Inserted ELIPPSIS option to doctests giving floating point significant digit errors

hamsal · hamsal · commit b4db6f24b716 · 2014-05-29T11:12:50.000-04:00
diff --git a/doc/modules/sgd.rst b/doc/modules/sgd.rst
@@ -109,7 +109,7 @@ Using ``loss="log"`` or ``loss="modified_huber"`` enables the
 :math:`P(y|x)` per sample :math:`x`::
 
     >>> clf = SGDClassifier(loss="log").fit(X, y)
-    >>> clf.predict_proba([[1., 1.]])
+    >>> clf.predict_proba([[1., 1.]]) # doctest: +ELLIPSIS
     array([[ 0.0000005,  0.9999995]])
 
 The concrete penalty can be set via the ``penalty`` parameter.
diff --git a/doc/tutorial/statistical_inference/supervised_learning.rst b/doc/tutorial/statistical_inference/supervised_learning.rst
@@ -175,7 +175,7 @@ Linear models: :math:`y = X\beta + \epsilon`
     >>> regr = linear_model.LinearRegression()
     >>> regr.fit(diabetes_X_train, diabetes_y_train)
     LinearRegression(copy_X=True, fit_intercept=True, normalize=False)
-    >>> print(regr.coef_)
+    >>> print(regr.coef_) # doctest: +ELLIPSIS
     [   0.30349955 -237.63931533  510.53060544  327.73698041 -814.13170937
       492.81458798  102.84845219  184.60648906  743.51961675   76.09517222]
 
