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: low_order_moms_dense_distr.py
 # 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 low_order_moms_dense_distr.py
 import os
 import sys
 from daal import step1Local, step2Master
 from daal.algorithms import low_order_moments
 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
 nBlocks = 4
 datasetFileNames = [
     os.path.join(DAAL_PREFIX, 'distributed', 'covcormoments_dense_1.csv'),
     os.path.join(DAAL_PREFIX, 'distributed', 'covcormoments_dense_2.csv'),
     os.path.join(DAAL_PREFIX, 'distributed', 'covcormoments_dense_3.csv'),
     os.path.join(DAAL_PREFIX, 'distributed', 'covcormoments_dense_4.csv')
 partialResult = [0] * nBlocks
 result = None
 def computestep1Local(block):
     global partialResult
     # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
     dataSource = FileDataSource(
         datasetFileNames[block], DataSourceIface.doAllocateNumericTable,
     # Retrieve the data from the input file
     # Create algorithm objects to compute low order moments in the distributed processing mode using the default method
     algorithm = low_order_moments.Distributed(step1Local)
     # Set input objects for the algorithm
     algorithm.input.set(low_order_moments.data, dataSource.getNumericTable())
     # Compute partial low order moments estimates on nodes
     partialResult[block] = algorithm.compute()  # Get the computed partial estimates
 def computeOnMasterNode():
     global result
     # Create algorithm objects to compute low order moments in the distributed processing mode using the default method
     algorithm = low_order_moments.Distributed(step2Master)
     # Set input objects for the algorithm
     for i in range(nBlocks):
         algorithm.input.add(low_order_moments.partialResults, partialResult[i])
     # Compute a partial low order moments estimate on the master node from the partial estimates on local nodes
     # Finalize the result in the distributed processing mode
     result = algorithm.finalizeCompute() # Get the computed low order moments
 def printResults(res):
     printNumericTable(res.get(low_order_moments.minimum),              "Minimum:")
     printNumericTable(res.get(low_order_moments.maximum),              "Maximum:")
     printNumericTable(res.get(low_order_moments.sum),                  "Sum:")
     printNumericTable(res.get(low_order_moments.sumSquares),           "Sum of squares:")
     printNumericTable(res.get(low_order_moments.sumSquaresCentered),   "Sum of squared difference from the means:")
     printNumericTable(res.get(low_order_moments.mean),                 "Mean:")
     printNumericTable(res.get(low_order_moments.secondOrderRawMoment), "Second order raw moment:")
     printNumericTable(res.get(low_order_moments.variance),             "Variance:")
     printNumericTable(res.get(low_order_moments.standardDeviation),    "Standard deviation:")
     printNumericTable(res.get(low_order_moments.variation),            "Variation:")
 if __name__ == "__main__":
     for i in range(nBlocks):
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