Python* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 4

minmax_dense_batch.py

1 # file: minmax_dense_batch.py
2 #===============================================================================
3 # Copyright 2014-2019 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 # ! Content:
18 # ! Python example of Min-max normalization algorithm.
19 # !*****************************************************************************
20 
21 #
22 
23 
24 #
25 
26 import os
27 import sys
28 
29 import daal.algorithms.normalization.minmax as minmax
30 from daal.data_management import DataSourceIface, FileDataSource
31 
32 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
33 if utils_folder not in sys.path:
34  sys.path.insert(0, utils_folder)
35 from utils import printNumericTable
36 
37 # Input data set parameters
38 datasetName = os.path.join('..', 'data', 'batch', 'normalization.csv')
39 
40 if __name__ == "__main__":
41 
42  # Retrieve the input data
43  dataSource = FileDataSource(datasetName,
44  DataSourceIface.doAllocateNumericTable,
45  DataSourceIface.doDictionaryFromContext)
46  dataSource.loadDataBlock()
47 
48  data = dataSource.getNumericTable()
49 
50  # Create an algorithm
51  algorithm = minmax.Batch(method=minmax.defaultDense)
52 
53  # Set lower and upper bounds for the algorithm
54  algorithm.parameter.lowerBound = -1.0
55  algorithm.parameter.upperBound = 1.0
56 
57  # Set an input object for the algorithm
58  algorithm.input.set(minmax.data, data)
59 
60  # Compute Min-max normalization function
61  res = algorithm.compute()
62 
63  printNumericTable(data, "First 10 rows of the input data:", 10)
64  printNumericTable(res.get(minmax.normalizedData), "First 10 rows of the min-max normalization result:", 10)

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