[pymvpa] Fw: Hyperalignment

[Yaroslav Halchenko]

On November 30, 2016 5:54:36 PM EST, Salim Al-wasity <salim_alwasity at yahoo.com> wrote:
>Dears
>
>
>I am running a Hyperalignment analysis to investigate the effect of the
>reference subject on the common model and between subject
>classification. I ran two scenarios to double check my results, and in
>both cases I got different classification accuracies.If I am not wrong,
>the below scenarios must giving me identical results.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)ds_hyper
>= [ hypmaps[i].forward(d_all) for i, d_all in
>enumerate(ds_task)]..........Then continue with classification
>
>SincerelySalim
>
>   
>
>------------------------------------------------------------------------
>
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http://www.pymvpa.org/generated/mvpa2.algorithms.hyperalignment.Hyperalignment.html#mvpa2.algorithms.hyperalignment.Hyperalignment

ref_ds : int or None, optional

Index of a dataset to use as 1st-level common space reference. If None, then the dataset with the maximum number of features is used. Constraints: (value must be in range [0, inf], and value must be convertible to type ‘int’), or value must be None. [Default: None]

So utter is not necessarily the first (0th) dataset which is chosen... So you get the same results if your set ref_ds=0

You could enable_ca=["chosen_ref_ds"] and then check ca.chosen_ref_ds on which one is chosen it you don't explicitly specify any
-- 
Sent from a phone which beats iPhone.
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