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📄 gauss2d.m

📁 含有多种ICA算法的eeglab工具箱
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% gauss2d() - generate a 2-dimensional gaussian matrix%% Usage:%   >> [ gaussmatrix ] = gauss2d( rows, columns, ...%                              sigmaR, sigmaC, peakR, peakC, mask)%% Example:%   >> imagesc(gauss2d(50, 50)); % image a size (50,50) 2-D gaussian matrix%% Inputs:%   rows    - number of rows in matrix%   columns - number of columns in matrix%   sigmaR  - width of standard deviation in rows (default: rows/5)%   sigmaC  - width of standard deviation in columns (default: columns/5)%   peakR   - location of the peak in each row (default: rows/2)%   peakC   - location of the peak in each column (default: columns/2)%   mask    - (0->1) portion of the matrix to mask with zeros (default: 0)%% Ouput:%   gaussmatrix - 2-D gaussian matrix%% Author: Arnaud Delorme, CNL / Salk Institute, 2001%123456789012345678901234567890123456789012345678901234567890123456789012% Copyright (C) 2001 Arnaud Delorme, Salk Institute, arno@salk.edu%% 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 2 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, write to the Free Software% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA% $Log: gauss2d.m,v $% Revision 1.3  2004/02/07 00:55:50  scott% helpd message edits%% Revision 1.2  2004/02/07 00:35:36  arno% header typo%% Revision 1.1  2002/04/05 17:39:45  jorn% Initial revision%% 01-25-02 reformated help & license -ad function mat = gauss2d( sizeX, sizeY, sigmaX, sigmaY, meanX, meanY, cut);if nargin < 2	help gauss2d	return; end;if nargin < 3	sigmaX = sizeX/5;end;if nargin < 4	sigmaY = sizeY/5;end;if nargin < 5	meanX = (sizeX+1)/2;end;if nargin < 6	meanY = (sizeY+1)/2;end;if nargin < 7	cut = 0;end;X = linspace(1, sizeX, sizeX)'* ones(1,sizeY);Y = ones(1,sizeX)'   		  * linspace(1, sizeY, sizeY);%[-sizeX/2:sizeX/2]'*ones(1,sizeX+1);%Y = ones(1,sizeY+1)'   *[-sizeY/2:sizeY/2];mat = exp(-0.5*(  ((X-meanX)/sigmaX).*((X-meanX)/sigmaX)...				+((Y-meanY)/sigmaY).*((Y-meanY)/sigmaY)))...             			/((sigmaX*sigmaY)^(0.5)*pi); if cut > 0	maximun = max(max(mat))*cut;	I = find(mat < maximun);	mat(I) = 0;end;return;

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