Python* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 1

low_order_moms_csr_distr.py

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40 
41 
42 
43 
44 import os
45 import sys
46 
47 from daal import step1Local, step2Master
48 from daal.algorithms import low_order_moments
49 
50 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
51 if utils_folder not in sys.path:
52  sys.path.insert(0, utils_folder)
53 from utils import printNumericTable, createSparseTable
54 
55 DAAL_PREFIX = os.path.join('..', 'data')
56 
57 # Input data set parameters
58 nBlocks = 4
59 
60 datasetFileNames = [
61  os.path.join(DAAL_PREFIX, 'distributed', 'covcormoments_csr_1.csv'),
62  os.path.join(DAAL_PREFIX, 'distributed', 'covcormoments_csr_2.csv'),
63  os.path.join(DAAL_PREFIX, 'distributed', 'covcormoments_csr_3.csv'),
64  os.path.join(DAAL_PREFIX, 'distributed', 'covcormoments_csr_4.csv')
65 ]
66 
67 partialResult = [0] * nBlocks
68 result = None
69 
70 
71 def computestep1Local(block):
72 
73  dataTable = createSparseTable(datasetFileNames[block])
74 
75  # Create algorithm objects to compute low order moments in the distributed processing mode using the default method
76  algorithm = low_order_moments.Distributed(step1Local, method=low_order_moments.fastCSR)
77 
78  # Set input objects for the algorithm
79  algorithm.input.set(low_order_moments.data, dataTable)
80 
81  # Compute partial low order moments estimates on nodes
82  partialResult[block] = algorithm.compute() # Get the computed partial estimates
83 
84 
85 def computeOnMasterNode():
86  global result
87 
88  # Create algorithm objects to compute low order moments in the distributed processing mode using the default method
89  algorithm = low_order_moments.Distributed(step2Master, method=low_order_moments.fastCSR)
90 
91  # Set input objects for the algorithm
92  for i in range(nBlocks):
93  algorithm.input.add(low_order_moments.partialResults, partialResult[i])
94 
95  # Compute a partial low order moments estimate on the master node from the partial estimates on local nodes
96  algorithm.compute()
97 
98  # Finalize the result in the distributed processing mode and get the computed low order moments
99  result = algorithm.finalizeCompute()
100 
101 
102 def printResults(res):
103 
104  printNumericTable(res.get(low_order_moments.minimum), "Minimum:")
105  printNumericTable(res.get(low_order_moments.maximum), "Maximum:")
106  printNumericTable(res.get(low_order_moments.sum), "Sum:")
107  printNumericTable(res.get(low_order_moments.sumSquares), "Sum of squares:")
108  printNumericTable(res.get(low_order_moments.sumSquaresCentered), "Sum of squared difference from the means:")
109  printNumericTable(res.get(low_order_moments.mean), "Mean:")
110  printNumericTable(res.get(low_order_moments.secondOrderRawMoment), "Second order raw moment:")
111  printNumericTable(res.get(low_order_moments.variance), "Variance:")
112  printNumericTable(res.get(low_order_moments.standardDeviation), "Standard deviation:")
113  printNumericTable(res.get(low_order_moments.variation), "Variation:")
114 
115 if __name__ == "__main__":
116  for block in range(nBlocks):
117  computestep1Local(block)
118 
119  computeOnMasterNode()
120  printResults(result)

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