{"id":21280641,"url":"https://github.com/borgwardtlab/mid","last_synced_at":"2025-07-11T10:32:38.337Z","repository":{"id":8152450,"uuid":"9572620","full_name":"BorgwardtLab/MID","owner":"BorgwardtLab","description":"MID (Mutual Information Dimension) for measuring statistical dependence between two random variables","archived":false,"fork":false,"pushed_at":"2013-04-21T01:03:28.000Z","size":524,"stargazers_count":10,"open_issues_count":0,"forks_count":4,"subscribers_count":2,"default_branch":"master","last_synced_at":"2023-06-09T12:05:13.430Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"C","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/BorgwardtLab.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}},"created_at":"2013-04-21T00:08:39.000Z","updated_at":"2020-05-21T21:03:44.000Z","dependencies_parsed_at":"2022-09-07T14:52:46.579Z","dependency_job_id":null,"html_url":"https://github.com/BorgwardtLab/MID","commit_stats":null,"previous_names":[],"tags_count":0,"template":null,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BorgwardtLab%2FMID","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BorgwardtLab%2FMID/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BorgwardtLab%2FMID/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BorgwardtLab%2FMID/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/BorgwardtLab","download_url":"https://codeload.github.com/BorgwardtLab/MID/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":225715635,"owners_count":17512905,"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":"2024-11-21T10:37:53.834Z","updated_at":"2024-11-21T10:37:54.285Z","avatar_url":"https://github.com/BorgwardtLab.png","language":"C","funding_links":[],"categories":[],"sub_categories":[],"readme":"MID (Mutual Information Dimension)\n==================================\n\nMID for measuring statistical dependence between two random variables.\n\n\nSummary\n-------\n\nAn estimation algorithm for MID (Mutual Information Dimension), which measures statistical dependence between two random variables.\nThis algorithm has the following advantages:\n\n* **Nonlinear dependences** (and also linear dependences) can be measured,\n* **Scalable**; the average-case time complexity is O(nlogn), where *n* is the number of data points, and\n* **Parameter-free**.\n\nPlease see the following article for detailed information and refer it in your published research:\n\n* Sugiyama, M., Borgwardt, K. M.: **Measuring Statistical Dependence via the Mutual Information Dimension**,\n\t*Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013)*, to appear.\n\n\nInstallation\n------------\n\nThe code consists of only one C file \"MID.c\".\nThus you can use by compiling it, for example, type into your terminal:\n\n\t$ gcc -O3 MID.c -o MID\n\n\nUsage\n-----\n\nTo calculate MID between two variables, type:\n\n\t$ ./MID \u003cinput_file\u003e\n\t\n`\u003cinput_file\u003e` is a comma-separated text file with two columns without row and column names.\nColumns correspond to respective variables.\nFor example,\t\n\n\t0.921,0.930\n\t0.491,0.492\n\t0.990,0.993\n\t0.775,0.777\n\t...\n\t0.577,0.561\n\nThe followings are shown at standard output.\n\n* **dimX** (the information dimension of the first variable)\n* **dimY** (the information dimension of the second variable)\n* **dimXY** (the information dimension of X and Y)\n* **MID** (equivalent to dimX + dimY - dimXY)\n\nExample\n-------\n\n\t$ gcc -O3 MID.c -o MID\n\t$ ./MID ./sampledata/linear.csv\n\tidim_x:  0.994690\n\tidim_y:  0.994690\n\tidim_xy: 0.994690\n\tMID:     0.994690\n\t$ ./MID ./sampledata/noise.csv\n\tidim_x:  0.995130\n\tidim_y:  0.996233\n\tidim_xy: 1.755107\n\tMID:     0.236256\n\n\nInformation\n-----------\n\n* Author: Mahito Sugiyama\n* Affiliation: Machine Learning \u0026 Computational Biology Research Group, MPIs Tübingen, Germany\n* Mail: mahito.sugiyama@tuebingen.mpg.de","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fborgwardtlab%2Fmid","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fborgwardtlab%2Fmid","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fborgwardtlab%2Fmid/lists"}