diff --git a/numpy_questions.py b/numpy_questions.py
index 21fcec4b..5560f383 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -41,6 +41,14 @@ def max_index(X):
     j = 0
 
     # TODO
+    if not isinstance(X, np.ndarray):
+        raise ValueError("The input is not a numpy array")
+
+    if X.ndim != 2:
+        raise ValueError("The shape is not 2D")
+
+    id = np.argmax(X)
+    i, j = np.unravel_index(id, X.shape)
 
     return i, j
 
@@ -64,4 +72,7 @@ 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
+    res = np.prod(np.array([4*n**2/(4*n**2 - 1) for n in range(1, n_terms+1)]))
+    return 2 * res
diff --git a/sklearn_questions.py b/sklearn_questions.py
index f65038c6..4d5c0821 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -28,16 +28,28 @@
 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.
+        """Implement the fitting of a nearest neighbor classifier.
+
+        Parameters
+        ----------
+        X : numpy.darray of shape (number of samples, number of features)
+            The input array we want to fit on.
+
+        y : numpy.darray of shape (number of samples)
+            The target of the samples present in X.
+
+        Returns
+        -------
+        self : OneNearestNeighbor
+            Fitted estimator.
 
-        And describe parameters
         """
         X, y = check_X_y(X, y)
         check_classification_targets(y)
@@ -45,12 +57,24 @@ 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):
-        """Write docstring.
-
-        And describe parameters
+        """Implement the prediction of a target y according to the entry X.
+
+        Parameters
+        ----------
+        X : numpy.darray of shape (number of samples, number of features)
+            The input array we want to predict.
+
+        Returns
+        -------
+        y : numpy.darray of shape (number of samples)
+            The prediction of the samples in X according to the
+            nearest neighbor classifier.
         """
         check_is_fitted(self)
         X = check_array(X)
@@ -60,15 +84,46 @@ def predict(self, X):
         )
 
         # XXX fix
+
+        if X.shape[1] != self.n_features_in_:
+            raise ValueError(
+                "X has {} features, but {} is expecting {} features as input"
+                .format(
+                    X.shape[1],
+                    self.__class__.__name__,
+                    self.n_features_in_,
+                )
+            )
+
+        diffs = X[:, np.newaxis, :] - self.X_[np.newaxis, :, :]
+        dist_sq = np.sum(diffs ** 2, axis=2)
+
+        nearest_indices = np.argmin(dist_sq, axis=1)
+
+        y_pred = self.y_[nearest_indices]
+
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
+        """Give the score of a prediction.
+
+        Parameters
+        ----------
+        X : numpy.darray of shape (number of samples, number of features)
+            The samples we test.
+
+        y : numpy.darray of shape (number of samples)
+            The true labels of the X we try to predict with the classifier.
+        Returns
+        -------
+        score : float
+            The mean accuracy of the predictor on the given data and target.
 
-        And describe parameters
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
 
         # XXX fix
+        y_pred = (y_pred == y).astype(float) / len(y)
+
         return y_pred.sum()