Update dimensionality_reduction.py

Author: Sid101098

machine_learning/dimensionality_reduction.py

@@ -179,15 +179,21 @@ def linear_discriminant_analysis(
     """
     assert classes > dimensions
 
-    if features.any:
-        _, eigenvectors = eigh(
-            covariance_between_classes(features, labels, classes),
-            covariance_within_classes(features, labels, classes),
-        )
-        filtered_eigenvectors = eigenvectors[:, ::-1][:, :dimensions]
-        svd_matrix, _, _ = np.linalg.svd(filtered_eigenvectors)
-        filtered_svd_matrix = svd_matrix[:, 0:dimensions]
-        projected_data = np.dot(filtered_svd_matrix.T, features)
+    if features.any():  # FIXED: Added missing parentheses
+        sb = covariance_between_classes(features, labels, classes)
+        sw = covariance_within_classes(features, labels, classes)
+
+        # Solve the generalized eigenvalue problem: Sb v = λ Sw v
+        eigenvalues, eigenvectors = eigh(sb, sw)
+
+        # Sort eigenvectors by eigenvalues (descending)
+        idx = np.argsort(eigenvalues)[::-1]
+        eigenvectors = eigenvectors[:, idx]
+
+        # Take top "dimensions"
+        filtered_eigenvectors = eigenvectors[:, :dimensions]
+
+        projected_data = np.dot(filtered_eigenvectors.T, features)
         logging.info("Linear Discriminant Analysis computed")
         return projected_data
     else: