function ds=cosmo_synthetic_dataset(varargin)
% generate synthetic dataset
%
% ds=cosmo_synthetic_dataset(varargin)
%
% Inputs:
% 'size', s determines the number of features. One of
% 'tiny', 'small', 'normal', 'big', or 'huge'
% 'type', t type of dataset. One of 'fmri', 'meeg',
% 'timelock', 'timefreq', 'surface', or 'source'.
% 'meeg' is equivalent to 'timelock'
% 'sens', c for meeg datasets ('time{lock}, 'meeg'), this
% sets which sensor type is used. Supported are
% - neuromag306{all,planar,combined,mag}
% - ctf151
% - 4d{1,2}48[planar]
% - yokogawa{64,160}[planar]
% - eeg10{05,10,20}
% 'data_field', f If the type t is 'source', this can be 'pow' or
% 'mom'.
% 'sigma', sigma parameter to influence class distance. The
% larger this value, the more discrimable the
% classes are.
%
% 'ntargets', nt number of unique targets (default: 2)
% 'nchunks', nc number of unique chunks (default: 3)
%
% 'nmodalities', nm number of unique modalities (default: [])
% 'nsubjects', ns number of unique subjects (default: [])
% 'nreps', nr number of sample repeats (default: [])
% 'seed', seed seed value for pseudo-random number generator
% (default: 1). Different seed values give
% different random numbers.
%
% Output:
% ds dataset struct according to parameters.
% The output has (nt*nc*nm*ns*nr) samples, and
% the number of features is determined by s.
% If any of the nt, nc, nm, ns, nr values are not
% empty, there is a corresponding sample
% attribute.
%
% Examples:
% ds=cosmo_synthetic_dataset();
% cosmo_disp(ds);
% %|| .samples
% %|| [ 2.03 -0.892 -0.826 1.16 1.16 -1.29
% %|| 0.584 1.84 1.17 -0.848 3.49 -0.199
% %|| -1.44 -0.262 -1.92 3.09 -1.37 1.73
% %|| -0.518 2.34 0.441 1.86 0.479 0.0832
% %|| 1.19 -0.204 -0.209 1.76 -0.955 0.501
% %|| -1.33 2.72 0.148 0.502 3.41 -0.48 ]
% %|| .fa
% %|| .i
% %|| [ 1 2 3 1 2 3 ]
% %|| .j
% %|| [ 1 1 1 2 2 2 ]
% %|| .k
% %|| [ 1 1 1 1 1 1 ]
% %|| .a
% %|| .fdim
% %|| .labels
% %|| { 'i' 'j' 'k' }
% %|| .values
% %|| { [ 1 2 3 ] [ 1 2 ] [ 1 ] }
% %|| .vol
% %|| .mat
% %|| [ 2 0 0 -3
% %|| 0 2 0 -3
% %|| 0 0 2 -3
% %|| 0 0 0 1 ]
% %|| .dim
% %|| [ 3 2 1 ]
% %|| .xform
% %|| 'scanner_anat'
% %|| .sa
% %|| .targets
% %|| [ 1
% %|| 2
% %|| 1
% %|| 2
% %|| 1
% %|| 2 ]
% %|| .chunks
% %|| [ 1
% %|| 1
% %|| 2
% %|| 2
% %|| 3
% %|| 3 ]
%
% ds=cosmo_synthetic_dataset('sigma',5,...
% 'nchunks',5,'ntargets',4,...
