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Cluster data using K-Means or DBSCAN
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Test model robustness with adversarial attacks and defenses
Multi-armed bandit simulation — epsilon-greedy, UCB1, Thompson sampling
Benchmark and compare ix algorithm performance
Embedded Redis-like cache with TTL, LRU, pub/sub, and RESP protocol
Category theory primitives — monad laws verification, free-forgetful adjunction
Chaos theory analysis — Lyapunov exponents, bifurcation, attractors, fractals
| name | ix-cluster |
| description | Cluster data using K-Means or DBSCAN |
| disable-model-invocation | true |
Group data points into clusters using unsupervised learning.
When the user has unlabeled data and wants to find natural groupings, segments, or patterns.
If the user doesn't specify k, suggest the elbow method:
use ix_unsupervised::kmeans::KMeans;
use ix_unsupervised::dbscan::DBSCAN;
// K-Means
let model = KMeans::new(k, max_iter, seed);
let assignments = model.fit(&data);
// DBSCAN
let model = DBSCAN::new(eps, min_points);
let labels = model.fit(&data); // -1 = noise