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

PivotedQRDenseBatch.java

/* file: PivotedQRDenseBatch.java */
/*******************************************************************************
* Copyright 2014-2019 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 computing pivoted QR decomposition
*/
package com.intel.daal.examples.pivoted_qr;
import com.intel.daal.algorithms.pivoted_qr.Batch;
import com.intel.daal.algorithms.pivoted_qr.InputId;
import com.intel.daal.algorithms.pivoted_qr.Method;
import com.intel.daal.algorithms.pivoted_qr.Result;
import com.intel.daal.algorithms.pivoted_qr.ResultId;
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 PivotedQRDenseBatch {
/* Input data set parameters */
private static final String dataset = "../data/batch/qr.csv";
private static DaalContext context = new DaalContext();
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
/* Initialize FileDataSource to retrieve the input data from a .csv file */
FileDataSource dataSource = new FileDataSource(context, dataset,
DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
DataSource.NumericTableAllocationFlag.DoAllocateNumericTable);
/* Retrieve the data from the input file */
dataSource.loadDataBlock();
NumericTable input = dataSource.getNumericTable();
/* Create an algorithm to compute pivoted QR decomposition */
Batch pivotedQRAlgorithm = new Batch(context, Float.class, Method.defaultDense);
pivotedQRAlgorithm.input.set(InputId.data, input);
/* Compute pivoted QR decomposition */
Result res = pivotedQRAlgorithm.compute();
NumericTable matrixQ = res.get(ResultId.matrixQ);
NumericTable matrixR = res.get(ResultId.matrixR);
NumericTable permutationMatrix = res.get(ResultId.permutationMatrix);
/* Print the results */
printResults(input, matrixQ, matrixR, permutationMatrix);
context.dispose();
}
private static void printResults(NumericTable data, NumericTable Q, NumericTable R, NumericTable P) {
Service.printNumericTable("Orthogonal matrix Q:", Q, 10);
Service.printNumericTable("Triangular matrix R:", R);
Service.printNumericTable("Permutation matrix P:", P);
}
}

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