// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt /* This is an example showing how to use sparse feature vectors with the dlib C++ library's machine learning tools. 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 100 dimensional data and will come from a simple linearly separable distribution. */ #include <iostream> #include <ctime> #include <vector> #include <dlib/svm.h> using namespace std; using namespace dlib;intmain(){// In this example program we will be dealing with feature vectors that are sparse (i.e. most // of the values in each vector are zero). So rather than using a dlib::matrix we can use // one of the containers from the STL to represent our sample vectors. In particular, we // can use the std::map to represent sparse vectors. (Note that you don't have to use std::map. // Any STL container of std::pair objects that is sorted can be used. So for example, you could // use a std::vector<std::pair<unsigned long,double> > here so long as you took care to sort every vector) typedef std::map<unsignedlong,double> sample_type; // This is a typedef for the type of kernel we are going to use in this example. // Since our data is linearly separable I picked the linear kernel. Note that if you // are using a sparse vector representation like std::map then you have to use a kernel // meant to be used with that kind of data type. typedef sparse_linear_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 a parameter to this object. See the dlib documentation for a // description of what this parameter does. trainer.set_lambda(0.00001); // Let's also use the svm trainer specially optimized for the linear_kernel and // sparse_linear_kernel. svm_c_linear_trainer<kernel_type> linear_trainer; // This trainer solves the "C" formulation of the SVM. See the documentation for // details. linear_trainer.set_c(10); std::vector<sample_type> samples; std::vector<double> labels; // make an instance of a sample vector so we can use it below sample_type sample; // Now let's go into a loop and randomly generate 10000 samples. srand(time(0));doublelabel = +1; for (inti = 0; i < 10000; ++i){// flip this flag label *= -1; sample.clear(); // now make a random sparse sample with at most 10 non-zero elements for (intj = 0; j < 10; ++j){intidx = std::rand()%100;doublevalue = static_cast<double>(std::rand())/RAND_MAX; sample[idx] = label*value;}// let the svm_pegasos learn about this sample. trainer.train(sample,label); // Also save the samples we are generating so we can let the svm_c_linear_trainer // learn from them below. samples.push_back(sample); labels.push_back(label);}// In addition to the rule we learned with the pegasos trainer, let's also use our // linear_trainer to learn a decision rule. decision_function<kernel_type> df = linear_trainer.train(samples, labels); // Now we have trained our SVMs. Let's test them out a bit. // Each of these statements prints the output of the SVMs given a particular sample. // Each 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.clear(); sample[4] = 0.3; sample[10] = 0.9; cout << "This is a +1 example, its SVM output is: " << trainer(sample) << endl; cout << "df: " << df(sample) << endl; sample.clear(); sample[83] = -0.3; sample[26] = -0.9; sample[58] = -0.7; cout << "This is a -1 example, its SVM output is: " << trainer(sample) << endl; cout << "df: " << df(sample) << endl; sample.clear(); sample[0] = -0.2; sample[9] = -0.8; cout << "This is a -1 example, its SVM output is: " << trainer(sample) << endl; cout << "df: " << df(sample) << endl;}