Assignment （Litong Hou）

numpy_questions.py

@@ -37,12 +37,16 @@ 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 must be a numpy array")
 
-    # TODO
+    if X.ndim != 2:
+        raise ValueError("Input must be 2D")
 
-    return i, j
+    flat_index = np.argmax(X)
+    i, j = np.unravel_index(flat_index, X.shape)
+
+    return int(i), int(j)
 
 
 def wallis_product(n_terms):
@@ -62,6 +66,16 @@ 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):
+        numerator = (2 * n) ** 2
+        denominator = (2 * n - 1) * (2 * n + 1)
+        product *= numerator / denominator
+
+    pi = 2 * product
+
+    return pi

sklearn_questions.py

@@ -28,47 +28,77 @@
 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.
-
-        And describe parameters
+        """Train the model by storing training data.
+
+        Parameters
+        ----------
+        X : array-like, shape (n_samples, n_features)
+            Training samples.
+        y : array-like, shape (n_samples,)
+            Target values.
+
+        Returns
+        -------
+        self : object
+            Returns self.
         """
         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.
+        """Perform classification on test samples.
+
+        Parameters
+        ----------
+        X : array-like, shape (n_samples, n_features)
+            Test samples.
 
-        And describe parameters
+        Returns
+        -------
+        y_pred : array, shape (n_samples,)
+            Class labels for samples in X.
         """
         check_is_fitted(self)
         X = check_array(X)
-        y_pred = np.full(
-            shape=len(X), fill_value=self.classes_[0],
-            dtype=self.classes_.dtype
-        )
-
-        # XXX fix
+        if X.shape[1] != self.n_features_in_:
+            raise ValueError(
+                f"X has {X.shape[1]} features, but OneNearestNeighbor "
+                f"is expecting {self.n_features_in_} features as input"
+            )
+        y_pred = np.zeros(len(X), dtype=self.y_.dtype)
+        for i in range(len(X)):
+            distances = np.sqrt(np.sum((self.X_ - X[i]) ** 2, axis=1))
+            nearest_idx = np.argmin(distances)
+            y_pred[i] = self.y_[nearest_idx]
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Calculate mean accuracy.
+
+        Parameters
+        ----------
+        X : array-like, shape (n_samples, n_features)
+            Test samples.
+        y : array-like, shape (n_samples,)
+            True labels.
+
+        Returns
+        -------
+        score : float
+            Mean accuracy.
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
-
-        # XXX fix
-        return y_pred.sum()
+        return np.mean(y_pred == y)