def linear_indx(r,x,y,c,i_max,j_max):
d0,d1,d2,d3 = r.shape
np.put(r,np.arange(i_max)[:,None]*d1*d2*d3 + np.arange(j_max)*d2*d3 + x*d3 +y,c)
return r
def org_app(r,x,y,c,i_max,j_max):
for i in range(i_max):
for j in range(j_max):
r[i, j, x[i,j], y[i,j]] = c[i,j]
return r
设置输入数组和基准-
In [134]: # Setup input arrays
...: i_max = 40
...: j_max = 50
...: D0 = 60
...: D1 = 70
...: N = 80
...:
...: r = np.zeros((D0,D1,N,N))
...: c = np.random.rand(i_max,j_max)
...:
...: x = np.random.randint(0,N,(i_max,j_max))
...: y = np.random.randint(0,N,(i_max,j_max))
...:
In [135]: # Make copies for testing, as both functions make in-situ changes
...: r1 = r.copy()
...: r2 = r.copy()
...:
In [136]: # Verify results by comparing with original loopy approach
...: np.allclose(linear_indx(r1,x,y,c,i_max,j_max),org_app(r2,x,y,c,i_max,j_max))
Out[136]: True
In [137]: # Make copies for testing, as both functions make in-situ changes
...: r1 = r.copy()
...: r2 = r.copy()
...:
In [138]: %timeit linear_indx(r1,x,y,c,i_max,j_max)
10000 loops, best of 3: 115 µs per loop
In [139]: %timeit org_app(r2,x,y,c,i_max,j_max)
100 loops, best of 3: 2.25 ms per loop
使用^{} -
基准测试和验证
定义函数-
设置输入数组和基准-
索引数组必须是broadcastable才能工作。唯一需要做的更改是在第一个索引
i
中添加一个轴,以使形状与其余的匹配。实现这一点的快速方法是使用None
(相当于numpy.newaxis
)进行索引:相关问题 更多 >
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