numpy:取log(matrix)时有效避免0 [英] numpy: Efficiently avoid 0s when taking log(matrix)

问题描述

from numpy import *

m = array([[1,0],
           [2,3]])

我想计算元素级log2(m)，但仅在m不为0的地方.在这些地方，我希望为0.

I would like to compute the element-wise log2(m), but only in the places where m is not 0. In those places, I would like to have 0 as a result.

我现在正在反对:

RuntimeWarning: divide by zero encountered in log2

---

尝试1:使用where

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Try 1: using where

res = where(m != 0, log2(m), 0)

可以计算出正确的结果，但仍然会记录RuntimeWarning: divide by zero encountered in log2.看起来(从语法上来说，这很明显)，numpy仍然在完整矩阵上计算log2(m)，然后才where选择要保留的值.

which computes me the correct result, but I still get logged a RuntimeWarning: divide by zero encountered in log2. It looks like (and syntactically it is quite obvious) numpy still computes log2(m) on the full matrix and only afterwards where picks the values to keep.

我想避免这个警告.

尝试2:使用口罩

from numpy import ma

res = ma.filled(log2(ma.masked_equal(m, 0)), 0)

确保掩盖零将阻止log2应用于它们，不是吗?不幸的是:我们仍然得到RuntimeWarning: divide by zero encountered in log2.

Sure masking away the zeros will prevent log2 to get applied to them, won't it? Unfortunately not: We still get RuntimeWarning: divide by zero encountered in log2.

即使矩阵被遮罩，log2似乎仍然适用于每个元素.

Even though the matrix is masked, log2 still seems to be applied to every element.

如何在不获取除数为零的警告的情况下有效地计算numpy数组的逐元素日志?

• 当然，我可以使用seterr暂时禁用这些警告的记录，但这看起来并不干净.
• 并且确保双 for 循环将特别有助于处理0，但会降低numpy的效率.

• Of course I could temporarily disable the logging of these warnings using seterr, but that doesn't look like a clean solution.
• And sure a double for loop would help with treating 0s specially, but defeats the efficiency of numpy.

有什么想法吗?

推荐答案

我们可以为此使用掩码数组:

We can use masked arrays for this:

>>> from numpy import *
>>> m = array([[1,0], [2,3]])
>>> x = ma.log(m)
>>> print x.filled(0)
[[ 0.          0.        ]
 [ 0.69314718  1.09861229]]

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