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

pca_cor_dense_online.cpp

/* file: pca_cor_dense_online.cpp */
/*******************************************************************************
* Copyright 2014-2019 Intel Corporation.
*
* This software and the related documents are Intel copyrighted materials, and
* your use of them is governed by the express license under which they were
* provided to you (License). Unless the License provides otherwise, you may not
* use, modify, copy, publish, distribute, disclose or transmit this software or
* the related documents without Intel's prior written permission.
*
* This software and the related documents are provided as is, with no express
* or implied warranties, other than those that are expressly stated in the
* License.
*******************************************************************************/
/*
! Content:
! C++ example of principal component analysis (PCA) using the correlation
! method 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 size_t nVectorsInBlock = 250;
const string dataFileName = "../data/online/pca_normalized.csv";
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);
/* Create an algorithm for principal component analysis using the correlation method */
pca::Online<> algorithm;
while(dataSource.loadDataBlock(nVectorsInBlock) == nVectorsInBlock)
{
/* Set the input data to the algorithm */
algorithm.input.set(pca::data, dataSource.getNumericTable());
/* Update PCA decomposition */
algorithm.compute();
}
/* Finalize computations */
algorithm.finalizeCompute();
/* Print the results */
pca::ResultPtr result = algorithm.getResult();
printNumericTable(result->get(pca::eigenvalues), "Eigenvalues:");
printNumericTable(result->get(pca::eigenvectors), "Eigenvectors:");
return 0;
}

For more complete information about compiler optimizations, see our Optimization Notice.