dlib C++ Library - svm_pegasos_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 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;
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;
 // 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 (int i = 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;
}