diff --git a/numpy_questions.py b/numpy_questions.py
index 21fcec4b..077ef756 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -39,8 +39,11 @@ def max_index(X):
     """
     i = 0
     j = 0
-
-    # TODO
+    if type(X) is not np.ndarray:
+        raise ValueError("X must be a numpy array.")
+    if X.ndim != 2:
+        raise ValueError("X must be a 2D array")
+    i, j = np.unravel_index(np.argmax(X), X.shape)
 
     return i, j
 
@@ -62,6 +65,10 @@ 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:
+        return 1
+    k = np.arange(1, n_terms + 1)
+    terms = (4 * k ** 2) / (4 * k ** 2 - 1)
+    pi = 2 * np.prod(terms)
+
+    return pi
diff --git a/sklearn_questions.py b/sklearn_questions.py
index 948c2e54..3c973a85 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -25,33 +25,51 @@
 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(ClassifierMixin, BaseEstimator):
-    "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)
+            The training points X_i.
+        y : ndarray of shape (n_samples,)
+            The target values (class labels).
+
+        Returns
+        -------
+        self : OneNearestNeighbor
+            The fitted classifier.
         """
         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.
+        """Predict the class labels for the provided data.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The input samples.
 
-        And describe parameters
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            The predicted class labels.
         """
         check_is_fitted(self)
         X = check_array(X)
@@ -59,17 +77,32 @@ def predict(self, X):
             shape=len(X), fill_value=self.classes_[0],
             dtype=self.classes_.dtype
         )
-
-        # XXX fix
+        if X.shape[1] != self.n_features_in_:
+            raise ValueError(
+                f"X has {X.shape[1]} features, but OneNearestNeighbor "
+                f"is expecting {self.n_features_in_} features as input."
+            )
+        V = np.linalg.norm(self.X_train_[None, :, :] - X[:, None, :], axis=2)
+        neighbor = np.argmin(V, axis=1)
+        y_pred = self.y_train_[neighbor]
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Return the accuracy on the given test data and labels.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The test samples.
+        y : ndarray of shape (n_samples,)
+            The true labels for X.
+
+        Returns
+        -------
+        score : float
+            The proportion of correctly classified samples.
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
 
-        # XXX fix
-        return y_pred.sum()
+        return np.mean(y_pred == y)