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 //  Content:
 //     Java example of sparse K-Means clustering in the batch processing mode
 //     for calculation assignments without centroids update



class KMeansCSRBatchAssign {
    /* Input data set parameters */
    private static final String datasetFileName = "../data/batch/kmeans_csr.csv";
    private static final int    nClusters       = 20;

    private static DaalContext context = new DaalContext();

    public static void main(String[] args) throws, {
        /* Retrieve the input data */
        CSRNumericTable input = Service.createSparseTable(context, datasetFileName);

        /* Calculate initial clusters for K-Means clustering */
        InitBatch init = new InitBatch(context, Float.class, InitMethod.randomCSR, nClusters);
        init.input.set(, input);
        InitResult initResult = init.compute();
        NumericTable inputCentroids = initResult.get(InitResultId.centroids);

        /* Create an algorithm for K-Means clustering to calculate only assignments */
        Batch algorithm = new Batch(context, Float.class, Method.lloydCSR, nClusters, 0);

        /* Set an input object for the algorithm */
        algorithm.input.set(, input);
        algorithm.input.set(InputId.inputCentroids, inputCentroids);

        /* Clusterize the data */
        Result result = algorithm.compute();

        /* Print the results */
        Service.printNumericTable("First 10 cluster assignments:", result.get(ResultId.assignments), 10);

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