[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KGCESA79D351RXBZBXYTJR":3,"skill-same:01M6KGCESA79D351RXBZBXYTJR":56},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":29,"linkOnly":30,"markdownContent":31,"markdownBody":32,"frontMatter":33,"refName":34,"commitSha":35,"skillDirectory":36,"forksCount":37,"repositoryHtmlUrl":38,"repositoryHomepageUrl":39,"ownerHtmlUrl":40,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":41,"install":42,"resources":55},"01M6KGCESA79D351RXBZBXYTJR","run2_custom-distance-metrics",null,"How to compute precomputed distance matrices for custom distance metrics in Scikit-Learn DBSCAN.","cxcscmu\u002FSkillLearnBench","cxcscmu","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F142838272?v=4","01M6KCQDFQWPDSN0MKZY96MSQP","01M6KCQDM7G70TKJ28NTSQC0RZ",92,"MIT","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb2-self-feedback-gemini-3.1-pro-preview\u002Fdbscan-parameter-tuning\u002Frun2_custom-distance-metrics\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28],"metrics","custom","custom distance metrics","run2 custom distance metrics","learn","how","compute","scikit-learn","matrices","how to","for","2026-11-10 10:12:30",false,"---\nname: run2_custom-distance-metrics\ndescription: How to compute precomputed distance matrices for custom distance metrics in Scikit-Learn DBSCAN.\n---\n\n# Fast Custom Distance Metrics in Scikit-Learn\n\nTo effectively use custom distance metrics with DBSCAN, it is highly recommended to precompute the distance matrix. Python scalar functions passed to `metric` are typically too slow. Instead, use numpy broadcasting to compute the matrix.\n\n## Installation\nEnsure you have scikit-learn and numpy installed:\n`pip install scikit-learn numpy`\n\n## Usage\n```python\nimport numpy as np\nfrom sklearn.cluster import DBSCAN\n\ndef custom_dist_matrix(points, w):\n    \"\"\"\n    Computes a distance matrix for N points.\n    d(a, b) = sqrt((w * Δx)^2 + ((2 - w) * Δy)^2)\n    \"\"\"\n    points = np.asarray(points)\n    # Broadcasting to find all pairwise differences\n    dx = w * (points[:, 0:1] - points[:, 0:1].T)\n    dy = (2 - w) * (points[:, 1:2] - points[:, 1:2].T)\n    return np.sqrt(dx**2 + dy**2)\n\n# X is an N by 2 array of points\n# dist_matrix = custom_dist_matrix(X, w=1.5)\n# db = DBSCAN(eps=10, min_samples=5, metric='precomputed').fit(dist_matrix)\n```\n","# Fast Custom Distance Metrics in Scikit-Learn\n\nTo effectively use custom distance metrics with DBSCAN, it is highly recommended to precompute the distance matrix. Python scalar functions passed to `metric` are typically too slow. Instead, use numpy broadcasting to compute the matrix.\n\n## Installation\nEnsure you have scikit-learn and numpy installed:\n`pip install scikit-learn numpy`\n\n## Usage\n```python\nimport numpy as np\nfrom sklearn.cluster import DBSCAN\n\ndef custom_dist_matrix(points, w):\n    \"\"\"\n    Computes a distance matrix for N points.\n    d(a, b) = sqrt((w * Δx)^2 + ((2 - w) * Δy)^2)\n    \"\"\"\n    points = np.asarray(points)\n    # Broadcasting to find all pairwise differences\n    dx = w * (points[:, 0:1] - points[:, 0:1].T)\n    dy = (2 - w) * (points[:, 1:2] - points[:, 1:2].T)\n    return np.sqrt(dx**2 + dy**2)\n\n# X is an N by 2 array of points\n# dist_matrix = custom_dist_matrix(X, w=1.5)\n# db = DBSCAN(eps=10, min_samples=5, metric='precomputed').fit(dist_matrix)\n```\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb2-self-feedback-gemini-3.1-pro-preview\u002Fdbscan-parameter-tuning\u002Frun2_custom-distance-metrics",7,"https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench","https:\u002F\u002Fcxcscmu.github.io\u002FSkillLearnBench","https:\u002F\u002Fgithub.com\u002Fcxcscmu","2026-10-02 22:55:36",{"source":8,"skillName":5,"commands":43,"prompt":53,"zipAvailable":54},[44,47,50],{"tool":45,"command":46},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill run2_custom-distance-metrics",{"tool":48,"command":49},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill run2_custom-distance-metrics",{"tool":51,"command":52},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill run2_custom-distance-metrics","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb2-self-feedback-gemini-3.1-pro-preview\u002Fdbscan-parameter-tuning\u002Frun2_custom-distance-metrics\u002FSKILL.md",true,[],[57,75,99,117,136,148],{"skillNo":58,"name":59,"nameZh":6,"description":60,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":61,"categories":62,"keywords":68,"publishedAt":29},"01M6KM5KH175P5ABP5JMX2SBDQ","object_counter","Count occurrences of an object in the image using computer vision algorithm.