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

pca_svd_dense_batch.py

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40 
41 ## <a name="DAAL-EXAMPLE-PY-PCA_SVD_DENSE_BATCH"></a>
42 ## \example pca_svd_dense_batch.py
43 
44 import os
45 import sys
46 
47 from daal.algorithms import pca
48 from daal.data_management import FileDataSource, DataSourceIface
49 
50 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
51 if utils_folder not in sys.path:
52  sys.path.insert(0, utils_folder)
53 from utils import printNumericTable
54 
55 DAAL_PREFIX = os.path.join('..', 'data')
56 
57 # Input data set parameters
58 dataFileName = os.path.join(DAAL_PREFIX, 'batch', 'pca_normalized.csv')
59 nVectors = 1000
60 
61 if __name__ == "__main__":
62 
63  # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
64  dataSource = FileDataSource(
65  dataFileName, DataSourceIface.doAllocateNumericTable, DataSourceIface.doDictionaryFromContext
66  )
67 
68  # Retrieve the data from the input file
69  dataSource.loadDataBlock(nVectors)
70 
71  # Create an algorithm for principal component analysis using the SVD method
72  algorithm = pca.Batch(method=pca.svdDense)
73 
74  # Set the algorithm input data
75  algorithm.input.setDataset(pca.data, dataSource.getNumericTable())
76 
77  # Compute results of the PCA algorithm
78  result = algorithm.compute()
79 
80  # Print the results
81  printNumericTable(result.get(pca.eigenvalues), "Eigenvalues:")
82  printNumericTable(result.get(pca.eigenvectors), "Eigenvectors:")

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