low_order_moms_csr_distr.py

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 # file: low_order_moms_csr_distr.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
 # use, modify, copy, publish, distribute,  disclose or transmit this software or
 # the related documents without Intel's prior written permission.
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 #===============================================================================
 
 ## <a name="DAAL-EXAMPLE-PY-LOW_ORDER_MOMENTS_CSR_DISTRIBUTED"></a>
 ## \example low_order_moms_csr_distr.py
 
 import os
 import sys
 
 from daal import step1Local, step2Master
 from daal.algorithms import low_order_moments
 
 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, 'distributed', 'covcormoments_csr_1.csv'),
     os.path.join(DAAL_PREFIX, 'distributed', 'covcormoments_csr_2.csv'),
     os.path.join(DAAL_PREFIX, 'distributed', 'covcormoments_csr_3.csv'),
     os.path.join(DAAL_PREFIX, 'distributed', 'covcormoments_csr_4.csv')
 ]
 
 partialResult = [0] * nBlocks
 result = None
 
 
 def computestep1Local(block):
 
     dataTable = createSparseTable(datasetFileNames[block])
 
     # Create algorithm objects to compute low order moments in the distributed processing mode using the default method
     algorithm = low_order_moments.Distributed(step1Local, method=low_order_moments.fastCSR)
 
     # Set input objects for the algorithm
     algorithm.input.set(low_order_moments.data, dataTable)
 
     # 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, method=low_order_moments.fastCSR)
 
     # 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
     algorithm.compute()
 
     # Finalize the result in the distributed processing mode and get the computed low order moments
     result = algorithm.finalizeCompute()
 
 
 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 block in range(nBlocks):
         computestep1Local(block)
 
     computeOnMasterNode()
     printResults(result)
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