Java* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 3

CovDenseBatch.java

/* file: CovDenseBatch.java */
/*******************************************************************************
* Copyright 2014-2018 Intel Corporation.
*
* This software and the related documents are Intel copyrighted materials, and
* your use of them is governed by the express license under which they were
* provided to you (License). Unless the License provides otherwise, you may not
* use, modify, copy, publish, distribute, disclose or transmit this software or
* the related documents without Intel's prior written permission.
*
* This software and the related documents are provided as is, with no express
* or implied warranties, other than those that are expressly stated in the
* License.
*******************************************************************************/
/*
// Content:
// Java example of dense 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.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 CovDenseBatch {
/* Input data set parameters */
private static final String datasetFileName = "../data/batch/covcormoments_dense.csv";
private static DaalContext context = new DaalContext();
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
/* Retrieve the input data */
FileDataSource dataSource = new FileDataSource(context, datasetFileName,
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.DoAllocateNumericTable);
dataSource.loadDataBlock();
/* Create an algorithm to compute a variance-covariance matrix using the single-pass method */
Batch alg = new Batch(context, Float.class, Method.defaultDense);
NumericTable input = dataSource.getNumericTable();
alg.input.set(InputId.data, input);
/* 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:", covariance);
Service.printNumericTable("Mean vector:", mean);
context.dispose();
}
}

For more complete information about compiler optimizations, see our Optimization Notice.