Python* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 1

pca_svd_dense_distr.py

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40 
41 
42 
43 
44 import os
45 import sys
46 
47 import daal
48 from daal.algorithms import pca
49 from daal.data_management import FileDataSource, DataSourceIface
50 
51 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
52 if utils_folder not in sys.path:
53  sys.path.insert(0, utils_folder)
54 from utils import printNumericTable
55 
56 DAAL_PREFIX = os.path.join('..', 'data')
57 
58 # Input data set parameters
59 nBlocks = 4
60 nVectorsInBlock = 250
61 
62 dataFileNames = [
63  os.path.join(DAAL_PREFIX, 'distributed', 'pca_normalized_1.csv'),
64  os.path.join(DAAL_PREFIX, 'distributed', 'pca_normalized_2.csv'),
65  os.path.join(DAAL_PREFIX, 'distributed', 'pca_normalized_3.csv'),
66  os.path.join(DAAL_PREFIX, 'distributed', 'pca_normalized_4.csv')
67 ]
68 
69 if __name__ == "__main__":
70 
71  # Create an algorithm for principal component analysis using the SVD method on the master node
72  masterAlgorithm = pca.Distributed(step=daal.step2Master, method=pca.svdDense)
73 
74  for i in range(nBlocks):
75  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
76  dataSource = FileDataSource(
77  dataFileNames[i], DataSourceIface.doAllocateNumericTable,
78  DataSourceIface.doDictionaryFromContext
79  )
80 
81  # Retrieve the input data
82  dataSource.loadDataBlock(nVectorsInBlock)
83 
84  # Create an algorithm for principal component analysis using the SVD method on the local node
85  localAlgorithm = pca.Distributed(step=daal.step1Local, method=pca.svdDense)
86 
87  # Set the input data to the algorithm
88  localAlgorithm.input.setDataset(pca.data, dataSource.getNumericTable())
89 
90  # Compute PCA decomposition
91  # Set local partial results as input for the master-node algorithm
92  masterAlgorithm.input.add(pca.partialResults, localAlgorithm.compute())
93 
94  # Merge and finalize PCA decomposition on the master node
95  masterAlgorithm.compute()
96  result = masterAlgorithm.finalizeCompute()
97 
98  # Print the results
99  printNumericTable(result.get(pca.eigenvalues), "Eigenvalues:")
100  printNumericTable(result.get(pca.eigenvectors), "Eigenvectors:")

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