Science Score: 44.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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○Academic publication links
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○Committers with academic emails
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (13.5%) to scientific vocabulary
Repository
Switching Autoregressive Model
Basic Info
- Host: GitHub
- Owner: biaslab
- Language: Jupyter Notebook
- Default Branch: main
- Size: 9.15 MB
Statistics
- Stars: 0
- Watchers: 4
- Forks: 1
- Open Issues: 0
- Releases: 1
Metadata Files
README.md
This repository contains a set of experiments for the inference in Switching Autoregressive Models through message-passing.
Dependencies
Before running the experiments, you need to have Julia 1.6.x installed on your machine. We use the DrWatson.jl package to structure our experiments such that you can reproduce them quickly. DrWatson.jl has to be installed in your global Julia environment before running the experiments. You can add DrWatson.jl by calling
(@v1.6) pkg> add DrWatson
or
julia -e 'import Pkg; Pkg.add("DrWatson")
We use git-lfs to commit binaries (e.g. plots and images) in the repository. While it's not required, it's highly recommended to have it installed on your machine.
Instantiating
For reproducibility, we have fixed the versions of all required packages in this project. To instantiate the project, you may use the following command in a terminal:
julia --project -e 'import Pkg; Pkg.instantiate()'
This command will install all required packages and will prepare the project environment.
Experiments
All experiments are located in the experiments folder. To run an individual experiment, you may use the following command in a terminal:
julia experiments/<experiment_name>.jl
It is also possible to run experiments from any IDE (Visual Studio Code, or within the experiments folder directly:
cd experiments
julia <experiment_name>.jl
It is unnecessary to activate a project environment before running experiments since DrWatson.jl will do this automatically.
Cached results
Some experiments may take a lot of time to complete. Optionally you can download the dump.zip archive from the GitHub releases section, containing precomputed JLD2 files for the synthetic experiments. By default, the experiments pipeline searches for cached results in the dump folder and doesn't recompute them if the corresponding cache exists. It is possible to reload the precomputed results and analyze them in REPL or Visual Studio Code without running all experiments from scratch.
To force the experiments pipeline to recompute results, you may either remove the corresponding cached results from the dump folder or modify experiments to use the force = true flag in the produce_or_load method:
result, _ = produce_or_load(..., force = true) do params
run_experiment(params)
end
Project structure
data- datasets of real-world data used for experimentsdump- (optional), cached results of the experiments in JLD2 filesexperiments- code/scripts for experimentsresults- plots for each result, both for the real-world and synthetic datasetssrc- reused code, project module, model definitions, and utilities
Owner
- Name: BIASlab
- Login: biaslab
- Kind: organization
- Email: info@biaslab.org
- Location: Eindhoven, the Netherlands
- Website: http://biaslab.org
- Repositories: 47
- Profile: https://github.com/biaslab
Bayesian Intelligent Autonomous Systems lab
Citation (Citation.cff)
cff-version: 1.2.0
message: "Please cite this research as below."
authors:
- family-names: "Podusenko"
given-names: "Albert"
- family-names: "van Erp"
given-names: "Bart"
orcid: "https://orcid.org/0000-0002-5619-7071"
- family-names: "Bagaev"
given-names: "Dmitry"
- family-names: "Ïsmail"
given-names: "Şenöz"
- family-names: "de Vries"
given-names: "Bert"
title: "Message Passing-Based Inference in Switching Autoregressive Models"
version: 1.0.0
date-released: 2022-08
url: "https://github.com/biaslab/SwAR"
preferred-citation:
type: conference-paper
authors:
- family-names: "Podusenko"
given-names: "Albert"
- family-names: "van Erp"
given-names: "Bart"
orcid: "https://orcid.org/0000-0002-5619-7071"
- family-names: "Bagaev"
given-names: "Dmitry"
- family-names: "Ïsmail"
given-names: "Şenöz"
- family-names: "de Vries"
given-names: "Bert"
title: "Message Passing-Based Inference in Switching Autoregressive Models"
year: 2022
month: 08
conference:
- name: 2022 30th European Signal Processing Conference (EUSIPCO)
start: 1497
end: 1501
GitHub Events
Total
Last Year
Committers
Last synced: 9 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Albert Podusenko | a****o@g****m | 20 |
| Bart van Erp | 4****p | 1 |
Issues and Pull Requests
Last synced: 9 months ago
All Time
- Total issues: 0
- Total pull requests: 0
- Average time to close issues: N/A
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- Total issue authors: 0
- Total pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0