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

kmeans_dense_batch.cpp

/* file: kmeans_dense_batch.cpp */
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/*
! Content:
! C++ example of dense K-Means clustering in the batch processing mode
!******************************************************************************/
#include "daal.h"
#include "service.h"
using namespace std;
using namespace daal;
using namespace daal::algorithms;
/* Input data set parameters */
string datasetFileName = "../data/batch/kmeans_dense.csv";
/* K-Means algorithm parameters */
const size_t nClusters = 20;
const size_t nIterations = 5;
int main(int argc, char *argv[])
{
checkArguments(argc, argv, 1, &datasetFileName);
/* Initialize FileDataSource to retrieve the input data from a .csv file */
FileDataSource<CSVFeatureManager> dataSource(datasetFileName, DataSource::doAllocateNumericTable,
DataSource::doDictionaryFromContext);
/* Retrieve the data from the input file */
dataSource.loadDataBlock();
/* Get initial clusters for the K-Means algorithm */
kmeans::init::Batch<float, kmeans::init::randomDense> init(nClusters);
init.input.set(kmeans::init::data, dataSource.getNumericTable());
init.compute();
NumericTablePtr centroids = init.getResult()->get(kmeans::init::centroids);
/* Create an algorithm object for the K-Means algorithm */
kmeans::Batch<> algorithm(nClusters, nIterations);
algorithm.input.set(kmeans::data, dataSource.getNumericTable());
algorithm.input.set(kmeans::inputCentroids, centroids);
algorithm.compute();
/* Print the clusterization results */
printNumericTable(algorithm.getResult()->get(kmeans::assignments), "First 10 cluster assignments:", 10);
printNumericTable(algorithm.getResult()->get(kmeans::centroids ), "First 10 dimensions of centroids:", 20, 10);
printNumericTable(algorithm.getResult()->get(kmeans::objectiveFunction), "Objective function value:");
return 0;
}

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