Complete assignment - Hae In KEUM

numpy_questions.py

@@ -41,6 +41,13 @@ 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")
+    flat_index = np.argmax(X)
+    i, j = np.unravel_index(flat_index, X.shape)
+    i, j = int(i), int(j)
 
     return i, j
 
@@ -64,4 +71,14 @@ 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 not isinstance(n_terms, (int, np.integer)):
+        raise ValueError("n_terms should be an integer")
+    if n_terms < 0:
+        raise ValueError("n_terms should be non-negative")
+    if n_terms == 0:
+        return 1.0
+    k = np.arange(1, n_terms + 1, dtype=float)
+    terms = (4.0 * k**2) / (4.0 * k**2 - 1.0)
+    product = np.prod(terms)
+    pi_approx = 2.0 * product
+    return float(pi_approx)

sklearn_questions.py

@@ -28,47 +28,80 @@
 from sklearn.utils.multiclass import check_classification_targets
 
 
-class OneNearestNeighbor(BaseEstimator, ClassifierMixin):
-    "OneNearestNeighbor classifier."
+class OneNearestNeighbor(ClassifierMixin, BaseEstimator):
+    """OneNearestNeighbor classifier.
+
+    To predict the label of a sample as the label of the closest sample in
+    Euclidean distance in the training set.
+    """
 
     def __init__(self):  # noqa: D107
         pass
 
     def fit(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Fit the one nearest neighbor classifier.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training data.
+        y : array-like of shape (n_samples,)
+            Target labels.
+
+        Returns
+        -------
+        self : OneNearestNeighbor
+            Fitted estimator.
         """
         X, y = check_X_y(X, y)
         check_classification_targets(y)
+        self.X_train_ = X
+        self.y_train_ = y
         self.classes_ = np.unique(y)
         self.n_features_in_ = X.shape[1]
-
-        # XXX fix
         return self
 
     def predict(self, X):
-        """Write docstring.
+        """Predict class labels for samples in X.
+
+        Parameters
+        ----------
+        X: array-like of shape (n_samples, n_features)
+            Samples to classify.
 
-        And describe parameters
+        Returns
+        -------
+        y_pred : array-like of shape (n_samples,)
+            Predicted class 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
-        )
-
-        # XXX fix
+        if X.shape[1] != self.n_features_in_:
+            raise ValueError(
+                f"X has {X.shape[1]} features, but "
+                f"{self.__class__.__name__} is expecting "
+                f"{self.n_features_in_} features as input."
+            )
+        diff = self.X_train_[np.newaxis, :, :] - X[:, np.newaxis, :]
+        dist_sq = np.sum(diff ** 2, axis=2)
+        nearest_indices = np.argmin(dist_sq, axis=1)
+        y_pred = self.y_train_[nearest_indices]
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Return the mean accuracy on the given test data and labels.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Test samples.
+        y : array-like of shape (n_samples,)
+            True labels for X.
+        Returns
+        -------
+        score : float
+            Mean accuracy of the predictions on X.
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
-
-        # XXX fix
-        return y_pred.sum()
+        return float(np.mean(y_pred == y))