# cosmo pdistΒΆ

```function d=cosmo_pdist(x, distance)
% compute pair-wise distance between samples in a matrix
%
% d=cosmo_pdist(x[, distance])
%
% Inputs:
%   x            PxM matrix for P samples and M features
%   distance     distance metric: one of 'euclidean' (default),
%                'correlation' (computing one-minus-correlation), or any
%                other metric supported by matlab's built-in 'pdist'
%
% Outputs:
%   d            1xN row vector with pairwise distances, where N=M*(M-1),
%                containing the distances of the lower triangle of the
%                distance matrix
%
% Examples:
%     data=[1 4 3; 2 2 3; 4 2 0;0 1 1];
%     % compute pair-wise distances with euclidean distance metric.
%     % since there are 4 samples, there are 4*3/2=6 pairs of samples.
%     d=cosmo_pdist(data);
%     cosmo_disp(d);
%     %|| [ 2.24      4.69      3.74      3.61         3      4.24 ]
%     %
%     % this gives the same output
%     d=cosmo_pdist(data,'euclidean');
%     cosmo_disp(d);
%     %|| [ 2.24      4.69      3.74      3.61         3      4.24 ]
%     %
%     % compute distances with one-minus-correlation distance metric
%     d_c=cosmo_pdist(data,'correlation');
%     cosmo_disp(d_c);
%     %|| [ 0.811      1.65    0.0551      1.87       0.5      1.87 ]
%
% Notes:
%   - this function provides a native implementation for 'euclidean' and
%     'correlation'; other distance metrics require the pdist function
%     supplied in the matlab stats toolbox, or the octave statistics
%     package
%   - the rationale for providing this function is to support pair-wise
%     distances on platforms without the stats toolbox
%   - to compute pair-wise distances on a dataset struct, use
%     cosmo_dissimilarity_matrix_measure
%   - the output of this function can be given to [cosmo_]squareform to
%     recreate a PxP distance matrix
%
%
% #   see the COPYING file distributed with CoSMoMVPA.           #

if nargin<2 || isempty(distance), distance='euclidean'; end

ns=size(x,1);
d=zeros(1,ns*(ns-1)/2);

switch distance
case 'euclidean'
pos=0;
for k=1:ns
ji=((k+1):ns);
nj=numel(ji);
idxs=pos+(1:nj);

delta=bsxfun(@minus,x(k,:),x(ji,:));
d_idxs=sqrt(sum(delta.^2,2));

d(idxs)=d_idxs;
pos=pos+nj;
end

case 'correlation'
% it's faster to compute all correlations that just the lower
% diagonal
dfull=cosmo_corr(x');
ns_rng=1:ns;

msk=bsxfun(@gt,ns_rng',ns_rng);
d=1-dfull(msk)';

otherwise
is_octave=cosmo_wtf('is_octave');
if is_octave || cosmo_check_external('@stats',false)
d=pdist(x, distance);
else
error(['Matlab requires the stats toolbox for distance '...
'metric %s'], distance);
end

end
```