/* file: kmeans_csr_batch.cpp */
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!  Content:
!    C++ example of sparse K-Means clustering in the batch 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 */

/* Input data set parameters */
string datasetFileName     = "../data/batch/kmeans_csr.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);

    /* Retrieve the data from the input file */
    CSRNumericTablePtr dataTable(createSparseTable<float>(datasetFileName));

    /* Get initial clusters for the K-Means algorithm */
    kmeans::init::Batch<algorithmFPType, kmeans::init::randomCSR> init(nClusters);

    init.input.set(kmeans::init::data, dataTable);

    NumericTablePtr centroids = init.getResult()->get(kmeans::init::centroids);

    /* Create an algorithm object for the K-Means algorithm */
    kmeans::Batch<algorithmFPType, kmeans::lloydCSR> algorithm(nClusters, nIterations);

    algorithm.input.set(kmeans::data,           dataTable);
    algorithm.input.set(kmeans::inputCentroids, centroids);


    /* 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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