Add Jaccard distance and similarity computations for KmerSet and KmerSetGroup

Add Jaccard distance and similarity computations for KmerSet and KmerSetGroup

This commit introduces Jaccard distance and similarity methods for KmerSet and KmerSetGroup.

For KmerSet:
- Added JaccardDistance method to compute the Jaccard distance between two KmerSets
- Added JaccardSimilarity method to compute the Jaccard similarity between two KmerSets

For KmerSetGroup:
- Added JaccardDistanceMatrix method to compute a pairwise Jaccard distance matrix
- Added JaccardSimilarityMatrix method to compute a pairwise Jaccard similarity matrix

Also includes:
- New DistMatrix implementation in pkg/obidist for storing and computing distance/similarity matrices
- Updated version handling with bump-version target in Makefile
- Added tests for all new methods

pkg/obikmer/kmer_set.go

@@ -158,6 +158,54 @@ func (ks *KmerSet) Difference(other *KmerSet) *KmerSet {
 	return NewKmerSetFromBitmap(ks.k, result)
 }
 
+// JaccardDistance computes the Jaccard distance between two KmerSets.
+// The Jaccard distance is defined as: 1 - (|A ∩ B| / |A ∪ B|)
+// where A and B are the two sets.
+//
+// Returns:
+//   - 0.0 when sets are identical (distance = 0, similarity = 1)
+//   - 1.0 when sets are completely disjoint (distance = 1, similarity = 0)
+//   - 1.0 when both sets are empty (by convention)
+//
+// Time complexity: O(|A| + |B|) for Roaring Bitmap operations
+// Space complexity: O(1) as operations are done in-place on temporary bitmaps
+func (ks *KmerSet) JaccardDistance(other *KmerSet) float64 {
+	if ks.k != other.k {
+		panic(fmt.Sprintf("Cannot compute Jaccard distance between KmerSets with different k values: %d vs %d", ks.k, other.k))
+	}
+
+	// Compute intersection cardinality
+	intersectionCard := ks.bitmap.AndCardinality(other.bitmap)
+
+	// Compute union cardinality
+	unionCard := ks.bitmap.OrCardinality(other.bitmap)
+
+	// If union is empty, both sets are empty - return 1.0 by convention
+	if unionCard == 0 {
+		return 1.0
+	}
+
+	// Jaccard similarity = |A ∩ B| / |A ∪ B|
+	similarity := float64(intersectionCard) / float64(unionCard)
+
+	// Jaccard distance = 1 - similarity
+	return 1.0 - similarity
+}
+
+// JaccardSimilarity computes the Jaccard similarity coefficient between two KmerSets.
+// The Jaccard similarity is defined as: |A ∩ B| / |A ∪ B|
+//
+// Returns:
+//   - 1.0 when sets are identical (maximum similarity)
+//   - 0.0 when sets are completely disjoint (no similarity)
+//   - 0.0 when both sets are empty (by convention)
+//
+// Time complexity: O(|A| + |B|) for Roaring Bitmap operations
+// Space complexity: O(1) as operations are done in-place on temporary bitmaps
+func (ks *KmerSet) JaccardSimilarity(other *KmerSet) float64 {
+	return 1.0 - ks.JaccardDistance(other)
+}
+
 // Iterator returns an iterator over all k-mers in the set
 func (ks *KmerSet) Iterator() roaring64.IntIterable64 {
 	return ks.bitmap.Iterator()