Python* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 4

cor_csr_online.py

Deprecation Notice: With the introduction of daal4py, a package that supersedes PyDAAL, Intel is deprecating PyDAAL and will discontinue support starting with Intel® DAAL 2021 and Intel® Distribution for Python 2021. Until then Intel will continue to provide compatible pyDAAL pip and conda packages for newer releases of Intel DAAL and make it available in open source. However, Intel will not add the new features of Intel DAAL to pyDAAL. Intel recommends developers switch to and use daal4py.

Note: To find daal4py examples, refer to daal4py documentation or browse github repository.

1 # file: cor_csr_online.py
2 #===============================================================================
3 # Copyright 2014-2019 Intel Corporation.
4 #
5 # This software and the related documents are Intel copyrighted materials, and
6 # your use of them is governed by the express license under which they were
7 # provided to you (License). Unless the License provides otherwise, you may not
8 # use, modify, copy, publish, distribute, disclose or transmit this software or
9 # the related documents without Intel's prior written permission.
10 #
11 # This software and the related documents are provided as is, with no express
12 # or implied warranties, other than those that are expressly stated in the
13 # License.
14 #===============================================================================
15 
16 ## <a name="DAAL-EXAMPLE-PY-CORRELATION_CSR_ONLINE"></a>
17 ## \example cor_csr_online.py
18 
19 import os
20 import sys
21 
22 from daal.algorithms import covariance
23 
24 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
25 if utils_folder not in sys.path:
26  sys.path.insert(0, utils_folder)
27 from utils import printNumericTable, createSparseTable
28 
29 DAAL_PREFIX = os.path.join('..', 'data')
30 
31 # Input data set parameters
32 nBlocks = 4
33 datasetFileNames = [
34  os.path.join(DAAL_PREFIX, 'online', 'covcormoments_csr_1.csv'),
35  os.path.join(DAAL_PREFIX, 'online', 'covcormoments_csr_2.csv'),
36  os.path.join(DAAL_PREFIX, 'online', 'covcormoments_csr_3.csv'),
37  os.path.join(DAAL_PREFIX, 'online', 'covcormoments_csr_4.csv'),
38 ]
39 
40 if __name__ == "__main__":
41 
42  # Create algorithm objects for correlation matrix computing in online mode using default method
43  algorithm = covariance.Online()
44 
45  # Set the parameter to choose the type of the output matrix
46  algorithm.parameter.outputMatrixType = covariance.correlationMatrix
47 
48  for i in range(nBlocks):
49  dataTable = createSparseTable(datasetFileNames[i])
50 
51  # Set input arguments of the algorithm
52  algorithm.input.set(covariance.data, dataTable)
53 
54  # Compute partial correlation estimates
55  algorithm.compute()
56 
57  # Finalize online result and get computed correlation
58  res = algorithm.finalizeCompute()
59 
60  printNumericTable(res.get(covariance.correlation), "Correlation matrix (upper left square 10*10) :", 10, 10)
61  printNumericTable(res.get(covariance.mean), "Mean vector:", 1, 10)

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