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Eric Coissac
8c7017a99d
- Update obioptions.Version from "Release 4.4.29" to "/v/ Release v5" - Update version.txt from 4.29 → .30 (automated by Makefile)
35 lines
2.0 KiB
Markdown
35 lines
2.0 KiB
Markdown
# `obistats` Package: K-Means Clustering Implementation
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The `obistats` package provides a concurrent, type-generic implementation of the **K-means clustering algorithm** for numerical datasets.
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## Core Utilities
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- `SquareDist` / `EuclideanDist`: Compute squared and Euclidean distances between vectors (generic over `float64` or `int`).
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- `DefaultRG`: Returns a seeded random number generator (`*rand.Rand`) for reproducibility control.
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## Data Structure
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- `KmeansClustering`: Encapsulates dataset (`*obiutils.Matrix[float64]`), cluster assignments, centers, and metadata (sizes, distances to nearest center).
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- Supports dynamic addition of clusters via `AddACenter()`.
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## Initialization & Management
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- `MakeKmeansClustering`: Initializes the structure with data, number of clusters *k*, and RNG.
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- `SetCenterTo`, `AddACenter`: Assign or grow centers; uses **k-means++**-inspired weighted sampling for new centers.
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- `ResetEmptyCenters`: Reinitializes empty clusters using distance-weighted sampling.
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## Core Algorithm Steps
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- `AssignToClass`: Parallel assignment of points to nearest centers (uses goroutines + mutex).
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- `ComputeCenters`: Computes new cluster centroids *as the closest original data point* to the arithmetic mean (robust for non-Euclidean spaces).
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- `Run`: Executes iterative refinement until convergence (`max_cycle` iterations or inertia drop ≤ threshold).
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## Accessors & Diagnostics
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- `K()`, `N()`, `Dimension()`: Return number of clusters, dataset size, and feature dimension.
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- `Inertia()`: Sum of squared distances to assigned centers (convergence metric).
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- `Centers`, `Classes`, `Sizes`: Expose internal clustering state.
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## Design Highlights
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- Fully concurrent (goroutine-based) for performance.
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- Generic distance functions support both `int` and `float64`.
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- Explicit handling of edge cases (empty clusters, convergence).
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- Logging via `logrus` for debugging (`obilog.Warnf`).
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> *Note: High-level wrapper functions (e.g., standalone `Kmeans`) are commented out but outline intended API usage.*
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