Merge pull request #76 from ilabcode/dev

Shared Parameters feature

Author: Anna280

.github/workflows/automerge_CompatHelper.yml

@@ -0,0 +1,28 @@
+# GitHub action to automerge compathelper PRs. Only merges if tests pass due to branch protections in the repo.
+
+name: automerge-CompatHelper-prs
+
+on: pull_request
+
+permissions:
+  contents: write
+  pull-requests: write
+
+jobs:
+  dependabot-automerge:
+    runs-on: ubuntu-latest
+    if: ${{ github.actor == 'CompatHelper Julia'}}    
+    
+    steps:
+    # Checkout action is required for token to persist
+    - name: Enable auto-merge for CompatHelper PRs
+      run: gh pr merge --auto --merge "$PR_URL" # Use Github CLI to merge automatically the PR
+      env:
+        PR_URL: ${{github.event.pull_request.html_url}}
+        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+    
+    - name: Auto approve CompatHelper PRs
+      if: github.actor == 'CompatHelper Julia'
+      uses: hmarr/auto-approve-action@v3
+      with: 
+        github-token: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/automerge_dependabot.yml

@@ -23,6 +23,6 @@ jobs:
 
     - name: Auto approve dependabot PRs
       if: github.actor == 'dependabot[bot]'
-      uses: hmarr/auto-approve-action@v2
+      uses: hmarr/auto-approve-action@v3
       with: 
         github-token: ${{ secrets.GITHUB_TOKEN }}

.gitignore

@@ -3,8 +3,9 @@
 *.jl.mem
 /docs/build/
 .DS_Store
-testing_script.jl
+testing_script*.jl
 settings.json
 Manifest.toml
 docs/Manifest.toml
-test/Manifest.toml
+test/Manifest.toml
+/docs/src/generated_markdowns/*.md

Project.toml

@@ -1,7 +1,7 @@
 name = "HierarchicalGaussianFiltering"
 uuid = "63d42c3e-681c-42be-892f-a47f35336a79"
-authors = ["Peter Thestrup Waade [email protected]", "Jacopo Comoglio [email protected]", "Christoph Mathys [email protected]\n and contributors"]
-version = "0.3.0"
+authors = ["Peter Thestrup Waade [email protected]", "Anna Hedvig Møller [email protected]", "Jacopo Comoglio [email protected]", "Christoph Mathys [email protected]\n and contributors"]
+version = "0.3.1"
 
 [deps]
 ActionModels = "320cf53b-cc3b-4b34-9a10-0ecb113566a3"

README.md

@@ -1,6 +1,122 @@
 # HGF
-
 [![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://ilabcode.github.io/HierarchicalGaussianFiltering.jl)
 [![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/>)
+
+
+# Welcome to The Hierarchical Gaussian Filtering Package!
+
+Hierarchical Gaussian Filtering (HGF) is a novel and adaptive package for doing cognitive and behavioral modelling. With the HGF you can fit time series data fit participant-level individual parameters, measure group differences based on model-specific parameters or use the model for any time series with underlying change in uncertainty.
+
+The HGF consists of a network of probabilistic nodes hierarchically structured. The hierarchy is determined by the coupling between nodes. A node (child node) in the network can inheret either its value or volatility sufficient statistics from a node higher in the hierarchy (a parent node).
+
+The presentation of a new observation at the lower level of the hierarchy (i.e. the input node) trigger a recursuve update of the nodes belief throught the bottom-up propagation of precision-weigthed prediction error.
+
+The HGF will be explained in more detail in the theory section of the documentation
+
+It is also recommended to check out the ActionModels.jl pacakge for stronger intuition behind the use of agents and action models.
+
+## Getting started
+
+The last official release can be downloaded from Julia with
+
+````@example index
+"] add Luxor"
+````
+
+We provide a script for getting started with commonly used functions and use cases
+
+Load packages
+
+````@example index
+using HierarchicalGaussianFiltering
+using ActionModels
+````
+
+### Get premade agent
+
+````@example index
+premade_agent("help")
+````
+
+### Create agent
+
+````@example index
+agent = premade_agent("hgf_binary_softmax_action")
+````
+
+### Get states and parameters
+
+````@example index
+get_states(agent)
+````
+
+![Image1](docs/src/images/readme/get_states.png)
+
+````@example index
+get_parameters(agent)
+````
+
+![Image1](docs/src/images/readme/get_parameters.png)
+
+Set a new parameter for initial precision of x2 and define some inputs
+
+````@example index
+set_parameters!(agent, ("x2", "initial_precision"), 0.9)
+inputs = [1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0];
+nothing #hide
+````
+
+### Give inputs to the agent
+
+````@example index
+actions = give_inputs!(agent, inputs)
+````
+![Image1](docs/src/images/readme/actions.png)
+### Plot state trajectories of input and prediction
+
+````@example index
+using StatsPlots
+using Plots
+plot_trajectory(agent, ("u", "input_value"))
+plot_trajectory!(agent, ("x1", "prediction"))
+````
+![Image1](docs/src/images/readme/plot_trajectory.png)
+
+Plot state trajectory of input value, action and prediction of x1
+
+````@example index
+plot_trajectory(agent, ("u", "input_value"))
+plot_trajectory!(agent, "action")
+plot_trajectory!(agent, ("x1", "prediction"))
+````
+![Image1](docs/src/images/readme/plot_trajectory_2.png)
+### Fitting parameters
+
+````@example index
+using Distributions
+prior = Dict(("x2", "evolution_rate") => Normal(1, 0.5))
+
+model = fit_model(agent, prior, inputs, actions, n_iterations = 20)
+````
+![Image1](docs/src/images/readme/fit_model.png)
+### Plot chains
+
+````@example index
+plot(model)
+````
+![Image1](docs/src/images/readme/chains.png)
+### Plot prior angainst posterior
+
+````@example index
+plot_parameter_distribution(model, prior)
+````
+![Image1](docs/src/images/readme/prior_posterior.png)
+### Get posterior
+
+````@example index
+get_posteriors(model)
+````
+
+---

