V2 Lab4 - Camille Valenciennes

numpy_questions.py

@@ -40,7 +40,15 @@ def max_index(X):
     i = 0
     j = 0
 
-    # TODO
+    if not isinstance(X, np.ndarray):
+        raise ValueError("Only Numpy arrays are allowed")
+    elif X.ndim != 2:
+        raise ValueError("Only 2D arrays are allowed")
+    elif X.size == 0:
+        raise ValueError("Only non-empty arrays are allowed")
+
+    i, j = np.unravel_index(X.argmax(), X.shape)
+    i, j = int(i), int(j)
 
     return i, j
 
@@ -62,6 +70,12 @@ 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:
+        raise ValueError("n_terms must be non-negative")
+    if n_terms == 0:
+        return 1
+
+    squares = 4 * np.arange(1, n_terms+1) ** 2
+    prod = np.prod(squares / (squares - 1))
+
+    return prod * 2

sklearn_questions.py

@@ -25,33 +25,50 @@
 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 one-nearest-neighbor classifier.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The training samples.
+        y : ndarray of shape (n_samples, )
+            The training labels.
+
+        Returns
+        -------
+        self : OneNearestNeighbor
+            The 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.X_train_ = X
+        self.y_train_ = y
         return self
 
     def predict(self, X):
-        """Write docstring.
+        """Predict the class of each sample of X.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The samples to classify.
 
-        And describe parameters
+        Returns
+        -------
+        y_pred : ndarray of shape (p_samples, )
+            The predicted class of each sample of X.
         """
         check_is_fitted(self)
         X = check_array(X)
@@ -60,16 +77,37 @@ def predict(self, X):
             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 = X[:, np.newaxis, :] - self.X_train_[np.newaxis, :, :]
+        dist = np.linalg.norm(diff, axis=2)
+        nearest = dist.argmin(axis=1)
+        y_pred = self.y_train_[nearest]
+
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Return the accuracy of the classifier on (X,y).
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The test samples.
+        y : ndarray of shape (n_samples, )
+            The test labels.
+
+        Returns
+        -------
+        score : float
+            The fraction of samples accurately classified.
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
 
-        # XXX fix
-        return y_pred.sum()
+        y_pred = y_pred == y
+        return y_pred.mean()