I have this numpy array with points, something like
[(x1,y1), (x2,y2), (x3,y3), (x4,y4), (x5,y5)]
What I would like to do, is to get an array of all minimum distances. So for point 1
(x1, y1), I want the distance of the point closest to it, same for point 2
(x2,y2), etc... Distance being
sqrt((x1-x2)^2 + (y1-y2)^2).
This will obviously be an array with the same length as my array with point (in this case: 5 points -> 5 minimum distances).
Any concise way of doing this without resorting to loops?
Best How To :
This solution really focuses on readability over performance - It explicitly calculates and stores the whole
n x n distance matrix and therefore cannot be considered efficient.
But: It is very concise and readable.
import numpy as np
from scipy.spatial.distance import pdist, squareform
#create n x d matrix (n=observations, d=dimensions)
A = np.array([[1,9,2,4], [1,2,3,1]]).T
# explicitly calculate the whole n x n distance matrix
dist_mat = squareform(pdist(A, metric="euclidean"))
# mask the diagonal
# and calculate the minimum of each row (or column)