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Diffstat (limited to 'ml/dlib/examples/multiclass_classification_ex.cpp')
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diff --git a/ml/dlib/examples/multiclass_classification_ex.cpp b/ml/dlib/examples/multiclass_classification_ex.cpp new file mode 100644 index 000000000..782511ca3 --- /dev/null +++ b/ml/dlib/examples/multiclass_classification_ex.cpp @@ -0,0 +1,248 @@ +// 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 multiclass classification tools + from the dlib C++ Library. Specifically, this example will make points from + three classes and show you how to train a multiclass classifier to recognize + these three classes. + + The classes are as follows: + - class 1: points very close to the origin + - class 2: points on the circle of radius 10 around the origin + - class 3: points that are on a circle of radius 4 but not around the origin at all +*/ + +#include <dlib/svm_threaded.h> + +#include <iostream> +#include <vector> + +#include <dlib/rand.h> + +using namespace std; +using namespace dlib; + +// Our data will be 2-dimensional data. So declare an appropriate type to contain these points. +typedef matrix<double,2,1> sample_type; + +// ---------------------------------------------------------------------------------------- + +void generate_data ( + std::vector<sample_type>& samples, + std::vector<double>& labels +); +/*! + ensures + - make some 3 class data as described above. + - Create 60 points from class 1 + - Create 70 points from class 2 + - Create 80 points from class 3 +!*/ + +// ---------------------------------------------------------------------------------------- + +int main() +{ + try + { + std::vector<sample_type> samples; + std::vector<double> labels; + + // First, get our labeled set of training data + generate_data(samples, labels); + + cout << "samples.size(): "<< samples.size() << endl; + + // The main object in this example program is the one_vs_one_trainer. It is essentially + // a container class for regular binary classifier trainer objects. In particular, it + // uses the any_trainer object to store any kind of trainer object that implements a + // .train(samples,labels) function which returns some kind of learned decision function. + // It uses these binary classifiers to construct a voting multiclass classifier. If + // there are N classes then it trains N*(N-1)/2 binary classifiers, one for each pair of + // labels, which then vote on the label of a sample. + // + // In this example program we will work with a one_vs_one_trainer object which stores any + // kind of trainer that uses our sample_type samples. + typedef one_vs_one_trainer<any_trainer<sample_type> > ovo_trainer; + + + // Finally, make the one_vs_one_trainer. + ovo_trainer trainer; + + + // Next, we will make two different binary classification trainer objects. One + // which uses kernel ridge regression and RBF kernels and another which uses a + // support vector machine and polynomial kernels. The particular details don't matter. + // The point of this part of the example is that you can use any kind of trainer object + // with the one_vs_one_trainer. + typedef polynomial_kernel<sample_type> poly_kernel; + typedef radial_basis_kernel<sample_type> rbf_kernel; + + // make the binary trainers and set some parameters + krr_trainer<rbf_kernel> rbf_trainer; + svm_nu_trainer<poly_kernel> poly_trainer; + poly_trainer.set_kernel(poly_kernel(0.1, 1, 2)); + rbf_trainer.set_kernel(rbf_kernel(0.1)); + + + // Now tell the one_vs_one_trainer that, by default, it should use the rbf_trainer + // to solve the individual binary classification subproblems. + trainer.set_trainer(rbf_trainer); + // We can also get more specific. Here we tell the one_vs_one_trainer to use the + // poly_trainer to solve the class 1 vs class 2 subproblem. All the others will + // still be solved with the rbf_trainer. + trainer.set_trainer(poly_trainer, 1, 2); + + // Now let's do 5-fold cross-validation using the one_vs_one_trainer we just setup. + // As an aside, always shuffle the order of the samples before doing cross validation. + // For a discussion of why this is a good idea see the svm_ex.cpp example. + randomize_samples(samples, labels); + cout << "cross validation: \n" << cross_validate_multiclass_trainer(trainer, samples, labels, 5) << endl; + // The output is shown below. It is the confusion matrix which describes the results. Each row + // corresponds to a class of data and each column to a prediction. Reading from top to bottom, + // the rows correspond to the class labels if the labels have been listed in sorted order. So the + // top row corresponds to class 1, the middle row to class 2, and the bottom row to class 3. The + // columns are organized similarly, with the left most column showing how many samples were predicted + // as members of class 1. + // + // So in