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When we apply a function to an argument, or when we compose two functions, we have to check that their types match.
Processing operators apply a function to each and every element of their input streams.
Though other thumbboards apply a function or shift key to the next character you type, this one makes you hold down two keys at once, even though there's only one shift key.
With the remaining KVPs, which all represent alleles, we apply a function to convert these from the strings found in VCF files (i.e. '0|1'), to doubles, where the double is the Hamming distance to the reference.
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We develop and apply a function-constrained sampling approach in order to compare disease sets with sample sets with a similar functional composition.
It is, for example, possible to apply a mapping function several times to the same mappings using different parameter sets, in order to compare the resulting mappings.
We have already encountered map, which applies a function to each element in a sequence and collects the results.
A powerful method of transforming one tuple into another is by applying a function to each element and collecting the results.
Now lets consider the reduce operation, which like map applies a function to every element of a list, but in doing so accumulates a result rather than just producing another list.
The main ideas are applying a function to an argument and forming functions by abstraction.
In detail, first for the dataset it creates workflow graph from workflow descriptions, and applies a function mapping on each trace of the workflow event log.
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