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

CovCSRBatch.java

/* file: CovCSRBatch.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 CovCSRBatch {
/*
* 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, Float.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();
}
}

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