Peter Otten於 2014年12月12日星期五UTC+8下午5時13分58秒寫道： > KK Sasa wrote: > > > Mark Lawrence於 2014年12月12日星期五UTC+8下午3時17分43秒寫道： > >> On 12/12/2014 06:22, KK Sasa wrote: > >> > Hi there, > >> > > >> > The list comprehension is results = [d2(t[k]) for k in xrange(1000)], > >> > where d2 is a function returning a list, say [x1,x2,x3,x4] for one > >> > example. So "results" is a list consisting of 1000 lists, each of > >> > length four. Here, what I want to get is the sum of 1000 lists, and > >> > then the result is a list of length four. Is there any efficient way to > >> > do this? Because I found it is slow in my case. I tried sum(d2(t[k]) > >> > for k in xrange(1000)), but it returned error: TypeError: unsupported > >> > operand type(s) for +: 'int' and 'list'. Thanks. > >> > > >> > >> I think you need something like this > >> http://stackoverflow.com/questions/19339/a-transpose-unzip-function-in-python-inverse-of-zip > >> > >> I'll let you add the finishing touches if I'm correct :) > >> > >> -- > >> My fellow Pythonistas, ask not what our language can do for you, ask > >> what you can do for our language. > >> > >> Mark Lawrence > > > > Hi Mark and Yotam, > > Thanks for kind reply. I think I didn't make my problem clear enough. > > The slow part is "[d2(t[k]) for k in xrange(1000)]". In addition, I > > don't need to construct a list of 1000 lists inside, but my aim is to > > get the sum of all "d2(t[k])". I wonder if there is any method to sum up > > efficiently. > > If that is slow the culprit is probably the d2() function. If so > > results = [0] * 4 > for k in xrange(1000): > for i, v in enumerate(d2(t[k])): > results[i] += v > > won't help. Can you tell us what's inside d2()?
Thanks for reply, Christian and Peter. Actually, the d2() is the Hessian function of a simple function (derived by using "ad" package, http://pythonhosted.org//ad/). The package hasn't supported Numpy array so far. And I am still not how to take a look inside d2(). Because I have to use d2() in a heavy way for iterative algorithm, the efficiency is a issue in my case. Following is an example. import scipy from scipy import stats import numpy from ad import adnumber from ad.admath import * from ad import jacobian from ad import gh # the gradient and hessian functions generator from ad import * import time people = 1000 range_people = xrange(people) dim0 = 2; mean0 = [0,0]; cov0 = [[1,0],[0,1]] seed([1]) t = stats.multivariate_normal.rvs(mean0,cov0,people) t = t.reshape(people,dim0) t = t.tolist() # back to list x = [0, 0, 1, 2] point = 2 def p(x,t,point,z,obs): d = x[0] tau = [0]+[x[1:point]] a = x[point:len(x)] at = sum(i*j for i, j in zip(a, t)) nu = [exp(z[k]*(at-d)-sum(tau[k])) for k in xrange(point)] de = sum(nu, axis=0) probability = [nu[k]/de for k in xrange(point)] return probability[obs] d1, d2 = gh(p) tStart = time.time() z = range(point) re = [d2(x,t[k],2,z,1) for k in range_people] tEnd = time.time() print "It cost %f sec" % (tEnd - tStart) -- https://mail.python.org/mailman/listinfo/python-list
