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29 lines
1.5 KiB
Markdown
29 lines
1.5 KiB
Markdown
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# `obidist` Package: Efficient Symmetric Distance/Similarity Matrix Management
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The `*DistMatrix` type provides a memory-efficient, symmetric matrix implementation for distance or similarity data.
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- **Storage Strategy**: Only the upper triangle (i < j) is stored, reducing memory usage from *O(n²)* to *n(n−1)/2*.
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- **Diagonal Handling**: Diagonal entries are fixed (0.0 for distances, 1.0 for similarities); assignments to diagonal indices are silently ignored.
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- **Symmetry Guarantee**: `Get(i, j)` and `Set(i, j, v)` automatically handle both (i,j) and (j,i), ensuring consistency.
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## Constructors
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| Function | Description |
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|---------|-------------|
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| `NewDistMatrix(n)` / `WithLabels(labels)` | Creates *n×n* distance matrix (diag = 0). |
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| `NewSimilarityMatrix(n)` / `WithLabels(labels)` | Creates *n×n* similarity matrix (diag = 1). |
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## Core Operations
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- `Get(i, j)` / `Set(i, j, v)`: Access/update symmetric entries.
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- `Size() int`, `GetLabel(i)` / `SetLabel(i, label)`: Query/mutate element labels.
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- `Labels() []string`, `GetRow(i)` / `GetColumn(j)`: Retrieve full rows/columns (as copies).
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## Analysis Helpers
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- `MinDistance()`, `MaxDistance()` → `(value, i, j)` of the extremal off-diagonal entry.
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- `Copy() *DistMatrix`: Deep copy for immutability-safe operations.
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- `ToFullMatrix()` → `[][]float64`: Converts to dense representation (use sparingly).
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Designed for clustering, phylogenetics, or any domain requiring fast symmetric matrix access with minimal footprint.
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