% 'nsubjects',10);
% cosmo_disp(ds)
% %|| .samples
% %|| [ 3.53 1.65 1.74 0.453 1.22 3.28
% %|| 0.584 3.88 0.856 0.841 -0.899 1.23
% %|| -3.68 1.38 1.89 -1.4 0.216 0.68
% %|| : : : : : :
% %|| -0.725 4.41 -0.313 0.111 0.437 -1.03
% %|| 0.297 0.0279 1.8 -0.0303 -0.451 0.346
% %|| 1.64 0.103 -1.22 4.8 0.754 1.03 ]@200x6
% %|| .fa
% %|| .i
% %|| [ 1 2 3 1 2 3 ]
% %|| .j
% %|| [ 1 1 1 2 2 2 ]
% %|| .k
% %|| [ 1 1 1 1 1 1 ]
% %|| .a
% %|| .fdim
% %|| .labels
% %|| { 'i' 'j' 'k' }
% %|| .values
% %|| { [ 1 2 3 ] [ 1 2 ] [ 1 ] }
% %|| .vol
% %|| .mat
% %|| [ 2 0 0 -3
% %|| 0 2 0 -3
% %|| 0 0 2 -3
% %|| 0 0 0 1 ]
% %|| .dim
% %|| [ 3 2 1 ]
% %|| .xform
% %|| 'scanner_anat'
% %|| .sa
% %|| .targets
% %|| [ 1
% %|| 2
% %|| 3
% %|| :
% %|| 2
% %|| 3
% %|| 4 ]@200x1
% %|| .chunks
% %|| [ 1
% %|| 1
% %|| 1
% %|| :
% %|| 5
% %|| 5
% %|| 5 ]@200x1
% %|| .subject
% %|| [ 1
% %|| 1
% %|| 1
% %|| :
% %|| 10
% %|| 10
% %|| 10 ]@200x1
%
% % example of MEEG dataset with ctf151 layout
% ds=cosmo_synthetic_dataset('type','meeg',...
% 'sens','ctf151','size','huge');
% % show labels and values for feature dimensions
% cosmo_disp(ds.a.fdim,'edgeitems',2)
% %|| .labels
% %|| { 'chan'
% %|| 'time' }
% %|| .values
% %|| { { 'MLC11' 'MLC12' ... 'MZP01' 'MZP02' }@1x151
% %|| [ -0.2 -0.15 ... 0.55 0.6 ]@1x17 }
%
% % example of MEEG dataset with 4d148 layout (3 channels only)
% ds=cosmo_synthetic_dataset('type','meeg','sens','4d148_planar');
% cosmo_disp(ds.a.fdim)
% %|| .labels
% %|| { 'chan'
% %|| 'time' }
% %|| .values
% %|| { { 'A148_dH' 'A147_dH' 'A146_dH' }
% %|| [ -0.2 -0.15 ] }
%
% # For CoSMoMVPA's copyright information and license terms, #
% # see the COPYING file distributed with CoSMoMVPA. #
default=struct();
default.ntargets=2;
default.nchunks=3;
default.sigma=3;
default.size='small';
default.type='fmri';
default.nreps=[];
default.nmodalities=[];
default.nsubjects=[];
default.chunks=[];
default.targets=[];
default.target1=1;
% for PRNG
default.seed=1;
% for MEEG
default.sens='neuromag306_all';
% for MEEG source
default.data_field='pow'; % 'pow' or 'mom'
opt=cosmo_structjoin(default,varargin);
[ds, nfeatures]=get_fdim_fa(opt);
sa_labels_pl={'ntargets','nchunks','nmodalities','nsubjects','nreps'};
cp=cosmo_cartprod(cellfun(@(x)1:max([opt.(x) 1]),sa_labels_pl,...
