feat: introduce trait-based distance aggregation and layered store

Introduces ColumnWeights, CountPartials, and BitPartials traits to compute and finalize partial distance matrices. Implements these traits for PersistentBitMatrix, PersistentCompactIntMatrix, and a new LayeredStore<S> wrapper that aggregates metrics across layers via parallel reduction. Adds ndarray for numerical aggregation and updates architecture documentation to reflect the trait-driven design and pending refactoring roadmap.

src/obicompactvec/src/intmatrix.rs

@@ -203,6 +203,35 @@ where
     m
 }
 
+// ── Trait impls ───────────────────────────────────────────────────────────────
+
+use crate::traits::{ColumnWeights, CountPartials};
+
+impl ColumnWeights for PersistentCompactIntMatrix {
+    fn col_weights(&self) -> Array1<u64> { self.sum() }
+}
+
+impl CountPartials for PersistentCompactIntMatrix {
+    fn partial_bray(&self) -> Array2<u64> {
+        self.partial_bray_dist_matrix()
+    }
+    fn partial_euclidean(&self) -> Array2<f64> {
+        self.partial_euclidean_dist_matrix()
+    }
+    fn partial_threshold_jaccard(&self, threshold: u32) -> (Array2<u64>, Array2<u64>) {
+        self.partial_threshold_jaccard_dist_matrix(threshold)
+    }
+    fn partial_relfreq_bray(&self, global: &Array1<u64>) -> Array2<f64> {
+        self.partial_relfreq_bray_dist_matrix(global)
+    }
+    fn partial_relfreq_euclidean(&self, global: &Array1<u64>) -> Array2<f64> {
+        self.partial_relfreq_euclidean_dist_matrix(global)
+    }
+    fn partial_hellinger(&self, global: &Array1<u64>) -> Array2<f64> {
+        self.partial_hellinger_euclidean_dist_matrix(global)
+    }
+}
+
 // ── Builder ───────────────────────────────────────────────────────────────────
 
 pub struct PersistentCompactIntMatrixBuilder {