Python* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 4

svd_dense_online.py

1 # file: svd_dense_online.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 
17 
18 
19 import os
20 import sys
21 import numpy as np
22 
23 from daal.algorithms import svd
24 from daal.data_management import FileDataSource, DataSourceIface
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
30 
31 DAAL_PREFIX = os.path.join('..', 'data')
32 
33 # Input data set parameters
34 nRowsInBlock = 4000
35 dataFileName = os.path.join(DAAL_PREFIX, 'batch', 'svd.csv')
36 
37 if __name__ == "__main__":
38 
39  # Initialize FileDataSource to retrieve input data from .csv file
40  dataSource = FileDataSource(
41  dataFileName,
42  DataSourceIface.doAllocateNumericTable,
43  DataSourceIface.doDictionaryFromContext
44  )
45 
46  # Create algorithm object to compute SVD decomposition in online mode
47  algorithm = svd.Online(fptype=np.float64)
48 
49  while dataSource.loadDataBlock(nRowsInBlock):
50  # Set input arguments of the algorithm
51  algorithm.input.set(svd.data, dataSource.getNumericTable())
52 
53  # Compute partial SVD decomposition estimates
54  algorithm.compute()
55 
56  # Finalize online result and get computed SVD decomposition
57  res = algorithm.finalizeCompute()
58 
59  # Print results
60  printNumericTable(res.get(svd.singularValues), "Singular values:")
61  printNumericTable(res.get(svd.rightSingularMatrix), "Right orthogonal matrix V:")
62  printNumericTable(res.get(svd.leftSingularMatrix), "Left orthogonal matrix U:", 10)

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