[pymvpa] Fw: Hyperalignment

[Swaroop Guntupalli]

Ok. Can you check if data in hyper.commonspace is the same after training
in the two scenarios?


On Wed, Nov 30, 2016 at 8:08 PM, Salim Al-wasity <salim_alwasity at yahoo.com>
wrote:

> Hi Swaroop
> Do you mean like this:
>
> Scenario-3-:
>> new_ds_task=[ds_task[2], ds_task[0], ds_task[1], ds_task[3], ds_task[4],
>> ds_task[5], ds_task[6], ds_task[7], ds_task[8], ds_task[9]]
>>
>> new_ds_h=[ds_h[2], ds_h[0], ds_h[1], ds_h[3], ds_h[4], ds_h[5], ds_h[6],
>> ds_h[7], ds_h[8], ds_h[9]]
>>
>> hyper=Hyperalignment()
>>
> hypermaps=hyper(new_ds_h)
>> ds_hyper = [ hypmaps[i].forward(d_all) for i, d_all in
>> enumerate(new_ds_task)]
>>
>>
>>
>> Same result as scenario 2, still different than scenario 1!.
>>
>>
>>
>> Sent from Yahoo Mail on Android
> <https://overview.mail.yahoo.com/mobile/?.src=Android>
>
> On Thu, 1 Dec, 2016 at 12:55 am, Swaroop Guntupalli
> <swaroopgj at gmail.com> wrote:
>
>>
>> I had 10 subjects whose Hyperalignment data are stored in (ds_h) and task
>> data in (ds_task) which I am classifying
>>
>> Scenario-1-:
>>
>> hyper=Hyperalignment(ref_ds=2)
>> hypermaps=hyper(ds_h)
>> ds_hyper = [ hypmaps[i].forward(d_all) for i, d_all in enumerate(ds_task)]
>> ..........Then continue with classification
>>
>>
>> Scenario-2-:
>> new_ds_h=[ds_h[2], ds_h[0], ds_h[1], ds_h[3], ds_h[4], ds_h[5], ds_h[6],
>> ds_h[7], ds_h[8], ds_h[9]]
>> hyper=Hyperalignment()
>> hyper.train(new_ds_h)
>>
> hypermaps=hyper(ds_h)
>>
>
> Can you combine the above lines into one call and re-order the mappers and
> see how it behaves?
>
>
>
>> ds_hyper = [ hypmaps[i].forward(d_all) for i, d_all in enumerate(ds_task)]
>> ..........Then continue with classification
>>
>>
>> Sincerely
>> Salim
>>
>>
>>
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>>
>
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