text: /* file: bf_knn_dense_batch.cpp */
  /*******************************************************************************
  * Copyright 2020 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 in the batch processing mode.
  !******************************************************************************/
  
  /**
   * <a name="DAAL-EXAMPLE-CPP-bf_KNN_DENSE_BATCH"></a>
   * \example bf_knn_dense_batch.cpp
   */
  
  #include "daal.h"
  #include "service.h"
  #include <cstdio>
  #include <cstdlib>
  
  using namespace std;
  using namespace daal;
  using namespace daal::algorithms;
  using namespace daal::data_management;
  
  /* Input data set parameters */
  string trainDatasetFileName = "../data/batch/k_nearest_neighbors_train.csv";
  string testDatasetFileName  = "../data/batch/k_nearest_neighbors_test.csv";
  
  size_t nFeatures = 5;
  size_t nClasses = 5;
  
  bf_knn_classification::training::ResultPtr trainingResult;
  bf_knn_classification::prediction::ResultPtr predictionResult;
  NumericTablePtr testGroundTruth;
  NumericTablePtr testData;
  
  void trainModel();
  void testModel();
  void printResults();
  
  int main(int argc, char *argv[]) {
      checkArguments(argc, argv, 2, &trainDatasetFileName, &testDatasetFileName);
  
      trainModel();
      testModel();
      printResults();
  
      return 0;
  }
  
  void trainModel() {
      /* 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(
          new HomogenNumericTable<>(nFeatures, 0, NumericTable::doNotAllocate));
      NumericTablePtr trainGroundTruth(
          new HomogenNumericTable<>(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 KD-tree based kNN model */
      bf_knn_classification::training::Batch<> algorithm;
  
      /* Pass the training data set and dependent values to the algorithm */
      algorithm.input.set(classifier::training::data, trainData);
      algorithm.input.set(classifier::training::labels, trainGroundTruth);
      algorithm.parameter().nClasses = nClasses;
  
      /* Train the KD-tree based kNN model */
      algorithm.compute();
  
      /* Retrieve the results of the training algorithm  */
      trainingResult = algorithm.getResult();
  }
  
  void testModel() {
      /* 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 */
      testData = NumericTablePtr(
          new HomogenNumericTable<>(nFeatures, 0, NumericTable::doNotAllocate));
      testGroundTruth = NumericTablePtr(
          new HomogenNumericTable<>(1, 0, NumericTable::doNotAllocate));
      NumericTablePtr mergedData(new MergedNumericTable(testData, testGroundTruth));
  
      /* Retrieve the data from input file */
      testDataSource.loadDataBlock(mergedData.get());
  
      /* Create algorithm objects for brute force based kNN prediction with the default
       * method */
      bf_knn_classification::prediction::Batch<> algorithm;
  
      /* Pass the testing data set and trained model to the algorithm */
      algorithm.input.set(classifier::prediction::data, testData);
      algorithm.input.set(classifier::prediction::model,
                          trainingResult->get(classifier::training::model));
      algorithm.parameter().nClasses = nClasses;
      algorithm.parameter().resultsToCompute = bf_knn_classification::computeDistances | bf_knn_classification::computeIndicesOfNeighbors;
  
      /* Compute prediction results */
      algorithm.compute();
  
      /* Retrieve algorithm results */
      predictionResult = algorithm.getResult();
  }
  
  void printResults() {
      printNumericTables<int, int>(
          testGroundTruth,
          predictionResult->get(bf_knn_classification::prediction::prediction),
          "Ground truth", "Classification results",
          "Brute force kNN classification results (first 20 observations):", 20);
      printNumericTables<int, float>(
          predictionResult->get(bf_knn_classification::prediction::indices),
          predictionResult->get(bf_knn_classification::prediction::distances),
          "Indices", "Distances",
          "Brute force kNN classification results (first 20 observations):", 20);
  }
  