Mehdi Benhiba El Idrissi — Assignment 2 Submission

numpy_questions.py

@@ -37,10 +37,13 @@ def max_index(X):
         If the input is not a numpy array or
         if the shape is not 2D.
     """
-    i = 0
-    j = 0
+    if not isinstance(X, np.ndarray):
+        raise ValueError("X must be a numpy ndarray.")
+    if X.ndim != 2:
+        raise ValueError("X must be a 2D array.")
 
-    # TODO
+    flat_idx = np.argmax(X)
+    i, j = np.unravel_index(flat_idx, X.shape)
 
     return i, j
 
@@ -62,6 +65,14 @@ 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 not isinstance(n_terms, int) or n_terms < 0:
+        raise ValueError("n_terms must be a non-negative integer.")
+
+    if n_terms == 0:
+        return 1.0
+
+    k = np.arange(1, n_terms + 1, dtype=float)
+    terms = (4 * k ** 2) / (4 * k ** 2 - 1)
+    product = np.prod(terms)
+
+    return 2 * product

sklearn_questions.py

@@ -28,47 +28,107 @@
 from sklearn.utils.multiclass import check_classification_targets
 
 
-class OneNearestNeighbor(BaseEstimator, ClassifierMixin):
-    "OneNearestNeighbor classifier."
+class OneNearestNeighbor(ClassifierMixin, BaseEstimator):
+    """OneNearestNeighbor classifier.
+
+    A simple classifier that assigns to each sample the label of the
+    closest training sample according to the Euclidean distance.
+    """
 
     def __init__(self):  # noqa: D107
+        """Initialize the estimator."""
         pass
 
     def fit(self, X, y):
         """Write docstring.
 
         And describe parameters
+
+        Fit the nearest neighbor classifier.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Training data.
+
+        y : ndarray of shape (n_samples,)
+            Target labels.
+
+        Returns
+        -------
+        self : object
+            Fitted estimator.
+
         """
         X, y = check_X_y(X, y)
         check_classification_targets(y)
+        self.X_ = X
+        self.y_ = y
         self.classes_ = np.unique(y)
         self.n_features_in_ = X.shape[1]
-
-        # XXX fix
         return self
 
     def predict(self, X):
         """Write docstring.
 
         And describe parameters
+
+        Predict class labels for samples in X.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Test samples.
+
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            Predicted labels.
         """
         check_is_fitted(self)
         X = check_array(X)
+
+        # Vérifier la cohérence du nombre de features
+        if X.shape[1] != self.n_features_in_:
+            raise ValueError(
+                f"X has {X.shape[1]} features, but "
+                f"{self.__class__.__name__} is expecting "
+                f"{self.n_features_in_} features as input"
+            )
+
         y_pred = np.full(
-            shape=len(X), fill_value=self.classes_[0],
-            dtype=self.classes_.dtype
+            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_idx = np.argmin(distances)
+            y_pred[i] = self.y_[nearest_idx]
+
         return y_pred
 
     def score(self, X, y):
         """Write docstring.
 
+        Compute accuracy of the classifier.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Test samples.
+
+        y : ndarray of shape (n_samples,)
+            True labels.
+
+        Returns
+        -------
+        accuracy : float
+            Proportion of correctly classified samples.
+
         And describe parameters
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
-
-        # XXX fix
-        return y_pred.sum()
+        return np.mean(y_pred == y)