CVobserver_example.cpp

#include <shogun/base/init.h>
#include <shogun/features/DenseFeatures.h>
#include <shogun/labels/RegressionLabels.h>
#include <shogun/kernel/LinearKernel.h>
#include <shogun/regression/KernelRidgeRegression.h>
#include <shogun/evaluation/CrossValidation.h>
#include <shogun/evaluation/CrossValidationSplitting.h>
#include <shogun/evaluation/MeanSquaredError.h>
#include <shogun/lib/parameter_observers/ParameterObserverCV.h>

using namespace shogun;

int main(int argc, char ** argv)
{
    init_shogun_with_defaults();

    // data matrix dimensions
    index_t num_vectors=100;
    index_t num_features=1;

    // training label data
    SGVector<float64_t> lab(num_vectors);

    // fill data matrix and labels
    SGMatrix<float64_t> train_dat(num_features, num_vectors);
    SGVector<float64_t>::range_fill_vector(train_dat.matrix, num_vectors);
    for (index_t i=0; i<num_vectors; ++i)
    {
    	// labels are linear plus noise
    	lab.vector[i]=i+CMath::normal_random(0, 1.0);

    }

    // training features
    CDenseFeatures<float64_t>* features = new CDenseFeatures<float64_t>(train_dat);
    SG_REF(features);

    // training labels
    CRegressionLabels* labels=new CRegressionLabels(lab);

    // kernel
    CLinearKernel* kernel=new CLinearKernel();
    kernel->init(features, features);

    // kernel ridge regression
    float64_t tau=0.0001;
    CKernelRidgeRegression* krr=new CKernelRidgeRegression(tau, kernel, labels);

    // evaluation criterion
    CMeanSquaredError* eval_crit= new CMeanSquaredError();

    // train and output
    krr->train(features);

    // splitting strategy
    index_t n_folds=5;
    CCrossValidationSplitting* splitting=
    		new CCrossValidationSplitting(labels, n_folds);

    // cross validation instance, 100 runs, 95% confidence interval
    CCrossValidation* cross=new CCrossValidation(krr, features, labels,
    		splitting, eval_crit);
    cross->set_num_runs(100);

    // Create the parameter observer
    // By setting false, we disable the observer verbosity
    ParameterObserverCV par {false};
    cross->subscribe_to_parameters(&par);

    // We get all the observations catched
    auto obs = par.get_observations();
    for (auto o : obs)
    {
        // For each of the observations folds we print the
        // train indices used.
        for (auto fold : o->get_folds_result())
    	      fold->get_train_indices().display_vector("Train indices ");
    }

    // clean up
    SG_UNREF(cross);
    SG_UNREF(features);

    exit_shogun();

return 0;
}