diff --git a/numpy_questions.py b/numpy_questions.py
index 21fcec4b..7a26ca59 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 array is not 2D")
 
-    return i, j
+    flat_idx = np.argmax(X)
+    i, j = np.unravel_index(flat_idx, X.shape)
+
+    return int(i), int(j)
 
 
 def wallis_product(n_terms):
@@ -64,4 +71,11 @@ 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
+
+    n = np.arange(1, n_terms + 1, dtype=float)
+    terms = (2 * n / (2 * n - 1)) * (2 * n / (2 * n + 1))
+    product = np.prod(terms)
+
+    return 2.0 * product
diff --git a/sklearn_questions.py b/sklearn_questions.py
index f65038c6..229a06ec 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -28,8 +28,8 @@
 from sklearn.utils.multiclass import check_classification_targets
 
 
-class OneNearestNeighbor(BaseEstimator, ClassifierMixin):
-    "OneNearestNeighbor classifier."
+class OneNearestNeighbor(ClassifierMixin, BaseEstimator):
+    """OneNearestNeighbor classifier."""
 
     def __init__(self):  # noqa: D107
         pass
@@ -41,6 +41,8 @@ def fit(self, X, y):
         """
         X, y = check_X_y(X, y)
         check_classification_targets(y)
+        self.X_train_ = X
+        self.y_train_ = y
         self.classes_ = np.unique(y)
         self.n_features_in_ = X.shape[1]
 
@@ -54,12 +56,14 @@ def predict(self, X):
         """
         check_is_fitted(self)
         X = check_array(X)
-        y_pred = np.full(
-            shape=len(X), fill_value=self.classes_[0],
-            dtype=self.classes_.dtype
-        )
+        self._check_n_features(X, reset=False)
+        y_pred = np.empty(X.shape[0], dtype=self.classes_.dtype)
 
         # XXX fix
+        for i in range(len(X)):
+            distances = np.linalg.norm(self.X_train_ - X[i], axis=1)
+            nearest_index = np.argmin(distances)
+            y_pred[i] = self.y_train_[nearest_index]
         return y_pred
 
     def score(self, X, y):
@@ -71,4 +75,4 @@ def score(self, X, y):
         y_pred = self.predict(X)
 
         # XXX fix
-        return y_pred.sum()
+        return np.mean(y_pred == y)