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

svd_dense_online.py

1 # file: svd_dense_online.py
2 #===============================================================================
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40 #===============================================================================
41 
42 
43 
44 
45 import os
46 import sys
47 import numpy as np
48 
49 from daal.algorithms import svd
50 from daal.data_management import FileDataSource, DataSourceIface
51 
52 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
53 if utils_folder not in sys.path:
54  sys.path.insert(0, utils_folder)
55 from utils import printNumericTable
56 
57 DAAL_PREFIX = os.path.join('..', 'data')
58 
59 # Input data set parameters
60 nRowsInBlock = 4000
61 dataFileName = os.path.join(DAAL_PREFIX, 'batch', 'svd.csv')
62 
63 if __name__ == "__main__":
64 
65  # Initialize FileDataSource to retrieve input data from .csv file
66  dataSource = FileDataSource(
67  dataFileName,
68  DataSourceIface.doAllocateNumericTable,
69  DataSourceIface.doDictionaryFromContext
70  )
71 
72  # Create algorithm object to compute SVD decomposition in online mode
73  algorithm = svd.Online(fptype=np.float64)
74 
75  while dataSource.loadDataBlock(nRowsInBlock):
76  # Set input arguments of the algorithm
77  algorithm.input.set(svd.data, dataSource.getNumericTable())
78 
79  # Compute partial SVD decomposition estimates
80  algorithm.compute()
81 
82  # Finalize online result and get computed SVD decomposition
83  res = algorithm.finalizeCompute()
84 
85  # Print results
86  printNumericTable(res.get(svd.singularValues), "Singular values:")
87  printNumericTable(res.get(svd.rightSingularMatrix), "Right orthogonal matrix V:")
88  printNumericTable(res.get(svd.leftSingularMatrix), "Left orthogonal matrix U:", 10)

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