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diff --git a/ml/dlib/examples/svm_ex.cpp b/ml/dlib/examples/svm_ex.cpp new file mode 100644 index 000000000..3d5d0bb84 --- /dev/null +++ b/ml/dlib/examples/svm_ex.cpp @@ -0,0 +1,255 @@ +// 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 support 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 svm 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 svm 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 are two + // parameters to the training. These are the nu and gamma parameters. Our choice for + // these parameters will influence how good the resulting decision function is. To + // test how good a particular choice of these 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 + // above. 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); + + + // The nu parameter has a maximum value that is dependent on the ratio of the +1 to -1 + // labels in the training data. This function finds that value. + const double max_nu = maximum_nu(labels); + + // here we make an instance of the svm_nu_trainer object that uses our kernel type. + svm_nu_trainer<kernel_type> trainer; + + // Now we loop over some different nu and 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 = 0.00001; gamma <= 1; gamma *= 5) + { + for (double nu = 0.00001; nu < max_nu; nu *= 5) + { + // tell the trainer the parameters we want to use + trainer.set_kernel(kernel_type(gamma)); + trainer.set_nu(nu); + + cout << "gamma: " << gamma << " nu: " << nu; + // Print out the cross validation accuracy for 3-fold cross validation using + // the current gamma and nu. 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 nu + // and gamma for this problem is 0.15625 for both. 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 0.15625 for nu and 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(0.15625)); + trainer.set_nu(0.15625); + 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 SVM training and save the results + + // print out the number of support vectors in the resulting decision function + cout << "\nnumber of support 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 support vectors in the resulting decision function. + // (it should be the same as in the one above) + cout << "\nnumber of support 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; + + // Note that there is also an example program that comes with dlib called the + // file_to_code_ex.cpp example. It is a simple program that takes a file and outputs a + // piece of C++ code that is able to fully reproduce the file's contents in the form of + // a std::string object. So you can use that along with the std::istringstream to save + // learned decision functions inside your actual C++ code files if you want. + + + + + // Lastly, note that the decision functions we trained above involved well over 200 + // basis vectors. Support vector machines in general tend to find decision functions + // that involve a lot of basis vectors. This is significant because the more basis + // vectors in a decision function, the longer it takes to classify new examples. So + // dlib provides the ability to find an approximation to the normal output of a trainer + // using fewer basis vectors. + + // Here we determine the cross validation accuracy when we approximate the output using + // only 10 basis vectors. To do this we use the reduced2() function. It takes a + // trainer object and the number of basis vectors to use and returns a new trainer + // object that applies the necessary post processing during the creation of decision + // function objects. + cout << "\ncross validation accuracy with only 10 support vectors: " + << cross_validate_trainer(reduced2(trainer,10), samples, labels, 3); + + // Let's print out the original cross validation score too for comparison. + cout << "cross validation accuracy with all the original support vectors: " + << cross_validate_trainer(trainer, samples, labels, 3); + + // When you run this program you should see that, for this problem, you can reduce the + // number of basis vectors down to 10 without hurting the cross validation accuracy. + + + // To get the reduced decision function out we would just do this: + learned_function.function = reduced2(trainer,10).train(samples, labels); + // And similarly for the probabilistic_decision_function: + learned_pfunct.function = train_probabilistic_decision_function(reduced2(trainer,10), samples, labels, 3); +} + |