import itertools
from agent import Agent
import random

# Created by <NAME> (22704832) and <NAME> (22730325)


class BayesJond(Agent):
    """
    All players should start with equal chance of being spy, IF resistance in game of 5 players, each other player has a 50% chance of being a spy, 
    as it is 2/4 chance of being a spy if we are not included in the count. This should be kept in mind and update when playing as spy too, as it can help avoid detection
    
    self.worlds stores a dictionary of all possible worlds, where each key is a tuple of the a combinations players in the world, 
                assuming the players of whose names are in the tuple are both spies, and the value is the likelihood of that world being the real world.
                eg. if (1,2): 0.4, then there is a 40% chance of the real world having players 1 and 2 as spies.
    self.spy_count stores a dict of how many spies are in a game in regards to how many players are in the game, eg a game with 5 players has 2 spies
    """

    def __init__(self, name='BayesJond 007'): # Initialises the agent.
        self.name = name

    def new_game(self, number_of_players, player_number, spy_list): 
        '''
        initialises the game, informing the agent of the 
        number_of_players, the player_number (an id number for the agent in the game),
        and a list of agent indexes which are the spies, if the agent is a spy, or empty otherwise
        '''
        self.spy_count = {5:2, 6:2, 7:3, 8:3, 9:3, 10:4} # Returns the number of spies in a game of a relavent size eg a game with 9 players, self.spy_count[9] returns 3
        self.number_of_worlds = {5:10, 6:15, 7:35, 8:56, 9:84, 10:210}
        self.number_of_players = number_of_players # The number of players in the game
        self.player_number = player_number # The agent's player number
        self.spy_list = spy_list # The list of spy indexes, empty if the agent is not a spy
        self.worlds = {} # Stores the worlds and their probabilities, will be updated as the game progresses
        self.votecount = 0 # Stores the number of failed votes
        self.mission_Number = 0 # The index of the current mission number, for indentifying how many betrayals are required
        self.spy_score = 0 # The number of points that the spies have, 3 == win for spies
        
        # Generates the dictionary all the possible worlds, where each key is a tuple of the a combinations players in the world,
        # and each value is the probability that the world is true. Creates number of worlds based on number of players in the game
        startingChance = 1/self.number_of_worlds[number_of_players]
        if self.spy_count[number_of_players] == 2: # Creates the worlds for a game of 2 spies
            for x in range(number_of_players):
                for y in range(x+1, number_of_players):
                    self.worlds[(x,y)] = startingChance
        elif self.spy_count[number_of_players] == 3: # Creates the worlds for a game of 3 spies
            for x in range(number_of_players):
                for y in range(x+1, number_of_players):
                    for z in range(y+1, number_of_players):
                        self.worlds[(x,y,z)] = startingChance
        elif self.spy_count[number_of_players] == 4: # Creates the worlds for a game of 4 spies
            for x in range(number_of_players):
                for y in range(x+1, number_of_players):
                    for z in range(y+1, number_of_players):
                        for w in range(z+1, number_of_players):
                            self.worlds[(x,y,z,w)] = startingChance


    def is_spy(self): # returns True iff the agent is a spy
        return self.player_number in self.spy_list

    def calculate_probabilities(self):
        '''
        calculates and sorts the suspiciousness of each player using values from self.worlds
        returns a dictionary mapping suspiciousness to player numbers and the average suspiciousness
        '''

        orderedProbs = {} # dictionary to populate
        for x in range(self.number_of_players): # Loop through all players
            temp = [] # list to hold probabilities of worlds containing player x
            for key, value in self.worlds.items(): # Loops through all worlds
                if x in key: 
                    temp.append(value) 
            orderedProbs[x] = (sum(temp) / len(temp)) # Averages the suspicion of each player in the worlds in which they are spies
            
        orderedProbs = {x: y for x, y in sorted(orderedProbs.items(), key=lambda item: item[1], reverse=True)} # Sorts the list in descending order of suspicion
        total = 0 # variable used to calculate average suspiciousness
        for agents, suspicion in orderedProbs.items(): # Loop through each player's suspicion scores
            total += suspicion
        average = total/len(orderedProbs) # Calculates average suspicion of all players in the game
        return orderedProbs, average

    def propose_mission(self, team_size, betrayals_required = 1): # Calculate the probability of each player being a spy, then select the "team_size" lowest people
        '''
        team_size is the required mission size
        betrayals_required is the number of betrayals needed for the mission to fail
        '''
        team = []
        probabilities, average = self.calculate_probabilities() # returns list of agents in descending order of suspicion
        probabilities = list(probabilities) # get only the keys of the dictionary

