2 Commits

Author	SHA1	Message	Date
lars	72bd65ff9f	Add post_pos as a model target via travel_dir decomposition ... step_length already encodes |post_pos - pre_pos| by definition, so a raw post_pos target would duplicate that magnitude and could drift inconsistent with step_length during sampling. Instead add travel_dir, a unit vector (local frame) giving only the direction of pre_pos->post_pos; post_pos is reconstructed at inference as pre_pos + step_length * travel_dir, keeping the two self-consistent. Target grows from 6D to 9D. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-06-18 10:36:55 +02:00
lars	9277d79dff	Implement Phase 1: full data pipeline, model, training, and config support ... - Data pipeline: loader (parquet→numpy), transforms (log, local-frame Rodrigues rotation, Normalizer), StepsDataset with event-ID-based split - Model: SinusoidalEmbedding, ConditionEncoder, ResBlock, DenoisingMLP - Schedule: cosine DDPM and conditional flow matching loss (Lipman 2022) - Samplers: flow (Euler ODE), DDPM ancestral, DDIM deterministic - Training loop: AdamW + cosine LR, grad clipping, best-val checkpoint - Validation: per-dimension marginal summary (normalised space) - CLI: TOML config support with CLI-overrides; hyperparam-encoded output directory; config.toml with git hash saved into each run's checkpoint dir - 21 unit tests covering transforms, network, flow/DDPM losses, dataset splits Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-06-17 10:48:03 +02:00