On 12/13/2009 05:13 AM, Jasper van de Gronde wrote:
Bruce Southey wrote:
Really I would suggest asking the list for the real problem because it is often amazing what solutions have been given.
So far this is the fastest code I've got: ------------------------------------------------------------------------ import numpy as np
nmax = 100
def minover(Xi,S): P,N = Xi.shape SXi = Xi.copy() for i in xrange(0,P): SXi[i] *= S[i] SXi2 = np.dot(SXi,SXi.T) SXiSXi2divN = np.concatenate((SXi,SXi2),axis=1)/N w = np.random.standard_normal((N)) E = np.dot(SXi,w) wE = np.concatenate((w,E)) for s in xrange(0,nmax*P): mu = wE[N:].argmin() wE += SXiSXi2divN[mu] # E' = dot(SXi,w') # = dot(SXi,w + SXi[mu,:]/N) # = dot(SXi,w) + dot(SXi,SXi[mu,:])/N # = E + dot(SXi,SXi.T)[:,mu]/N # = E + dot(SXi,SXi.T)[mu,:]/N return wE[:N] ------------------------------------------------------------------------
I am particularly interested in cleaning up the initialization part, but any suggestions for improving the overall performance are of course appreciated.
What is Xi and S? I think that your SXi is just: SXi=Xi*S But really I do not understand what you are actually trying to do. As previously indicated, some times simplifying an algorithm can make it computationally slower. Bruce