I would like to do the below but using PyTorch.
The below example and description is from this post.
I have a numeric matrix with 25 columns and 23 rows, and a vector of length 25. How can I multiply each row of the matrix by the vector without using a for loop?
The result should be a 25x23 matrix (the same size as the input), but each row has been multiplied by the vector.
Example Code in R (source: reproducible example from @hatmatrix's answer):
matrix <- matrix(rep(1:3,each=5),nrow=3,ncol=5,byrow=TRUE)
[,1] [,2] [,3] [,4] [,5]
[1,] 1 1 1 1 1
[2,] 2 2 2 2 2
[3,] 3 3 3 3 3
vector <- 1:5
Desired output:
[,1] [,2] [,3] [,4] [,5]
[1,] 1 2 3 4 5
[2,] 2 4 6 8 10
[3,] 3 6 9 12 15
What is the best way of doing this using Pytorch?
The answer was so trivial that I overlooked it.
For simplicity I used a smaller vector and matrix in this answer.
Multiply rows of matrix by vector:
X = torch.tensor([[1,2,3],[5,6,7]])
y = torch.tensor([7,4])
X.transpose(0,1)*y
# or alternatively
y*X.transpose(0,1)
output:
tensor([[ 7, 20],
[14, 24],
[21, 28]])
tensor([[ 7, 20],
[14, 24],
[21, 28]])
Multiply columns of matrix by vector:
To multiply the columns of matrix by a vector you can use the same operator '*' but without the need to transpose the matrix (or vector) first
X = torch.tensor([[3, 5],[5, 5],[1, 0]])
y = torch.tensor([7,4])
X*y
# or alternatively
y*X
output:
tensor([[21, 20],
[35, 20],
[ 7, 0]])
tensor([[21, 20],
[35, 20],
[ 7, 0]])