[SciPy-user] PyEM: custom (non-Euclidean) distance function?

[Emanuele Olivetti]

Emanuele Olivetti wrote:
> Hi All,
>
> I'm playing with PyEM [0] in scikits and would like to feed
> a dataset for which Euclidean distance is not supposed to
> work. So I'm wondering how simple is to modify the code with
> a custom distance (e.g., 1-norm).
>
>   

Additional info. My final goal is to run the EM algorithm
and estimate the Gaussian mixture from data, but assuming
a different distance function. I had a look to densities.py
which seems to be the relevant file for this question. I
can see the computation of Euclidean distance in:
- _scalar_gauss_den()
- _diag_gauss_den()
- _full_gauss_den()


So the question is: if I change those functions according to a
new distance function, is it expected the EM estimation
em.train() to work meaningfully? Are there other parts of PyEM
that assumes Euclidean distance function?


Emanuele