R sugawara patch 5

.github/workflows/scan.yml

@@ -0,0 +1,21 @@
+on:
+  # Trigger analysis when pushing in master or pull requests, and when creating
+  # a pull request.
+  push:
+    branches:
+      - master
+  pull_request:
+      types: [opened, synchronize, reopened]
+name: Main Workflow
+jobs:
+  sonarcloud:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v4
+      with:
+        # Disabling shallow clone is recommended for improving relevancy of reporting
+        fetch-depth: 0
+    - name: SonarCloud Scan
+      uses: sonarsource/sonarcloud-github-action@master # Ex: v2.1.0, See the latest version at https://github.com/marketplace/actions/sonarcloud-scan
+      env:
+        SONAR_TOKEN: ${{ secrets.SONAR_TOKEN }}

DIRECTORY.md

@@ -243,6 +243,15 @@
     * [Min Heap](data_structures/heap/min_heap.py)
     * [Randomized Heap](data_structures/heap/randomized_heap.py)
     * [Skew Heap](data_structures/heap/skew_heap.py)
+  * Kd Tree
+    * [Build Kdtree](data_structures/kd_tree/build_kdtree.py)
+    * Example
+      * [Example Usage](data_structures/kd_tree/example/example_usage.py)
+      * [Hypercube Points](data_structures/kd_tree/example/hypercube_points.py)
+    * [Kd Node](data_structures/kd_tree/kd_node.py)
+    * [Nearest Neighbour Search](data_structures/kd_tree/nearest_neighbour_search.py)
+    * Tests
+      * [Test Kdtree](data_structures/kd_tree/tests/test_kdtree.py)
   * Linked List
     * [Circular Linked List](data_structures/linked_list/circular_linked_list.py)
     * [Deque Doubly](data_structures/linked_list/deque_doubly.py)
@@ -285,12 +294,6 @@
   * Trie
     * [Radix Tree](data_structures/trie/radix_tree.py)
     * [Trie](data_structures/trie/trie.py)
-  * KD Tree
-    * [KD Tree Node](data_structures/kd_tree/kd_node.py)
-    * [Build KD Tree](data_structures/kd_tree/build_kdtree.py)
-    * [Nearest Neighbour Search](data_structures/kd_tree/nearest_neighbour_search.py)
-    * [Hypercibe Points](data_structures/kd_tree/example/hypercube_points.py)
-    * [Example Usage](data_structures/kd_tree/example/example_usage.py)
 
 ## Digital Image Processing
   * [Change Brightness](digital_image_processing/change_brightness.py)

data_structures/kd_tree/example/example_usage.py

@@ -19,7 +19,7 @@ def main() -> None:
     hypercube_kdtree = build_kdtree(points.tolist())
 
     # Generate a random query point within the same space
-    rng = np.random.default_rng()
+    rng = np.random.default_rng(5)
     query_point: list[float] = rng.random(num_dimensions).tolist()
 
     # Perform nearest neighbor search

data_structures/kd_tree/example/hypercube_points.py

@@ -16,6 +16,6 @@ def hypercube_points(
         An array of shape (num_points, num_dimensions)
                     with generated points.
     """
-    rng = np.random.default_rng()
+    rng = np.random.default_rng(5)
     shape = (num_points, num_dimensions)
     return hypercube_size * rng.random(shape)

sonar-project.properties

@@ -0,0 +1,2 @@
+sonar.organization=r-sugawara-1
+sonar.projectKey=r-sugawara-1_desctop_notification