From 0bc4df6a1ab14ecf55d68cc86134d14b26eca6e5 Mon Sep 17 00:00:00 2001
From: Tavian Barnes <tavianator@tavianator.com>
Date: Thu, 28 May 2020 14:47:10 -0400
Subject: exhaustive: Implement an exhaustive search index

---
 Cargo.toml        |  1 +
 src/exhaustive.rs | 89 +++++++++++++++++++++++++++++++++++++++++++++++++++++++
 src/lib.rs        | 32 ++++++++++++++++++++
 3 files changed, 122 insertions(+)
 create mode 100644 src/exhaustive.rs

diff --git a/Cargo.toml b/Cargo.toml
index 724f6ea..5971ae6 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -11,3 +11,4 @@ categories = ["algorithms", "data-structures"]
 
 [dependencies]
 num-traits = "0.2.11"
+rand = "0.7.3"
diff --git a/src/exhaustive.rs b/src/exhaustive.rs
new file mode 100644
index 0000000..909edda
--- /dev/null
+++ b/src/exhaustive.rs
@@ -0,0 +1,89 @@
+//! Exhaustive nearest neighbor search.
+
+use crate::distance::Proximity;
+use crate::{ExactNeighbors, NearestNeighbors, Neighborhood};
+
+use std::iter::FromIterator;
+
+/// A [NearestNeighbors] implementation that does exhaustive search.
+#[derive(Debug)]
+pub struct ExhaustiveSearch<T>(Vec<T>);
+
+impl<T> ExhaustiveSearch<T> {
+    /// Create an empty ExhaustiveSearch index.
+    pub fn new() -> Self {
+        Self(Vec::new())
+    }
+
+    /// Add a new item to the index.
+    pub fn push(&mut self, item: T) {
+        self.0.push(item);
+    }
+
+    /// Get the size of this index.
+    pub fn len(&self) -> usize {
+        self.0.len()
+    }
+
+    /// Check if this index is empty.
+    pub fn is_empty(&self) -> bool {
+        self.0.is_empty()
+    }
+}
+
+impl<T> Default for ExhaustiveSearch<T> {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+impl<T> FromIterator<T> for ExhaustiveSearch<T> {
+    fn from_iter<I: IntoIterator<Item = T>>(items: I) -> Self {
+        Self(items.into_iter().collect())
+    }
+}
+
+impl<T> IntoIterator for ExhaustiveSearch<T> {
+    type Item = T;
+    type IntoIter = std::vec::IntoIter<T>;
+
+    fn into_iter(self) -> Self::IntoIter {
+        self.0.into_iter()
+    }
+}
+
+impl<T> Extend<T> for ExhaustiveSearch<T> {
+    fn extend<I: IntoIterator<Item = T>>(&mut self, iter: I) {
+        for value in iter {
+            self.push(value);
+        }
+    }
+}
+
+impl<K: Proximity<V>, V> NearestNeighbors<K, V> for ExhaustiveSearch<V> {
+    fn search<'k, 'v, N>(&'v self, mut neighborhood: N) -> N
+    where
+        K: 'k,
+        V: 'v,
+        N: Neighborhood<&'k K, &'v V>,
+    {
+        for e in &self.0 {
+            neighborhood.consider(e);
+        }
+        neighborhood
+    }
+}
+
+impl<K: Proximity<V>, V> ExactNeighbors<K, V> for ExhaustiveSearch<V> {}
+
+#[cfg(test)]
+pub mod tests {
+    use super::*;
+
+    use crate::tests::test_nearest_neighbors;
+
+    #[test]
+    fn test_exhaustive_index() {
+        test_nearest_neighbors(ExhaustiveSearch::from_iter);
+    }
+}
diff --git a/src/lib.rs b/src/lib.rs
index b1639d7..2f6504a 100644
--- a/src/lib.rs
+++ b/src/lib.rs
@@ -5,6 +5,7 @@
 pub mod coords;
 pub mod distance;
 pub mod euclid;
+pub mod exhaustive;
 
 pub use coords::Coordinates;
 pub use distance::{Distance, Metric, Proximity};
@@ -303,6 +304,12 @@ pub trait ExactNeighbors<K: Proximity<V>, V = K>: NearestNeighbors<K, V> {}
 pub mod tests {
     use super::*;
 
+    use crate::exhaustive::ExhaustiveSearch;
+
+    use rand::prelude::*;
+
+    use std::iter::FromIterator;
+
     type Point = Euclidean<[f32; 3]>;
 
     /// Test a [NearestNeighbors] implementation.
@@ -313,6 +320,7 @@ pub mod tests {
     {
         test_empty(&from_iter);
         test_pythagorean(&from_iter);
+        test_random_points(&from_iter);
     }
 
     fn test_empty<T, F>(from_iter: &F)
@@ -385,4 +393,28 @@ pub mod tests {
             ]
         );
     }
+
+    fn test_random_points<T, F>(from_iter: &F)
+    where
+        T: NearestNeighbors<Point>,
+        F: Fn(Vec<Point>) -> T,
+    {
+        let mut points = Vec::new();
+        for _ in 0..256 {
+            points.push(Euclidean([random(), random(), random()]));
+        }
+
+        let index = from_iter(points.clone());
+        let eindex = ExhaustiveSearch::from_iter(points.clone());
+
+        let target = Euclidean([random(), random(), random()]);
+
+        assert_eq!(
+            index.k_nearest(&target, 3),
+            eindex.k_nearest(&target, 3),
+            "target: {:?}, points: {:#?}",
+            target,
+            points,
+        );
+    }
 }
-- 
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