C++ API Reference for Intel® Data Analytics Acceleration Library 2018 Update 1

pca_cor_dense_distr.cpp

/* file: pca_cor_dense_distr.cpp */
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/*
! Content:
! C++ example of principal component analysis (PCA) using the correlation
! method 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 size_t nVectorsInBlock = 250;
size_t nFeatures;
const string dataFileNames[] =
{
"../data/distributed/pca_normalized_1.csv", "../data/distributed/pca_normalized_2.csv",
"../data/distributed/pca_normalized_3.csv", "../data/distributed/pca_normalized_4.csv"
};
int main(int argc, char *argv[])
{
checkArguments(argc, argv, 4, &dataFileNames[0], &dataFileNames[1], &dataFileNames[2], &dataFileNames[3]);
/* Create an algorithm for principal component analysis using the correlation method on the master node */
pca::Distributed<step2Master> masterAlgorithm;
for (size_t i = 0; i < nBlocks; i++)
{
/* Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file */
FileDataSource<CSVFeatureManager> dataSource(dataFileNames[i], DataSource::doAllocateNumericTable,
DataSource::doDictionaryFromContext);
/* Retrieve the input data */
dataSource.loadDataBlock(nVectorsInBlock);
/* Create an algorithm for principal component analysis using the correlation method on the local node */
pca::Distributed<step1Local> localAlgorithm;
/* Set the input data to the algorithm */
localAlgorithm.input.set(pca::data, dataSource.getNumericTable());
/* Compute PCA decomposition */
localAlgorithm.compute();
/* Set local partial results as input for the master-node algorithm */
masterAlgorithm.input.add(pca::partialResults, localAlgorithm.getPartialResult());
}
/* Merge and finalize PCA decomposition on the master node */
masterAlgorithm.compute();
masterAlgorithm.finalizeCompute();
/* Retrieve the algorithm results */
pca::ResultPtr result = masterAlgorithm.getResult();
/* Print the results */
printNumericTable(result->get(pca::eigenvalues), "Eigenvalues:");
printNumericTable(result->get(pca::eigenvectors), "Eigenvectors:");
return 0;
}

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