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

out_detect_mult_dense_batch.cpp

/* file: out_detect_mult_dense_batch.cpp */
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/*
! Content:
! C++ example of multivariate outlier detection
!******************************************************************************/
#include "daal.h"
#include "service.h"
using namespace std;
using namespace daal;
using namespace algorithms;
/* Input data set parameters */
string datasetFileName = "../data/batch/outlierdetection.csv";
int main(int argc, char *argv[])
{
checkArguments(argc, argv, 1, &datasetFileName);
/* Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file */
FileDataSource<CSVFeatureManager> dataSource(datasetFileName, DataSource::doAllocateNumericTable,
DataSource::doDictionaryFromContext);
/* Retrieve the data from the input file */
dataSource.loadDataBlock();
/* Create an algorithm to detect outliers using the default method */
multivariate_outlier_detection::Batch<float, multivariate_outlier_detection::defaultDense> algorithm;
algorithm.input.set(multivariate_outlier_detection::data, dataSource.getNumericTable());
/* Compute outliers */
algorithm.compute();
/* Get the computed results */
multivariate_outlier_detection::ResultPtr res = algorithm.getResult();
printNumericTables(dataSource.getNumericTable().get(), res->get(multivariate_outlier_detection::weights).get(),
"Input data", "Weights",
"Outlier detection result (Default method)");
return 0;
}

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