CovarianceCSRBatch.java

/* file: CovarianceCSRBatch.java */
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/*
 //  Content:
 //     Java example of variance-covariance matrix computation in the batch
 //     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;

class CovarianceCSRBatch {
    /*
     * Input data set parameters
     * Input matrix is stored in the compressed sparse row format with one-based indexing
     */
    private static final String datasetFileName = "../data/batch/covcormoments_csr.csv";

    private static DaalContext context = new DaalContext();

    public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
        /* Read a data set from a file and create a numeric table for storing the input data */
        CSRNumericTable dataTable = Service.createSparseTable(context, datasetFileName);

        /* Create an algorithm to compute a variance-covariance matrix using the default method */
        Batch alg = new Batch(context, Double.class, Method.fastCSR);
        alg.input.set(InputId.data, dataTable);

        /* Compute the variance-covariance matrix */
        Result res = alg.compute();

        HomogenNumericTable covariance = (HomogenNumericTable) res.get(ResultId.covariance);
        HomogenNumericTable mean = (HomogenNumericTable) res.get(ResultId.mean);

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

        context.dispose();
    }
}