[EMAIL PROTECTED] wrote: > I was trying to create a sparse matrix using scipy.sparse (100000 X > 100000) with just the first row and first column filled with ones. > Lets call this matrix Asp. This is how I created Asp > > from scipy import sparse > Asp = scipy.lil_matrix(100000,100000) > for i in range(100000): > Asp[i,0] = i > Asp[0,i] = i > Bsp = (Asp.tocsr())*(Asp.tocsr()) > > This multiplication to get Bsp is taking a long time (to the order of > over an hour). Is there some other way of storing such huge sparse > matrices and doing their multiplications or is this the most optimal > way?
I think sparse matrices don't help here because the result isn't sparse. Looking at your example I think you can see that B[i,k] == A[i,0]*A[0,k] for i,k != 0,0, so for a matrix of that structure you might get away with something like # use at your own risk import numpy N = 10**4 # I get a MemoryError for a bigger exponent b = numpy.array(range(N)) a = numpy.zeros((N, N)) + b a *= a.transpose() a[0,0] = (b*b).sum() print a But for expert advice you better ask on the numpy mailing list... Peter -- http://mail.python.org/mailman/listinfo/python-list
