def query_blockages(self, evidence_list):
print_info("Printing probability that each of the edges is blocked:")
blockage_nodes = [n for n in self.networkObjects if n.n_type == 'E']
for bl_node in blockage_nodes:
blockage_chances = self.enumerate_ask(bl_node, evidence_list)
print_info("\tP(" + str(bl_node) + " Blockage = True) = " +
str(blockage_chances[0]))
def add_agent(self, s_vertex):
index = 1
initial_state = AgentState(s_vertex, self.graph.get_people_array(),
self.k_value)
ag = ValueIterationAgent(index, initial_state)
print_info("ADDED AGENT TO ENVIRONMENT.")
self.agent = ag
def query_floodings(self, evidence_list):
print_info(
"Printing probability that each of the vertices is flooded:")
flood_nodes = [n for n in self.networkObjects if n.n_type == 'V']
for fl_node in flood_nodes:
flood_chances = self.enumerate_ask(fl_node, evidence_list)
print_info("\tP(" + str(fl_node) + " Flooding = True) = " +
str(flood_chances[0]))
def print_changes(self):
print_info("TIME IS: " + str(self.env_state.time))
print_info("OUR VERTICES PEOPLE COUNT: ")
people_arr = self.graph.get_people_array_with_shelter()
print_info(str(people_arr))
ppl_saved = sum(
[v.ppl_count for v in self.graph.vertices if v.is_shelter()])
print_info("PEOPLE SAVED: " + str(ppl_saved) + "/" +
str(self.total_ppl))
print_info("AGENT STATE: " + str(str(self.agent.curr_state)))
def __init__(self, config_file_path, k_value=K_DEFAULT_VALUE):
self.graph = Graph(config_file_path)
self.grapCopy = deepcopy(self.graph)
# self.env_time = 0
# Array of the actual agents running in the environment
self.agent = None
self.agent_score = 0
self.k_value = k_value # Penalty related constant
self.dead_ppl = 0
self.total_ppl = sum(self.graph.get_people_array_with_shelter())
s_vertex = self.graph.vertices[0]
people_at_vertices_count = self.graph.get_people_array()
edges_blocked_status = self.graph.get_edges_blocked_status()
self.env_state = EnvState(s_vertex, people_at_vertices_count,
edges_blocked_status, 0, 0, 0, False)
Environment.PERCEPT = self
self.all_possible_states = []
self.stateUtilityAndPolicyDict = {}
print_info("CREATED ENVIRONMENT WITH " +
str(self.graph.num_of_vertices()) + " VERTICES, AND " +
str(self.graph.num_of_roads()) + " ROADS.")
print_info("OUR GRAPH:")
print(str(self.graph))
print_info("ENVIRONMENT STARTING STATE: " + str(self.env_state))
def simulation(self):
print("------------------------------------------------")
self.initializeStatesDict()
self.runValueIteration(Environment.VALUE_ITERATION_DELTA)
dictCopy = deepcopy(self.stateUtilityAndPolicyDict)
play = 'Y'
while play == 'Y':
self.stateUtilityAndPolicyDict = deepcopy(dictCopy)
print_info("STARTING SIMULATION:")
self.setRealEdgesStatus()
self.add_agent(self.env_state.ag_loc)
print_info("CREATED RANDOM INSTANCE OF ENVIRONMENT:")
self.print_env()
if not self.agent:
self.print_env()
else:
ag = self.agent
while not ag.curr_state.is_terminated:
if ag.is_traversing():
ag.traverse_update()
for v in self.graph.vertices:
if not v.is_shelter(
) and v.deadline < self.env_state.time:
self.dead_ppl += v.ppl_count
v.ppl_count = 0
print_debug("PRINTING ENVIRONMENT STATUS:")
self.print_changes()
print_debug("AGENTS OPERATING IN ENVIRONMENT:")
self.update()
print_debug("DONE WITH AGENTS OPERATING IN ENVIRONMENT.")
self.env_state.time += 1
print("------------------------------------------------")
print_debug("GAME OVER")
print_info("PRINTING ENVIRONMENT STATUS:")
self.print_env()
print_query("Play again? (Y/N)")
play = input()
self.graph = deepcopy(self.grapCopy)
self.initEnvironmentVariables()
def simulation(self):
evidenceList = []
inp = 1
while 1 <= inp <= 3:
print_query("Enter what you want to do:")
print_query("\t1. Add piece of evidence to evidence list.")
print_query("\t2. Do probabilistic reasoning.")
print_query("\t3. Reset evidence list to empty")
print_query("\t4. Quit the program.")
inp = int(input())
if inp == 1:
print_info("You chose to add evidence.")
print_info("Examples of evidence input:")
print_info("\tFlooding at vertex 1 at time 1 -> V11")
print_info("\tEdge 2 not blocked at time 0 -> not E20")
print_query("Enter evidence:")
ev = str(input()).split()
if len(ev) is 1:
evidenceVar = self.bayesNet.getBayesNodeByName(ev[0])
evidenceList.append(
bayesNetwork.VarWithVal(evidenceVar, True))
else:
evidenceVar = self.bayesNet.getBayesNodeByName(ev[1])
evidenceList.append(
bayesNetwork.VarWithVal(evidenceVar, False))
elif inp == 2:
print_info(
"You chose to do probabilistic reasoning. Choose your query:"
)
print_query(
"\t1. What is the probability that each of the vertices is flooded?"
