part B done

numpy_questions.py

@@ -42,6 +42,15 @@ def max_index(X):
 
     # TODO
 
+    if not isinstance(X, np.ndarray):
+        raise ValueError("Input is not a numpy array")  # Check for numpy array
+
+    if X.ndim != 2:
+        raise ValueError("Input array is not 2D")  # Check for 2D shape
+
+    # Find indices of maximum value
+    i, j = np.unravel_index(np.argmax(X), X.shape)
+
     return i, j
 
 
@@ -64,4 +73,14 @@ 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:
+        # Wallis product with 0 terms is defined as 1
+        return 1.0
+
+    n = np.arange(1, n_terms + 1)  # Create an array from 1 to n_terms
+    terms = (2 * n / (2 * n - 1)) * (2 * n / (2 * n + 1))
+    product = np.prod(terms)  # Compute the product of all terms
+
+    # Return the approximation of pi
+    return 2.0 * product

sklearn_questions.py

@@ -29,28 +29,49 @@
 
 
 class OneNearestNeighbor(BaseEstimator, ClassifierMixin):
-    "OneNearestNeighbor classifier."
+    """OneNearestNeighbor classifier."""
 
     def __init__(self):  # noqa: D107
         pass
 
     def fit(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Fit the OneNearestNeighbor classifier.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Training data.
+        y : ndarray of shape (n_samples,)
+            Target labels corresponding to X.
+
+        Returns
+        -------
+        self : object
+            Fitted estimator.
         """
         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 class labels for the given samples.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Input data to predict.
 
-        And describe parameters
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            Predicted class labels for each sample in X.
         """
         check_is_fitted(self)
         X = check_array(X)
@@ -60,15 +81,32 @@ def predict(self, X):
         )
 
         # XXX fix
+        # euclidean distances between each point in X and each point in self.X_
+        distances = np.linalg.norm(
+            self.X_[np.newaxis, :, :] - X[:, np.newaxis, :],
+            axis=2,
+        )
+        nearest_idx = np.argmin(distances, axis=1)
+        y_pred = self.y_[nearest_idx]
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Compute the accuracy of the classifier.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Test samples.
+        y : ndarray of shape (n_samples,)
+            True labels for X.
+
+        Returns
+        -------
+        score : float
+            Mean accuracy of the classifier on the provided data.
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
 
         # XXX fix
-        return y_pred.sum()
+        return np.mean(y_pred == y)