Hi,
I'm trying to compute the distance matrix (weighted p-norm [*])
between two sets of vectors (data1 and data2). Example:
import numpy as N
p = 3.0
data1 = N.random.randn(100,20)
data2 = N.random.randn(80,20)
weight = N.random.rand(20)
distance_matrix = N.zeros((data1.shape[0],data2.shape[0]))
for d in range(data1.shape[1]):
distance_matrix +=
(N.abs(N.subtract.outer(data1[:,d],data2[:,d]))*weight[d])**p
pass
distance_matrix = distance_matrix**(1.0/p)
Is there a way to speed up the for loop? When the dimension
of the vectors becomes big (e.g. >1000) the for loop
becomes really annoying.
Thanks,
Emanuele
[*] : ||x - x'||_w = (\sum_{i=1...N} (w_i*|x_i - x'_i|)**p)**(1/p)
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