docs/make.jl

@@ -2,16 +2,42 @@ using HierarchicalGaussianFiltering
 using Documenter
 using Literate
 
-# #Remove old tutorial markdown files
-# for filename in readdir("src/tutorials")
-#     rm("src/tutorials/" * filename)
-# end
-# #Generate new tutorial markdown files
-# for filename in readdir("tutorials")
-#     if endswith(filename, ".jl")
-#         Literate.markdown("tutorials/" * filename, "src/tutorials", documenter = true)
-#     end
-# end
+#Remove old tutorial markdown files
+for filename in readdir("docs/src/generated_markdowns")
+    rm("docs/src/generated_markdowns/" * filename)
+end
+rm("docs/src/index.md")
+#Generate new markdown files from the documentation source files
+for filename in readdir("docs/src/Julia_src_files")
+    if endswith(filename, ".jl")
+
+        #Place the index file in another folder than the rest of the documentation
+        if startswith(filename, "index")
+            Literate.markdown(
+                "docs/src/Julia_src_files/" * filename,
+                "docs/src",
+                documenter = true,
+            )
+        else
+            Literate.markdown(
+                "docs/src/Julia_src_files/" * filename,
+                "docs/src/generated_markdowns",
+                documenter = true,
+            )
+        end
+    end
+end
+
+#Generate new tutorial markdown files from the tutorials
+for filename in readdir("docs/src/tutorials")
+    if endswith(filename, ".jl")
+        Literate.markdown(
+            "docs/src/tutorials/" * filename,
+            "docs/src/generated_markdowns",
+            documenter = true,
+        )
+    end
+end
 
 #Set documenter metadata
 DocMeta.setdocmeta!(
@@ -33,10 +59,32 @@ makedocs(;
         canonical = "https://ilabcode.github.io/HierarchicalGaussianFiltering.jl",
         assets = String[],
     ),
-    pages = ["Home" => "index.md"],
+    pages = [
+        "Introduction to Hierarchical Gaussian Filtering" => "./index.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",
+            "Updating the HGF" => "./generated_markdowns/updating_the_HGF.md",
+            "List Of Premade Agent Models" => "./generated_markdowns/premade_models.md",
+            "List Of Premade HGF's" => "./generated_markdowns/premade_HGF.md",
+            "Fitting an HGF-agent model to data" => "./generated_markdowns/fitting_hgf_models.md",
+            "Utility Functions" => "./generated_markdowns/utility_functions.md",
+        ],
+        "Tutorials" => [
+            "classic binary" => "./generated_markdowns/classic_binary.md",
+            "classic continouous" => "./generated_markdowns/classic_usdchf.md",
+        ],
+        "All Functions" => "./generated_markdowns/all_functions.md",
+    ],
 )
-
 deploydocs(;
     repo = "github.com/ilabcode/HierarchicalGaussianFiltering.jl",
-    devbranch = "dev",
+    devbranch = "main",
+    push_preview = false,
 )

docs/src/Julia_src_files/all_functions.jl

@@ -0,0 +1,5 @@
+# # Overview of all functions in the Hierarchical Gaussian Filtering package
+
+# ```@autodocs
+# Modules = [HierarchicalGaussianFiltering]
+# ```