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

pca_cor_dense_online.py

1 # file: pca_cor_dense_online.py
2 #===============================================================================
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40 #===============================================================================
41 
42 
44 
45 import os
46 import sys
47 
48 import numpy as np
49 
50 from daal.algorithms import pca
51 from daal.data_management import FileDataSource, DataSourceIface
52 
53 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
54 if utils_folder not in sys.path:
55  sys.path.insert(0, utils_folder)
56 from utils import printNumericTable
57 
58 DAAL_PREFIX = os.path.join('..', 'data')
59 
60 # Input data set parameters
61 nVectorsInBlock = 250
62 dataFileName = os.path.join(DAAL_PREFIX, 'online', 'pca_normalized.csv')
63 
64 if __name__ == "__main__":
65 
66  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
67  dataSource = FileDataSource(
68  dataFileName, DataSourceIface.doAllocateNumericTable,
69  DataSourceIface.doDictionaryFromContext
70  )
71 
72  # Create an algorithm for principal component analysis using the correlation method
73  algorithm = pca.Online(fptype=np.float64)
74 
75  while(dataSource.loadDataBlock(nVectorsInBlock) == nVectorsInBlock):
76  # Set the input data to the algorithm
77  algorithm.input.setDataset(pca.data, dataSource.getNumericTable())
78 
79  # Update PCA decomposition
80  algorithm.compute()
81 
82  result = algorithm.finalizeCompute()
83 
84  # Print the results
85  printNumericTable(result.get(pca.eigenvalues), "Eigenvalues:")
86  printNumericTable(result.get(pca.eigenvectors), "Eigenvectors:")

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