Cattykit
Cattykit is a framework for implementing artificial intelligence models based on components from the cognitive models of the Fluid Analogies Research Group including a shared workspace, slipnet, coderack, and codelets.
Usage
To install and run a model once on a single problem:
import cattykit
from cattykit.logging import PrintLogger
# Install a model package once. The source can also be a model name or HTTPS URL.
cattykit.install_model("./models/copycat")
print(cattykit.available_models())
# ("copycat",)
logger = PrintLogger()
try:
copycat = cattykit.load_model(
"copycat",
config={"seed": 1234},
logger=logger,
)
copycat.solve("abc -> abd ==> ijk -> ?")
finally:
logger.close()
To run a suite of problems on a single model:
from cattykit import install_model, run_experiment
# Install the model if necessary.
cattykit.install_model("./models/copycat")
results_db = run_experiment(
model_name="copycat",
problems=[
"abc -> abd ==> ijk -> ?",
"abc -> abc ==> iijjkk -> ?",
"abc -> abc ==> kji -> ?",
"abc -> abc ==> mrrjjj -> ?",
"abc -> abc ==> xyz -> ?",
],
iterations=1000
)
print(results_db)
This will generate a database located at results_db containing logs of the runs and summary statistics. These can be explored using the Cattycam package.
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