- training params are (again) attrubute of Entity

- ditched layout concept

src/rbm/entity.py

@@ -1,6 +1,48 @@
 from .state import RbmState
 from .matrix import prob, Mat
 from enum import Enum
+
+class TrainingParams:
+	def __init__(self,
+				 learning_rate: float = 0.1,
+				 momentum: float = 0.5,
+				 weight_decay: float = 0.0,
+				 num_epochs: int = 1000,
+				 num_gibbs_samples: int = 1,
+				 mini_batch_size: int = 0,
+				 do_rao_blackwell: bool = False,
+				 do_gibbs_sample_visible: bool = False,
+				 do_gibbs_sample_hidden: bool = False,
+				 do_batch_sample: bool = False
+				 ):
+		# Training parameters
+		self.learning_rate = learning_rate
+		self.momentum = momentum
+		self.weight_decay = weight_decay
+		self.num_epochs = num_epochs
+		self.num_gibbs_samples = num_gibbs_samples
+		self.mini_batch_size = mini_batch_size
+		self.do_rao_blackwell = do_rao_blackwell
+		self.do_gibbs_sample_visible = do_gibbs_sample_visible
+		self.do_gibbs_sample_hidden = do_gibbs_sample_hidden
+		self.do_batch_sample = do_batch_sample
+
+	@classmethod
+	def from_dict(cls, params: dict):
+		obj = TrainingParams()
+		obj.learning_rate = params["learningRate"]
+		obj.momentum = params["momentum"]
+		obj.weight_decay = params["weightDecay"]
+		obj.num_epochs = params["numEpochs"]
+		obj.num_gibbs_samples = params["numGibbs"]
+		obj.mini_batch_size = params["miniBatchSize"]
+		obj.do_rao_blackwell = params["doRaoBlackwell"]
+		obj.do_gibbs_sample_visible = params["gibbsDoSampleVisible"]
+		obj.do_gibbs_sample_hidden = params["gibbsDoSampleHidden"]
+		obj.do_batch_sample = params["doSampleBatch"]
+
+		return obj
+
 class EntityParams:
 	def __init__(self, do_gaussian_visible: bool = False, do_gaussian_hidden: bool = False, num_gibbs_samples: int = 1):
 		# Entity parameters
@@ -27,9 +69,10 @@ class Entity:
 		GB_RBM = "GB-RBM"
 		GG_RBM = "GG-RBM"
 
-	def __init__(self, shape: tuple[int, int], params: EntityParams):
+	def __init__(self, shape: tuple[int, int], params: EntityParams, training_params: TrainingParams|None = None):
 		self.shape = shape
 		self.params = params
+		self.training_params = training_params
 		self.state = RbmState.from_layer_params(shape)
 		self.grad = RbmState.from_layer_params(shape)
 		self.type = None