'UniformOutput',false));
sa_labels_si={'targets','chunks','modality','subject','rep'};
for k=1:numel(sa_labels_si)
if ~isempty(opt.(sa_labels_pl{k}))
ds.sa.(sa_labels_si{k})=cp(:,k);
end
end
nelem=log(nfeatures*max([opt.nreps 1]));
class_distance=opt.sigma/sqrt(nelem);
ds.samples=generate_samples(ds, class_distance, opt.seed);
ds=assign_sa(ds, opt, {'chunks','targets'});
ds.sa.targets=ds.sa.targets+opt.target1-1;
cosmo_check_dataset(ds);
function ds=assign_sa(ds, opt, names)
nsamples=size(ds.samples,1);
for k=1:numel(names)
name=names{k};
v=opt.(name);
if ~isempty(v)
if ~isscalar(v)
error('Value for ''%s'' must be a scalar', name);
end
ds.sa.(name)=ones(nsamples,1)*v;
end
end
function samples=generate_samples(ds, class_distance, seed)
targets=ds.sa.targets;
nsamples=numel(targets);
nclasses=numel(unique(targets));
fa_names=fieldnames(ds.fa);
nfeatures=size(ds.fa.(fa_names{1}),2);
r=cosmo_rand(nsamples,nfeatures,'seed',seed);
samples=cosmo_norminv(r);
add_msk=mod(bsxfun(@minus,1:nclasses+1,targets),nclasses+1)==0;
add_msk_full=repmat(add_msk,1,ceil(nfeatures/(nclasses+1)));
add_msk_full=add_msk_full(:,1:nfeatures);
samples(add_msk_full)=samples(add_msk_full)+class_distance;
function a=dim_labels_values(opt)
data_type=opt.type;
sens_type=opt.sens;
a=struct();
switch data_type
case 'fmri'
a.vol.mat=eye(4);
a.vol.mat(1:3,1:3)=a.vol.mat(1:3,1:3)*2;
% ensure negative origin, so that MRIcron displays
% the matrix correctly
a.vol.mat(1:3,4)=-3;
labels={'i';'j';'k'};
values={1:20;1:20;1:20};
case {'meeg','timelock','timefreq'}
% simulate full neuromag 306 system
chan=get_meeg_channels(sens_type);
time=(-.2:.05:1.3);
switch data_type
case 'timefreq'
freq=(2:2:40);
values={chan;freq;time};
labels={'chan';'freq';'time'};
a.meeg.samples_type='freq';
a.meeg.samples_field='powspctrm';
otherwise % meeg and timelock
values={chan;time};
labels={'chan';'time'};
a.meeg.samples_type='timelock';
a.meeg.samples_field='trial';
end
a.meeg.samples_label='rpt';
case 'surface'
labels={'node_indices'};
values={1:4000};
case 'source'
xyz=cosmo_cartprod({-70:10:70,-60:10:60,-110:10:110})';
[unused,i]=sort(sum(abs(xyz),1));
switch opt.data_field
case 'pow'
labels={'pos','time'};
values={xyz(:,i),-1:.5:14};
a.meeg.samples_field='trial.pow';
case 'mom'
labels={'pos','mom','time'};
values={xyz(:,i),{'x','y','z'},-1:.5:14};
a.meeg.samples_field='trial.mom';
otherwise
error('illegal data_field ''%s''', opt.data_field);
end
otherwise
error('Unsupported type ''%s''', data_type);
end
a.fdim.values=values;
a.fdim.labels=labels;
function labels=get_meeg_channels(sens_type)
% mapping from name to helper function
% each name is prefixed with 'T' to allow names that start with a digit
sens2func=struct();
sens2func.Tneuromag306=@get_neuromag_chan;
sens2func.Tctf151=@get_CTF151_chan;
sens2func.T4d148=@get_4d148_chan;
sens2func.T4d248=@get_4d248_chan;
sens2func.Teeg1020=@get_eeg1020_chan;
sens2func.Teeg1010=@get_eeg1010_chan;
sens2func.Teeg1005=@get_eeg1005_chan;
sens2func.Tyokogawa64=@get_yokogawa64_chan;
sens2func.Tyokogawa160=@get_yokogawa160_chan;
sp=cosmo_strsplit(['T' sens_type],'_');
key=sp{1};
if ~isfield(sens2func,key)
supported_sens=fieldnames(sens2func);
supported_labels=cellfun(@(x)[x(2:end) '*'],supported_sens,...
