Python* API Reference for Intel® Data Analytics Acceleration Library 2020 Update 1

kernel_func_rbf_dense_batch.py

1 # file: kernel_func_rbf_dense_batch.py
2 #===============================================================================
3 # Copyright 2014-2020 Intel Corporation
4 #
5 # Licensed under the Apache License, Version 2.0 (the "License");
6 # you may not use this file except in compliance with the License.
7 # You may obtain a copy of the License at
8 #
9 # http://www.apache.org/licenses/LICENSE-2.0
10 #
11 # Unless required by applicable law or agreed to in writing, software
12 # distributed under the License is distributed on an "AS IS" BASIS,
13 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 # See the License for the specific language governing permissions and
15 # limitations under the License.
16 #===============================================================================
17 
18 
19 
20 
21 import os
22 import sys
23 
24 from daal.algorithms import kernel_function
25 from daal.data_management import FileDataSource, DataSourceIface
26 
27 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
28 if utils_folder not in sys.path:
29  sys.path.insert(0, utils_folder)
30 from utils import printNumericTable
31 
32 DAAL_PREFIX = os.path.join('..', 'data')
33 
34 # Input data set parameters
35 leftDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'kernel_function.csv')
36 rightDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'kernel_function.csv')
37 
38 # Kernel algorithm parameters
39 sigma = 1.0
40 
41 if __name__ == "__main__":
42 
43  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
44  leftDataSource = FileDataSource(
45  leftDatasetFileName, DataSourceIface.doAllocateNumericTable,
46  DataSourceIface.doDictionaryFromContext
47  )
48 
49  rightDataSource = FileDataSource(
50  rightDatasetFileName, DataSourceIface.doAllocateNumericTable,
51  DataSourceIface.doDictionaryFromContext
52  )
53 
54  # Retrieve the data from the input file
55  leftDataSource.loadDataBlock()
56  rightDataSource.loadDataBlock()
57 
58  # Create algorithm objects for the kernel algorithm using the default method
59  algorithm = kernel_function.rbf.Batch()
60 
61  # Set the kernel algorithm parameter
62  algorithm.parameter.sigma = sigma
63  algorithm.parameter.computationMode = kernel_function.matrixMatrix
64 
65  # Set an input data table for the algorithm
66  algorithm.input.set(kernel_function.X, leftDataSource.getNumericTable())
67  algorithm.input.set(kernel_function.Y, rightDataSource.getNumericTable())
68 
69  # Compute the RBF kernel and get the computed results
70  result = algorithm.compute()
71 
72  # Print the results
73  printNumericTable(result.get(kernel_function.values), "Values")

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