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

low_order_moms_dense_online.py

1 # file: low_order_moms_dense_online.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 datasetFileName = os.path.join(DAAL_PREFIX, 'online', 'covcormoments_dense.csv')
36 nVectorsInBlock = 50
37 
38 
39 def printResults(res):
40 
41  printNumericTable(res.get(low_order_moments.minimum), "Minimum:")
42  printNumericTable(res.get(low_order_moments.maximum), "Maximum:")
43  printNumericTable(res.get(low_order_moments.sum), "Sum:")
44  printNumericTable(res.get(low_order_moments.sumSquares), "Sum of squares:")
45  printNumericTable(res.get(low_order_moments.sumSquaresCentered), "Sum of squared difference from the means:")
46  printNumericTable(res.get(low_order_moments.mean), "Mean:")
47  printNumericTable(res.get(low_order_moments.secondOrderRawMoment), "Second order raw moment:")
48  printNumericTable(res.get(low_order_moments.variance), "Variance:")
49  printNumericTable(res.get(low_order_moments.standardDeviation), "Standard deviation:")
50  printNumericTable(res.get(low_order_moments.variation), "Variation:")
51 
52 if __name__ == "__main__":
53 
54  # Initialize FileDataSource<CSVFeatureManager> to retrieve input data from .csv file
55  dataSource = FileDataSource(
56  datasetFileName, DataSourceIface.doAllocateNumericTable,
57  DataSourceIface.doDictionaryFromContext
58  )
59 
60  # Create algorithm objects for low order moments computing in online mode using default method
61  algorithm = low_order_moments.Online()
62 
63  while(dataSource.loadDataBlock(nVectorsInBlock) == nVectorsInBlock):
64  # Set input arguments of the algorithm
65  algorithm.input.set(low_order_moments.data, dataSource.getNumericTable())
66 
67  # Compute partial low order moments estimates
68  algorithm.compute()
69 
70  # Finalize online result and get computed low order moments
71  res = algorithm.finalizeCompute()
72 
73  printResults(res)

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