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Author:
Tyson A. Neuroth
Summary:
Construct epsilon-machines to generate symbol sequences with ground truth causal structure and information-theoretic complexity for studying neural network learning dynamics.
Latest version:
0.3.0
Required dependencies:
automata-lib
|
graphviz
|
hnswlib
|
imageio
|
lemminflect
|
matplotlib
|
nanobind
|
networkx
|
nltk
|
numba
|
numpy
|
openai
|
orjson
|
ortools
|
pdoc
|
pyarrow
|
pydantic-settings
|
pyenchant
|
python-ternary
|
pytoulbar2
|
scikit-umfpack
|
scipy
|
spacy
|
sympy
|
unidecode
|
word-forms
Optional dependencies:
a-machine
|
aim
|
cupy-cuda13x
|
cuvs-cu13
|
datasets
|
fastapi
|
nnsight
|
orjson
|
pydantic
|
pytest
|
pytorch-optimizer
|
torch
|
transformers
|
uvicorn
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