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Fvideo-object-counting\u002Fobject_counter\u002FSKILL.md",[63],{"categoryNo":64,"categoryName":65,"taxonomyType":66,"taxonomyTypeDesc":67},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[69,70,71,72,73,74],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":76,"name":77,"nameZh":6,"description":78,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":79,"categories":80,"keywords":84,"publishedAt":29},"01M6KM51TMVXG79MMPCS6Y8G86","travel-planning-workflow","Invoke this skill when performing travel planning. It defines a structured workflow for generating itineraries. Following this workflow is essential to ensure the final plan satisfies user requirements.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftravel-planning\u002Ftravel-planning-workflow\u002FSKILL.md",[81],{"categoryNo":82,"categoryName":83,"taxonomyType":66,"taxonomyTypeDesc":67},"01JBSEEDCAT000000000000023","Skill与智能体开发",[85,86,87,88,89,90,91,92,93,28,94,95,96,97,98],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","user","it","requirements","essential","itineraries",{"skillNo":100,"name":101,"nameZh":6,"description":102,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":103,"categories":104,"keywords":105,"publishedAt":29},"01M6KM4DY820XHS9YTSK5QP5HD","bundled-dataset-search-tool","invoke this skill when you need to perform database search for travel planning. This skill provides some useful pre-packaged tools to look up accommodations, attractions, cities, driving distance, flights, and restaurants from the bundled dataset.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftravel-planning\u002Fbundled-dataset-search-tool\u002FSKILL.md",[],[106,107,108,109,110,111,112,88,113,114,89,115,28,116,86],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","up","look","flights","you","database",{"skillNo":118,"name":119,"nameZh":6,"description":120,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":121,"categories":122,"keywords":123,"publishedAt":29},"01M6KM465BB9PQ0B548HQSVAWZ","glm-output","Output-processing guidance for GLM tasks. Especially useful after glm-basics and glm-calibration when you need verifier-matching metrics from output.nc and a final \u002Froot\u002Fmetrics.json.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftemperature-simulation\u002Fglm-output\u002FSKILL.md",[],[124,125,126,28,127,18,128,129,130,131,132,133,115,134,135],"glm output","output","glm","tasks","json","basics","guidance","matching","verifier","processing","root","calibration",{"skillNo":137,"name":138,"nameZh":6,"description":139,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":140,"categories":141,"keywords":143,"publishedAt":29},"01M6KM40KY3QWDW5W97S39R2BC","glm-calibration","Calibration guidance for GLM tasks. Often most effective after glm-basics has clarified the setup; glm-output is the companion skill for exact final metric computation.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftemperature-simulation\u002Fglm-calibration\u002FSKILL.md",[142],{"categoryNo":82,"categoryName":83,"taxonomyType":66,"taxonomyTypeDesc":67},[144,135,126,125,130,129,127,145,146,28,147],"glm calibration","setup","companion","metric",{"skillNo":149,"name":150,"nameZh":6,"description":151,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":152,"categories":153,"keywords":154,"publishedAt":29},"01M6KM3VHHVSGMGHJQB3PPT6YG","glm-basics","A good starting point for GLM calibration tasks. Use to inspect glm3.nml, confirm how GLM runs, and identify the relevant files before moving on to calibration and output evaluation.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftemperature-simulation\u002Fglm-basics\u002FSKILL.md",[],[155,126,129,156,144,135,28,125,157,158,159,160,23,161,127],"glm basics","inspect","relevant","files","good","use","Evaluation"]