- adapted test to TrainingParameter as parat of Entity

- learn_norbs_labes start working

src/tests/test_learn_encoded_labels.py

@@ -3,14 +3,13 @@ import matplotlib.pyplot as plt
 
 from rbm.model import Model
 from rbm.label import Label
-from rbm.entity import Entity, EntityParams
+from rbm.entity import Entity, EntityParams, TrainingParams
 from rbm.matrix import Mat, np
-from rbm.train import TrainingParams
 
 class LabelLearner(Model):
 	def __init__(self, name: str, work_dir: str = '.'):
 		super().__init__(name, work_dir)
-		self.unit1 = Entity((16, 8), EntityParams())
+		self.unit1 = Entity((16, 8), EntityParams(), TrainingParams(learning_rate=0.01, momentum=0.9, do_rao_blackwell=True, num_epochs=10000))
 
 	def forward(self, x: Mat):
 		x = self.unit1.forward(x)
@@ -45,7 +44,7 @@ if __name__ == "__main__":
 	model.load()
 
 	# Train
-	model.train(encoded, TrainingParams(learning_rate=0.01, momentum=0.9, do_rao_blackwell=True, num_epochs=1000))
+	model.train(encoded)
 
 	# save state
 	model.save()
@@ -57,7 +56,7 @@ if __name__ == "__main__":
 		axes[index].imshow(img)
 		axes[index].axis('off')
 		axes[index].set_title(f'{test_labels[index]}')
-		print(inp)
+
 	plt.show()
 
 	print("Test: [passed]")