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[binary_autoencoder] - add BB-RBM autoencoder test and tests README

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Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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jens
2026-05-30 14:24:49 +02:00
co-authored by Claude Sonnet 4.6
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# Tests
Run any test from the `src/tests/` directory:
```bash
cd src/tests
python test_<name>.py
```
---
## Self-contained tests
These generate their own data and run without external files.
| File | RBM type | What it tests |
|---|---|---|
| `test_binary_autoencoder.py` | BB-RBM | Auto-encoder on synthetic bars-and-stripes binary images (8×8). Trains, reconstructs, and plots original vs. reconstruction. |
| `test_model.py` | BB-RBM (4 layers) | Deep auto-encoder on random binary data (1024-dim). Prints per-pattern reconstruction error. |
| `test_xor.py` | BB-RBM | Auto-encoder on XOR-like 3-bit patterns using the older `Layer` API. |
| `test_linear.py` | GB-RBM or GG-RBM | GB/GG-RBM on random continuous data (3000-dim). Toggle `do_gaussian_hidden` at the top of the file. |
| `test_sub_image.py` | — | Unit test for `SubImage` (patch extraction). No RBM involved. |
| `test_conv2d.py` | — | Unit test for `conv2d`. No RBM involved. |
---
## Tests requiring external data
These load data from `/home/jens/work/repos/Rbm/` (Armadillo `.dat` files).
| File | RBM type | What it tests |
|---|---|---|
| `test_norbs.py` | GB-RBM or GG-RBM | Single-layer auto-encoder on NORB small (96×96 images). Toggle `DO_HIDDEN_GAUSSIAN` at the top of the file. |
| `test_deep_norbs.py` | GB-RBM + BB-RBM | Two-layer deep auto-encoder: loads pre-trained GB-RBM weights, trains a BB-RBM on top. |
| `test_learn_norbs_labels.py` | GB-RBM + BB-RBM | Multimodal model combining NORB image features with one-hot label embeddings in a joint hidden layer. |
| `test_rbm.py` | Any (from `.prj` file) | Full pipeline via `StackFactory`. Takes a project name as CLI argument (`python test_rbm.py <name>`). Uses OpenCV for interactive reconstruction display. |
---
## Tests requiring saved state
These expect a trained model to already exist in `results/`.
| File | Prerequisite |
|---|---|
| `test_label.py` | Requires a saved `Label` fitter. Trains a binary label encoder (vocabulary of 100 integers → 5×5 binary codes) and visualises encoded labels. Set `do_train = True` on first run. |
| `test_learn_encoded_labels.py` | Requires a saved one-hot label fitter. Trains a BB-RBM on one-hot encoded integer labels and plots hidden representation, reconstruction, and original. Set `do_train = True` on first run. |
---
## RBM type reference
| Type | Visible | Hidden | `EntityParams` |
|---|---|---|---|
| BB-RBM | Binary | Binary | `EntityParams()` |
| GB-RBM | Gaussian | Binary | `EntityParams(do_gaussian_visible=True)` |
| BG-RBM | Binary | Gaussian | `EntityParams(do_gaussian_hidden=True)` |
| GG-RBM | Gaussian | Gaussian | `EntityParams(do_gaussian_visible=True, do_gaussian_hidden=True)` |