<div dir="ltr"><p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt" id="docs-internal-guid-66372fd7-2315-5a54-1692-32f05571e9e7"><span style="font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">Dear PyMVPA experts,</span></p>
<br><span style="font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"></span><p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt">
<span style="font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">I’m trying to use Hyperalignment() procedure to align different subjects’ brain. </span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">I am mainly referring to the example described in this webpage - </span><a href="http://dev.pymvpa.org/examples/hyperalignment.html" style="text-decoration:none"><span style="font-size:15px;font-family:Arial;color:rgb(17,85,204);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:underline;vertical-align:baseline">http://dev.pymvpa.org/examples/hyperalignment.html</span></a><span style="font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"></span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">However, when I try to compute the common space on the training set (10 participant, single - participant matrix: 301 time-point x 1000 voxels ) I get the following message:</span></p>
<br><span style="font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:italic;font-variant:normal;text-decoration:none;vertical-align:baseline"></span><p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt">
<span style="font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:italic;font-variant:normal;text-decoration:none;vertical-align:baseline">LinAlgError, 'SVD did not converge'</span></p>
<br><span style="font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"></span><p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt">
<span style="font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">To the best of my knowledge, singular value decomposition could fail for several reasons, here is what I checked:</span></p>
<ul style="margin-top:0pt;margin-bottom:0pt"><li dir="ltr" style="list-style-type:disc;font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">Matrices have no Nan’s. </span></p>
</li><li dir="ltr" style="list-style-type:disc;font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">Standard deviation for each voxel over time is > 0, meaning that there are no zero or constant voxels.</span></p>
</li><li dir="ltr" style="list-style-type:disc;font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">By aligning each pair of participants independently, I couldn’t find any specifically ‘compromised’ participant, as far as no clear pattern emerged (depending on the pairs the SVD could-couldn’t converge).</span></p>
</li></ul><br><span style="font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"></span><p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt">
<span style="font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">Further informations:</span></p>
<ul style="margin-top:0pt;margin-bottom:0pt"><li style="list-style-type:disc;font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">
<p style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">Data come from Human Connectome Project (HCP)- Working memory task</span></p>
</li><li style="list-style-type:disc;font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"><span style="font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">We tried also with another task (motor) that comes from HCP using the same subjects but we get a same error<br>
</span></li><li style="list-style-type:disc;font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">
<p style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">The </span><span style="font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:italic;font-variant:normal;text-decoration:none;vertical-align:baseline">ds_all</span><span style="font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"> container with participants’ information (samples and features):<br class="">
<a href="http://nilab.cimec.unitn.it/people/vittorioiacovella/ds_all.pickle">http://nilab.cimec.unitn.it/people/vittorioiacovella/ds_all.pickle</a></span></p></li><li style="list-style-type:disc;font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">
<p style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">Please, find below the code I am using for this task.</span></p>
</li></ul><br><span style="font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"></span><p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt">
<span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">import numpy as np</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">from mvpa2.suite import *</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">import pickle as pk</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">import sys</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">#Insert all the part of loading ds_all</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">path=sys.argv[1]</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">ds_all = pk.load(open(path))</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">### DEFINE THE CLASSIFIER</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">## use same linear support vector machine</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">clf = LinearCSVMC()</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"># STARTING THE HYPERALIGNMENT</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">verbose(2, "between-subject (hyperaligned)...", cr=False, lf=False)</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">hyper_start_time = time.time()</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">bsc_hyper_results = []</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">#cross-validation over subjects</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">cv = CrossValidation(clf, NFoldPartitioner(attr='subject'),</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"> errorfx=mean_match_accuracy)</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"> </span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"> # HYPERALIGNMENT</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"> # - Leave-one-run-out for hyperalignment training</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">nruns = 1,2</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">for test_run in nruns: </span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"> #Split in training and testing set:</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"> # For leave one run out, use this:</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"> ds_train = [sd[sd.sa.chunks != test_run,:] for sd in ds_all]</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"> ds_test = [sd[sd.sa.chunks == test_run,:] for sd in ds_all]</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"> #Defining hyper-function and computing hyperalignment parameters.</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"> hyper = Hyperalignment()</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"> hypmaps = hyper(ds_train)</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"> #Applying hyperalignment parameters on the test set (the run left out)</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"> ds_hyper = [ hypmaps[i].forward(sd) for i, sd in enumerate(ds_test)]</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"> # zscore each subject individually after transformation for optimal performance</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"> zscore(ds_hyper, chunks_attr='subject')</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"> #Encoding by simple avaraging each block after all subjects data are in common space.</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"> averager = mean_group_sample(['targets', 'block', 'chunks'])</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"> ds_hyper_encoded = [sd.get_mapped(averager) for sd in ds_hyper]</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"> ds_hyper = vstack(ds_hyper_encoded)</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"> #Store results of classification for different partitions.</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"> res_cv = cv(ds_hyper)</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"> #Append results on a list (each element comes from one run)</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"> bsc_hyper_results.append(res_cv)</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"> #Final results</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">bsc_hyper_results = hstack(bsc_hyper_results)</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">#Showing final results.</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">verbose(2, "done in %.1f seconds" , (time.time() - hyper_start_time,))</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">verbose(2, "between-subject (hyperaligned): %.2f +/-%.3f" \</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"> , (np.mean(bsc_hyper_results),</span></p>
<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt"><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"> np.std(np.mean(bsc_hyper_results, axis=1)) / np.sqrt(nsubjs - 1)))</span></p>
<br><span style="font-size:15px;font-family:'Courier New';color:rgb(0,0,0);background-color:rgb(217,217,217);font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"></span><br>
<span style="font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"></span><p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt">
<span style="font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">Thank you.</span></p><p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt">
<span style="font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline">Best,</span></p><br><span style="font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"></span><br>
<span style="font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"></span><br><span style="font-size:15px;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:none;vertical-align:baseline"></span></div>