diff --git a/numpy_questions.py b/numpy_questions.py
index 21fcec4b..58427bc2 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -41,7 +41,13 @@ def max_index(X):
     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 2D")
 
+    flat_index = np.argmax(X)
+    i, j = np.unravel_index(flat_index, X.shape)
     return i, j
 
 
@@ -64,4 +70,10 @@ 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)
+    term = term = (4 * n ** 2) / (4 * n ** 2 - 1)
+    product = np.prod(term)
+
+    return 2.0 * product
diff --git a/sklearn_questions.py b/sklearn_questions.py
index f65038c6..86f3f5aa 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
@@ -45,6 +45,8 @@ 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):
@@ -60,6 +62,10 @@ def predict(self, X):
         )
 
         # XXX fix
+        for i, x_test in enumerate(X):
+            distances = np.sum((self.X_ - x_test)**2, axis=1)
+            closest_idx = np.argmin(distances)
+            y_pred[i] = self.y_[closest_idx]
         return y_pred
 
     def score(self, X, y):
@@ -69,6 +75,5 @@ 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)