diff --git a/numpy_questions.py b/numpy_questions.py
index 21fcec4b..368d85b3 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -39,9 +39,12 @@ def max_index(X):
     """
     i = 0
     j = 0
-
-    # TODO
-
+    if not isinstance(X, np.ndarray):
+        raise ValueError("Input must be a numpy array")
+    if X.ndim != 2:
+        raise ValueError("Input must be a 2D array")
+    idx_flat = np.argmax(X)
+    i, j = np.unravel_index(idx_flat, X.shape)
     return i, j
 
 
@@ -62,6 +65,9 @@ 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.
+    product = 1.0
+    for k in range(1, n_terms + 1):
+        product *= (4 * k ** 2) / (4 * k ** 2 - 1)
+    if n_terms == 0:
+        return 1.0
+    return product * 2
diff --git a/sklearn_questions.py b/sklearn_questions.py
index f65038c6..178a8a3f 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -29,7 +29,7 @@
 
 
 class OneNearestNeighbor(BaseEstimator, ClassifierMixin):
-    "OneNearestNeighbor classifier."
+    """OneNearestNeighbor classifier."""
 
     def __init__(self):  # noqa: D107
         pass
@@ -42,9 +42,10 @@ def fit(self, X, y):
         X, y = check_X_y(X, y)
         check_classification_targets(y)
         self.classes_ = np.unique(y)
+        self.X_train_ = X
+        self.y_train_ = y
         self.n_features_in_ = X.shape[1]
 
-        # XXX fix
         return self
 
     def predict(self, X):
@@ -58,8 +59,10 @@ def predict(self, X):
             shape=len(X), fill_value=self.classes_[0],
             dtype=self.classes_.dtype
         )
-
-        # XXX fix
+        for i, x in enumerate(X):
+            dist = np.linalg.norm(self.X_train_ - x, axis=1)
+            idx_min = np.argmin(dist)
+            y_pred[i] = self.y_train_[idx_min]
         return y_pred
 
     def score(self, X, y):
@@ -70,5 +73,4 @@ def score(self, X, 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)