        # EXPERT RULE: Always include self in missions proposition
        team.append(self.player_number)
        team_size -= 1 # Ensures that we don't add too many players
        
        if self.is_spy(): # If the agent is a spy, choose a team most likely to pass the vote, but still contain a spy
            spiesSelected = 1 # Keeps track of how many spies have been selected, starts as 1 as self is always selected
            for x in range(len(probabilities)-1, 0, -1): # looping backwards over the list, find the first spy (least suspicious), and add them to the team
                # Check that the number of spies selected is less than the number of spies required
                # Check that agent is not adding itself again
                if probabilities[x] in self.spy_list and spiesSelected < betrayals_required and probabilities[x] != self.player_number: 
                    team.append(probabilities[x])
                    spiesSelected += 1 # Increment the number of spies selected, If the number of spies selected is equal to the number of betrayals required, then do not select more spies
            for x in range(team_size - spiesSelected+1): # for the remaining number of players required, add the next least suspicious players to the team, more likely to succeed proposal from other opponents
                if probabilities[x] not in self.spy_list and len(team) < team_size + 1 and probabilities[x] != self.player_number:
                    team.append(probabilities[x])
        else: # If the agent is not a spy, choose a team with the least suspicion, least chance of containing a spy
            probabilities.reverse() # Reverse the list so that the first element is the least likely to be a spy
            for x in range(team_size):
                if probabilities[x] != self.player_number: # To avoid adding self to the team again
                    team.append(probabilities[x]) 
        random.shuffle(team) # Shuffle the team so that the spy is not always last, so that opponents can't model us based off of team proposal order
        return team

    def vote(self, mission, proposer): # If proposer is suspected spy, be careful of voting yes, if members of mission are suspected spies, vote no
        '''
        mission is a list of agents to be sent on a mission. 
        The agents on the mission are distinct and indexed between 0 and number_of_players.
        proposer is an int between 0 and number_of_players and is the index of the player who proposed the mission.
        The function should return True if the vote is for the mission, and False if the vote is against the mission.
        '''
        probabilities, average = self.calculate_probabilities() # returns list of agents in descending order of suspicion

        # EXPERT RULE
        # Always accept the final mission proposal, as voting no is bad for the resistance and will make you suspicious if you are a spy
        if self.votecount == 4:
            return True
        
        if not self.is_spy(): # If Resistance, deny missions with suspicious proposers or mission members
            # EXPERT RULE
            # If you are not in a mission when the number of players on the mission is equal to the number of resistance members in the game
            # then the mission is guaranteed to have a spy, so deny mission
            if len(mission) == (self.number_of_players - self.spy_count[self.number_of_players]) and self.player_number not in mission:
                return False
            # EXPERT RULE
            # Trust players that cannot possibly be spies, assume they will make informed decisions
            if probabilities.get(proposer) == 0: 
                return True
            if probabilities.get(proposer) > average: # If the proposer is more suspicious than average, deny mission
                return False
            else: # If the proposer is less suspicious than average, check the mission members
                for x in mission:
                    if probabilities.get(x) > average: # If any agents in mission are more suspicious than average, deny mission
                        return False
                return True # If mission and proposer pass the suspicion test, vote yes
        else: # If the agent is a spy, pass missions with spies on them, but not too suspicious, as it may incriminate yourself
            if probabilities.get(proposer) > average: # If the proposer is more suspicious than average, then vote no
                return False
            else:
                for x in mission:
                    if probabilities.get(x) > average: # if a player on the mission is more suspicious than average, deny mission
                        return False
                if sorted(mission) == sorted(self.spy_list):
                    # If mission of only spies, would be incriminating, deny mission
                    return False
                return True

    def vote_outcome(self, mission, proposer, votes): # use to keep track of how many votes have failed
        '''
        mission is a list of agents to be sent on a mission. 
        The agents on the mission are distinct and indexed between 0 and number_of_players.
        proposer is an int between 0 and number_of_players and is the index of the player who proposed the mission.
        votes is a list of players that voted in favour of the mission
        No return value is required or expected.
        '''
        if len(votes) >= self.number_of_players/2: # increment if the vote fails
            self.votecount += 1
        else: # reset if the vote succeeds
            self.votecount = 0

    def betray(self, mission, proposer): 
        '''
        mission is a list of agents to be sent on a mission. 
        The agents on the mission are distinct and indexed between 0 and number_of_players, and include this agent.
        proposer is an int between 0 and number_of_players and is the index of the player who proposed the mission.
        The method should return True if this agent chooses to betray the mission, and False otherwise. 
        By default, spies will betray 30% of the time. 
        '''
        
