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

em_gmm_dense_batch.cpp

/* file: em_gmm_dense_batch.cpp */
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* Copyright 2014-2018 Intel Corporation.
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/*
! Content:
! C++ example of the expectation-maximization (EM) algorithm for the
! Gaussian mixture model (GMM)
!******************************************************************************/
#include "daal.h"
#include "service.h"
using namespace std;
using namespace daal;
using namespace daal::algorithms;
using namespace daal::data_management;
typedef float dataFPType; /* Data floating-point type */
/* Input data set parameters */
const std::string datasetFileName = "../data/batch/em_gmm.csv" ;
const size_t nComponents = 2;
size_t nFeatures;
int main(int argc, char *argv[])
{
checkArguments(argc, argv, 1, &datasetFileName);
/* Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file */
FileDataSource<CSVFeatureManager> dataSource(datasetFileName, DataSource::doAllocateNumericTable,
DataSource::doDictionaryFromContext);
nFeatures = dataSource.getNumberOfColumns();
/* Retrieve the data from the input file */
dataSource.loadDataBlock();
/* Create algorithm objects to initialize the EM algorithm for the GMM
* computing the number of components using the default method */
em_gmm::init::Batch<> initAlgorihm(nComponents);
/* Set an input data table for the initialization algorithm */
initAlgorihm.input.set(em_gmm::init::data, dataSource.getNumericTable());
/* Compute initial values for the EM algorithm for the GMM with the default parameters */
initAlgorihm.compute();
em_gmm::init::ResultPtr resultInit = initAlgorihm.getResult();
/* Create algorithm objects for the EM algorithm for the GMM computing the number of components using the default method */
em_gmm::Batch<> algorithm(nComponents);
/* Set an input data table for the algorithm */
algorithm.input.set(em_gmm::data, dataSource.getNumericTable());
algorithm.input.set(em_gmm::inputValues, initAlgorihm.getResult());
/* Compute the results of the EM algorithm for the GMM with the default parameters */
algorithm.compute();
em_gmm::ResultPtr result = algorithm.getResult();
/* Print the results */
printNumericTable(result->get(em_gmm::weights), "Weights");
printNumericTable(result->get(em_gmm::means), "Means");
for(size_t i = 0; i < nComponents; i++)
{
printNumericTable(result->get(em_gmm::covariances, i), "Covariance");
}
return 0;
}

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