这是矢量化代码、进行解释和演示的好地方。
#Generate two random arrays, shape must be the same
>>> Mt = np.random.rand(2,2)
>>> It = np.random.rand(2,2)
>>> Mt
array([[ 0.47961753, 0.74107574],
[ 0.94540074, 0.05287875]])
>>> It
array([[ 0.86232671, 0.45408798],
[ 0.99468912, 0.87005204]])
#Create a mask based on some condition
>>> mask = Mt > It
>>> mask
array([[False, True],
[False, False]], dtype=bool)
#Update in place
>>> Mt[mask]+=1
>>> Mt[~mask]-=1 #Numpy logical not
>>> Mt
array([[-0.52038247, 1.74107574],
[-0.05459926, -0.94712125]])
您可能需要创建第二个掩码,因为当前的减法掩码是Mt <= It
not Mt < It
,然而,这是一个证明逻辑不的好地方。
要准确地重现您的代码,请使用以下代码:
Mt[Mt > It]+=1
Mt[Mt < It]-=1
因为我对这些事情感兴趣:
def looper(Mt,It):
for x in range (Mt.shape[0]):
for y in range (Mt.shape[1]):
if Mt [x,y] > It[x,y]:
Mt [x,y] +=1
elif Mt [x,y] < It[x,y]:
Mt [x,y] -=1
nlooper = autojit(looper)
Mt = np.random.rand(500,500)
It = np.random.rand(500,500)
%timeit looper(Mt,It)
1 loops, best of 3: 531 ms per loop
%timeit Mt[Mt > It]+=1;Mt[Mt < It]-=1
100 loops, best of 3: 2.27 ms per loop
%timeit nlooper(Mt,It)
1 loops, best of 3: 308 µs per loop
autojit
是 python/numpy 的 JIT 编译器,来自numba http://numba.pydata.org module.