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Review algorithms and data structures looking for optimization opportunities. The idea is to prioritize good design, code readability etc but without wasting time/memory unnecessarily.
Measure performance (time/memory) against standard MeTTa interpreter spaces
Eventually, other downstream called classes may be refactored as well. Two known issue are
The test-cases to benchmark can be extracted from MeTTa examples provided in the MeTTa interpreter repo: https://github.com/trueagi-io/hyperon-experimental but it'd be interesting to collect more test MeTTa code from users. There's a channel in Mattermost for MeTTa user group ("Metta Coders" in public World server)
The text was updated successfully, but these errors were encountered:
EPIC
das-query-engine
withdas-atom-db
after refactoring work das-query-engine#304AtomDB.get_matched_links
and related code das-atom-db#185AtomDB.get_atom
method das-atom-db#182Dict
withdataclasses
when possible das-atom-db#162get_atom_as_dict()
das-atom-db#126Dict
withdataclass
when possible das-query-engine#283Goals:
Eventually, other downstream called classes may be refactored as well. Two known issue are
get_atom_as_dict()
das-atom-db#126but there may be others.
The test-cases to benchmark can be extracted from MeTTa examples provided in the MeTTa interpreter repo: https://github.com/trueagi-io/hyperon-experimental but it'd be interesting to collect more test MeTTa code from users. There's a channel in Mattermost for MeTTa user group ("Metta Coders" in public World server)
The text was updated successfully, but these errors were encountered: