pca_cor_dense_batch.py

Deprecation Notice: With the introduction of daal4py, a package that supersedes PyDAAL, Intel is deprecating PyDAAL and will discontinue support starting with Intel® DAAL 2021 and Intel® Distribution for Python 2021. Until then Intel will continue to provide compatible pyDAAL pip and conda packages for newer releases of Intel DAAL and make it available in open source. However, Intel will not add the new features of Intel DAAL to pyDAAL. Intel recommends developers switch to and use daal4py.

Note: To find daal4py examples, refer to daal4py documentation or browse github repository.

 # file: pca_cor_dense_batch.py
 #===============================================================================
 # Copyright 2014-2019 Intel Corporation.
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 # the related documents without Intel's prior written permission.
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 #===============================================================================
 
 ## <a name="DAAL-EXAMPLE-PY-PCA_CORRELATION_DENSE_BATCH"></a>
 ## \example pca_cor_dense_batch.py
 
 import os
 import sys
 import numpy as np
 
 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
 dataFileName = os.path.join(DAAL_PREFIX, 'batch', 'pca_normalized.csv')
 nVectors = 1000
 
 if __name__ == "__main__":
     # Initialize FileDataSource<CSVFeatureManager> to retrieve the input data from a .csv file
 
     dataSource = FileDataSource(
         dataFileName, DataSourceIface.doAllocateNumericTable, DataSourceIface.doDictionaryFromContext
     )
 
     # Retrieve the data from the input file
     dataSource.loadDataBlock(nVectors)
 
     # Create an algorithm for principal component analysis using the correlation method
     algorithm = pca.Batch(fptype=np.float64)
 
     # Set the algorithm input data
     algorithm.input.setDataset(pca.data, dataSource.getNumericTable())
     algorithm.parameter.resultsToCompute = pca.mean | pca.variance | pca.eigenvalue;
     algorithm.parameter.isDeterministic = True;
 
     # Compute results of the PCA algorithm
     result = algorithm.compute()
 
     # Print the results
     printNumericTable(result.get(pca.eigenvalues), "Eigenvalues:")
     printNumericTable(result.get(pca.eigenvectors), "Eigenvectors:")
     printNumericTable(result.get(pca.means), "Means:")
     printNumericTable(result.get(pca.variances), "Variances:")
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