the results below we can see that, for the class 1 samples, 60 of them were correctly predicted + // to be members of class 1 and 0 were incorrectly classified. Similarly, the other two classes of data + // are perfectly classified. + /* + cross validation: + 60 0 0 + 0 70 0 + 0 0 80 + */ + + // Next, if you wanted to obtain the decision rule learned by a one_vs_one_trainer you + // would store it into a one_vs_one_decision_function. + one_vs_one_decision_function<ovo_trainer> df = trainer.train(samples, labels); + + cout << "predicted label: "<< df(samples[0]) << ", true label: "<< labels[0] << endl; + cout << "predicted label: "<< df(samples[90]) << ", true label: "<< labels[90] << endl; + // The output is: + /* + predicted label: 2, true label: 2 + predicted label: 1, true label: 1 + */ + + + // If you want to save a one_vs_one_decision_function to disk, you can do + // so. However, you must declare what kind of decision functions it contains. + one_vs_one_decision_function<ovo_trainer, + decision_function<poly_kernel>, // This is the output of the poly_trainer + decision_function<rbf_kernel> // This is the output of the rbf_trainer + > df2, df3; + + + // Put df into df2 and then save df2 to disk. Note that we could have also said + // df2 = trainer.train(samples, labels); But doing it this way avoids retraining. + df2 = df; + serialize("df.dat") << df2; + + // load the function back in from disk and store it in df3. + deserialize("df.dat") >> df3; + + + // Test df3 to see that this worked. + cout << endl; + cout << "predicted label: "<< df3(samples[0]) << ", true label: "<< labels[0] << endl; + cout << "predicted label: "<< df3(samples[90]) << ", true label: "<< labels[90] << endl; + // Test df3 on the samples and labels and print the confusion matrix. + cout << "test deserialized function: \n" << test_multiclass_decision_function(df3, samples, labels) << endl; + + + + + + // Finally, if you want to get the binary classifiers from inside a multiclass decision + // function you can do it by calling get_binary_decision_functions() like so: + one_vs_one_decision_function<ovo_trainer>::binary_function_table functs; + functs = df.get_binary_decision_functions(); + cout << "number of binary decision functions in df: " << functs.size() << endl; + // The functs object is a std::map which maps pairs of labels to binary decision + // functions. So we can access the individual decision functions like so: + decision_function<poly_kernel> df_1_2 = any_cast<decision_function<poly_kernel> >(functs[make_unordered_pair(1,2)]); + decision_function<rbf_kernel> df_1_3 = any_cast<decision_function<rbf_kernel> >(functs[make_unordered_pair(1,3)]); + // df_1_2 contains the binary decision function that votes for class 1 vs. 2. + // Similarly, df_1_3 contains the classifier that votes for 1 vs. 3. + + // Note that the multiclass decision function doesn't know what kind of binary + // decision functions it contains. So we have to use any_cast to explicitly cast + // them back into the concrete type. If you make a mistake and try to any_cast a + // binary decision function into the wrong type of function any_cast will throw a + // bad_any_cast exception. + } + catch (std::exception& e) + { + cout << "exception thrown!" << endl; + cout << e.what() << endl; + } +} + +// ---------------------------------------------------------------------------------------- + +void generate_data ( + std::vector<sample_type>& samples, + std::vector<double>& labels +) +{ + const long num = 50; + + sample_type m; + + dlib::rand rnd; + + + // make some samples near the origin + double radius = 0.5; + for (long i = 0; i < num+10; ++i) + { + double sign = 1; + if (rnd.get_random_double() < 0.5) + sign = -1; + m(0) = 2*radius*rnd.get_random_double()-radius; + m(1) = sign*sqrt(radius*radius - m(0)*m(0)); + + // add this sample to our set of training samples + samples.push_back(m); + labels.push_back(1); + } + + // make some samples in a circle around the origin but far away + radius = 10.0; + for (long i = 0; i < num+20; ++i) + { + double sign = 1; + if (rnd.get_random_double() < 0.5) + sign = -1; + m(0) = 2*radius*rnd.get_random_double()-radius; + m(1) = sign*sqrt(radius*radius - m(0)*m(0)); + + // add this sample to our set of training samples + samples.push_back(m); + labels.push_back(2); + } + + // make some samples in a circle around the point (25,25) + radius = 4.0; + for (long i = 0; i < num+30; ++i) + { + double sign = 1; + if (rnd.get_random_double() < 0.5) + sign = -1; + m(0) = 2*radius*rnd.get_random_double()-radius; + m(1) = sign*sqrt(radius*radius - m(0)*m(0)); + + // translate this point away from the origin + m(0) += 25; + m(1) += 25; + + // add this sample to our set of training samples + samples.push_back(m); + labels.push_back(3); + } +} + +// ---------------------------------------------------------------------------------------- + |