القائمة
Cluster data using K-Means or DBSCAN
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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