pca_svd_dense_distr.py

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 # file: pca_svd_dense_distr.py
 #===============================================================================
 # Copyright 2014-2019 Intel Corporation.
 #
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 # your use of  them is  governed by the  express license  under which  they were
 # provided to you (License).  Unless the License provides otherwise, you may not
 # use, modify, copy, publish, distribute,  disclose or transmit this software or
 # the related documents without Intel's prior written permission.
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 # This software and the related documents  are provided as  is,  with no express
 # or implied  warranties,  other  than those  that are  expressly stated  in the
 # License.
 #===============================================================================
 
 ## <a name="DAAL-EXAMPLE-PY-PCA_SVD_DENSE_DISTRIBUTED"></a>
 ## \example pca_svd_dense_distr.py
 
 import os
 import sys
 
 import daal
 from daal.algorithms import pca
 from daal.data_management import FileDataSource, DataSourceIface
 
 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
 if utils_folder not in sys.path:
     sys.path.insert(0, utils_folder)
 from utils import printNumericTable
 
 DAAL_PREFIX = os.path.join('..', 'data')
 
 # Input data set parameters
 nBlocks = 4
 nVectorsInBlock = 250
 
 dataFileNames = [
     os.path.join(DAAL_PREFIX, 'distributed', 'pca_normalized_1.csv'),
     os.path.join(DAAL_PREFIX, 'distributed', 'pca_normalized_2.csv'),
     os.path.join(DAAL_PREFIX, 'distributed', 'pca_normalized_3.csv'),
     os.path.join(DAAL_PREFIX, 'distributed', 'pca_normalized_4.csv')
 ]
 
 if __name__ == "__main__":
 
     # Create an algorithm for principal component analysis using the SVD method on the master node
     masterAlgorithm = pca.Distributed(step=daal.step2Master, method=pca.svdDense)
 
     for i in range(nBlocks):
         # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
         dataSource = FileDataSource(
             dataFileNames[i], DataSourceIface.doAllocateNumericTable,
             DataSourceIface.doDictionaryFromContext
         )
 
         # Retrieve the input data
         dataSource.loadDataBlock(nVectorsInBlock)
 
         # Create an algorithm for principal component analysis using the SVD method on the local node
         localAlgorithm = pca.Distributed(step=daal.step1Local, method=pca.svdDense)
 
         # Set the input data to the algorithm
         localAlgorithm.input.setDataset(pca.data, dataSource.getNumericTable())
 
         # Compute PCA decomposition
         # Set local partial results as input for the master-node algorithm
         masterAlgorithm.input.add(pca.partialResults, localAlgorithm.compute())
 
     # Merge and finalize PCA decomposition on the master node
     masterAlgorithm.compute()
     result = masterAlgorithm.finalizeCompute()
 
     # Print the results
     printNumericTable(result.get(pca.eigenvalues), "Eigenvalues:")
     printNumericTable(result.get(pca.eigenvectors), "Eigenvectors:")
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