How do I vectorize this cumulative operation?
Let be W
some matrix of dimension (x, nP)
[see. end of question]
I am currently doing the following code:
uUpperDraw = np.zeros(W.shape)
for p in np.arange(0, nP):
uUpperDraw[s, p] = (W[s+1,:(p+1)]).sum()
I want to vectorize this for efficiency. Given pGrid = [0, 1, ...]
how can I reproduce the following?
uUpperDraw = np.array([sum(W[x, 0]), sum(W[x,0] + W[x, 1]), sum(W[x,0] + W[x, 1] + W[x, 2]) ...
Here are some reproducible examples.
>>> 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. ]])
+3
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