diff --git a/numpy_questions.py b/numpy_questions.py
index 21fcec4b..9098e3b4 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -40,7 +40,12 @@ def max_index(X):
     i = 0
     j = 0
 
-    # TODO
+    if not isinstance(X, np.ndarray) or X.ndim != 2:
+        raise ValueError
+
+    max_index = np.argmax(X)
+    i = max_index // X.shape[1]
+    j = max_index % X.shape[1]
 
     return i, j
 
@@ -64,4 +69,14 @@ 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.
+
+    product = 1
+    for i in range(1, n_terms+1):
+        numerator = 4*i*i
+        denominator = numerator - 1
+        product *= numerator/denominator
+
+    if n_terms == 0:
+        return product
+    else:
+        return product*2
diff --git a/sklearn_questions.py b/sklearn_questions.py
index f65038c6..ba945244 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -29,28 +29,50 @@
 
 
 class OneNearestNeighbor(BaseEstimator, ClassifierMixin):
-    "OneNearestNeighbor classifier."
+    """OneNearestNeighbor classifier."""
 
     def __init__(self):  # noqa: D107
         pass
 
     def fit(self, X, y):
-        """Write docstring.
+        """Store training data.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The input array of samples.
+
+        y : ndarray of shape (n_samples,).
+            The input array of targets.
+
+        Returns
+        -------
+        self : OneNearestNeighbor
+            The fitted estimator.
 
-        And describe parameters
         """
         X, y = check_X_y(X, y)
         check_classification_targets(y)
         self.classes_ = np.unique(y)
         self.n_features_in_ = X.shape[1]
+        self._X_train = X
+        self._y_train = y
 
-        # XXX fix
         return self
 
     def predict(self, X):
-        """Write docstring.
+        """Predict the targets of training samples.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The input array of test samples.
+
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            The output array of predictions.
 
-        And describe parameters
         """
         check_is_fitted(self)
         X = check_array(X)
@@ -59,16 +81,31 @@ def predict(self, X):
             dtype=self.classes_.dtype
         )
 
-        # XXX fix
+        for i, x in enumerate(X):
+            distances = np.linalg.norm(self._X_train - x, axis=1)
+            nearest_index = np.argmin(distances)
+            y_pred[i] = self._y_train[nearest_index]
+
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
+        """Compute accuracy score of predictions.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The input array of test samples.
+
+        y : ndarray of shape (n_samples,).
+            The input array of targets.
+
+        Returns
+        -------
+        accuracy : float
+            Fraction of correctly classified samples.
 
-        And describe parameters
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
-
-        # XXX fix
-        return y_pred.sum()
+        accuracy = (y_pred == y).mean()
+        return accuracy