numpy.vectorize返回不正确的值 [英] numpy.vectorize returns incorrect values

问题描述

我在使用numpy.vectorize函数时遇到了一些问题.

I am having some problems with the numpy.vectorize function.

我定义了一个对单个元素输入效果很好的函数，但是矢量化版本返回的结果不同-我在做什么错了?

I have defined a function that works well for single element input but the vectorized version returns different results - What am I doing wrong?

代码:

def c_inf_comp(z):
    if z>0:
        return np.exp(-1./(z*z))
    else:
        return 0


>>> x = np.array([-10., 10.])
>>> x
array([-10.,  10.])
>>> c_inf_comp(x[0])
0
>>> c_inf_comp(x[1])
0.99004983374916811
>>> vfunz = np.vectorize(c_inf_comp)
>>> vfunz(x)
array([0, 0])

推荐答案

由于对向量进行矢量化时未指定otypes(输出数据类型)，因此NumPy假定要返回int32数组价值观.

Because you don't specify otypes (the output data type) when you vectorize your function, NumPy assumes you want to return an array of int32 values.

给定x时，矢量化函数vfunz首先会看到-10.，返回整数0，因此确定返回数组的dtype应该为int32.

When given x the vectorized function vfunz first sees -10., returns the integer 0, and so decides that the dtype of the returned array should be int32.

要解决此问题，请将otypes指定为np.float值:

To fix this, specify otypes to be np.float values:

vfunz = np.vectorize(c_inf_comp, otypes=[np.float])

然后您将获得预期的结果:

You then get your expected result:

>>> vfunz(x)
array([ 0.        ,  0.99004983])

(或者，可以通过在c_inf_comp的else条件下返回浮点值(即return 0.0来解决此问题.这样，由np.vectorize(c_inf_comp)生成的函数将返回一个浮点值数组，即使如果它首先看到负数.)

(Alternatively, the issue can be fixed by returning a float value in the else condition of c_inf_comp, i.e. return 0.0. That way, the function generated by np.vectorize(c_inf_comp) will return an array of float values even if it sees a negative number first.)

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