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\LS_SVMlab\plotlssvm.m
function model = plotlssvm(model,ab,grain, princdim)
% Plot the LS-SVM results in the environment of the training data
%
% >> plotlssvm({X,Y,type,gam, sig2, kernel})
% >> plotlssvm({X,Y,type,gam, sig2, kernel}, {alpha,b})
% >> model = plotlssvm(model)
%
% The first argument specifies the LS-SVM. The latter specifies the
% results of the training if already known. Otherwise, the training
% algorithm is first called. One can specify the precision of the
% plot by specifying the grain of the grid. By default this value
% is 50. The dimensions (seldims) of the input data to display can
% be selected as an optional argument in case of higher dimensional
% inputs (> 2). A grid will be taken over this dimension, while the
% other inputs remain constant (0).
%
%
% Full syntax
%
% 1. Using the functional interface:
%
% >> plotlssvm({X,Y,type,gam,sig2,kernel,preprocess}, {alpha,b})
% >> plotlssvm({X,Y,type,gam,sig2,kernel,preprocess}, {alpha,b}, grain)
% >> plotlssvm({X,Y,type,gam,sig2,kernel,preprocess}, {alpha,b}, grain, seldims)
% >> plotlssvm({X,Y,type,gam,sig2,kernel,preprocess})
% >> plotlssvm({X,Y,type,gam,sig2,kernel,preprocess}, [], , grain)
% >> plotlssvm({X,Y,type,gam,sig2,kernel,preprocess}, [], , grain, seldims)
%
% Inputs
% X : N x d matrix with the inputs of the training data
% Y : N x 1 vector with the outputs of the training data
% type : 'function estimation' ('f') or 'classifier' ('c')
% gam : Regularization parameter
% sig2 : Kernel parameter (bandwidth in the case of the 'RBF_kernel')
% kernel(*) : Kernel type (by default 'RBF_kernel')
% preprocess(*) : 'preprocess'(*) or 'original'
% alpha(*) : support values obtained from training
% b(*) : Bias term obtained from training
% grain(*) : The grain of the grid evaluated to compose the surface (by default 50)
% seldims(*) : The principal inputs one wants to span a grid (by default [1 2])
%
%
% 2. Using the object oriented interface:
%
% >> model = plotlssvm(model)
% >> model = plotlssvm(model, [], grain)
% >> model = plotlssvm(model, [], grain, seldims)
%
% Outputs
% model(*) : Trained object oriented representation of the LS-SVM model
% Inputs
% model : Object oriented representation of the LS-SVM model
% grain(*) : The grain of the grid evaluated to compose the surface (by default 50)
% seldims(*) : The principal inputs one wants to span a grid (by default [1 2])
%
% See also:
% trainlssvm, simlssvm.
% Copyright (c) 2002, KULeuven-ESAT-SCD, License & help @ http://www.esat.kuleuven.ac.be/sista/lssvmlab
fprintf('Start Plotting...')
%
% initiating the model...
%
if iscell(model),
model = initlssvm(model{:});
eval('model.alpha = ab{1}; model.b = ab{2};model.status = ''trained'';','model=trainlssvm(model);');
end
%figure;
clf
model = trainlssvm(model);
% reconstruct the original support vectors ...
[osvX,osvY] = postlssvm(model,model.xtrain(:,1:model.x_dim),model.ytrain(:,1:model.y_dim));
%
% define the principal dimensions one plots
%
if (model.x_dim>2)
% plotted principal dimensions
eval('princdim; restdim = setdiff(1:model.x_dim,princdim);','princdim=[1 2 3];');
elseif (model.x_dim==2),
princdim = [1 2]; restdim = [];
else
princdim = [1]; restdim = [];
end
if max(princdim)>model.x_dim,
error('Given dimensions exceed input dimensions...');
end
% classification (x_dim=2, y_dim=1:...) %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if model.type(1)=='c', % 'classification'
%
% precision of plot
%
eval('grain;','grain = 50;');
if model.x_dim>=2,
%%%%%%%%%%%%%%%%%%
% Determine plot limits
xmin1=min(osvX(:,princdim(1))); if xmin1<0, xmin1=1.05*xmin1; else xmin1 = 0.98*xmin1; end
xmax1=max(osvX(:,princdim(1))); if xmax1>0, xmax1=1.05*xmax1; else xmax1 = 0.98*xmax1; end
xmin2=min(osvX(:,princdim(2))); if xmin2<0, xmin2=1.05*xmin2; else xmin2 = 0.98*xmin2; end
xmax2=max(osvX(:,princdim(2))); if xmax2>0, xmax2=1.05*xmax2; else xmax2 = 0.98*xmax2; end
xrange1 = xmin1:(xmax1-xmin1)/grain:xmax1;
xrange2 = xmin2:(xmax2-xmin2)/grain:xmax2;
[XX,YY] = meshgrid(xrange1,xrange2);
Xt = [reshape(XX,prod(size(XX)),1) reshape(YY,prod(size(YY)),1)];
xsteps = length(xrange1);
ysteps = length(xrange2);
%
% simulate the points
%
restdim = setdiff(1:model.x_dim, princdim);
rest = zeros(size(Xt,1),model.x_dim-2);
Xt = [Xt rest];
[ZZ,ff,model] = simlssvm(model,Xt(:,[princdim restdim]));
if min(ZZ)==max(ZZ), warning('Simulation over the input space results in only one class...'); end
%
% for plotting, the categorical format is required
%
if ~strcmpi(model.codetype,'none'),
if size(model.codebook1,1)~=1,
eval('[ZZ,codebook_cat] = code(ZZ,''code_cat'',model.codebook2,model.code_distfct);',...
