{"id":18046659,"url":"https://github.com/ltla/oknn2018","last_synced_at":"2025-04-05T04:25:00.267Z","repository":{"id":82716816,"uuid":"138182505","full_name":"LTLA/OkNN2018","owner":"LTLA","description":"Code for performance testing of the kmknn package at https://github.com/LTLA/kmknn.","archived":false,"fork":false,"pushed_at":"2018-07-24T12:25:58.000Z","size":21,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-02-10T12:29:36.861Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"R","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/LTLA.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":"2018-06-21T14:35:51.000Z","updated_at":"2018-07-24T12:25:59.000Z","dependencies_parsed_at":null,"dependency_job_id":"5319a250-97fe-436a-b16f-fe997ab09680","html_url":"https://github.com/LTLA/OkNN2018","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LTLA%2FOkNN2018","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LTLA%2FOkNN2018/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LTLA%2FOkNN2018/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LTLA%2FOkNN2018/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/LTLA","download_url":"https://codeload.github.com/LTLA/OkNN2018/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247288270,"owners_count":20914331,"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":[],"created_at":"2024-10-30T19:08:25.626Z","updated_at":"2025-04-05T04:25:00.238Z","avatar_url":"https://github.com/LTLA.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Testing kmknn performance on real and simulated data\n\n## Overview\n\nThis repository tests the performance of the [_kmknn_ package](https://github.com/LTLA/kmknn) for detecting nearest neighbours.\nSpeed comparisons are performed to existing R packages, mostly based on the ANN library (i.e., [RANN](https://cran.r-project.org/web/packages/RANN/index.html) and [FNN](https://cran.r-project.org/web/packages/FNN/index.html)). \nWe focus on performance for data sets with moderately high (10-50) dimensions, as discussed by [Wang (2012)](https://dx.doi.org/10.1016/j.patcog.2010.01.003).\n\n## Simulations \n\nScenarios in `simulations/` include:\n\n- `sim_hypercube.R`, consisting of uniformly distributed points in a hypercube.\n- `sim_gaussclust.R`, consisting of Gaussian clusters.\n- `sim_helical.R`, consisting of a helical trajectory.\n\nSome of the scripts have tunable parameters that should be specified by the calling process.\nThis is controlled during job submission, which can be executed by calling `submitter.sh` for SLURM clusters.\n\n## Real data\n\nEach dataset in `real/` should contain:\n\n- `proc_*.R`, which processes the data into a RDS file for nearest neighbor detection.\n- `run_*.R`, which runs the algorithm timings on the processed data.\n\nCurrent data sets are:\n\n- PBMC 68K single-cell RNA-seq data from 10X Genomics\n- MNIST data sets of handwritten digits\n\n## Plot generation\n\nThe `plot_results.R` scripts in both directories will generate summary plots for each scenario/dataset.\nEach plot will show the effect of dimensionality and choice of `k`, as well as the number of points for the simulations.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fltla%2Foknn2018","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fltla%2Foknn2018","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fltla%2Foknn2018/lists"}