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Updates to Epstein example #2429
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* shift to von neuman grid * update of perceived risk with round operator * introduction of enum for state * cleanup of statistics gathering to use enum * update of visualization code to be consistent with what is currently supported when drawing new_style discrete grids
for more information, see https://pre-commit.ci
for more information, see https://pre-commit.ci
Performance benchmarks:
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for cell in self.grid.all_cells: | ||
if self.random.random() < self.cop_density: | ||
cop = Cop(self, vision=self.cop_vision) | ||
cop.move_to(cell) | ||
klass = self.random.choices( | ||
[Citizen, Cop, None], | ||
cum_weights=[citizen_density, citizen_density + cop_density, 1], |
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Seems a bit complicated. Can't we just generate cop_density * cells cops and citizen_density * cells citizens, and place them randomly?
Or aren't they allowed to start at the same cell?
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There can be at most one agent per cell.
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Few minor comments, generally seems like a nice improvement.
Have you any idea about performance?
I haven't done any testing, this was really focussed on getting the proper behavior back. |
for more information, see https://pre-commit.ci
This is an update to the Epstein model to address #2423. Next to shifting to a von Neumann grid, there are several other changes.
round
. This is based on an extensive literature that shows that without it, the original Epstein results cannot be reproduced.The net result is shown below.