Entity: refactored taining algo into train module

src/rbm/layer.py

@@ -1,12 +1,12 @@
-from params import RbmParams
+from params import EntityParams
 from state import RbmState
 from status import Status
-from cd_train import cd_jens
+from train import train
 from entity import Entity
 from matrix import Mat, np
 
 class Layer:
-	def __init__(self, name: str, shape: tuple[int, int, int, int], params: RbmParams):
+	def __init__(self, name: str, shape: tuple[int, int, int, int], params: EntityParams):
 		self.name = name
 		self.shape = shape
 		self.entity = Entity((shape[0]*shape[1]+shape[2], shape[3]), params)
@@ -29,7 +29,7 @@ class Layer:
 
 def xor():
 	# Create params
-	params = RbmParams()
+	params = EntityParams()
 	params.do_rao_blackwell = True
 	params.num_gibbs_samples = 3
 
@@ -46,7 +46,7 @@ def xor():
 	training_batch = Mat([[0,1,1], [0,0,0], [1,1,0], [1,0,1]], dtype=np.float64)
 
 	# Train layer
-	layer.entity.train(training_batch, cd_jens, Status())
+	train(layer.entity, training_batch, Status())
 
 	# Save weights
 	layer.save()