- added training params as list for model.train()

- added gaussian sample
- introduced layout concept
- updated README

src/rbm/model.py

@@ -20,11 +20,14 @@ class Model(ABC):
 				obj_list.append(value)
 		return obj_list
 
-	def train(self, batch: Mat, params: TrainingParams):
+	def train(self, batch: Mat, params: TrainingParams|list[TrainingParams]):
+		entities = self.objects(Entity)
+		if isinstance(params, TrainingParams):
+			params = [params]*len(entities)
 		_batch = np.copy(batch)
-		for entity in self.objects(Entity):
-			train(entity, _batch, params, Status(), cd_jens)
-			_batch = entity.forward(_batch, num_gibbs=params.num_gibbs_samples)
+		for entity, param in zip(entities, params):
+			train(entity, _batch, param, Status(), cd_jens)
+			_batch = entity.forward(_batch, num_gibbs=param.num_gibbs_samples)
 
 	@abstractmethod
 	def forward(self, x: Mat) -> Mat: