van Erp <[email protected]>", "Ismail Senoz <[email protected]>"]
-version = "2.4.1"
+version = "2.5.1"
 
 [deps]
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
@@ -25,7 +25,6 @@ SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
 StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
-StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"
 TupleTools = "9d95972d-f1c8-5527-a6e0-b4b365fa01f6"
 Unrolled = "9602ed7d-8fef-5bc8-8597-8f21381861e8"
 

docs/make.jl

@@ -44,7 +44,7 @@ makedocs(
             "Hidden Markov Model"              => "examples/hidden_markov_model.md",
             "Hierarchical Gaussian Filter"     => "examples/hierarchical_gaussian_filter.md",
             "Autoregressive Model"             => "examples/autoregressive.md",
-            "Normalizing Flows Tutorial"       => "examples/flow_tutorial.md",
+            "Invertible Neural Networks"       => "examples/invertible_neural_network_tutorial.md",
             "Univariate Normal Mixture"        => "examples/univariate_normal_mixture.md",
             "Multivariate Normal Mixture"      => "examples/multivariate_normal_mixture.md",
             "Gamma Mixture"                    => "examples/gamma_mixture.md",

docs/src/examples/overview.md

@@ -12,7 +12,7 @@ We are going to perform an exact inference to assess the skills of a student giv
 - [Hidden Markov Model](@ref examples-hidden-markov-model): An example of structured variational Bayesian inference in Hidden Markov Model with unknown transition and observational matrices.
 - [Hierarchical Gaussian Filter](@ref examples-hgf): An example of online inference procedure for Hierarchical Gaussian Filter with univariate noisy observations using Variational Message Passing algorithm. Reference: [Ismail Senoz, Online Message Passing-based Inference in the Hierarchical Gaussian Filter](https://ieeexplore.ieee.org/document/9173980).
 - [Autoregressive Model](@ref examples-autoregressive): An example of variational Bayesian Inference on full graph for Autoregressive model. Reference: [Albert Podusenko, Message Passing-Based Inference for Time-Varying Autoregressive Models](https://www.mdpi.com/1099-4300/23/6/683).
-- [Normalising Flows](@ref examples-flow): An example of variational Bayesian Inference with Normalizing Flows. Reference: Bard van Erp, Hybrid Inference with Invertible Neural Networks in Factor Graphs (submitted).
+- [Invertible Neural Networks](@ref examples-inn): An example of variational Bayesian Inference with invertible neural networks. Reference: Bart van Erp, Hybrid Inference with Invertible Neural Networks in Factor Graphs (accepted).
 - [Univariate Gaussian Mixture](@ref examples-univariate-gaussian-mixture): This example implements variational Bayesian inference in a univariate Gaussian mixture model with mean-field assumption.
 - [Multivariate Gaussian Mixture](@ref examples-multivariate-gaussian-mixture): This example implements variational Bayesian inference in a multivariate Gaussian mixture model with mean-field assumption.
 - [Gamma Mixture](@ref examples-gamma-mixture): This example implements one of the experiments outlined in https://biaslab.github.io/publication/mp-based-inference-in-gmm/ .

src/algebra/correction.jl

@@ -26,6 +26,7 @@ struct NoCorrection <: AbstractCorrection end
 
 correction!(::NoCorrection, value::Real)            = value
 correction!(::NoCorrection, matrix::AbstractMatrix) = matrix
+correction!(::Nothing, something)                   = correction!(NoCorrection(), something)
 
 """
     TinyCorrection

src/nodes/gcv.jl

@@ -18,7 +18,13 @@ const DefaultGCVNodeMetadata = GCVMetadata(GaussHermiteCubature(20))
 
 default_meta(::Type{GCV}) = DefaultGCVNodeMetadata
 
-@average_energy GCV (q_y_x::MultivariateNormalDistributionsFamily, q_z::NormalDistributionsFamily, q_κ::Any, q_ω::Any) =
+@average_energy GCV (
+    q_y_x::MultivariateNormalDistributionsFamily,
+    q_z::NormalDistributionsFamily,
+    q_κ::Any,
+    q_ω::Any,
+    meta::Union{<:GCVMetadata, Nothing}
+) =
     begin
         y_x_mean, y_x_cov = mean_cov(q_y_x)
         z_mean, z_var     = mean_var(q_z)

