Merge pull request #150 from ilabcode/dev

Version 0.5.2
ComputationalPsychiatry · May 9, 2024 · d931cd1 · d931cd1 · PTWaade · May 9, 2024
2 parents e46e07b + 9fc5ced
commit d931cd1
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diff --git a/.github/workflows/CI_full.yml b/.github/workflows/CI_full.yml
@@ -37,7 +37,7 @@ jobs:
         with:
           version: ${{ matrix.version }}
           arch: ${{ matrix.arch }}
-      - uses: julia-actions/cache@v1
+      - uses: julia-actions/cache@v2
       - uses: julia-actions/julia-buildpkg@v1
       - uses: julia-actions/julia-runtest@v1
       - uses: julia-actions/julia-processcoverage@v1

diff --git a/.github/workflows/CI_small.yml b/.github/workflows/CI_small.yml
@@ -33,7 +33,7 @@ jobs:
         with:
           version: ${{ matrix.version }}
           arch: ${{ matrix.arch }}
-      - uses: julia-actions/cache@v1
+      - uses: julia-actions/cache@v2
       - uses: julia-actions/julia-buildpkg@v1
       - uses: julia-actions/julia-runtest@v1
       - uses: julia-actions/julia-processcoverage@v1

diff --git a/Project.toml b/Project.toml
@@ -4,7 +4,7 @@ authors = [ "Peter Thestrup Waade [email protected]",
             "Anna Hedvig Møller [email protected]",
             "Jacopo Comoglio [email protected]",
             "Christoph Mathys [email protected]"]
-version = "0.5.1"
+version = "0.5.2"
 
 [deps]
 ActionModels = "320cf53b-cc3b-4b34-9a10-0ecb113566a3"
@@ -15,4 +15,4 @@ RecipesBase = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
 ActionModels = "0.5"
 Distributions = "0.25"
 RecipesBase = "1"
-julia = "1.9"
+julia = "1.10"
diff --git a/README.md b/README.md
@@ -3,6 +3,7 @@
 [![Build Status](https://github.com/ilabcode/HierarchicalGaussianFiltering.jl/actions/workflows/CI_full.yml/badge.svg?branch=main)](https://github.com/ilabcode/HierarchicalGaussianFiltering.jl/actions/workflows/CI_full.yml?query=branch%3Amain)
 [![Coverage](https://codecov.io/gh/ilabcode/HierarchicalGaussianFiltering.jl/branch/main/graph/badge.svg?token=NVFiiPydFA)](https://codecov.io/gh/ilabcode/HierarchicalGaussianFiltering.jl)
 [![License: GNU](https://img.shields.io/badge/License-GNU-yellow)](<https://www.gnu.org/licenses/>)
+[![Aqua QA](https://raw.githubusercontent.com/JuliaTesting/Aqua.jl/master/badge.svg)](https://github.com/JuliaTesting/Aqua.jl)
 
 
 # Welcome to The Hierarchical Gaussian Filtering Package!

diff --git a/docs/Project.toml b/docs/Project.toml
@@ -1,12 +1,15 @@
 [deps]
 ActionModels = "320cf53b-cc3b-4b34-9a10-0ecb113566a3"
+Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 Glob = "c27321d9-0574-5035-807b-f59d2c89b15c"
 HierarchicalGaussianFiltering = "63d42c3e-681c-42be-892f-a47f35336a79"
+JET = "c3a54625-cd67-489e-a8e7-0a5a0ff4e31b"
 JuliaFormatter = "98e50ef6-434e-11e9-1051-2b60c6c9e899"
 Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"

diff --git a/src/ActionModels_variations/utils/get_history.jl b/src/ActionModels_variations/utils/get_history.jl
@@ -7,7 +7,7 @@ Gets the history of a state from a specific node in an HGF. A vector of states c
 
 Gets the history of all states for a specific node in an HGF. If only a node object is passed, it will return the history of all states in that node. If only an HGF object is passed, it will return the history of all states in all nodes in the HGF.
 """
-function ActionModels.get_history() end
+# function ActionModels.get_history() end
 
 ### For getting histories of specific states ###
 function ActionModels.get_history(hgf::HGF, target_state::Tuple{String,String})

diff --git a/src/ActionModels_variations/utils/get_parameters.jl b/src/ActionModels_variations/utils/get_parameters.jl
@@ -7,7 +7,7 @@ Gets a single parameter value from a specific node in an HGF. A vector of parame
 
 Gets all parameter values for a specific node in an HGF. If only a node object is passed, returns all parameters in that node. If only an HGF object is passed, returns all parameters of all nodes in the HGF.
 """
-function ActionModels.get_parameters() end
+# function ActionModels.get_parameters() end
 
 ### For getting a specific parameter from a specific node ###
 #For parameters other than coupling strengths

diff --git a/src/ActionModels_variations/utils/get_states.jl b/src/ActionModels_variations/utils/get_states.jl
@@ -7,7 +7,7 @@ Gets a single state value from a specific node in an HGF. A vector of states can
 
