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

cor_dense_online.py

1 # file: cor_dense_online.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 covariance
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 datasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'covcormoments_dense.csv')
36 nObservations = 50
37 
38 if __name__ == "__main__":
39 
40  # Initialize FileDataSource<CSVFeatureManager> to retrieve input data from .csv file
41  dataSource = FileDataSource(
42  datasetFileName, DataSourceIface.doAllocateNumericTable,
43  DataSourceIface.doDictionaryFromContext
44  )
45 
46  # Create algorithm objects for correlation matrix computing in online mode using default method
47  algorithm = covariance.Online()
48 
49  # Set the parameter to choose the type of the output matrix
50  algorithm.parameter.outputMatrixType = covariance.correlationMatrix
51 
52  while (dataSource.loadDataBlock(nObservations) == nObservations):
53  # Set input arguments of the algorithm
54  algorithm.input.set(covariance.data, dataSource.getNumericTable())
55 
56  # Compute partial correlation estimates
57  algorithm.compute()
58 
59  # Finalize online result and get computed correlation
60  res = algorithm.finalizeCompute()
61 
62  printNumericTable(res.get(covariance.correlation), "Correlation matrix:")
63  printNumericTable(res.get(covariance.mean), "Mean vector:")

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