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Exploring the evolution of languages on complex networks through agent-based simulations.
Save data after every simulation by appending to an existing file.
For parquet
data, append with the append=True
argument for writing.
For csv
data, append using the Pandas to_csv
method, with mode='a'
and header=False
-- see here.
Find a way to keep it for legacy reasons, but otherwise phase out the "regenerating" network update approach. Would simplify parameters and execution and improve readability.
In order to test that all aspects of the simulation are working correctly, I could:
This way I can make sure future versions of the simulation won't affect the validity of previous results
Would be worth adding a few test cases on the deterministic seeding itself.
Need to figure out what type of package to use -- MongoDB?
Would allow the simulation to run on any type of network if using the relabel strategy.
Possibly implement through some sort of constructor object instead of checks in __init__
?
Would allow getting rid of some sim parameters and simplifying implementation in some cases.
Instead of the current learning strategy parameter, switch to the sample_influence
with a sample_proportional
or something similar, to specify whether neighbor sampling is proportional to fitness or not.
default_params
_params
from default_params
and init positional/keyword arguments_params
Use parameters to specify a certain number of reports for payoffs. Would reduce memory usage without too much loss of information, assuming the number of reports is large enough.
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