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Intrinsic surprise scoring for PyTorch via statistical coding.

Project description

Nervecode Docs

This directory will contain documentation for the Nervecode project.

Developer Setup

  • Install dev tools (in your virtualenv): pip install -e .[dev]
  • Install git hooks: pre-commit install
  • Run on all files once: pre-commit run --all-files

Included hooks:

  • Formatting: ruff-format
  • Linting and import sorting: ruff (with --fix)
  • Type-checking: mypy

Tests

  • Unit tests: pytest tests/unit (fast correctness checks)
  • Integration tests: pytest tests/integration (heavier, cross-module)
  • Smoke tests: pytest tests/smoke (repo wiring and API surface)

Deterministic runs

  • Tests auto-seed RNGs per test using NERVECODE_SEED (default: 1234).
  • You can control reproducibility in your own scripts via:
    from nervecode.utils.seed import seed_everything, seed_from_env
    seed_everything(seed_from_env())
    

Performance Notes

  • Overhead estimates and guidance for pooled Conv2d coding: see docs/overhead.md.
  • Scaling and tradeoffs for layer selection and coding dimension: see docs/scaling.md.

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