src/nodes/uniform.jl

@@ -11,3 +11,8 @@ function prod(::ProdAnalytical, left::Uniform, right::Beta)
     # The special case for `Uniform(0, 1)` which is essentially `p(x) = 1` and does not change anything
     return right
 end
+
+@average_energy Uniform (q_out::Beta, q_a::PointMass, q_b::PointMass) = begin
+    @assert (mean(q_a), mean(q_b)) == (0.0, 1.0) "a and b must be equal to 0 and 1 respectively"
+    0.0
+end

src/rule.jl

@@ -86,6 +86,12 @@ function rule_macro_parse_on_tag(on)
         return :(Type{Val{$(QuoteNode(name))}}), nothing, nothing
     elseif @capture(on, (:name_, index_Symbol))
         return :(Tuple{Val{$(QuoteNode(name))}, Int}), index, :($index = on[2])
+    elseif @capture(on, (:name_, k_ = index_Int))
+        return :(Tuple{Val{$(QuoteNode(name))}, Int}),
+        index,
+        :(error(
+            "`k = ...` syntax in the edge specification is only allowed in the `@call_rule` and `@call_marginalrule` macros"
+        ))
     else
         error(
             "Error in macro. `on` specification is incorrect: $(on). Must be either a quoted symbol expression (e.g. `:out` or `:mean`) or tuple expression with quoted symbol and index identifier (e.g. `(:m, k)` or `(:w, k)`)"
@@ -197,6 +203,17 @@ function call_rule_macro_parse_fn_args(inputs; specname, prefix, proxy)
     return names_arg, values_arg
 end
 
+call_rule_macro_construct_on_arg(on_type, on_index::Nothing) = MacroHelpers.bottom_type(on_type)
+
+function call_rule_macro_construct_on_arg(on_type, on_index::Int)
+    bottomtype = MacroHelpers.bottom_type(on_type)
+    if @capture(bottomtype, Tuple{Val{R_}, Int})
+        return :((Val($R), $on_index))
+    else
+        error("Internal indexed call rule error: Invalid `on_type` in the `call_rule_macro_construct_on_arg` function.")
+    end
+end
+
 function rule_function_expression(
     body::Function,
     fuppertype,
@@ -273,7 +290,7 @@ macro rule(fform, lambda)
     fuppertype                       = MacroHelpers.upper_type(fformtype)
     on_type, on_index, on_index_init = rule_macro_parse_on_tag(on)
     whereargs                        = whereargs === nothing ? [] : whereargs
-    metatype                         = metatype === nothing ? :Any : metatype
+    metatype                         = metatype === nothing ? :Nothing : metatype
 
     options = map(options) do option
         @capture(option, name_ = value_) || error("Error in macro. Option specification '$(option)' is incorrect")x
@@ -418,7 +435,7 @@ macro call_rule(fform, args)
     q_names_arg, q_values_arg =
         call_rule_macro_parse_fn_args(inputs, specname = :marginals, prefix = :q_, proxy = :(ReactiveMP.Marginal))
 
-    on_arg = MacroHelpers.bottom_type(on_type)
+    on_arg = call_rule_macro_construct_on_arg(on_type, on_index)
 
     output = quote
         ReactiveMP.rule(
@@ -589,7 +606,7 @@ macro marginalrule(fform, lambda)
     fuppertype                       = MacroHelpers.upper_type(fformtype)
     on_type, on_index, on_index_init = rule_macro_parse_on_tag(on)
     whereargs                        = whereargs === nothing ? [] : whereargs
-    metatype                         = metatype === nothing ? :Any : metatype
+    metatype                         = metatype === nothing ? :Nothing : metatype
 
     inputs = map(inputs) do input
         @capture(input, iname_::itype_) || error("Error in macro. Input $(input) is incorrect")
@@ -647,7 +664,7 @@ macro call_marginalrule(fform, args)
     q_names_arg, q_values_arg =
         call_rule_macro_parse_fn_args(inputs, specname = :marginals, prefix = :q_, proxy = :(ReactiveMP.Marginal))
 
-    on_arg = MacroHelpers.bottom_type(on_type)
+    on_arg = call_rule_macro_construct_on_arg(on_type, on_index)
 
     output = quote
         ReactiveMP.marginalrule(
@@ -824,8 +841,11 @@ end
 rule_method_error_extract_fform(f::Function) = string("typeof(", f, ")")
 rule_method_error_extract_fform(f)           = string(f)
 
