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

pca_cor_csr_online.py

1 # file: pca_cor_csr_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 covariance
51 from daal.algorithms import pca
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, createSparseTable
57 
58 DAAL_PREFIX = os.path.join('..', 'data')
59 
60 # Input data set parameters
61 nBlocks = 4
62 datasetFileNames = [
63  os.path.join(DAAL_PREFIX, 'distributed', 'covcormoments_csr_1.csv'),
64  os.path.join(DAAL_PREFIX, 'distributed', 'covcormoments_csr_2.csv'),
65  os.path.join(DAAL_PREFIX, 'distributed', 'covcormoments_csr_3.csv'),
66  os.path.join(DAAL_PREFIX, 'distributed', 'covcormoments_csr_4.csv')
67 ]
68 
69 if __name__ == "__main__":
70 
71  # Create an algorithm for principal component analysis using the correlation method
72  algorithm = pca.Online(fptype=np.float64)
73 
74  # Use covariance algorithm for sparse data inside the PCA algorithm
75  algorithm.parameter.covariance = covariance.Online(fptype=np.float64,method=covariance.fastCSR)
76 
77  for i in range(nBlocks):
78  # Read data from a file and create a numeric table to store input data
79  dataTable = createSparseTable(datasetFileNames[i])
80 
81  # Set input objects for the algorithm
82  algorithm.input.setDataset(pca.data, dataTable)
83 
84  # Update PCA decomposition
85  algorithm.compute()
86 
87  # Finalize computations
88  result = algorithm.finalizeCompute()
89 
90  printNumericTable(result.get(pca.eigenvalues), "Eigenvalues:")
91  printNumericTable(result.get(pca.eigenvectors), "Eigenvectors:")

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