low_order_moms_csr_distr.cpp

/* file: low_order_moms_csr_distr.cpp */
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* Copyright 2014-2019 Intel Corporation.
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/*
!  Content:
!    C++ example of computing low order moments in the distributed processing
!    mode.
!
!    Input matrix is stored in the compressed sparse row (CSR) format with
!    one-based indexing.
!******************************************************************************/

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

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

void computestep1Local(size_t block);
void computeOnMasterNode();

void printResults(const low_order_moments::ResultPtr &res);

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

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

    computeOnMasterNode();

    printResults(result);

    return 0;
}

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

    /* Create an algorithm to compute low order moments in the distributed processing mode using the default method */
    low_order_moments::Distributed<step1Local, algorithmFPType, low_order_moments::fastCSR> algorithm;

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

    /* Compute partial low order moments estimates on nodes */
    algorithm.compute();


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

void computeOnMasterNode()
{
    /* Create an algorithm to compute low order moments in the distributed processing mode using the default method */
    low_order_moments::Distributed<step2Master, algorithmFPType, low_order_moments::fastCSR> algorithm;

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

    /* Compute a partial low order moments 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 low order moments */
    result = algorithm.getResult();
}

void printResults(const low_order_moments::ResultPtr &res)
{
    printNumericTable(res->get(low_order_moments::minimum),              "Minimum:");
    printNumericTable(res->get(low_order_moments::maximum),              "Maximum:");
    printNumericTable(res->get(low_order_moments::sum),                  "Sum:");
    printNumericTable(res->get(low_order_moments::sumSquares),           "Sum of squares:");
    printNumericTable(res->get(low_order_moments::sumSquaresCentered),   "Sum of squared difference from the means:");
    printNumericTable(res->get(low_order_moments::mean),                 "Mean:");
    printNumericTable(res->get(low_order_moments::secondOrderRawMoment), "Second order raw moment:");
    printNumericTable(res->get(low_order_moments::variance),             "Variance:");
    printNumericTable(res->get(low_order_moments::standardDeviation),    "Standard deviation:");
    printNumericTable(res->get(low_order_moments::variation),            "Variation:");
}
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