pca_svd_dense_distr.cpp

/* file: pca_svd_dense_distr.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 singular value
!    decomposition (SVD) method in the distributed 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 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 SVD method on the master node */
    pca::Distributed<step2Master, algorithmFPType, pca::svdDense> 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 SVD method on the local node */
        pca::Distributed<step1Local, algorithmFPType, pca::svdDense> 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;
}
For more complete information about compiler optimizations, see our Optimization Notice.
Select sticky button color: 
Orange (only for download buttons)