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Diffstat (limited to 'ml/dlib/examples/rvm_ex.cpp')
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diff --git a/ml/dlib/examples/rvm_ex.cpp b/ml/dlib/examples/rvm_ex.cpp new file mode 100644 index 000000000..d1d5935e7 --- /dev/null +++ b/ml/dlib/examples/rvm_ex.cpp @@ -0,0 +1,217 @@ +// 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; + +} + |