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.
# file: cov_csr_batch.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. #=============================================================================== ## <a name="DAAL-EXAMPLE-PY-COVARIANCE_CSR_BATCH"></a> ## \example cov_csr_batch.py import os import sys from daal.algorithms import covariance 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 matrix is stored in one-based sparse row storage format datasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'covcormoments_csr.csv') if __name__ == "__main__": # Read datasetFileName from file and create numeric table for storing input data dataTable = createSparseTable(datasetFileName) # Create algorithm to compute covariance matrix using default method algorithm = covariance.Batch() algorithm.input.set(covariance.data, dataTable) # Get computed covariance res = algorithm.compute() printNumericTable(res.get(covariance.covariance), "Covariance matrix (upper left square 10*10) :", 10, 10) printNumericTable(res.get(covariance.mean), "Mean vector:", 1, 10)