aic_from_rss — DRIVE `ball` op

• Data kinds: none → scalar (an op determined by its arguments alone — it takes no image or data input)

• Call: import fullseye as fs; fs.ledger.aic_from_rss(rss: 'float', n: 'int', k: 'int') -> 'float' (to call the implementation directly, import ballistics; ballistics.aic_from_rss(rss: 'float', n: 'int', k: 'int') -> 'float'; from the registry, opsdrive.get("aic_from_rss"))

• GPU: none (CPU reference implementation only)

Usage

Akaike information criterion from a least-squares residual sum of squares: AIC = n ln(RSS/n) + 2k (Gaussian noise, variance estimated by maximum likelihood, constant term dropped). Only differences between models fitted to the same data are meaningful (the absolute value depends on scale and n). n ≤ k, RSS ≤ 0 and non-finite values raise ValueError (RSS = 0 is AIC = −∞ and the model-selection question does not arise).

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• poc_video_physics_consistency — py -3.11 examples/poc_video_physics_consistency.py

Ops the type connects to (they accept `scalar` as input)

spin_from_marker_sequence · carla_transform_matrix · carla_pose_to_world · carla_camera_pose · carla_xy_yaw · carla_scene_load · town_chain · osm_parse

Same category (`ball`)

ball_params · impact_params · flight_vacuum · flight_ode · flight_simulate · flight_state_at · magnus_lift_coefficient · drag_coefficient_sphere

*Provenance: ballistics.py — DRIVE operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

aic_from_rss — DRIVE ball op