diff --git a/numpy_questions.py b/numpy_questions.py
index 21fcec4b..844d61b6 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -37,10 +37,14 @@ 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("The input is not a numpy array")
 
-    # TODO
+    if X.ndim != 2:
+        raise ValueError("The shape is not 2D")
+
+    flat_index = np.argmax(X)
+    i, j = np.unravel_index(flat_index, 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.
+    k_array = np.arange(1, n_terms+1)
+    fractions_array = (4*(k_array**2))/(4*(k_array**2)-1)
+    pi = 2 * np.prod(fractions_array)
+    return pi
diff --git a/sklearn_questions.py b/sklearn_questions.py
index 948c2e54..5b491f70 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -23,53 +23,86 @@
 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 one nearest neighbor 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)
+        self.n_features_in_ = X.shape[1]
         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 input samples.
+
+        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 label for each sample.
         """
         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 i in range(len(X)):
+            distances = np.linalg.norm(self.X_train_ - X[i], axis=1)
+            nearest = np.argmin(distances)
+            y_pred[i] = self.y_train_[nearest]
+
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Compute the accuracy of the classifier on the given data.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The input samples.
+        y : ndarray of shape (n_samples,)
+            The true class labels.
+
+        Returns
+        -------
+        score : float
+            The fraction of correctly classified samples.
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
 
-        # XXX fix
-        return y_pred.sum()
+        accuracy = (y_pred == y).sum() / len(y)
+        return accuracy