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Incident command system wikipedia Time-series clustering users can now get per-sample silhouette coefficients with time-series metrics. Previously only the scalar silhouette_score was available; per-sample values (needed for silhouette plots and identifying misassigned or outlier series) required a manual two-step workaround of computing a cross-distance matrix and calling sklearn directly. Insta Story Inspo

Incident vo venle wikipédia tslearn.clustering.silhouette_samples() accepts time-series data or a precomputed distance matrix, computes the cross-distance matrix with the requested metric (dtw, softdtw, euclidean, a callable, or "precomputed"), and delegates the per-sample computation to sklearn's silhouette_samples with metric="precomputed" — mirroring the existing silhouette_score wrapper. The function deliberately omits sample_size/random_state (sklearn's silhouette_samples has no subsampling), rejecting non-None values with a TypeError. Cool Calligraphy Reels

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• New tslearn.clustering.silhouette_samples() in tslearn/clustering/utils.py, exported from tslearn.clustering.
• Per-sample values are (n_ts,) and satisfy the documented invariant mean(silhouette_samples(...)) == silhouette_score(...) for the same inputs.
• Callable metrics receive metric_params/**kwds and return backend-normalized values, so a callable behaves like the equivalent string metric under both numpy and pytorch backends.
• Docstring with doctests, API reference entry, CHANGELOG entry, and a silhouette-plot example (docs/examples/clustering/plot_silhouette.py).

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• test_silhouette_samples() covers shape, value range, mean-vs-score equality per metric, TypeError rejection of sample_size/random_state across all metric branches, None no-op semantics, and metric_params forwarding to callable metrics.
• pytest tests/test_clustering.py --doctest-modules tslearn/clustering/utils.py: 11 passed.
• Example verified headless (matplotlib Agg): mean silhouette 0.71 on the Trace dataset.

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Accident investigation vs incident investigation guide ✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 95.21%. Comparing base (Why Do People Write) to head (Best Unsecured Credit Cards For Bad Credit).
⚠️ Report is 3 commits behind head on main. Give Me An Example Of A Blog Post

Additional details and impacted files

@@ Coverage Diff @@ ## main #703 +/- ## ========================================== + Coverage 95.14% 95.21% +0.06%  ========================================== Files 83 83 Lines 7814 7902 +88 ========================================== + Hits 7435 7524 +89  + Misses 379 378 -1 

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Safety and incident reporting enterprise guide alertops Hi What Does A Technology Blog Post Look Like , thanks for the PR, that would be indeed a nice addition to tslearn!!! Structure Blog Post For Student

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I analyzed the 5 best incident response tools in 2026 Checked the coverage concern as well: the Codecov patch-coverage gap (2 uncovered lines in tslearn/clustering/utils.py) was closed in Day Out Facebook Story before this review — current checks show codecov/patch at 100.00% of diff hit and codecov/project at 95.20% (+0.05%), no annotations. All CI is green and the two residual P3 items are already tracked separately as Flyer Produto and Launch Logo Round (non-blocking). Fashion Blog Post Ideas

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Book Full Of Products And Description Design, thanks for your time and effort making this PR go through!!! Nice addition to tslearn code base!!! Post. Read Stack Image