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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 deleted file mode 100644 index 42648351f..000000000 --- a/ml/dlib/examples/krr_classification_ex.cpp +++ /dev/null @@ -1,205 +0,0 @@ -// 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; - -} - |