src/mlpack/tests/adaboost_test.cpp
/**
 * This test case runs the AdaBoost.mh algorithm on non-linearly separable
 * dataset.  It checks whether the hamming loss breaches the upperbound, which
 * is provided by ztAccumulator.
 */
This test case runs the AdaBoost.mh algorithm on non-linearly separable
dataset.  It checks whether the hamming loss breaches the upperbound, which
is provided by ztAccumulator.
This test case runs the AdaBoost . mh algorithm on non - linearly separable dataset . It checks whether the hamming loss breaches the upperbound which is provided by ztAccumulator .
BOOST_AUTO_TEST_CASE(HammingLossBoundNonLinearSepData)
{
  arma::mat inputData;
  if (!data::Load("train_nonlinsep.txt", inputData))
    BOOST_FAIL("Cannot load test dataset train_nonlinsep.txt!");
  arma::Mat<size_t> labels;
  if (!data::Load("train_labels_nonlinsep.txt", labels))
    BOOST_FAIL("Cannot load labels for train_labels_nonlinsep.txt");
  const size_t numClasses = max(labels.row(0)) + 1;
  size_t perceptronIter = 800;
  Perceptron<> p(inputData, labels.row(0), numClasses, perceptronIter);
  size_t iterations = 50;
  double tolerance = 1e-10;
  AdaBoost<> a(inputData, labels.row(0), numClasses, p, iterations, tolerance);
  arma::Row<size_t> predictedLabels;
  a.Classify(inputData, predictedLabels);
  size_t countError = 0;
  for (size_t i = 0; i < labels.n_cols; i++)
    if (labels(i) != predictedLabels(i))
      countError++;
  double hammingLoss = (double) countError / labels.n_cols;
  BOOST_REQUIRE_LE(hammingLoss, a.ZtProduct());
}
BOOST_AUTO_TEST_CASE ( HammingLossBoundNonLinearSepData ) { arma :: mat inputData ; if ( ! data :: Load ( " " , inputData ) ) BOOST_FAIL ( " " ) ; arma :: Mat < size_t > labels ; if ( ! data :: Load ( " " , labels ) ) BOOST_FAIL ( " " ) ; const size_t numClasses = max ( labels . row ( 0 ) ) + 1 ; size_t perceptronIter = 800 ; Perceptron < > p ( inputData , labels . row ( 0 ) , numClasses , perceptronIter ) ; size_t iterations = 50 ; double tolerance = 1e-10 ; AdaBoost < > a ( inputData , labels . row ( 0 ) , numClasses , p , iterations , tolerance ) ; arma :: Row < size_t > predictedLabels ; a . Classify ( inputData , predictedLabels ) ; size_t countError = 0 ; for ( size_t i = 0 ; i < labels . n_cols ; i ++ ) if ( labels ( i ) != predictedLabels ( i ) ) countError ++ ; double hammingLoss = ( double ) countError / labels . n_cols ; BOOST_REQUIRE_LE ( hammingLoss , a . ZtProduct ( ) ) ; }
This test case runs the AdaBoost.mh algorithm on non-linearly separable
 dataset.
This test case runs the AdaBoost . mh algorithm on non - linearly separable dataset .
// Define your own weak learner, perceptron in this case. // Run the perceptron for perceptronIter iterations. // Define parameters for AdaBoost.