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

kernel_func_rbf_dense_batch.py

1 # file: kernel_func_rbf_dense_batch.py
2 #===============================================================================
3 # Copyright 2014-2018 Intel Corporation
4 # All Rights Reserved.
5 #
6 # If this software was obtained under the Intel Simplified Software License,
7 # the following terms apply:
8 #
9 # The source code, information and material ("Material") contained herein is
10 # owned by Intel Corporation or its suppliers or licensors, and title to such
11 # Material remains with Intel Corporation or its suppliers or licensors. The
12 # Material contains proprietary information of Intel or its suppliers and
13 # licensors. The Material is protected by worldwide copyright laws and treaty
14 # provisions. No part of the Material may be used, copied, reproduced,
15 # modified, published, uploaded, posted, transmitted, distributed or disclosed
16 # in any way without Intel's prior express written permission. No license under
17 # any patent, copyright or other intellectual property rights in the Material
18 # is granted to or conferred upon you, either expressly, by implication,
19 # inducement, estoppel or otherwise. Any license under such intellectual
20 # property rights must be express and approved by Intel in writing.
21 #
22 # Unless otherwise agreed by Intel in writing, you may not remove or alter this
23 # notice or any other notice embedded in Materials by Intel or Intel's
24 # suppliers or licensors in any way.
25 #
26 #
27 # If this software was obtained under the Apache License, Version 2.0 (the
28 # "License"), the following terms apply:
29 #
30 # You may not use this file except in compliance with the License. You may
31 # obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
32 #
33 #
34 # Unless required by applicable law or agreed to in writing, software
35 # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
36 # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
37 #
38 # See the License for the specific language governing permissions and
39 # limitations under the License.
40 #===============================================================================
41 
42 
44 
45 import os
46 import sys
47 
48 from daal.algorithms import kernel_function
49 from daal.data_management import FileDataSource, DataSourceIface
50 
51 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
52 if utils_folder not in sys.path:
53  sys.path.insert(0, utils_folder)
54 from utils import printNumericTable
55 
56 DAAL_PREFIX = os.path.join('..', 'data')
57 
58 # Input data set parameters
59 leftDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'kernel_function.csv')
60 rightDatasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'kernel_function.csv')
61 
62 # Kernel algorithm parameters
63 sigma = 1.0
64 
65 if __name__ == "__main__":
66 
67  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
68  leftDataSource = FileDataSource(
69  leftDatasetFileName, DataSourceIface.doAllocateNumericTable,
70  DataSourceIface.doDictionaryFromContext
71  )
72 
73  rightDataSource = FileDataSource(
74  rightDatasetFileName, DataSourceIface.doAllocateNumericTable,
75  DataSourceIface.doDictionaryFromContext
76  )
77 
78  # Retrieve the data from the input file
79  leftDataSource.loadDataBlock()
80  rightDataSource.loadDataBlock()
81 
82  # Create algorithm objects for the kernel algorithm using the default method
83  algorithm = kernel_function.rbf.Batch()
84 
85  # Set the kernel algorithm parameter
86  algorithm.parameter.sigma = sigma
87  algorithm.parameter.computationMode = kernel_function.matrixMatrix
88 
89  # Set an input data table for the algorithm
90  algorithm.input.set(kernel_function.X, leftDataSource.getNumericTable())
91  algorithm.input.set(kernel_function.Y, rightDataSource.getNumericTable())
92 
93  # Compute the RBF kernel and get the computed results
94  result = algorithm.compute()
95 
96  # Print the results
97  printNumericTable(result.get(kernel_function.values), "Values")

For more complete information about compiler optimizations, see our Optimization Notice.