hi Olivier,
Thanks for the reply.
In my case each row of X contains two normal distributions (one 1-D and
second 2-D).
So a row of X looks like this : [ [mean1(1x1)] [variance1(1x1)] [mean2
(1x2)] [variance2(2x2)] ]
In case of normal distributions, do you think features will be preserved if
i flatten mean2 and variance2 ?
Thanks,
On Wed, Dec 4, 2013 at 4:14 PM, Olivier Grisel <[email protected]>wrote:
> 2013/12/4 abhishek <[email protected]>:
> > Hello everyone,
> >
> > Im familiar with the scikit-learn classifiers and have used them a lot of
> > times in some research. The problem I'm facing right now is my data is in
> > the form of numpy object array.
> >
> > For example X is:
> >
> > X = [ [1,2] [2,5] [[1,2],[3,4]],
> > [2,4] [54,52] [[11,22],[13,4]],
> > [1,23] [2,25] [[1,2],[3,4]] ]
> >
> > the labels are 0 and 1.
> >
> > Is there any sklearn classifier which will work on this type of data? If
> not
> > are there any other libraries that can handle this kind of data?
>
> All scikit-learn models expect homogeneous features (e.g. a 2D array
> with shape (n_samples, n_features)). You have to flatten your
> descriptors.
>
> How to do that depends on the feature "meaning" and the problem you
> are trying to solve. In particular integers don't have a meaning per
> se: they can encode categorical values, ordinal values or absolute
> numerical values.
>
> List of integers could encode disjunction of categorical values if
> individual element describe categorical variables.
>
> Have a look at:
> http://scikit-learn.org/stable/modules/feature_extraction.html if you
> are not familiar with what I mean by categorical values.
>
> --
> Olivier
> http://twitter.com/ogrisel - http://github.com/ogrisel
>
>
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--
Regards
Abhishek Thakur
- de.linkedin.com/in/abhisvnit/
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