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_group.go

@@ -3,6 +3,7 @@ package obikmer
 import (
 	"fmt"
 
+	"git.metabarcoding.org/obitools/obitools4/obitools4/pkg/obidist"
 	"git.metabarcoding.org/obitools/obitools4/obitools4/pkg/obiseq"
 )
 
@@ -260,3 +261,79 @@ Set breakdown:
 
 	return result
 }
+
+// JaccardDistanceMatrix computes a pairwise Jaccard distance matrix for all KmerSets in the group.
+// Returns a triangular distance matrix where element (i, j) represents the Jaccard distance
+// between set i and set j.
+//
+// The Jaccard distance is: 1 - (|A ∩ B| / |A ∪ B|)
+//
+// The matrix labels are set to the IDs of the individual KmerSets if available,
+// otherwise they are set to "set_0", "set_1", etc.
+//
+// Time complexity: O(n² × (|A| + |B|)) where n is the number of sets
+// Space complexity: O(n²) for the distance matrix
+func (ksg *KmerSetGroup) JaccardDistanceMatrix() *obidist.DistMatrix {
+	n := len(ksg.sets)
+
+	// Create labels from set IDs
+	labels := make([]string, n)
+	for i, ks := range ksg.sets {
+		if ks.Id() != "" {
+			labels[i] = ks.Id()
+		} else {
+			labels[i] = fmt.Sprintf("set_%d", i)
+		}
+	}
+
+	dm := obidist.NewDistMatrixWithLabels(labels)
+
+	// Compute pairwise distances
+	for i := 0; i < n-1; i++ {
+		for j := i + 1; j < n; j++ {
+			distance := ksg.sets[i].JaccardDistance(ksg.sets[j])
+			dm.Set(i, j, distance)
+		}
+	}
+
+	return dm
+}
+
+// JaccardSimilarityMatrix computes a pairwise Jaccard similarity matrix for all KmerSets in the group.
+// Returns a similarity matrix where element (i, j) represents the Jaccard similarity
+// between set i and set j.
+//
+// The Jaccard similarity is: |A ∩ B| / |A ∪ B|
+//
+// The diagonal is 1.0 (similarity of a set to itself).
+//
+// The matrix labels are set to the IDs of the individual KmerSets if available,
+// otherwise they are set to "set_0", "set_1", etc.
+//
+// Time complexity: O(n² × (|A| + |B|)) where n is the number of sets
+// Space complexity: O(n²) for the similarity matrix
+func (ksg *KmerSetGroup) JaccardSimilarityMatrix() *obidist.DistMatrix {
+	n := len(ksg.sets)
+
+	// Create labels from set IDs
+	labels := make([]string, n)
+	for i, ks := range ksg.sets {
+		if ks.Id() != "" {
+			labels[i] = ks.Id()
+		} else {
+			labels[i] = fmt.Sprintf("set_%d", i)
+		}
+	}
+
+	sm := obidist.NewSimilarityMatrixWithLabels(labels)
+
+	// Compute pairwise similarities
+	for i := 0; i < n-1; i++ {
+		for j := i + 1; j < n; j++ {
+			similarity := ksg.sets[i].JaccardSimilarity(ksg.sets[j])
+			sm.Set(i, j, similarity)
+		}
+	}
+
+	return sm
+}