dlib C++ Library - rvm_ex.cpp

// 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 relevance vector machine
 utilities from the dlib C++ Library. 
 This example creates a simple set of data to train on and then shows
 you how to use the cross validation and rvm training functions
 to find a good decision function that can classify examples in our
 data set.
 The data used in this example will be 2 dimensional data and will
 come from a distribution where points with a distance less than 10
 from the origin are labeled +1 and all other points are labeled
 as -1.
 
*/
#include <iostream>
#include <dlib/svm.h>
using namespace std;
using namespace dlib;
int main()
{
 // The rvm functions use column vectors to contain a lot of the data on which they 
 // operate. So the first thing we do here is declare a convenient typedef. 
 // This typedef declares a matrix with 2 rows and 1 column. It will be the
 // object that contains each of our 2 dimensional samples. (Note that if you wanted 
 // more than 2 features in this vector you can simply change the 2 to something else.
 // Or if you don't know how many features you want until runtime then you can put a 0
 // here and use the matrix.set_size() member function)
 typedef matrix<double, 2, 1> sample_type;
 // This is a typedef for the type of kernel we are going to use in this example.
 // In this case I have selected the radial basis kernel that can operate on our
 // 2D sample_type objects
 typedef radial_basis_kernel<sample_type> kernel_type;
 // Now we make objects to contain our samples and their respective labels.
 std::vector<sample_type> samples;
 std::vector<double> labels;
 // Now let's put some data into our samples and labels objects. We do this
 // by looping over a bunch of points and labeling them according to their
 // distance from the origin.
 for (int r = -20; r <= 20; ++r)
 {
 for (int c = -20; c <= 20; ++c)
 {
 sample_type samp;
 samp(0) = r;
 samp(1) = c;
 samples.push_back(samp);
 // if this point is less than 10 from the origin
 if (sqrt((double)r*r + c*c) <= 10)
 labels.push_back(+1);
 else
 labels.push_back(-1);
 }
 }
 // Here we normalize all the samples by subtracting their mean and dividing by their standard deviation.
 // This is generally a good idea since it often heads off numerical stability problems and also 
 // prevents one large feature from smothering others. Doing this doesn't matter much in this example
 // so I'm just doing this here so you can see an easy way to accomplish this with 
 // the library. 
 vector_normalizer<sample_type> normalizer;
 // let the normalizer learn the mean and standard deviation of the samples
 normalizer.train(samples);
 // now normalize each sample
 for (unsigned long i = 0; i < samples.size(); ++i)
 samples[i] = normalizer(samples[i]); 
 // Now that we have some data we want to train on it. However, there is a parameter to the 
 // training. This is the gamma parameter of the RBF kernel. Our choice for this parameter will 
 // influence how good the resulting decision function is. To test how good a particular choice of
 // kernel parameters is we can use the cross_validate_trainer() function to perform n-fold cross
 // validation on our training data. However, there is a problem with the way we have sampled 
 // our distribution. The problem is that there is a definite ordering to the samples. 
 // That is, the first half of the samples look like they are from a different distribution 
 // than the second half. This would screw up the cross validation process but we can 
 // fix it by randomizing the order of the samples with the following function call.
 randomize_samples(samples, labels);
 // here we make an instance of the rvm_trainer object that uses our kernel type.
 rvm_trainer<kernel_type> trainer;
 // One thing you can do to reduce the RVM training time is to make its
 // stopping epsilon bigger. However, this might make the outputs less
 // reliable. But sometimes it works out well. 0.001 is the default.
 trainer.set_epsilon(0.001);
 // You can also set an explicit limit on the number of iterations used by the numeric
 // solver. The default is 2000.
 trainer.set_max_iterations(2000);
 // Now we loop over some different gamma values to see how good they are. Note
 // that this is a very simple way to try out a few possible parameter choices. You 
 // should look at the model_selection_ex.cpp program for examples of more sophisticated 
 // strategies for determining good parameter choices.
 cout << "doing cross validation" << endl;
 for (double gamma = 0.000001; gamma <= 1; gamma *= 5)
 {
 // tell the trainer the parameters we want to use
 trainer.set_kernel(kernel_type(gamma));
 cout << "gamma: " << gamma;
 // Print out the cross validation accuracy for 3-fold cross validation using the current gamma. 
