A Divendres 25 Novembre 2005 15:24, gf va escriure:
from numarray import add, array, asarray, absolute, argsort, floor, take, size
def mean(m,axis=0): m = asarray(m) return add.reduce(m,axis)/float(m.shape[axis])
def eliminate_outliers(dat,frac): num_to_eliminate = int(floor(size(dat,0)*frac)) for i in range(num_to_eliminate): ind = argsort(absolute(dat-mean(dat)),0) sdat = take(dat,ind,0)[:,0] dat = sdat[:-1] return dat
#--------------------------------------------------------------------
if __name__ == "__main__": from MLab import rand sz = 100 nn = rand(sz,1) nn[:10] = 20*rand(10,1) nn[sz-10:] = -20*rand(10,1) print eliminate_outliers(nn,0.10)
For sz=100, the next line of code is 10x faster on my machine (more if sz is bigger): print nn[argsort(abs(nn_c-nn_c.mean()),0)][:-int(sz*0.10),0] I haven't checked it very carefully, so you should double check it. BTW, you will need to use the numarray MLab interface: from numarray.mlab import rand Cheers, --
0,0< Francesc Altet http://www.carabos.com/ V V Cárabos Coop. V. Enjoy Data "-"