/* file: pca_cor_csr_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;

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

/* Input data set parameters */
const size_t nBlocks = 4;
const string datasetFileNames[] =

int main(int argc, char *argv[])
    checkArguments(argc, argv, 4, &datasetFileNames[0], &datasetFileNames[1], &datasetFileNames[2], &datasetFileNames[3]);

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

    /* Use covariance algorithm for sparse data inside the PCA algorithm */
    algorithm.parameter.covariance = services::SharedPtr<covariance::Online<algorithmFPType, covariance::fastCSR> >
                                     (new covariance::Online<algorithmFPType, covariance::fastCSR>());

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

        /* Set input objects for the algorithm */
        algorithm.input.set(pca::data, CSRNumericTablePtr(dataTable));

        /* 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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