Hi, I'm looking to replicate the behaviour of python sklearn.preprocessing StandardScaler library. I use the 'proc standard' to achieve the result of 'transform' as is the method called in the StandardScaler. However the StandardScaler has the functionality to later inverse_transform data based on the same values that were used to transform the data. The reason this is useful is because it can do this to datasets that are not the original, but have been derived form the original (Think the genetic algorithm implementation on the standardised data). I need to 'unstandardise' the mutated standardised data... Is there something in SAS that can do this or the implementation would have do be done manually by preserving the values used to standardise the dataset and then apply them to reverse it? Thanks!
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I am writing a genetic algorithm implementation from python and I need to call some sas procedures inside the ObjectFunction that i am defining. For example when i try to use proc compare base=selected_row compare=evolving_individual outstats=compare_result noprint; run; inside my objective function i get an error saying ERROR: Subroutine 'fitness_sso' was not terminated with ENDSUB. ERROR: Execution terminating because of previous errors. If i remove the call in my objective function to the 'proc compare' then the genetic algorithm runs to completion. Any suggestions how i can call procedures inside the objective function? What about my own defined macros? If i reference my own macro like so %macro_call(var1, var2); then i also get the same error of the objective function not termintated with ENDSUB... Thanks for any help on this, but there is just not enough documentation and examples on genetic algorithm implementation.
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