>
>
> In other words, I would like to get in an "API freeze" state where we
add/modify only essentials stuff to the API.
Ok, then I suppose, the easiest way would be to create 2 kind of
transformers for each method: one that transforms the space so that
Euclidean distance acts like Mahalanobis' one, and another that transforms
the data into a similarity matrix.
Any objections?
I removed kernel ITML from the timeline (though it's still there in case
I'd have time), and added a week for each iteration to add documentation
and tests as I go.
New version:
https://github.com/scikit-learn/scikit-learn/wiki/%5BWIP%5D-GSoC-2015-Proposal:-Metric-Learning-module
On Tue, Mar 24, 2015 at 4:06 PM, Joel Nothman <joel.noth...@gmail.com>
wrote:
> On 25 March 2015 at 00:01, Gael Varoquaux <gael.varoqu...@normalesup.org>
> wrote:
>
>>
>> > To make this more concrete, the MetricLearner().metric_ estimator would
>> > require specialised set_params or clone behaviour, I assume. I.e. it
>> > involves hacking API fundamentals.
>>
>> It's more a general principle of "freeze": to be able to settle down on
>> something that we _know_ works and is robust, understandable, bugless...
>> we need to stop changing or adding things.
>>
>
> Yes, I get that too. GSoC tends to pull in the opposite direction by way
> of being project oriented.
>
>
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