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.

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 # file: cor_csr_online.py
 #===============================================================================
 # Copyright 2014-2019 Intel Corporation.
 #
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 # 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
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 # the related documents without Intel's prior written permission.
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 #===============================================================================
 
 ## <a name="DAAL-EXAMPLE-PY-CORRELATION_CSR_ONLINE"></a>
 ## \example cor_csr_online.py
 
 import os
 import sys
 
 from daal.algorithms import covariance
 
 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
 if utils_folder not in sys.path:
     sys.path.insert(0, utils_folder)
 from utils import printNumericTable, createSparseTable
 
 DAAL_PREFIX = os.path.join('..', 'data')
 
 # Input data set parameters
 nBlocks = 4
 datasetFileNames = [
     os.path.join(DAAL_PREFIX, 'online', 'covcormoments_csr_1.csv'),
     os.path.join(DAAL_PREFIX, 'online', 'covcormoments_csr_2.csv'),
     os.path.join(DAAL_PREFIX, 'online', 'covcormoments_csr_3.csv'),
     os.path.join(DAAL_PREFIX, 'online', 'covcormoments_csr_4.csv'),
 ]
 
 if __name__ == "__main__":
 
     # Create algorithm objects for correlation matrix computing in online mode using default method
     algorithm = covariance.Online()
 
     # Set the parameter to choose the type of the output matrix
     algorithm.parameter.outputMatrixType = covariance.correlationMatrix
 
     for i in range(nBlocks):
         dataTable = createSparseTable(datasetFileNames[i])
 
         # Set input arguments of the algorithm
         algorithm.input.set(covariance.data, dataTable)
 
         # Compute partial correlation estimates
         algorithm.compute()
 
     # Finalize online result and get computed correlation
     res = algorithm.finalizeCompute()
 
     printNumericTable(res.get(covariance.correlation), "Correlation matrix (upper left square 10*10) :", 10, 10)
     printNumericTable(res.get(covariance.mean),        "Mean vector:", 1, 10)
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