From 47a386b50d904d3f1b7ce3cfc13c29ea96dd1e43 Mon Sep 17 00:00:00 2001
From: Douglas Rumbaugh <dbr4@psu.edu>
Date: Tue, 30 Apr 2024 14:18:08 -0400
Subject: Added VPTree BSM benchmark

---
 benchmarks/include/vptree_bsm.h | 317 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 317 insertions(+)
 create mode 100644 benchmarks/include/vptree_bsm.h

(limited to 'benchmarks/include/vptree_bsm.h')

diff --git a/benchmarks/include/vptree_bsm.h b/benchmarks/include/vptree_bsm.h
new file mode 100644
index 0000000..2f12fcb
--- /dev/null
+++ b/benchmarks/include/vptree_bsm.h
@@ -0,0 +1,317 @@
+#pragma once
+
+#include <cstdlib>
+#include <vector>
+#include <algorithm>
+#include <limits>
+#include <gsl/gsl_rng.h>
+
+#include "psu-ds/PriorityQueue.h"
+#include "framework/interface/Record.h"
+
+template <typename R, size_t LEAFSZ=100>
+class BSMVPTree {
+public:
+    struct KNNQueryParms {
+        R point;
+        size_t k;
+    };
+
+public:
+    static BSMVPTree *build(std::vector<R> &records) {
+        return new BSMVPTree(records);
+    }
+
+    static BSMVPTree *build_presorted(std::vector<R> &records) {
+        return new BSMVPTree(records);
+    }
+
+    std::vector<R> unbuild() {
+        return std::move(m_data);
+    }
+
+    std::vector<R> query(void *q) {
+        std::vector<R> rs;
+
+        /* return an empty result set if q is invalid */
+        if (q == nullptr) {
+            return rs;
+        }
+
+        auto parms = (BSMVPTree::KNNQueryParms*) q;
+        auto pq = psudb::PriorityQueue<R, de::DistCmpMax<R>>(parms->k, &parms->point);
+
+        if (parms->k >= m_data.size()) {
+            for (size_t i=0; i<m_data.size(); i++) {
+                if (m_ptrs[i].ptr != nullptr) {
+                    pq.push(m_ptrs[i].ptr);
+                }
+            }
+        } else {
+            double farthest = std::numeric_limits<double>::max();
+            internal_search(m_root, parms->point, parms->k, pq, &farthest);
+        }
+
+        size_t i=0;
+        while (pq.size() > 0) {
+           rs.push_back(*pq.peek().data);
+           pq.pop();
+        }
+        return std::move(rs);
+    }
+
+    std::vector<R> query_merge(std::vector<R> &rsa, std::vector<R> &rsb, void* parms) {
+        KNNQueryParms *p = (KNNQueryParms *) parms;
+        R rec = p->point;
+        size_t k = p->k;
+
+        std::vector<R> output;
+
+        psudb::PriorityQueue<R, de::DistCmpMax<R>> pq(k, &rec);
+
+        for (size_t i=0; i<rsa.size(); i++) {
+            if (pq.size() < k) {
+                pq.push(&rsa[i]);
+            } else {
+                double head_dist = pq.peek().data->calc_distance(rec);
+                double cur_dist = rsa[i].calc_distance(rec);
+
+                if (cur_dist < head_dist) {
+                    pq.pop();
+                    pq.push(&rsa[i]);
+                }
+            }
+        }
+
+        for (size_t i=0; i<rsb.size(); i++) {
+            if (pq.size() < k) {
+                pq.push(&rsb[i]);
+            } else {
+                double head_dist = pq.peek().data->calc_distance(rec);
+                double cur_dist = rsb[i].calc_distance(rec);
+
+                if (cur_dist < head_dist) {
+                    pq.pop();
+                    pq.push(&rsb[i]);
+                }
+            }
+        }
+
+        while (pq.size() > 0) {
+            output.emplace_back(*pq.peek().data);
+            pq.pop();
+        }
+
+        return std::move(output);
+    }
+
+    size_t record_count() {
+        return m_data.size();
+    }
+
+    ~BSMVPTree() {
+        delete m_root;
+    }
+
+private:
+
+    struct vp_ptr {
+        R *ptr;
+        double dist;
+    };
+
+    struct vpnode {
+        size_t start;
+        size_t stop;
+        bool leaf;
+
+        double radius;
+        vpnode *inside;
+        vpnode *outside;
+
+        vpnode() : start(0), stop(0), leaf(false), radius(0.0), inside(nullptr), outside(nullptr) {}
+
+        ~vpnode() {
+            delete inside;
+            delete outside;
+        }
+    };
+
+    std::vector<R> m_data;
+    std::vector<vp_ptr> m_ptrs;
+    vpnode *m_root;
+
+    size_t m_node_cnt;
+
+    BSMVPTree(std::vector<R> &records) {
+        m_data = std::move(records);
+        m_node_cnt = 0;
+
+        for (size_t i=0; i<m_data.size(); i++) {
+            m_ptrs.push_back({&m_data[i], 0});
+        }
+
+        m_root = build_vptree();
+    }
+
+    vpnode *build_vptree() {
+        if (m_data.size() == 0) {
+            return nullptr;
+        }
+
+        size_t lower = 0;
+        size_t upper = m_data.size() - 1;
+
+        auto rng = gsl_rng_alloc(gsl_rng_mt19937);
+        auto root = build_subtree(lower, upper, rng);
+        gsl_rng_free(rng);
+        return root;
+    }
+
