text: /* file: bf_knn_dense_batch.cpp */
  /*******************************************************************************
  * Copyright 2014-2019 Intel Corporation
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
  * You may obtain a copy of the License at
  *
  *     http://www.apache.org/licenses/LICENSE-2.0
  *
  * Unless required by applicable law or agreed to in writing, software
  * distributed under the License is distributed on an "AS IS" BASIS,
  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  * See the License for the specific language governing permissions and
  * limitations under the License.
  *******************************************************************************/
  
  /*
  !  Content:
  !    C++ example of k-Nearest Neighbor for GPU in the batch processing mode.
  !******************************************************************************/
  
  /**
   * <a name="DAAL-EXAMPLE-CPP-BF_KNN_DENSE_BATCH"></a>
   * \example bf_knn_dense_batch.cpp
   */
  
  #include "daal_sycl.h"
  #include "service.h"
  #include "service_sycl.h"
  #include <cstdio>
  
  using namespace std;
  using namespace daal;
  using namespace daal::algorithms;
  
  /* Input data set parameters */
  const string trainDatasetFileName            = "../data/batch/k_nearest_neighbors_train.csv";
  const string testDatasetFileName             = "../data/batch/k_nearest_neighbors_test.csv";
  
  const size_t nFeatures = 5;
  const size_t nClasses  = 5;
  
  void trainModel(bf_knn_classification::training::ResultPtr& trainingResult);
  void testModel(bf_knn_classification::training::ResultPtr& trainingResult,
                  classifier::prediction::ResultPtr& predictionResult,
                  NumericTablePtr& testGroundTruth);
  void printResults(NumericTablePtr& testGroundTruth, classifier::prediction::ResultPtr& predictionResult);
  
  int main(int argc, char *argv[])
  {
      checkArguments(argc, argv, 2, &trainDatasetFileName, &testDatasetFileName);
  
      for (const auto& deviceSelector : getListOfDevices())
      {
          const auto& nameDevice = deviceSelector.first;
          const auto& device = deviceSelector.second;
          if(!device.is_gpu())
              continue;
          cl::sycl::queue queue(device);
          std::cout << "Running on " << nameDevice << "\n\n";
  
          daal::services::SyclExecutionContext ctx(queue);
          services::Environment::getInstance()->setDefaultExecutionContext(ctx);
  
          bf_knn_classification::training::ResultPtr trainingResult;
          classifier::prediction::ResultPtr predictionResult;
          NumericTablePtr testGroundTruth;
  
          trainModel(trainingResult);
          testModel(trainingResult, predictionResult, testGroundTruth);
          printResults(testGroundTruth, predictionResult);
      }
      return 0;
  }
  
  void trainModel(bf_knn_classification::training::ResultPtr& trainingResult)
  {
      /* Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file */
      FileDataSource<CSVFeatureManager> trainDataSource(trainDatasetFileName,
                                                        DataSource::notAllocateNumericTable,
                                                        DataSource::doDictionaryFromContext);
  
      /* Create Numeric Tables for training data and labels */
      NumericTablePtr trainData = SyclHomogenNumericTable<>::create(nFeatures, 0, NumericTable::notAllocate);
      NumericTablePtr trainGroundTruth = SyclHomogenNumericTable<>::create(1, 0, NumericTable::doNotAllocate);
      NumericTablePtr mergedData(new MergedNumericTable(trainData, trainGroundTruth));
      /* Retrieve the data from the input file */
      trainDataSource.loadDataBlock(mergedData.get());
  
      /* Create an algorithm object to train the BF kNN model */
      bf_knn_classification::training::Batch<> algorithm;
  
      /* Pass the training data set and dependent values to the algorithm */
      algorithm.getInput()->set(classifier::training::data, trainData);
      algorithm.getInput()->set(classifier::training::labels, trainGroundTruth);
      algorithm.parameter().nClasses = nClasses;
      /* Train the BF kNN model */
      algorithm.compute();
      /* Retrieve the results of the training algorithm  */
      trainingResult = algorithm.getResult();
  }
  
  void testModel(bf_knn_classification::training::ResultPtr& trainingResult,
                  classifier::prediction::ResultPtr& predictionResult,
                  NumericTablePtr& testGroundTruth)
  {
      /* Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file */
      FileDataSource<CSVFeatureManager> testDataSource(testDatasetFileName,
                                                       DataSource::notAllocateNumericTable,
                                                       DataSource::doDictionaryFromContext);
  
      /* Create Numeric Tables for testing data and labels */
      NumericTablePtr testData = SyclHomogenNumericTable<>::create(nFeatures, 0, NumericTable::doNotAllocate);
      testGroundTruth = SyclHomogenNumericTable<>::create(1, 0, NumericTable::doNotAllocate);
      NumericTablePtr mergedData(new MergedNumericTable(testData, testGroundTruth));
  
      /* Retrieve the data from input file */
      testDataSource.loadDataBlock(mergedData.get());
  
      /* Create algorithm objects for BF kNN prediction with the default method */
      bf_knn_classification::prediction::Batch<> algorithm;
  
      /* Pass the testing data set and trained model to the algorithm */
      algorithm.getInput()->set(classifier::prediction::data,  testData);
      algorithm.getInput()->set(classifier::prediction::model, trainingResult->get(classifier::training::model));
  
      /* Compute prediction results */
      algorithm.compute();
  
      /* Retrieve algorithm results */
      predictionResult = algorithm.getResult();
  }
  
  void printResults(NumericTablePtr& testGroundTruth, classifier::prediction::ResultPtr& predictionResult)
  {
      printNumericTables<int, int>(testGroundTruth,
                                   predictionResult->get(classifier::prediction::prediction),
                                   "Ground truth", "Classification results",
                                   "BF kNN classification results (first 20 observations):", 20);
  }
  