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

kmeans_dense_distr.cpp

/* file: kmeans_dense_distr.cpp */
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/*
! Content:
! C++ example of dense K-Means clustering 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 */
/* K-Means algorithm parameters */
const size_t nClusters = 20;
const size_t nIterations = 5;
const size_t nBlocks = 4;
const size_t nVectorsInBlock = 2500;
const string dataFileNames[] =
{
"../data/distributed/kmeans_dense_1.csv", "../data/distributed/kmeans_dense_2.csv",
"../data/distributed/kmeans_dense_3.csv", "../data/distributed/kmeans_dense_4.csv"
};
int main(int argc, char *argv[])
{
checkArguments(argc, argv, 4, &dataFileNames[0], &dataFileNames[1], &dataFileNames[2], &dataFileNames[3]);
kmeans::Distributed<step2Master> masterAlgorithm(nClusters);
NumericTablePtr data[nBlocks];
NumericTablePtr centroids;
NumericTablePtr assignments[nBlocks];
NumericTablePtr objectiveFunction;
kmeans::init::Distributed<step2Master, algorithmFPType, kmeans::init::randomDense> masterInit(nClusters);
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 data from the input file */
dataSource.loadDataBlock();
data[i] = dataSource.getNumericTable();
/* Create an algorithm object for the K-Means algorithm */
kmeans::init::Distributed<step1Local, algorithmFPType, kmeans::init::randomDense> localInit(nClusters, nBlocks*nVectorsInBlock, i*nVectorsInBlock);
localInit.input.set(kmeans::init::data, data[i]);
localInit.compute();
masterInit.input.add(kmeans::init::partialResults, localInit.getPartialResult());
}
masterInit.compute();
masterInit.finalizeCompute();
centroids = masterInit.getResult()->get(kmeans::init::centroids);
/* Calculate centroids */
for(size_t it = 0; it < nIterations; it++)
{
for (size_t i = 0; i < nBlocks; i++)
{
/* Create an algorithm object for the K-Means algorithm */
kmeans::Distributed<step1Local> localAlgorithm(nClusters, false);
/* Set the input data to the algorithm */
localAlgorithm.input.set(kmeans::data, data[i]);
localAlgorithm.input.set(kmeans::inputCentroids, centroids);
localAlgorithm.compute();
masterAlgorithm.input.add(kmeans::partialResults, localAlgorithm.getPartialResult());
}
masterAlgorithm.compute();
masterAlgorithm.finalizeCompute();
centroids = masterAlgorithm.getResult()->get(kmeans::centroids);
objectiveFunction = masterAlgorithm.getResult()->get(kmeans::objectiveFunction);
}
/* Calculate assignments */
for (size_t i = 0; i < nBlocks; i++)
{
/* Create an algorithm object for the K-Means algorithm */
kmeans::Batch<> localAlgorithm(nClusters, 0);
/* Set the input data to the algorithm */
localAlgorithm.input.set(kmeans::data, data[i]);
localAlgorithm.input.set(kmeans::inputCentroids, centroids);
localAlgorithm.compute();
assignments[i] = localAlgorithm.getResult()->get(kmeans::assignments);
}
/* Print the clusterization results */
printNumericTable(assignments[0], "First 10 cluster assignments from 1st node:", 10);
printNumericTable(centroids, "First 10 dimensions of centroids:", 20, 10);
printNumericTable(objectiveFunction, "Objective function value:");
return 0;
}

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