Python* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 5

minmax_dense_batch.py

Deprecation Notice: With the introduction of daal4py, a package that supersedes PyDAAL, Intel is deprecating PyDAAL and will discontinue support starting with Intel® DAAL 2021 and Intel® Distribution for Python 2021. Until then Intel will continue to provide compatible pyDAAL pip and conda packages for newer releases of Intel DAAL and make it available in open source. However, Intel will not add the new features of Intel DAAL to pyDAAL. Intel recommends developers switch to and use daal4py.

Note: To find daal4py examples, refer to daal4py documentation or browse github repository.

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 ## <a name="DAAL-EXAMPLE-PY-MINMAX_BATCH"></a>
23 ## \example minmax_dense_batch.py
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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