From 1b654ef4a05ca8e48b9ad8f4222c546ecb1ac5cd Mon Sep 17 00:00:00 2001
From: Alessandro Tavolato <alessandro.tavolato@gmail.com>
Date: Tue, 10 Nov 2026 16:33:10 +0100
Subject: [PATCH 1/2] Implement numpy and sklearn questions

---
 numpy_questions.py   | 20 ++++++++++--
 sklearn_questions.py | 78 ++++++++++++++++++++++++++++++--------------
 2 files changed, 72 insertions(+), 26 deletions(-)

diff --git a/numpy_questions.py b/numpy_questions.py
index 21fcec4b..545e1448 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -37,10 +37,19 @@ def max_index(X):
         If the input is not a numpy array or
         if the shape is not 2D.
     """
+    if not isinstance(X, np.ndarray):
+        raise ValueError("X must be a numpy array")
+    if X.ndim != 2:
+        raise ValueError("X must be 2D")
+
     i = 0
     j = 0
 
-    # TODO
+    for row in range(X.shape[0]):
+        for col in range(X.shape[1]):
+            if X[row, col] > X[i, j]:
+                i = row
+                j = col
 
     return i, j
 
@@ -64,4 +73,11 @@ 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
+
+    prod = 1.0
+    for n in range(1, n_terms + 1):
+        prod *= (4 * n ** 2) / (4 * n ** 2 - 1)
+    return 2 * prod
diff --git a/sklearn_questions.py b/sklearn_questions.py
index f65038c6..ac76f03a 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -23,52 +23,82 @@
 from sklearn.base import BaseEstimator
 from sklearn.base import ClassifierMixin
 from sklearn.utils.validation import check_X_y
-from sklearn.utils.validation import check_array
 from sklearn.utils.validation import check_is_fitted
+from sklearn.utils.validation import validate_data
 from sklearn.utils.multiclass import check_classification_targets
 
 
-class OneNearestNeighbor(BaseEstimator, ClassifierMixin):
-    "OneNearestNeighbor classifier."
+class OneNearestNeighbor(ClassifierMixin, BaseEstimator):
+    """OneNearestNeighbor classifier.
+
+    Predicts for each sample the label of the closest training sample,
+    using the Euclidean distance.
+    """
 
     def __init__(self):  # noqa: D107
         pass
 
     def fit(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Fit the classifier by storing the training data.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The training samples.
+        y : ndarray of shape (n_samples,)
+            The target labels of the training samples.
+
+        Returns
+        -------
+        self : OneNearestNeighbor
+            The fitted classifier.
         """
-        X, y = check_X_y(X, y)
+        X, y = validate_data(self, X, y)
         check_classification_targets(y)
         self.classes_ = np.unique(y)
-        self.n_features_in_ = X.shape[1]
 
-        # XXX fix
+        self.X_train_ = X
+        self.y_train_ = y
         return self
 
     def predict(self, X):
-        """Write docstring.
+        """Predict the label of each sample in X.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The samples to classify.
 
-        And describe parameters
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            The label of the closest training sample for each sample.
         """
         check_is_fitted(self)
-        X = check_array(X)
-        y_pred = np.full(
-            shape=len(X), fill_value=self.classes_[0],
-            dtype=self.classes_.dtype
-        )
+        X = validate_data(self, X, reset=False)
 
-        # XXX fix
-        return y_pred
+        # distances[i, k] = Euclidean distance between X[i] and X_train_[k]
+        diff = X[:, np.newaxis, :] - self.X_train_[np.newaxis, :, :]
+        distances = np.sqrt(np.sum(diff ** 2, axis=2))
 
-    def score(self, X, y):
-        """Write docstring.
+        closest = np.argmin(distances, axis=1)
+        return self.y_train_[closest]
 
-        And describe parameters
+    def score(self, X, y):
+        """Compute the accuracy of the classifier on X and y.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The test samples.
+        y : ndarray of shape (n_samples,)
+            The true labels of the test samples.
+
+        Returns
+        -------
+        score : float
+            The fraction of samples correctly classified.
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
-
-        # XXX fix
-        return y_pred.sum()
+        return np.mean(y_pred == y)

From 39c3087e39750ee86a622bfabab50b5e4d4e89de Mon Sep 17 00:00:00 2001
From: Alessandro Tavolato <alessandro.tavolato@gmail.com>
Date: Tue, 10 Nov 2026 16:42:25 +0100
Subject: [PATCH 2/2] Resolve merge conflict

---
 sklearn_questions.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/sklearn_questions.py b/sklearn_questions.py
index 9194463f..ac76f03a 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -29,15 +29,11 @@
 
 
 class OneNearestNeighbor(ClassifierMixin, BaseEstimator):
-<<<<<<< HEAD
     """OneNearestNeighbor classifier.
 
     Predicts for each sample the label of the closest training sample,
     using the Euclidean distance.
     """
-=======
-    "OneNearestNeighbor classifier."
->>>>>>> b2d89059424004be5fd3b7f1d02a049f9ce720dc
 
     def __init__(self):  # noqa: D107
         pass