From 58daab21cd043e1dc37024a7f99b396788372918 Mon Sep 17 00:00:00 2001 From: Daniel Baumann Date: Sat, 9 Mar 2024 14:19:48 +0100 Subject: Merging upstream version 1.44.3. Signed-off-by: Daniel Baumann --- ml/dlib/examples/krls_ex.cpp | 94 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 94 insertions(+) create mode 100644 ml/dlib/examples/krls_ex.cpp (limited to 'ml/dlib/examples/krls_ex.cpp') diff --git a/ml/dlib/examples/krls_ex.cpp b/ml/dlib/examples/krls_ex.cpp new file mode 100644 index 000000000..968f1a6dd --- /dev/null +++ b/ml/dlib/examples/krls_ex.cpp @@ -0,0 +1,94 @@ +// 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 +#include + +#include + +using namespace std; +using namespace dlib; + +// Here is the sinc function we will be trying to learn with the krls +// object. +double sinc(double x) +{ + if (x == 0) + return 1; + return sin(x)/x; +} + +int main() +{ + // 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 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 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 test(kernel_type(0.1),0.001); + + // now we train our object on a few samples of the sinc function. + sample_type m; + for (double x = -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: + serialize("saved_krls_object.dat") << test; + + // Now let's open that file back up and load the krls object it contains. + deserialize("saved_krls_object.dat") >> test; + + // 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 funct = test.get_decision_function(); + serialize("saved_krls_function.dat") << funct; +} + + -- cgit v1.2.3