<html><head></head><body><div class="gmail_quote">On November 30, 2016 5:54:36 PM EST, Salim Al-wasity <salim_alwasity@yahoo.com> wrote:<blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
<div style="color:#000; background-color:#fff; font-family:arial, helvetica, sans-serif;font-size:16px"><div id="yui_3_16_0_ym19_1_1480546394726_5882">Dears<br /></div><div class="qtdSeparateBR"><br /><br /></div><div class="yahoo_quoted" id="yui_3_16_0_ym19_1_1480546394726_5937" style="display: block;"><div style="font-family: arial, helvetica, sans-serif; font-size: 16px;" id="yui_3_16_0_ym19_1_1480546394726_5936"><div style="font-family: HelveticaNeue, Helvetica Neue, Helvetica, Arial, Lucida Grande, Sans-Serif; font-size: 16px;" id="yui_3_16_0_ym19_1_1480546394726_5935"><div class="y_msg_container" id="yui_3_16_0_ym19_1_1480546394726_5948"><div id="yiv6131933817"><div id="yui_3_16_0_ym19_1_1480546394726_5947"><div style="color:#000;background-color:#fff;font-family:arial, helvetica, sans-serif;font-size:16px;" id="yui_3_16_0_ym19_1_1480546394726_5946"><div id="yiv6131933817yui_3_16_0_ym19_1_1480544289680_12187" dir="ltr">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</div><div id="yiv6131933817yui_3_16_0_ym19_1_1480544289680_12187" dir="ltr">.</div><div id="yiv6131933817yui_3_16_0_ym19_1_1480544289680_12187" dir="ltr">I had 10 subjects whose Hyperalignment data are stored in (ds_h) and task data in (ds_task) which I am classifying</div><div id="yiv6131933817yui_3_16_0_ym19_1_1480544289680_12187" dir="ltr"><br /></div><div id="yiv6131933817yui_3_16_0_ym19_1_1480544289680_12187">Scenario-1-:</div><div id="yiv6131933817yui_3_16_0_ym19_1_1480544289680_12187"><br /></div><div id="yiv6131933817yui_3_16_0_ym19_1_1480544289680_12187">hyper=Hyperalignment(ref_ds=2)</div><div id="yiv6131933817yui_3_16_0_ym19_1_1480544289680_12187" dir="ltr">hypermaps=hyper(ds_h)</div><div
id="yiv6131933817yui_3_16_0_ym19_1_1480544289680_12187" dir="ltr">ds_hyper = [ hypmaps[i].forward(d_all) for i, d_all in enumerate(ds_task)]</div><div id="yiv6131933817yui_3_16_0_ym19_1_1480544289680_12187" dir="ltr">..........Then continue with classification</div><div id="yiv6131933817yui_3_16_0_ym19_1_1480544289680_12187" dir="ltr"><br /></div><div id="yiv6131933817yui_3_16_0_ym19_1_1480544289680_12187" dir="ltr"><br /></div><div id="yiv6131933817yui_3_16_0_ym19_1_1480544289680_12187" dir="ltr">Scenario-2-:</div><div id="yiv6131933817yui_3_16_0_ym19_1_1480544289680_12187" dir="ltr">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]]</div><div id="yiv6131933817yui_3_16_0_ym19_1_1480544289680_12187" dir="ltr">hyper=Hyperalignment()</div><div id="yiv6131933817yui_3_16_0_ym19_1_1480544289680_12187" dir="ltr">hyper.train(new_ds_h)</div><div id="yiv6131933817yui_3_16_0_ym19_1_1480544289680_12187" dir="ltr">hypermaps=hyper(ds_h)</div><div
dir="ltr" id="yiv6131933817yui_3_16_0_ym19_1_1480544289680_12566">ds_hyper = [ hypmaps[i].forward(d_all) for i, d_all in enumerate(ds_task)]</div><div dir="ltr" id="yiv6131933817yui_3_16_0_ym19_1_1480544289680_12567">..........Then continue with classification</div><div dir="ltr" id="yiv6131933817yui_3_16_0_ym19_1_1480544289680_12568"><br id="yiv6131933817yui_3_16_0_ym19_1_1480544289680_12569" /></div><div dir="ltr" id="yiv6131933817yui_3_16_0_ym19_1_1480544289680_12568"><br /></div><div dir="ltr" id="yiv6131933817yui_3_16_0_ym19_1_1480544289680_12568">Sincerely</div><div dir="ltr" id="yiv6131933817yui_3_16_0_ym19_1_1480544289680_12568">Salim</div></div></div></div><br /><br /></div> </div> </div> </div></div><p style="margin-top: 2.5em; margin-bottom: 1em; border-bottom: 1px solid #000"></p><pre class="k9mail"><hr /><br />Pkg-ExpPsy-PyMVPA mailing list<br />Pkg-ExpPsy-PyMVPA@lists.alioth.debian.org<br /><a
href="http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa">http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa</a></pre></blockquote></div><br clear="all"><a href="http://www.pymvpa.org/generated/mvpa2.algorithms.hyperalignment.Hyperalignment.html#mvpa2.algorithms.hyperalignment.Hyperalignment">http://www.pymvpa.org/generated/mvpa2.algorithms.hyperalignment.Hyperalignment.html#mvpa2.algorithms.hyperalignment.Hyperalignment</a><br>
<br>
ref_ds : int or None, optional<br>
<br>
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]<br>
<br>
So utter is not necessarily the first (0th) dataset which is chosen... So you get the same results if your set ref_ds=0<br>
<br>
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<br>
-- <br>
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