Reputation: 389
I have a one dimensional array, lets say:
import numpy as np
inp_vec = np.array([1, 2, 3])
Now, I would like to construct a matrix of the form
[[1 - 1, 1 - 2, 1 - 3],
[2 - 1, 2 - 2, 2 - 3],
[3 - 1, 3 - 2, 3 - 3]])
Of course it can be done with for loops but is there a more elegant way to do this?
Upvotes: 4
Views: 1803
Reputation: 155
This is a quick and simple alternative.
import numpy as np
inp_vec = np.array([1, 2, 3])
N = len(inp_vec)
np.reshape(inp_vec,(N,1)) - np.reshape(inp_vec,(1,N))
Upvotes: 1
Reputation: 11
Using np.nexaxis
import numpy as np
inp_vec = np.array([1, 2, 3])
output = inp_vec[:, np.newaxis] - inp_vec
Output
array([[ 0, -1, -2],
[ 1, 0, -1],
[ 2, 1, 0]])
Upvotes: 1
Reputation:
This I find a nice way as well:
np.subtract.outer([1,2,3], [1,2,3])
Upvotes: 6
Reputation: 65791
This seems to work:
In [1]: %paste
import numpy as np
inp_vec = np.array([1, 2, 3])
## -- End pasted text --
In [2]: inp_vec.reshape(-1, 1) - inp_vec
Out[2]:
array([[ 0, -1, -2],
[ 1, 0, -1],
[ 2, 1, 0]])
Explanation:
You first reshape the array to nx1
. When you subtract a 1D array, they are both broadcast to nxn
:
array([[ 1, 1, 1],
[ 2, 2, 2],
[ 3, 3, 3]])
and
array([[ 1, 2, 3],
[ 1, 2, 3],
[ 1, 2, 3]])
Then the subtraction is done element-wise, which yields the desired result.
Upvotes: 4
Reputation: 31
import numpy as np
inp_vec = np.array([1, 2, 3])
a, b = np.meshgrid(inp_vec, inp_vec)
print(b - a)
Output:
Array([[ 0 -1 -2],
[ 1 0 -1],
[ 2 1 0]])
Upvotes: 3