doc string changed

fvgp/fvgp.py

@@ -79,9 +79,8 @@ class provides all the methods described for the GP class.
         False. Note, the training will always use a linear solve instead of the inverse for stability reasons.
     ram_economy : bool, optional
         Only of interest if the gradient and/or Hessian of the marginal log_likelihood is/are used for the training.
-    args : user defined, optional
-        These optional arguments will be available as attribute in kernel and mean function definitions.
-
+    args : any, optional
+        args will be a class attribute and therefore available to kernel and and prior mean functions.
 
     """
     def __init__(

fvgp/gp.py

@@ -81,6 +81,8 @@ class GP():
         and return a 2-D numpy array of shape V x V.
         If ram_economy=False, the function should be of the form f(points1, points2, hyperparameters) and return a numpy array of shape
         H x V x V, where H is the number of hyperparameters. V is the number of points. CAUTION: This array will be stored and is very large.
+    args : any, optional
+        args will be a class attribute and therefore available to kernel and and prior mean functions.