fully dynamic test

README.md

@@ -2,7 +2,7 @@
 [![crates.io](https://img.shields.io/crates/v/rindex)](https://crates.io/crates/rindex)
 
 # rindex
-Rindex: reverse nearest neighbor search index for high-dimensional clustered datasets.
+Rindex: fully dynamic nearest neighbor search index for high-dimensional clustered datasets.
 
 ## License
 This project is licensed under the [Apache License, Version 2.0](LICENSE.md) - See the [LICENSE.md](https://github.com/azizkayumov/rindex/blob/main/LICENSE) file for details.

lib/README.md

@@ -1,5 +1,38 @@
 # rindex
-Rindex: reverse nearest neighbor search index for high-dimensional clustered datasets.
+Rindex: fully dynamic nearest neighbor search index for high-dimensional clustered datasets.
+
+## Usage
+
+The following example shows how to update and query Rindex:
+```
+let mut rindex = Rindex::default();
+
+// Insert some points
+let a = rindex.insert([1.0, 1.0]);
+let b = rindex.insert([2.0, 2.0]);
+let c = rindex.insert([3.0, 3.0]);
+let d = rindex.insert([20.0, 20.0]);
+
+// Query the tree for 3 nearest neighbors of the query point
+let query_point = [0.0, 0.0];
+let result = rindex.query_neighbors(query_point, 3);
+
+// The result should contain the points a, b, and c
+assert_eq!(result.len(), 3);
+assert!(result.contains(&a));
+assert!(result.contains(&b));
+assert!(result.contains(&c));
+
+// Delete the point c
+rindex.delete(c);
+
+// Query the tree again (c should not be in the result)
+let result = rindex.query_neighbors(query_point, 3);
+assert_eq!(result.len(), 3);
+assert!(result.contains(&a));
+assert!(result.contains(&b));
+assert!(result.contains(&d));
+```
 
 ## License
 This project is licensed under the [Apache License, Version 2.0](LICENSE.md) - See the [LICENSE.md](https://github.com/azizkayumov/rindex/blob/main/LICENSE) file for details.

lib/tests/random.rs

@@ -0,0 +1,56 @@
+use rand::{rngs::StdRng, Rng, SeedableRng};
+use rindex::Rindex;
+
+#[test]
+fn test_random() {
+    let mut rindex = Rindex::default();
+
+    let mut rng = StdRng::seed_from_u64(0);
+    let deletion_probability = 0.2;
+
+    let num_ops = 1000;
+    let mut points = Vec::new();
+
+    for _ in 0..num_ops {
+        // Randomly insert or delete a point
+        let should_delete = rng.gen_bool(deletion_probability);
+        if should_delete && !points.is_empty() {
+            let idx = rng.gen_range(0..points.len());
+            let (point_id, _) = points.swap_remove(idx);
+            rindex.delete(point_id);
+        } else {
+            let x = rng.gen_range(-100.0..100.0);
+            let y = rng.gen_range(-100.0..100.0);
+            let point_id = rindex.insert([x, y]);
+            points.push((point_id, [x, y]));
+        }
+
+        // Creata a random query point and radius
+        let x = rng.gen_range(-100.0..100.0);
+        let y = rng.gen_range(-100.0..100.0);
+        let query_point = [x, y];
+        let query_radius = rng.gen_range(5.0..10.0);
+
+        // Compute the expected results
+        let mut expected = Vec::new();
+        for (id, point) in &points {
+            let dx = point[0] - query_point[0];
+            let dy = point[1] - query_point[1];
+            let distance = (dx * dx + dy * dy).sqrt();
+            if distance <= query_radius {
+                expected.push(*id);
+            }
+        }
+        expected.sort();
+
+        // Compute the actual results using the range query
+        let mut actual = rindex.query(query_point, query_radius);
+        actual.sort();
+        assert_eq!(expected, actual);
+
+        // Compute the actual results using the k nearest neighbors query
+        let mut actual = rindex.query_neighbors(query_point, expected.len());
+        actual.sort();
+        assert_eq!(expected, actual);
+    }
+}

lib/tests/usage.rs

@@ -0,0 +1,32 @@
+use rindex::Rindex;
+
+#[test]
+fn basic_usage() {
+    let mut rindex = Rindex::default();
+
+    // Insert some points
+    let a = rindex.insert([1.0, 1.0]);
+    let b = rindex.insert([2.0, 2.0]);
+    let c = rindex.insert([3.0, 3.0]);
+    let d = rindex.insert([20.0, 20.0]);
+
+    // Query the tree for nearest neighbors of the query point
+    let query_point = [0.0, 0.0];
+    let result = rindex.query_neighbors(query_point, 3);
+
+    // The result should contain the points a, b, and c
+    assert_eq!(result.len(), 3);
+    assert!(result.contains(&a));
+    assert!(result.contains(&b));
+    assert!(result.contains(&c));
+
+    // Delete the point c
+    rindex.delete(c);
+
+    // Query the tree again (c should not be in the result)
+    let result = rindex.query_neighbors(query_point, 3);
+    assert_eq!(result.len(), 3);
+    assert!(result.contains(&a));
+    assert!(result.contains(&b));
+    assert!(result.contains(&d));
+}