From acaa456d217dff530c0f05aa0c29a7922c4fc8b5 Mon Sep 17 00:00:00 2001
From: Ryan Xiao Wei Lin <rxwl2@cam.ac.uk>
Date: Tue, 14 Jan 2025 18:49:25 +0800
Subject: [PATCH] linting

---
 .../ProbabilisticGraphicalModels/bayesnet.jl  | 20 +++++++++----------
 1 file changed, 9 insertions(+), 11 deletions(-)

diff --git a/src/experimental/ProbabilisticGraphicalModels/bayesnet.jl b/src/experimental/ProbabilisticGraphicalModels/bayesnet.jl
index 0d3a4ca3..2d5a3d2f 100644
--- a/src/experimental/ProbabilisticGraphicalModels/bayesnet.jl
+++ b/src/experimental/ProbabilisticGraphicalModels/bayesnet.jl
@@ -113,7 +113,6 @@ function decondition!(bn::BayesianNetwork{V}, deconditioning_variables::Vector{V
     return bn
 end
 
-
 """
 Add a stochastic vertex to the BayesianNetwork.
 - `dist` can be a `Distribution` or a function returning a `Distribution`.
@@ -124,8 +123,8 @@ function add_stochastic_vertex!(
     bn::BayesianNetwork{V,T},
     name::V,
     dist::Any,
-    node_type::Symbol = :continuous;  
-    is_observed::Bool = false
+    node_type::Symbol=:continuous;
+    is_observed::Bool=false,
 )::T where {V,T}
     Graphs.add_vertex!(bn.graph) || return 0
     id = nv(bn.graph)
@@ -161,7 +160,7 @@ Add a directed edge `from -> to` in the BayesianNetwork's graph.
 """
 function add_edge!(bn::BayesianNetwork{V,T}, from::V, to::V)::Bool where {T,V}
     from_id = bn.names_to_ids[from]
-    to_id   = bn.names_to_ids[to]
+    to_id = bn.names_to_ids[to]
     return Graphs.add_edge!(bn.graph, from_id, to_id)
 end
 
@@ -398,7 +397,7 @@ function compute_full_logpdf(bn::BayesianNetwork)
                 end
             end
             dist = get_distribution(bn, sid)
-            val  = bn.values[varname]
+            val = bn.values[varname]
             lpdf = logpdf(dist, val)
             if isinf(lpdf)
                 return -Inf
@@ -419,19 +418,18 @@ Enumerate all discrete node values for unobserved discrete nodes.
 Returns a *probability sum*, i.e. sum over exp(logpdf).
 """
 function sum_discrete_configurations(
-    bn::BayesianNetwork,
-    discrete_ids::Vector{Int},
-    idx::Int
+    bn::BayesianNetwork, discrete_ids::Vector{Int}, idx::Int
 )::Float64
     if idx > length(discrete_ids)
-        return exp( compute_full_logpdf(bn) )
+        return exp(compute_full_logpdf(bn))
     else
         node_id = discrete_ids[idx]
         dist = get_distribution(bn, node_id)
         total_prob = 0.0
         for val in support(dist)
-            bn.values[ bn.names[node_id] ] = val
-            total_prob += sum_discrete_configurations(bn, discrete_ids, idx+1) * pdf(dist, val)
+            bn.values[bn.names[node_id]] = val
+            total_prob +=
+                sum_discrete_configurations(bn, discrete_ids, idx + 1) * pdf(dist, val)
         end
         delete!(bn.values, bn.names[node_id])
         return total_prob