Python Numpy Tutorials： Broadcasting

Broadcasting typically makes your code more concise and faster, so you should strive to use it where possible.

# -*- coding: utf-8 -*-"""Python Version: 3.5Created on Fri May 12 12:50:49 2017E-mail: [e-mail]@author: DidiLv"""import numpy as np# Compute outer product of vectorsv = np.array([1,2,3])  # v has shape (3,)w = np.array([4,5])    # w has shape (2,)# To compute an outer product, we first reshape v to be a column# vector of shape (3, 1); we can then broadcast it against w to yield# an output of shape (3, 2), which is the outer product of v and w:# [[ 4  5]#  [ 8 10]#  [12 15]]print(np.reshape(v, (3, 1)) * w)x = np.array([[1,2,3], [4,5,6]])# x has shape (2, 3) and v has shape (3,) so they broadcast to (2, 3),# giving the following matrix:# [[2 4 6]#  [5 7 9]]print(x + v) #这里注意reshape()函数调用并不改变原数组的形状print((x.T + w).T)# Another solution is to reshape w to be a row vector of shape (2, 1);# we can then broadcast it directly against x to produce the same# output.print(x + np.reshape(w, (2, 1)))# Multiply a matrix by a constant:# x has shape (2, 3). Numpy treats scalars as arrays of shape ();# these can be broadcast together to shape (2, 3), producing the# following array:# [[ 2  4  6]#  [ 8 10 12]]print(x * 2)