设置
np.random.seed(314)
x = np.random.rand(10, 4)
mask np.array([True, False, False, True])
x
array([[ 0.91687358, 0.58854191, 0.26504775, 0.78320538],
[ 0.91800106, 0.82735501, 0.72795148, 0.26048042],
[ 0.9117634 , 0.26075656, 0.76637602, 0.26153114],
[ 0.12229137, 0.38600554, 0.84008124, 0.27817936],
[ 0.06991369, 0.63310965, 0.58476603, 0.58123194],
[ 0.6772054 , 0.6871551 , 0.43892737, 0.3209265 ],
[ 0.57055222, 0.47984862, 0.86107434, 0.83480474],
[ 0.10576611, 0.06040804, 0.59688219, 0.79239497],
[ 0.22635574, 0.5352008 , 0.13606616, 0.37224445],
[ 0.15197674, 0.42982185, 0.79270622, 0.40695651]])
我可以像这样屏蔽 x:
y = x[:, mask]
y
array([[ 0.91687358, 0.78320538],
[ 0.91800106, 0.26048042],
[ 0.9117634 , 0.26153114],
[ 0.12229137, 0.27817936],
[ 0.06991369, 0.58123194],
[ 0.6772054 , 0.3209265 ],
[ 0.57055222, 0.83480474],
[ 0.10576611, 0.79239497],
[ 0.22635574, 0.37224445],
[ 0.15197674, 0.40695651]])
问题
给定y和mask如何生成:
array([[ 0.91687358, 0. , 0. , 0.78320538],
[ 0.91800106, 0. , 0. , 0.26048042],
[ 0.9117634 , 0. , 0. , 0.26153114],
[ 0.12229137, 0. , 0. , 0.27817936],
[ 0.06991369, 0. , 0. , 0.58123194],
[ 0.6772054 , 0. , 0. , 0.3209265 ],
[ 0.57055222, 0. , 0. , 0.83480474],
[ 0.10576611, 0. , 0. , 0.79239497],
[ 0.22635574, 0. , 0. , 0.37224445],
[ 0.15197674, 0. , 0. , 0.40695651]])
最佳答案
解决方案
z = np.zeros((y.shape[0], len(mask)))
z[:, mask] = y
z
array([[ 0.91687358, 0. , 0. , 0.78320538],
[ 0.91800106, 0. , 0. , 0.26048042],
[ 0.9117634 , 0. , 0. , 0.26153114],
[ 0.12229137, 0. , 0. , 0.27817936],
[ 0.06991369, 0. , 0. , 0.58123194],
[ 0.6772054 , 0. , 0. , 0.3209265 ],
[ 0.57055222, 0. , 0. , 0.83480474],
[ 0.10576611, 0. , 0. , 0.79239497],
[ 0.22635574, 0. , 0. , 0.37224445],
[ 0.15197674, 0. , 0. , 0.40695651]])
关于python - numpy 中 bool 掩码的反向影响，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/37038817/