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

kernel_func_rbf_dense_batch.py

1 # file: kernel_func_rbf_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 
18 
19 import os
20 import sys
21 
22 from daal.algorithms import kernel_function
23 from daal.data_management import FileDataSource, DataSourceIface
24 
25 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
26 if utils_folder not in sys.path:
27  sys.path.insert(0, utils_folder)
28 from utils import printNumericTable
29 
30 DAAL_PREFIX = os.path.join('..', 'data')
31 
32 # Input data set parameters
33 leftDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'kernel_function.csv')
34 rightDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'kernel_function.csv')
35 
36 # Kernel algorithm parameters
37 sigma = 1.0
38 
39 if __name__ == "__main__":
40 
41  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
42  leftDataSource = FileDataSource(
43  leftDatasetFileName, DataSourceIface.doAllocateNumericTable,
44  DataSourceIface.doDictionaryFromContext
45  )
46 
47  rightDataSource = FileDataSource(
48  rightDatasetFileName, DataSourceIface.doAllocateNumericTable,
49  DataSourceIface.doDictionaryFromContext
50  )
51 
52  # Retrieve the data from the input file
53  leftDataSource.loadDataBlock()
54  rightDataSource.loadDataBlock()
55 
56  # Create algorithm objects for the kernel algorithm using the default method
57  algorithm = kernel_function.rbf.Batch()
58 
59  # Set the kernel algorithm parameter
60  algorithm.parameter.sigma = sigma
61  algorithm.parameter.computationMode = kernel_function.matrixMatrix
62 
63  # Set an input data table for the algorithm
64  algorithm.input.set(kernel_function.X, leftDataSource.getNumericTable())
65  algorithm.input.set(kernel_function.Y, rightDataSource.getNumericTable())
66 
67  # Compute the RBF kernel and get the computed results
68  result = algorithm.compute()
69 
70  # Print the results
71  printNumericTable(result.get(kernel_function.values), "Values")

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