glmnetPredict.py 文件源码

def softmax(x, gap = False):
   d = x.shape
   maxdist = x[:, 0]
   pclass = scipy.zeros([d[0], 1], dtype = scipy.integer)
   for i in range(1, d[1], 1):
       l = x[:, i] > maxdist
       pclass[l] = i
       maxdist[l] = x[l, i]
   if gap == True:
       x = scipy.absolute(maxdist - x)
       x[0:d[0], pclass] = x*scipy.ones([d[1], d[1]])
       #gaps = pmin(x)# not sure what this means; gap is never called with True
       raise ValueError('gap = True is not implemented yet')

   result = dict()
   if gap == True:
       result['pclass'] = pclass
       #result['gaps'] = gaps
       raise ValueError('gap = True is not implemented yet')
   else:
       result['pclass'] = pclass;

   return(result)
# end of softmax
# =========================================