{"id":43545064,"url":"https://github.com/pointlander/entity","last_synced_at":"2026-02-03T18:42:40.336Z","repository":{"id":296120890,"uuid":"991659361","full_name":"pointlander/entity","owner":"pointlander","description":"Multivariate gaussian neural network","archived":false,"fork":false,"pushed_at":"2025-07-25T18:26:17.000Z","size":1623,"stargazers_count":0,"open_issues_count":1,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-07-25T23:11:48.746Z","etag":null,"topics":["multivariate-gaussian","neural-network"],"latest_commit_sha":null,"homepage":"","language":"Go","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/pointlander.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,"zenodo":null}},"created_at":"2025-05-28T01:03:08.000Z","updated_at":"2025-07-25T18:26:21.000Z","dependencies_parsed_at":"2025-06-16T18:23:52.212Z","dependency_job_id":"45480ffa-786b-4638-b385-95eba8236b67","html_url":"https://github.com/pointlander/entity","commit_stats":null,"previous_names":["pointlander/entity"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/pointlander/entity","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pointlander%2Fentity","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pointlander%2Fentity/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pointlander%2Fentity/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pointlander%2Fentity/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/pointlander","download_url":"https://codeload.github.com/pointlander/entity/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pointlander%2Fentity/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29053094,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-03T15:43:47.601Z","status":"ssl_error","status_checked_at":"2026-02-03T15:43:46.709Z","response_time":96,"last_error":"SSL_read: 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":["multivariate-gaussian","neural-network"],"created_at":"2026-02-03T18:42:39.620Z","updated_at":"2026-02-03T18:42:40.328Z","avatar_url":"https://github.com/pointlander.png","language":"Go","funding_links":[],"categories":[],"sub_categories":[],"readme":"# A multivariate gaussian neural network\nThis repo implements a [multivariate gaussian](https://en.wikipedia.org/wiki/Multivariate_normal_distribution) \nneural network and works by seeing how close in terms of the L2 norm the samples are to a query vector. \nTo generate the distributions the average vector is first calculated, and then the covariance matrix is produced. \nA standard deviation matrix is produced by taking the sqaure root of the covariance matrix. The average \nvector and standard deviation matrix form the model.\n## Implementation of the iris data set\nThe iris data set has 4 feature per flower with three types of flowers. The data set is divided up in terms of \nflower type creating 3 sets with 50 samples each. The 3 sets of samples are used to create 3 multivariate \ngaussians. To classify a query vector of 4 features the multivariate gaussian is sampled from. The L2 norm is \nused to calculate which multivariate guassian produces samples closest to the query vector.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpointlander%2Fentity","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpointlander%2Fentity","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpointlander%2Fentity/lists"}