[Numpy-discussion] distance matrix and (weighted) p-norm
Damian Eads
eads@soe.ucsc....
Mon Sep 8 10:15:24 CDT 2008
Emanuele Olivetti wrote:
> 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)
This feature could be implemented easily. However, I must admit I'm not
very familiar with weighted p-norms. What is the reason for raising w
to the p instead of w_i*(|x_i-x'_i|)**p?
Damian
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