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

kernel_func_lin_csr_batch.py

1 # file: kernel_func_lin_csr_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 # ! Content:
20 # ! Python example of computing a linear kernel function in the batch processing mode
21 # !
22 # !*****************************************************************************
23 
24 #
25 
26 
27 #
28 import os
29 import sys
30 
31 from daal.algorithms import kernel_function
32 
33 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
34 if utils_folder not in sys.path:
35  sys.path.insert(0, utils_folder)
36 from utils import printNumericTable, createSparseTable
37 
38 data_dir = os.path.join('..', 'data', 'batch')
39 # Input data set parameters
40 leftDatasetFileName = os.path.join(data_dir, 'kernel_function_csr.csv')
41 rightDatasetFileName = os.path.join(data_dir, 'kernel_function_csr.csv')
42 
43 # Kernel algorithm parameters
44 k = 1.0 # Linear kernel coefficient in the k(X,Y) + b model
45 b = 0.0 # Linear kernel coefficient in the k(X,Y) + b model
46 
47 if __name__ == "__main__":
48 
49  # Read datasetFileName from a file and create a numeric tables to store input data
50  leftData = createSparseTable(leftDatasetFileName)
51  rightData = createSparseTable(rightDatasetFileName)
52 
53  # Create algorithm objects for the kernel algorithm using the default method
54  algorithm = kernel_function.linear.Batch(method=kernel_function.linear.fastCSR)
55 
56  # Set the kernel algorithm parameter
57  algorithm.parameter.k = k
58  algorithm.parameter.b = b
59  algorithm.parameter.computationMode = kernel_function.matrixMatrix
60 
61  # Set an input data table for the algorithm
62  algorithm.input.set(kernel_function.X, leftData)
63  algorithm.input.set(kernel_function.Y, rightData)
64 
65  # Compute the linear kernel function
66  # (Result class from daal.algorithms.kernel_function)
67  result = algorithm.compute()
68 
69  # Print the results
70  printNumericTable(result.get(kernel_function.values), "Values")

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