"TotalGenerationsCount" returns the largest generation of any edge during the evolution:
In[] := WolframModel[{{1, 2}} -> {{1, 3}, {1, 3}, {3, 2}}, {{1, 1}},
<|"MaxEvents" -> 42|>, "TotalGenerationsCount"]
Out[] = 5"CompleteGenerationsCount" yields the number of generations that are "completely done". That is, no more matches
can be made involving this or earlier generations. If the
default evaluation order is used, this can only be either the same
as "TotalGenerationsCount" (if we just finished a step) or one less (if we are in the middle of a step). However, it
gets much more interesting if a different event order is used. For a random evolution, for instance, one can get
In[] := WolframModel[{{1, 2}} -> {{1, 3}, {1, 3}, {3, 2}}, {{1, 1}},
<|"MaxEvents" -> 42|>, "EventOrderingFunction" -> "Random"]
Note, in this case, only one generation is complete, and seven are partial. That happens because the states grow with each generation, so it becomes more likely for a random choice to pick an edge from a later generation. Thus earlier ones are left unevolved.
"PartialGenerationsCount" is simply a difference of "TotalGenerationsCount" and "CompleteGenerationsCount",
and "GenerationsCount" is equivalent to {"CompleteGenerationsCount", "PartialGenerationsCount"}.
"GenerationComplete" takes a generation number as an argument, and
gives True
or False depending on whether that particular generation is
complete:
In[] := WolframModel[{{1, 2}} -> {{1, 3}, {1, 3}, {3, 2}}, {{1, 1}},
<|"MaxEvents" -> 42|>]["GenerationComplete", 5]
Out[] = False