src/mlpack/tests/adaboost_test.cpp
BOOST_AUTO_TEST_CASE(ClassifyTest_VERTEBRALCOL)
{
  arma::mat inputData;
  if (!data::Load("vc2.csv", inputData))
    BOOST_FAIL("Cannot load test dataset vc2.csv!");

  arma::Mat<size_t> labels;
  if (!data::Load("vc2_labels.txt", labels))
    BOOST_FAIL("Cannot load labels for vc2_labels.txt");

  // Define your own weak learner, perceptron in this case.
  // Run the perceptron for perceptronIter iterations.
  size_t perceptronIter = 1000;

  arma::mat testData;

  if (!data::Load("vc2_test.csv", testData))
    BOOST_FAIL("Cannot load test dataset vc2_test.csv!");

  arma::Mat<size_t> trueTestLabels;

  if (!data::Load("vc2_test_labels.txt", trueTestLabels))
    BOOST_FAIL("Cannot load labels for vc2_test_labels.txt");

  const size_t numClasses = max(labels.row(0)) + 1;

  Row<size_t> perceptronPrediction(labels.n_cols);
  Perceptron<> p(inputData, labels.row(0), numClasses, perceptronIter);
  p.Classify(inputData, perceptronPrediction);

  // Define parameters for AdaBoost.
  size_t iterations = 100;
  double tolerance = 1e-10;
  AdaBoost<> a(inputData, labels.row(0), numClasses, p, iterations, tolerance);

  arma::Row<size_t> predictedLabels(testData.n_cols);
  a.Classify(testData, predictedLabels);

  size_t localError = 0;
  for (size_t i = 0; i < trueTestLabels.n_cols; i++)
    if (trueTestLabels(i) != predictedLabels(i))
      localError++;

  double lError = (double) localError / trueTestLabels.n_cols;
  BOOST_REQUIRE_LE(lError, 0.30);
}
/**
 * This test case runs the AdaBoost.mh algorithm on the UCI Vertebral Column
 * dataset.  It tests the Classify function and checks for a satisfactory error
 * rate.
 */
This test case runs the AdaBoost.mh algorithm on the UCI Vertebral Column
dataset.  It tests the Classify function and checks for a satisfactory error
rate.
This test case runs the AdaBoost . mh algorithm on the UCI Vertebral Column dataset . It tests the Classify function and checks for a satisfactory error rate .
BOOST_AUTO_TEST_CASE(ClassifyTest_VERTEBRALCOL)
{
  arma::mat inputData;
  if (!data::Load("vc2.csv", inputData))
    BOOST_FAIL("Cannot load test dataset vc2.csv!");
  arma::Mat<size_t> labels;
  if (!data::Load("vc2_labels.txt", labels))
    BOOST_FAIL("Cannot load labels for vc2_labels.txt");
  size_t perceptronIter = 1000;
  arma::mat testData;
  if (!data::Load("vc2_test.csv", testData))
    BOOST_FAIL("Cannot load test dataset vc2_test.csv!");
  arma::Mat<size_t> trueTestLabels;
  if (!data::Load("vc2_test_labels.txt", trueTestLabels))
    BOOST_FAIL("Cannot load labels for vc2_test_labels.txt");
  const size_t numClasses = max(labels.row(0)) + 1;
  Row<size_t> perceptronPrediction(labels.n_cols);
  Perceptron<> p(inputData, labels.row(0), numClasses, perceptronIter);
  p.Classify(inputData, perceptronPrediction);
  size_t iterations = 100;
  double tolerance = 1e-10;
  AdaBoost<> a(inputData, labels.row(0), numClasses, p, iterations, tolerance);
  arma::Row<size_t> predictedLabels(testData.n_cols);
  a.Classify(testData, predictedLabels);
  size_t localError = 0;
  for (size_t i = 0; i < trueTestLabels.n_cols; i++)
    if (trueTestLabels(i) != predictedLabels(i))
      localError++;
  double lError = (double) localError / trueTestLabels.n_cols;
  BOOST_REQUIRE_LE(lError, 0.30);
}
BOOST_AUTO_TEST_CASE ( ClassifyTest_VERTEBRALCOL ) { arma :: mat inputData ; if ( ! data :: Load ( " " , inputData ) ) BOOST_FAIL ( " " ) ; arma :: Mat < size_t > labels ; if ( ! data :: Load ( " " , labels ) ) BOOST_FAIL ( " " ) ; size_t perceptronIter = 1000 ; arma :: mat testData ; if ( ! data :: Load ( " " , testData ) ) BOOST_FAIL ( " " ) ; arma :: Mat < size_t > trueTestLabels ; if ( ! data :: Load ( " " , trueTestLabels ) ) BOOST_FAIL ( " " ) ; const size_t numClasses = max ( labels . row ( 0 ) ) + 1 ; Row < size_t > perceptronPrediction ( labels . n_cols ) ; Perceptron < > p ( inputData , labels . row ( 0 ) , numClasses , perceptronIter ) ; p . Classify ( inputData , perceptronPrediction ) ; size_t iterations = 100 ; double tolerance = 1e-10 ; AdaBoost < > a ( inputData , labels . row ( 0 ) , numClasses , p , iterations , tolerance ) ; arma :: Row < size_t > predictedLabels ( testData . n_cols ) ; a . Classify ( testData , predictedLabels ) ; size_t localError = 0 ; for ( size_t i = 0 ; i < trueTestLabels . n_cols ; i ++ ) if ( trueTestLabels ( i ) != predictedLabels ( i ) ) localError ++ ; double lError = ( double ) localError / trueTestLabels . n_cols ; BOOST_REQUIRE_LE ( lError , 0.30 ) ; }
This test case runs the AdaBoost.mh algorithm on the UCI Vertebral Column
 dataset.
This test case runs the AdaBoost . mh algorithm on the UCI Vertebral Column dataset .
// Define your own weak learner, perceptron in this case. // Run the perceptron for perceptronIter iterations. // Define parameters for AdaBoost.