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Where did I go wrong with support vector

1 次查看（过去 30 天）
G. Nardi
G. Nardi 2019-1-23
编辑： per isakson 2019-1-23
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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