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.

 # file:
 # Copyright 2014-2019 Intel Corporation.
 # This software and the related documents are Intel copyrighted  materials,  and
 # 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
 # use, modify, copy, publish, distribute,  disclose or transmit this software or
 # the related documents without Intel's prior written permission.
 # This software and the related documents  are provided as  is,  with no express
 # or implied  warranties,  other  than those  that are  expressly stated  in the
 # License.
 ## \example
 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 matrix is stored in one-based sparse row storage format
 datasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'covcormoments_csr.csv')
 if __name__ == "__main__":
     # Read datasetFileName from file and create numeric table for storing input data
     dataTable = createSparseTable(datasetFileName)
     # Create algorithm to compute covariance matrix using default method
     algorithm = covariance.Batch()
     algorithm.input.set(, dataTable)
     # Get computed covariance
     res = algorithm.compute()
     printNumericTable(res.get(covariance.covariance), "Covariance matrix (upper left square 10*10) :", 10, 10)
     printNumericTable(res.get(covariance.mean),       "Mean vector:", 1, 10)
For more complete information about compiler optimizations, see our Optimization Notice.
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