Where did I go wrong with support vector

4 次查看（过去 30 天）
G. Nardi
G. Nardi 2019-1-23
回答： Divyajyoti Nayak 2024-10-7
https://www.mathworks.com/help/stats/support-vector-machines-for-binary-classification.html
I am following the example above.
I am trying to get the two figures that look like this.
but mine ends up looking like this
Below is my code. Can you spot what I did wrong? I believe it's around where I commented %%%%%mysigmoid2
commandwindow;
rng(1); % For reproducibility
r = sqrt(rand(100,1)); % Radius
t = 2*pi*rand(100,1); % Angle
data1 = [r.*cos(t), r.*sin(t)]; % Points
r2 = sqrt(3*rand(100,1)+1); % Radius
t2 = 2*pi*rand(100,1); % Angle
data2 = [r2.*cos(t2), r2.*sin(t2)]; % points
data3 = [data1;data2];
theclass = ones(200,1);
theclass(1:100) = -1;
%Train the SVM Classifier
cl = fitcsvm(data3,theclass,'KernelFunction','rbf',...
'BoxConstraint',Inf,'ClassNames',[-1,1]);
% Predict scores over the grid
d = 0.02;
[x1Grid,x2Grid] = meshgrid(min(data3(:,1)):d:max(data3(:,1)),...
min(data3(:,2)):d:max(data3(:,2)));
xGrid = [x1Grid(:),x2Grid(:)];
[~,scores] = predict(cl,xGrid);
cl2 = fitcsvm(data3,theclass,'KernelFunction','rbf');
[~,scores2] = predict(cl2,xGrid);
figure;
h(1:2) = gscatter(data3(:,1),data3(:,2),theclass,'rb','.');
hold on
ezpolar(@(x)1);
h(3) = plot(data3(cl2.IsSupportVector,1),data3(cl2.IsSupportVector,2),'ko');
contour(x1Grid,x2Grid,reshape(scores2(:,2),size(x1Grid)),[0 0],'k');
legend(h,{'-1','+1','Support Vectors'});
axis equal
hold off
rng(1); % For reproducibility
n = 100; % Number of points per quadrant
r1 = sqrt(rand(2*n,1)); % Random radii
t1 = [pi/2*rand(n,1); (pi/2*rand(n,1)+pi)]; % Random angles for Q1 and Q3
X1 = [r1.*cos(t1) r1.*sin(t1)]; % Polar-to-Cartesian conversion
r2 = sqrt(rand(2*n,1));
t2 = [pi/2*rand(n,1)+pi/2; (pi/2*rand(n,1)-pi/2)]; % Random angles for Q2 and Q4
X2 = [r2.*cos(t2) r2.*sin(t2)];
X = [X1; X2]; % Predictors
Y = ones(4*n,1);
Y(2*n + 1:end) = -1; % Labels
%%%%%mysigmoid2
Mdl2 = fitcsvm(X,Y,'KernelFunction','mysigmoid2','Standardize',true);
[~,scores2] = predict(Mdl2,xGrid);
figure;
h(1:2) = gscatter(X(:,1),X(:,2),Y);
hold on
h(3) = plot(X(Mdl2.IsSupportVector,1),...
X(Mdl2.IsSupportVector,2),'ko','MarkerSize',10);
title('Scatter Diagram with the Decision Boundary')
contour(x1Grid,x2Grid,reshape(scores2(:,2),size(x1Grid)),[0 0],'k');
legend({'-1','1','Support Vectors'},'Location','Best');
hold off
CVMdl2 = crossval(Mdl2);
misclass2 = kfoldLoss(CVMdl2);
misclass2;
%mysigmoid
% Mdl1 = fitcsvm(X,Y,'KernelFunction','mysigmoid','Standardize',true);
%
% % Compute the scores over a grid
% d = 0.02; % Step size of the grid
% [x1Grid,x2Grid] = meshgrid(min(X(:,1)):d:max(X(:,1)),...
% min(X(:,2)):d:max(X(:,2)));
% xGrid = [x1Grid(:),x2Grid(:)]; % The grid
% [~,scores1] = predict(Mdl1,xGrid); % The scores
%
% figure;
% h(1:2) = gscatter(X(:,1),X(:,2),Y);
% hold on
% h(3) = plot(X(Mdl1.IsSupportVector,1),...
% X(Mdl1.IsSupportVector,2),'ko','MarkerSize',10);
% % Support vectors
% contour(x1Grid,x2Grid,reshape(scores1(:,2),size(x1Grid)),[0 0],'k');
% % Decision boundary
% title('Scatter Diagram with the Decision Boundary')
% legend({'-1','1','Support Vectors'},'Location','Best');
% hold off
mysigmoid2.m
function G = mysigmoid2(U,V)
% Sigmoid kernel function with slope gamma and intercept c
gamma = 0.5;
c = -1;
G = tanh(gamma*U*V' + c);
end

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回答（1 个）

Divyajyoti Nayak
Divyajyoti Nayak 2024-10-7
Hi @G. Nardi,
From what I understand. You are following an example and are not getting the same graphs. The graphs seem to be plotted correctly but don’t look the same. This is because the limits of the y axis are different from the example figure since you have used the same grid for the second figure as the first. Making another grid for the second figure should solve the issue.
%%%%%mysigmoid2
Mdl2 = fitcsvm(X,Y,'KernelFunction','mysigmoid2','Standardize',true);
%Making Grid for second figure
d = 0.02; % Step size of the grid
[x1Grid,x2Grid] = meshgrid(min(X(:,1)):d:max(X(:,1)),...
min(X(:,2)):d:max(X(:,2)));
xGrid = [x1Grid(:),x2Grid(:)];
[~,scores2] = predict(Mdl2,xGrid);
figure;
h(1:2) = gscatter(X(:,1),X(:,2),Y);
hold on
h(3) = plot(X(Mdl2.IsSupportVector,1),...
X(Mdl2.IsSupportVector,2),'ko','MarkerSize',10);
title('Scatter Diagram with the Decision Boundary')
contour(x1Grid,x2Grid,reshape(scores2(:,2),size(x1Grid)),[0 0],'k');
legend({'-1','1','Support Vectors'},'Location','Best');
hold off
CVMdl2 = crossval(Mdl2);
misclass2 = kfoldLoss(CVMdl2);
misclass2;
Here's the result:
Hope this helps!

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