ai_mouse/CHANGELOG.md

Changelog

All notable changes to this project will be documented here. Format follows Keep a Changelog; versioning follows Semantic Versioning.

[Unreleased]

Changed

• Mouse post-processing pipeline reworked to remove endpoint artifacts: hard forward clip → backtrack-tolerant soft monotonic with tanh overshoot compression (soften_forward); tail-drag endpoint snapping → whole-curve smoothstep residual correction (warp_endpoints); abrupt start damping → continuous smoothstep damping (damp_start); gaussian smoothing now applied to both axes.
• tests/unit/data/golden_mouse.npz re-baselined against the new pipeline (intentional behavior change; scroll goldens unchanged).

[0.2.0] - 2026-05-12

Changed (breaking)

• Inference no longer requires PyTorch. Runtime dependencies are now numpy + onnxruntime only.
• Public API additions: MouseModel and ScrollModel classes wrapping a persistent ORT InferenceSession.
• Function signatures generate() and generate_scroll() are now keyword-only past the positional start/end (or start_scroll_y/target_scroll_y).
• New parameters: click=True (mouse), seed= (both), viewport_height= (scroll).
• Removed config= parameter; use kwargs directly.
• model_dir= renamed to model_path=; accepts str or pathlib.Path.
• start_scrollY / target_scrollY renamed to start_scroll_y / target_scroll_y.
• Training, web UI, collector, eval, and data adapter code moved to repo-level tools/; no longer packaged in the wheel.

Added

• ONNX-format pre-trained weights bundled inside the wheel via importlib.resources (~3 MB).
• tools/export_onnx.py script with PyTorch/ORT parity check.
• Errors namespace ai_mouse.errors with AiMouseError, ModelLoadError, GenerationError.
• Custom ORT providers parameter for GPU / DirectML.
• Per-process lru_cache so generate() / generate_scroll() reuse the default model across calls.

Removed

• Legacy JointCVAE class.
• ai_mouse.config.GenerateConfig top-level export (parameters moved to kwargs).
• Source dependency on scipy.stats.truncnorm (replaced by numpy rejection sampling).