text: /* file: kernel_func_lin_dense_batch.cpp */
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  /*
  !  Content:
  !    C++ example of computing a linear kernel function
  !
  !******************************************************************************/
  
  /**
   * <a name="DAAL-EXAMPLE-CPP-KERNEL_FUNCTION_LINEAR_DENSE_BATCH"></a>
   * \example kernel_func_lin_dense_batch.cpp
   */
  
  #include "daal.h"
  #include "service.h"
  
  using namespace std;
  using namespace daal;
  using namespace daal::algorithms;
  
  /* Input data set parameters */
  string leftDatasetFileName  = "../data/batch/kernel_function.csv";
  string rightDatasetFileName = "../data/batch/kernel_function.csv";
  
  /* Kernel algorithm parameters */
  const double k = 1.0;            /* Linear kernel coefficient in the k(X,Y) + b model */
  const double b = 0.0;            /* Linear kernel coefficient in the k(X,Y) + b model */
  
  int main(int argc, char *argv[])
  {
      checkArguments(argc, argv, 1, &leftDatasetFileName);
      checkArguments(argc, argv, 1, &rightDatasetFileName);
  
      /* Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file */
      FileDataSource<CSVFeatureManager> leftDataSource(leftDatasetFileName, DataSource::doAllocateNumericTable,
                                                       DataSource::doDictionaryFromContext);
  
      FileDataSource<CSVFeatureManager> rightDataSource(rightDatasetFileName, DataSource::doAllocateNumericTable,
                                                        DataSource::doDictionaryFromContext);
  
      /* Retrieve the data from the input file */
      leftDataSource.loadDataBlock();
      rightDataSource.loadDataBlock();
  
      /* Create algorithm objects for the kernel algorithm using the default method */
      kernel_function::linear::Batch<> algorithm;
  
      /* Set the kernel algorithm parameter */
      algorithm.parameter.k = k;
      algorithm.parameter.b = b;
      algorithm.parameter.computationMode = kernel_function::matrixMatrix;
  
      /* Set an input data table for the algorithm */
      algorithm.input.set(kernel_function::X, leftDataSource.getNumericTable());
      algorithm.input.set(kernel_function::Y, rightDataSource.getNumericTable());
  
      /* Compute the linear kernel function */
      algorithm.compute();
  
      /* Get the computed results */
      kernel_function::ResultPtr result = algorithm.getResult();
  
      /* Print the results */
      printNumericTable(result->get(kernel_function::values), "Values");
  
      return 0;
  }
  