Multiplying numpy arrays

Question

I have written a function to multiply two numpy arrays.

def ra(self):
 """Multiply Rotation with initial Values"""
 rva = self.r_array() * self.va_array()
 rva = np.sum(rva, axis=1) # Sum rows of Matrix
 rva = np.array([[rva[0]], # Transpose Matrix
 [rva[1]],
 [rva[2]]])

where:

• r_array has 3 rows and 3 columns
• va_array has 3 rows and 1 column

I feel like this should be able to be written in one line. However, self.r_array() * self.va_array() always returns a 3 x 3 array.

Any suggestions would be greatly appreciated.

Cheers

• aki: stackoverflow.com/a/21563036 Use r_array.dot(va_array) or @ operator.
• GalacticPonderer: @Peilonrayz - Thank you for the welcome. My code did work as intended, hence why I posted it here and not at in Stack Overflow. I generated a workaround that produced the correct answer but was not elegant. The problem was that I did not know how to do this in one line. This was correctly answered below, however.
• Peilonrayz: So it does, my apologies.
• hpaulj: Questions like this are common on SO.

Answer 1

Actually the * operator does element-wise multiplication. So you need to use .dot() function to get the desired result.

Example :

import numpy as np
a = np.array([[1,2,3],
 [4,5,6],
 [7,8,9]])
b = np.array([[1]
 ,[2],
 [3]])
print(a * b)
print(a.dot(b))

output :

[[ 1 2 3]
 [ 8 10 12]
 [21 24 27]]
[[14]
 [32]
 [50]]

Observe that when I have used * operator, every column in a is multiplied with b element-wise

• GalacticPonderer: @sai-sreenivas - Thank you for the answer. That worked to get it down to one line. It was working as intended before (unlike was suggesting and thus I dispute that it was off-topic) but this has made it significantly more elegant.
• Peilonrayz: Why does this output different numbers to the OPs? OPs outputs 6, 30, 72 where yours outputs 14, 32, 50.

Answer 2

A one liner:

 np.sum(r_array*va_array, axis=1, keepdims=True)

To match r_array@va_array, use va_array.T in the 1liner.