From 5724fd950cf2bc019035baf383145370d8579bd0 Mon Sep 17 00:00:00 2001
From: akilanthevaraja-max <akilanthevaraja@icloud.com>
Date: Sat, 15 Nov 2025 17:41:53 +0100
Subject: [PATCH 1/3] Part B done Akilan-Thevaraja

---
 numpy_questions.py   | 18 ++++++++++++++++--
 sklearn_questions.py | 17 +++++++++++------
 2 files changed, 27 insertions(+), 8 deletions(-)

diff --git a/numpy_questions.py b/numpy_questions.py
index 21fcec4b..70d51b60 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -41,8 +41,15 @@ def max_index(X):
     j = 0
 
     # TODO
+    if not isinstance(X, np.ndarray):
+        raise ValueError("Input is not a numpy array.")
+    if X.ndim != 2:
+        raise ValueError("Input array is not 2D")
 
-    return i, j
+    flat_idx = np.argmax(X)
+    i, j = np.unravel_index(flat_idx, X.shape)
+
+    return int(i), int(j)
 
 
 def wallis_product(n_terms):
@@ -64,4 +71,11 @@ def wallis_product(n_terms):
     """
     # XXX : The n_terms is an int that corresponds to the number of
     # terms in the product. For example 10000.
-    return 0.
+    if n_terms == 0:
+        return 1.0
+
+    n = np.arange(1, n_terms + 1, dtype=float)
+    terms =(2 * n / (2 * n -1)) * (2 * n /(2 * n +1))
+    product = np.prod(terms)
+
+    return 2.0 * product
diff --git a/sklearn_questions.py b/sklearn_questions.py
index f65038c6..71909ede 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -28,7 +28,7 @@
 from sklearn.utils.multiclass import check_classification_targets
 
 
-class OneNearestNeighbor(BaseEstimator, ClassifierMixin):
+class OneNearestNeighbor(ClassifierMixin, BaseEstimator):
     "OneNearestNeighbor classifier."
 
     def __init__(self):  # noqa: D107
@@ -41,6 +41,8 @@ def fit(self, X, y):
         """
         X, y = check_X_y(X, y)
         check_classification_targets(y)
+        self.X_train_ = X
+        self.y_train_ = y 
         self.classes_ = np.unique(y)
         self.n_features_in_ = X.shape[1]
 
@@ -54,12 +56,15 @@ def predict(self, X):
         """
         check_is_fitted(self)
         X = check_array(X)
-        y_pred = np.full(
-            shape=len(X), fill_value=self.classes_[0],
-            dtype=self.classes_.dtype
-        )
+        self._check_n_features(X, reset=False)
+        y_pred = np.empty(X.shape[0], dtype=self.classes_.dtype)
 
         # XXX fix
+        for i in range(len(X)):
+            distances = np.linalg.norm(self.X_train_ - X[i], axis=1)
+            nearest_index = np.argmin(distances)
+            y_pred[i] = self.y_train_[nearest_index]
+        
         return y_pred
 
     def score(self, X, y):
@@ -71,4 +76,4 @@ def score(self, X, y):
         y_pred = self.predict(X)
 
         # XXX fix
-        return y_pred.sum()
+        return np.mean(y_pred == y)

From 289c07c49a91d8fb05e5b2f37aacd31f90503f5c Mon Sep 17 00:00:00 2001
From: akilanthevaraja-max <akilanthevaraja@icloud.com>
Date: Sat, 15 Nov 2025 17:53:00 +0100
Subject: [PATCH 2/3] error fixed

---
 numpy_questions.py   | 2 +-
 sklearn_questions.py | 3 +--
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/numpy_questions.py b/numpy_questions.py
index 70d51b60..7a26ca59 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -75,7 +75,7 @@ def wallis_product(n_terms):
         return 1.0
 
     n = np.arange(1, n_terms + 1, dtype=float)
-    terms =(2 * n / (2 * n -1)) * (2 * n /(2 * n +1))
+    terms = (2 * n / (2 * n - 1)) * (2 * n / (2 * n + 1))
     product = np.prod(terms)
 
     return 2.0 * product
diff --git a/sklearn_questions.py b/sklearn_questions.py
index 71909ede..51d0bd98 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -42,7 +42,7 @@ def fit(self, X, y):
         X, y = check_X_y(X, y)
         check_classification_targets(y)
         self.X_train_ = X
-        self.y_train_ = y 
+        self.y_train_ = y
         self.classes_ = np.unique(y)
         self.n_features_in_ = X.shape[1]
 
@@ -64,7 +64,6 @@ def predict(self, X):
             distances = np.linalg.norm(self.X_train_ - X[i], axis=1)
             nearest_index = np.argmin(distances)
             y_pred[i] = self.y_train_[nearest_index]
-        
         return y_pred
 
     def score(self, X, y):

From d073f1ede51c0d0d2eb509a2b206a431caba7be7 Mon Sep 17 00:00:00 2001
From: akilanthevaraja-max <akilanthevaraja@icloud.com>
Date: Sat, 15 Nov 2025 17:56:12 +0100
Subject: [PATCH 3/3] pydocstyle errors fixed

---
 sklearn_questions.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sklearn_questions.py b/sklearn_questions.py
index 51d0bd98..229a06ec 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -29,7 +29,7 @@
 
 
 class OneNearestNeighbor(ClassifierMixin, BaseEstimator):
-    "OneNearestNeighbor classifier."
+    """OneNearestNeighbor classifier."""
 
     def __init__(self):  # noqa: D107
         pass