diff --git a/numpy_questions.py b/numpy_questions.py
index 21fcec4b..8baf7f77 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -41,7 +41,11 @@ 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")
+    i, j = np.unravel_index(np.argmax(X), X.shape)
     return i, j
 
 
@@ -64,4 +68,10 @@ 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
+
+    product = 1
+    for i in range(1, n_terms + 1):
+        product *= (4 * i**2) / (4 * i**2 - 1)
+    return product * 2
diff --git a/sklearn_questions.py b/sklearn_questions.py
index f65038c6..138fe9d1 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -28,47 +28,83 @@
 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
 
     def fit(self, X, y):
-        """Write docstring.
+        """Fit the OneNearestNeighbor classifier.
 
-        And describe parameters
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The input array.
+
+        y : ndarray of shape (n_samples,)
+            Target labels for the training samples.
+
+        Returns
+        -------
+        self : OneNearestNeighbor
+            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_ = X
+        self.y_ = y
         return self
 
     def predict(self, X):
-        """Write docstring.
+        """Predict class labels for the input samples.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Samples for which to predict labels.
 
-        And describe parameters
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            Predicted class labels.
         """
         check_is_fitted(self)
         X = check_array(X)
+        self._check_n_features(X, reset=False)
         y_pred = np.full(
             shape=len(X), fill_value=self.classes_[0],
             dtype=self.classes_.dtype
         )
 
-        # XXX fix
+        for i in range(len(X)):
+            dist = np.linalg.norm(self.X_ - X[i], axis=1)
+            nearest_idx = np.argmin(dist)
+            y_pred[i] = self.y_[nearest_idx]
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
+        """Compute the accuracy of the classifier.
 
-        And describe parameters
+        The accuracy is the proportion of correctly classified samples.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Test samples.
+
+        y : ndarray of shape (n_samples,)
+            True labels corresponding to the test samples.
+
+        Returns
+        -------
+        score :
+            Classification accuracy.
         """
+        self._check_n_features(X, reset=False)
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
-
-        # XXX fix
+        y_pred = (y_pred == y).astype(int)/len(y)
         return y_pred.sum()