Matlab Machine Learning User's Guide

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Matlab Machine Learning User's Guide.pdf (52.61 MB)

2015-11-2 11:26:44 上传

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关键词：Learning machine earning MATLAB Guide

1. #load fisheriris
   
2. x = meas(:,3:4);
   
3. gscatter(x(:,1),x(:,2),species)
   
4. legend('Location','best')
   
5. #Plot the new point.
   
6. newpoint = [5 1.45];
   
7. line(newpoint(1),newpoint(2),'marker','x','color','k',...
   
8. 'markersize',10,'linewidth',2)
   
9. #Prepare a K d-tree neighbor searcher model.
   
10. Mdl = KDTreeSearcher(x)
    
11. 
    
12. #Find the 10 sample points closest to the new point.
    
13. [n,d] = knnsearch(Mdl,newpoint,'k',10);
    
14. line(x(n,1),x(n,2),'color',[.5 .5 .5],'marker','o',...
    
15. 'linestyle','none','markersize',10)
    
16. #Make the axes equal so the calculated distances correspond to the apparent distances on
    
17. the plot axis equal and zoom in to see the neighbors better.
    
18. xlim([4.5 5.5]);
    
19. ylim([1 2]);
    
20. axis square
    
21. 
    
22. #Find the species of the 10 neighbors.
    
23. tabulate(species(n))
    
24. 
    
25. #Using a rule based on the majority vote of the 10 nearest neighbors, you can classify this new point as a versicolor.Visually identify the neighbors by drawing a circle around the group of them. Define the
    
26. #center and diameter of a circle, based on the location of the new point.
    
27. ctr = newpoint - d(end);
    
28. 
    
29. diameter = 2*d(end);
    
30. % Draw a circle around the 10 nearest neighbors.
    
31. h = rectangle('position',[ctr,diameter,diameter],...
    
32. 'curvature',[1 1]);
    
33. h.LineStyle = ':';
    
34. 
    
35. #Using the same dataset, find the 10 nearest neighbors to three new points.
    
36. figure
    
37. newpoint2 = [5 1.45;6 2;2.75 .75];
    
38. gscatter(x(:,1),x(:,2),species)
    
39. legend('location','best')
    
40. [n2,d2] = knnsearch(Mdl,newpoint2,'k',10);
    
41. line(x(n2,1),x(n2,2),'color',[.5 .5 .5],'marker','o',...
    
42. 'linestyle','none','markersize',10)
    
43. line(newpoint2(:,1),newpoint2(:,2),'marker','x','color','k',...
    
44. 'markersize',10,'linewidth',2,'linestyle','none')

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1. Regularize Poisson Regression
   
2. This example shows how to identify and remove redundant predictors from a generalized
   
3. linear model.
   
4. 
   
5. #Create data with 20 predictors, and Poisson responses using just three of the predictors,
   
6. plus a constant.
   
7. rng('default') % for reproducibility
   
8. X = randn(100,20);
   
9. mu = exp(X(:,[5 10 15])*[.4;.2;.3] + 1);
   
10. y = poissrnd(mu);
    
11. #Construct a cross-validated lasso regularization of a Poisson regression model of the data.
    
12. [B FitInfo] = lassoglm(X,y,'poisson','CV',10);
    
13. 
    
14. #Examine the cross-validation plot to see the effect of the Lambda regularization parameter.
    
15. lassoPlot(B,FitInfo,'plottype','CV');
    
16. 
    
17. #Find the nonzero model coefficients corresponding to the two identified points.
    
18. minpts = find(B(:,FitInfo.IndexMinDeviance))
    
19. 
    
20. #The constant term is in the FitInfo.Intercept vector.
    
21. FitInfo.Intercept(FitInfo.Index1SE)

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Matlab Machine Learning User's Guide

Matlab Machine Learning User's Guide

kankan

good 3x

thanks

don't know