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

CovDenseOnline.java

/* file: CovDenseOnline.java */
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/*
// Content:
// Java example of dense variance-covariance matrix computation in the online
// 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 CovDenseOnline {
/* Input data set parameters */
private static final String datasetFileName = "../data/online/covcormoments_dense.csv";
private static final int nVectorsInBlock = 50;
private static FileDataSource dataSource;
private static Result result;
private static DaalContext context = new DaalContext();
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
/* Retrieve the input data from a .csv file */
dataSource = new FileDataSource(context, datasetFileName,
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.DoAllocateNumericTable);
/* Create algorithm objects to compute a variance-covariance matrix in the online processing mode using the default method */
Online algorithm = new Online(context, Float.class, Method.defaultDense);
/* Set input objects for the algorithm */
NumericTable input = dataSource.getNumericTable();
algorithm.input.set(InputId.data, input);
while (dataSource.loadDataBlock(nVectorsInBlock) == nVectorsInBlock) {
/* Compute partial estimates */
algorithm.compute();
}
/* Finalize the result in the online processing mode */
result = algorithm.finalizeCompute();
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();
}
}

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