As a follow up, I found a description of the parallel tree training
algorithm [2] that MSR used.
Regards,
Brian
[2] http://budiu.info/work/budiu-biglearn11.pdf
--
All the data continuously generated in your IT
Hi,
Currently the fit function in GaussianProcess throws a weird exception
when only one training example is passed to fit():
from sklearn.gaussian_process import GaussianProcess
from sklearn.gaussian_process import GaussianProcess
gp.fit([[1., 2.]], [-1.0])
gp.fit([[1., 2.]], [-1.0])
Hi Alex,
I would say:
if it makes sense to fit a GP with only one point:
it should be fixed
else:
raise a nicer error message
Alex
On Tue, Nov 29, 2011 at 7:10 PM, Alexandre Passos
alexandre...@gmail.com wrote:
Hi,
Currently the fit function in GaussianProcess throws a weird
On Tue, Nov 29, 2011 at 15:02, Alexandre Gramfort
alexandre.gramf...@inria.fr wrote:
Hi Alex,
I would say:
if it makes sense to fit a GP with only one point:
it should be fixed
The thing is, I'm not quite sure. In the code I was writing it made
sense to add points incrementally, but what
On Tue, Nov 29, 2011 at 10:02 PM, Alexandre Gramfort
alexandre.gramf...@inria.fr wrote:
Hi Alex,
I would say:
if it makes sense to fit a GP with only one point:
it should be fixed
Note that even though it might not make any sense in practice, unless
there's a mathematical reason that I'm
The thing is, I'm not quite sure. In the code I was writing it made
sense to add points incrementally, but what you get out of a GP with
only one point is something pretty silly, enough to be almost as
useless as the output of a logistic regression with only one point.
that would be my gut
There is no maximum likelihood solution to a GP with a single training
point, but you can certainly draw samples from the posterior; in fact,
you can draw samples from the prior (without conditioning on data).
That may help you determine if your covariance function is reasonable:
samples from the
Hi list,
Indeed, I did not think about this usage of the GP predictor (actually I
don't think DACE for Matlab handles this case either). In my opinion,
fitting a GP with only one point does not make much sense even if it
holds mathematically (i.e. you can compute the posterior distribution of
2011/11/29 Alexandre Passos alexandre...@gmail.com:
On Tue, Nov 29, 2011 at 16:18, Vincent Dubourg
vincent.dubo...@gmail.com wrote:
@AlexP: What are you trying to do with this iterative construction? Are
you trying to implement some optimization algorithm (like the efficient
global optimizer
On Tue, Nov 29, 2011 at 4:53 PM, Olivier Grisel
olivier.gri...@ensta.org wrote:
Now back to you problem I think we should support fitting models with
just one sample just for the sake of consistency / continuity even if
theds is no practical application of fitting models with a single
sample:
10 matches
Mail list logo