Notebook reconstructs the C++ context33.prj configuration exactly:
- shared-weights StackRnn (1 entity, reused every time step)
- visible = [context(128) | x_t(40)] = 168 units, hidden = 128
- all hyperparams from .prj: lr=0.05, momentum=0.5, epochs=1000,
mini_batch=100, gibbs=3, rao_blackwell=True, weight_decay=0
Training data loaded directly from context33.training.dat (Armadillo format,
12 × 168): sensory one-hot chars decoded as 'XURQLIFB9651'.
Key design: training pairs are (h_t, x_{t+1}) not (h_{t-1}, x_t) —
context is first advanced by seeing x_t, then the model is trained to
predict the next character x_{t+1} from that context. Evaluation using
clamped Gibbs on h_t achieves 100% next-step prediction accuracy.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
