Python列表对Numpy的理解 [英] Python list comprehension for Numpy
问题描述
（33）：
s [index_values [i]] + = 4.1
有没有一种方法可以消除for循环？
我不完全理解什么样的对象 index_values 是。但是，如果它是 ndarray ，或者可以转换为 ndarray ，您可以这样做：
>>> s = numpy.arange（20）
>>> index_values =（numpy.random.random（（3，3））* 20）.astype（'i'）
>>> s [index_values] = 4
>>> s
数组（[0,1,4,4,5,6,4,8,4,4,11，12，
13，4，15，4,4，4， 19]）
编辑：但似乎不行这个案例。根据你的编辑和评论，这里有一个我认为可能适合你的方法。随机列表的长度不同...
>>> index_values = [list（range（x，x + random.randrange（1，5）））
... for x in [random.randrange（0,50）for y in range（33）]]
...不难转换为数组：
>>> index_value_array = numpy.fromiter（itertools.chain（* index_values），
dtype ='i'）
如果您知道数组的长度，请指定 count 以获得更好的性能：
>>> index_value_array = numpy.fromiter（itertools.chain（* index_values），
dtype ='i'，count = 83）
正如Robert Kern指出的那样，由于您的编辑表明您需要类似直方图的行为，因此简单的索引不会执行。因此，使用 numpy.histogram ：
>>> hist = numpy.histogram（index_value_array，bins = range（0，51））
直方图确实是为浮点直方图构建的。这意味着箱子必须比预期的大一些，因为最后一个箱子包含在最后一个箱子里，所以如果我们使用更直观的范围（0， 50）。结果是一个带有 n 计数数组和元组边框的元组：
>>> （阵列（[2,2,1,2,1,0,0,0,1,1,1,1,1,0,1,1,1,5,5,5,3 ，3，
3,3,3,2,1,0,2,3,3,1,0,2,3,2,2,2,3,2,1,1,2,2 ，
2,0,0,0,1,0]），
数组（[0,1,2,3,4,5,6,7,8,9,10,11， 12，13，14，15，16，
17，18，19，20，21，22，23，24，25，26，27，28，29，30，31，32，33，
34，35，36，37，38，39，40，41，42，43，44，45，46，47，48，49，50]）
$ / code> / pre>
现在我们可以将计数增加4.1倍，并执行向量加法：
>>> s = numpy.arange（50，dtype ='f'）
>>> hist [0] * 4.1 + s
数组（[8.2,9.2,6.1,11.2,8.1,5.6,6.7，12.1，
13.1,14.1,15.1,16.1， ，18.1,19.1,20.1,37.5，
38.5,39.5,32.3,33.3,34.3,35.3,36.3,33.2,30.1，
27.，36.2,41.3,42.3,35.1,32.41.2 ，46.3,43.2，
44.2,45.2,50.3,47.2,44.1,45.1,50.2,51.2,52.2，
45.，46.，47.，52.1，49。]）
我不知道这是否适合您的目的，但它似乎是一个很好的方法，应该可能发生在接近c因为它只使用 numpy 和 itertools 。
I'm looking for list-comprehension method or similar in Numpy to eliminate use of a for-loop eg. index_values is a Python dictionary list of lists (each list containing a different number of index values) and s is a numpy vector:
for i in range(33):
s[index_values[i]] += 4.1
Is there a method available that allows eliminating the for-loop?
解决方案 I don't fully understand what kind of object index_values is. But if it were an ndarray, or could be converted to an ndarray, you could just do this:
>>> s = numpy.arange(20)
>>> index_values = (numpy.random.random((3, 3)) * 20).astype('i')
>>> s[index_values] = 4
>>> s
array([ 0, 1, 4, 4, 4, 5, 6, 4, 8, 4, 4, 11, 12,
13, 4, 15, 4, 4, 4, 19])
Edit: But it seems that won't work in this case. On the basis of your edits and comments, here's a method I think might work for you. A random list of lists with varying lengths...
>>> index_values = [list(range(x, x + random.randrange(1, 5)))
... for x in [random.randrange(0,50) for y in range(33)]]
...isn't hard to convert into an array:
>>> index_value_array = numpy.fromiter(itertools.chain(*index_values),
dtype='i')
If you know the length of the array, specify the count for better performance:
>>> index_value_array = numpy.fromiter(itertools.chain(*index_values),
dtype='i', count=83)
Since your edit indicates that you want histogram-like behavior, simple indexing won't do, as pointed out by Robert Kern. So use numpy.histogram:
>>> hist = numpy.histogram(index_value_array, bins=range(0, 51))
histogram is really constructed for floating point histograms. This means that bins has to be a bit larger than expected because the last value is included in the last bin, and so 48 and 49 would be in the same bin if we used the more intuitive range(0, 50). The result is a tuple with an array of n counts and an array of n + 1 bin borders:
>>> hist
(array([2, 2, 1, 2, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 5, 5, 5, 3, 3,
3, 3, 3, 2, 1, 0, 2, 3, 3, 1, 0, 2, 3, 2, 2, 2, 3, 2, 1, 1, 2, 2,
2, 0, 0, 0, 1, 0]),
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50]))
Now we can scale the counts up by a factor of 4.1 and perform vector addition:
>>> s = numpy.arange(50, dtype='f')
>>> hist[0] * 4.1 + s
array([ 8.2, 9.2, 6.1, 11.2, 8.1, 5. , 6. , 7. , 12.1,
13.1, 14.1, 15.1, 16.1, 13. , 18.1, 19.1, 20.1, 37.5,
38.5, 39.5, 32.3, 33.3, 34.3, 35.3, 36.3, 33.2, 30.1,
27. , 36.2, 41.3, 42.3, 35.1, 32. , 41.2, 46.3, 43.2,
44.2, 45.2, 50.3, 47.2, 44.1, 45.1, 50.2, 51.2, 52.2,
45. , 46. , 47. , 52.1, 49. ])
I have no idea if this suits your purposes, but it seems like a good approach, and should probably happen at near c speed since it uses only numpy and itertools.
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