diff --git a/numpy_questions.py b/numpy_questions.py
index 21fcec4b..bbbf46e1 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -41,8 +41,15 @@ def max_index(X):
     j = 0
 
     # TODO
+    if not isinstance(X, np.ndarray):
+        raise ValueError("Input is not a numpy array")
+    if X.ndim != 2:
+        raise ValueError("Input is not a 2D array")
 
-    return i, j
+    max_idx = np.unravel_index(np.argmax(X, axis=None), X.shape)
+    i, j = max_idx
+
+    return (i, j)
 
 
 def wallis_product(n_terms):
@@ -64,4 +71,9 @@ 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.
+    if n_terms == 0:
+        return 1.0
+    product = 1.0
+    for n in range(1, n_terms + 1):
+        product *= (4 * n ** 2) / (4 * n ** 2 - 1)
+    return product * 2.0
diff --git a/sklearn_questions.py b/sklearn_questions.py
index f65038c6..8008f718 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -29,15 +29,21 @@
 
 
 class OneNearestNeighbor(BaseEstimator, ClassifierMixin):
-    "OneNearestNeighbor classifier."
+    """OneNearestNeighbor classifier."""
 
     def __init__(self):  # noqa: D107
         pass
 
     def fit(self, X, y):
-        """Write docstring.
+        """Fit the model to the training data.
 
-        And describe parameters
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The input data.
+
+        y : ndarray of shape (n_samples,)
+            The target labels.
         """
         X, y = check_X_y(X, y)
         check_classification_targets(y)
@@ -45,12 +51,22 @@ def fit(self, X, 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 input data.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The input data.
 
-        And describe parameters
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            The predicted class labels.
         """
         check_is_fitted(self)
         X = check_array(X)
@@ -60,15 +76,29 @@ def predict(self, X):
         )
 
         # XXX fix
+        for i, x in enumerate(X):
+            dists = np.linalg.norm(self.X_ - x, axis=1)
+            y_pred[i] = self.y_[np.argmin(dists)]
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
+        """Compute the accuracy of the model.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The input data.
+
+        y : ndarray of shape (n_samples,)
+            The true labels.
 
-        And describe parameters
+        Returns
+        -------
+        score : float
+            The accuracy of the model.
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
 
         # XXX fix
-        return y_pred.sum()
+        return np.mean(y_pred == y)