{"id":20346252,"url":"https://github.com/zagoli/cmapdcostregression","last_synced_at":"2026-05-29T21:31:25.125Z","repository":{"id":113133910,"uuid":"595039916","full_name":"zagoli/cmapdCostRegression","owner":"zagoli","description":"Predict the cost of a CMAPD assignment with XGBoost regression","archived":false,"fork":false,"pushed_at":"2023-03-30T10:15:01.000Z","size":6459,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-04T15:48:15.529Z","etag":null,"topics":["multiagent-planning","pathfinding","regression","xgboost"],"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/zagoli.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":"2023-01-30T09:14:28.000Z","updated_at":"2023-04-05T10:39:00.000Z","dependencies_parsed_at":null,"dependency_job_id":"aaba8011-c434-45d8-bc5f-a292f0cf6ba5","html_url":"https://github.com/zagoli/cmapdCostRegression","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/zagoli/cmapdCostRegression","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zagoli%2FcmapdCostRegression","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zagoli%2FcmapdCostRegression/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zagoli%2FcmapdCostRegression/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zagoli%2FcmapdCostRegression/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/zagoli","download_url":"https://codeload.github.com/zagoli/cmapdCostRegression/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zagoli%2FcmapdCostRegression/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":27440583,"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":"2025-12-01T02:00:06.371Z","response_time":60,"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":["multiagent-planning","pathfinding","regression","xgboost"],"created_at":"2024-11-14T22:12:23.376Z","updated_at":"2025-12-01T23:02:26.186Z","avatar_url":"https://github.com/zagoli.png","language":"C++","funding_links":[],"categories":[],"sub_categories":[],"readme":"# CMAPD solution cost regression\nThis code tries to predict the cost of a conflict free solution\nfor a CMAPD instance (with already assigned tasks) using regression.\nThe best $R^2$ score achieved so far is 0.97!\n\n### Installation\nInstallation instructions for Linux and similar systems:\n1. create a conda environment with the provided requirements file: `conda_env_requirements.txt`;\n2. clone this repo;\n3. activate the environment and navigate to `cmapdCostRegression/oracle`;\n4. run `make`.\nAll done!\n\n### Usage\nHow to train the model:\n1. change the value of the configuration variables in `config.py` if needed;\n2. run `python datasetGenerator.py` to generate the dataset based on the config variables;\n3. run `python train.py` to train the model.\n\nThe trained model will also be saved for later usage. \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzagoli%2Fcmapdcostregression","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fzagoli%2Fcmapdcostregression","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzagoli%2Fcmapdcostregression/lists"}