Java* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 4

KMeansDenseBatchAssign.java

/* file: KMeansDenseBatchAssign.java */
/*******************************************************************************
* Copyright 2014-2019 Intel Corporation.
*
* This software and the related documents are Intel copyrighted materials, and
* your use of them is governed by the express license under which they were
* provided to you (License). Unless the License provides otherwise, you may not
* use, modify, copy, publish, distribute, disclose or transmit this software or
* the related documents without Intel's prior written permission.
*
* This software and the related documents are provided as is, with no express
* or implied warranties, other than those that are expressly stated in the
* License.
*******************************************************************************/
/*
// Content:
// Java example of dense K-Means clustering in the batch processing mode
// for calculation assignments without centroids update
*/
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;
class KMeansDenseBatchAssign {
/* Input data set parameters */
private static final String dataset = "../data/batch/kmeans_dense.csv";
private static final int nClusters = 20;
private static DaalContext context = new DaalContext();
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();
/* Calculate initial clusters for K-Means clustering */
InitBatch init = new InitBatch(context, Float.class, InitMethod.randomDense, nClusters);
init.input.set(InitInputId.data, 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.lloydDense, nClusters, 0);
/* Set an input object for the algorithm */
algorithm.input.set(InputId.data, 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);
context.dispose();
}
}

For more complete information about compiler optimizations, see our Optimization Notice.