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

CovCSRDistr.java

/* file: CovCSRDistr.java */
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/*
// Content:
// Java example of 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.CSRNumericTable;
import com.intel.daal.data_management.data.HomogenNumericTable;
import com.intel.daal.examples.utils.Service;
import com.intel.daal.services.DaalContext;
/*
// Input data set is stored in the compressed sparse row format
*/
class CovCSRDistr {
/* Input data set parameters */
private static final String datasetFileNames[] = new String[] { "../data/distributed/covcormoments_csr_1.csv",
"../data/distributed/covcormoments_csr_2.csv", "../data/distributed/covcormoments_csr_3.csv",
"../data/distributed/covcormoments_csr_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 (upper left square 10*10) :", covariance, 10, 10);
Service.printNumericTable("Mean vector:", mean, 1, 10);
context.dispose();
}
private static void computeOnLocalNode(int block) throws java.io.IOException {
/* Read the input data from a file */
CSRNumericTable dataTable = Service.createSparseTable(context, datasetFileNames[block]);
/* 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.fastCSR);
/* Set input objects for the algorithm */
algorithm.input.set(InputId.data, dataTable);
/* 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.fastCSR);
/* 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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