C++ API Reference for Intel® Data Analytics Acceleration Library 2019 Update 5

cor_dense_online.cpp

/* file: cor_dense_online.cpp */
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/*
! Content:
! C++ example of dense correlation matrix computation in the online
! processing mode
!******************************************************************************/
#include "daal.h"
#include "service.h"
using namespace std;
using namespace daal;
using namespace daal::algorithms;
/* Input data set parameters */
const string datasetFileName = "../data/batch/covcormoments_dense.csv";
const size_t nObservations = 50;
int main(int argc, char *argv[])
{
checkArguments(argc, argv, 1, &datasetFileName);
/* Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file */
FileDataSource<CSVFeatureManager> dataSource(datasetFileName, DataSource::doAllocateNumericTable,
DataSource::doDictionaryFromContext);
/* Create an algorithm to compute a dense correlation matrix in the online processing mode using the default method */
covariance::Online<> algorithm;
/* Set the parameter to choose the type of the output matrix */
algorithm.parameter.outputMatrixType = covariance::correlationMatrix;
while (dataSource.loadDataBlock(nObservations) == nObservations)
{
/* Set input objects for the algorithm */
algorithm.input.set(covariance::data, dataSource.getNumericTable());
/* Compute partial estimates */
algorithm.compute();
}
/* Finalize the result in the online processing mode */
algorithm.finalizeCompute();
/* Get the computed dense correlation matrix */
covariance::ResultPtr res = algorithm.getResult();
printNumericTable(res->get(covariance::correlation), "Correlation matrix:");
printNumericTable(res->get(covariance::mean), "Mean vector:");
return 0;
}

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