 // cross_validate_trainer() returns a row vector. The first element of the vector is the fraction
 // of +1 training examples correctly classified and the second number is the fraction of -1 training 
 // examples correctly classified.
 cout << " cross validation accuracy: " << cross_validate_trainer(trainer, samples, labels, 3);
 }
 // From looking at the output of the above loop it turns out that a good value for 
 // gamma for this problem is 0.08. So that is what we will use.
 // Now we train on the full set of data and obtain the resulting decision function. We use the
 // value of 0.08 for gamma. The decision function will return values >= 0 for samples it predicts
 // are in the +1 class and numbers < 0 for samples it predicts to be in the -1 class.
 trainer.set_kernel(kernel_type(0.08));
 typedef decision_function<kernel_type> dec_funct_type;
 typedef normalized_function<dec_funct_type> funct_type;
 // Here we are making an instance of the normalized_function object. This object provides a convenient 
 // way to store the vector normalization information along with the decision function we are
 // going to learn. 
 funct_type learned_function;
 learned_function.normalizer = normalizer; // save normalization information
 learned_function.function = trainer.train(samples, labels); // perform the actual RVM training and save the results
 // Print out the number of relevance vectors in the resulting decision function.
 cout << "\nnumber of relevance vectors in our learned_function is " 
 << learned_function.function.basis_vectors.size() << endl;
 // Now let's try this decision_function on some samples we haven't seen before 
 sample_type sample;
 sample(0) = 3.123;
 sample(1) = 2;
 cout << "This is a +1 class example, the classifier output is " << learned_function(sample) << endl;
 sample(0) = 3.123;
 sample(1) = 9.3545;
 cout << "This is a +1 class example, the classifier output is " << learned_function(sample) << endl;
 sample(0) = 13.123;
 sample(1) = 9.3545;
 cout << "This is a -1 class example, the classifier output is " << learned_function(sample) << endl;
 sample(0) = 13.123;
 sample(1) = 0;
 cout << "This is a -1 class example, the classifier output is " << learned_function(sample) << endl;
 // We can also train a decision function that reports a well conditioned probability 
 // instead of just a number > 0 for the +1 class and < 0 for the -1 class. An example 
 // of doing that follows:
 typedef probabilistic_decision_function<kernel_type> probabilistic_funct_type; 
 typedef normalized_function<probabilistic_funct_type> pfunct_type;
 pfunct_type learned_pfunct; 
 learned_pfunct.normalizer = normalizer;
 learned_pfunct.function = train_probabilistic_decision_function(trainer, samples, labels, 3);
 // Now we have a function that returns the probability that a given sample is of the +1 class. 
 // print out the number of relevance vectors in the resulting decision function. 
 // (it should be the same as in the one above)
 cout << "\nnumber of relevance vectors in our learned_pfunct is " 
 << learned_pfunct.function.decision_funct.basis_vectors.size() << endl;
 sample(0) = 3.123;
 sample(1) = 2;
 cout << "This +1 class example should have high probability. Its probability is: " 
 << learned_pfunct(sample) << endl;
 sample(0) = 3.123;
 sample(1) = 9.3545;
 cout << "This +1 class example should have high probability. Its probability is: " 
 << learned_pfunct(sample) << endl;
 sample(0) = 13.123;
 sample(1) = 9.3545;
 cout << "This -1 class example should have low probability. Its probability is: " 
 << learned_pfunct(sample) << endl;
 sample(0) = 13.123;
 sample(1) = 0;
 cout << "This -1 class example should have low probability. Its probability is: " 
 << learned_pfunct(sample) << endl;
 // Another thing that is worth knowing is that just about everything in dlib is serializable.
 // So for example, you can save the learned_pfunct object to disk and recall it later like so:
 serialize("saved_function.dat") << learned_pfunct;
 // Now let's open that file back up and load the function object it contains.
 deserialize("saved_function.dat") >> learned_pfunct;
}