diff --git a/numpy_questions.py b/numpy_questions.py
index 21fcec4b..ff36a3a0 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -40,7 +40,14 @@ def max_index(X):
     i = 0
     j = 0
 
-    # TODO
+    if not isinstance(X, np.ndarray):
+        raise ValueError("Input should be a numpy array.")
+
+    if X.ndim != 2:
+        raise ValueError("Input array should be 2D.")
+
+    flat_idx = np.argmax(X)
+    i, j = flat_idx // X.shape[1], flat_idx % X.shape[1]
 
     return i, j
 
@@ -62,6 +69,12 @@ 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.
+    pi = 2.
+
+    if n_terms == 0:
+        return 1.
+
+    for n in range(1, n_terms + 1):
+        pi *= (4 * n ** 2) / (4 * n ** 2 - 1)
+
+    return pi
diff --git a/sklearn_questions.py b/sklearn_questions.py
index f65038c6..bef72c12 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -29,46 +29,93 @@
 
 
 class OneNearestNeighbor(BaseEstimator, ClassifierMixin):
-    "OneNearestNeighbor classifier."
+    """OneNearestNeighbor classifier."""
 
     def __init__(self):  # noqa: D107
         pass
 
     def fit(self, X, y):
-        """Write docstring.
+        """Fit OneNearestNeighbor model according to the given training data.
 
-        And describe parameters
+        Check X and y for consistent length, enforce X to be 2D and y 1D,
+        and ensure that target y is of a non-regression type.
+        (The above is consistent with sklearn's documentation.)
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Training data.
+
+        y : ndarray of shape (n_samples,)
+            Target values.
+
+        Returns
+        -------
+        self : object
+            Model fit on the training data.
         """
         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.
-
-        And describe parameters
+        """Perform classification on test data X.
+
+        Check that the model is fitted and X is 2D.
+        By default, the input is checked to be a non-empty 2D array
+        containing only finite values.
+        (The above was taken from sklearn's documentation.)
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Test samples.
+
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            Predicted class labels for samples in X.
         """
-        check_is_fitted(self)
         X = check_array(X)
+        check_is_fitted(self)
+
         y_pred = np.full(
             shape=len(X), fill_value=self.classes_[0],
             dtype=self.classes_.dtype
         )
 
-        # XXX fix
+        distances = np.linalg.norm(X[:, np.newaxis] - self.X_, axis=2)
+        nearest_neighbor_idx = np.argmin(distances, axis=1)
+        y_pred = self.y_[nearest_neighbor_idx]
+
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
+        """Return the accuracy on the given test data and labels.
+
+        Check that the model is fitted and that X and y have consistent length.
+        (The above was taken from sklearn's documentation.)
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Test samples.
+
+        y : ndarray of shape (n_samples,)
+            True labels for X.
 
-        And describe parameters
+        Returns
+        -------
+        score : float
+            Accuracy of self.predict(X) with respect to y.
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
 
-        # XXX fix
-        return y_pred.sum()
+        return np.mean(y_pred == y)