refactor RnnModel: multi-unit delay training and unified vc interface

- Add batch_delay() to shift visible input per unit index
- Unify forward_step() to work with combined vc matrix
- Fix split() to always slice on axis=1
- Add index param to Entity for readable naming
- Rename test_xor.py to xor.py, replace Mat with np.array

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

tests/xor.py

@@ -0,0 +1,46 @@
+import os.path
+
+from rbm.layer import Layer
+from rbm.status import Status
+from rbm.train import train
+from rbm.matrix import Mat, np
+from rbm.entity import EntityParams, TrainingParams
+
+WORK_DIR = "../../results"
+USE_OPTIMIZER = True
+
+def xor():
+	# Create params
+	entity_params = EntityParams()
+	training_params = TrainingParams()
+	entity_params.do_rao_blackwell = True
+	entity_params.num_gibbs_samples = 3
+
+	# Create layer
+	layer = Layer("Layer_0", (3, 1, 0, 16), entity_params, training_params)
+
+	# Init weights
+	layer.init(0.01)
+
+	# Load weights (if exists)
+	layer.load(os.path.join(WORK_DIR, "xor_layer0_state.npz"))
+
+	# Prepare training data
+	training_batch = np.array([[0,1,1], [0,0,0], [1,1,0], [1,0,1]], dtype=np.float64)
+
+	# Train layer
+	train(layer.entity, training_batch, Status())
+
+	# Save weights
+	layer.save(os.path.join(WORK_DIR, "xor_layer0_state.npz"))
+
+	# Test with test data
+	test_batch = Mat([[0,0,0], [0,1,0], [1,0,0], [1,1,0]], dtype=np.float64)
+	for pattern in test_batch:
+		h = layer.entity.forward(pattern)
+		v = layer.entity.reconstruct(h)
+		print(f"P{pattern} : {v}")
+
+if __name__ == "__main__":
+	xor()
+	print("Test: [passed]")