gusucode.com > 支持向量机工具箱 - LIBSVM OSU_SVM LS_SVM源码程序 > 支持向量机工具箱 - LIBSVM OSU_SVM LS_SVM\stprtool\bayes\pbayescln.m
function pbayescln(MI,SIGMA,Pk,background, linestyle) % PBAYESCLN vizualizes Bayes classifier discriminant in 2D. % pbayescln(MI,SIGMA,Pk,background, linestyle ) % % This fucntion vizualizes discriminant functions of % Bayes classifier (see help bayescln). % % Intput: % (notation: K - number of classes) % MI [2xK] Matrix of K vectors of mean values of p(x|k). % MI=[mi_1,mi_2,...,mi_K], where mi_j is a column vector [Nx1] for % class j. % SIGMA [(2x2)xK] Matrix of covariance matrices of the density p(x|k). % SIGMA=[sigma_1,sigma_2,...,sigma_K], sigma_j is the covariance % matrix corresponding to the class j. % Pk [1xK] Vector with a priori probability densities. Pk(j) is an % a priori probability of class j. % background [1x1] if is 0 then only the border (discriminat fce = 0) % is displayed. If is 1 (default) then the colors of background % correspond to values of discriminant function. % linestyle [string] used line style (see help plot). % % Output: % Graph to current figure. % % See also BAYESCLN, PDISCRIM. % % Statistical Pattern Recognition Toolbox, Vojtech Franc, Vaclav Hlavac % (c) Czech Technical University Prague, http://cmp.felk.cvut.cz % Written Vojtech Franc (diploma thesis) 23.12.1999, 5.4.2000 % Modifications % 20-may-2001, V. Franc, created if nargin < 4 | isempty(background), background = 1; end if nargin < 5, linestyle = 'k'; end % grid GRIDX=50; GRIDY=50; if nargin < 3, error('Not enough input arguments.'); return; end V = axis; dx = (V(2)-V(1))/GRIDX; dy = (V(4)-V(3))/GRIDY; [X,Y] = meshgrid(V(1):dx:V(2),V(3):dy:V(4)); % make testing points Xtst=[reshape(X',1,prod(size(X)));reshape(Y',1,prod(size(Y)))]; % classify points [Ixy, D] = bayescln(Xtst,MI,SIGMA,Pk); pdiscrim( D, V(1):dx:V(2), V(3):dy:V(4), background, linestyle ); axis(V); return;