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The normalizer estimator scales the samples independently by the sample's norm (l1, l2). Use the IncrementalBasicStatistics
estimator to generate the sum squared data and use it for generating only the l2 version of the normalizer. Investigate where
the new implementation may be low performance and include guards in the code to use Scikit-learn as necessary. The final
deliverable would be to add this estimator to the 'spmd' interfaces which are effective on MPI-enabled supercomputers, this
will use the underlying MPI-enabled mean and variance calculators in IncrementalBasicStatistics. This is an easy difficulty project,
and would be a medium time commitment when combined with other pre-processing projects.
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The normalizer estimator scales the samples independently by the sample's norm (l1, l2). Use the IncrementalBasicStatistics
estimator to generate the sum squared data and use it for generating only the l2 version of the normalizer. Investigate where
the new implementation may be low performance and include guards in the code to use Scikit-learn as necessary. The final
deliverable would be to add this estimator to the 'spmd' interfaces which are effective on MPI-enabled supercomputers, this
will use the underlying MPI-enabled mean and variance calculators in IncrementalBasicStatistics. This is an easy difficulty project,
and would be a medium time commitment when combined with other pre-processing projects.
https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Normalizer.html
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