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Diffstat (limited to 'ml/dlib/examples/krr_classification_ex.cpp')
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diff --git a/ml/dlib/examples/krr_classification_ex.cpp b/ml/dlib/examples/krr_classification_ex.cpp new file mode 100644 index 000000000..42648351f --- /dev/null +++ b/ml/dlib/examples/krr_classification_ex.cpp @@ -0,0 +1,205 @@ +// 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 kernel ridge regression + object from the dlib C++ Library. + + This example creates a simple set of data to train on and then shows + you how to use the kernel ridge regression tool to find a good decision + function that can classify examples in our data set. + + + The data used in this example will be 2 dimensional data and will + come from a distribution where points with a distance less than 13 + from the origin are labeled +1 and all other points are labeled + as -1. All together, the dataset will contain 10201 sample points. + +*/ + + +#include <iostream> +#include <dlib/svm.h> + +using namespace std; +using namespace dlib; + + +int main() +{ + // 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; + + + // Now we make objects to contain our samples and their respective labels. + std::vector<sample_type> samples; + std::vector<double> labels; + + // Now let's put some data into our samples and labels objects. We do this + // by looping over a bunch of points and labeling them according to their + // distance from the origin. + for (double r = -20; r <= 20; r += 0.4) + { + for (double c = -20; c <= 20; c += 0.4) + { + sample_type samp; + samp(0) = r; + samp(1) = c; + samples.push_back(samp); + + // if this point is less than 13 from the origin + if (sqrt((double)r*r + c*c) <= 13) + labels.push_back(+1); + else + labels.push_back(-1); + + } + } + + cout << "samples generated: " << samples.size() << endl; + cout << " number of +1 samples: " << sum(mat(labels) > 0) << endl; + cout << " number of -1 samples: " << sum(mat(labels) < 0) << endl; + + // Here we normalize all the samples by subtracting their mean and dividing by their standard deviation. + // This is generally a good idea since it often heads off numerical stability problems and also + // prevents one large feature from smothering others. Doing this doesn't matter much in this example + // so I'm just doing this here so you can see an easy way to accomplish this with + // the library. + vector_normalizer<sample_type> normalizer; + // let the normalizer learn the mean and standard deviation of the samples + normalizer.train(samples); + // now normalize each sample + for (unsigned long i = 0; i < samples.size(); ++i) + samples[i] = normalizer(samples[i]); + + + // here we make an instance of the krr_trainer object that uses our kernel type. + krr_trainer<kernel_type> trainer; + + // The krr_trainer has the ability to perform leave-one-out cross-validation. + // It does this to automatically determine the regularization parameter. Since + // we are performing classification instead of regression we should be sure to + // call use_classification_loss_for_loo_cv(). This function tells it to measure + // errors in terms of the number of classification mistakes instead of mean squared + // error between decision function output values and labels. + trainer.use_classification_loss_for_loo_cv(); + + + // Now we loop over some different gamma values to see how good they are. + cout << "\ndoing leave-one-out cross-validation" << endl; + for (double gamma = 0.000001; gamma <= 1; gamma *= 5) + { + // tell the trainer the parameters we want to use + trainer.set_kernel(kernel_type(gamma)); + + // loo_values will contain the LOO predictions for each sample. In the case + // of perfect prediction it will end up being a copy of labels. + std::vector<double> loo_values; + trainer.train(samples, labels, loo_values); + + // Print gamma and the fraction of samples correctly classified during LOO cross-validation. + const double classification_accuracy = mean_sign_agreement(labels, loo_values); + cout << "gamma: " << gamma << " LOO accuracy: " << classification_accuracy << endl; + } + + + // From looking at the output of the above loop it turns out that a good value for + // gamma for this problem is 0.000625. So that is what we will use. + trainer.set_kernel(kernel_type(0.000625)); + typedef decision_function<kernel_type> dec_funct_type; + typedef normalized_function<dec_funct_type> funct_type; + + + // Here we are making an instance of the normalized_function object. This object provides a convenient + // way to store the vector normalization information along with the decision function we are + // going to learn. + funct_type learned_function; + learned_function.normalizer = normalizer; // save normalization information + learned_function.function = trainer.train(samples, labels); // perform the actual training and save the results + + // print out the number of basis vectors in the resulting decision function + cout << "\nnumber of basis vectors in our learned_function is " + << learned_function.function.basis_vectors.size() << endl; + + // Now let's try this decision_function on some samples we haven't seen before. + // The decision function will return values >= 0 for samples it predicts + // are in the +1 class and numbers < 0 for samples it predicts to be in the -1 class. + sample_type sample; + + sample(0) = 3.123; + sample(1) = 2; + cout << "This is a +1 class example, the classifier output is " << learned_function(sample) << endl; + + sample(0) = 3.123; + sample(1) = 9.3545; + cout << "This is a +1 class example, the classifier output is " << learned_function(sample) << endl; + + sample(0) = 13.123; + sample(1) = 9.3545; + cout << "This is a -1 class example, the classifier output is " << learned_function(sample) << endl; + + sample(0) = 13.123; + sample(1) = 0; + cout << "This is a -1 class example, the classifier output is " << learned_function(sample) << endl; + + + // We can also train a decision function that reports a well conditioned probability + // instead of just a number > 0 for the +1 class and < 0 for the -1 class. An example + // of doing that follows: + typedef probabilistic_decision_function<kernel_type> probabilistic_funct_type; + typedef normalized_function<probabilistic_funct_type> pfunct_type; + + // The train_probabilistic_decision_function() is going to perform 3-fold cross-validation. + // So it is important that the +1 and -1 samples be distributed uniformly across all the folds. + // calling randomize_samples() will make sure that is the case. + randomize_samples(samples, labels); + + pfunct_type learned_pfunct; + learned_pfunct.normalizer = normalizer; + learned_pfunct.function = train_probabilistic_decision_function(trainer, samples, labels, 3); + // Now we have a function that returns the probability that a given sample is of the +1 class. + + // print out the number of basis vectors in the resulting decision function. + // (it should be the same as in the one above) + cout << "\nnumber of basis vectors in our learned_pfunct is " + << learned_pfunct.function.decision_funct.basis_vectors.size() << endl; + + sample(0) = 3.123; + sample(1) = 2; + cout << "This +1 class example should have high probability. Its probability is: " + << learned_pfunct(sample) << endl; + + sample(0) = 3.123; + sample(1) = 9.3545; + cout << "This +1 class example should have high probability. Its probability is: " + << learned_pfunct(sample) << endl; + + sample(0) = 13.123; + sample(1) = 9.3545; + cout << "This -1 class example should have low probability. Its probability is: " + << learned_pfunct(sample) << endl; + + sample(0) = 13.123; + sample(1) = 0; + cout << "This -1 class example should have low probability. Its probability is: " + << learned_pfunct(sample) << endl; + + + + // Another thing that is worth knowing is that just about everything in dlib is serializable. + // So for example, you can save the learned_pfunct object to disk and recall it later like so: + serialize("saved_function.dat") << learned_pfunct; + + // Now let's open that file back up and load the function object it contains. + deserialize("saved_function.dat") >> learned_pfunct; + +} + |