From ecb042be3b58ec1a0ee65b7e0781ff533ad668f9 Mon Sep 17 00:00:00 2001
From: OMAR BENHARBIT <omar.benharbit.1@zimra.polytechnique.fr>
Date: Tue, 10 Nov 2026 16:04:17 +0100
Subject: [PATCH 1/2] Solved numpy exercise

---
 numpy_questions.py | 24 ++++++++++++++++++++----
 1 file changed, 20 insertions(+), 4 deletions(-)

diff --git a/numpy_questions.py b/numpy_questions.py
index 21fcec4b..028a6fb5 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -40,8 +40,13 @@ def max_index(X):
     i = 0
     j = 0
 
-    # TODO
+    if not isinstance(X, np.ndarray):
+        raise ValueError("Input must be a numpy array.")
 
+    if X.ndim != 2:
+        raise ValueError("Input must be a 2D numpy array.")
+
+    i, j = np.unravel_index(np.argmax(X), X.shape)
     return i, j
 
 
@@ -62,6 +67,17 @@ 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.
+    if n_terms < 0:
+        raise ValueError("n_terms must be a non-negative integer.")
+    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)
+
+    pi_approx = prod * 2
+    return pi_approx
+    
+
+    

From 88d99e04ba1997330b2c0ce6c5aec61a09a9f17d Mon Sep 17 00:00:00 2001
From: OMAR BENHARBIT <omar.benharbit.1@zimra.polytechnique.fr>
Date: Tue, 10 Nov 2026 16:37:24 +0100
Subject: [PATCH 2/2] Solved sklearn exercise

---
 numpy_questions.py   |  3 --
 sklearn_questions.py | 65 +++++++++++++++++++++++++++++++++-----------
 2 files changed, 49 insertions(+), 19 deletions(-)

diff --git a/numpy_questions.py b/numpy_questions.py
index 028a6fb5..4d729508 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -78,6 +78,3 @@ def wallis_product(n_terms):
 
     pi_approx = prod * 2
     return pi_approx
-    
-
-    
diff --git a/sklearn_questions.py b/sklearn_questions.py
index f65038c6..073f8b52 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -23,52 +23,85 @@
 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."""
 
     def __init__(self):  # noqa: D107
         pass
 
     def fit(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """
+        Fit the model according to the given training data.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training data.
+        y : array-like of shape (n_samples,)
+            Target values.
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
         """
         X, y = check_X_y(X, y)
         check_classification_targets(y)
         self.classes_ = np.unique(y)
         self.n_features_in_ = X.shape[1]
 
-        # XXX fix
+        self.X_ = X
+        self.y_ = y
+
         return self
 
     def predict(self, X):
-        """Write docstring.
+        """Predict the class labels for the provided data.
 
-        And describe parameters
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Test data.
+
+        Returns
+        -------
+        y_pred : array-like of shape (n_samples,)
+            The predicted class labels.
         """
         check_is_fitted(self)
-        X = check_array(X)
+        X = validate_data(self, X, reset=False)
         y_pred = np.full(
             shape=len(X), fill_value=self.classes_[0],
             dtype=self.classes_.dtype
         )
 
-        # XXX fix
+        for i, x in enumerate(X):
+            distances = np.linalg.norm(self.X_ - x, axis=1)
+            nearest_index = np.argmin(distances)
+            y_pred[i] = self.y_[nearest_index]
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Return the mean accuracy on the given test data and labels.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Test data.
+        y : array-like of shape (n_samples,)
+            True labels for X.
+
+        Returns
+        -------
+        score : float
+            Mean accuracy of self.predict(X) versus y.
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
 
-        # XXX fix
-        return y_pred.sum()
+        return np.mean(y_pred == y)