)
print_query(
"\t2. What is the probability that each of the edges is blocked?"
)
print_query(
"\t3. What is the probability that a certain path (set of edges) is free from blockages?"
)
print_query(
"\t4. BONUS: What is the path between 2 given vertices that has"
+
" the highest probability of being free from blockages at time t=1?"
)
print_query("\t5. Go back.")
query_choice = int(input())
print_info("Given our evidence: " +
str([str(ev) for ev in evidenceList]))
if query_choice == 1:
self.bayesNet.query_floodings(evidenceList)
elif query_choice == 2:
self.bayesNet.query_blockages(evidenceList)
elif query_choice == 3:
print_query("Enter Path:")
print_query("\tFor example: E11 E21")
pathElements = str(input()).split()
path = []
for edge in pathElements:
pathElement = self.bayesNet.getBayesNodeByName(edge)
path.append(pathElement)
res = self.bayesNet.query_pathNotBlocked(
path, evidenceList)
print_info("P(Path not blocked = True) = " + str(res))
elif query_choice == 4:
print_query("Enter 2 vertices:")
print_query("\tFor example: V1 V4")
pathElements = str(input()).split()
v1 = self.graph.get_vertex_from_string(pathElements[0])
v2 = self.graph.get_vertex_from_string(pathElements[1])
path, prob = self.bayesNet.getBestRoute(
v1, v2, evidenceList)
print_query("Best path is: " +
self.bayesNet.get_path_str(path) +
" with probability: " + str(prob))
X = 1
pass
elif inp == 3:
evidenceList = []
def printNodeInfo(self):
if self.n_type == 'V':
print_info("VERTEX " + str(self.index) + ", time " +
str(self.time) + ":")
if not self.parents:
print_info("\tP(Flooding = True) = " +
str(self.probabilityTable['1']))
print_info("\tP(Flooding = False) = " +
str(1 - self.probabilityTable['1']))
elif len(self.parents) == 1:
print_info("\tP(Flooding = True | Flooding " +
str(self.parents[0]) + " = True) = " +
str(self.probabilityTable['1']))
# print_info("\tP(Flooding = False | Flooding " + str(self.parents[0]) + " = True) = " +
# str(1 - self.probabilityTable['1']))
print_info("\tP(Flooding = True | Flooding " +
str(self.parents[0]) + " = False) = " +
str(self.probabilityTable['0']))
# print_info("\tP(Flooding = False | Flooding " + str(self.parents[0]) + " = False) = " +
# str(1 - self.probabilityTable['0']))
else: # 2 Parents
print_info("EDGE " + str(self.index) + ", time " +
str(self.time) + ":")
for pos_vals in [(True, True), (True, False), (False, True),
(False, False)]:
print_info(
"\tP(Blockage = True | Flooding " +
str(self.parents[0]) + " = " + str(pos_vals[0]) +
", Flooding " + str(self.parents[1]) + " = " +
str(pos_vals[1]) + ") = " +
str(self.probabilityTable[bin(pos_vals[0])[2:] +
bin(pos_vals[1])[2:]]))
def print_env(self):
print_info("TIME IS: " + str(self.env_state.time))
print_info("OUR VERTICES TYPES:")
v_types = []
for vertex in self.graph.vertices:
v_types.append(vertex.v_type)
print_info(str(v_types))
print_info("OUR VERTICES DEADLINES:")
deadlines = []
for vertex in self.graph.vertices:
deadlines.append(vertex.deadline)
print_info(str(deadlines))
print_info("OUR VERTICES PEOPLE COUNT: ")
people_arr = self.graph.get_people_array_with_shelter()
print_info(str(people_arr))
print_info("OUR EDGES ACTUAL BLOCKAGE STATUS:")
edges_actual_blockage_status = self.graph.get_edges_actual_blocked_status(
)
print_info(str(edges_actual_blockage_status))
ppl_saved = sum(
[v.ppl_count for v in self.graph.vertices if v.is_shelter()])
print_info("PEOPLE SAVED: " + str(ppl_saved) + "/" +
str(self.total_ppl))
print_info("OUR AGENTS:")
ag_names = []
ag_states = []
agent = self.agent
ag_names.append(str(agent))
ag_states.append(str(agent.curr_state))
print_info("AGENTS: " + str(ag_names))
print_info("AGENTS SCORES: " + str(self.agent_score))
print_info("AGENTS STATES: " + str(ag_states))
from environment import Environment as Env
from helper_funcs import print_debug, print_query, print_info
ENVIRONMENT_SETTINGS_FILE = "environment_example_forgrading.txt"
env = Env(ENVIRONMENT_SETTINGS_FILE)
print_info("OUR GRAPH:")
print_info(env.graph)
print("------------------------------------------------")
print_debug("STARTING SIMULATION:")
env.simulation()
