how to produce a matrix of the x[0] y[0], x[1] y[0]....... x[n] y[n]-CodePudding

I have a list x and y, both are list of list and trying to produce a matrix of the add up of each element of the each list

x = numpy.array ([[1,1,1,1],[2,2,2,2]])
y = numpy.array ([[0,1,2,3],[4,5,6,7]])

result: [x[0] y[0], x[0] y[1], x[1] y[0], x[1] y[1]]
=> numpy.array ([[1,2,3,4],[5,6,7,8],[2,3,4,5],[6,7,8,9]])

Do I have to reshape y before production? Is there any smarter and more effective way achieve it?

Thanks you

CodePudding user response：

You can express the sum of all row combinations between the two arrays using numpy.repeat and numpy.tile:

import numpy as np

x = np.array ([[1,1,1,1],[2,2,2,2],[3,3,3,3]]) 
y = np.array ([[0,1,2,3],[4,5,6,7]]) 

x_height = x.shape[0]
y_height = y.shape[0]

result = np.repeat(x, y_height, axis=0)   np.tile(y, (x_height, 1))
print(result)

results in

[[ 1  2  3  4]   # x[0]   y[0]
 [ 5  6  7  8]   # x[0]   y[1]
 [ 2  3  4  5]   # x[1]   y[0]
 [ 6  7  8  9]   # x[1]   y[1]
 [ 3  4  5  6]   # x[2]   y[0]
 [ 7  8  9 10]]  # x[2]   y[1]

This generalizes to x and y with arbitrary numbers of rows.

CodePudding user response：

With broadcasting followed by a reshape:

In [138]: x
Out[138]: 
array([[1, 1, 1, 1],
       [2, 2, 2, 2]])

In [139]: y
Out[139]: 
array([[0, 1, 2, 3],
       [4, 5, 6, 7]])

In [140]: (np.expand_dims(x, 1)   y).reshape(-1, x.shape[-1])
Out[140]: 
array([[1, 2, 3, 4],
       [5, 6, 7, 8],
       [2, 3, 4, 5],
       [6, 7, 8, 9]])