生活的戏剧化让人惊诧。我不知道自己是应该笑还是哭。疯了。
我看着它被巨大的校巴碾过，我就这样看着它发生…
神奇…很多东西就是这样说不在就不在的…人也是…
这个世界就是这样让我感到不安。
Enjoy the dramatic part.
Rest in peace, my mobile 2.
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好好珍惜拥有的东西，已经足够！
最近喜欢上了一样东西,魂牵梦绕…
这就是python~~一个字,美…
管他交大上没上,我就沉在我喜欢的事情里,不管别的…
至于简历,再议了,我就不信找不到工作…
贴一下google的pageranking的python版
#!/usr/bin/env python
from numpy import *
def pageRankGenerator( At = [array((), int32)], numLinks = array((), int32), ln = array((), int32), alpha = 0.85, convergence = 0.01, checkSteps = 10 ): """ Compute an approximate page rank vector of N pages to within some convergence factor. @param At a sparse square matrix with N rows. At[ii] contains the indices of pages jj linking to ii. @param numLinks iNumLinks[ii] is the number of links going out from ii. @param ln contains the indices of pages without links @param alpha a value between 0 and 1. Determines the relative importance of "stochastic" links. @param convergence a relative convergence criterion. smaller means better, but more expensive. @param checkSteps check for convergence after so many steps """
# the number of "pages" N = len(At)
# the number of "pages without links" M = ln.shape[0]
# initialize: single-precision should be good enough iNew = ones((N,), float32) / N iOld = ones((N,), float32) / N
done = False while not done:
# normalize every now and then for numerical stability iNew /= sum(iNew) for step in range(checkSteps):
# swap arrays iOld, iNew = iNew, iOld
# an element in the 1 x I vector. # all elements are identical. oneIv = (1 – alpha) * sum(iOld) / N
# an element of the A x I vector. # all elements are identical. oneAv = 0.0 if M > 0: oneAv = alpha * sum(iOld.take(ln, axis = 0)) * M / N # the elements of the H x I multiplication ii = 0 while ii < N: page = At[ii] h = 0 if page.shape[0]: h = alpha * dot( iOld.take(page, axis = 0), 1. / numLinks.take(page, axis = 0) ) iNew[ii] = h + oneAv + oneIv ii += 1 diff = iNew – iOld done = (sqrt(dot(diff, diff)) / N < convergence) yield iNew
def transposeLinkMatrix( outGoingLinks = [[]] ): """ Transpose the link matrix. The link matrix contains the pages each page points to. But what we want is to know which pages point to a given page, while retaining information about how many links each page contains (so store that in a separate array), as well as which pages contain no links at all (leaf nodes).
@param outGoingLinks outGoingLinks[ii] contains the indices of pages pointed to by page ii @return a tuple of (incomingLinks, numOutGoingLinks, leafNodes) """
nPages = len(outGoingLinks) # incomingLinks[ii] will contain the indices jj of the pages linking to page ii incomingLinks = [[] for ii in range(nPages)] # the number of links in each page numLinks = zeros(nPages, int32) # the indices of the leaf nodes leafNodes = []
for ii in range(nPages): if len(outGoingLinks[ii]) == 0: leafNodes.append(ii) else: numLinks[ii] = len(outGoingLinks[ii]) # transpose the link matrix for jj in outGoingLinks[ii]: incomingLinks[jj].append(ii) incomingLinks = [array(ii) for ii in incomingLinks] numLinks = array(numLinks) leafNodes = array(leafNodes)
return incomingLinks, numLinks, leafNodes
def pageRank( linkMatrix = [[]], alpha = 0.85, convergence = 0.01, checkSteps = 10 ): """ Convenience wrap for the link matrix transpose and the generator.
@see pageRankGenerator for parameter description """ incomingLinks, numLinks, leafNodes = transposeLinkMatrix(linkMatrix)
for gr in pageRankGenerator(incomingLinks, numLinks, leafNodes, alpha = alpha, convergence = convergence, checkSteps = checkSteps): final = gr
return final
To 随风：python..不看后面，你就差点吓死我了…~你像病毒一样来我blog…
To MW: 嗯。
过去的让它过去就好