/* file: mn_naive_bayes_csr_distr.cpp */
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!  Content:
!    C++ example of Naive Bayes classification in the distributed processing
!    mode.
!    The program trains the Naive Bayes model on a supplied training data set in
!    compressed sparse rows (CSR)__format and then performs classification of
!    previously unseen data.

#include "daal.h"
#include "service.h"

using namespace std;
using namespace daal;
using namespace daal::algorithms;
using namespace daal::algorithms::multinomial_naive_bayes;

typedef float algorithmFPType; /* Algorithm floating-point type */

/* Input data set parameters */
const string trainDatasetFileNames[4]     =
    "../data/distributed/naivebayes_train_csr_1.csv", "../data/distributed/naivebayes_train_csr_2.csv",
    "../data/distributed/naivebayes_train_csr_3.csv", "../data/distributed/naivebayes_train_csr_4.csv"
const string trainGroundTruthFileNames[4] =
    "../data/distributed/naivebayes_train_labels_1.csv", "../data/distributed/naivebayes_train_labels_2.csv",
    "../data/distributed/naivebayes_train_labels_3.csv", "../data/distributed/naivebayes_train_labels_4.csv"

string testDatasetFileName      = "../data/distributed/naivebayes_test_csr.csv";
string testGroundTruthFileName  = "../data/distributed/naivebayes_test_labels.csv";

const size_t nClasses             = 20;
const size_t nBlocks              = 4;
const size_t nTrainVectorsInBlock = 8000;
const size_t nTestObservations    = 2000;

void trainModel();
void testModel();
void printResults();

training::ResultPtr trainingResult;
classifier::prediction::ResultPtr predictionResult;
CSRNumericTablePtr trainData[nBlocks];
CSRNumericTablePtr testData;

int main(int argc, char *argv[])
    checkArguments(argc, argv, 10,
                   &trainDatasetFileNames[0], &trainDatasetFileNames[1],
                   &trainDatasetFileNames[2], &trainDatasetFileNames[3],
                   &trainGroundTruthFileNames[0], &trainGroundTruthFileNames[1],
                   &trainGroundTruthFileNames[2], &trainGroundTruthFileNames[3],
                   &testDatasetFileName, &testGroundTruthFileName);



    return 0;

void trainModel()
    training::Distributed<step2Master, algorithmFPType, training::fastCSR> masterAlgorithm(nClasses);

    for(size_t i = 0; i < nBlocks; i++)
        /* Read trainDatasetFileNames and create a numeric table to store the input data */
        trainData[i] = CSRNumericTablePtr(createSparseTable<float>(trainDatasetFileNames[i]));

        /* Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file */
        FileDataSource<CSVFeatureManager> trainLabelsSource(trainGroundTruthFileNames[i], DataSource::doAllocateNumericTable,

        /* Retrieve the data from an input file */

        /* Create an algorithm object to train the Naive Bayes model on the local-node data */
        training::Distributed<step1Local, algorithmFPType, training::fastCSR> localAlgorithm(nClasses);

        /* Pass a training data set and dependent values to the algorithm */
        localAlgorithm.input.set(classifier::training::data,   trainData[i]);
        localAlgorithm.input.set(classifier::training::labels, trainLabelsSource.getNumericTable());

        /* Build the Naive Bayes model on the local node */

        /* Set the local Naive Bayes model as input for the master-node algorithm */
        masterAlgorithm.input.add(training::partialModels, localAlgorithm.getPartialResult());

    /* Merge and finalize the Naive Bayes model on the master node */

    /* Retrieve the algorithm results */
    trainingResult = masterAlgorithm.getResult();

void testModel()
    /* Read testDatasetFileName and create a numeric table to store the input data */
    testData = CSRNumericTablePtr(createSparseTable<float>(testDatasetFileName));

    /* Create an algorithm object to predict Naive Bayes values */
    prediction::Batch<algorithmFPType, prediction::fastCSR> algorithm(nClasses);

    /* 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 Naive Bayes values */

    /* Retrieve the algorithm results */
    predictionResult = algorithm.getResult();

void printResults()
    FileDataSource<CSVFeatureManager> testGroundTruth(testGroundTruthFileName,

    printNumericTables<int, int>(testGroundTruth.getNumericTable().get(),
                                 "Ground truth", "Classification results",
                                 "NaiveBayes classification results (first 20 observations):", 20);
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