mirror of https://github.com/davisking/dlib.git
98 lines
3.5 KiB
C++
98 lines
3.5 KiB
C++
/*
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This is an example illustrating the use of the krls object
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from the dlib C++ Library.
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The krls object allows you to perform online regression. This
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example will train an instance of it on the sinc function.
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*/
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#include <iostream>
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#include <vector>
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#include "dlib/svm.h"
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using namespace std;
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using namespace dlib;
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// Here is the sinc function we will be trying to learn with the krls
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// object.
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double sinc(double x)
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{
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if (x == 0)
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return 1;
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return sin(x)/x;
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}
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int main()
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{
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// Here we declare that our samples will be 1 dimensional column vectors. The reason for
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// using a matrix here is that in general you can use N dimensional vectors as inputs to the
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// krls object. But here we only have 1 dimension to make the example simple.
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// (Note that if you don't know the dimensionality of your vectors at compile time
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// you can change the first number to a 0 and then set the size at runtime)
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typedef matrix<double,1,1> sample_type;
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// Now we are making a typedef for the kind of kernel we want to use. I picked the
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// radial basis kernel because it only has one parameter and generally gives good
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// results without much fiddling.
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typedef radial_basis_kernel<sample_type> kernel_type;
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// Here we declare an instance of the krls object. The first argument to the constructor
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// is the kernel we wish to use. The second is a parameter that determines the numerical
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// accuracy with which the object will perform part of the regression algorithm. Generally
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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 0.001.
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krls<kernel_type> test(kernel_type(0.1),0.001);
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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 = -10; x <= 4; x += 1)
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{
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m(0) = x;
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test.train(m, sinc(x));
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}
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// now we output the value of the sinc function for a few test points as well as the
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// value predicted by krls object.
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m(0) = 2.5; cout << sinc(m(0)) << " " << test(m) << endl;
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m(0) = 0.1; cout << sinc(m(0)) << " " << test(m) << endl;
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m(0) = -4; cout << sinc(m(0)) << " " << test(m) << endl;
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m(0) = 5.0; cout << sinc(m(0)) << " " << test(m) << endl;
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// The output is as follows:
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// 0.239389 0.239362
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// 0.998334 0.998333
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// -0.189201 -0.189201
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// -0.191785 -0.197267
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// The first column is the true value of the sinc function and the second
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// column is the output from the krls estimate.
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// Another thing that is worth knowing is that just about everything in dlib is serializable.
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// So for example, you can save the test object to disk and recall it later like so:
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ofstream fout("saved_krls_object.dat",ios::binary);
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serialize(test,fout);
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fout.close();
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// now lets open that file back up and load the krls object it contains
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ifstream fin("saved_krls_object.dat",ios::binary);
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deserialize(test, fin);
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// If you don't want to save the whole krls object (it might be a bit large)
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// you can save just the decision function it has learned so far. You can get
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// the decision function out of it by calling test.get_decision_function() and
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// then you can serialize that object instead. E.g.
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decision_function<kernel_type> funct = test.get_decision_function();
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fout.open("saved_krls_function.dat",ios::binary);
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serialize(funct, fout);
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}
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