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: out_detect_bacon_dense_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.
 ## \example out_detect_bacon_dense_batch.py
 import os
 import sys
 from daal.algorithms import bacon_outlier_detection
 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 printNumericTables
 DAAL_PREFIX = os.path.join('..', 'data')
 # Input data set parameters
 datasetFileName = os.path.join(DAAL_PREFIX, 'batch', 'outlierdetection.csv')
 if __name__ == "__main__":
     # Initialize FileDataSource<CSVFeatureManager> to retrieve the test data from a .csv file
     dataSource = FileDataSource(
     # Retrieve the data from the input file
     # Create an algorithm to detect outliers using the Bacon method
     algorithm = bacon_outlier_detection.Batch()
     algorithm.input.set(bacon_outlier_detection.data, dataSource.getNumericTable())
     # Compute outliers amd get the computed results
     res = algorithm.compute()
         dataSource.getNumericTable(), res.get(bacon_outlier_detection.weights),
         "Input data", "Weights",
         "Outlier detection result (Bacon method)"
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