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

kernel_func_lin_dense_batch.py

1 # file: kernel_func_lin_dense_batch.py
2 #===============================================================================
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40 #===============================================================================
41 
42 
43 
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 k = 1.0 # Linear kernel coefficient in the k(X,Y) + b model
64 b = 0.0 # Linear kernel coefficient in the k(X,Y) + b model
65 
66 if __name__ == "__main__":
67 
68  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
69  leftDataSource = FileDataSource(
70  leftDatasetFileName, DataSourceIface.doAllocateNumericTable,
71  DataSourceIface.doDictionaryFromContext
72  )
73 
74  rightDataSource = FileDataSource(
75  rightDatasetFileName, DataSourceIface.doAllocateNumericTable,
76  DataSourceIface.doDictionaryFromContext
77  )
78 
79  # Retrieve the data from the input file
80  leftDataSource.loadDataBlock()
81  rightDataSource.loadDataBlock()
82 
83  # Create algorithm objects for the kernel algorithm using the default method
84  algorithm = kernel_function.linear.Batch()
85 
86  # Set the kernel algorithm parameter
87  algorithm.parameter.k = k
88  algorithm.parameter.b = b
89  algorithm.parameter.computationMode = kernel_function.matrixMatrix
90 
91  # Set an input data table for the algorithm
92  algorithm.input.set(kernel_function.X, leftDataSource.getNumericTable())
93  algorithm.input.set(kernel_function.Y, rightDataSource.getNumericTable())
94 
95  # Compute the linear kernel function and get the computed results
96  # (Result class from daal.algorithms.kernel_function)
97  result = algorithm.compute()
98 
99  # Print the results
100  printNumericTable(result.get(kernel_function.values), "Values")

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