diff --git a/numpy_questions.py b/numpy_questions.py
index 21fcec4b..272c5216 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -37,10 +37,13 @@ def max_index(X):
         If the input is not a numpy array or
         if the shape is not 2D.
     """
-    i = 0
-    j = 0
+    if not isinstance(X, np.ndarray):
+        raise ValueError
 
-    # TODO
+    if X.ndim != 2:
+        raise ValueError
+
+    i, j = np.unravel_index(np.argmax(X), X.shape)
 
     return i, j
 
@@ -62,6 +65,12 @@ 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.
+    if n_terms == 0:
+        pi = 1
+    else:
+        pi = 1
+        for n in range(1, n_terms + 1):
+            pi = pi * (4 * (n**2)/((4*n**2)-1))
+        pi = 2*pi
+
+    return pi
diff --git a/sklearn_questions.py b/sklearn_questions.py
index 948c2e54..97ea90bd 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -23,53 +23,84 @@
 from sklearn.base import BaseEstimator
 from sklearn.base import ClassifierMixin
 from sklearn.utils.validation import check_X_y
-from sklearn.utils.validation import check_array
 from sklearn.utils.validation import check_is_fitted
 from sklearn.utils.validation import validate_data
 from sklearn.utils.multiclass import check_classification_targets
 
 
 class OneNearestNeighbor(ClassifierMixin, BaseEstimator):
-    "OneNearestNeighbor classifier."
+    """OneNearestNeighbor classifier."""
 
     def __init__(self):  # noqa: D107
         pass
 
     def fit(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Fit the OneNearestNeighbor classifier.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The training input samples.
+        y : ndarray of shape (n_samples,)
+            The target labels.
+
+        Returns
+        -------
+        self : OneNearestNeighbor
+            The fitted classifier.
         """
-        X, y = check_X_y(X, y)
+        X, y = validate_data(self, X, y)
         check_classification_targets(y)
         self.classes_ = np.unique(y)
         self.n_features_in_ = X.shape[1]
 
-        # XXX fix
+        self.X_train_ = X
+        self.y_train_ = y
         return self
 
     def predict(self, X):
-        """Write docstring.
+        """Predict the class labels for the samples in X.
+
+        Parameters
+        ----------
+        X :  ndarray of shape (n_samples, n_features)
+             The input samples to classify.
 
-        And describe parameters
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            The predicted class labels.
         """
         check_is_fitted(self)
-        X = check_array(X)
+        X = validate_data(self, X, reset=False)
         y_pred = np.full(
             shape=len(X), fill_value=self.classes_[0],
             dtype=self.classes_.dtype
         )
 
-        # XXX fix
+        for k in range(len(X)):
+            norm = np.linalg.norm(X[k] - self.X_train_, axis=1)
+            index = np.argmin(norm)
+            y_pred[k] = self.y_train_[index]
+
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Evaluate the accuracy of the predictions.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The test samples.
+        y : ndarray of shape (n_samples,)
+            The true labels for X.
+
+        Returns
+        -------
+        score : float
+                The accuracy of the classifier.
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
 
-        # XXX fix
-        return y_pred.sum()
+        return np.mean(y_pred == y)