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Forming a matrix by summing 2 arrays element-array wise using NumPy (2 answers)
Closed 3 years ago.
Given 2 numpy (1D) arrays, I want to compute the 2D array containing all possible sums of elements of those two arrays. For example, like this:
import numpy as np
v = np.array([1, 2, 3])
w = np.array([4, 5, 6])
sum_matrix = np.zeros((3,3))
for i in range(3):
for j in range(3):
sum_matrix[i,j] = v[i] + w[j]
print(sum_matrix)
This returns the correct answer
[[5. 6. 7.]
[6. 7. 8.]
[7. 8. 9.]]
but I want to avoid python loops (and certainly a double one). Is there some nice numpy functionality for this situation?
I can think of one solution that avoids all loops, but it feels kind of like a hack. I noticed that if v is a column matrix and w a row matrix, then the matrix product v @ w returns the matrix of all possible products. I can translate this to sums using log and exp:
v = v[np.newaxis].T # Turn v into a column matrix
w = w[np.newaxis] # Turn w into a row matrix
print(np.log(np.exp(v) @ np.exp(w)))
This prints the same as the above.
Question: Are there other, less hacky, numpy solutions to this problem?
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