C++ API Reference for Intel® Data Analytics Acceleration Library 2018 Update 2

pca_metrics_dense_batch.cpp

/* file: pca_metrics_dense_batch.cpp */
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/*
! Content:
! C++ example of pca quality metrics in batch processing mode.
!
! The program computes PCA and quality
! metrics for the PCA.
!******************************************************************************/
#include "daal.h"
#include "service.h"
#include <iostream>
using namespace std;
using namespace daal;
using namespace daal::algorithms;
using namespace daal::algorithms::pca::quality_metric;
using namespace daal::algorithms::pca::quality_metric_set;
/* Input data set parameters */
const string dataFileName = "../data/batch/pca_normalized.csv";
const size_t nVectors = 1000;
const size_t nComponents = 5;
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 SVD method */
pca::Batch<float, pca::svdDense> algorithm;
/* Set the algorithm input data */
algorithm.input.set(pca::data, dataSource.getNumericTable());
/* Compute results of the PCA algorithm */
algorithm.compute();
/* Create a quality metrics algorithm for explained variances, explained variances ratios and noise_variance */
pca::quality_metric_set::Batch qms(nComponents);
services::SharedPtr<algorithms::Input> algInput =
qms.getInputDataCollection()->getInput(explainedVariancesMetrics);
explained_variance::InputPtr varianceMetrics = explained_variance::Input::cast(algInput);
varianceMetrics->set(explained_variance::eigenvalues, algorithm.getResult()->get(pca::eigenvalues));
/* Compute quality metrics of the PCA algorithm */
qms.compute();
/* Output quality metrics of the PCA algorithm */
explained_variance::ResultPtr qmsResult = explained_variance::Result::cast
(qms.getResultCollection()->getResult(explainedVariancesMetrics));
printNumericTable(qmsResult->get(explained_variance::explainedVariances),
"Explained variances:");
printNumericTable(qmsResult->get(explained_variance::explainedVariancesRatios),
"Explained variance ratios:");
printNumericTable(qmsResult->get(explained_variance::noiseVariance),
"Noise variance:");
return 0;
}

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