-rule_method_error_extract_on(::Type{Val{T}}) where {T}         = T
-rule_method_error_extract_on(on::Tuple{Val{T}, Int}) where {T} = string("(:", rule_method_error_extract_on(typeof(on[1])), ", k)")
+rule_method_error_extract_on(::Type{Val{T}}) where {T}              = string(":", T)
+rule_method_error_extract_on(::Type{Tuple{Val{T}, Int}}) where {T}  = string("(", rule_method_error_extract_on(Val{T}), ", k)")
+rule_method_error_extract_on(::Type{Tuple{Val{T}, N}}) where {T, N} = string("(", rule_method_error_extract_on(Val{T}), ", ", convert(Int, N), ")")
+rule_method_error_extract_on(::Tuple{Val{T}, Int}) where {T}        = string("(", rule_method_error_extract_on(Val{T}), ", k)")
+rule_method_error_extract_on(::Tuple{Val{T}, N}) where {T, N}       = string("(", rule_method_error_extract_on(Val{T}), ", ", convert(Int, N), ")")
 
 rule_method_error_extract_vconstraint(something) = typeof(something)
 
@@ -894,7 +914,7 @@ function Base.showerror(io::IO, error::RuleMethodError)
         meta_spec      = rule_method_error_extract_meta(error.meta)
 
         possible_fix_definition = """
-        @rule $(spec_fform)(:$spec_on, $spec_vconstraint) ($arguments_spec, $meta_spec) = begin 
+        @rule $(spec_fform)($spec_on, $spec_vconstraint) ($arguments_spec, $meta_spec) = begin 
             return ...
         end
         """

src/rules/and/in2.jl

@@ -1,3 +1,3 @@
-@rule AND(:in2, Marginalisation) (m_out::Bernoulli, m_in1::Bernoulli, meta::Any) = begin
+@rule AND(:in2, Marginalisation) (m_out::Bernoulli, m_in1::Bernoulli) = begin
     return @call_rule AND(:in1, Marginalisation) (m_out = m_out, m_in2 = m_in1, meta = meta)
 end

src/rules/delta/extended/in.jl

@@ -9,7 +9,7 @@
         m = A * μ_out + b
         V = A * Σ_out * A'
 
-        F = val(m, Number) ? Univariate : Multivariate
+        F = isa(m, Number) ? Univariate : Multivariate
 
         return convert(promote_variate_type(F, NormalMeanVariance), m, V)
     end
@@ -26,7 +26,7 @@
         m = A * μ_in + b
         V = A * Σ_in * A'
 
-        F = val(m, Number) ? Univariate : Multivariate
+        F = isa(m, Number) ? Univariate : Multivariate
 
         return convert(promote_variate_type(F, NormalMeanVariance), m, V)
     end

src/rules/delta/helpers/shared.jl

@@ -76,7 +76,7 @@ end
 """
 Return the marginalized statistics of the Gaussian corresponding to an inbound inx
 """
-function marginalizeGaussianMV(m::Vector{T}, V::AbstractMatrix, ds::Vector, inx::Int64) where T<:Real
+function marginalizeGaussianMV(m::Vector{T}, V::AbstractMatrix, ds::Vector, inx::Int64) where {T <: Real}
     if ds[inx] == () # Univariate original
         return (m[inx], V[inx, inx]) # Return scalars
     else # Multivariate original

src/rules/gcv/marginals.jl

@@ -1,5 +1,12 @@
 
-@marginalrule GCV(:y_x) (m_y::UniNormalOrExpLinQuad, m_x::UniNormalOrExpLinQuad, q_z::Any, q_κ::Any, q_ω::Any) = begin
+@marginalrule GCV(:y_x) (
+    m_y::UniNormalOrExpLinQuad,
+    m_x::UniNormalOrExpLinQuad,
+    q_z::Any,
+    q_κ::Any,
+    q_ω::Any,
+    meta::Union{<:GCVMetadata, Nothing}
+) = begin
     y_mean, y_precision = mean_precision(m_y)
     x_mean, x_precision = mean_precision(m_x)
 

src/rules/gcv/x.jl

@@ -1,6 +1,12 @@
 export rule
 
-@rule GCV(:x, Marginalisation) (m_y::UniNormalOrExpLinQuad, q_z::Any, q_κ::Any, q_ω::Any) = begin
+@rule GCV(:x, Marginalisation) (
+    m_y::UniNormalOrExpLinQuad,
+    q_z::Any,
+    q_κ::Any,
+    q_ω::Any,
+    meta::Union{<:GCVMetadata, Nothing}
+) = begin
     y_mean, y_var = mean_var(m_y)
     z_mean, z_var = mean_var(q_z)
     κ_mean, κ_var = mean_var(q_κ)
@@ -13,7 +19,7 @@ export rule
     return NormalMeanVariance(y_mean, y_var + inv(A * B))
 end
 
