quickstartTutorial.cpp

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#include <shark/Data/Csv.h>
#include <shark/Algorithms/Trainers/LDA.h>
#include <shark/ObjectiveFunctions/Loss/ZeroOneLoss.h>
using namespace shark;
 
 
#include <iostream>
using namespace std;
 
 
int main(int argc, char **argv){
    //create a Dataset from the file "quickstartData"
    if(argc < 2) {
        cerr << "usage: " << argv[0] << " (filename)" << endl;
        exit(EXIT_FAILURE);
    }
    ClassificationDataset data;
    try {
        importCSV(data, argv[1], LAST_COLUMN, ' ');
    } 
    catch (...) {
        cerr << "unable to read data from file " <<  argv[1] << endl;
        exit(EXIT_FAILURE);
    }
    
    //create a test and training partition of the data
    ClassificationDataset test = splitAtElement(data,static_cast<std::size_t>(0.8*data.numberOfElements()));
    
    //create a classifier for the problem
    LinearClassifier<> classifier;
    //create the lda trainer
    LDA lda;
    //train the classifier using the training portion of the Data
    lda.train(classifier,data);
 
    ZeroOneLoss<> loss;
    Data<unsigned int> predictions = classifier(test.inputs());
    double error = loss(test.labels(),predictions);
    
    //print results
    cout << "RESULTS: " << endl;
    cout << "========\n" << endl;
    cout << "test data size: " << test.numberOfElements() << endl;
    cout << "error rate: " << error << endl;
}