Chloe Khazzaka Part B completed

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("Input is not a numpy array")
 
-    # TODO
+    if X.ndim != 2:
+        raise ValueError("Shape is not 2D")
+
+    max_index = np.argmax(X)
+    i, j = np.unravel_index(max_index, X.shape)
 
     return i, j
 
@@ -64,4 +68,9 @@ 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
+    n = np.arange(1, n_terms + 1)
+    terms = (4 * n * n) / (4 * n * n - 1)
+
+    return 2 * np.prod(terms)

sklearn_questions.py

@@ -29,28 +29,47 @@
 
 
 class OneNearestNeighbor(BaseEstimator, ClassifierMixin):
-    "OneNearestNeighbor classifier."
+    """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)
+            Training data.
+
+        y : ndarray of shape (n_samples,)
+            Target labels.
+
+        Returns
+        -------
+        self : object
+            Fitted estimator.
         """
         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 samples in X.
 
-        And describe parameters
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Input data.
+
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            Predicted labels.
         """
         check_is_fitted(self)
         X = check_array(X)
@@ -59,16 +78,29 @@ def predict(self, X):
             dtype=self.classes_.dtype
         )
 
-        # XXX fix
+        for i, x in enumerate(X):
+            distances = np.linalg.norm(self.X_ - x, axis=1)
+            nearest_idx = np.argmin(distances)
+            y_pred[i] = self.y_[nearest_idx]
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
+        """Return the accuracy score on the given test data.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Test samples.
+
+        y : ndarray of shape (n_samples,)
+            True labels.
 
-        And describe parameters
+        Returns
+        -------
+        accuracy : float
+            Mean accuracy of predictions.
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
 
-        # XXX fix
-        return y_pred.sum()
+        return np.mean(y_pred == y)