-@rule GCV(:x, Marginalisation) (q_y::Any, q_z::Any, q_κ::Any, q_ω::Any) = begin
+@rule GCV(:x, Marginalisation) (q_y::Any, q_z::Any, q_κ::Any, q_ω::Any, meta::Union{<:GCVMetadata, Nothing}) = begin
     y_mean        = mean(q_y)
     z_mean, z_var = mean_var(q_z)
     κ_mean, κ_var = mean_var(q_κ)

src/rules/gcv/y.jl

@@ -1,6 +1,12 @@
 export rule
 
-@rule GCV(:y, Marginalisation) (m_x::UniNormalOrExpLinQuad, q_z::Any, q_κ::Any, q_ω::Any) = begin
+@rule GCV(:y, Marginalisation) (
+    m_x::UniNormalOrExpLinQuad,
+    q_z::Any,
+    q_κ::Any,
+    q_ω::Any,
+    meta::Union{<:GCVMetadata, Nothing}
+) = begin
     x_mean, x_var = mean_var(m_x)
     z_mean, z_var = mean_var(q_z)
     κ_mean, κ_var = mean_var(q_κ)
@@ -13,7 +19,7 @@ export rule
     return NormalMeanVariance(x_mean, x_var + inv(A * B))
 end
 
-@rule GCV(:y, Marginalisation) (q_x::Any, q_z::Any, q_κ::Any, q_ω::Any) = begin
+@rule GCV(:y, Marginalisation) (q_x::Any, q_z::Any, q_κ::Any, q_ω::Any, meta::Union{<:GCVMetadata, Nothing}) = begin
     x_mean        = mean(q_x)
     z_mean, z_var = mean_var(q_z)
     κ_mean, κ_var = mean_var(q_κ)

src/rules/multiplication/A.jl

@@ -1,15 +1,20 @@
 
-@rule typeof(*)(:A, Marginalisation) (m_out::PointMass, m_in::PointMass) = PointMass(mean(m_in) \ mean(m_out))
+@rule typeof(*)(:A, Marginalisation) (m_out::PointMass, m_in::PointMass, meta::Union{<:AbstractCorrection, Nothing}) =
+    PointMass(mean(m_in) \ mean(m_out))
 
-@rule typeof(*)(:A, Marginalisation) (m_out::GammaDistributionsFamily, m_in::PointMass{<:Real}) = begin
+@rule typeof(*)(:A, Marginalisation) (
+    m_out::GammaDistributionsFamily,
+    m_in::PointMass{<:Real},
+    meta::Union{<:AbstractCorrection, Nothing}
+) = begin
     return GammaShapeRate(shape(m_out), rate(m_out) * mean(m_in))
 end
 
 # if A is a matrix, then the result is multivariate
 @rule typeof(*)(:A, Marginalisation) (
     m_out::MultivariateNormalDistributionsFamily,
     m_in::PointMass{<:AbstractMatrix},
-    meta::AbstractCorrection
+    meta::Union{<:AbstractCorrection, Nothing}
 ) = begin
     A = mean(m_in)
     ξ_out, W_out = weightedmean_precision(m_out)
@@ -22,7 +27,7 @@ end
 @rule typeof(*)(:A, Marginalisation) (
     m_out::MultivariateNormalDistributionsFamily,
     m_in::PointMass{<:AbstractVector},
-    meta::AbstractCorrection
+    meta::Union{<:AbstractCorrection, Nothing}
 ) = begin
     A = mean(m_in)
     ξ_out, W_out = weightedmean_precision(m_out)
@@ -34,7 +39,7 @@ end
 @rule typeof(*)(:A, Marginalisation) (
     m_out::F,
     m_in::PointMass{<:Real},
-    meta::AbstractCorrection
+    meta::Union{<:AbstractCorrection, Nothing}
 ) where {F <: NormalDistributionsFamily} = begin
     A = mean(m_in)
     ξ_out, W_out = weightedmean_precision(m_out)
@@ -46,7 +51,7 @@ end
 @rule typeof(*)(:A, Marginalisation) (
     m_out::MvNormalMeanCovariance,
     m_in::PointMass{<:AbstractMatrix},
-    meta::AbstractCorrection
+    meta::Union{<:AbstractCorrection, Nothing}
 ) = begin
     A = mean(m_in)
     μ_out, Σ_out = mean_cov(m_out)
@@ -61,7 +66,7 @@ end
 @rule typeof(*)(:A, Marginalisation) (
     m_out::MvNormalMeanCovariance,
     m_in::PointMass{<:AbstractVector},
-    meta::AbstractCorrection
+    meta::Union{<:AbstractCorrection, Nothing}
 ) = begin
     A = mean(m_in)
     μ_out, Σ_out = mean_cov(m_out)

