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

cor_csr_batch.py

1 # file: cor_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 
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 matrix is stored in one-based sparse row storage format
34 datasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'covcormoments_csr.csv')
35 
36 if __name__ == "__main__":
37 
38  # Read datasetFileName from file and create numeric table for storing input data
39  dataTable = createSparseTable(datasetFileName)
40 
41  # Create algorithm to compute correlation matrix using default method
42  algorithm = covariance.Batch()
43  algorithm.input.set(covariance.data, dataTable)
44 
45  # Set the parameter to choose the type of the output matrix
46  algorithm.parameter.outputMatrixType = covariance.correlationMatrix
47 
48  # Get computed correlation
49  res = algorithm.compute()
50 
51  printNumericTable(res.get(covariance.correlation), "Correlation matrix (upper left square 10*10) :", 10, 10)
52  printNumericTable(res.get(covariance.mean), "Mean vector:", 1, 10)

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