Python* API Reference for Intel® Data Analytics Acceleration Library 2020 Update 1

low_order_moms_dense_batch.py

1 # file: low_order_moms_dense_batch.py
2 #===============================================================================
3 # Copyright 2014-2020 Intel Corporation
4 #
5 # Licensed under the Apache License, Version 2.0 (the "License");
6 # you may not use this file except in compliance with the License.
7 # You may obtain a copy of the License at
8 #
9 # http://www.apache.org/licenses/LICENSE-2.0
10 #
11 # Unless required by applicable law or agreed to in writing, software
12 # distributed under the License is distributed on an "AS IS" BASIS,
13 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 # See the License for the specific language governing permissions and
15 # limitations under the License.
16 #===============================================================================
17 
18 
19 
20 
21 import os
22 import sys
23 
24 from daal.algorithms import low_order_moments
25 from daal.data_management import FileDataSource, DataSourceIface
26 
27 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
28 if utils_folder not in sys.path:
29  sys.path.insert(0, utils_folder)
30 from utils import printNumericTable
31 
32 DAAL_PREFIX = os.path.join('..', 'data')
33 
34 # Input data set parameters
35 dataFileName = os.path.join(DAAL_PREFIX, 'batch', 'covcormoments_dense.csv')
36 
37 
38 def printResults(res):
39  printNumericTable(res.get(low_order_moments.minimum), "Minimum:")
40  printNumericTable(res.get(low_order_moments.maximum), "Maximum:")
41  printNumericTable(res.get(low_order_moments.sum), "Sum:")
42  printNumericTable(res.get(low_order_moments.sumSquares), "Sum of squares:")
43  printNumericTable(res.get(low_order_moments.sumSquaresCentered), "Sum of squared difference from the means:")
44  printNumericTable(res.get(low_order_moments.mean), "Mean:")
45  printNumericTable(res.get(low_order_moments.secondOrderRawMoment), "Second order raw moment:")
46  printNumericTable(res.get(low_order_moments.variance), "Variance:")
47  printNumericTable(res.get(low_order_moments.standardDeviation), "Standard deviation:")
48  printNumericTable(res.get(low_order_moments.variation), "Variation:")
49 
50 if __name__ == "__main__":
51 
52  # Initialize FileDataSource to retrieve input data from .csv file
53  dataSource = FileDataSource(
54  dataFileName,
55  DataSourceIface.doAllocateNumericTable,
56  DataSourceIface.doDictionaryFromContext
57  )
58 
59  # Retrieve the data from input file
60  dataSource.loadDataBlock()
61 
62  # Create algorithm for computing low order moments in batch processing mode
63  algorithm = low_order_moments.Batch()
64 
65  # Set input arguments of the algorithm
66  algorithm.input.set(low_order_moments.data, dataSource.getNumericTable())
67 
68  # Get computed low order moments
69  res = algorithm.compute()
70 
71  printResults(res)

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