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

kmeans_csr_distr.cpp

/* file: kmeans_csr_distr.cpp */
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/*
! Content:
! C++ example of sparse 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 = 8000;
const string dataFileNames[] =
{
"../data/distributed/kmeans_csr_1.csv", "../data/distributed/kmeans_csr_2.csv",
"../data/distributed/kmeans_csr_3.csv", "../data/distributed/kmeans_csr_4.csv"
};
CSRNumericTablePtr dataTable[nBlocks];
int main(int argc, char *argv[])
{
checkArguments(argc, argv, 4, &dataFileNames[0], &dataFileNames[1], &dataFileNames[2], &dataFileNames[3]);
kmeans::Distributed<step2Master, algorithmFPType, kmeans::lloydCSR> masterAlgorithm(nClusters);
NumericTablePtr centroids;
NumericTablePtr assignments[nBlocks];
NumericTablePtr objectiveFunction;
kmeans::init::Distributed<step2Master, algorithmFPType, kmeans::init::randomCSR> masterInit(nClusters);
for (size_t i = 0; i < nBlocks; i++)
{
/* Read dataFileNames and create a numeric table to store the input data */
dataTable[i] = CSRNumericTablePtr(createSparseTable<float>(dataFileNames[i]));
/* Create an algorithm object for the K-Means algorithm */
kmeans::init::Distributed<step1Local, algorithmFPType, kmeans::init::randomCSR> localInit(nClusters, nBlocks*nVectorsInBlock, i*nVectorsInBlock);
localInit.input.set(kmeans::init::data, dataTable[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, algorithmFPType, kmeans::lloydCSR> localAlgorithm(nClusters, false);
/* Set the input data to the algorithm */
localAlgorithm.input.set(kmeans::data, dataTable[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<algorithmFPType, kmeans::lloydCSR> localAlgorithm(nClusters, 0);
/* Set the input data to the algorithm */
localAlgorithm.input.set(kmeans::data, dataTable[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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