Merge pull request #109 from ilabcode/documentation_polish

Documentation polish

docs/make.jl

@@ -61,12 +61,12 @@ makedocs(;
     ),
     pages = [
         "Introduction to Hierarchical Gaussian Filtering" => "./index.md",
-        #"Theory" => [
-        #    "./theory/genmodel.md",
-        #    "./theory/node.md",
-        #    "./theory/vape.md",
-        #    "./theory/vope.md",
-        #],
+        "Theory" => [
+            "./theory/genmodel.md",
+            "./theory/node.md",
+            "./theory/vape.md",
+            "./theory/vope.md",
+        ],
         "Using the package" => [
             "The HGF Nodes" => "./generated_markdowns/the_HGF_nodes.md",
             "Building an HGF" => "./generated_markdowns/building_an_HGF.md",

docs/src/Julia_src_files/premade_HGF.jl

@@ -9,7 +9,6 @@
 # - [Categorical 3-level HGF](#Categorical-3-level-HGF)
 # - [Categorical 3-level state transition HGF](#Categorical-3-level-state-transition)
 
-# ![continuous 2-level graph](../images/HGF_structures/all_models.png)
 
 # #Load data for examples
 
@@ -40,13 +39,19 @@ inputs_binary = CSV.read(hgf_path_binary * "classic_binary_inputs.csv", DataFram
 
 # ## Continuous 2-level HGF
 
+
+
+# ![continuous 2-level graph](../images/HGF_structures/continuous.svg)
+
+
 # The continuous 2-level HGF is structured with following nodes:
 
 # - input node: continuous 
 # - state nodes: 
 #   - 1st level: continuous (value coupling to input node)
 #   - 2nd level: continous (volatility coupling to 1st level)
 
+
 #Create HGF and Agent
 continuous_2_level = premade_hgf("continuous_2level");
 agent_continuous_2_level =
@@ -93,6 +98,9 @@ plot_trajectory(
 
 # ## Binary 2-level HGF
 
+# ![continuous 2-level graph](../images/HGF_structures/binary.svg)
+
+
 # - input node: binary
 # - state nodes:
 #   - 1st level: binary (value coupling to input node)
@@ -143,6 +151,9 @@ plot_trajectory(agent_binary_3_level, ("x3", "posterior"))
 
 # ## Categorical 3-level HGF
 
+# ![continuous 2-level graph](../images/HGF_structures/categorical.svg)
+
+
 # The categorical 3-level HGF model takes an input from one of m categories and learns the probability of a category appearing.
 
 # - input node: categorical

docs/src/Julia_src_files/the_HGF_nodes.jl

@@ -2,67 +2,13 @@
 
 # In this section we will cover the types of nodes, their parameters and the rules for structuring your own HGF. 
 
-
 # ### Overview
 
-#   - [The Node Types In an HGF and their states](#The-Node-Types-In-an-HGF)
-#   - [Building principles ](#Building-principles )
-
-# ### The Node Types In an HGF
-
-# We have six types of nodes in the HGF: binary (state node and input node), categorical (state node and input node), and continuous (state node and input node).
-
-# If a node's parameters are configured with a default value, they are stated below as well. 
-
-# We provide an overview of the states in each of the nodes. 
-
-# 1. [Binary Nodes](#Binary-Nodes)
-#     1. [State Node: States and parameters](#The-states-of-binary-state-nodes-and-parameters)
-#     2. [Input Node: States and parameters](#The-states-of-binary-input-nodes-and-parameters)
-
-# 3. [Categorical Nodes](#Categorical-Nodes)
-#    1. [State Node: States and parameters](#The-states-of-Categorical-state-nodes-and-parameters)
-#    2. [Input Node: States and parameters](#The-states-of-Categorical-Input-nodes-and-parameters)
-
-# 4. [Continuous Nodes](#Continuous-Nodes)
-#    1. [State Node: States and parameters](#The-states-of-Continuous-state-nodes-and-parameters)
-#    2. [Input Node: States and parameters](#The-states-of-Continuous-Input-nodes-and-parameters)
-
+# ![states and parameters](../images/states_nodes/all_states.svg)
 
-# ## Binary Nodes
-
-# ### The states of binary state nodes
-
-# - posterior_mean
-# - posterior_precision
-# - value\_prediction\_error
-# - prediction_mean
-# - prediction_precision
-
-# ### Parameters
-
-# - Value coupling
-
-
-# ### The states of binary input nodes and parameters
-
-# - input value
-# - value prediction error
-
-# ### Parameters
-
-# - Category means (default is [0,1])
-# - Input precision (default is  infinite input precision)
-
-
-# ## Categorical Nodes
-
-# ### The states of Categorical state nodes and parameters
-
-# - posterior
-# - value prediction error
-# - prediction
+# ## Building principles
 
+# The following rules apply for connecting nodes, when customizing your own HGF structure:
 # ### Parameters
 
 # - no parameters in the categorical state node
@@ -112,17 +58,15 @@
 # - input noise (default is 0)
 # - value coupling 
 # - volatility coupling
-
-
-
-# ## Building principles 
 
 # ### Binary state node rules:
 
 # - Can only have exactly one value parent
 # - Can only have excatly one value child
 # - Can only have a contionus state node as value parent
 
+# ![states and parameters](../images/states_nodes/binary_nodes.svg)
+
 # ### continuous state node rules:
 
 # - Can’t have binary input node as child
@@ -132,8 +76,12 @@
 # - Can’t have the same value parent as volatility parent
 # - Can’t have the same value child as volatility child
 
+# ![states and parameters](../images/states_nodes/continuous_nodes.svg)
+
 # ### Categorical state node rules:
 
 # - Can only have exactly one value child
 # - Can only have categorical input node as child
 # - Can only have binary state node as parents
+
+# ![states and parameters](../images/states_nodes/categorical_nodes.svg)