C++ API Reference for Intel® Data Analytics Acceleration Library 2019 Update 4

low_order_moms_dense_distr.cpp

/* file: low_order_moms_dense_distr.cpp */
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/*
! Content:
! C++ example of computing low order moments 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"
};
low_order_moments::PartialResultPtr partialResult[nBlocks];
low_order_moments::ResultPtr result;
void computestep1Local(size_t i);
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 i = 0; i < nBlocks; i++)
{
computestep1Local(i);
}
computeOnMasterNode();
printResults(result);
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 low order moments in the distributed processing mode using the default method */
low_order_moments::Distributed<step1Local> algorithm;
/* Set input objects for the algorithm */
algorithm.input.set(low_order_moments::data, dataSource.getNumericTable());
/* 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> 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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