My Unit class is supposed to represent a neuron that can be linked to any other unit. The neuron itself is merely a (float) potential that can vary along time under the influence of other units and learning. I gather these units into groups which are in fact 2D matrix of units. Currently, I implemented the Unit and Group and I "talk" with numpy through an attribute of groups which represent all available potentials. Finally my group is like a simple 2d matrix of float but I need to have an underlying object to perform computation on each Unit at each time step. Currently I'm able to write something like:
group = Unit()*[2,2] group.potentials = numpy.zeros([2,2]) print group.potentials [[ 0. 0.] [ 0. 0.]] group[0,0].potential = 1 [[ 1. 0.] [ 0. 0.]]
Nicolas On Thu, 2008-07-10 at 16:30 -0700, Christopher Barker wrote:
Nicolas Rougier wrote:
Concerning the dtype argument during array creation, I thought it was here for somehow controlling the type of array elements. For example, if I use a "regular" numpy array (let's say a float array), I cannot set an item to a string value (it raises a ValueError: setting an array element with a sequence).
Yes, but numpy is designed primarily for numeric types: ints, floats, etc. It can also be used for custom types that are essentially like C structs (see recarray). The key is that a dtype desribes a data type in terms of bytes and that they represent -- It can not be a python type.
The only way to use arbitrary python types is a object array, which you've discovered, but then numpy doesn't know any thing about the objects, other than that they are python objects.
So what would be the best way to use numpy arrays with "foreign" types (or is it possible at all) ? I've designed the "real" Unit in C++ and exported it to python (via boost and shared pointers) and I would like to create array of such Units
If your type is a C++ class, then it may be possible, with some C hackary to get numpy to understand it, but you're getting beyong my depth here -- also I doubt that you'd get the full features like array math and all anyway -- that's all set up for basic numeric types.
Maybe others will have some idea, but I think you're pushing what numpy is capable of.
(in fact, I also created an array-like class but I would prefer to use directly the real array interface to benefit from the great work of numpy instead of re-inventing the wheel).
What operations do you expect to perform with these arrays of Units?
-Chris