On Sat, May 17, 2008 at 7:22 PM, Brian Blais <[EMAIL PROTECTED]> wrote: > at least for me, that was the motivation. I am trying to build a simulation > framework for part of the brain, which requires connected layers of nodes. > A layer is either a 1D or 2D structure of nodes, with each node a > relatively complex beast. Rather than reinvent the indexing (1D, 2D, > slicing, etc...), I just inherited from ndarray. I thought, after the fact, > that some numpy functions on arrays would help speed up the code, which > consists mostly of calling an update function on all nodes, passing each > them an input vector. I wasn't sure if there would be any speed up for > this, compared to > for n in self.flat: > n.update(input_vector) > From the response, the answer seems to be no, and that I should stick with > the python loops for clarity. But also, the words of Anne Archibald, makes > me think that I have made a bad choice by inheriting from ndarray, although > I am not sure what a convenient alternative would be. > > > bb
Hello, It depends on what you are doing but to really exploit the performance gains of numpy it can be better to change the way you are doing the model to have arrays representing the various properties of each node seperately. For example, instead of having each node with a voltage and a number of channels node.V node.C (list of n channel conductances), you have big arrays holding the values for all of them. So V would be an array 1xn_nodes and C would be an array of n_nodes x max number of channels (some of them could be zero if not all nodes have the same number of channels). Another global type array n_nodes x n_nodes could hold all the connections... Then instead of updating each node individually you can update all nodes together with vectorised operations utilising ATLAS etc. The indexing can get a bit complicated and it may not be possible depending on how your nodes interact (but usually even if a node depends on values of other nodes it does so only at the previous timestep so you can store the full previous state and reference it in the update function). Just a suggestion - it was much more efficient for me to do it this way with integrate and fire type neural networks... Also I hope I've clearly expressed what I mean - it's getting late here. Cheers Robin _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
