Python* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 2

minmax_dense_batch.py

1 # file: minmax_dense_batch.py
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40 #===============================================================================
41 
42 #
43 # ! Content:
44 # ! Python example of Min-max normalization algorithm.
45 # !*****************************************************************************
46 
47 #
48 
51 
52 import os
53 import sys
54 
55 import daal.algorithms.normalization.minmax as minmax
56 from daal.data_management import DataSourceIface, FileDataSource
57 
58 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
59 if utils_folder not in sys.path:
60  sys.path.insert(0, utils_folder)
61 from utils import printNumericTable
62 
63 # Input data set parameters
64 datasetName = os.path.join('..', 'data', 'batch', 'normalization.csv')
65 
66 if __name__ == "__main__":
67 
68  # Retrieve the input data
69  dataSource = FileDataSource(datasetName,
70  DataSourceIface.doAllocateNumericTable,
71  DataSourceIface.doDictionaryFromContext)
72  dataSource.loadDataBlock()
73 
74  data = dataSource.getNumericTable()
75 
76  # Create an algorithm
77  algorithm = minmax.Batch(method=minmax.defaultDense)
78 
79  # Set lower and upper bounds for the algorithm
80  algorithm.parameter.lowerBound = -1.0
81  algorithm.parameter.upperBound = 1.0
82 
83  # Set an input object for the algorithm
84  algorithm.input.set(minmax.data, data)
85 
86  # Compute Min-max normalization function
87  res = algorithm.compute()
88 
89  printNumericTable(data, "First 10 rows of the input data:", 10)
90  printNumericTable(res.get(minmax.normalizedData), "First 10 rows of the min-max normalization result:", 10)

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