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

cor_csr_batch.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_batch.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_BATCH"></a>
17 ## \example cor_csr_batch.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 matrix is stored in one-based sparse row storage format
32 datasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'covcormoments_csr.csv')
33 
34 if __name__ == "__main__":
35 
36  # Read datasetFileName from file and create numeric table for storing input data
37  dataTable = createSparseTable(datasetFileName)
38 
39  # Create algorithm to compute correlation matrix using default method
40  algorithm = covariance.Batch()
41  algorithm.input.set(covariance.data, dataTable)
42 
43  # Set the parameter to choose the type of the output matrix
44  algorithm.parameter.outputMatrixType = covariance.correlationMatrix
45 
46  # Get computed correlation
47  res = algorithm.compute()
48 
49  printNumericTable(res.get(covariance.correlation), "Correlation matrix (upper left square 10*10) :", 10, 10)
50  printNumericTable(res.get(covariance.mean), "Mean vector:", 1, 10)

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