covariance_csr_distributed.cpp

/* file: covariance_csr_distributed.cpp */
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/*
!  Content:
!    C++ example of variance-covariance matrix computation in the distributed
!    processing mode
!******************************************************************************/

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

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

typedef float  dataFPType;          /* Data floating-point type */
typedef double algorithmFPType;     /* Algorithm floating-point type */

/* Input data set parameters */
const size_t nBlocks         = 4;

const string datasetFileNames[] =
{
    "../data/distributed/covcormoments_csr_1.csv",
    "../data/distributed/covcormoments_csr_2.csv",
    "../data/distributed/covcormoments_csr_3.csv",
    "../data/distributed/covcormoments_csr_4.csv"
};

services::SharedPtr<covariance::PartialResult> partialResult[nBlocks];
services::SharedPtr<covariance::Result> 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::covariance), "Covariance matrix (upper left square 10*10) :", 10, 10);
    printNumericTable(result->get(covariance::mean),       "Mean vector:", 1, 10);

    return 0;
}

void computestep1Local(size_t block)
{
    CSRNumericTable *dataTable = createSparseTable<dataFPType>(datasetFileNames[block]);

    /* Create algorithm objects to compute a variance-covariance matrix in the distributed processing mode using the default method */
    covariance::Distributed<step1Local, algorithmFPType, covariance::fastCSR> algorithm;

    /* Set input objects for the algorithm */
    algorithm.input.set(covariance::data, services::SharedPtr<CSRNumericTable>(dataTable));

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

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

void computeOnMasterNode()
{
    /* Create algorithm objects to compute a variance-covariance matrix in the distributed processing mode using the default method */
    covariance::Distributed<step2Master, algorithmFPType, covariance::fastCSR> algorithm;

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

    /* 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 variance-covariance matrix */
    result = algorithm.getResult();
}