/* file: pca_cor_dense_online.cpp */
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!  Content:
!    C++ example of principal component analysis (PCA) using the correlation
!    method in the online processing mode

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

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

/* Input data set parameters */
const size_t nVectorsInBlock = 250;
const string dataFileName = "../data/online/pca_normalized.csv";

int main(int argc, char *argv[])
    checkArguments(argc, argv, 1, &dataFileName);

    /* Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file */
    FileDataSource<CSVFeatureManager> dataSource(dataFileName, DataSource::doAllocateNumericTable,

    /* Create an algorithm for principal component analysis using the correlation method */
    pca::Online<> algorithm;

    while(dataSource.loadDataBlock(nVectorsInBlock) == nVectorsInBlock)
        /* Set the input data to the algorithm */
        algorithm.input.set(pca::data, dataSource.getNumericTable());

        /* Update PCA decomposition */

    /* Finalize computations */

    /* Print the results */
    pca::ResultPtr result = algorithm.getResult();
    printNumericTable(result->get(pca::eigenvalues), "Eigenvalues:");
    printNumericTable(result->get(pca::eigenvectors), "Eigenvectors:");

    return 0;
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