On Wed, May 20, 2009 at 14:24, dmitrey <dmitrey.kros...@scipy.org> wrote: > hi all, > > suppose I have A that is numpy ndarray of floats, with shape n x n. > > I want to obtain dot(A, b), b is vector of length n and norm(b)=1, but > instead of exact multiplication I want to approximate b as a vector > [+/- 2^m0, ± 2^m1, ± 2^m2 ,,, ± 2^m_n], m_i are integers, and then > invoke left_shift(vector_m) for rows of A.
You don't shift floats. You only shift integers. For floats, multiplying by an integer power of 2 should be fast because of the floating point representation (the exponent just gets incremented or decremented), so just do the multiplication. > So, what is the simplest way to do it, without cycles of course? Or it > cannot be implemented w/o cycles with current numpy version? It might help if you showed us an example of an actual b vector decomposed the way you describe. Your description is ambiguous. -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
