From 6b434ec5f2182cb9624a011bd8d65587cd5a0759 Mon Sep 17 00:00:00 2001
From: Douglas Rumbaugh <dbr4@psu.edu>
Date: Mon, 24 Jul 2023 11:40:14 -0400
Subject: VPTree: KNN query initial implementation

---
 include/shard/VPTree.h | 130 +++++++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 126 insertions(+), 4 deletions(-)

(limited to 'include/shard/VPTree.h')

diff --git a/include/shard/VPTree.h b/include/shard/VPTree.h
index 7376d4c..05ffd50 100644
--- a/include/shard/VPTree.h
+++ b/include/shard/VPTree.h
@@ -26,8 +26,6 @@
 
 namespace de {
 
-thread_local size_t wss_cancelations = 0;
-
 template <NDRecordInterface R>
 struct KNNQueryParms {
     R point;
@@ -278,6 +276,41 @@ private:
         m_data[idx2] = tmp;
     }
 
+
+    void search(vpnode *node, const R &point, size_t k, PriorityQueue<R> &pq, double *farthest) {
+        if (node == nullptr) return;
+
+        double d = point.calc_distance(m_data[node->idx].rec);
+
+        if (d < *farthest) {
+            if (pq.size() == k) pq.pop();
+            pq.push(&m_data[node->idx]);
+            if (pq.size() == k) {
+                *farthest = point.calc_distance(*pq.peek().data.rec);
+            }
+        }
+
+        if (!node->inside && !node->outside) return;
+
+        if (d < node->radius) {
+            if (d - *farthest <= node->radius) {
+                search(node->inside, point, k, pq, farthest);
+            }
+
+            if (d + *farthest >= node->radius) {
+                search(node->outside, point, k, pq, farthest);
+            }
+        } else {
+            if (d + *farthest >= node->radius) {
+                search(node->outside, point, k, pq, farthest);
+            }
+
+            if (d - *farthest <= node->radius) {
+                search(node->inside, point, k, pq, farthest);
+            }
+        }
+    }
+
     Wrapped<R>* m_data;
     std::unordered_map<R, size_t, RecordHash<R>> m_lookup_map;
     size_t m_reccnt;
@@ -285,9 +318,19 @@ private:
     size_t m_node_cnt;
 
     vpnode *m_root;
-
 };
 
+template <NDRecordInterface R, R P>
+class KNNDistCmp {
+public:
+    inline bool operator()(queue_record<R> *a, queue_record<R> *b) requires WrappedInterface<R> {
+        return a->rec.calc_distance(P.rec) > b->rec.calc_distance(P.rec); 
+    }
+
+    inline bool operator()(queue_record<R> *a, queue_record<R> *b) requires (!WrappedInterface<R>){
+        return a->data.calc_distance(P) > b->data.calc_distance(P); 
+    }
+};
 
 template <NDRecordInterface R>
 class KNNQuery {
@@ -301,15 +344,94 @@ public:
     }
 
     static void process_query_states(void *query_parms, std::vector<void*> shard_states, void *buff_state) {
+        return;
     }
 
     static std::vector<Wrapped<R>> query(VPTree<R> *wss, void *q_state, void *parms) {
+        std::vector<Wrapped<R>> results;
+        KNNQueryParms<R> *p = (KNNQueryParms<R> *) parms;
+        Wrapped<R> wrec;
+        wrec.rec = p->point;
+        wrec.header = 0;
+
+        PriorityQueue<Wrapped<R>, KNNDistCmp<Wrapped<R>, wrec>> pq;
+
+        double farthest = std::numeric_limits<double>::max();
+
+        wss->search(wss->m_root, p->point, p->k, pq, &farthest);
+
+        while (pq.size() > 0) {
+            results.emplace_back(*pq.peek().data);
+            pq.pop();
+        }
+
+        return results;
     }
 
     static std::vector<Wrapped<R>> buffer_query(MutableBuffer<R> *buffer, void *state, void *parms) {
+        KNNQueryParms<R> *p = (KNNQueryParms<R> *) parms;
+        Wrapped<R> wrec;
+        wrec.rec = p->point;
+        wrec.header = 0;
+
+        size_t k = p->k;
+
+        PriorityQueue<Wrapped<R>, KNNDistCmp<Wrapped<R>, wrec>> pq;
+        for (size_t i=0; i<buffer->get_record_count(); i++) {
+            // Skip over deleted records (under tagging)
+            if (buffer->get_data()[i]->is_deleted()) {
+                continue;
+            }
+
+            if (pq.size() < k) {
+                pq.push(buffer->get_data() + i);
+            } else {
+                double head_dist = pq.peek().data.rec->calc_distance(wrec.rec);
+                double cur_dist = (buffer->get_data() + i)->calc_distance(wrec.rec);
+
+                if (cur_dist < head_dist) {
+                    pq.pop();
+                    pq.push(buffer->get_data() + i);
+                }
+            }
+        }
+
+        std::vector<Wrapped<R>> results;
+        while (pq.size() > 0) {
+            results.emplace(*pq.peek().data);
+            pq.pop();
+        }
     }
 
-    static std::vector<R> merge(std::vector<std::vector<R>> &results) {
+    static std::vector<R> merge(std::vector<std::vector<R>> &results, void *parms) {
+        KNNQueryParms<R> *p = (KNNQueryParms<R> *) parms;
+        R rec = p->point;
+        size_t k = p->k;
+
+        PriorityQueue<R, KNNDistCmp<R, rec>> pq;
+        for (size_t i=0; i<results.size(); i++) {
+            for (size_t j=0; j<results.size(); j++) {
+                if (pq.size() < k) {
+                    pq.push(&results[i][j]);
+                } else {
+                    double head_dist = pq.peek().data->calc_distance(rec);
+                    double cur_dist = results[i][j].calc_distance(rec);
+
+                    if (cur_dist < head_dist) {
+                        pq.pop();
+                        pq.push(&results[i][j]);
+                    }
+                }
+            }
+        }
+
+        std::vector<R> output;
+        while (pq.size() > 0) {
+            output.emplace(*pq.peek().data);
+            pq.pop();
+        }
+
+        return output;
     }
 
     static void delete_query_state(void *state) {
-- 
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