I should emphasise it will not affect the results: the example will still
work fine.
On 19 February 2014 13:29, Joel Nothman wrote:
> Hi Tommy,
>
> I'm pretty sure this isn't related to Python 3. As far as I understand
> it's a deprecation warning from numpy. It may well be fixed now, and
> cer
Hi Tommy,
I'm pretty sure this isn't related to Python 3. As far as I understand it's
a deprecation warning from numpy. It may well be fixed now, and certainly
the warning won't show with default warning settings anymore.
Thanks for the report,
- Joel
On 19 February 2014 07:23, Tommy Carstense
To scikit-learn-general,
I was under the impression that scikit-learn is fully compatible with
Python 3 after reading this thread:
http://stackoverflow.com/questions/11910481/best-machine-learning-packag
e-for-python-3x
However, I get the warning below, when I run the first test example on
this p
Agreed.
Gaël
Original message From: Sturla Molden
Date:18/02/2014 20:00 (GMT+01:00)
To: [email protected] Subject:
Re: [Scikit-learn-general] fit a GMM to a histogram (or weighted
data points) ?
Whether the initial data reduction is core s
I would say that you need core sets.
We don't have them in scikit-learn but I'd love to get them. Worst case do a
data reduction using a streaming k means.
Histograms are really a ugly way of doing data réduction. They don't adapt to
the data distribution.
Gaël
Original message --
Whether the initial data reduction is core sets or histograms, the
procedure for fitting a weighted GMM would still be the same.
Sturla
Charles Greenberg wrote:
> To give some more info, my application is fitting to processed data.
> Basically, it's too expensive to keep all individual data poi
To give some more info, my application is fitting to processed data.
Basically, it's too expensive to keep all individual data points so we keep
a (3D) histogram, but we still want to fit a GMM to the data. Additionally,
we perform processing on the histogram, averaging from multiple
experiments. M
So this is now fixed in Cython, just get the most recent master from
github. :-)
Sturla
Sturla Molden
wrote:
> On 16/02/14 18:36, Sturla Molden wrote:
>> On 16/02/14 17:59, Sturla Molden wrote:
>> > Eh, I am not even sure it should be legal to call an iterator like this:
>> >
>> > typ
https://github.com/scikit-learn/scikit-learn/issues/1649
have a look at the implemention from @ksemb
https://gist.github.com/ksemb/4c3c86c6b62e7a99989b
and the discussion on the issue
HTH
A
--
Managing the Performance
I didn't find anything on the issue tracker related to this topic. Could you
point me to the relevant entries please?
Clemens
On Feb 17, 2014, at 21:42, Alexandre Gramfort
wrote:
> hi Clemens,
>
> you might want to look at the open issues on the topic.
>
> A
>
> On Mon, Feb 17, 2014 at 1
Hey Juan,
For scaling data, sklearn provides the preprocessing module. You can use
one scaler object per batch, and that will normalize each batch
indipendently.
Depending on what the angles represent, you should probably rescale them in
a way that they end up on a linear scale. Naively - maybe
Hi All,
I have a "biggish" dataset (to use Gaël's terminology ;), 45K samples x 300
features, that I want to cluster. I have very heterogeneous features -- some
are continuous, others are quasi-continuous (high counts), others are
discrete (counts of rare events), others are angles (uniformly dist
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