Java* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 1

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.DoubleBuffer;
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 double getSingleDouble(NumericTable nt) {
DoubleBuffer result = DoubleBuffer.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);
/* Clusterize the data */
Result result = algorithm.compute();
final double goalFunc = getSingleDouble(result.get(ResultId.objectiveFunction));
final int nIterations = getSingleInt(result.get(ResultId.nIterations));
/* Print the results */
System.out.println("K-means algorithm results: Objective function = " + goalFunc*1e-6 +
"*1e+6, number of iterations = " + nIterations);
}
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", 0.); /* oversamplingFactor doesn't mater */
runKmeans(input, InitMethod.randomDense, "randomDense", 0.); /* oversamplingFactor doesn't mater */
runKmeans(input, InitMethod.plusPlusDense, "plusPlusDense", 0.); /* oversamplingFactor doesn't mater */
runKmeans(input, InitMethod.parallelPlusDense, "plusPlusDense", 0.5);
runKmeans(input, InitMethod.parallelPlusDense, "plusPlusDense", 2.0);
context.dispose();
}
}

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