src/rules/multiplication/in.jl

@@ -1,15 +1,20 @@
 
-@rule typeof(*)(:in, Marginalisation) (m_out::PointMass, m_A::PointMass) = PointMass(mean(m_A) \ mean(m_out))
+@rule typeof(*)(:in, Marginalisation) (m_out::PointMass, m_A::PointMass, meta::Union{<:AbstractCorrection, Nothing}) =
+    PointMass(mean(m_A) \ mean(m_out))
 
-@rule typeof(*)(:in, Marginalisation) (m_out::GammaDistributionsFamily, m_A::PointMass{<:Real}) = begin
+@rule typeof(*)(:in, Marginalisation) (
+    m_out::GammaDistributionsFamily,
+    m_A::PointMass{<:Real},
+    meta::Union{<:AbstractCorrection, Nothing}
+) = begin
     return GammaShapeRate(shape(m_out), rate(m_out) * mean(m_A))
 end
 
 # if A is a matrix, then the result is multivariate
 @rule typeof(*)(:in, Marginalisation) (
     m_out::MultivariateNormalDistributionsFamily,
     m_A::PointMass{<:AbstractMatrix},
-    meta::AbstractCorrection
+    meta::Union{<:AbstractCorrection, Nothing}
 ) = begin
     A = mean(m_A)
     ξ_out, W_out = weightedmean_precision(m_out)
@@ -22,7 +27,7 @@ end
 @rule typeof(*)(:in, Marginalisation) (
     m_out::MultivariateNormalDistributionsFamily,
     m_A::PointMass{<:AbstractVector},
-    meta::AbstractCorrection
+    meta::Union{<:AbstractCorrection, Nothing}
 ) = begin
     A = mean(m_A)
     ξ_out, W_out = weightedmean_precision(m_out)
@@ -34,7 +39,7 @@ end
 @rule typeof(*)(:in, Marginalisation) (
     m_out::F,
     m_A::PointMass{<:Real},
-    meta::AbstractCorrection
+    meta::Union{<:AbstractCorrection, Nothing}
 ) where {F <: NormalDistributionsFamily} = begin
     A = mean(m_A)
     ξ_out, W_out = weightedmean_precision(m_out)
@@ -46,7 +51,7 @@ end
 @rule typeof(*)(:in, Marginalisation) (
     m_out::MvNormalMeanCovariance,
     m_A::PointMass{<:AbstractMatrix},
-    meta::AbstractCorrection
+    meta::Union{<:AbstractCorrection, Nothing}
 ) = begin
     A = mean(m_A)
     μ_out, Σ_out = mean_cov(m_out)
@@ -61,7 +66,7 @@ end
 @rule typeof(*)(:in, Marginalisation) (
     m_out::MvNormalMeanCovariance,
     m_A::PointMass{<:AbstractVector},
-    meta::AbstractCorrection
+    meta::Union{<:AbstractCorrection, Nothing}
 ) = begin
     A = mean(m_A)
     μ_out, Σ_out = mean_cov(m_out)

src/rules/multiplication/marginals.jl

@@ -3,7 +3,7 @@
     m_out::NormalDistributionsFamily,
     m_A::PointMass,
     m_in::NormalDistributionsFamily,
-    meta::Any
+    meta::Union{<:AbstractCorrection, Nothing}
 ) = begin
     b_in = @call_rule typeof(*)(:in, Marginalisation) (m_out = m_out, m_A = m_A, meta = meta)
     q_in = prod(ProdAnalytical(), b_in, m_in)
@@ -17,7 +17,7 @@ end
     m_out::UnivariateNormalDistributionsFamily,
     m_A::UnivariateNormalDistributionsFamily,
     m_in::PointMass{<:Real},
-    meta::Any
+    meta::Union{<:AbstractCorrection, Nothing}
 ) = begin
     return @call_marginalrule typeof(*)(:A_in) (m_out = m_out, m_A = m_in, m_in = m_A, meta = meta)
 end