custom-distance-metrics SKILL.md skillA
custom-distance-metrics from cxcscmu/skilllearnbench is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add cxcscmu/skilllearnbench/custom-distance-metrics@git:20260424.d50cf5aPins git:20260424.d50cf5a in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- cxcscmu/skilllearnbench
- Path
skills/b1-one-shot-claude-opus-4-6/dbscan-parameter-tuning/custom-distance-metrics/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260424.d50cf5a, committed Apr 24, 2026
- Size
- 1,332 bytes
- Repository stars
- 91
- License
- MIT
Define custom distance/similarity metrics for DBSCAN clustering with sklearn, using weighted Euclidean distances.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Custom Distance Metrics for DBSCAN
## Overview
DBSCAN in sklearn supports custom distance metrics via `metric='precomputed'` (pass a distance matrix) or `metric=callable` with the pairwise distance function.
## Approach: Precomputed Distance Matrix
For small-to-medium datasets per image, computing a full pairwise distance matrix is efficient:
```python
from sklearn.cluster import DBSCAN
from scipy.spatial.distance import pdist, squareform
import numpy as np
def weighted_euclidean(points, w):
"""Compute pairwise weighted Euclidean distance.
d(a,b) = sqrt((w*dx)^2 + ((2-w)*dy)^2)
"""
scaled = points * [w, 2 - w]
return squareform(pdist(scaled, metric='euclidean'))
# Usage
dist_matrix = weighted_euclidean(points_xy, shape_weight)
db = DBSCAN(eps=epsilon, min_samples=min_samples, metric='precomputed')
labels = db.fit_predict(dist_matrix)
```
## Key Points
- `pdist` + `squareform` is faster than looping over pairs
- Scale the coordinates before computing standard Euclidean = same as custom weighted metric
- When w=1, this equals standard Euclidean distance
- Cluster centroids are computed from original (unscaled) coordinatesRead the whole file at its exact version.
How to install
mdr add cxcscmu/skilllearnbench/custom-distance-metrics@sha256:a7107392e03a2545The line at the top of this page pins the label git:20260424.d50cf5a, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_dcko54fkpjathiz5)
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Versions
The registry has recorded 1 version of this file, committed on Apr 24, 2026.
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Source
cxcscmu/skilllearnbench · 91 stars · license MIT · pushed 2026-10-02 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_dcko54fkpjathiz5 GET https://markdownregistry.com/api/v1/resolve?ref=cxcscmu/skilllearnbench/custom-distance-metrics GET https://markdownregistry.com/api/v1/blob/a7107392e03a25453b688ee30f8175a3fb4bb3a6bd4dbe2a2bb1ce0b484a6b5c
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