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

- added gaussian sample
- introduced layout concept
- updated README

src/rbm/layout.py

@@ -0,0 +1,29 @@
+import numpy as np
+
+from .entity import Entity
+from .matrix import Mat
+from .train import train, cd_jens, TrainingParams
+from .status import Status
+
+class Horizontal:
+	def __init__(self, entities: list[Entity]):
+		self.units: list[Entity] = entities
+
+	def forward(self, x: Mat):
+		res: Mat = Mat([])
+		for unit in self.units:
+			x = unit.forward(x)
+			res = np.concat((res, x))
+
+	def reconstruct(self, x: Mat):
+		res: Mat = Mat([])
+		for unit in self.units:
+			x = unit.forward(x)
+			res = np.concat((res, x))
+
+	def train(self, batch: Mat, params: TrainingParams):
+		_batch = np.copy(batch)
+		for unit in self.units:
+			train(unit, _batch, params, Status(), cd_jens)
+			_batch = unit.forward(_batch, num_gibbs=params.num_gibbs_samples)
+

src/rbm/matrix.py

@@ -18,11 +18,18 @@ def uniform(shape: tuple, mu: float = 0.5, std: float = 1.0) -> Mat:
 	return std * (np.random.rand(shape[0], shape[1]) + mu - 0.5)
 
 def gaussian(shape: tuple, mu: float = 0.0, std: float = 1.0) -> Mat:
-	return std * (np.random.randn(shape[0], shape[1]) + mu)
+	if shape.__len__() == 1:
+		return std * (np.random.randn(shape[0]) + mu)
+	else:
+		return std * (np.random.randn(shape[0], shape[1]) + mu)
+
 
 def sample(src: Mat) -> Mat:
 	return (src > uniform(src.shape)).astype(float)
 
+def gaussian_sample(src: Mat, mu=0, std=1.0) -> Mat:
+	return gaussian(src.shape, mu, std)
+
 def prob(src: Mat) -> Mat:
 	return 1.0 / (1 + np.exp(-src))
 

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: