问题
Consider the following exercise in Numpy array broadcasting.
import numpy as np
v = np.array([[1.0, 2.0]]).T # column array
A2 = np.random.randn(2,10) # 2D array
A3 = np.random.randn(2,10,10) # 3D
v * A2 # works great
# causes error:
v * A3 # error
I know the Numpy rules for broadcasting, and I'm familiar with bsxfun functionality in Matlab. I understand why attempting to broadcast a (2,1) array into a (2,N,N) array fails, and that I have to reshape the (2,1) array into a (2,1,1) array before this broadcasting goes through.
My question is: is there any way to tell Python to automatically pad the dimensionality of an array when it attempts to broadcast, without me having to specifically tell it the necessary dimension?
I don't want to explicitly couple the (2,1) vector with the multidimensional array it's going to be broadcast against---otherwise I could do something stupid and absurdly ugly like mult_v_A = lambda v,A: v.reshape([v.size] + [1]*(A.ndim-1)) * A. I don't know ahead of time if the "A" array will be 2D or 3D or N-D.
Matlab's bsxfun broadcasting functionality implicitly pads the dimensions as needed, so I'm hoping there's something I could do in Python.
回答1:
It's ugly, but this will work:
(v.T * A3.T).T
If you don't give it any arguments, transposing reverses the shape tuple, so you can now rely on the broadcasting rules to do their magic. The last transpose returns everything to the right order.
回答2:
NumPy broadcasting adds additional axes on the left.
So if you arrange your arrays so the shared axes are on the right and the broadcastable axes are on the left, then you can use broadcasting with no problem:
import numpy as np
v = np.array([[1.0, 2.0]]) # shape (1, 2)
A2 = np.random.randn(10,2) # shape (10, 2)
A3 = np.random.randn(10,10,2) # shape (10, 10, 2)
v * A2 # shape (10, 2)
v * A3 # shape (10, 10, 2)
来源：https://stackoverflow.com/questions/17577336/numpy-python-automatically-expand-dimensions-of-arrays-when-broadcasting