Louan BARDOU numpy&sklearn PART @

numpy_questions.py

@@ -37,11 +37,11 @@ def max_index(X):
         If the input is not a numpy array or
         if the shape is not 2D.
     """
-    i = 0
-    j = 0
-
-    # TODO
+    if not isinstance(X, np.ndarray) or X.ndim != 2:
+        raise ValueError("X must be a 2D numpy array")
 
+    flat_index = np.argmax(X)
+    i, j = np.unravel_index(flat_index, 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 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.
+    pi = 2.
+    for i in range(1, n_terms + 1):
+        pi *= (4 * i**2) / (4 * i**2 - 1)
+    return pi

sklearn_questions.py

@@ -28,47 +28,81 @@
 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
+        """Fit the OneNearestNeighbor classifier.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training data.
+        y : array-like of shape (n_samples,)
+            Target values.
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
         """
         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_train_ = X
+        self.y_train_ = y
         return self
 
     def predict(self, X):
-        """Write docstring.
+        """Predict the class labels for the provided data.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Test samples.
 
-        And describe parameters
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            Class labels for each data sample.
         """
         check_is_fitted(self)
-        X = check_array(X)
+        X = check_array(X, ensure_2d=True)
+        if X.shape[1] != self.n_features_in_:
+            raise ValueError(
+                "X has {} features, but {} is expecting {} features as input"
+                .format(
+                    X.shape[1], self.__class__.__name__, self.n_features_in_)
+            )
         y_pred = np.full(
             shape=len(X), fill_value=self.classes_[0],
             dtype=self.classes_.dtype
         )
-
-        # XXX fix
+        for i, x in enumerate(X):
+            distances = np.sqrt(np.sum((self.X_train_ - x)**2, axis=1))
+            nearest_idx = np.argmin(distances)
+            y_pred[i] = self.y_train_[nearest_idx]
         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 self.predict(X) with respect to y.
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
-
-        # XXX fix
-        return y_pred.sum()
+        return np.mean(y_pred == y)