refactored

src/tests/test_xor.py

@@ -1,20 +1,21 @@
 import os.path
 
-from rbm.params import EntityParams
 from rbm.layer import Layer
 from rbm.status import Status
-from rbm.train import train
+from rbm.train import train, TrainingParams
 from rbm.matrix import Mat, np
+from rbm.entity import EntityParams
 
 work_dir = "../../results"
 def xor():
 	# Create params
-	params = EntityParams()
-	params.do_rao_blackwell = True
-	params.num_gibbs_samples = 3
+	entity_params = EntityParams()
+	training_params = TrainingParams()
+	entity_params.do_rao_blackwell = True
+	entity_params.num_gibbs_samples = 3
 
 	# Create layer
-	layer = Layer("Layer_0", (3, 1, 0, 16), params)
+	layer = Layer("Layer_0", (3, 1, 0, 16), entity_params, training_params)
 
 	# Init weights
 	layer.init(0.01)
@@ -26,7 +27,7 @@ def xor():
 	training_batch = Mat([[0,1,1], [0,0,0], [1,1,0], [1,0,1]], dtype=np.float64)
 
 	# Train layer
-	train(layer.entity, training_batch, Status())
+	train(layer.entity, training_batch, training_params, Status())
 
 	# Save weights
 	layer.save(os.path.join(work_dir, "xor_layer0_state.npz"))
@@ -34,8 +35,8 @@ def xor():
 	# Test with test data
 	test_batch = Mat([[0,0,0], [0,1,0], [1,0,0], [1,1,0]], dtype=np.float64)
 	for pattern in test_batch:
-		h = layer.entity.gibbs_v_to_h(pattern)
-		v = layer.entity.gibbs_h_to_v(h)
+		h = layer.entity.forward(pattern)
+		v = layer.entity.reconstruct(h)
 		print(f"P{pattern} : {v}")
 
 if __name__ == "__main__":