C++ API Reference for Intel® Data Analytics Acceleration Library 2018 Update 3

cov_dense_distr.cpp

/* file: cov_dense_distr.cpp */
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/*
! Content:
! C++ example of dense 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;
/* 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::covariance), "Covariance 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 variance-covariance 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 variance-covariance 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]);
}
/* 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 variance-covariance matrix */
result = algorithm.getResult();
}

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