Hi, I think the best way to solve this issue to not use a state at all. It is fast, reproducible even in parallel (if wanted), and doesn't suffer from the shared issue. Would be nice if numpy provided such a stateless RNG as implemented in Random123: www.deshawresearch.com/resources_random123.html Roland On Tue, May 12, 2015 at 2:18 PM, Neal Becker <ndbecker2@gmail.com> wrote:

In order to make sure all my random number generators have good independence, it is a good practice to use a single shared instance (because it is already known to have good properties). A less-desirable alternative is to used rng's seeded with different starting states - in this case the independence properties are not generally known.

So I have some fairly deeply nested data structures (classes) that somewhere contain a reference to a RandomState object.

I need to be able to clone these data structures, producing new independent copies, but I want the RandomState part to be the shared, singleton rs object.

In python, no problem:

--- from numpy.random import RandomState

class shared_random_state (RandomState): def __init__ (self, rs): RandomState.__init__(self, rs)

def __deepcopy__ (self, memo): return self ---

Now I can copy.deepcopy the data structures, but the randomstate part is shared. I just use

rs = shared_random_state (random.RandomState(0))

and provide this rs to all my other objects. Pretty nice!

-- Those who fail to understand recursion are doomed to repeat it

_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion

-- ORNL/UT Center for Molecular Biophysics cmb.ornl.gov 865-241-1537, ORNL PO BOX 2008 MS6309