Update example and notes

example/README.md

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+# Example: HAI 21.03 Feature Split
+
+This folder contains a small, reproducible example that inspects the HAI 21.03
+CSV (train1) and produces a continuous/discrete split using a simple heuristic.
+
+## Files
+- analyze_hai21_03.py: reads a sample of the data and writes results.
+- data_utils.py: CSV loading, vocab, normalization, and batching helpers.
+- feature_split.json: column split for HAI 21.03.
+- hybrid_diffusion.py: hybrid model + diffusion utilities.
+- prepare_data.py: compute vocab and normalization stats.
+- train_stub.py: end-to-end scaffold for loss computation.
+- train.py: minimal training loop with checkpoints.
+- sample.py: minimal sampling loop.
+- model_design.md: step-by-step design notes.
+- results/feature_split.txt: comma-separated feature lists.
+- results/summary.txt: basic stats (rows sampled, column counts).
+
+## Run
+```
+python /home/anay/Dev/diffusion/mask-ddpm/example/analyze_hai21_03.py
+```
+
+Prepare vocab + stats (writes to `example/results`):
+```
+python /home/anay/Dev/diffusion/mask-ddpm/example/prepare_data.py
+```
+
+Train a small run:
+```
+python /home/anay/Dev/diffusion/mask-ddpm/example/train.py
+```
+
+Sample from the trained model:
+```
+python /home/anay/Dev/diffusion/mask-ddpm/example/sample.py
+```
+
+## Notes
+- Heuristic: integer-like values with low cardinality (<=10) are treated as
+  discrete. All other numeric columns are continuous.
+- The script only samples the first 5000 rows to stay fast.
+- `prepare_data.py` runs without PyTorch, but `train.py` and `sample.py` require it.
+- `train.py` and `sample.py` auto-select GPU if available; otherwise they fall back to CPU.