Pull svmbatch development history into main MethodsCore repo

svmbatch/README

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+SVM Batch
+
+Written 2011 by Daniel Kessler, kesslerd@umich.edu
+
+**Prerequisites**
+This was code was written and tested in AFNI_2011_05_26_1457
+
+
+This batch script should not require any modification by you. Instead, you customize it by editing the support text files that come with it, namely:
+
+*svmdir
+This file should contain a path to an extant directory where the script should store all of its intermediates and results
+
+*filelist1
+This should contain a whitespace-delimted list of (ideally absolute) paths to files that 3dsvm will consider to be examples of class 1. If you are giving it data from img/hdr pairs, be sure to point to the hdr (it will figure out where the img is based on the hdr).
+
+*filelist2
+Just like filelist1, except these are files that represent examples from class 2.
+
+That's it. Other support files may be added as we extend functionality.
+
+
+**Likely future additions
+
+1) Automatic testing of model on training set
+2) Automated partitioning of examples into training set and test set for model cross-validation
+3) Permutation testing. This will be computationally intensive, but not too hard to implement.

svmbatch/matlab_Scripts/permutation_test.m

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+%function [Y] = permutation_test(Pt,Pdir,name)
+% Calculate nonparametric distributions based on svm permutation tests
+% Pt   - filename of weight vector from real model
+% Pdir - file directory in which to search for results of permutation test
+% img/hdr pairs
+% name - string of name to write out (without .img extension)
+
+%% Make list of files
+Pp=spm_select('List',Pdir,'[0-9]+\.hdr');
+
+%% Read in files
+th=spm_vol(Pt);
+tvol=spm_read_vols(th);
+
+
+cd(Pdir);
+ph=spm_vol(Pp);
+pvol=spm_read_vols(ph);
+cd ..
+
+%% Create dumping space for comparisons
+
+rvol=zeros(size(tvol));
+rh=th;
+rh.fname=[name '.img'];
+rh.descrip='SPM{T_[10000000]}';
+
+%% Loop over dimensions
+
+% For each voxel, calculate proportion of permutations that resulted in
+% smaller or equal value. This returns the nonparametric CDF. We then
+% convert this, using norminv, to a z-score. Extremely positive values will
+% have high CDF scores, high p scores, and high z scores. Very negative
+% values will have very small CDF scores, small p scores, and negative z
+% scores. We convert to z for easier visualization in xjview.
+
+for i=1:size(rvol,1), for j=1:size(rvol,2), for k=1:size(rvol,3)
+            rvol(i,j,k)=norminv(sum(pvol(i,j,k,:)<=tvol(i,j,k))/size(pvol,4));
+        end,end,end
+
+%% Mask based on tvol
+% Some voxels that were zero in the real weight vector are out-of-brain
+% voxels. Implicit masking during first level testing should have set them
+% to 0. All permutation tests will likely also return 0, yielding a bizarre
+% value for the CDF. To protect against this, we set the z-score of these
+% points to be 0, for a corresponding p of .5
+
+for i=1:size(rvol,1), for j=1:size(rvol,2), for k=1:size(rvol,3)
+            if tvol(i,j,k)==0, rvol(i,j,k)=0; end
+        end,end,end
+
+%% Replace Inf and -Inf values with prior min and max, respectively
+% rvol(find(rvol==Inf))=max(rvol(~isinf(rvol)));
+% rvol(find(rvol==-Inf))=min(rvol(~isinf(rvol)));
+
+rvol(find(rvol==Inf))=norminv((1-1/size(pvol,4)));
+rvol(find(rvol==-Inf))=norminv((1/size(pvol,4)));
+
+%Fix the origin for 
+
+rh.mat(:,4)=[-81 -115 -53 1];
+
+
+%% Write out the resulting p values in one giant file
+spm_write_vol(rh,rvol);
+
+%%Slice up the file into its sub-totems, and write them out separately from
+%%bottom up
+
+for i=1:(th.dim(3)/46)
+    cth_range=[1:46] + (i-1)*46;
+    cth=rh;
+    cth.fname=sprintf('%s%s%s%.3d%s',pwd,'/', name ,i,'.img');
+    cth.dim(3)=[46];
+    ctvol=rvol(:,:,cth_range);
+    spm_write_vol(cth,ctvol);
+end