'UniformOutput',false);
error('Unsupported sens_type %s, supported are: %s ',...
key(2:end),cosmo_strjoin(supported_labels, ', '));
end
func=sens2func.(key);
chan_type=[cosmo_strjoin(sp(2:end),'_') '']; % ensure string
labels=func(chan_type);
labels=labels(:)';
function labels=get_eeg1020_chan(chan_type)
labels=get_eeg10XX_chan_helper(chan_type,'1020');
function labels=get_eeg1010_chan(chan_type)
labels=get_eeg10XX_chan_helper(chan_type,'1010');
function labels=get_eeg1005_chan(chan_type)
labels=get_eeg10XX_chan_helper(chan_type,'1005');
function labels=get_eeg10XX_chan_helper(chan_type,sens_type)
assert(isempty(chan_type));
% build mapping from prefix to postfixes
ch=struct();
switch sens_type
case '1020'
ch.Fp={ '1' '2' 'z' };
ch.F={ '3' '4' '7' '8' 'z' };
ch.T={ '3' '4' '5' '6' '7' '8' };
ch.C={ '3' '4' 'z' };
ch.P={ '3' '4' '7' '8' 'z' };
ch.O={ '1' '2' 'z' };
ch.A={ '1' '2' };
ch.M={ '1' '2' };
case '1010'
ch.TP={ '10' '7' '8' '9' };
ch.CP={ '1' '2' '3' '4' '5' '6' 'z' };
ch.Fp={ '1' '2' 'z' };
ch.AF={ '1' '10' '2' '3' '4' '5' '6' '7' '8' '9' 'z' };
ch.F={ '1' '10' '2' '3' '4' '5' '6' '7' '8' '9' 'z' };
ch.FT={ '10' '7' '8' '9' };
ch.FC={ '1' '2' '3' '4' '5' '6' 'z' };
ch.T={ '10' '3' '4' '5' '6' '7' '8' '9' };
ch.C={ '1' '2' '3' '4' '5' '6' 'z' };
ch.P={ '1' '10' '2' '3' '4' '5' '6' '7' '8' '9' 'z' };
ch.PO={ '1' '10' '2' '3' '4' '5' '6' '7' '8' '9' 'z' };
ch.O={ '1' '2' 'z' };
ch.I={ '1' '2' 'z' };
ch.A={ '1' '2' };
ch.M={ '1' '2' };
case '1005'
ch.FCC={ '1' '1h' '2' '2h' '3' '3h' '4' '4h' '5' '5h' '6'...
'6h' 'z' };
ch.F={ '1' '10' '10h' '1h' '2' '2h' '3' '3h' '4' '4h'...
'5' '5h' '6' '6h' '7' '7h' '8' '8h' '9' '9h' ...
'p1' 'p1h' 'p2' 'p2h' 'pz' 'z' };
ch.AF={ '1' '10' '10h' '1h' '2' '2h' '3' '3h' '4' '4h'...
'5' '5h' '6' '6h' '7' '7h' '8' '8h' '9' '9h' ...
'p1' 'p10' 'p10h' 'p1h' 'p2' 'p2h' 'p3' 'p3h' ...
'p4' 'p4h' 'p5' 'p5h' 'p6' 'p6h' 'p7' 'p7h' ...
'p8' 'p8h' 'p9' 'p9h' 'pz' 'z' };
ch.FT={ '10' '10h' '7' '7h' '8' '8h' '9' '9h' };
ch.FC={ '1' '1h' '2' '2h' '3' '3h' '4' '4h' '5' '5h'...
'6' '6h' 'z' };
ch.T={ '10' '10h' '3' '4' '5' '6' '7' '7h' '8' '8h'...
'9' '9h' };
ch.C={ '1' '1h' '2' '2h' '3' '3h' '4' '4h' '5' '5h'...
'6' '6h' 'z' };
ch.TP={ '10' '10h' '7' '7h' '8' '8h' '9' '9h' };
ch.CP={ '1' '1h' '2' '2h' '3' '3h' '4' '4h' '5' '5h'...
'6' '6h' 'z' };
ch.P={ '1' '10' '10h' '1h' '2' '2h' '3' '3h' '4' '4h'...
'5' '5h' '6' '6h' '7' '7h' '8' '8h' '9' '9h' 'z' };
ch.PO={ '1' '10' '10h' '1h' '2' '2h' '3' '3h' '4' '4h'...
'5' '5h' '6' '6h' '7' '7h' '8' '8h' '9' '9h' 'z' };
ch.O={ '1' '1h' '2' '2h' 'z' };
ch.I={ '1' '1h' '2' '2h' 'z' };
ch.AFF={ '1' '10' '10h' '1h' '2' '2h' '3' '3h' '4' '4h'...
