diff options
Diffstat (limited to 'ml/dlib/examples/svm_pegasos_ex.cpp')
| -rw-r--r-- | ml/dlib/examples/svm_pegasos_ex.cpp | 160 |
1 files changed, 160 insertions, 0 deletions
diff --git a/ml/dlib/examples/svm_pegasos_ex.cpp b/ml/dlib/examples/svm_pegasos_ex.cpp new file mode 100644 index 00000000..e69b485f --- /dev/null +++ b/ml/dlib/examples/svm_pegasos_ex.cpp @@ -0,0 +1,160 @@ +// 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; + + +} + |