{"id":26222295,"url":"https://github.com/alonfnt/pcax","last_synced_at":"2025-07-02T07:05:49.833Z","repository":{"id":170591744,"uuid":"646756487","full_name":"alonfnt/pcax","owner":"alonfnt","description":"Differentiable Principal Component Analysis (PCA) implementation in JAX","archived":false,"fork":false,"pushed_at":"2025-04-19T07:43:25.000Z","size":36,"stargazers_count":28,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-06-25T14:05:30.098Z","etag":null,"topics":["differentiable-programming","dimensionality-reduction","jax","pca"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/alonfnt.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2023-05-29T09:15:33.000Z","updated_at":"2025-06-12T16:12:51.000Z","dependencies_parsed_at":null,"dependency_job_id":"eeda563d-d23c-4e0a-ac28-5bc94f0516b1","html_url":"https://github.com/alonfnt/pcax","commit_stats":null,"previous_names":["alonfnt/pcax"],"tags_count":2,"template":false,"template_full_name":null,"purl":"pkg:github/alonfnt/pcax","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alonfnt%2Fpcax","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alonfnt%2Fpcax/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alonfnt%2Fpcax/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alonfnt%2Fpcax/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/alonfnt","download_url":"https://codeload.github.com/alonfnt/pcax/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alonfnt%2Fpcax/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":261926381,"owners_count":23231363,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":["differentiable-programming","dimensionality-reduction","jax","pca"],"created_at":"2025-03-12T16:51:45.069Z","updated_at":"2025-07-02T07:05:49.823Z","avatar_url":"https://github.com/alonfnt.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cp align=center\u003e\n  \u003cimg src=\"https://github.com/user-attachments/assets/4f48d642-ca12-42c4-91a3-32f2ef464b3a\" style=\"width: 640px; height: auto;\" /\u003e\n\u003c/p\u003e\n\n[![tests](https://github.com/alonfnt/pcax/actions/workflows/pytest.yml/badge.svg)](https://github.com/alonfnt/pcax/actions/workflows/pytest.yml)\n[![PyPI](https://img.shields.io/pypi/v/pcax.svg)](https://pypi.org/project/pcax/)\n\n`pcax` is a minimal PCA implementation in [JAX](https://github.com/jax-ml/jax) that’s both GPU/TPU/CPU‑native and fully differentiable.\nIt keeps data and computation on-device with zero-copy transfers, lets you backpropagate through your dimensionality reduction step, and plugs directly your JAX workflows for seamless, efficient model integration.\n\n## Usage\n```python\nimport pcax\n\n# Fit the PCA model with 3 components on your data X\nstate = pcax.fit(X, n_components=3)\n\n# Transform X to its principal components\nX_pca = pcax.transform(state, X)\n\n# Recover the original X from its principal components\nX_recover = pcax.recover(state, X_pca)\n```\n\n## Installation\n`pcax` can be installed from PyPI via `pip`\n```\npip install pcax\n```\n\n## Citation\nIf you use `pcax` in your research and need to reference it, please cite it as follows:\n```\n@software{alonso_pcax,\n  author = {Alonso, Albert},\n  title = {pcax: Minimal Principal Component Analysis (PCA) Implementation in JAX},\n  url = {https://github.com/alonfnt/pcax},\n  version = {0.1.0}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falonfnt%2Fpcax","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Falonfnt%2Fpcax","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falonfnt%2Fpcax/lists"}