{"id":39214216,"url":"https://github.com/igomezv/neuralike","last_synced_at":"2026-01-17T23:19:05.470Z","repository":{"id":69557797,"uuid":"483033651","full_name":"igomezv/neuralike","owner":"igomezv","description":"Using machine learning to speed-up Bayesian inference.","archived":false,"fork":false,"pushed_at":"2024-10-15T21:59:45.000Z","size":2969,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":4,"default_branch":"main","last_synced_at":"2024-10-17T10:53:28.270Z","etag":null,"topics":["bayesian-inference","deep-learning","nested-sampling","neural-networks","observational-cosmology","torch"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/igomezv.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-04-18T23:55:02.000Z","updated_at":"2024-10-15T21:59:48.000Z","dependencies_parsed_at":"2024-09-16T11:38:06.158Z","dependency_job_id":"ffb3b675-b819-4eef-824a-d31cdb7cf239","html_url":"https://github.com/igomezv/neuralike","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/igomezv/neuralike","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/igomezv%2Fneuralike","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/igomezv%2Fneuralike/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/igomezv%2Fneuralike/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/igomezv%2Fneuralike/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/igomezv","download_url":"https://codeload.github.com/igomezv/neuralike/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/igomezv%2Fneuralike/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28521783,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-17T22:11:28.393Z","status":"ssl_error","status_checked_at":"2026-01-17T22:11:27.841Z","response_time":85,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bayesian-inference","deep-learning","nested-sampling","neural-networks","observational-cosmology","torch"],"created_at":"2026-01-17T23:19:04.806Z","updated_at":"2026-01-17T23:19:05.463Z","avatar_url":"https://github.com/igomezv.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003ca href=\"https://arxiv.org/abs/2405.03293\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/arXiv-2405.03293-b31b1b.svg\" alt=\"arXiv:2405.03293\"\u003e\n\u003c/a\u003e\n\n# neuralike\n\n## Deep Learning and Genetic Algorithms for Cosmological Bayesian Inference Speed-up\n\nCode of our paper *Deep Learning and genetic algorithms for cosmological Bayesian inference speed-up*, Physical Review D, 110(8) 083518. Available at https://arxiv.org/abs/2405.03293.\n\n## Repository Structure\n\n- neuralike/\n    - **NeuraLike.py**.- Main class, gathers all other classes.\n    - **NeuralManager.py**.-  API class, Manager for neural networks to learn likelihood function over a grid.\n    - **NeuralNet.py**.- Class with neural net architecture in PyTorch.\n    - **RandomSampling.py**.- Creates random samples in the parameter space and evaluates the likelihood in them. This is used to generate the training set for a neural network.\n    - **pytorchtools.py**.- Methods and utilities for PyTorch.\n\n\n## Usage\n\nIn the branch **neuralike** of the repository https://github.com/igomezv/simplemc_tests it is available neuralike integrated within the dynesty library for nested sampling within the SimpleMC cosmological parameter estimation code (https://igomezv.github.io/SimpleMC/).\n\n## Acknowledgments\n\nWe based our work on the following external codes:\n\n- Philosophy of the method\n  - https://arxiv.org/abs/1110.2997\n  - https://github.com/DarkMachines/pyBAMBI\n- Nested sampling library\n  - https://dynesty.readthedocs.io/en/stable\n- Cosmological parameter estimation\n  - https://igomezv.github.io/SimpleMC\n- Genetic algorithms library\n  - https://deap.readthedocs.io/en/master\n- Deep learning library\n  - https://pytorch.org\n\n\n## Citation\n\nIf you use this work in your research, please cite:\n\n```bibtex\n@article{gomez2024neuralike,\n  title = {Deep learning and genetic algorithms for cosmological Bayesian inference speed-up},\n  author = {G\\'omez-Vargas, Isidro and V\\'azquez, J. Alberto},\n  journal = {Phys. Rev. D},\n  volume = {110},\n  issue = {8},\n  pages = {083518},\n  numpages = {15},\n  year = {2024},\n  month = {Oct},\n  publisher = {American Physical Society},\n  doi = {10.1103/PhysRevD.110.083518},\n  url = {https://link.aps.org/doi/10.1103/PhysRevD.110.083518}\n}\n```\n\nIf you find useful our [`nnogada`](https://github.com/igomezv/Nnogada) framework for hyperparameter tuning of neural networks with genetic algorithms:\n\n```bibtex\n@article{nnogada,\n  title={Neural networks optimized by genetic algorithms in cosmology},\n  author={Gómez-Vargas, I. and Andrade, J. B. and Vázquez, J. A.},\n  journal={Physical Review D},\n  volume={107},\n  number={4},\n  pages={043509},\n  year={2023},\n  publisher={American Physical Society},\n  doi={https://doi.org/10.1103/PhysRevD.107.043509},\n  url={https://doi.org/10.48550/arXiv.2209.02685}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Figomezv%2Fneuralike","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Figomezv%2Fneuralike","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Figomezv%2Fneuralike/lists"}