NumPy的：矢量分配

Question

期间Evaulate索引阵列我想向量化这个NumPy的操作：

for j in range(yt): 
    for i in range(xt): 
     y[j, i] = x[idx[j, i], j, i] 

其中idx包含轴-0索引到一个x切片。有一些简单的方法来做到这一点？

Answer 1

您可以使用：

J, I = np.ogrid[:yt, :xt] 
x[idx, J, I] 

下面是测试：

import numpy as np 

yt, xt = 3, 5 
x = np.random.rand(10, 6, 7) 
y = np.zeros((yt, xt)) 
idx = np.random.randint(0, 10, (yt, xt)) 

for j in range(yt): 
    for i in range(xt): 
     y[j, i] = x[idx[j, i], j, i] 

J, I = np.ogrid[:yt, :xt] 
np.all(x[idx, J, I] == y) 

Answer 2

下面是使用linear indexing一个方法 -

zt,yt,xt = x.shape 
out = x.reshape(zt,-1)[idx.ravel(),np.arange(yt*xt)].reshape(-1,xt) 

---

运行测试&验证输出

本节比较本文中的建议方法和other orgid based solution的性能，并验证输出。

函数定义 -

def original_app(x,idx): 
    _,yt,xt = x.shape 
    y = np.zeros((yt,xt)) 
    for j in range(yt): 
     for i in range(xt): 
      y[j, i] = x[idx[j, i], j, i] 
    return y 

def ogrid_based(x,idx): 
    _,yt,xt = x.shape 
    J, I = np.ogrid[:yt, :xt] 
    return x[idx, J, I] 

def reshape_based(x,idx):        
    zt,yt,xt = x.shape 
    return x.reshape(zt,-1)[idx.ravel(),np.arange(yt*xt)].reshape(-1,xt) 

设置输入 -

In [56]: # Inputs 
    ...: zt,yt,xt = 100,100,100 
    ...: x = np.random.rand(zt,yt,xt) 
    ...: idx = np.random.randint(0,zt,(yt,xt)) 
...: 

验证输出 -

In [57]: np.allclose(original_app(x,idx),ogrid_based(x,idx)) 
Out[57]: True 

In [58]: np.allclose(original_app(x,idx),reshape_based(x,idx)) 
Out[58]: True 

计时 -

In [68]: %timeit original_app(x,idx) 
100 loops, best of 3: 6.97 ms per loop 

In [69]: %timeit ogrid_based(x,idx) 
1000 loops, best of 3: 391 µs per loop 

In [70]: %timeit reshape_based(x,idx) 
1000 loops, best of 3: 230 µs per loop