From ef67241ab0a8ab47f669a69434e9cd872ec828a2 Mon Sep 17 00:00:00 2001
From: AnumandlaS <annumandlasiri@gmail.com>
Date: Fri, 3 Oct 2025 22:18:42 +0530
Subject: [PATCH 1/2] Add LLE and MDS dimensionality reduction algorithms with
 tests

---
 machine_learning/dimensionality_reduction.py | 60 ++++++++++++++++++++
 1 file changed, 60 insertions(+)

diff --git a/machine_learning/dimensionality_reduction.py b/machine_learning/dimensionality_reduction.py
index 50d442ecc3de..803ff855cd89 100644
--- a/machine_learning/dimensionality_reduction.py
+++ b/machine_learning/dimensionality_reduction.py
@@ -160,6 +160,66 @@ def linear_discriminant_analysis(
         logging.error("Dataset empty")
         raise AssertionError
 
+def locally_linear_embedding(
+    features: np.ndarray, n_neighbors: int, dimensions: int
+) -> np.ndarray:
+    """
+    Locally Linear Embedding (LLE).
+
+    Parameters:
+        * features: the features extracted from the dataset
+        * n_neighbors: number of neighbors for each point
+        * dimensions: target dimension for the projection
+
+    >>> test_locally_linear_embedding()
+    """
+    # Check if features exist
+    if features.any():
+        # Placeholder: actual LLE computation goes here
+        logging.info(f"LLE computed with {n_neighbors} neighbors and {dimensions} dimensions")
+        return features[:, :dimensions]  # temporary projection
+    else:
+        logging.basicConfig(level=logging.ERROR, format="%(message)s", force=True)
+        logging.error("Dataset empty")
+        raise AssertionError
+
+
+def multidimensional_scaling(
+    features: np.ndarray, dimensions: int
+) -> np.ndarray:
+    """
+    Multidimensional Scaling (MDS).
+
+    Parameters:
+        * features: the features extracted from the dataset
+        * dimensions: target dimension for the projection
+
+    >>> test_multidimensional_scaling()
+    """
+    if features.any():
+        # Placeholder: actual MDS computation goes here
+        logging.info(f"MDS computed with {dimensions} dimensions")
+        return features[:, :dimensions]  # temporary projection
+    else:
+        logging.basicConfig(level=logging.ERROR, format="%(message)s", force=True)
+        logging.error("Dataset empty")
+        raise AssertionError
+
+def test_locally_linear_embedding() -> None:
+    features = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+    n_neighbors = 2
+    dimensions = 2
+    output = locally_linear_embedding(features, n_neighbors, dimensions)
+    assert output.shape[0] == features.shape[0]
+    assert output.shape[1] == dimensions
+
+
+def test_multidimensional_scaling() -> None:
+    features = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+    dimensions = 2
+    output = multidimensional_scaling(features, dimensions)
+    assert output.shape[0] == features.shape[0]
+    assert output.shape[1] == dimensions
 
 def test_linear_discriminant_analysis() -> None:
     # Create dummy dataset with 2 classes and 3 features

From 8c1edd76a73fd29943f942733e8dca9b1126e35a Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Fri, 3 Oct 2025 16:59:26 +0000
Subject: [PATCH 2/2] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 machine_learning/dimensionality_reduction.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/machine_learning/dimensionality_reduction.py b/machine_learning/dimensionality_reduction.py
index 803ff855cd89..622668666ef1 100644
--- a/machine_learning/dimensionality_reduction.py
+++ b/machine_learning/dimensionality_reduction.py
@@ -160,6 +160,7 @@ def linear_discriminant_analysis(
         logging.error("Dataset empty")
         raise AssertionError
 
+
 def locally_linear_embedding(
     features: np.ndarray, n_neighbors: int, dimensions: int
 ) -> np.ndarray:
@@ -176,7 +177,9 @@ def locally_linear_embedding(
     # Check if features exist
     if features.any():
         # Placeholder: actual LLE computation goes here
-        logging.info(f"LLE computed with {n_neighbors} neighbors and {dimensions} dimensions")
+        logging.info(
+            f"LLE computed with {n_neighbors} neighbors and {dimensions} dimensions"
+        )
         return features[:, :dimensions]  # temporary projection
     else:
         logging.basicConfig(level=logging.ERROR, format="%(message)s", force=True)
@@ -184,9 +187,7 @@ def locally_linear_embedding(
         raise AssertionError
 
 
-def multidimensional_scaling(
-    features: np.ndarray, dimensions: int
-) -> np.ndarray:
+def multidimensional_scaling(features: np.ndarray, dimensions: int) -> np.ndarray:
     """
     Multidimensional Scaling (MDS).
 
@@ -205,6 +206,7 @@ def multidimensional_scaling(
         logging.error("Dataset empty")
         raise AssertionError
 
+
 def test_locally_linear_embedding() -> None:
     features = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
     n_neighbors = 2
@@ -221,6 +223,7 @@ def test_multidimensional_scaling() -> None:
     assert output.shape[0] == features.shape[0]
     assert output.shape[1] == dimensions
 
+
 def test_linear_discriminant_analysis() -> None:
     # Create dummy dataset with 2 classes and 3 features
     features = np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]])