- simplified TrainingParams.from_dict

- fixed test_rbm

src/rbm/entity.py

@@ -32,17 +32,8 @@ class TrainingParams:
 	@classmethod
 	def from_dict(cls, params: dict):
 		obj = TrainingParams()
-		obj.learning_rate = params["learningRate"]
+		for key, value in params.items():
-		obj.momentum = params["momentum"]
+			setattr(obj, key, value)
-		obj.weight_decay = params["weightDecay"]
-		obj.l2_lambda = params["l2_lambda"]
-		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
 

src/tests/test_rbm.py

@@ -5,6 +5,7 @@ from rbm.stack_factory import StackFactory
 from rbm.status import Status
 from rbm.stack_deep import StackDeep
 from rbm.matrix import Mat, np, convert, read_armadillo
+from rbm.entity import Entity
 
 def cv_show(name: str, vec: Mat, shape):
 	img = cv.Mat(convert(np.resize(vec, shape)))
@@ -18,10 +19,10 @@ class MyStatus(Status):
 		self.batch = _batch
 		self.index = 0
 
-	def on_report(self, status: dict) -> bool:
+	def on_report(self, entity: Entity, status: dict) -> bool:
 		do_continue = True
 		# print status values
-		Status.print_status(status)
+		Status.print_status(entity, status)
 		# Shape of training vector
 		shape = self.stack.from_index(0).shape[0:2] + (1,)