CovCSROnline.java

/* file: CovCSROnline.java */
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/*
 //  Content:
 //     Java example of 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.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 CovCSROnline {

    /* Input data set parameters */
    private static final String datasetFileNames[] = new String[] { "../data/online/covcormoments_csr_1.csv",
            "../data/online/covcormoments_csr_2.csv", "../data/online/covcormoments_csr_3.csv",
            "../data/online/covcormoments_csr_4.csv" };
    private static final int    nBlocks            = 4;

    private static Result result;

    private static DaalContext context = new DaalContext();

    public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {

        /* 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.fastCSR);

        for (int i = 0; i < nBlocks; i++) {
            /* Read the input data from a file */
            CSRNumericTable dataTable = Service.createSparseTable(context, datasetFileNames[i]);

            /* Set input objects for the algorithm */
            algorithm.input.set(InputId.data, dataTable);

            /* 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 (upper left square 10*10) :", covariance, 10, 10);
        Service.printNumericTable("Mean vector:", mean, 1, 10);

        context.dispose();
    }
}
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