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

cov_csr_online.py

1 # file: cov_csr_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 
26 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
27 if utils_folder not in sys.path:
28  sys.path.insert(0, utils_folder)
29 from utils import printNumericTable, createSparseTable
30 
31 DAAL_PREFIX = os.path.join('..', 'data')
32 
33 # Input data set parameters
34 nBlocks = 4
35 datasetFileNames = [
36  os.path.join(DAAL_PREFIX, 'online', 'covcormoments_csr_1.csv'),
37  os.path.join(DAAL_PREFIX, 'online', 'covcormoments_csr_2.csv'),
38  os.path.join(DAAL_PREFIX, 'online', 'covcormoments_csr_3.csv'),
39  os.path.join(DAAL_PREFIX, 'online', 'covcormoments_csr_4.csv'),
40 ]
41 
42 if __name__ == "__main__":
43 
44  # Create algorithm objects for covariance matrix computing in online mode using default method
45  algorithm = covariance.Online()
46 
47  for i in range(nBlocks):
48  dataTable = createSparseTable(datasetFileNames[i])
49 
50  # Set input arguments of the algorithm
51  algorithm.input.set(covariance.data, dataTable)
52 
53  # Compute partial covariance estimates
54  algorithm.compute()
55 
56  # Finalize online result and get computed covariance
57  res = algorithm.finalizeCompute()
58 
59  printNumericTable(res.get(covariance.covariance), "Covariance matrix (upper left square 10*10) :", 10, 10)
60  printNumericTable(res.get(covariance.mean), "Mean vector:", 1, 10)

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