Expression or statement is incorrect--possibly unbalanced (, {, or [.

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ghali ahmed
ghali ahmed 2017-11-10
评论： Walter Roberson 2017-11-11
hello every body i have an error ??? Error: File: test.m Line: 31 Column: 3 exactely in [~,scores] = predict(cl,xGrid); i have Matlab 7.8.0 (R2009a)
  1. rand(1); % For reproducibility
  2. r = sqrt(rand(100,1)); % Radius
  3. t = 2*pi*rand(100,1); % Angle
  4. data1 = [r.*cos(t), r.*sin(t)]; % Points
  6. r2 = sqrt(3*rand(100,1)+1); % Radius
  7. t2 = 2*pi*rand(100,1); % Angle
  8. data2 = [r2.*cos(t2), r2.*sin(t2)]; % points
  10. figure;
  11. plot(data1(:,1),data1(:,2),'r.','MarkerSize',15)
  12. hold on
  13. plot(data2(:,1),data2(:,2),'b.','MarkerSize',15)
  14. ezpolar(@(x)1);ezpolar(@(x)2);
  15. axis equal
  16. hold off
  18. data3 = [data1;data2];
  19. theclass = ones(200,1);
  20. theclass(1:100) = -1;
  22. %Train the SVM Classifier
  23. cl = fitcsvm(data3,theclass,'KernelFunction','rbf',...
  24. 'BoxConstraint',Inf,'ClassNames',[-1,1]);
  26. % Predict scores over the grid
  27. d = 0.02;
  28. [x1Grid,x2Grid] = meshgrid(min(data3(:,1)):d:max(data3(:,1)),...
  29. min(data3(:,2)):d:max(data3(:,2)));
  30. xGrid = [x1Grid(:),x2Grid(:)];
  31. [~,scores] = predict(cl,xGrid);
  33. % Plot the data and the decision boundary
  34. figure;
  35. h(1:2) = gscatter(data3(:,1),data3(:,2),theclass,'rb','.');
  36. hold on
  37. ezpolar(@(x)1);
  38. h(3) = plot(data3(cl.IsSupportVector,1),data3(cl.IsSupportVector,2),'ko');
  39. contour(x1Grid,x2Grid,reshape(scores(:,2),size(x1Grid)),[0 0],'k');
  40. legend(h,{'-1','+1','Support Vectors'});
  41. axis equal
  42. hold off

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回答（1 个）

Steven Lord
Steven Lord 2017-11-10
The ability to ignore specific input or output arguments in function calls using the tilde operator was introduced in release R2009b. Replace ~ with a dummy variable name, like dummy, for older releases.
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per isakson
per isakson 2017-11-11
fitcsvm - Train binary support vector machine classifier
fitcsvm trains or cross-validates a support vector machine (SVM)
model for two-class (binary) classification on a low- through
moderate-dimensional predictor data set. fitcsvm supports...
Documentation > Statistics and Machine Learning Toolbox > Classification > Support Vector Machine Classification
Walter Roberson
Walter Roberson 2017-11-11
That routine was introduced in R2014a.
In your software release there was no built-in SVM in any toolbox, so people would compile and link the third party libsvm

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