text: /* file: svm_two_class_dense_batch.cpp */
  /*******************************************************************************
  * Copyright 2014-2017 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 two-class support vector machine (SVM) classification
  !
  !******************************************************************************/
  
  /**
   * <a name="DAAL-EXAMPLE-CPP-SVM_TWO_CLASS_DENSE_BATCH"></a>
   * \example svm_two_class_dense_batch.cpp
   */
  
  #include "daal.h"
  #include "service.h"
  
  using namespace std;
  using namespace daal;
  using namespace daal::algorithms;
  
  /* Input data set parameters */
  string trainDatasetFileName     = "../data/batch/svm_two_class_train_dense.csv";
  
  string testDatasetFileName      = "../data/batch/svm_two_class_test_dense.csv";
  
  const size_t nFeatures          = 20;
  
  /* Parameters for the SVM kernel function */
  services::SharedPtr<kernel_function::KernelIface> kernel(new kernel_function::linear::Batch<>());
  
  /* Model object for the SVM algorithm */
  svm::training::ResultPtr trainingResult;
  classifier::prediction::ResultPtr predictionResult;
  NumericTablePtr testGroundTruth;
  
  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 SVM model */
      svm::training::Batch<> algorithm;
  
      algorithm.parameter.kernel = kernel;
      algorithm.parameter.cacheSize = 40000000;
  
      /* Pass a training data set and dependent values to the algorithm */
      algorithm.input.set(classifier::training::data, trainData);
      algorithm.input.set(classifier::training::labels, trainGroundTruth);
  
      /* Build the SVM model */
      algorithm.compute();
  
      /* Retrieve the algorithm results */
      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 */
      NumericTablePtr testData(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 an algorithm object to predict SVM values */
      svm::prediction::Batch<> algorithm;
  
      algorithm.parameter.kernel = kernel;
  
      /* Pass a testing data set and the trained model to the algorithm */
      algorithm.input.set(classifier::prediction::data, testData);
      algorithm.input.set(classifier::prediction::model,
                          trainingResult->get(classifier::training::model));
  
      /* Predict SVM values */
      algorithm.compute();
  
      /* Retrieve the algorithm results */
      predictionResult = algorithm.getResult();
  }
  
  void printResults()
  {
      printNumericTables<int, float>(testGroundTruth,
                                      predictionResult->get(classifier::prediction::prediction),
                                      "Ground truth\t", "Classification results",
                                      "SVN classification results (first 20 observations):", 20);
  }
  