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我正在尝试对类方法内部的 for 循环进行矢量化。for 循环具有以下形式：它遍历一堆点并根据某个变量（下面称为“self.condition_met”）是否为真，在该点上调用一对函数，并将结果添加到列表中. 这里的每个点都是列表向量中的一个元素，即看起来像数组（[[1,2,3]，[4,5,6]，...]）的数据结构。这是有问题的功能：

def myClass:
   def my_inefficient_method(self):
       final_vector = []
       # Assume 'my_vector' and 'my_other_vector' are defined numpy arrays
       for point in all_points:
         if not self.condition_met:
             a = self.my_func1(point, my_vector)
             b = self.my_func2(point, my_other_vector)
         else:
             a = self.my_func3(point, my_vector)
             b = self.my_func4(point, my_other_vector)
         c = a + b
         final_vector.append(c)
       # Choose random element from resulting vector 'final_vector'

self.condition_met 是在调用 my_inefficient_method 之前设置的，所以似乎没有必要每次都检查它，但我不知道如何更好地写这个。由于这里没有破坏性操作，似乎我可以将整个事情重写为矢量化操作——这可能吗？任何想法如何做到这一点？

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3 回答 3

2

This only takes a couple lines of code in NumPy (the rest is just creating a data set, a couple of functions, and set-up).

import numpy as NP

# create two functions 
fnx1 = lambda x : x**2
fnx2 = lambda x : NP.sum(fnx1(x))

# create some data
M = NP.random.randint(10, 99, 40).reshape(8, 5)

# creates index array based on condition satisfaction
# (is the sum (of that row/data point) even or odd)
ndx = NP.where( NP.sum(M, 0) % 2 == 0 )

# only those data points that satisfy the condition (are even) 
# are passed to one function then another and the result off applying both 
# functions to each data point is stored in an array
res = NP.apply_along_axis( fnx2, 1, M[ndx,] )

print(res)
# returns: [[11609 15309 15742 12406  4781]]

From your description i abstracted this flow:

  • check for condition (boolean) 'if True'
  • calls pair functions on those data points (rows) that satisfy the condition
  • appends result from each set of calls to a list ('res' below)
于 2010-04-19T19:52:09.230 回答
2

你可以重写my_funcx为矢量化吗？如果是这样，你可以这样做

def myClass:
   def my_efficient_method(self):
       # Assume 'all_points', 'my_vector' and 'my_other_vector' are defined numpy arrays
       if not self.condition_met:
           a = self.my_func1(all_points, my_vector)
           b = self.my_func2(all_points, my_other_vector)
       else:
           a = self.my_func3(all_points, my_vector)
           b = self.my_func4(all_points, my_other_vector)
       final_vector = a + b
       # Choose random element from resulting vector 'final_vector'
于 2010-04-19T19:35:48.943 回答
0

最好做 mtrw 什么，但如果你不确定矢量化，你可以尝试 numpy.vectorize 在my_funcs

http://docs.scipy.org/doc/numpy/reference/generated/numpy.vectorize.html

于 2010-04-19T19:45:06.283 回答