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Hi,<br>
<br>
I worked on this problem long ago and attempted a solution within
PyMVPA,<br>
but my commitment was not enough to get generic hyperparameters'
optimization<br>
so that the GPR way of minimizing the marginal likelihood would have
had its proper place.<br>
I'm the one to blame for this part of PyMVPA that is not fully
developed ;-)<br>
<br>
Anyway what was done is in mvpa/clfs/model_selector.py<br>
which is imported in gpr.py and used by the class GPRWeights(). My
goal<br>
at that time was exactly to implement a Python version of what is in
the GPML<br>
book. Yarik rearranged that part in later evolutions of PyMVPA. So
my suggestion<br>
is to dig that part of the code. Yarik might want to add more
comments on this.<br>
<br>
Unfortunately I had not much opportunity to work on it after that<br>
attempt.<br>
<br>
Best,<br>
<br>
Emanuele<br>
<br>
On 05/31/2011 01:00 PM, Martin Fergie wrote:
<blockquote
cite="mid:BANLkTink9o3oQo6LEX0S5iXVVsOwHP8Yyw@mail.gmail.com"
type="cite">Hi,<br>
<br>
I've been looking into using PyMVPA recently for performing
Gaussian process regression. I can't seem to find a method from
gpr.py or the examples of minimizing the log marginal likelihood
with respect to the kernel hyper parameters. <br>
<br>
Is there a recommended way of doing this? or would I have to
implement some sort of wrapper to combine gpr.py with a gradient
ascent routine?<br>
<br>
I currently use the GPML matlab package, however I'd like to
replace it with a python solution so I can easily parallelize
training multiple Gaussian processes over multiple machines. <br>
<br>
Thanks for your help,<br>
<font color="#888888"><font color="#888888">Martin</font>
</font><br>
<br>
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