Assignment Part B Yuqi Xin

numpy_questions.py

@@ -41,6 +41,17 @@ def max_index(X):
     j = 0
 
     # TODO
+    if not isinstance(X, np.ndarray):
+        raise ValueError("Input is not a numpy array")
+
+    if X.ndim != 2:
+        raise ValueError("Input array is not 2D")
+
+    # np.argmax returns the index of the maximum value in the flattened array
+    # np.unravel_index converts it back to 2D indices
+    max_idx = np.unravel_index(np.argmax(X, axis=None), X.shape)
+
+    i, j = max_idx
 
     return i, j
 
@@ -64,4 +75,18 @@ 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 or not isinstance(n_terms, int):
+        raise ValueError("n_terms: non-negative integer")
+
+    if n_terms == 0:
+        return 1
+
+    product = 1
+
+    for n in range(1, n_terms + 1):
+        numerator = 4 * n ** 2
+        denominator = numerator - 1
+        product *= numerator / denominator
+
+    return 2 * product

sklearn_questions.py

@@ -29,28 +29,49 @@
 
 
 class OneNearestNeighbor(BaseEstimator, ClassifierMixin):
-    "OneNearestNeighbor classifier."
+    """OneNearestNeighbor classifier."""
 
     def __init__(self):  # noqa: D107
         pass
 
     def fit(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Fit the OneNearestNeighbor model.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The training input samples.
+        y : ndarray of shape (n_samples,)
+            The target values (class 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 using the OneNearestNeighbor model.
 
-        And describe parameters
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The input samples to predict.
+
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            The predicted class labels.
         """
         check_is_fitted(self)
         X = check_array(X)
@@ -60,15 +81,34 @@ def predict(self, X):
         )
 
         # XXX fix
+        for i in range(X.shape[0]):
+            # Compute distances from X[i] to all training samples
+            distances = np.linalg.norm(self.X_ - X[i, :], axis=1)
+
+            nearest_index = np.argmin(distances)
+            y_pred[i] = self.y_[nearest_index]
+
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Compute the accuracy of the OneNearestNeighbor model.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The input samples to predict.
+        y : ndarray of shape (n_samples,)
+            The true class labels.
+
+        Returns
+        -------
+        score : float
+            The accuracy of the model.
         """
         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