Run multiple experiments for the same model with different input data in a loop

Scalar values are simply replaced when they are assigned new values. If you are re-initializing an array with new data there are 2 cases: (a) the new data set defines the same index tuples: reading will overwrite existing contents, so there is no need for 'reset' or dynamic; (b) the data sets have different index tuples: you need to make sure to clear the entire array before re-initializing it, this is done when applying 'reset' to a sparse array (array declared as 'dynamic' or 'hashmap'). The 'reset' needs to be called individually for every array.

An alternative to the above might be to move the contents of the loop along with the declarations into a subroutine so that the loop invokes a subroutine in every iteration: by moving declarations into a subroutine you turn them into local entities that only exist within the respective subroutine, that is, there is no need for an explicit call to 'reset' as the arrays will disappear at the end of the subroutine where they have been declared.