KMeansInitDenseBatch.java

/* file: KMeansInitDenseBatch.java */
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/*
 //  Content:
 //     Java example of dense K-Means clustering with different initialization
 //      methods in the batch processing mode
 */

package com.intel.daal.examples.kmeans;

import com.intel.daal.algorithms.kmeans.*;
import com.intel.daal.algorithms.kmeans.init.*;
import com.intel.daal.data_management.data.NumericTable;
import com.intel.daal.data_management.data_source.DataSource;
import com.intel.daal.data_management.data_source.FileDataSource;
import com.intel.daal.examples.utils.Service;
import com.intel.daal.services.DaalContext;
import java.nio.FloatBuffer;
import java.nio.IntBuffer;

class KMeansInitDenseBatch {
    /* Input data set parameters */
    private static final String dataset       = "../data/batch/kmeans_init_dense.csv";
    private static final int    nClusters     = 20;

    /* K-Means algorithm parameters */
    private static final int maxIterations = 1000;
    private static final double accuracyThreshold = 0.01;

    private static DaalContext context = new DaalContext();

    private static float getSingleFloat(NumericTable nt) {
        FloatBuffer result = FloatBuffer.allocate(1);
        result = nt.getBlockOfRows(0, 1, result);
        return result.get(0);
    }

    private static int getSingleInt(NumericTable nt) {
        IntBuffer result = IntBuffer.allocate(1);
        result = nt.getBlockOfRows(0, 1, result);
        return result.get(0);
    }

    private static void runKmeans(NumericTable input, InitMethod method, final String methodName, double oversamplingFactor) {
        /* Calculate initial clusters for K-Means clustering */
        InitBatch init = new InitBatch(context, Float.class, method, nClusters);
        init.input.set(InitInputId.data, input);
        if (oversamplingFactor > 0)
            init.parameter.setOversamplingFactor(oversamplingFactor);
        System.out.print("K-means init parameters: method = " + methodName);
        if (method == InitMethod.parallelPlusDense)
            System.out.print(", oversamplingFactor = " + init.parameter.getOversamplingFactor() +
                ", nRounds = " + init.parameter.getNRounds());
        System.out.println("");

        InitResult initResult = init.compute();
        NumericTable inputCentroids = initResult.get(InitResultId.centroids);

        /* Create an algorithm for K-Means clustering */
        Batch algorithm = new Batch(context, Float.class, Method.lloydDense, nClusters, maxIterations);

        /* Set an input object for the algorithm */
        algorithm.input.set(InputId.data, input);
        algorithm.input.set(InputId.inputCentroids, inputCentroids);
        algorithm.parameter.setAccuracyThreshold(accuracyThreshold);
        System.out.println("K-means algorithm parameters: maxIterations = " + algorithm.parameter.getMaxIterations() +
            ", accuracyThreshold = " +  algorithm.parameter.getAccuracyThreshold());
        /* Clusterize the data */
        Result result = algorithm.compute();

        final float goalFunc = getSingleFloat(result.get(ResultId.objectiveFunction));
        final int nIterations = getSingleInt(result.get(ResultId.nIterations));

        /* Print the results */
        System.out.println("K-means algorithm results: Objective function value = " + goalFunc*1e-6 +
            "*1E+6, number of iterations = " + nIterations + "\n");
    }

    public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
        /* Retrieve the input data */
        FileDataSource dataSource = new FileDataSource(context, dataset,
                DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
                DataSource.NumericTableAllocationFlag.DoAllocateNumericTable);
        dataSource.loadDataBlock();
        NumericTable input = dataSource.getNumericTable();

        runKmeans(input, InitMethod.deterministicDense, "deterministicDense",-1.0); /* oversamplingFactor doesn't mater */
        runKmeans(input, InitMethod.randomDense, "randomDense",-1.0); /* oversamplingFactor doesn't mater */
        runKmeans(input, InitMethod.plusPlusDense, "plusPlusDense",-1.0); /* oversamplingFactor doesn't mater */
        runKmeans(input, InitMethod.parallelPlusDense, "parallelPlusDense", 0.5);
        runKmeans(input, InitMethod.parallelPlusDense, "parallelPlusDense", 2.0);
        context.dispose();
    }
}
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