cor_dense_distr.cpp

/* file: cor_dense_distr.cpp */
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/*
!  Content:
!    C++ example of dense correlation matrix computation in the
!    distributed 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 nBlocks         = 4;

const string datasetFileNames[] =
{
    "../data/distributed/covcormoments_dense_1.csv",
    "../data/distributed/covcormoments_dense_2.csv",
    "../data/distributed/covcormoments_dense_3.csv",
    "../data/distributed/covcormoments_dense_4.csv"
};

covariance::PartialResultPtr partialResult[nBlocks];
covariance::ResultPtr result;

void computestep1Local(size_t i);
void computeOnMasterNode();

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

    for(size_t i = 0; i < nBlocks; i++)
    {
        computestep1Local(i);
    }

    computeOnMasterNode();

    printNumericTable(result->get(covariance::correlation), "Correlation matrix:");
    printNumericTable(result->get(covariance::mean),        "Mean vector:");

    return 0;
}

void computestep1Local(size_t block)
{
    /* Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file */
    FileDataSource<CSVFeatureManager> dataSource(datasetFileNames[block], DataSource::doAllocateNumericTable,
                                         DataSource::doDictionaryFromContext);

    /* Retrieve the data from the input file */
    dataSource.loadDataBlock();

    /* Create an algorithm to compute a dense correlation matrix in the distributed processing mode using the default method */
    covariance::Distributed<step1Local> algorithm;

    /* Set input objects for the algorithm */
    algorithm.input.set(covariance::data, dataSource.getNumericTable());

    /* Compute partial estimates on local nodes */
    algorithm.compute();

    /* Get the computed partial estimates */
    partialResult[block] = algorithm.getPartialResult();
}

void computeOnMasterNode()
{
    /* Create an algorithm to compute a dense correlation matrix in the distributed processing mode using the default method */
    covariance::Distributed<step2Master> algorithm;

    /* Set input objects for the algorithm */
    for (size_t i = 0; i < nBlocks; i++)
    {
        algorithm.input.add(covariance::partialResults, partialResult[i]);
    }

    /* Set the parameter to choose the type of the output matrix */
    algorithm.parameter.outputMatrixType = covariance::correlationMatrix;

    /* Compute a partial estimate on the master node from the partial estimates on local nodes */
    algorithm.compute();

    /* Finalize the result in the distributed processing mode */
    algorithm.finalizeCompute();

    /* Get the computed dense correlation matrix */
    result = algorithm.getResult();
}
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