# Sample data — NBA (100 scenes)

A **tiny smoke-test subset** so you can verify the code runs end-to-end before wiring up the
full datasets. **Not** for reproducing any reported number.

```
sample_data/nba/original/nba_train.npy   # (100, 30, 11, 2) float32, 258 KB
sample_data/nba/original/nba_test.npy    # (100, 30, 11, 2) float32, 258 KB
```

* Layout: `(scenes, frames, agents, xy)` — 30 frames @ 5 Hz = 10 past + 20 future (4.0 s),
  11 agents (10 players + ball, ball = index 10), absolute court coordinates.
* Each file is the **exact first-100 prefix** of the corresponding full split
  (`trajs[:100]`), which is the same slice the loaders take — so scene indices line up
  with the full dataset.
* Full splits are 32 500 train / 12 500 test scenes.

## Use it

`--data_dir` must point at the **parent** of `original/` (the loader appends `original/` itself):

```bash
cd MoFlow
CUDA_VISIBLE_DEVICES=0 python fm_nba_graph_v6.py \
  --cfg cfg/nba/cor_fm.yml --exp smoke \
  --data_dir ../sample_data/nba \
  --n_train 100 --n_test 100 \
  --batch_size 8 --epochs 1 \
  --fm_in_scaling --tied_noise --top_n_neighbors 5 --uncertainty_weight 0.01
```

Verified to load and collate:

```
past_traj                 (B, 11, 10, 6)
past_traj_original_scale  (B, 11, 10, 6)
fut_traj_original_scale   (B, 11, 20, 2)
```

> With only 100 scenes the model cannot learn anything meaningful — expect high ADE/FDE.
> This subset exists purely to confirm the data path, model construction, training step and
> evaluation loop all execute.

## Provenance

Derived from the NBA player-tracking (SportVU) movement data as preprocessed by prior
trajectory-prediction work. Redistributed here only as a minimal fixture for code testing;
please refer to the original data source for licensing and terms of use.