Python* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 5

kernel_func_lin_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: kernel_func_lin_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 ## <a name="DAAL-EXAMPLE-PY-KERNEL_FUNCTION_LINEAR_DENSE_BATCH"></a>
17 ## \example kernel_func_lin_dense_batch.py
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 k = 1.0 # Linear kernel coefficient in the k(X,Y) + b model
38 b = 0.0 # Linear kernel coefficient in the k(X,Y) + b model
39 
40 if __name__ == "__main__":
41 
42  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
43  leftDataSource = FileDataSource(
44  leftDatasetFileName, DataSourceIface.doAllocateNumericTable,
45  DataSourceIface.doDictionaryFromContext
46  )
47 
48  rightDataSource = FileDataSource(
49  rightDatasetFileName, DataSourceIface.doAllocateNumericTable,
50  DataSourceIface.doDictionaryFromContext
51  )
52 
53  # Retrieve the data from the input file
54  leftDataSource.loadDataBlock()
55  rightDataSource.loadDataBlock()
56 
57  # Create algorithm objects for the kernel algorithm using the default method
58  algorithm = kernel_function.linear.Batch()
59 
60  # Set the kernel algorithm parameter
61  algorithm.parameter.k = k
62  algorithm.parameter.b = b
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 linear kernel function and get the computed results
70  # (Result class from daal.algorithms.kernel_function)
71  result = algorithm.compute()
72 
73  # Print the results
74  printNumericTable(result.get(kernel_function.values), "Values")

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