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function [R,tix]=histo4(Y)
% HISTO4 calculates histogram for rows and supports data compression
%
% R = HISTO4(Y)
% 	R is a struct with th fields 
%       R.X  are the bin-values 
%       R.H  is the frequency of occurence of value X 
%  	R.N  are the number of samples 
%
% HISTO4 might be useful for data compression, because
% [R,tix] = histo4(Y) 
%     	is the compression step
% R.X(tix,:) 
%  	is the decompression step
%
% The effort (in memory and speed) for compression is O(n*log(n))
% The effort (in memory and speed) for decompression is only O(n)
% 
% see also: HISTO, HISTO2, HISTO3, HISTO4
%
% REFERENCE(S):
%  C.E. Shannon and W. Weaver 'The mathematical theory of communication' University of Illinois Press, Urbana 1949 (reprint 1963).


%	$Id: histo4.m 6075 2009-08-04 12:51:17Z schloegl $
%	Copyright (C) 1996-2005,2008,2009 by Alois Schloegl <a.schloegl@ieee.org>	
%    	This is part of the TSA-toolbox 
%	http://hci.tugraz.at/~schloegl/matlab/tsa/
%
%    This program is free software: you can redistribute it and/or modify
%    it under the terms of the GNU General Public License as published by
%    the Free Software Foundation, either version 3 of the License, or
%    (at your option) any later version.
%
%    This program is distributed in the hope that it will be useful,
%    but WITHOUT ANY WARRANTY; without even the implied warranty of
%    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
%    GNU General Public License for more details.
%
%    You should have received a copy of the GNU General Public License
%    along with this program.  If not, see <http://www.gnu.org/licenses/>.


if 0, exist('histo_mex','file')
	% TODO: Performance tests showing an advantage  
	[R,tix] = histo_mex(Y,'rows');
	return; 
end; 	

[yr, yc] = size(Y);
if yr==1,
        % Makes sure there is a second row
        % Sort does not support the DIM-argument, therefore,
        % this function would not work correctly with this software
        % Once this is fixed, this part can be removed. 
        Y = [Y; NaN+ones(size(Y))];  
end;

% identify all possible X's and overall Histogram
[Y,   idx] = sortrows(Y);
[tmp, idx] = sort(idx);        % inverse index

%[ix, iy] = (diff(Y,1)>0);
ix = logical(zeros(yr-1,1));
for k = 1:yr-1,
        d = Y(k,:)-Y(k+1,:);
        d = (~isnan(d) & d~=0) | (isnan(Y(k,:)) ~= isnan(Y(k+1,:)));
        ix(k) = any(d);
end;

tmp = [find(ix); yr];
R.H = diff([0; tmp]);
R.X = Y(tmp,:);
R.N = yr;
R.datatype = 'HISTOGRAM';

% generate inverse index
if nargout>1,
        tix = cumsum([1;ix]);	% rank 
        cc  = 1;
        tmp = sum(ix)+1;
	if 0, exist('OCTAVE_VERSION','builtin'),
		; % NOP; no support for integer datatyp 
        elseif tmp <= 2^8;
                tix = uint8(tix);
                cc = 8/1;
        elseif tmp <= 2^16;
                tix = uint16(tix);
                cc = 8/2;
        elseif tmp <= 2^32;
                tix = uint32(tix);
                cc = 8/4;
        end;
        tix = tix(idx);		% inverse sort rank
        
        R.compressionratio = (prod(size(R.X)) + yr/cc) / (yr*yc);
        R.tix = tix;
end;