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161 lines
6.3 KiB
C++
161 lines
6.3 KiB
C++
// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
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/*
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This is an example illustrating the use of the dlib C++ library's
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implementation of the pegasos algorithm for online training of support
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vector machines.
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This example creates a simple binary classification problem and shows
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you how to train a support vector machine on that data.
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The data used in this example will be 2 dimensional data and will
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come from a distribution where points with a distance less than 10
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from the origin are labeled +1 and all other points are labeled
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as -1.
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*/
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#include <iostream>
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#include <ctime>
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#include <vector>
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#include <dlib/svm.h>
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using namespace std;
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using namespace dlib;
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int main()
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{
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// The svm functions use column vectors to contain a lot of the data on which they
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// operate. So the first thing we do here is declare a convenient typedef.
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// This typedef declares a matrix with 2 rows and 1 column. It will be the
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// object that contains each of our 2 dimensional samples. (Note that if you wanted
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// more than 2 features in this vector you can simply change the 2 to something else.
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// Or if you don't know how many features you want until runtime then you can put a 0
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// here and use the matrix.set_size() member function)
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typedef matrix<double, 2, 1> sample_type;
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// This is a typedef for the type of kernel we are going to use in this example.
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// In this case I have selected the radial basis kernel that can operate on our
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// 2D sample_type objects
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typedef radial_basis_kernel<sample_type> kernel_type;
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// Here we create an instance of the pegasos svm trainer object we will be using.
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svm_pegasos<kernel_type> trainer;
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// Here we setup the parameters to this object. See the dlib documentation for a
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// description of what these parameters are.
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trainer.set_lambda(0.00001);
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trainer.set_kernel(kernel_type(0.005));
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// Set the maximum number of support vectors we want the trainer object to use
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// in representing the decision function it is going to learn. In general,
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// supplying a bigger number here will only ever give you a more accurate
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// answer. However, giving a smaller number will make the algorithm run
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// faster and decision rules that involve fewer support vectors also take
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// less time to evaluate.
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trainer.set_max_num_sv(10);
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std::vector<sample_type> samples;
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std::vector<double> labels;
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// make an instance of a sample matrix so we can use it below
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sample_type sample, center;
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center = 20, 20;
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// Now lets go into a loop and randomly generate 1000 samples.
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srand(time(0));
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for (int i = 0; i < 10000; ++i)
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{
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// Make a random sample vector.
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sample = randm(2,1)*40 - center;
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// Now if that random vector is less than 10 units from the origin then it is in
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// the +1 class.
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if (length(sample) <= 10)
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{
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// let the svm_pegasos learn about this sample
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trainer.train(sample,+1);
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// save this sample so we can use it with the batch training examples below
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samples.push_back(sample);
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labels.push_back(+1);
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}
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else
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{
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// let the svm_pegasos learn about this sample
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trainer.train(sample,-1);
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// save this sample so we can use it with the batch training examples below
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samples.push_back(sample);
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labels.push_back(-1);
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}
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}
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// Now we have trained our SVM. Lets see how well it did.
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// Each of these statements prints out the output of the SVM given a particular sample.
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// The SVM outputs a number > 0 if a sample is predicted to be in the +1 class and < 0
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// if a sample is predicted to be in the -1 class.
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sample(0) = 3.123;
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sample(1) = 4;
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cout << "This is a +1 example, its SVM output is: " << trainer(sample) << endl;
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sample(0) = 13.123;
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sample(1) = 9.3545;
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cout << "This is a -1 example, its SVM output is: " << trainer(sample) << endl;
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sample(0) = 13.123;
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sample(1) = 0;
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cout << "This is a -1 example, its SVM output is: " << trainer(sample) << endl;
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// The previous part of this example program showed you how to perform online training
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// with the pegasos algorithm. But it is often the case that you have a dataset and you
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// just want to perform batch learning on that dataset and get the resulting decision
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// function. To support this the dlib library provides functions for converting an online
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// training object like svm_pegasos into a batch training object.
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// First lets clear out anything in the trainer object.
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trainer.clear();
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// Now to begin with, you might want to compute the cross validation score of a trainer object
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// on your data. To do this you should use the batch_cached() function to convert the svm_pegasos object
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// into a batch training object. Note that the second argument to batch_cached() is the minimum
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// learning rate the trainer object must report for the batch_cached() function to consider training
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// complete. So smaller values of this parameter cause training to take longer but may result
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// in a more accurate solution.
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// Here we perform 4-fold cross validation and print the results
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cout << "cross validation: " << cross_validate_trainer(batch_cached(trainer,0.1), samples, labels, 4);
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// Here is an example of creating a decision function. Note that we have used the verbose_batch_cached()
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// function instead of batch_cached() as above. They do the same things except verbose_batch_cached() will
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// print status messages to standard output while training is under way.
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decision_function<kernel_type> df = verbose_batch_cached(trainer,0.1).train(samples, labels);
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// At this point we have obtained a decision function from the above batch mode training.
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// Now we can use it on some test samples exactly as we did above.
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sample(0) = 3.123;
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sample(1) = 4;
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cout << "This is a +1 example, its SVM output is: " << df(sample) << endl;
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sample(0) = 13.123;
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sample(1) = 9.3545;
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cout << "This is a -1 example, its SVM output is: " << df(sample) << endl;
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sample(0) = 13.123;
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sample(1) = 0;
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cout << "This is a -1 example, its SVM output is: " << df(sample) << endl;
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}
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