'[ZZ,codebook_cat] = code(ZZ,''code_cat'',model.codebook2);');
else
codebook_cat = model.codebook1;
end
eval('osvY = code(osvY, codebook_cat,{}, model.codebook2, model.codedist_fct, model.codedist_args);',...
'osvY = code(osvY, codebook_cat,{}, model.codebook2);');
if max(max(ZZ))==-inf,
error('bad coding scheme, no classes found after training');
end
else
if model.y_dim>1,
warning(['only first dimension is plotted, for multiclass' ...
' classification use categorical representation, ev.'...
' combined with a coding technique.']);
end
osvY = osvY(:,1);
ZZ = ZZ(:,1);
sosvY = sort(osvY);
codebook_cat = sosvY([1;find(sosvY(2:end)~=sosvY(1:end-1))+1])';
end
% contour plot
colormap cool;
map = colormap;
%cindex = [min(codebook1)+.1 codebook1 max(codebook1)-.1];
ZZd = reshape(ZZ(:,1),size(XX,1),size(XX,2));
eval('[C,h]=contourf(XX,YY,ZZd);','warning(''no surface plot feasable'');');
hold on;
eval('clabel(C,h,codebook_cat);',' ');
%
% plotting the datapoints
%
markers = {'*','s','+','o','x','d','v','p','h'};
for c=1:length(codebook_cat),
s = find(osvY(:,1)==codebook_cat(c));
plot(osvX(s,princdim(1)),osvX(s,princdim(2)) ,[markers{1+mod(c-1,9)} 'k']);
legstr{c} = ['class ' num2str(c)];
end
eval('legend(legstr);',' ');
% arrange axis
xlabel(['X_{' num2str(princdim(1)) '}']);
ylabel(['X_{' num2str(princdim(2)) '}']);
title(['LS-SVM_{\gamma=' num2str(model.gam(1)) ',\sigma^2=' num2str(model.kernel_pars(1:min(1,length(model.kernel_pars)))) '}^{' model.kernel_type(1:3) '}, with ' num2str(length(codebook_cat)) ' different classes']);
axis([xmin1 xmax1 xmin2 xmax2]);
hold off;
else
error('cannot display this dimension..');
end
% function estimation (x_dim=1,2; y_dim=1)%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
elseif model.type(1)=='f',
eval('grain;','grain = 200;');
% Determine plot limits
xmin1=min(osvX(:,princdim(1)));
xmax1=max(osvX(:,princdim(1)));
if model.x_dim>=2 & length(princdim)==2,
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Determine plot limits
xmin2=min(osvX(:,princdim(2))); if xmin2<0, xmin2=1.05*xmin2; else xmin2 = 0.975*xmin2; end
xmax2=max(osvX(:,princdim(2))); if xmax2>0, xmax2=1.05*xmax2; else xmax2 = 0.975*xmax2; end
range1 = (xmin1:(xmax1-xmin1)/grain:xmax1)';
range2 = (xmin2:(xmax2-xmin2)/grain:xmax2)';
rest = zeros(size(range1,1),model.x_dim-2);
for i=1:length(range2),
Xt = [range1 ones(size(range1,1)).*range2(i) rest];
[r,ff,model] = simlssvm(model, Xt(:,[princdim,restdim]));
z(i,:)=r';
end
surf(range1, range2,z);
hold on;
plot3(osvX(model.selector,princdim(1)),osvX(model.selector,princdim(2)), osvY(model.selector,1),'k*');
shading interp;
xlabel(['X_' num2str(princdim(1))]);
ylabel(['X_' num2str(princdim(2))]);
zlabel('Y');
title([' function estimation using LS-SVM_{\gamma=' num2str(model.gam(1)) ',\sigma^2=' num2str(model.kernel_pars(1:min(1,length(model.kernel_pars)))) '}^{' model.kernel_type(1:3) '} ']);
view(-30,50);
hold off;
elseif and(model.x_dim==1,model.y_dim==1) | length(princdim)==1,
range1 = (xmin1:(xmax1-xmin1)/grain:xmax1)';
rest = zeros(size(range1,1),model.x_dim-1);
grid = [range1 rest];
[z,ff,model] = simlssvm(model,grid(:,[princdim(1) restdim]) );
plot(range1,z,'b');
hold on;
plot(osvX(model.selector,princdim(1)),osvY(model.selector,1),'k*');
xlabel('X');
ylabel('Y');
title([' function estimation using LS-SVM_{\gamma=' num2str(model.gam(1)) ',\sigma^2=' num2str(model.kernel_pars(1:min(1,length(model.kernel_pars)))) '}^{' model.kernel_type(1:3) '}']);
%eval('title(['' function estimation using LS-SVM_{\gamma='' num2str(model.gam(1)) '',\sigma^2='' num2str(model.kernel_pars) ''}^{'' kerneltype ''} datapoints (black *), and estimation (blue line)'']);',' title(''function approximation using LS-SVM'')');
hold off;
else
Yh = simlssvm(model,osvX);
plot(Yh);
hold on;
plot(osvY,'*k');
xlabel('time');
ylabel('Y');
title([' function estimation using '...
' LS-SVM_{\gamma=' num2str(model.gam(1)) ...
',\sigma^2=' num2str(model.kernel_pars(1:min(1,length(model.kernel_pars)))) '}^{' model.kernel_type(1:3) '}'...
' datapoints (black *), and estimation (blue line)']);
hold off
end
else
end
fprintf('finished\n');