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Diffstat (limited to 'src/ml/dlib/examples/kcentroid_ex.cpp')
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diff --git a/src/ml/dlib/examples/kcentroid_ex.cpp b/src/ml/dlib/examples/kcentroid_ex.cpp new file mode 100644 index 000000000..1f9311bc1 --- /dev/null +++ b/src/ml/dlib/examples/kcentroid_ex.cpp @@ -0,0 +1,129 @@ +// 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 kcentroid object + from the dlib C++ Library. + + The kcentroid object is an implementation of an algorithm that recursively + computes the centroid (i.e. average) of a set of points. The interesting + thing about dlib::kcentroid is that it does so in a kernel induced feature + space. This means that you can use it as a non-linear one-class classifier. + So you might use it to perform online novelty detection (although, it has + other uses, see the svm_pegasos or kkmeans examples for example). + + This example will train an instance of it on points from the sinc function. + +*/ + +#include <iostream> +#include <vector> + +#include <dlib/svm.h> +#include <dlib/statistics.h> + +using namespace std; +using namespace dlib; + +// Here is the sinc function we will be trying to learn with the kcentroid +// object. +double sinc(double x) +{ + if (x == 0) + return 1; + return sin(x)/x; +} + +int main() +{ + // Here we declare that our samples will be 2 dimensional column vectors. + // (Note that if you don't know the dimensionality of your vectors at compile time + // you can change the 2 to a 0 and then set the size at runtime) + typedef matrix<double,2,1> 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 kcentroid object. The kcentroid has 3 parameters + // you need to set. 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 the centroid estimation. Generally, smaller values + // give better results but cause the algorithm to attempt to use more dictionary vectors + // (and thus run slower and use more memory). The third argument, however, is the + // maximum number of dictionary vectors a kcentroid is allowed to use. So you can use + // it to control the runtime complexity. + kcentroid<kernel_type> test(kernel_type(0.1),0.01, 15); + + + // now we train our object on a few samples of the sinc function. + sample_type m; + for (double x = -15; x <= 8; x += 1) + { + m(0) = x; + m(1) = sinc(x); + test.train(m); + } + + running_stats<double> rs; + + // Now let's output the distance from the centroid to some points that are from the sinc function. + // These numbers should all be similar. We will also calculate the statistics of these numbers + // by accumulating them into the running_stats object called rs. This will let us easily + // find the mean and standard deviation of the distances for use below. + cout << "Points that are on the sinc function:\n"; + m(0) = -1.5; m(1) = sinc(m(0)); cout << " " << test(m) << endl; rs.add(test(m)); + m(0) = -1.5; m(1) = sinc(m(0)); cout << " " << test(m) << endl; rs.add(test(m)); + m(0) = -0; m(1) = sinc(m(0)); cout << " " << test(m) << endl; rs.add(test(m)); + m(0) = -0.5; m(1) = sinc(m(0)); cout << " " << test(m) << endl; rs.add(test(m)); + m(0) = -4.1; m(1) = sinc(m(0)); cout << " " << test(m) << endl; rs.add(test(m)); + m(0) = -1.5; m(1) = sinc(m(0)); cout << " " << test(m) << endl; rs.add(test(m)); + m(0) = -0.5; m(1) = sinc(m(0)); cout << " " << test(m) << endl; rs.add(test(m)); + + cout << endl; + // Let's output the distance from the centroid to some points that are NOT from the sinc function. + // These numbers should all be significantly bigger than previous set of numbers. We will also + // use the rs.scale() function to find out how many standard deviations they are away from the + // mean of the test points from the sinc function. So in this case our criterion for "significantly bigger" + // is > 3 or 4 standard deviations away from the above points that actually are on the sinc function. + cout << "Points that are NOT on the sinc function:\n"; + m(0) = -1.5; m(1) = sinc(m(0))+4; cout << " " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc." << endl; + m(0) = -1.5; m(1) = sinc(m(0))+3; cout << " " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc." << endl; + m(0) = -0; m(1) = -sinc(m(0)); cout << " " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc." << endl; + m(0) = -0.5; m(1) = -sinc(m(0)); cout << " " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc." << endl; + m(0) = -4.1; m(1) = sinc(m(0))+2; cout << " " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc." << endl; + m(0) = -1.5; m(1) = sinc(m(0))+0.9; cout << " " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc." << endl; + m(0) = -0.5; m(1) = sinc(m(0))+1; cout << " " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc." << endl; + + // And finally print out the mean and standard deviation of points that are actually from sinc(). + cout << "\nmean: " << rs.mean() << endl; + cout << "standard deviation: " << rs.stddev() << endl; + + // The output is as follows: + /* + Points that are on the sinc function: + 0.869913 + 0.869913 + 0.873408 + 0.872807 + 0.870432 + 0.869913 + 0.872807 + + Points that are NOT on the sinc function: + 1.06366 is 119.65 standard deviations from sinc. + 1.02212 is 93.8106 standard deviations from sinc. + 0.921382 is 31.1458 standard deviations from sinc. + 0.918439 is 29.3147 standard deviations from sinc. + 0.931428 is 37.3949 standard deviations from sinc. + 0.898018 is 16.6121 standard deviations from sinc. + 0.914425 is 26.8183 standard deviations from sinc. + + mean: 0.871313 + standard deviation: 0.00160756 + */ + + // So we can see that in this example the kcentroid object correctly indicates that + // the non-sinc points are definitely not points from the sinc function. +} + + |