Leo Burtin

numpy_questions.py

@@ -42,6 +42,12 @@ def max_index(X):
 
     # TODO
 
+    if not isinstance(X, np.ndarray):
+        raise ValueError("Input must be a numpy array.")
+    if X.ndim != 2:
+        raise ValueError("Input must be 2D.")
+    flat_idx = np.argmax(X)
+    i, j = np.unravel_index(flat_idx, X.shape)
     return i, j
 
 
@@ -64,4 +70,13 @@ 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) or n_terms < 0:
+        raise ValueError("n_terms must be a non-negative integer.")
+    if n_terms == 0:
+        return 1.0
+
+    product = 1.0
+    for n in range(1, n_terms + 1):
+        numerator = 4 * n * n
+        product *= numerator / (numerator - 1)
+    return 2.0 * product

sklearn_questions.py

@@ -28,29 +28,51 @@
 from sklearn.utils.multiclass import check_classification_targets
 
 
-class OneNearestNeighbor(BaseEstimator, ClassifierMixin):
+class OneNearestNeighbor(ClassifierMixin, BaseEstimator):
     "OneNearestNeighbor classifier."
 
     def __init__(self):  # noqa: D107
         pass
 
     def fit(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """
+        Fit the OneNearestNeighbor classifier.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training samples with one row per example.
+        y : array-like of shape (n_samples,)
+            Target labels aligned with rows of `X`.
+
+        Returns
+        -------
+        self : OneNearestNeighbor
+            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._fit_X = X
+        self._fit_y = y
         return self
 
     def predict(self, X):
-        """Write docstring.
+        """
+        Predict class labels for the provided samples.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Samples to classify.
 
-        And describe parameters
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            Predicted class labels.
         """
         check_is_fitted(self)
         X = check_array(X)
@@ -60,15 +82,38 @@ def predict(self, X):
         )
 
         # 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"
+            )
+
+        distances = np.linalg.norm(self._fit_X[None, :, :] - X[:, None, :], axis=2)
+        nearest_idx = np.argmin(distances, axis=1)
+        y_pred[:] = self._fit_y[nearest_idx]
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """
+        Evaluate the classifier on labeled data.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Samples on which to evaluate.
+        y : array-like of shape (n_samples,)
+            True labels corresponding to `X`.
+
+        Returns
+        -------
+        score : float
+            Fraction of correctly classified samples.
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
 
         # XXX fix
+        y_pred = (y_pred == y).astype(float) / len(y_pred)
         return y_pred.sum()