From ec90ac6734b9c0aeb464d9edcf3464b96a2e75ed Mon Sep 17 00:00:00 2001
From: silabou <sila.boukhobza@gmail.com>
Date: Thu, 13 Nov 2025 14:43:20 +0100
Subject: [PATCH 1/3] PR Sila BOUKHOBZA

---
 numpy_questions.py   | 1 +
 sklearn_questions.py | 1 +
 2 files changed, 2 insertions(+)

diff --git a/numpy_questions.py b/numpy_questions.py
index 21fcec4b..371b8d58 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -15,6 +15,7 @@
 This will be enforced with `flake8`. You can check that there is no flake8
 errors by calling `flake8` at the root of the repo.
 """
+# Modification
 import numpy as np
 
 
diff --git a/sklearn_questions.py b/sklearn_questions.py
index f65038c6..d885e860 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -19,6 +19,7 @@
 for the methods you code and for the class. The docstring will be checked using
 `pydocstyle` that you can also call at the root of the repo.
 """
+#Modification
 import numpy as np
 from sklearn.base import BaseEstimator
 from sklearn.base import ClassifierMixin

From 1cebecefdb4530b78242e6ec3b0926304c2e5719 Mon Sep 17 00:00:00 2001
From: silabou <sila.boukhobza@gmail.com>
Date: Thu, 13 Nov 2025 16:06:55 +0100
Subject: [PATCH 2/3] Sila BOUKHOBZA

---
 numpy_questions.py   | 17 ++++++++++++-----
 sklearn_questions.py | 32 ++++++++++++++++++++------------
 2 files changed, 32 insertions(+), 17 deletions(-)

diff --git a/numpy_questions.py b/numpy_questions.py
index 371b8d58..49f25073 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -38,11 +38,13 @@ def max_index(X):
         If the input is not a numpy array or
         if the shape is not 2D.
     """
-    i = 0
-    j = 0
-
-    # TODO
+    if not isinstance(X, np.ndarray):
+        raise ValueError("Input is not a numpy array.")
+    if X.ndim != 2:
+        raise ValueError("Shape is not 2D")
 
+    idx_flat = np.argmax(X)
+    i, j = np.unravel_index(idx_flat, X.shape)
     return i, j
 
 
@@ -65,4 +67,9 @@ 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.
+    res = 1
+    if n_terms == 0:
+        return res
+    for i in range(1, n_terms+1):
+        res *= (4 * i**2) / (4 * i**2 - 1)
+    return 2*res
diff --git a/sklearn_questions.py b/sklearn_questions.py
index d885e860..b299ffe4 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -19,7 +19,6 @@
 for the methods you code and for the class. The docstring will be checked using
 `pydocstyle` that you can also call at the root of the repo.
 """
-#Modification
 import numpy as np
 from sklearn.base import BaseEstimator
 from sklearn.base import ClassifierMixin
@@ -29,29 +28,34 @@
 from sklearn.utils.multiclass import check_classification_targets
 
 
-class OneNearestNeighbor(BaseEstimator, ClassifierMixin):
+class OneNearestNeighbor(ClassifierMixin, BaseEstimator):
     "OneNearestNeighbor classifier."
 
     def __init__(self):  # noqa: D107
         pass
 
     def fit(self, X, y):
-        """Write docstring.
+        """
+        Fits the model to data X and y
 
-        And describe parameters
+        X : training features
+        y : training targets
         """
         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_train_ = X
+        self.y_train_ = y
+
         return self
 
     def predict(self, X):
-        """Write docstring.
+        """
+        Predicts data X according to fit.
 
-        And describe parameters
+        X : estimators
         """
         check_is_fitted(self)
         X = check_array(X)
@@ -60,16 +64,20 @@ def predict(self, X):
             dtype=self.classes_.dtype
         )
 
-        # XXX fix
+        for i, x in enumerate(X):
+            distances = np.linalg.norm(self.X_train_ - x, axis=1)
+            min_dist = np.argmin(distances)
+            y_pred[i] = self.y_train_[min_dist]
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
+        """
+        Computes the accuracy score for evaluation
 
-        And describe parameters
+        X : estimators
+        y : target
         """
         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 8864c7ead70ff5971dc1f602d8d521677d3241d0 Mon Sep 17 00:00:00 2001
From: silabou <sila.boukhobza@gmail.com>
Date: Thu, 13 Nov 2025 16:13:06 +0100
Subject: [PATCH 3/3] Sila BOUKHOBZA

---
 sklearn_questions.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/sklearn_questions.py b/sklearn_questions.py
index b299ffe4..0320d7a3 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -29,14 +29,14 @@
 
 
 class OneNearestNeighbor(ClassifierMixin, BaseEstimator):
-    "OneNearestNeighbor classifier."
+    """OneNearestNeighbor classifier."""
 
     def __init__(self):  # noqa: D107
         pass
 
     def fit(self, X, y):
         """
-        Fits the model to data X and y
+        Fits the model to data X and y.
 
         X : training features
         y : training targets
@@ -72,7 +72,7 @@ def predict(self, X):
 
     def score(self, X, y):
         """
-        Computes the accuracy score for evaluation
+        Compute the accuracy score for evaluation.
 
         X : estimators
         y : target