Feature: Implement constrained k-mer generation and clique search

This commit introduces core functionality for oligonucleotide family tagging, constrained k-mer generation, and clique finding. It also includes updates to the Go language version and refactors the logic for k-mer dissimilarity metrics.

pkg/obikmer/kmer_dissimilarity.go

@@ -0,0 +1,84 @@
+package obikmer
+
+import (
+	"fmt"
+	"strings"
+	"sync"
+)
+
+// KmerMetric selects the distance used to compare two k-mers.
+type KmerMetric int
+
+const (
+	// HammingMetric counts the positions at which two k-mers differ.
+	HammingMetric KmerMetric = iota
+	// LCSMetric is the alignment length minus the LCS length (see KmerLCS).
+	LCSMetric
+)
+
+// ParseKmerMetric converts "hamming" or "lcs" (case insensitive) to a KmerMetric.
+func ParseKmerMetric(name string) (KmerMetric, error) {
+	switch strings.ToLower(name) {
+	case "hamming":
+		return HammingMetric, nil
+	case "lcs":
+		return LCSMetric, nil
+	}
+	return HammingMetric, fmt.Errorf("unknown distance type %q (expected hamming or lcs)", name)
+}
+
+func (m KmerMetric) String() string {
+	if m == LCSMetric {
+		return "lcs"
+	}
+	return "hamming"
+}
+
+type lcsScratch struct {
+	partners []uint64
+	results  []KmerLCSResult
+}
+
+// NewKmerDissimilarityFilter describes the graph whose vertices are kmers[i]
+// (k-mers of size k, 1 <= k <= 31) and where two vertices are linked when
+// their distance is at least dmin. The returned function has the signature of
+// obigraph.NeighborFilter: it appends to out[:0] the candidates (indices in
+// kmers) linked to v. It is safe for concurrent use.
+func NewKmerDissimilarityFilter(kmers []uint64, k int, metric KmerMetric, dmin int) func(v int32, candidates, out []int32) []int32 {
+	if metric == HammingMetric {
+		return func(v int32, candidates, out []int32) []int32 {
+			q := kmers[v]
+			out = out[:0]
+			for _, c := range candidates {
+				if HammingDistance(q, kmers[c], k) >= dmin {
+					out = append(out, c)
+				}
+			}
+			return out
+		}
+	}
+
+	pool := sync.Pool{New: func() any { return new(lcsScratch) }}
+	return func(v int32, candidates, out []int32) []int32 {
+		s := pool.Get().(*lcsScratch)
+		defer pool.Put(s)
+
+		s.partners = s.partners[:0]
+		for _, c := range candidates {
+			s.partners = append(s.partners, kmers[c])
+		}
+		if cap(s.results) < len(candidates) {
+			s.results = make([]KmerLCSResult, len(candidates))
+		}
+		s.results = s.results[:len(candidates)]
+		KmerLCSBatch(kmers[v], s.partners, k, s.results)
+
+		out = out[:0]
+		for i, c := range candidates {
+			if s.results[i].Distance() >= dmin {
+				out = append(out, c)
+			}
+		}
+		return out
+	}
+}