 Gets all parameter values for a specific node in an HGF. If only a node object is passed, returns all states in that node. If only an HGF object is passed, returns all states of all nodes in the HGF.
 """
-function ActionModels.get_states() end
+# function ActionModels.get_states() end
 
 
 ### For getting a specific state from a specific node ###
@@ -36,7 +36,9 @@ function ActionModels.get_states(node::AbstractNode, state_name::String)
     #If the state does not exist in the node
     if !(Symbol(state_name) in fieldnames(typeof(node.states)))
         #throw an error
-        throw(ArgumentError("The node $node_name does not have the state $state_name"))
+        throw(
+            ArgumentError("The node $(node.node_name) does not have the state $state_name"),
+        )
     end
 
     #If a prediction state has been specified

diff --git a/src/ActionModels_variations/utils/give_inputs.jl b/src/ActionModels_variations/utils/give_inputs.jl
@@ -3,7 +3,7 @@ give_inputs!(hgf::HGF, inputs)
 
 Give inputs to an agent. Input can be a single value, a vector of values, or an array of values.
 """
-function ActionModels.give_inputs!() end
+# function ActionModels.give_inputs!() end
 
 
 ### Giving a single input ###

diff --git a/src/ActionModels_variations/utils/set_parameters.jl b/src/ActionModels_variations/utils/set_parameters.jl
@@ -7,7 +7,7 @@ Setting a single parameter value for an HGF.
 
 Set mutliple parameters values for an HGF. Takes a dictionary of parameter names and values.
 """
-function ActionModels.set_parameters!() end
+# function ActionModels.set_parameters!() end
 
 ### For setting a single parameter ###
 

diff --git a/src/HierarchicalGaussianFiltering.jl b/src/HierarchicalGaussianFiltering.jl
@@ -4,7 +4,7 @@ module HierarchicalGaussianFiltering
 using ActionModels, Distributions, RecipesBase
 
 #Export functions
-export init_node, init_hgf, premade_hgf, check_hgf, check_node, update_hgf!
+export init_node, init_hgf, premade_hgf, check_hgf, update_hgf!
 export get_prediction, get_surprise
 export premade_agent,
     init_agent, plot_predictive_simulation, plot_trajectory, plot_trajectory!

diff --git a/src/create_hgf/hgf_structs.jl b/src/create_hgf/hgf_structs.jl
@@ -79,7 +79,7 @@ end
 """
 """
 Base.@kwdef mutable struct OrderedNodes
-    all_nodes::Vector{AbstractNode} = []
+    all_nodes::Vector{AbstractNode} = AbstractNode[]
     input_nodes::Vector{AbstractInputNode} = []
     all_state_nodes::Vector{AbstractStateNode} = []
     early_update_state_nodes::Vector{AbstractStateNode} = []

diff --git a/src/update_hgf/node_updates/continuous_state_node.jl b/src/update_hgf/node_updates/continuous_state_node.jl
@@ -45,8 +45,10 @@ function calculate_prediction_mean(node::ContinuousStateNode, stepsize::Real)
         #Transform the parent's value
         drift_increment = transform_parent_value(
             parent.states.posterior_mean,
-            coupling_transform,
+            coupling_transform;
             derivation_level = 0,
+            child = node,
+            parent = parent,
         )
 
         #Add the drift increment
@@ -225,17 +227,21 @@ function calculate_posterior_precision_increment(
     #Calculate the increment
     child.states.prediction_precision * (
         coupling_strength^2 * transform_parent_value(
-            coupling_transform,
             node.states.posterior_mean,
+            coupling_transform;
             derivation_level = 1,
+            parent = node,
+            child = child,
         ) -
         coupling_strength *
-        node.states.value_prediction_error *
         transform_parent_value(
-            coupling_transform,
             node.states.posterior_mean,
+            coupling_transform;
             derivation_level = 2,
-        )
+            parent = node,
+            child = child,
+        ) *
+        child.states.value_prediction_error
     )
 
 end
@@ -396,9 +402,11 @@ function calculate_posterior_mean_increment(
         (
             child.parameters.coupling_strengths[node.name] *
             transform_parent_value(
-                child.parameters.coupling_transforms[node.name],
                 node.states.posterior_mean,
+                child.parameters.coupling_transforms[node.name];
                 derivation_level = 1,
+                parent = node,
+                child = child,
             ) *
             child.states.prediction_precision
         ) / node.states.posterior_precision
@@ -416,9 +424,11 @@ function calculate_posterior_mean_increment(
         (
             child.parameters.coupling_strengths[node.name] *
             transform_parent_value(
-                child.parameters.coupling_transforms[node.name],
                 node.states.posterior_mean,
+                child.parameters.coupling_transforms[node.name];
                 derivation_level = 1,
+                parent = node,
+                child = child,
             ) *
             child.states.prediction_precision
         ) / node.states.prediction_precision

diff --git a/src/update_hgf/nonlinear_transforms.jl b/src/update_hgf/nonlinear_transforms.jl
@@ -1,14 +1,16 @@
 #Transformation (of varioius derivations) for a linear transformation
 function transform_parent_value(
-    transform_type::LinearTransform,
-    parent_value::Real;
+    parent_value::Real,
+    transform_type::LinearTransform;
     derivation_level::Integer,
+    parent::AbstractNode,
+    child::AbstractNode,
 )
     if derivation_level == 0
         return parent_value
-    elseif derivation_level == 0
+    elseif derivation_level == 1
         return 1
-    elseif derivation_level == 0
+    elseif derivation_level == 2
         return 0
     else
         @error "derivation level is misspecified"
@@ -17,26 +19,28 @@ end
 
