VersatileClassificationTutorial-NN.cpp

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#include <shark/Data/Dataset.h>
#include <shark/Data/Csv.h>
#include <shark/ObjectiveFunctions/Loss/ZeroOneLoss.h>

#include <shark/Models/Trees/KDTree.h>
#include <shark/Models/NearestNeighborClassifier.h>
#include <shark/Algorithms/NearestNeighbors/TreeNearestNeighbors.h>


using namespace shark;

int main()
{
    // Load data, use 70% for training and 30% for testing.
    // The path is hard coded; make sure to invoke the executable
    // from a place where the data file can be found. It is located
    // under [shark]/examples/Supervised/data.
    ClassificationDataset traindata, testdata;
    importCSV(traindata, "data/quickstartData.csv", LAST_COLUMN, ' ');
    testdata = splitAtElement(traindata, 70 * traindata.numberOfElements() / 100);

    unsigned int k = 3;   // number of neighbors
    KDTree<RealVector> tree(traindata.inputs());
    TreeNearestNeighbors<RealVector, unsigned int> algorithm(traindata, &tree);
    NearestNeighborClassifier<RealVector> model(&algorithm, k);

    Data<unsigned int> prediction = model(testdata.inputs());

    ZeroOneLoss<unsigned int> loss;
    double error_rate = loss(testdata.labels(), prediction);

    std::cout << "model: " << model.name() << std::endl
        << "test error rate: " << error_rate << std::endl;
}