xor: add better documentation

src/rbm/layer.py

@@ -109,6 +109,7 @@ class RbmLayer:
 		return v
 
 def xor():
+	# Create params
 	params = RbmParams()
 	params.do_rao_blackwell = True
 	params.num_gibbs_samples = 3
@@ -117,17 +118,25 @@ def xor():
 	params.momentum = 0.5
 	params.num_epochs = 1000
 
-	status = Status()
+	# Create layer
 	layer = RbmLayer("Layer_0", 3, 16, params)
+
+	# Init weights
 	layer.init(0.01)
+
+	# Load weights (if exists)
 	layer.load()
 
-	# Train
+	# Prepare training data
 	training_batch = np.array([[0,1,1], [0,0,0], [1,1,0], [1,0,1]], dtype=np.float64)
-	layer.train(training_batch, cd_jens, status)
+
+	# Train layer
+	layer.train(training_batch, cd_jens, Status())
+
+	# Save weights
 	layer.save()
 
-	# Test
+	# Test with test data
 	test_batch = np.array([[0,0,0], [0,1,0], [1,0,0], [1,1,0]], dtype=np.float64)
 	for pattern in test_batch:
 		h = layer.gibbs_v_to_h(pattern)