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<double,kmeans::init::randomDense> init(nClusters);

    init.input.set(kmeans::init::data, dataSource.getNumericTable());
    init.compute();

    services::SharedPtr<NumericTable> 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::goalFunction), "Goal function value:");

    return 0;
}