Python* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 2

qr_dense_online.py

1 # file: qr_dense_online.py
2 #===============================================================================
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40 #===============================================================================
41 
42 
44 
45 import os
46 import sys
47 
48 from daal.algorithms import qr
49 from daal.data_management import FileDataSource, DataSourceIface
50 
51 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
52 if utils_folder not in sys.path:
53  sys.path.insert(0, utils_folder)
54 from utils import printNumericTable
55 
56 DAAL_PREFIX = os.path.join('..', 'data')
57 
58 # Input data set parameters
59 nRowsInBlock = 4000
60 dataFileName = os.path.join(DAAL_PREFIX, 'batch', 'qr.csv')
61 
62 if __name__ == "__main__":
63 
64  # Initialize FileDataSource to retrieve input data from .csv file
65  dataSource = FileDataSource(
66  dataFileName,
67  DataSourceIface.doAllocateNumericTable,
68  DataSourceIface.doDictionaryFromContext
69  )
70 
71  # Create algorithm to compute QR decomposition
72  algorithm = qr.Online()
73 
74  while dataSource.loadDataBlock(nRowsInBlock) == nRowsInBlock:
75  # Set input arguments of the algorithm
76  algorithm.input.set(qr.data, dataSource.getNumericTable())
77 
78  # Compute partial QR decompisition estimates
79  algorithm.compute()
80 
81  # Finalize online result and get computed QR decomposition
82  res = algorithm.finalizeCompute()
83 
84  # Print values
85  printNumericTable(res.get(qr.matrixQ), "Orthogonal matrix Q:", 10)
86  printNumericTable(res.get(qr.matrixR), "Triangular matrix R:")

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