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

cov_csr_online.py

1 # file: cov_csr_online.py
2 #===============================================================================
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40 #===============================================================================
41 
42 
43 
44 
45 import os
46 import sys
47 
48 from daal.algorithms import covariance
49 
50 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
51 if utils_folder not in sys.path:
52  sys.path.insert(0, utils_folder)
53 from utils import printNumericTable, createSparseTable
54 
55 DAAL_PREFIX = os.path.join('..', 'data')
56 
57 # Input data set parameters
58 nBlocks = 4
59 datasetFileNames = [
60  os.path.join(DAAL_PREFIX, 'online', 'covcormoments_csr_1.csv'),
61  os.path.join(DAAL_PREFIX, 'online', 'covcormoments_csr_2.csv'),
62  os.path.join(DAAL_PREFIX, 'online', 'covcormoments_csr_3.csv'),
63  os.path.join(DAAL_PREFIX, 'online', 'covcormoments_csr_4.csv'),
64 ]
65 
66 if __name__ == "__main__":
67 
68  # Create algorithm objects for covariance matrix computing in online mode using default method
69  algorithm = covariance.Online()
70 
71  for i in range(nBlocks):
72  dataTable = createSparseTable(datasetFileNames[i])
73 
74  # Set input arguments of the algorithm
75  algorithm.input.set(covariance.data, dataTable)
76 
77  # Compute partial covariance estimates
78  algorithm.compute()
79 
80  # Finalize online result and get computed covariance
81  res = algorithm.finalizeCompute()
82 
83  printNumericTable(res.get(covariance.covariance), "Covariance matrix (upper left square 10*10) :", 10, 10)
84  printNumericTable(res.get(covariance.mean), "Mean vector:", 1, 10)

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