如何繪製SVM結果的三維超平面圖？

例如，在這裏我們使用了兩個特徵，我們可以在2D中繪製決策邊界。但是，如果我們如何使用3個特徵如何繪製3D超平面？

load fisheriris; 

features = meas(1:100,:); 
featureSelcted = features(1:100,1:2); % For example, featureSelcted = features(1:100,1:3) can not be plotted 
groundTruthGroup = species(1:100); 


svmStruct        = svmtrain(featureSelcted, groundTruthGroup, ... 
    'Kernel_Function', 'rbf', 'boxconstraint', Inf, 'showplot', true, 'Method', 'QP'); 
svmClassified       = svmclassify(svmStruct,featureSelcted,'showplot',true);

在R A類似的解決方案可以在 svm-fit-hyperplane 找到但Matlab的實現將是得心應手。非常感謝。

來源

2013-04-22 Samo Jerom

這裏是繪製在MATLAB 3D SVM結果的功能。

function [] = svm_3d_matlab_vis(svmStruct,Xdata,group) 
sv = svmStruct.SupportVectors; 
alphaHat = svmStruct.Alpha; 
bias = svmStruct.Bias; 
kfun = svmStruct.KernelFunction; 
kfunargs = svmStruct.KernelFunctionArgs; 
sh = svmStruct.ScaleData.shift; % shift vector 
scalef = svmStruct.ScaleData.scaleFactor; % scale vector 

group = group(~any(isnan(Xdata),2)); 
Xdata =Xdata(~any(isnan(Xdata),2),:); % remove rows with NaN 

% scale and shift data 
Xdata1 = repmat(scalef,size(Xdata,1),1).*(Xdata+repmat(sh,size(Xdata,1),1)); 
k = 50; 
cubeXMin = min(Xdata1(:,1)); 
cubeYMin = min(Xdata1(:,2)); 
cubeZMin = min(Xdata1(:,3)); 

cubeXMax = max(Xdata1(:,1)); 
cubeYMax = max(Xdata1(:,2)); 
cubeZMax = max(Xdata1(:,3)); 
stepx = (cubeXMax-cubeXMin)/(k-1); 
stepy = (cubeYMax-cubeYMin)/(k-1); 
stepz = (cubeZMax-cubeZMin)/(k-1); 
[x, y, z] = meshgrid(cubeXMin:stepx:cubeXMax,cubeYMin:stepy:cubeYMax,cubeZMin:stepz:cubeZMax); 
mm = size(x); 
x = x(:); 
y = y(:); 
z = z(:); 
f = (feval(kfun,sv,[x y z],kfunargs{:})'*alphaHat(:)) + bias; 
t = strcmp(group, group{1}); 

% unscale and unshift data 
Xdata1 =(Xdata1./repmat(scalef,size(Xdata,1),1)) - repmat(sh,size(Xdata,1),1); 
x =(x./repmat(scalef(1),size(x,1),1)) - repmat(sh(1),size(x,1),1); 
y =(y./repmat(scalef(2),size(y,1),1)) - repmat(sh(2),size(y,1),1); 
z =(z./repmat(scalef(3),size(z,1),1)) - repmat(sh(3),size(z,1),1); 
figure 
plot3(Xdata1(t, 1), Xdata1(t, 2), Xdata1(t, 3), 'b.'); 
hold on 
plot3(Xdata1(~t, 1), Xdata1(~t, 2), Xdata1(~t, 3), 'r.'); 
hold on 
% load unscaled support vectors for plotting 
sv = svmStruct.SupportVectorIndices; 
sv = [Xdata1(sv, :)]; 
plot3(sv(:, 1), sv(:, 2), sv(:, 3), 'go'); 
legend(group{1},group{end},'support vectors') 

x0 = reshape(x, mm); 
y0 = reshape(y, mm); 
z0 = reshape(z, mm); 
v0 = reshape(f, mm); 

[faces,verts,colors] = isosurface(x0, y0, z0, v0, 0, x0); 
patch('Vertices', verts, 'Faces', faces, 'FaceColor','k','edgecolor', 'none', 'FaceAlpha', 0.5); 
grid on 
box on 
view(3) 
hold off 
end

例情節：

% load data 
    load fisheriris; 
% train svm using three features for two species 
    svmStruct = svmtrain(meas(1:100,1:3),species(1:100),'showplot','false','kernel_function','rbf',... 
     'boxconstraint',1,'kktviolationlevel',0.05,'tolkkt',5e-3); 
    % run function described above 
    svm_3d_matlab_vis(svmStruct,meas(1:100,1:3),species(1:100))

3D Data Plot with SVM Hyperplane

來源

2013-11-14 04:11:49 hkf

如何繪製SVM結果的三維超平面圖？

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