Questions Answered

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.
     """
+    if not isinstance(X, np.ndarray):
+        raise ValueError("Input is not a numpy array")
+    if X.ndim != 2:
+        raise ValueError("X must be a 2D array")
     i = 0
     j = 0
-
-    # TODO
+    index = np.argmax(X)
+    i, j = np.unravel_index(index, X.shape)
 
     return i, j
 
@@ -62,6 +66,13 @@ 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.
+    product = 1
+
+    for i in range(1, n_terms + 1):
+        numerator = 4 * (i ** 2)
+        denominator = numerator - 1
+        product *= numerator / denominator
+
+    if n_terms == 0:
+        return 1.0
+    return 2 * product

sklearn_questions.py

@@ -22,45 +22,57 @@
 import numpy as np
 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_X_y, check_array
 from sklearn.utils.validation import check_is_fitted
 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.
+        """
+        Fits data.
 
-        And describe parameters
+        Parameters:
+        X: ndarray shape (n_samples, n_features)
+        y: ndarray shape (n_samples,)
+
+        Returns:
+        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.
+        """
+        Make predictions.
 
-        And describe parameters
+        Parameters:
+        X: ndarray of shape (n_samples, n_features)
+
+        Returns:
+        predicted labels
         """
         check_is_fitted(self)
         X = check_array(X)
-        y_pred = np.full(
-            shape=len(X), fill_value=self.classes_[0],
-            dtype=self.classes_.dtype
+        distances = np.linalg.norm(
+            self.X_[None, :, :] - X[:, None, :], axis=2
         )
 
-        # XXX fix
-        return y_pred
+        nearest_neighbour = np.argmin(distances, axis=1)
+
+        return self.y_[nearest_neighbour]
 
     def score(self, X, y):
         """Write docstring.
@@ -70,5 +82,4 @@ def score(self, X, y):
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
 
-        # XXX fix
-        return y_pred.sum()
+        return np.mean(y_pred == y)