/**
 * @file tests/main_tests/mean_shift_test.cpp
 * @author Tan Jun An
 *
 * Test mlpackMain() of mean_shift_main.cpp.
 *
 * mlpack is free software; you may redistribute it and/or modify it under the
 * terms of the 3-clause BSD license.  You should have received a copy of the
 * 3-clause BSD license along with mlpack.  If not, see
 * http://www.opensource.org/licenses/BSD-3-Clause for more information.
 */
#include <string>

#define BINDING_TYPE BINDING_TYPE_TEST
static const std::string testName = "MeanShift";

#include <mlpack/core.hpp>
#include <mlpack/core/util/mlpack_main.hpp>
#include <mlpack/methods/mean_shift/mean_shift_main.cpp>
#include "test_helper.hpp"

#include <boost/test/unit_test.hpp>
#include "../test_tools.hpp"

using namespace mlpack;

struct MeanShiftTestFixture
{
 public:
  MeanShiftTestFixture()
  {
    // Cache in the options for this program.
    IO::RestoreSettings(testName);
  }

  ~MeanShiftTestFixture()
  {
    // Clear the settings.
    bindings::tests::CleanMemory();
    IO::ClearSettings();
  }
};

static void ResetSettings()
{
  bindings::tests::CleanMemory();
  IO::ClearSettings();
  IO::RestoreSettings(testName);
}

BOOST_FIXTURE_TEST_SUITE(MeanShiftMainTest, MeanShiftTestFixture);

/**
 * Ensure that the output has 1 extra row for the labels and
 * check the number of points for output remain the same.
 */
BOOST_AUTO_TEST_CASE(MeanShiftOutputDimensionTest)
{
  arma::mat x;
  x.randu(3, 100); // 100 points in 3 dimension

  // Input random data points.
  SetInputParam("input", std::move(x));

  mlpackMain();

  // Now check that the output has 1 extra row for labels.
  BOOST_REQUIRE_EQUAL(IO::GetParam<arma::mat>("output").n_rows, 3 + 1);
  // Check number of output points are the same.
  BOOST_REQUIRE_EQUAL(IO::GetParam<arma::mat>("output").n_cols, 100);
}

/**
 * Ensure that if we ask for labels_only, output has 1 row and
 * same number of columns for each point's label.
 */
BOOST_AUTO_TEST_CASE(MeanShiftLabelOnlyOutputDimensionTest)
{
  arma::mat x;
  x.randu(3, 100); // 100 points in 3 dimension

  // Input random data points.
  SetInputParam("input", std::move(x));
  SetInputParam("labels_only", true);

  mlpackMain();

  // Check that there is only 1 row containing all the labels.
  BOOST_REQUIRE_EQUAL(IO::GetParam<arma::mat>("output").n_rows, 1);
  // Check number of output points are the same.
  BOOST_REQUIRE_EQUAL(IO::GetParam<arma::mat>("output").n_cols, 100);
}

/**
 * Ensure that the updated input file has 1 extra row for the labels 
 * and check the number of points remain the same if the --in_place
 * flag is set.
 */
BOOST_AUTO_TEST_CASE(MeanShiftInPlaceTest)
{
  arma::mat x;
  if (!data::Load("iris_test.csv", x))
    BOOST_FAIL("Cannot load test dataset iris_test.csv!");

  // Get initial number of rows and columns in file.
  int numRows = x.n_rows;
  int numCols = x.n_cols;

  // Input random data points.
  SetInputParam("input", std::move(x));
  SetInputParam("in_place", true);

  mlpackMain();

  // Now check that the output has 1 extra row for labels.
  BOOST_REQUIRE_EQUAL(IO::GetParam<arma::mat>("output").n_rows, numRows + 1);
  // Check number of output points are the same.
  BOOST_REQUIRE_EQUAL(IO::GetParam<arma::mat>("output").n_cols, numCols);
}

/**
 * Ensure that force_convergence is used by testing that the
 * force_convergence flag makes a difference in the program.
 */
BOOST_AUTO_TEST_CASE(MeanShiftForceConvergenceTest)
{
  arma::mat x;
  if (!data::Load("iris_test.csv", x))
    BOOST_FAIL("Cannot load test dataset iris_test.csv!");

  // Input random data points.
  SetInputParam("input", x);
  // Set a very small max_iterations.
  SetInputParam("max_iterations", (int) 1);

  mlpackMain();

  const int numCentroids1 = IO::GetParam<arma::mat>("centroid").n_cols;

  ResetSettings();

  // Input same random data points.
  SetInputParam("input", std::move(x));
  // Set the same small max_iterations.
  SetInputParam("max_iterations", (int) 1);
  // Set the force_convergence flag on.
  SetInputParam("force_convergence", true);

  mlpackMain();

  const int numCentroids2 = IO::GetParam<arma::mat>("centroid").n_cols;
  // Resulting number of centroids should be different.
  BOOST_REQUIRE_NE(numCentroids1, numCentroids2);
}

/**
 * Ensure that radius is used by testing that the radius
 * makes a difference in the program.
 */
BOOST_AUTO_TEST_CASE(MeanShiftRadiusTest)
{
  arma::mat x;
  if (!data::Load("iris_test.csv", x))
    BOOST_FAIL("Cannot load test dataset iris_test.csv!");

  // Input random data points.
  SetInputParam("input", x);
  // Set a small radius.
  SetInputParam("radius", (double) 0.1);

  mlpackMain();

  const int numCentroids1 = IO::GetParam<arma::mat>("centroid").n_cols;

  ResetSettings();

  // Input same random data points.
  SetInputParam("input", std::move(x));
  // Set a larger radius.
  SetInputParam("radius", (double) 1.0);

  mlpackMain();

  const int numCentroids2 = IO::GetParam<arma::mat>("centroid").n_cols;
  // Resulting number of centroids should be different.
  BOOST_REQUIRE_NE(numCentroids1, numCentroids2);
}

/**
 * Ensure that max_iterations is used by testing that the
 * max_iteration makes a difference in the program.
 */
BOOST_AUTO_TEST_CASE(MeanShiftMaxIterationsTest)
{
  arma::mat x;
  if (!data::Load("iris_test.csv", x))
    BOOST_FAIL("Cannot load test dataset iris_test.csv!");

  // Input random data points.
  SetInputParam("input", x);
  // Set a small max_iterations.
  SetInputParam("max_iterations", (int) 4);

  mlpackMain();

  const int numCentroids1 = IO::GetParam<arma::mat>("centroid").n_cols;

  ResetSettings();

  // Input same random data points.
  SetInputParam("input", std::move(x));
  // Set a larger max_iterations.
  SetInputParam("max_iterations", (int) 20);

  mlpackMain();

  const int numCentroids2 = IO::GetParam<arma::mat>("centroid").n_cols;
  // Resulting number of centroids should be different.
  BOOST_REQUIRE_NE(numCentroids1, numCentroids2);
}

/**
 * Ensure that we can't specify an invalid max number of iterations.
 */
BOOST_AUTO_TEST_CASE(MeanShiftInvalidMaxIterationsTest)
{
  arma::mat x;
  x.randu(3, 100); // 100 points in 3 dimension

  // Input random data points.
  SetInputParam("input", std::move(x));
  // Input invalid max number of iterations.
  SetInputParam("max_iterations", (int) -1);

  Log::Fatal.ignoreInput = true;
  BOOST_REQUIRE_THROW(mlpackMain(), std::runtime_error);
  Log::Fatal.ignoreInput = false;
}

BOOST_AUTO_TEST_SUITE_END();