mirror of https://github.com/davisking/dlib.git
130 lines
6.3 KiB
C++
130 lines
6.3 KiB
C++
// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
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/*
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This is an example illustrating the use of the kcentroid object
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from the dlib C++ Library.
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The kcentroid object is an implementation of an algorithm that recursively
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computes the centroid (i.e. average) of a set of points. The interesting
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thing about dlib::kcentroid is that it does so in a kernel induced feature
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space. This means that you can use it as a non-linear one-class classifier.
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So you might use it to perform online novelty detection (although, it has
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other uses, see the svm_pegasos or kkmeans examples for example).
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This example will train an instance of it on points from 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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#include <dlib/statistics.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 kcentroid
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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 2 dimensional column vectors.
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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 2 to a 0 and then set the size at runtime)
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typedef matrix<double,2,1> 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 kcentroid object. The kcentroid has 3 parameters
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// you need to set. The first argument to the constructor is the kernel we wish to
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// use. The second is a parameter that determines the numerical accuracy with which
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// the object will perform the centroid estimation. Generally, smaller values
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// give better results but cause the algorithm to attempt to use more dictionary vectors
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// (and thus run slower and use more memory). The third argument, however, is the
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// maximum number of dictionary vectors a kcentroid is allowed to use. So you can use
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// it to control the runtime complexity.
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kcentroid<kernel_type> test(kernel_type(0.1),0.01, 15);
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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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{
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m(0) = x;
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m(1) = sinc(x);
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test.train(m);
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}
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running_stats<double> rs;
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// Now lets output the distance from the centroid to some points that are from the sinc function.
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// These numbers should all be similar. We will also calculate the statistics of these numbers
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// by accumulating them into the running_stats object called rs. This will let us easily
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// find the mean and standard deviation of the distances for use below.
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cout << "Points that are on the sinc function:\n";
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m(0) = -1.5; m(1) = sinc(m(0)); cout << " " << test(m) << endl; rs.add(test(m));
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m(0) = -1.5; m(1) = sinc(m(0)); cout << " " << test(m) << endl; rs.add(test(m));
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m(0) = -0; m(1) = sinc(m(0)); cout << " " << test(m) << endl; rs.add(test(m));
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m(0) = -0.5; m(1) = sinc(m(0)); cout << " " << test(m) << endl; rs.add(test(m));
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m(0) = -4.1; m(1) = sinc(m(0)); cout << " " << test(m) << endl; rs.add(test(m));
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m(0) = -1.5; m(1) = sinc(m(0)); cout << " " << test(m) << endl; rs.add(test(m));
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m(0) = -0.5; m(1) = sinc(m(0)); cout << " " << test(m) << endl; rs.add(test(m));
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cout << endl;
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// Lets output the distance from the centroid to some points that are NOT from the sinc function.
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// These numbers should all be significantly bigger than previous set of numbers. We will also
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// use the rs.scale() function to find out how many standard deviations they are away from the
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// mean of the test points from the sinc function. So in this case our criterion for "significantly bigger"
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// is > 3 or 4 standard deviations away from the above points that actually are on the sinc function.
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cout << "Points that are NOT on the sinc function:\n";
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m(0) = -1.5; m(1) = sinc(m(0))+4; cout << " " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc." << endl;
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m(0) = -1.5; m(1) = sinc(m(0))+3; cout << " " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc." << endl;
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m(0) = -0; m(1) = -sinc(m(0)); cout << " " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc." << endl;
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m(0) = -0.5; m(1) = -sinc(m(0)); cout << " " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc." << endl;
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m(0) = -4.1; m(1) = sinc(m(0))+2; cout << " " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc." << endl;
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m(0) = -1.5; m(1) = sinc(m(0))+0.9; cout << " " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc." << endl;
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m(0) = -0.5; m(1) = sinc(m(0))+1; cout << " " << test(m) << " is " << rs.scale(test(m)) << " standard deviations from sinc." << endl;
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// And finally print out the mean and standard deviation of points that are actually from sinc().
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cout << "\nmean: " << rs.mean() << endl;
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cout << "standard deviation: " << rs.stddev() << endl;
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// The output is as follows:
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/*
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Points that are on the sinc function:
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0.869913
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0.869913
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0.873408
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0.872807
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0.870432
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0.869913
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0.872807
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Points that are NOT on the sinc function:
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1.06366 is 119.65 standard deviations from sinc.
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1.02212 is 93.8106 standard deviations from sinc.
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0.921382 is 31.1458 standard deviations from sinc.
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0.918439 is 29.3147 standard deviations from sinc.
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0.931428 is 37.3949 standard deviations from sinc.
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0.898018 is 16.6121 standard deviations from sinc.
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0.914425 is 26.8183 standard deviations from sinc.
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mean: 0.871313
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standard deviation: 0.00160756
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*/
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// So we can see that in this example the kcentroid object correctly indicates that
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// the non-sinc points are definitely not points from the sinc function.
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}
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