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

normal_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: normal_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 normal distribution
19 # !
20 # !*****************************************************************************
21 
22 #
23 ## <a name="DAAL-EXAMPLE-PY-NORMAL_DENSE_BATCH"></a>
24 ## \example normal_dense_batch.py
25 #
26 
27 import os
28 import sys
29 
30 import daal.algorithms.distributions as distributions
31 import daal.algorithms.distributions.normal as normal
32 from daal.algorithms.engines.mt19937 import Batch_Float64DefaultDense_create as create
33 from daal.data_management import HomogenNumericTable, NumericTableIface
34 
35 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
36 if utils_folder not in sys.path:
37  sys.path.insert(0, utils_folder)
38 from utils import printNumericTable
39 
40 if __name__ == "__main__":
41  # Create input table to fill with random numbers
42  dataTable = HomogenNumericTable(1, 10, NumericTableIface.doAllocate)
43 
44  # Create the algorithm
45  normal = normal.Batch()
46 
47  # Set the algorithm input
48  normal.input.set(distributions.tableToFill, dataTable)
49 
50  # Set the Mersenne Twister engine to the distribution
51  normal.parameter.engine = create(777)
52 
53  # Perform computations
54  normal.compute()
55 
56  # Print the results
57  printNumericTable(dataTable, "Normal distribution output:")

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