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

pca_cor_dense_batch.cpp

/* file: pca_cor_dense_batch.cpp */
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/*
! Content:
! C++ example of principal component analysis (PCA) using the correlation
! method in the batch processing mode
!
!******************************************************************************/
#include "daal.h"
#include "service.h"
using namespace std;
using namespace daal;
using namespace daal::algorithms;
/* Input data set parameters */
const string dataFileName = "../data/batch/pca_normalized.csv";
const size_t nVectors = 1000;
int main(int argc, char *argv[])
{
checkArguments(argc, argv, 1, &dataFileName);
/* Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file */
FileDataSource<CSVFeatureManager> dataSource(dataFileName, DataSource::doAllocateNumericTable,
DataSource::doDictionaryFromContext);
/* Retrieve the data from the input file */
dataSource.loadDataBlock(nVectors);
/* Create an algorithm for principal component analysis using the correlation method */
pca::Batch<> algorithm;
/* Set the algorithm input data */
algorithm.input.set(pca::data, dataSource.getNumericTable());
algorithm.parameter.resultsToCompute = pca::mean | pca::variance | pca::eigenvalue;
algorithm.parameter.isDeterministic = true;
/* Compute results of the PCA algorithm */
algorithm.compute();
/* Print the results */
pca::ResultPtr result = algorithm.getResult();
printNumericTable(result->get(pca::eigenvalues), "Eigenvalues:");
printNumericTable(result->get(pca::eigenvectors), "Eigenvectors:");
printNumericTable(result->get(pca::means), "Means:");
printNumericTable(result->get(pca::variances), "Variances:");
return 0;
}

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