+   vpnode *build_subtree(size_t start, size_t stop, gsl_rng *rng) {
+        /* 
+         * base-case: sometimes happens (probably because of the +1 and -1
+         * in the first recursive call)
+         */
+        if (start > stop) {
+            return nullptr;
+        }
+
+        /* base-case: create a leaf node */
+        if (stop - start <= LEAFSZ) {
+            vpnode *node = new vpnode();
+            node->start = start;
+            node->stop = stop;
+            node->leaf = true;
+
+            m_node_cnt++;
+            return node;
+        }
+
+        /* 
+         * select a random element to be the root of the
+         * subtree
+         */
+        auto i = start + gsl_rng_uniform_int(rng, stop - start + 1);
+        swap(start, i);
+
+        /* for efficiency, we'll pre-calculate the distances between each point and the root */
+        for (size_t i=start+1; i<=stop; i++) {
+            m_ptrs[i].dist = m_ptrs[start].ptr->calc_distance(*m_ptrs[i].ptr);
+        }
+
+        /* 
+         * partition elements based on their distance from the start,
+         * with those elements with distance falling below the median
+         * distance going into the left sub-array and those above 
+         * the median in the right. This is easily done using QuickSelect.
+         */
+        auto mid = (start + 1 + stop) / 2;
+        quickselect(start + 1, stop, mid, m_ptrs[start].ptr, rng);
+
+        /* Create a new node based on this partitioning */
+        vpnode *node = new vpnode();
+        node->start = start;
+
+        /* store the radius of the circle used for partitioning the node. */
+        node->radius = m_ptrs[start].ptr->calc_distance(*m_ptrs[mid].ptr);
+        m_ptrs[start].dist = node->radius;
+
+        /* recursively construct the left and right subtrees */
+        node->inside = build_subtree(start + 1, mid-1, rng); 
+        node->outside = build_subtree(mid, stop, rng);
+
+        m_node_cnt++;
+
+        return node;
+    }
+
+    void quickselect(size_t start, size_t stop, size_t k, R *p, gsl_rng *rng) {
+        if (start == stop) return;
+
+        auto pivot = partition(start, stop, p, rng);
+
+        if (k < pivot) {
+            quickselect(start, pivot - 1, k, p, rng);
+        } else if (k > pivot) {
+            quickselect(pivot + 1, stop, k, p, rng);
+        }
+    }
+
+    size_t partition(size_t start, size_t stop, R *p, gsl_rng *rng) {
+        auto pivot = start + gsl_rng_uniform_int(rng, stop - start);
+
+        swap(pivot, stop);
+
+        size_t j = start;
+        for (size_t i=start; i<stop; i++) {
+            if (m_ptrs[i].dist < m_ptrs[stop].dist)  {
+                swap(j++, i);
+            }
+        }
+
+        swap(j, stop);
+        return j;
+    }
+
+    void swap(size_t idx1, size_t idx2) {
+        auto tmp = m_ptrs[idx1];
+        m_ptrs[idx1] = m_ptrs[idx2];
+        m_ptrs[idx2] = tmp;
+    }
+
+     void internal_search(vpnode *node, const R &point, size_t k, psudb::PriorityQueue<R, 
+                de::DistCmpMax<R>> &pq, double *farthest) {
+
+        if (node == nullptr) return;
+
+        if (node->leaf) {
+            for (size_t i=node->start; i<=node->stop; i++) {
+                double d = point.calc_distance(*m_ptrs[i].ptr);
+                if (d < *farthest) {
+                    if (pq.size() == k) {
+                        pq.pop();
+                    }
+
+                    pq.push(m_ptrs[i].ptr);
+                    if (pq.size() == k) {
+                        *farthest = point.calc_distance(*pq.peek().data);
+                    }
+                }
+            }
+
+            return;
+        }
+
+        double d = point.calc_distance(*m_ptrs[node->start].ptr);
+
+        if (d < *farthest) {
+            if (pq.size() == k) {
+                auto t = pq.peek().data;
+                pq.pop();
+            }
+            pq.push(m_ptrs[node->start].ptr);
+            if (pq.size() == k) {
+                *farthest = point.calc_distance(*pq.peek().data);
+            }
+        }
+
+        if (d < node->radius) {
+            if (d - (*farthest) <= node->radius) {
+                internal_search(node->inside, point, k, pq, farthest);
+            }
+
+            if (d + (*farthest) >= node->radius) {
+                internal_search(node->outside, point, k, pq, farthest);
+            }
+        } else {
+            if (d + (*farthest) >= node->radius) {
+                internal_search(node->outside, point, k, pq, farthest);
+            }
+
+            if (d - (*farthest) <= node->radius) {
+                internal_search(node->inside, point, k, pq, farthest);
+            }
+        }
+    }
+};
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