Update dimensionality_reduction.py

Author: Sid101098

machine_learning/dimensionality_reduction.py

@@ -7,12 +7,20 @@
 Notes:
   - Each column of the features matrix corresponds to a class item
 """
+"""
+Implementation of dimensionality reduction algorithms.
+
+Includes:
+- Principal Component Analysis (PCA)
+- Linear Discriminant Analysis (LDA)
+- Locally Linear Embedding (LLE)
+- Multidimensional Scaling (MDS)
+"""
 
 import doctest
 import logging
 
 import numpy as np
-import pytest
 from scipy.linalg import eigh
 from scipy.spatial.distance import cdist
 from sklearn.neighbors import NearestNeighbors
@@ -423,7 +431,9 @@ def test_principal_component_analysis() -> None:
     """Test function for Principal Component Analysis."""
     features = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
     dimensions = 2
-    expected_output = np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]])
+    expected_output = np.array(
+        [[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]]
+    )
 
     output = principal_component_analysis(features, dimensions)
     if not np.allclose(expected_output, output):