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_csr_online.py
 # Copyright 2014-2019 Intel Corporation.
 # This software and the related documents are Intel copyrighted  materials,  and
 # 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.
 # 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.
 ## \example pca_cor_csr_online.py
 import os
 import sys
 import numpy as np
 from daal.algorithms import covariance
 from daal.algorithms import pca
 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, createSparseTable
 DAAL_PREFIX = os.path.join('..', 'data')
 # Input data set parameters
 nBlocks = 4
 datasetFileNames = [
     os.path.join(DAAL_PREFIX, 'distributed', 'covcormoments_csr_1.csv'),
     os.path.join(DAAL_PREFIX, 'distributed', 'covcormoments_csr_2.csv'),
     os.path.join(DAAL_PREFIX, 'distributed', 'covcormoments_csr_3.csv'),
     os.path.join(DAAL_PREFIX, 'distributed', 'covcormoments_csr_4.csv')
 if __name__ == "__main__":
     # Create an algorithm for principal component analysis using the correlation method
     algorithm = pca.Online(fptype=np.float64)
     # Use covariance algorithm for sparse data inside the PCA algorithm
     algorithm.parameter.covariance = covariance.Online(fptype=np.float64,method=covariance.fastCSR)
     for i in range(nBlocks):
         # Read data from a file and create a numeric table to store input data
         dataTable = createSparseTable(datasetFileNames[i])
         # Set input objects for the algorithm
         algorithm.input.setDataset(pca.data, dataTable)
         # Update PCA decomposition
     # Finalize computations
     result = algorithm.finalizeCompute()
     printNumericTable(result.get(pca.eigenvalues), "Eigenvalues:")
     printNumericTable(result.get(pca.eigenvectors), "Eigenvectors:")
For more complete information about compiler optimizations, see our Optimization Notice.
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