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.

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 # 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.
 ## <a name="DAAL-EXAMPLE-PY-SVD_ONLINE"></a>
 ## \example
 import os
 import sys
 import numpy as np
 from daal.algorithms import svd
 from daal.data_management import FileDataSource, DataSourceIface
 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
 DAAL_PREFIX = os.path.join('..', 'data')
 #  Input data set parameters
 nRowsInBlock = 4000
 dataFileName = os.path.join(DAAL_PREFIX, 'batch', 'svd.csv')
 if __name__ == "__main__":
     # Initialize FileDataSource to retrieve input data from .csv file
     dataSource = FileDataSource(
     # Create algorithm object to compute SVD decomposition in online mode
     algorithm = svd.Online(fptype=np.float64)
     while dataSource.loadDataBlock(nRowsInBlock):
         # Set input arguments of the algorithm
         algorithm.input.set(, dataSource.getNumericTable())
         # Compute partial SVD decomposition estimates
     # Finalize online result and get computed SVD decomposition
     res = algorithm.finalizeCompute()
     # Print results
     printNumericTable(res.get(svd.singularValues),      "Singular values:")
     printNumericTable(res.get(svd.rightSingularMatrix), "Right orthogonal matrix V:")
     printNumericTable(res.get(svd.leftSingularMatrix),  "Left orthogonal matrix U:", 10)
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