 #Transformation (of varioius derivations) for a nonlinear transformation
 function transform_parent_value(
-    transform_type::NonlinearTransform,
     parent_value::Real,
+    transform_type::NonlinearTransform;
     derivation_level::Integer,
+    parent::AbstractNode,
+    child::AbstractNode,
 )
 
     #Get the transformation function that fits the derivation level
     if derivation_level == 0
-        transform_function = node.parameters.coupling_transforms[parent.name].base_function
+        transform_function = child.parameters.coupling_transforms[parent.name].base_function
     elseif derivation_level == 1
         transform_function =
-            node.parameters.coupling_transforms[parent.name].first_derivation
+            child.parameters.coupling_transforms[parent.name].first_derivation
     elseif derivation_level == 2
         transform_function =
-            node.parameters.coupling_transforms[parent.name].second_derivation
+            child.parameters.coupling_transforms[parent.name].second_derivation
     else
         @error "derivation level is misspecified"
     end
 
     #Get the transformation parameters
-    transform_parameters = node.parameters.coupling_transforms[parent.name].parameters
+    transform_parameters = child.parameters.coupling_transforms[parent.name].parameters
 
     #Transform the value
     return transform_function(parent_value, transform_parameters)

diff --git a/test/Project.toml b/test/Project.toml
@@ -4,6 +4,7 @@ Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+GR = "28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71"
 Glob = "c27321d9-0574-5035-807b-f59d2c89b15c"
 JET = "c3a54625-cd67-489e-a8e7-0a5a0ff4e31b"
 JuliaFormatter = "98e50ef6-434e-11e9-1051-2b60c6c9e899"

diff --git a/test/testsuite/Aqua.jl b/test/testsuite/Aqua.jl
@@ -0,0 +1,3 @@
+using HierarchicalGaussianFiltering
+using Aqua
+Aqua.test_all(HierarchicalGaussianFiltering, ambiguities = false)
diff --git a/test/testsuite/test_custom_structures.jl b/test/testsuite/test_custom_structures.jl
@@ -0,0 +1,32 @@
+using Test
+using HierarchicalGaussianFiltering
+
+
+@testset "test custom structures" begin
+
+    @testset "Many continuous nodes" begin
+        nodes = [
+            ContinuousInput(name = "u"),
+            ContinuousInput(name = "u2"),
+            ContinuousState(name = "x1"),
+            ContinuousState(name = "x2"),
+            ContinuousState(name = "x3"),
+            ContinuousState(name = "x4"),
+            ContinuousState(name = "x5"),
+        ]
+
+        edges = Dict(
+            ("u", "x1") => ObservationCoupling(),
+            ("u2", "x2") => ObservationCoupling(),
+            ("x1", "x2") => DriftCoupling(),
+            ("x1", "x3") => VolatilityCoupling(),
+            ("u2", "x3") => NoiseCoupling(),
+            ("x2", "x4") => VolatilityCoupling(),
+            ("x3", "x5") => DriftCoupling(),
+        )
+
+        hgf = init_hgf(nodes = nodes, edges = edges, verbose = false)
+
+        update_hgf!(hgf, [1, 1])
+    end
+end
diff --git a/test/testsuite/test_nonlinear_transforms.jl b/test/testsuite/test_nonlinear_transforms.jl
@@ -0,0 +1,41 @@
+using Test
+using HierarchicalGaussianFiltering
+
+@testset "Testing nonlinear transforms" begin
+
+    @testset "Sinoid transform" begin
+        nodes = [
+            ContinuousInput(name = "u"),
+            ContinuousState(name = "x1"),
+            ContinuousState(name = "x2"),
+        ]
+
+        base = function (x, parameters::Dict)
+            sin(x)
+        end
+        first_derivative = function (x, parameters::Dict)
+            cos(x)
+        end
+        second_derivative = function (x, parameters::Dict)
+            -sin(x)
+        end
+        transform_parameters = Dict()
+
+        edges = Dict(
+            ("u", "x1") => ObservationCoupling(),
+            ("x1", "x2") => DriftCoupling(
+                2,
+                NonlinearTransform(
+                    base,
+                    first_derivative,
+                    second_derivative,
+                    transform_parameters,
+                ),
+            ),
+        )
+
+        hgf = init_hgf(nodes = nodes, edges = edges, verbose = false)
+
+        update_hgf!(hgf, 1)
+    end
+end