edited errors for transformations

docs/Project.toml

@@ -5,6 +5,7 @@ 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"
 JuliaFormatter = "98e50ef6-434e-11e9-1051-2b60c6c9e899"

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

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)

test/testsuite/test_custom_structures.jl

@@ -0,0 +1,39 @@
+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

test/testsuite/test_nonlinear_transforms.jl

@@ -0,0 +1,40 @@
+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
+
+
+