Clément Combe

numpy_questions.py

@@ -37,10 +37,10 @@ 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) or X.ndim != 2:
+        raise ValueError
 
-    # TODO
+    i, j = np.unravel_index(np.argmax(X), X.shape)
 
     return i, j
 
@@ -62,6 +62,11 @@ 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
+
+    i = np.arange(1, n_terms + 1)
+
+    pi_approx = 2 * np.prod((4 * i ** 2) / (4 * i ** 2 - 1))
+
+    return pi_approx

sklearn_questions.py

@@ -22,54 +22,90 @@
 import numpy as np
 from sklearn.base import BaseEstimator
 from sklearn.base import ClassifierMixin
-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."
+    """OneNearestNeighbor classifier."""
 
     def __init__(self):  # noqa: D107
         pass
 
     def fit(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Fit the nearest neighbor classifier.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training samples.
+        y : array-like of shape (n_samples,)
+            Target class labels.
+
+        Returns
+        -------
+        self : OneNearestNeighbor
+        Fitted classifier.
         """
-        X, y = check_X_y(X, y)
+        X, y = validate_data(self, X, y)
         check_classification_targets(y)
         self.classes_ = np.unique(y)
         self.n_features_in_ = X.shape[1]
 
-        # XXX fix
+        self.X_train_ = X
+        self.y_train_ = y
+
         return self
 
     def predict(self, X):
-        """Write docstring.
+        """Predict the 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 : ndarray of shape (n_samples,)
+        Predicted class labels.
         """
         check_is_fitted(self)
-        X = check_array(X)
+        X = validate_data(self, X, reset=False)
+
         y_pred = np.full(
             shape=len(X), fill_value=self.classes_[0],
             dtype=self.classes_.dtype
         )
 
-        # XXX fix
+        distances = np.sqrt(((X[:, np.newaxis, :]
+                            - self.X_train_[np.newaxis, :, :]) ** 2)
+                            .sum(axis=2))
+
+        nearest_indices = np.argmin(distances, axis=1)
+
+        y_pred = self.y_train_[nearest_indices]
+
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
+        """Return the mean accuracy on the given test data.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+        Test samples.
+
+        y : array-like of shape (n_samples,)
+        True class labels.
 
-        And describe parameters
+        Returns
+        -------
+        score : float
+        Mean accuracy of the classifier.
         """
-        X, y = check_X_y(X, y)
+        X, y = validate_data(self, X, y, reset=False)
         y_pred = self.predict(X)
 
-        # XXX fix
-        return y_pred.sum()
+        return np.mean(y_pred == y)