layer: refactored xor() into test_xor.py

src/rbm/layer.py

@@ -27,42 +27,6 @@ class Layer:
 		if state is not None:
 			self.entity.state = state
 
-def xor():
-	# Create params
-	params = EntityParams()
-	params.do_rao_blackwell = True
-	params.num_gibbs_samples = 3
-
-	# Create layer
-	layer = Layer("Layer_0", (3, 1, 0, 16), params)
-
-	# Init weights
-	layer.init(0.01)
-
-	# Load weights (if exists)
-	layer.load()
-
-	# Prepare training data
-	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())
-
-	# Save weights
-	layer.save()
-
-	# 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)
-		print(f"P{pattern} : {v}")
-
-
-if __name__ == "__main__":
-	xor()
-	print("Test: [passed]")
-
 
 
 

src/rbm/test_xor.py

@@ -0,0 +1,40 @@
+from rbm.params import EntityParams
+from rbm.layer import Layer
+from rbm.status import Status
+from rbm.train import train
+from rbm.matrix import Mat, np
+
+def xor():
+	# Create params
+	params = EntityParams()
+	params.do_rao_blackwell = True
+	params.num_gibbs_samples = 3
+
+	# Create layer
+	layer = Layer("Layer_0", (3, 1, 0, 16), params)
+
+	# Init weights
+	layer.init(0.01)
+
+	# Load weights (if exists)
+	layer.load()
+
+	# Prepare training data
+	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())
+
+	# Save weights
+	layer.save()
+
+	# 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)
+		print(f"P{pattern} : {v}")
+
+if __name__ == "__main__":
+	xor()
+	print("Test: [passed]")