mirror of https://github.com/davisking/dlib.git
252 lines
12 KiB
C++
252 lines
12 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 support vector machine
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utilities from the dlib C++ Library.
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This example creates a simple set of data to train on and then shows
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you how to use the cross validation and svm training functions
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to find a good decision function that can classify examples in our
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data set.
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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 "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 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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// Now we make objects to contain our samples and their respective labels.
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std::vector<sample_type> samples;
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std::vector<double> labels;
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// Now lets put some data into our samples and labels objects. We do this
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// by looping over a bunch of points and labeling them according to their
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// distance from the origin.
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for (int r = -20; r <= 20; ++r)
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{
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for (int c = -20; c <= 20; ++c)
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{
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sample_type samp;
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samp(0) = r;
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samp(1) = c;
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samples.push_back(samp);
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// if this point is less than 10 from the origin
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if (sqrt((double)r*r + c*c) <= 10)
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labels.push_back(+1);
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else
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labels.push_back(-1);
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}
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}
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// Here we normalize all the samples by subtracting their mean and dividing by their standard deviation.
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// This is generally a good idea since it often heads off numerical stability problems and also
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// prevents one large feature from smothering others. Doing this doesn't matter much in this example
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// so I'm just doing this here so you can see an easy way to accomplish this with
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// the library.
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vector_normalizer<sample_type> normalizer;
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// let the normalizer learn the mean and standard deviation of the samples
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normalizer.train(samples);
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// now normalize each sample
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for (unsigned long i = 0; i < samples.size(); ++i)
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samples[i] = normalizer(samples[i]);
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// Now that we have some data we want to train on it. However, there are two parameters to the
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// training. These are the nu and gamma parameters. Our choice for these parameters will
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// influence how good the resulting decision function is. To test how good a particular choice
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// of these parameters are we can use the cross_validate_trainer() function to perform n-fold cross
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// validation on our training data. However, there is a problem with the way we have sampled
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// our distribution above. The problem is that there is a definite ordering to the samples.
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// That is, the first half of the samples look like they are from a different distribution
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// than the second half do. This would screw up the cross validation process but we can
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// fix it by randomizing the order of the samples with the following function call.
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randomize_samples(samples, labels);
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// The nu parameter has a maximum value that is dependent on the ratio of the +1 to -1
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// labels in the training data. This function finds that value.
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const double max_nu = maximum_nu(labels);
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// here we make an instance of the svm_nu_trainer object that uses our kernel type.
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svm_nu_trainer<kernel_type> trainer;
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// Now we loop over some different nu and gamma values to see how good they are. Note
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// that this is just a simple brute force way to try out a few possible parameter
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// choices. You may want to investigate more sophisticated strategies for determining
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// good parameter choices.
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cout << "doing cross validation" << endl;
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for (double gamma = 0.00001; gamma <= 1; gamma += 0.1)
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{
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for (double nu = 0.00001; nu < max_nu; nu += 0.1)
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{
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// tell the trainer the parameters we want to use
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trainer.set_kernel(kernel_type(gamma));
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trainer.set_nu(nu);
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cout << "gamma: " << gamma << " nu: " << nu;
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// Print out the cross validation accuracy for 3-fold cross validation using the current gamma and nu.
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// cross_validate_trainer() returns a row vector. The first element of the vector is the fraction
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// of +1 training examples correctly classified and the second number is the fraction of -1 training
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// examples correctly classified.
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cout << " cross validation accuracy: " << cross_validate_trainer(trainer, samples, labels, 3);
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}
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}
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// From looking at the output of the above loop it turns out that a good value for
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// nu and gamma for this problem is 0.1 for both. So that is what we will use.
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// Now we train on the full set of data and obtain the resulting decision function. We use the
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// value of 0.1 for nu and gamma. The decision function will return values >= 0 for samples it predicts
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// are in the +1 class and numbers < 0 for samples it predicts to be in the -1 class.
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trainer.set_kernel(kernel_type(0.1));
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trainer.set_nu(0.1);
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typedef decision_function<kernel_type> dec_funct_type;
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typedef normalized_function<dec_funct_type> funct_type;
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// Here we are making an instance of the normalized_function object. This object provides a convenient
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// way to store the vector normalization information along with the decision function we are
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// going to learn.
