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

cov_csr_online.cpp

/* file: cov_csr_online.cpp */
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/*
! Content:
! C++ example of variance-covariance matrix computation in the online
! processing mode
!******************************************************************************/
#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/online/covcormoments_csr_1.csv",
"../data/online/covcormoments_csr_2.csv",
"../data/online/covcormoments_csr_3.csv",
"../data/online/covcormoments_csr_4.csv"
};
int main(int argc, char *argv[])
{
checkArguments(argc, argv, 4, &datasetFileNames[0], &datasetFileNames[1], &datasetFileNames[2], &datasetFileNames[3]);
/* Create an algorithm to compute a variance-covariance matrix in the online processing mode using the default method */
covariance::Online<algorithmFPType, covariance::fastCSR> algorithm;
for(size_t i = 0; i < nBlocks; i++)
{
CSRNumericTable *dataTable = createSparseTable<float>(datasetFileNames[i]);
/* Set input objects for the algorithm */
algorithm.input.set(covariance::data, CSRNumericTablePtr(dataTable));
/* Compute partial estimates */
algorithm.compute();
}
/* Finalize the result in the online processing mode */
algorithm.finalizeCompute();
/* Get the computed variance-covariance matrix */
covariance::ResultPtr res = algorithm.getResult();
printNumericTable(res->get(covariance::covariance), "Covariance matrix (upper left square 10*10) :", 10, 10);
printNumericTable(res->get(covariance::mean), "Mean vector:", 1, 10);
return 0;
}

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