tune hyperparams, add README, simplify training loop

- reduce WIN to 3 and H_SIZE to 32 for faster iteration
- increase NUM_EPOCHS to 1000 and collapse NUM_ITERATIONS to a single pass
- add README_JayRnn.md with algorithm description and ASCII architecture diagrams

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

JayRnn.py

@@ -24,13 +24,13 @@ from rbm.train import train
 from rbm.status import Status
 
 # ── Hyper-parameters ──────────────────────────────────────────────────────────
-TEXT    = "  HALLO SUPER JENS UND SUPER MAUSI!" # the character sequence to learn (matches diagram example)
-WIN    = 5         # sliding-window width  (= N in the diagram)
+TEXT    = "HALLO SUPER JENS UND SUPER MAUSI!" # the character sequence to learn (matches diagram example)
+WIN    = 3         # sliding-window width  (= N in the diagram)
 STRIDE = 1     	   # sliding-window step size
 UNITS  = 1
-H_SIZE = 180         # hidden units per RBM cell
-NUM_EPOCHS = 100
-NUM_ITERATIONS = 10
+H_SIZE = 32         # hidden units per RBM cell
+NUM_EPOCHS = 1000
+NUM_ITERATIONS = 1
 
 TRAIN_PARAMS = TrainingParams(
 	learning_rate    = 0.04,
@@ -80,7 +80,7 @@ class RnnModel(Model):
 	def train(self, vc: Mat, status: Status = None):
 		for unit in self.units:
 			train(unit, vc, status)
-			# Update context portion of vc
+			# For the next unit: Update context portion of vc
 			_c = unit.forward(vc)
 			vc[1:, WIN*vocab_size():] = _c[0:-1,:]
 
@@ -120,9 +120,8 @@ if __name__ == "__main__":
 	# context will be updated after training
 	c_train = np.zeros([len(batch), H_SIZE])
 	vc_train = concat(batch, c_train, axis=1)
-	for k in range(NUM_ITERATIONS):
-		model.train(vc_train, status=Status())
-		model.save()
+	model.train(vc_train, status=Status())
+	model.save()
 
 	# test the model
 	seed_str = 'HA^'