np.zeros和np.zeros_like

np.zeros类似于C中的calloc，预定义后随着使用而动态分配内存

而np.zeros_like类似于C中的malloc+memset，在预定义时就直接初始化内存，之后直接使用内存，系统不再重新分配

以下测试结果取自https://stackoverflow.com/questions/27464039/why-the-performance-difference-between-numpy-zeros-and-numpy-zeros-like

This simple test with the memory_profile package supports the claim that zeros and empty allocate memory 'on-the-fly', while zeros_like allocates everything up front:

N = (1000, 1000) 
M = (slice(None, 500, None), slice(500, None, None))

Line #    Mem usage    Increment   Line Contents
================================================
     2   17.699 MiB    0.000 MiB   @profile
     3                             def test1(N, M):
     4   17.699 MiB    0.000 MiB       print(N, M)
     5   17.699 MiB    0.000 MiB       x = np.zeros(N)   # no memory jump
     6   17.699 MiB    0.000 MiB       y = np.empty(N)
     7   25.230 MiB    7.531 MiB       z = np.zeros_like(x) # initial jump
     8   29.098 MiB    3.867 MiB       x[M] = 1     # jump on usage
     9   32.965 MiB    3.867 MiB       y[M] = 1
    10   32.965 MiB    0.000 MiB       z[M] = 1
    11   32.965 MiB    0.000 MiB       return x,y,z