Alexandre Gramfort

2010-12-12 19:30:32 UTC

Hi,

the question has been raised many time and I think it's time to

address it in a common way.

We regularly need to compute the pairwise distances / metrics

between two sets of samples in the same space. See for example

in the affinity_propagation, in the manifold module, in the gaussian

process module etc...

Shall we create :

scikits.learn.pairwise_metrics

and then

from scikits.learn.pairwise_metrics import euclidian_distances

D = euclidian_distances(X, Y)

where D is a symmetric matrix D[i,j] = linalg.norm(X[i] - Y[j])

what do you think?

Alex

the question has been raised many time and I think it's time to

address it in a common way.

We regularly need to compute the pairwise distances / metrics

between two sets of samples in the same space. See for example

in the affinity_propagation, in the manifold module, in the gaussian

process module etc...

Shall we create :

scikits.learn.pairwise_metrics

and then

from scikits.learn.pairwise_metrics import euclidian_distances

D = euclidian_distances(X, Y)

where D is a symmetric matrix D[i,j] = linalg.norm(X[i] - Y[j])

what do you think?

Alex