From c4408a6d393e12a38719dedc3dfa3e1e85109a2f Mon Sep 17 00:00:00 2001
From: JulienK03 <julienkanaan@hotmail.com>
Date: Sun, 16 Nov 2025 16:58:18 +0100
Subject: [PATCH 1/3] Debugging for wallis product

---
 numpy_questions.py | 35 ++++++++++++++++++++++++++---------
 1 file changed, 26 insertions(+), 9 deletions(-)

diff --git a/numpy_questions.py b/numpy_questions.py
index 21fcec4b..1a5508f6 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -37,12 +37,20 @@ def max_index(X):
         If the input is not a numpy array or
         if the shape is not 2D.
     """
-    i = 0
-    j = 0
-
-    # TODO
-
-    return i, j
+    # Check if input is a numpy array
+    if not isinstance(X, np.ndarray):
+        raise ValueError("Input must be a numpy array")
+    
+    # Check if array is 2D
+    if X.ndim != 2:
+        raise ValueError("Input must be a 2D array")
+    
+    # Find the index of the maximum value
+    # np.argmax returns the flattened index, so we use unravel_index
+    flat_index = np.argmax(X)
+    i, j = np.unravel_index(flat_index, X.shape)
+    
+    return int(i), int(j)
 
 
 def wallis_product(n_terms):
@@ -62,6 +70,15 @@ def wallis_product(n_terms):
     pi : float
         The approximation of order `n_terms` of pi using the Wallis product.
     """
-    # XXX : The n_terms is an int that corresponds to the number of
-    # terms in the product. For example 10000.
-    return 0.
+    # Wallis product: pi/2 = (2/1 * 2/3) * (4/3 * 4/5) * (6/5 * 6/7) * ...
+    # Or: pi/2 = product of (4n^2) / (4n^2 - 1) for n from 1 to n_terms
+    if n_terms == 0:
+        return 1.0
+
+    product = 1.0
+    
+    for n in range(1, n_terms + 1):
+        product *= (4 * n**2) / (4 * n**2 - 1)
+    
+    # Multiply by 2 to get pi
+    return 2 * product
\ No newline at end of file

From 492fabff059020e68719ef16805e1d33a9e860cf Mon Sep 17 00:00:00 2001
From: JulienK03 <julienkanaan@hotmail.com>
Date: Sun, 16 Nov 2025 17:03:29 +0100
Subject: [PATCH 2/3] Completed OneNearestNeighbor class

---
 sklearn_questions.py | 21 +++++++++++++++++----
 1 file changed, 17 insertions(+), 4 deletions(-)

diff --git a/sklearn_questions.py b/sklearn_questions.py
index f65038c6..85ed5fe5 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -44,7 +44,10 @@ def fit(self, X, y):
         self.classes_ = np.unique(y)
         self.n_features_in_ = X.shape[1]
 
-        # XXX fix
+        # Store training data
+        self.X_ = X
+        self.y_ = y
+
         return self
 
     def predict(self, X):
@@ -59,7 +62,15 @@ def predict(self, X):
             dtype=self.classes_.dtype
         )
 
-        # XXX fix
+        # For each test sample, find the nearest training sample
+        for i, x in enumerate(X):
+            # Calculate Euclidean distances to all training samples
+            distances = np.sqrt(np.sum((self.X_ - x) ** 2, axis=1))
+            # Find index of nearest neighbor
+            nearest_idx = np.argmin(distances)
+            # Assign the label of the nearest neighbor
+            y_pred[i] = self.y_[nearest_idx]
+
         return y_pred
 
     def score(self, X, y):
@@ -70,5 +81,7 @@ def score(self, X, y):
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
 
-        # XXX fix
-        return y_pred.sum()
+        # Calculate accuracy: proportion of correct predictions
+        accuracy = np.mean(y_pred == y)
+
+        return accuracy

From c3d2ba0f1552b53431a6eb6e7591fee33d0cdc4d Mon Sep 17 00:00:00 2001
From: JulienK03 <julienkanaan@hotmail.com>
Date: Sun, 16 Nov 2025 17:08:19 +0100
Subject: [PATCH 3/3] Fixed flake8 and pydocstyle comments

---
 numpy_questions.py   | 12 ++++++------
 sklearn_questions.py |  2 +-
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/numpy_questions.py b/numpy_questions.py
index 1a5508f6..9a3301f8 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -40,16 +40,16 @@ def max_index(X):
     # Check if input is a numpy array
     if not isinstance(X, np.ndarray):
         raise ValueError("Input must be a numpy array")
-    
+
     # Check if array is 2D
     if X.ndim != 2:
         raise ValueError("Input must be a 2D array")
-    
+
     # Find the index of the maximum value
     # np.argmax returns the flattened index, so we use unravel_index
     flat_index = np.argmax(X)
     i, j = np.unravel_index(flat_index, X.shape)
-    
+
     return int(i), int(j)
 
 
@@ -76,9 +76,9 @@ def wallis_product(n_terms):
         return 1.0
 
     product = 1.0
-    
+
     for n in range(1, n_terms + 1):
         product *= (4 * n**2) / (4 * n**2 - 1)
-    
+
     # Multiply by 2 to get pi
-    return 2 * product
\ No newline at end of file
+    return 2 * product
diff --git a/sklearn_questions.py b/sklearn_questions.py
index 85ed5fe5..e827d503 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -29,7 +29,7 @@
 
 
 class OneNearestNeighbor(BaseEstimator, ClassifierMixin):
-    "OneNearestNeighbor classifier."
+    """OneNearestNeighbor classifier."""
 
     def __init__(self):  # noqa: D107
         pass