Java* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 3

CovDenseDistr.java

/* file: CovDenseDistr.java */
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/*
// Content:
// Java example of dense variance-covariance matrix computation in the
// distributed processing mode
*/
package com.intel.daal.examples.covariance;
import com.intel.daal.algorithms.covariance.*;
import com.intel.daal.data_management.data.HomogenNumericTable;
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 CovDenseDistr {
/* Input data set parameters */
private static final String datasetFileNames[] = new String[] { "../data/distributed/covcormoments_dense_1.csv",
"../data/distributed/covcormoments_dense_2.csv", "../data/distributed/covcormoments_dense_3.csv",
"../data/distributed/covcormoments_dense_4.csv" };
private static final int nBlocks = 4;
private static PartialResult[] partialResult = new PartialResult[nBlocks];
private static Result result;
private static DaalContext context = new DaalContext();
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
for (int i = 0; i < nBlocks; i++) {
computeOnLocalNode(i);
}
computeOnMasterNode();
HomogenNumericTable covariance = (HomogenNumericTable) result.get(ResultId.covariance);
HomogenNumericTable mean = (HomogenNumericTable) result.get(ResultId.mean);
Service.printNumericTable("Covariance matrix:", covariance);
Service.printNumericTable("Mean vector:", mean);
context.dispose();
}
private static void computeOnLocalNode(int block) {
/* Retrieve the input data from a .csv file */
FileDataSource dataSource = new FileDataSource(context, datasetFileNames[block],
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.DoAllocateNumericTable);
/* Retrieve the data from the input file */
dataSource.loadDataBlock();
/* Create algorithm objects to compute a variance-covariance matrix in the distributed processing mode using the default method */
DistributedStep1Local algorithm = new DistributedStep1Local(context, Float.class, Method.defaultDense);
/* Set input objects for the algorithm */
NumericTable input = dataSource.getNumericTable();
algorithm.input.set(InputId.data, input);
/* Compute partial estimates on nodes */
partialResult[block] = algorithm.compute();
}
private static void computeOnMasterNode() {
/* Create algorithm objects to compute a variance-covariance matrix in the distributed processing mode using the default method */
DistributedStep2Master algorithm = new DistributedStep2Master(context, Float.class, Method.defaultDense);
/* Set input objects for the algorithm */
for (int i = 0; i < nBlocks; i++) {
algorithm.input.add(DistributedStep2MasterInputId.partialResults, partialResult[i]);
}
/* Compute a partial estimate on the master node from the partial estimates on local nodes */
algorithm.compute();
/* Finalize the result in the distributed processing mode */
result = algorithm.finalizeCompute();
}
}

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