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

abs_dense_batch.py

1 # file: abs_dense_batch.py
2 #===============================================================================
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40 #===============================================================================
41 
42 #
43 # ! Content:
44 # ! Python example of abs algorithm.
45 # !
46 # !*****************************************************************************
47 
48 #
49 ## <a name="DAAL-EXAMPLE-PY-ABS_DENSE_BATCH"></a>
50 ## \example abs_dense_batch.py
51 #
52 
53 import os
54 import sys
55 
56 import daal.algorithms.math.abs
57 from daal.algorithms import math
58 from daal.data_management import FileDataSource, DataSourceIface
59 
60 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
61 if utils_folder not in sys.path:
62  sys.path.insert(0, utils_folder)
63 from utils import printNumericTable
64 
65 # Input data set parameters
66 datasetName = os.path.join('..', 'data', 'batch', 'covcormoments_dense.csv')
67 
68 if __name__ == "__main__":
69 
70  # Retrieve the input data
71  dataSource = FileDataSource(datasetName,
72  DataSourceIface.doAllocateNumericTable,
73  DataSourceIface.doDictionaryFromContext)
74  dataSource.loadDataBlock()
75 
76  # Create an algorithm
77  algorithm = math.abs.Batch()
78 
79  # Set an input object for the algorithm
80  algorithm.input.set(math.abs.data, dataSource.getNumericTable())
81 
82  # Compute Abs function
83  res = algorithm.compute()
84 
85  # Print the results of the algorithm
86  printNumericTable(res.get(math.abs.value), "Abs result (first 5 rows):", 5)

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