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funct_type learned_function;
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learned_function.normalizer = normalizer; // save normalization information
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learned_function.function = trainer.train(samples, labels); // perform the actual SVM training and save the results
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// print out the number of support vectors in the resulting decision function
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cout << "\nnumber of support vectors in our learned_function is "
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<< learned_function.function.support_vectors.nr() << endl;
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// now lets try this decision_function on some samples we haven't seen before
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sample_type sample;
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sample(0) = 3.123;
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sample(1) = 2;
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cout << "This sample should be >= 0 and it is classified as a " << learned_function(sample) << endl;
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sample(0) = 3.123;
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sample(1) = 9.3545;
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cout << "This sample should be >= 0 and it is classified as a " << learned_function(sample) << endl;
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sample(0) = 13.123;
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sample(1) = 9.3545;
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cout << "This sample should be < 0 and it is classified as a " << learned_function(sample) << endl;
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sample(0) = 13.123;
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sample(1) = 0;
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cout << "This sample should be < 0 and it is classified as a " << learned_function(sample) << endl;
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// We can also train a decision function that reports a well conditioned probability
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// instead of just a number > 0 for the +1 class and < 0 for the -1 class. An example
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// of doing that follows:
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typedef probabilistic_decision_function<kernel_type> probabilistic_funct_type;
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typedef normalized_function<probabilistic_funct_type> pfunct_type;
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pfunct_type learned_pfunct;
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learned_pfunct.normalizer = normalizer;
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learned_pfunct.function = train_probabilistic_decision_function(trainer, samples, labels, 3);
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// Now we have a function that returns the probability that a given sample is of the +1 class.
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// print out the number of support vectors in the resulting decision function.
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// (it should be the same as in the one above)
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cout << "\nnumber of support vectors in our learned_pfunct is "
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<< learned_pfunct.function.decision_funct.support_vectors.nr() << endl;
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sample(0) = 3.123;
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sample(1) = 2;
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cout << "This +1 example should have high probability. It's probability is: " << learned_pfunct(sample) << endl;
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sample(0) = 3.123;
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sample(1) = 9.3545;
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cout << "This +1 example should have high probability. It's probability is: " << learned_pfunct(sample) << endl;
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sample(0) = 13.123;
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sample(1) = 9.3545;
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cout << "This -1 example should have low probability. It's probability is: " << learned_pfunct(sample) << endl;
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sample(0) = 13.123;
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sample(1) = 0;
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cout << "This -1 example should have low probability. It's probability is: " << learned_pfunct(sample) << endl;
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// Another thing that is worth knowing is that just about everything in dlib is serializable.
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// So for example, you can save the learned_pfunct object to disk and recall it later like so:
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ofstream fout("saved_function.dat",ios::binary);
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serialize(learned_pfunct,fout);
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fout.close();
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// now lets open that file back up and load the function object it contains
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ifstream fin("saved_function.dat",ios::binary);
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deserialize(learned_pfunct, fin);
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// Note that there is also an example program that comes with dlib called the file_to_code_ex.cpp
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// example. It is a simple program that takes a file and outputs a piece of C++ code
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// that is able to fully reproduce the file's contents in the form of a std::string object.
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// So you can use that along with the std::istringstream to save learned decision functions
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// inside your actual C++ code files if you want.
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// Lastly, note that the decision functions we trained above involved well over 100
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// support vectors. Support vector machines in general tend to find decision functions
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// that involve a lot of support vectors. This is significant because the more
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// support vectors in a decision function, the longer it takes to classify new examples.
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// So dlib provides the ability to find an approximation to the normal output of a
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// support vector machine using fewer support vectors.
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// Here we determine the cross validation accuracy when we approximate the output
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// using only 10 support vectors. To do this we use the reduced2() function. It
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// takes a trainer object and the number of support vectors to use and returns
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// a new trainer object that applies the necessary post processing during the creation
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// of decision function objects.
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cout << "\ncross validation accuracy with only 10 support vectors: "
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<< cross_validate_trainer(reduced2(trainer,10), samples, labels, 3);
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// Lets print out the original cross validation score too for comparison.
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cout << "cross validation accuracy with all the original support vectors: "
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<< cross_validate_trainer(trainer, samples, labels, 3);
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// When you run this program you should see that, for this problem, you can reduce
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// the number of support vectors down to 10 without hurting the cross validation
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// accuracy.
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// To get the reduced decision function out we would just do this:
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learned_function.function = reduced2(trainer,10).train(samples, labels);
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// And similarly for the probabilistic_decision_function:
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learned_pfunct.function = train_probabilistic_decision_function(reduced2(trainer,10), samples, labels, 3);
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}
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