{"id":17797089,"url":"https://github.com/anselmoo/rbf_networkfitting","last_synced_at":"2025-06-20T17:07:39.033Z","repository":{"id":113963322,"uuid":"208275828","full_name":"Anselmoo/RBF_NetworkFitting","owner":"Anselmoo","description":"Radial-Basis-Function-Network for solving the 1D- and 2D-minimization problem","archived":false,"fork":false,"pushed_at":"2020-08-14T17:28:41.000Z","size":8198,"stargazers_count":6,"open_issues_count":0,"forks_count":2,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-06-20T17:06:23.394Z","etag":null,"topics":["fitting-algorithm","genetic-algorithm","neural-network","python","spectroscopy"],"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/Anselmoo.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":"2019-09-13T13:59:24.000Z","updated_at":"2024-08-13T02:42:25.000Z","dependencies_parsed_at":null,"dependency_job_id":"31037cd8-6d9f-4918-b9fd-5ab7956654a4","html_url":"https://github.com/Anselmoo/RBF_NetworkFitting","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/Anselmoo/RBF_NetworkFitting","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Anselmoo%2FRBF_NetworkFitting","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Anselmoo%2FRBF_NetworkFitting/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Anselmoo%2FRBF_NetworkFitting/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Anselmoo%2FRBF_NetworkFitting/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Anselmoo","download_url":"https://codeload.github.com/Anselmoo/RBF_NetworkFitting/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Anselmoo%2FRBF_NetworkFitting/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":260985171,"owners_count":23092885,"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":["fitting-algorithm","genetic-algorithm","neural-network","python","spectroscopy"],"created_at":"2024-10-27T11:50:47.979Z","updated_at":"2025-06-20T17:07:34.003Z","avatar_url":"https://github.com/Anselmoo.png","language":"Python","readme":"[![Build Status](https://travis-ci.com/Anselmoo/RBF_NetworkFitting.svg?branch=master)](https://travis-ci.com/Anselmoo/RBF_NetworkFitting)\n[![CodeFactor](https://www.codefactor.io/repository/github/anselmoo/rbf_networkfitting/badge)](https://www.codefactor.io/repository/github/anselmoo/rbf_networkfitting)\n[![codebeat badge](https://codebeat.co/badges/9ef976e1-f0f3-4d03-a9d0-23d71a44584b)](https://codebeat.co/projects/github-com-anselmoo-rbf_networkfitting-master)\n[![Mergify Status](https://img.shields.io/endpoint.svg?url=https://gh.mergify.io/badges/Anselmoo/RBF_NetworkFitting\u0026style=flat)](https://github.com/Anselmoo/RBF_NetworkFitting/commits/master)\n[![DOI](https://zenodo.org/badge/208275828.svg)](https://zenodo.org/badge/latestdoi/208275828)\n[![GitHub](https://img.shields.io/github/license/Anselmoo/RBF_NetworkFitting)](https://github.com/Anselmoo/RBF_NetworkFitting/blob/master/LICENSE)\n[![GitHub release (latest SemVer)](https://img.shields.io/github/v/release/Anselmoo/RBF_NetworkFitting)](https://github.com/Anselmoo/RBF_NetworkFitting/releases)\n# RBF Network Fitting\n\n**RBF Network Fitting** is an in Python developed fitting routine, which is using the [Radial-Basis-Function-Network for solving](https://en.wikipedia.org/wiki/Radial_basis_function_network) the 1D- and 2D-minimization problem. During the *Self-Consistent-Field-Optimization* of the RBF-Network, the `mean-squared-error` will be evaluated for each cycle, and a *difference- and gradient-correction* will be applied to the input-parameter of the Fitting-Model. As Fitting-Models can be choosen: \n * [Normal Distribution](https://en.wikipedia.org/wiki/Normal_distribution)\n * [Cauchy/Lorentzian Distribution](https://en.wikipedia.org/wiki/Cauchy_distribution)\n * [Pseudo-Voigt Profile](https://en.wikipedia.org/wiki/Voigt_profile#Pseudo-Voigt_Approximation)\n\nIn order to optimize the *Hyperparameter-Finding* for the number of layers and the kind of choosen models, a [Genetic Algorithm](https://en.wikipedia.org/wiki/Genetic_algorithm) can be optionally used. The combination of both *Radial-Basis-Function-Network* and *Genetic Algorithm* allows using **RBF Network Fitting** as a real `black-box-method` in the absence of empirical parameters.\n\n\n## Examples\n\n* Detecting peaks of an oscillating function\n\nExample - I             |  Example - II\n:-------------------------:|:-------------------------:\n![osci_1](https://github.com/Anselmoo/RBF_NetworkFitting/blob/master/docu/example_2.png)|![osci_2](https://github.com/Anselmoo/RBF_NetworkFitting/blob/master/docu/example_6.png)\n\n\n* Fitting of experimental data\n\nExample - III             |  \n:-------------------------:|\n![d6_example](https://github.com/Anselmoo/RBF_NetworkFitting/blob/master/docu/example_5.png)|\n\n* Following patterns of 3D-Functions\n\nExample - IV             |  Example - V\n:-------------------------:|:-------------------------:\n![3D-I](https://github.com/Anselmoo/RBF_NetworkFitting/blob/master/docu/example_4.png)|![3D-II](https://github.com/Anselmoo/RBF_NetworkFitting/blob/master/docu/example_7.png)\n\n**RBF Network Fitting** requires:\n  * [numpy](https://github.com/numpy/numpy)\n  * [matplotlib](https://github.com/matplotlib/matplotlib)\n  \n \n Installing and Running:\n```python \npython setup.py install\n# as command line application \npython -m RBFN \n# as library\nfrom RBFN import GeneticFitter\nfrom RBFN import RBFNetwork\nfrom RBFN import PlotResults\n```\n\n## Further Readings:\n```\nGenetic Algorithms and Machine Learning for Programmers: Create AI Models and Evolve Solutions\nFrances Buontempo\nPragmatic Bookshelf, 2019\n```\n\n```\nGenetic Algorithms with Python\nClinton Sheppard\nClinton Sheppard, 2018\n```\n[https://github.com/handcraftsman/GeneticAlgorithmsWithPython/blob/master/ch08/genetic.py](https://github.com/handcraftsman/GeneticAlgorithmsWithPython/blob/master/ch08/genetic.py)\n[https://en.wikipedia.org/wiki/Radial_basis_function_network](https://en.wikipedia.org/wiki/Radial_basis_function_network)    \n    \n\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanselmoo%2Frbf_networkfitting","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fanselmoo%2Frbf_networkfitting","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanselmoo%2Frbf_networkfitting/lists"}