Update dimensionality_reduction.py

Author: Sid101098

machine_learning/dimensionality_reduction.py

@@ -6,7 +6,6 @@
 Notes:
   - Each column of the features matrix corresponds to a class item
 """
-
 """
 Implementation of dimensionality reduction algorithms.
 
@@ -124,7 +123,7 @@ def covariance_between_classes(
 def principal_component_analysis(features: np.ndarray, dimensions: int) -> np.ndarray:
     """Principal Component Analysis (PCA).
 
-    For more details: https://en.wikipedia.org/wiki/Principal_component_analysis
+    For more details: https://en.wikipedia.org/wiki/Principal_component_analysis 
 
     Args:
         features: Input features matrix (n_features x n_samples).
@@ -158,7 +157,7 @@ def linear_discriminant_analysis(
 ) -> np.ndarray:
     """Linear Discriminant Analysis (LDA).
 
-    For more details: https://en.wikipedia.org/wiki/Linear_discriminant_analysis
+    For more details: https://en.wikipedia.org/wiki/Linear_discriminant_analysis 
 
     Args:
         features: Input features matrix (n_features x n_samples).
@@ -199,7 +198,7 @@ def locally_linear_embedding(
 ) -> np.ndarray:
     """Locally Linear Embedding (LLE).
 
-    For more details: https://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction
+    For more details: https://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction 
 
     Args:
         features: Input features matrix (shape: [n_features, n_samples]).
@@ -284,7 +283,7 @@ def multidimensional_scaling(
 ) -> np.ndarray:
     """Multidimensional Scaling (MDS).
 
-    For more details: https://en.wikipedia.org/wiki/Multidimensional_scaling
+    For more details: https://en.wikipedia.org/wiki/Multidimensional_scaling 
 
     Args:
         features: Input features matrix (shape: [n_features, n_samples]).
@@ -430,7 +429,9 @@ 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):