// 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 dlib C++ library's implementation of the pegasos algorithm for online training of support vector machines. This example creates a simple binary classification problem and shows you how to train a support vector machine on that data. 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 <ctime> #include <vector> #include <dlib/svm.h> using namespace std; using namespace dlib;intmain(){// 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; // Here we create an instance of the pegasos svm trainer object we will be using. svm_pegasos<kernel_type> trainer; // Here we setup the parameters to this object. See the dlib documentation for a // description of what these parameters are. trainer.set_lambda(0.00001); trainer.set_kernel(kernel_type(0.005)); // Set the maximum number of support vectors we want the trainer object to use // in representing the decision function it is going to learn. In general, // supplying a bigger number here will only ever give you a more accurate // answer. However, giving a smaller number will make the algorithm run // faster and decision rules that involve fewer support vectors also take // less time to evaluate. trainer.set_max_num_sv(10); std::vector<sample_type> samples; std::vector<double> labels; // make an instance of a sample matrix so we can use it below sample_type sample, center; center = 20, 20; // Now let's go into a loop and randomly generate 1000 samples. srand(time(0)); for (inti = 0; i < 10000; ++i){// Make a random sample vector. sample = randm(2,1)*40 - center; // Now if that random vector is less than 10 units from the origin then it is in // the +1 class. if (length(sample) <= 10){// let the svm_pegasos learn about this sample trainer.train(sample,+1); // save this sample so we can use it with the batch training examples below samples.push_back(sample); labels.push_back(+1);}else{// let the svm_pegasos learn about this sample trainer.train(sample,-1); // save this sample so we can use it with the batch training examples below samples.push_back(sample); labels.push_back(-1);}}// Now we have trained our SVM. Let's see how well it did. // Each of these statements prints out the output of the SVM given a particular sample. // The SVM outputs a number > 0 if a sample is predicted to be in the +1 class and < 0 // if a sample is predicted to be in the -1 class. sample(0) = 3.123; sample(1) = 4; cout << "This is a +1 example, its SVM output is: " << trainer(sample) << endl; sample(0) = 13.123; sample(1) = 9.3545; cout << "This is a -1 example, its SVM output is: " << trainer(sample) << endl; sample(0) = 13.123; sample(1) = 0; cout << "This is a -1 example, its SVM output is: " << trainer(sample) << endl; // The previous part of this example program showed you how to perform online training // with the pegasos algorithm. But it is often the case that you have a dataset and you // just want to perform batch learning on that dataset and get the resulting decision // function. To support this the dlib library provides functions for converting an online // training object like svm_pegasos into a batch training object. // First let's clear out anything in the trainer object. trainer.clear(); // Now to begin with, you might want to compute the cross validation score of a trainer object // on your data. To do this you should use the batch_cached() function to convert the svm_pegasos object // into a batch training object. Note that the second argument to batch_cached() is the minimum // learning rate the trainer object must report for the batch_cached() function to consider training // complete. So smaller values of this parameter cause training to take longer but may result // in a more accurate solution. // Here we perform 4-fold cross validation and print the results cout << "cross validation: " << cross_validate_trainer(batch_cached(trainer,0.1), samples, labels, 4); // Here is an example of creating a decision function. Note that we have used the verbose_batch_cached() // function instead of batch_cached() as above. They do the same things except verbose_batch_cached() will // print status messages to standard output while training is under way. decision_function<kernel_type> df = verbose_batch_cached(trainer,0.1).train(samples, labels); // At this point we have obtained a decision function from the above batch mode training. // Now we can use it on some test samples exactly as we did above. sample(0) = 3.123; sample(1) = 4; cout << "This is a +1 example, its SVM output is: " << df(sample) << endl; sample(0) = 13.123; sample(1) = 9.3545; cout << "This is a -1 example, its SVM output is: " << df(sample) << endl; sample(0) = 13.123; sample(1) = 0; cout << "This is a -1 example, its SVM output is: " << df(sample) << endl;}