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