Some minor comments inserted: > On 8 Sep 2017, at 17:52, Pegah Kassraian Fard <pega...@gmail.com> wrote: > > > from glob import glob > import os > import numpy as np > > from mvpa2.suite import * > > %matplotlib inline > > > # enable debug output for searchlight call > if __debug__: > debug.active += ["SLC"] > > > # change working directory to 'WB' > os.chdir('mypath/WB') > > # use glob to get the filenames of .nii data into a list > nii_fns = glob('beta*.nii') > > # read data > > labels = [ > 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, > 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, > 7, 7, 7, 7, 7, 7, 7, > 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, > 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, > 7, 7, 7, 7, 7, 7, 7 > ] > grps = np.repeat([0, 1], 37, axis=0) # used for `chuncks` > > db = mvpa2.datasets.mri.fmri_dataset( > nii_fns, targets=labels, chunks=grps, mask=None, sprefix='vxl', > tprefix='tpref', add_fa=None > )
Is there a reason not to use a mask? At least a brain mask to avoid stuff stuff like skull and air? > > # use only the samples of which labels are 1 or 2 > db12 = db[np.array([label in [1, 2] for label in labels], dtype='bool')] > > # in-place z-score normalization > zscore(db12) > > # choose classifier > clf = LinearNuSVMC() Have you tried a different classifier, for example Naive Bayes? That one is simpler (though usually a bit less sensitive than SVM / LDA in my experience)? > > # setup measure to be computed by Searchlight > # cross-validated mean transfer using an N-fold dataset splitter > cv = CrossValidation(clf, NFoldPartitioner()) > > # define searchlight methods > radius_ = 1 That's a tiny radius - why not use something like 3? _______________________________________________ Pkg-ExpPsy-PyMVPA mailing list Pkg-ExpPsy-PyMVPA@lists.alioth.debian.org http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa