Calculate Euclidean Distance With Numpy
Solution 1:
A different solution would be to use the spatial module from scipy, the KDTree in particular.
This class learn from a set of data and can be interrogated given a new dataset:
from scipy.spatial import KDTree
# create some fake data
x = arange(20)
y = rand(20)
z = x**2
# put them togheter, should have a form [n_points, n_dimension]
data = np.vstack([x, y, z]).T
# create the KDTree
kd = KDTree(data)
now if you have a point you can ask the distance and the index of the closet point (or the N closest points) simply by doing:
kd.query([1, 2, 3])
# (1.8650720813822905, 2)# your may differs
or, given an array of positions:
#bogus position
x2 = rand(20)*20
y2 = rand(20)*20
z2 = rand(20)*20
# join them togheter as the input
data2 = np.vstack([x2, y2, z2]).T
#query them
kd.query(data2)
#(array([ 14.96118553, 9.15924813, 16.08269197, 21.50037074,# 18.14665096, 13.81840533, 17.464429 , 13.29368755,# 20.22427196, 9.95286671, 5.326888 , 17.00112683,# 3.66931946, 20.370496 , 13.4808055 , 11.92078034,# 5.58668204, 20.20004206, 5.41354322, 4.25145521]),#array([4, 3, 2, 4, 2, 2, 4, 2, 3, 3, 2, 3, 4, 4, 3, 3, 3, 4, 4, 4]))
Solution 2:
You can calculate the difference from each xa to each xb with np.subtract.outer(xa, xb)
. The distance to the nearest xb is given by
np.min(np.abs(np.subtract.outer(xa, xb)), axis=1)
To extend this to 3D,
distances = np.sqrt(np.subtract.outer(xa, xb)**2 + \
np.subtract.outer(ya, yb)**2 + np.subtract.outer(za, zb)**2)
distance_to_nearest = np.min(distances, axis=1)
If you actually want to know which of the b points is the nearest, you use argmin
in place of min
.
index_of_nearest = np.argmin(distances, axis=1)
Solution 3:
There is more than one way of doing this. Most importantly, there's a trade-off between memory-usage and speed. Here's the wasteful method:
s = (1, -1)
d = min((xa.reshape(s)-xb.reshape(s).T)**2
+ (ya.reshape(s)-yb.reshape(s).T)**2
+ (za.reshape(s)-zb.reshape(s).T)**2), axis=0)
The other method would be to iterate over the point set in b
to avoid the expansion to the full blown matrix.
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