'5' '5h' '6' '6h' '7' '7h' '8' '8h' '9' '9h' 'z' };
ch.FFT={ '10' '10h' '7' '7h' '8' '8h' '9' '9h' };
ch.FFC={ '1' '1h' '2' '2h' '3' '3h' '4' '4h' '5' '5h' '6'...
'6h' 'z' };
ch.FTT={ '10' '10h' '7' '7h' '8' '8h' '9' '9h' };
ch.TTP={ '10' '10h' '7' '7h' '8' '8h' '9' '9h' };
ch.CCP={ '1' '1h' '2' '2h' '3' '3h' '4' '4h' '5' '5h' '6'...
'6h' 'z' };
ch.TPP={ '10' '10h' '7' '7h' '8' '8h' '9' '9h' };
ch.CPP={ '1' '1h' '2' '2h' '3' '3h' '4' '4h' '5' '5h' '6'...
'6h' 'z' };
ch.PPO={ '1' '10' '10h' '1h' '2' '2h' '3' '3h' '4' '4h' '5'...
'5h' '6' '6h' '7' '7h' '8' '8h' '9' '9h' 'z' };
ch.POO={ '1' '10' '10h' '1h' '2' '2h' '3' '3h' '4' '4h' '5'...
'5h' '6' '6h' '7' '7h' '8' '8h' '9' '9h' 'z' };
ch.OI={ '1' '1h' '2' '2h' 'z' };
ch.A={ '1' '2' };
ch.M={ '1' '2' };
otherwise
error('illegal senstype %s',sens_type);
end
prefixes=fieldnames(ch);
nprefix=numel(prefixes);
label_cell=cell(nprefix,1);
for k=1:nprefix
prefix=prefixes{k};
postfixes=ch.(prefix);
npostfix=numel(postfixes);
prefix_labels=cell(npostfix,1);
for j=1:npostfix
postfix=postfixes{j};
prefix_labels{j}=[prefix postfix];
end
label_cell{k}=prefix_labels;
end
labels=cat(1,label_cell{:});
function labels=get_4d248_chan(chan_type)
labels=get_4dX48_chan_helper(chan_type,248);
function labels=get_4d148_chan(chan_type)
labels=get_4dX48_chan_helper(chan_type,148);
function labels=get_yokogawa64_chan(chan_type)
labels=get_yokogawaX_chan_helper(chan_type,64);
function labels=get_yokogawa160_chan(chan_type)
labels=get_yokogawaX_chan_helper(chan_type,160);
function labels=get_yokogawaX_chan_helper(chan_type,nchan)
labels=get_general_chan_helper(chan_type,nchan,...
'AG%03d','AG%03d_dH','AG%03d_dV');
function labels=get_4dX48_chan_helper(chan_type,nchan)
labels=get_general_chan_helper(chan_type,nchan,...
'A%d','A%d_dH','A%d_dV');
function labels=get_general_chan_helper(chan_type,chan_vals,...
pat_combined, pat_planar1, pat_planar2)
% if chan_vals is a scalar, it indicates the number of channels
if isnumeric(chan_vals) && numel(chan_vals)==1
% take the last channels
chan_vals=num2cell(chan_vals:-1:1);
end
generate=@(pat) cellfun(@(x)sprintf(pat,x),chan_vals,...
'UniformOutput',false);
switch chan_type
case {'','planar_combined'}
labels=generate(pat_combined)';
case 'planar'
labels=[generate(pat_planar1) generate(pat_planar2)]';
otherwise
error('illegal chan type %s', chan_type);
end
function labels=get_CTF151_chan(chan_type)
chan_vals=get_CTF151_chan_prefixes();
labels=get_general_chan_helper(chan_type,chan_vals,...
'%s','%s_dH','%s_dV');
function labels=get_CTF151_chan_prefixes()
% get channels for CTF151
lats='LRZ'; % lateralities
locs='CFOPT'; % brain part
counts=[repmat({{[0,5,4,3,3],[0,2,3,4,5,2],[0,2,2,3,3],...
