A quick refactoring to reduce repeated code (#251)

use `_tempered_evalaute!!` function introduced in
#247 to reduce duplicated
code

Author: sunxd3

Project.toml

@@ -1,6 +1,6 @@
 name = "JuliaBUGS"
 uuid = "ba9fb4c0-828e-4473-b6a1-cd2560fee5bf"
-version = "0.7.3"
+version = "0.7.4"
 
 [deps]
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"

src/model.jl

@@ -491,53 +491,10 @@ function AbstractPPL.evaluate!!(model::BUGSModel)
 end
 
 function AbstractPPL.evaluate!!(model::BUGSModel, flattened_values::AbstractVector)
-    var_lengths = if model.transformed
-        model.transformed_var_lengths
-    else
-        model.untransformed_var_lengths
-    end
-
-    evaluation_env = deepcopy(model.evaluation_env)
-    current_idx = 1
-    logp = 0.0
-    for (i, vn) in enumerate(model.flattened_graph_node_data.sorted_nodes)
-        is_stochastic = model.flattened_graph_node_data.is_stochastic_vals[i]
-        is_observed = model.flattened_graph_node_data.is_observed_vals[i]
-        node_function = model.flattened_graph_node_data.node_function_vals[i]
-        loop_vars = model.flattened_graph_node_data.loop_vars_vals[i]
-        if !is_stochastic
-            value = node_function(evaluation_env, loop_vars)
-            evaluation_env = BangBang.setindex!!(evaluation_env, value, vn)
-        else
-            dist = node_function(evaluation_env, loop_vars)
-            if !is_observed
-                l = var_lengths[vn]
-                if model.transformed
-                    b = Bijectors.bijector(dist)
-                    b_inv = Bijectors.inverse(b)
-                    reconstructed_value = reconstruct(
-                        b_inv,
-                        dist,
-                        view(flattened_values, current_idx:(current_idx + l - 1)),
-                    )
-                    value, logjac = Bijectors.with_logabsdet_jacobian(
-                        b_inv, reconstructed_value
-                    )
-                else
-                    value = reconstruct(
-                        dist, view(flattened_values, current_idx:(current_idx + l - 1))
-                    )
-                    logjac = 0.0
-                end
-                current_idx += l
-                logp += logpdf(dist, value) + logjac
-                evaluation_env = BangBang.setindex!!(evaluation_env, value, vn)
-            else
-                logp += logpdf(dist, AbstractPPL.get(evaluation_env, vn))
-            end
-        end
-    end
-    return evaluation_env, logp
+    evaluation_env, (logprior, loglikelihood, tempered_logjoint) = _tempered_evaluate!!(
+        model, flattened_values; temperature=1.0
+    )
+    return evaluation_env, tempered_logjoint
 end
 
 """