Java* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 5

LowOrderMomsDenseDistr.java

/* file: LowOrderMomsDenseDistr.java */
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/*
// Content:
// Java example of computing low order moments in the distributed processing
// mode
*/
package com.intel.daal.examples.moments;
import com.intel.daal.algorithms.low_order_moments.*;
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 LowOrderMomsDenseDistr {
/* 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();
printResults();
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 an input file */
dataSource.loadDataBlock();
/* Create algorithm objects to compute low order moments 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 low order moments estimates on local nodes */
partialResult[block] = algorithm.compute();
}
private static void computeOnMasterNode() {
/* Create algorithm objects to compute low order moments 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 low order moments 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();
}
private static void printResults() {
NumericTable minimum = result.get(ResultId.minimum);
NumericTable maximum = result.get(ResultId.maximum);
NumericTable sum = result.get(ResultId.sum);
NumericTable sumSquares = result.get(ResultId.sumSquares);
NumericTable sumSquaresCentered = result.get(ResultId.sumSquaresCentered);
NumericTable mean = result.get(ResultId.mean);
NumericTable secondOrderRawMoment = result.get(ResultId.secondOrderRawMoment);
NumericTable variance = result.get(ResultId.variance);
NumericTable standardDeviation = result.get(ResultId.standardDeviation);
NumericTable variation = result.get(ResultId.variation);
Service.printNumericTable("Minimum:", minimum);
Service.printNumericTable("Maximum:", maximum);
Service.printNumericTable("Sum:", sum);
Service.printNumericTable("Sum of squares:", sumSquares);
Service.printNumericTable("Sum of squared difference from the means:", sumSquaresCentered);
Service.printNumericTable("Mean:", mean);
Service.printNumericTable("Second order raw moment:", secondOrderRawMoment);
Service.printNumericTable("Variance:", variance);
Service.printNumericTable("Standard deviation:", standardDeviation);
Service.printNumericTable("Variation:", variation);
}
}

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