def setMatch(self, match, address):
node = getNode(self.rules, address)
if node["left"] is not None or node["right"] is not None:
raise Exception(
"Cant set rule at LogWatch {} since address {} is not a leaf".
format(self.lwID, address))
node["value"] = match
def delMatch(self, address: tuple):
node = getNode(self.rules, address)
if node["left"] is not None or node["right"] is not None:
raise Exception(
"Cant delete rule at LogWatch {} since address {} is not a leaf"
.format(self.lwID, address))
if address == ():
self.rules = createNode()
else:
parentNode = getNode(self.rules, address[:-1])
if address[-1] == 0:
survivorNode = parentNode["right"]
else:
survivorNode = parentNode["left"]
parentNode["value"] = survivorNode["value"]
parentNode["left"] = survivorNode["left"]
parentNode["right"] = survivorNode["right"]
def combineMatch(self, match, connector, address):
node = getNode(self.rules, address)
if node["left"] is not None or node["right"] is not None:
raise Exception(
"Cant combine rule at LogWatch {} since address {} is not a leaf"
.format(self.lwID, address))
node["left"] = createNode(node["value"])
node["value"] = connector
node["right"] = createNode(match)
def test_getNode(self):
rule = {
'value': 'val2',
'left': {
'value': 'val1',
'left': {'left'},
'right': {'right'}
},
'right': {
'value': 'val1',
'left': {'left'},
'right': {'right'}
}
}
self.assertEqual(rule, getNode(rule, ()))
self.assertEqual(rule['left'], getNode(rule, (0, )))
self.assertEqual(rule['right'], getNode(rule, (1, )))
self.assertEqual(rule['left']['left'], getNode(rule, (0, 0)))
self.assertEqual(rule['left']['right'], getNode(rule, (0, 1)))
self.assertEqual(rule['right']['left'], getNode(rule, (1, 0)))
self.assertEqual(rule['right']['right'], getNode(rule, (1, 1)))
for step in range(0, k):
print("step: ", step)
# print("isSelected \n", graph.isSelected)
# select node to be added
probOfRandomSelection = max(pow(0.1, step), 0.8)
# print("probOfRandomSelection is : ", probOfRandomSelection)
if (step == 0):
action_t = util.getRandomNode(
episode, step
) #graphEnv.graphEnvironment[episode].top_tenpct_nodes[0] action_t=
# graphEnv.graphEnvironment[episode].top_tenpct_nodes[0]#util.getRandomNode(episode,step)#graphEnv.graphEnvironment[episode].top_tenpct_nodes[0]
# index of the selected node
else:
action_t = util.getNode(probOfRandomSelection, episode, step)
print("Node selected: ", action_t)
# add action_t to the partial solution
graphEnv.graphEnvironment[episode].isSelected[action_t] = step
graphEnv.graphEnvironment[episode].isCounted[action_t] = True
neighbors_of_chosen_node = graphEnv.graphEnvironment[
episode].dict_node_sampled_neighbors[
action_t] #neighbors(action_t))
print("num nbrs of chosen node ", len(neighbors_of_chosen_node))
new_neighbors_length = len(
neighbors_of_chosen_node -
graphEnv.graphEnvironment[episode].neighbors_chosen_till_now)
graphEnv.graphEnvironment[
episode].neighbors_chosen_till_now = graphEnv.graphEnvironment[
episode].neighbors_chosen_till_now.union(