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@ -46,8 +46,23 @@ int main()
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// smaller values give better results but cause the algorithm to run slower. You just have
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// to play with it to decide what balance of speed and accuracy is right for your problem.
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// Here we have set it to 0.01.
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//
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// Also, since we are using the radial basis kernel we have to pick the RBF width parameter.
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// Here we have it set to 0.1. But in general, a reasonable way of picking this value is
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// to start with some initial guess and to just run the algorithm. Then print out
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// test.dictionary_size() to see how many support vectors the kcentroid object is using.
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// And a good rule of thumb is that you should have somewhere in the range of 10-100
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// support vectors. So if you aren't in that range then you can change the RBF parameter.
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// Making it smaller will decrease the dictionary size and making it bigger will increase
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// the dictionary size.
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//
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// So what I often do is I set the kcentroid's second parameter to 0.01 or 0.001. Then
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// I find an RBF kernel parameter that gives me the number of support vectors that I
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// feel is appropriate for the problem I'm trying to solve. Again, this just comes down
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// to playing with it and getting a feel for how things work.
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kcentroid<kernel_type> test(kernel_type(0.1),0.01);
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// now we train our object on a few samples of the sinc function.
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sample_type m;
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for (double x = -15; x <= 8; x += 1)
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