Anyways, for now I can live with non-sparse implementation. This is mostly prototyping code for integeration in to a largely Java system (with some things written in C). So this will be ported to Java or C eventually. Whether or not I will need to protoype something that scales to thousands of nodes remains to be seen.
Dave
On 8/1/06, Charles R Harris <[EMAIL PROTECTED]> wrote:
Hi David,
I often have several thousand nodes in a graph, sometimes clustered into connected components. I suspect that using an adjacency matrix is an inefficient representation for graphs of that size while for smaller graphs the overhead of more complicated structures wouldn't be noticeable. Have you looked at the boost graph library? I don't like all their stuff but it is a good start with lots of code and a suitable license.
ChuckOn 8/1/06, David Grant < [EMAIL PROTECTED]> wrote:I have written my own graph class, it doesn't really do much, just has a few methods, it might do more later. Up until now it has just had one piece of data, an adjacency matrix, so it looks something like this:
class Graph:
def __init__(self, Adj):
self.Adj = Adj
I had the idea of changing Graph to inherit numpy.ndarray instead, so then I can just access itself directly rather than having to type self.Adj. Is this the right way to go about it? To inherit from numpy.ndarray?
The reason I'm using a numpy array to store the graph by the way is the following:
-Memory is not a concern (yet) so I don't need to use a sparse structure like a sparse array or a dictionary
-I run a lot of sums on it, argmin, blanking out of certain rows and columns using fancy indexing, grabbing subgraphs using vector indexing
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