Python* API Reference for Intel® Data Analytics Acceleration Library 2019

low_order_moms_dense_batch.py

1 # file: low_order_moms_dense_batch.py
2 #===============================================================================
3 # Copyright 2014-2018 Intel Corporation.
4 #
5 # This software and the related documents are Intel copyrighted materials, and
6 # your use of them is governed by the express license under which they were
7 # provided to you (License). Unless the License provides otherwise, you may not
8 # use, modify, copy, publish, distribute, disclose or transmit this software or
9 # the related documents without Intel's prior written permission.
10 #
11 # This software and the related documents are provided as is, with no express
12 # or implied warranties, other than those that are expressly stated in the
13 # License.
14 #===============================================================================
15 
16 
17 
18 
19 import os
20 import sys
21 
22 from daal.algorithms import low_order_moments
23 from daal.data_management import FileDataSource, DataSourceIface
24 
25 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
26 if utils_folder not in sys.path:
27  sys.path.insert(0, utils_folder)
28 from utils import printNumericTable
29 
30 DAAL_PREFIX = os.path.join('..', 'data')
31 
32 # Input data set parameters
33 dataFileName = os.path.join(DAAL_PREFIX, 'batch', 'covcormoments_dense.csv')
34 
35 
36 def printResults(res):
37  printNumericTable(res.get(low_order_moments.minimum), "Minimum:")
38  printNumericTable(res.get(low_order_moments.maximum), "Maximum:")
39  printNumericTable(res.get(low_order_moments.sum), "Sum:")
40  printNumericTable(res.get(low_order_moments.sumSquares), "Sum of squares:")
41  printNumericTable(res.get(low_order_moments.sumSquaresCentered), "Sum of squared difference from the means:")
42  printNumericTable(res.get(low_order_moments.mean), "Mean:")
43  printNumericTable(res.get(low_order_moments.secondOrderRawMoment), "Second order raw moment:")
44  printNumericTable(res.get(low_order_moments.variance), "Variance:")
45  printNumericTable(res.get(low_order_moments.standardDeviation), "Standard deviation:")
46  printNumericTable(res.get(low_order_moments.variation), "Variation:")
47 
48 if __name__ == "__main__":
49 
50  # Initialize FileDataSource to retrieve input data from .csv file
51  dataSource = FileDataSource(
52  dataFileName,
53  DataSourceIface.doAllocateNumericTable,
54  DataSourceIface.doDictionaryFromContext
55  )
56 
57  # Retrieve the data from input file
58  dataSource.loadDataBlock()
59 
60  # Create algorithm for computing low order moments in batch processing mode
61  algorithm = low_order_moments.Batch()
62 
63  # Set input arguments of the algorithm
64  algorithm.input.set(low_order_moments.data, dataSource.getNumericTable())
65 
66  # Get computed low order moments
67  res = algorithm.compute()
68 
69  printResults(res)

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