{"id":19243119,"url":"https://github.com/sangyx/mlkit","last_synced_at":"2026-04-21T10:02:15.501Z","repository":{"id":108325083,"uuid":"228018790","full_name":"sangyx/mlkit","owner":"sangyx","description":"A head-only library provides sklearn-api with gpu support.","archived":false,"fork":false,"pushed_at":"2020-03-03T06:54:40.000Z","size":137,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-01-05T03:42:15.830Z","etag":null,"topics":["c-plus-plus","gpu-support","header-only","machine-learning","sklearn"],"latest_commit_sha":null,"homepage":"","language":"C++","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/sangyx.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":"2019-12-14T12:24:30.000Z","updated_at":"2020-03-03T06:54:42.000Z","dependencies_parsed_at":"2023-03-13T14:28:13.778Z","dependency_job_id":null,"html_url":"https://github.com/sangyx/mlkit","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/sangyx%2Fmlkit","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sangyx%2Fmlkit/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sangyx%2Fmlkit/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sangyx%2Fmlkit/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sangyx","download_url":"https://codeload.github.com/sangyx/mlkit/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":240331313,"owners_count":19784644,"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":["c-plus-plus","gpu-support","header-only","machine-learning","sklearn"],"created_at":"2024-11-09T17:16:44.202Z","updated_at":"2026-04-21T10:02:15.406Z","avatar_url":"https://github.com/sangyx.png","language":"C++","readme":"# MLKIT\r\n\r\n![language](https://img.shields.io/badge/language-cpp-orange.svg) [![Build Status](https://travis-ci.com/sangyx/mlkit.svg?branch=master)](https://travis-ci.com/sangyx/mlkit) [![codecov](https://codecov.io/gh/sangyx/mlkit/branch/master/graph/badge.svg)](https://codecov.io/gh/sangyx/mlkit) ![license](https://img.shields.io/github/license/sangyx/mlkit)\r\n\r\n\u003e A HEADER-ONLY LIBRARY PROVIDES SKLEARN-LIKE API WITH GPU SUPPORT.\r\n\r\n## Dependencies\r\n* [ArrayFire](http://arrayfire.org/): a general purpose GPU library.\r\n* [Googletest](https://github.com/google/googletest): Google Testing and Mocking Framework.\r\n\r\n## Examples\r\n```cpp\r\n#include \"mlkit.hpp\"\r\n\r\nusing namespace std;\r\nusing namespace mk;\r\n\r\nint main(int argc, char **argv)\r\n{\r\n    int device = argc \u003e 1 ? atoi(argv[1]) : -1; // default -1\r\n    try {\r\n        if(device \u003e= 0)\r\n            af::setBackend(AF_BACKEND_CUDA); // use gpu\r\n        else\r\n            af::setBackend(AF_BACKEND_CPU); // use cpu\r\n\r\n        af::info();\r\n        af::array X = af::randn(100, 3);\r\n        af::array y = 1 * X.col(0) + 2 * X.col(1) + 3 * X.col(2) + 4 + af::randu(100, 1) * 0.5;\r\n        linear_model::LinearRegression lr = linear_model::LinearRegression(true);\r\n        lr.fit(X, y);\r\n        cout \u003c\u003c endl \\\r\n             \u003c\u003c \"[linear regression]\" \u003c\u003c endl \\\r\n             \u003c\u003c \"-----------------------------------------------\" \u003c\u003c endl \\\r\n             \u003c\u003c \"expect coef: [1, 2, 3], expect intercept: 4\" \u003c\u003c endl \\\r\n             \u003c\u003c \"-----------------------------------------------\" \u003c\u003c endl \\\r\n             \u003c\u003c \"fit result: \" \u003c\u003c endl;\r\n        af_print(lr.coef_);\r\n        af_print(lr.intercept_)\r\n        cout \u003c\u003c \"-----------------------------------------------\" \u003c\u003c endl;\r\n        lr.score(X, y);\r\n    } catch (af::exception \u0026ae) {\r\n        cerr \u003c\u003c ae.what() \u003c\u003c endl;\r\n    }\r\n    return 0;\r\n}\r\n```\r\n\r\nThe output:\r\n```bash\r\n# compiler command\r\ng++ -std=c++11 -g example.cpp -o test -I/opt/arrayfire/include -Imlkit/include -laf -L/opt/arrayfire/lib\r\n\r\n# output\r\nArrayFire v3.7.0 (CPU, 64-bit Linux, build c30d5455)\r\n[0] Intel: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz, 95293 MB, Max threads(20) GNU Compiler Collection(GCC/G++) 7.4.0\r\n\r\n[linear regression]\r\n-----------------------------------------------\r\nexpect coef: [1, 2, 3], expect intercept: 4\r\n-----------------------------------------------\r\nfit result:\r\nlr.coef_\r\n[3 1 1 1]\r\n   Offset: 1\r\n   Strides: [1 4 4 4]\r\n    0.9999\r\n    1.9851\r\n    2.9896\r\n\r\nlr.intercept_\r\n[1 1 1 1]\r\n   Offset: 0\r\n   Strides: [1 4 4 4]\r\n    4.2475\r\n-----------------------------------------------\r\nMean Sqaure Error: 0.01791\r\n```\r\n\r\n## Algorithms\r\n* Statistical Learning：\r\n    - [x] linear_model.LinearRegression\r\n    - [x] linear_model.LogisticRegression\r\n    - [x] neighbors.KNeighborsClassifier\r\n    - [x] cluster.KMeans\r\n    - [x] decomposition.PCA\r\n    - [x] tree.DecisionTreeClassifier\r\n    - [x] mixture.GaussianMixture\r\n    - [x] svm.LinearSVC\r\n\r\n\u003c!-- ## 手记系列\r\n\r\n### 数学基础\r\n* [数学分析](https://www.sangyx.cn/281)\r\n* [概率论与数理统计](https://www.sangyx.cn/1155)\r\n* [线性代数](https://www.sangyx.cn/1161)\r\n\r\n### 统计学习\r\n* [线性回归](https://www.sangyx.cn/304)\r\n* [逻辑回归](https://www.sangyx.cn/331)\r\n* [KNN](https://www.sangyx.cn/1193)\r\n* [决策树](https://www.sangyx.cn/1195)\r\n\r\n### 优化\r\n* [梯度下降](https://www.sangyx.cn/261) --\u003e\r\n\r\n## Reference\r\n* 李航. 统计学习方法[M]. 2012.\r\n* Harrington P. Machine Learning in Action[M]. 2012.\r\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsangyx%2Fmlkit","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsangyx%2Fmlkit","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsangyx%2Fmlkit/lists"}