设 W 是一些维度为 (x, nP) 的矩阵 [见问题结尾]
现在，我正在执行以下代码:
uUpperDraw = np.zeros(W.shape)
for p in np.arange(0, nP):
uUpperDraw[s, p] = (W[s+1,:(p+1)]).sum()
我想对其进行矢量化以提高效率。给定一个 pGrid = [0, 1, ...]，我如何重现以下内容？
uUpperDraw = np.array([sum(W[x, 0]), sum(W[x,0] + W[x, 1]), sum(W[x,0] + W[x, 1] + W[x, 2]) ...
这是一些可重现的例子。
>>> s, nP
(3, 10)
>>> W
array([[ 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[ 2. , 1.63636364, 1.38461538, 1.2 , 1.05882353,
0.94736842, 0.85714286, 0.7826087 , 0.72 , 0.66666667]])
>>> uUpperDraw
array([[ 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. ,
0. , 0. ],
[ 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. ,
0. , 0. ],
[ 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. ,
0. , 0. ],
[ 2. , 3.63636364, 5.02097902, 6.22097902,
7.27980255, 8.22717097, 9.08431383, 9.86692252,
10.58692252, 11.25358919],
[ 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. ,
0. , 0. ]])
最佳答案
这看起来像是累计和。当你想分别计算每一行的累计总和时，这里就可以了
uUpperDraw = np.cumsum(W,axis=1)
关于python - 如何向量化这个累积运算？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/29922182/