{"id":22707101,"url":"https://github.com/cair/regression-tsetlin-machine","last_synced_at":"2025-07-31T02:33:55.153Z","repository":{"id":82388715,"uuid":"186170571","full_name":"cair/regression-tsetlin-machine","owner":"cair","description":"Implementation of the Regression Tsetlin Machine","archived":false,"fork":false,"pushed_at":"2019-05-14T07:15:23.000Z","size":419,"stargazers_count":9,"open_issues_count":0,"forks_count":1,"subscribers_count":7,"default_branch":"master","last_synced_at":"2025-04-13T12:35:45.009Z","etag":null,"topics":["machine-learning","regression","tsetlin-machine"],"latest_commit_sha":null,"homepage":"https://arxiv.org/abs/1905.04206","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/cair.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":"2019-05-11T18:51:42.000Z","updated_at":"2022-06-11T02:25:23.000Z","dependencies_parsed_at":"2023-06-15T11:30:12.882Z","dependency_job_id":null,"html_url":"https://github.com/cair/regression-tsetlin-machine","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/cair/regression-tsetlin-machine","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cair%2Fregression-tsetlin-machine","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cair%2Fregression-tsetlin-machine/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cair%2Fregression-tsetlin-machine/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cair%2Fregression-tsetlin-machine/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/cair","download_url":"https://codeload.github.com/cair/regression-tsetlin-machine/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cair%2Fregression-tsetlin-machine/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":267977985,"owners_count":24175241,"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","status":"online","status_checked_at":"2025-07-31T02:00:08.723Z","response_time":66,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"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":["machine-learning","regression","tsetlin-machine"],"created_at":"2024-12-10T10:11:29.420Z","updated_at":"2025-07-31T02:33:55.108Z","avatar_url":"https://github.com/cair.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# The Regression Tsetlin Machine\n\nThe inner inference mechanism of the Tsetlin Machine (https://arxiv.org/abs/1804.01508) is modified so that input patterns are transformed into a single continuous output, rather than to distinct categories.\n\nThis is achieved by: \n\n* Using the conjunctive clauses of the Tsetlin Machine to capture arbitrarily complex patterns;\n* Mapping these patterns to a continuous output through a novel voting and normalization mechanism; and \n* Employing a feedback scheme that updates the Tsetlin Machine clauses to minimize the regression error. \n\nFurther details can be found in https://arxiv.org/abs/1905.04206.\n\n## Behaviour with noisy and noise-free data\n\nSix datasets have been given in order to study the behaviour of the Regression Tsetlin Machine.\n\n* **Dataset I** contains  2-bit  feature  input  and  the  output  is  100  times  larger  than  the  decimal value of the binary input (e.g., when the input is [1, 0], the output is 200). The training set consists of 8000 samples while testing set consists of 2000 samples, both without noise\n* **Dataset II** contains the same data as Dataset I, except that the output of the training data is perturbed to introduce noise\n* **Dataset III** has 3-bit input without noise \n* **Dataset IV** has 3-bit input with noise\n* **Dataset V** has 4-bit input without noise\n* **Dataset VI** has 4-bit input with noise\n\nDifferent datasets can be loaded by changing the following line in **_ArtificialDataDemo.py_**\n```\ndf = np.loadtxt(\"2inNoNoise.txt\").astype(dtype=np.float32)\n```\nThe training error variation for each dataset with different number of clauses can be seen in the following figure.\n\n\u003cimg src=\"https://github.com/cair/regression-tsetlin-machine/blob/master/Training.PNG\" width=\"600\" height=\"550\"\u003e\n\nDatasets without noise can be perfectly learned with a small number of clauses\n```\nAverage Absolute Error on Training Data: 0.0\nAverage Absolute Error on Test Data: 0.0\n```\nTraining and testing error for noisy data can be reduced by increasing the number of clauses and training rounds.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcair%2Fregression-tsetlin-machine","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcair%2Fregression-tsetlin-machine","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcair%2Fregression-tsetlin-machine/lists"}