Jia Zhu – Part B assignment

numpy_questions.py

@@ -37,11 +37,12 @@ def max_index(X):
         If the input is not a numpy array or
         if the shape is not 2D.
     """
-    i = 0
-    j = 0
-
-    # TODO
+    if not isinstance(X, np.ndarray):
+        raise ValueError("X must be a numpy array")
+    if X.ndim != 2:
+        raise ValueError("X must be a 2D numpy array")
 
+    i, j = np.unravel_index(np.argmax(X), X.shape)
     return i, j
 
 
@@ -62,6 +63,11 @@ 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:
+        return 1.0
+
+    product = 1.0
+    for n in range(1, n_terms + 1):
+        product *= (4.0 * n ** 2) / (4.0 * n ** 2 - 1.0)
+
+    return 2.0 * product

sklearn_questions.py

@@ -25,33 +25,51 @@
 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."
+    """Classify samples using the nearest training sample."""
 
     def __init__(self):  # noqa: D107
         pass
 
     def fit(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Fit the classifier with training samples and labels.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training samples.
+        y : array-like of shape (n_samples,)
+            Class labels for the training samples.
+
+        Returns
+        -------
+        self : OneNearestNeighbor
+            The fitted classifier.
         """
         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 labels for samples using their nearest training samples.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Samples to classify.
 
-        And describe parameters
+        Returns
+        -------
+        y : ndarray of shape (n_samples,)
+            Predicted class labels.
         """
         check_is_fitted(self)
         X = check_array(X)
@@ -60,16 +78,30 @@ def predict(self, X):
             dtype=self.classes_.dtype
         )
 
-        # XXX fix
+        distances = (
+            (X[:, np.newaxis, :] - self.X_[np.newaxis, :, :]) ** 2
+        ).sum(axis=2)
+
+        nearest_indices = np.argmin(distances, axis=1)
+        y_pred[:] = self.y_[nearest_indices]
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Return the mean accuracy on the given data.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Samples to classify.
+        y : array-like of shape (n_samples,)
+            True class labels for the samples.
+
+        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()
+        return np.mean(y_pred == y)