[0,3,2,4],[0,6,6,5,4]}},2,1);...
{{2,3,2,2}}];
labels=cell(1,151);
pos=0;
for i=1:numel(lats)
lat=lats(i);
count=counts{i};
for k=1:numel(count)
loc=locs(k);
cs=count{k};
for j=1:numel(cs)
for m=1:cs(j)
label=sprintf('M%s%s%d%d',lat,loc,j-1,m);
pos=pos+1;
labels{pos}=label;
end
end
end
end
function labels=get_neuromag_chan(chan_type)
% get channels for neuromag system
all_chan_idxs=[repmat((1:26)',4,1) kron((1:4)',ones(26,1))];
remove_msk=cosmo_match(all_chan_idxs(:,1), 8) & ...
cosmo_match(all_chan_idxs(:,2),[3 4]);
chan_idxs=all_chan_idxs(~remove_msk,:);
nloc=size(chan_idxs,1);
assert(nloc==102);
infix=reshape(sprintf('%02d%d',chan_idxs'),3,[])';
as_column=@(x) repmat(x,nloc,1);
meg=as_column('MEG');
one=as_column('1');
two=as_column('2');
three=as_column('3');
plus_=as_column('+');
labels=cell(nloc,4);
labels(:,1)=cellstr([meg infix one]);
labels(:,2)=cellstr([meg infix two]);
labels(:,3)=cellstr([meg infix three]);
labels(:,4)=cellstr([meg infix two plus_ infix three]);
keep_col=false(1,4);
types=cosmo_strsplit(chan_type,'+');
for k=1:numel(types)
tp=types{k};
switch tp
case {'all',''}
keep_col([1 2 3])=true;
case 'planar_combined'
keep_col(4)=true;
case 'planar'
keep_col([2 3])=true;
case 'axial'
keep_col(1)=true;
otherwise
error('Unsupported channel type %s', tp);
end
end
if any(keep_col(2:3)) && keep_col(4)
error('planar and cmb/combined are mutually exclusive');
end
labels=labels(:,keep_col)';
labels=labels(:);
function dim_size=get_dim_size(size_label,opt)
% get dimension size. NaN means use input size
size2dim=struct();
size2dim.tiny=[2 1 1];
size2dim.small=[3 2 1];
size2dim.normal=[3 2 5];
size2dim.big=[NaN 7 5];
size2dim.huge=[NaN 17 19];
if ~isfield(size2dim, size_label)
error('Unsupported size %s. Supported are: %s',...
size_label, cosmo_strjoin(fieldnames(size2dim),', '));
end
dim_size=size2dim.(size_label);
if strcmp(opt.type,'source')
dim_size(1)=4;
if strcmp(opt.data_field,'mom')
dim_size(2)=3;
end
end
function [ds, nfeatures]=get_fdim_fa(opt)
data_type=opt.type;
size_label=opt.size;
% get dimension labels
a=dim_labels_values(opt);
labels=a.fdim.labels;
values=a.fdim.values;
a=rmfield(a,'fdim');
% get dimension values
dim_sizes=get_dim_size(size_label,opt);
nlabel=numel(labels);
if nlabel==1
% surface case
dim_sizes=prod(dim_sizes);
else
% everything else
dim_sizes=dim_sizes(1:nlabel);
end
for k=1:numel(dim_sizes)
if isnan(dim_sizes(k))
dim_sizes(k)=size(values{k},2);
end
if isvector(values{k})
values{k}=values{k}(1:dim_sizes(k));
else
values{k}=values{k}(:,1:dim_sizes(k));
end
end
% get ds with proper size
ds=cosmo_flatten(zeros([1 dim_sizes]),labels,values,2,...
'matrix_labels',{'pos'});
ds.a=cosmo_structjoin(ds.a,a);
switch data_type
case 'fmri'
ds.a.vol.dim=dim_sizes;
ds.a.vol.xform='scanner_anat';
end
nfeatures=prod(dim_sizes);