        if self.is_spy():
            Spies_in_mission = 0
            # Check the number of spies in the mission
            for x in mission: 
                if x in self.spy_list:
                    Spies_in_mission += 1
            # If 2 missions have already failed, always betray the mission if there are enough spies, as it will guarantee a win
            if self.spy_score == 2 and self.fails_required[self.number_of_players][self.mission_Number-1] <= Spies_in_mission:
                return True
            # If there are enough spies to betray the mission, choose to betray
            if self.fails_required[self.number_of_players][self.mission_Number-1] <= Spies_in_mission:
                return True
            else: # If there are not enough spies to betray the mission, choose not to betray
                return False
        else:
            return False

    def mission_outcome(self, mission, proposer, betrayals, mission_success):
        # Update internal perception of worlds
        '''
        mission is a list of agents to be sent on a mission. 
        The agents on the mission are distinct and indexed between 0 and number_of_players.
        proposer is an int between 0 and number_of_players and is the index of the player who proposed the mission.
        betrayals is the number of people on the mission who betrayed the mission, 
        and mission_success is True if there were not enough betrayals to cause the mission to fail, False otherwise.
        It iss not expected or required for this function to return anything.
        '''
        # if there was at least 1 betrayal, the worlds probabilities can be updated
        if betrayals != 0:
            fail_chance = self.worlds.copy() # this dictionary will store the P(F|W) values
            total_fail = 0
            # iterate through all the worlds
            for combination in fail_chance:
                # overlap is the agents in both the mission and current combination
                overlap = set(combination)&set(mission)

                # comb is the combinations of which agents could have chosen to betray
                # e.g. if we know that there were 2 betrayals, and overlap was [0, 1, 2],
                #      then the possible betrayal outcomes are:
                #      (0, 1), (0, 2) and (1, 2)
                comb = list(itertools.combinations(overlap, betrayals))

                # total is the total number of combinations that a mission could end in
                # each agent either passes the mission (True), or fails the mission (False)
                # e.g. for a mission with 3 agents, the possible outcomes are:
                # (True, True, True), (True, True, False), (True, False, True), (True, False, False), (False, True, True), (False, True, False), (False, False, True), (False, False, False)
                total = list(itertools.product([True, False], repeat = len(mission)))
                
                # the chance of failing a mission is the number of combinations divided by the total
                fail_chance[combination] = len(comb)/len(total)
                
                # sum the product of P(C) and P(F|C) to get P(F)
                total_fail += fail_chance[combination] * self.worlds[combination]
            
            for combination in fail_chance: # set the world probabilities to the newly calculated values
                self.worlds[combination] = fail_chance[combination] * self.worlds[combination] / total_fail

    def round_outcome(self, rounds_complete, missions_failed):
        '''
        basic informative function, where the parameters indicate:
        rounds_complete, the number of rounds (0-5) that have been completed
        missions_failed, the number of missions (0-3) that have failed.
        '''
        #nothing to do here
        pass
    
    def game_outcome(self, spies_win, spies): 
        '''
        basic informative function, where the parameters indicate:
        spies_win, True iff the spies caused 3+ missions to fail
        spies, a list of the player indexes for the spies.
        '''
        # Outputs result of each game to csv, used for testing performance of the agent.
        # Commented out from Code for final submission and tournament play
        pass
        """
        if self.name == 'BayesJondRandom':
            if self.is_spy():
                if spies_win:
                    with open('BayesJondRandom.csv','a') as fd:
                        fd.write('{} players,I was spy,Won\n'.format(self.number_of_players))
                else:
                    with open('BayesJondRandom.csv','a') as fd:
                        fd.write('{} players,I was spy,Lost\n'.format(self.number_of_players))
            else:
                if spies_win:
                    with open('BayesJondRandom.csv','a') as fd:
                        fd.write('{} players,I was not spy,Lost\n'.format(self.number_of_players))
                else:
                    with open('BayesJondRandom.csv','a') as fd:
                        fd.write('{} players,I was not spy,Won\n'.format(self.number_of_players))
                        
        elif self.name == 'BayesJondBasic':
            if self.is_spy():
                if spies_win:
                    with open('BayesJondBasic.csv','a') as fd:
                        fd.write('{} players,I was spy,Won\n'.format(self.number_of_players))
                else:
                    with open('BayesJondBasic.csv','a') as fd:
                        fd.write('{} players,I was spy,Lost\n'.format(self.number_of_players))
            else:
                if spies_win:
                    with open('BayesJondBasic.csv','a') as fd:
                        fd.write('{} players,I was not spy,Lost\n'.format(self.number_of_players))
                else:
                    with open('BayesJondBasic.csv','a') as fd:
                        fd.write('{} players,I was not spy,Won\n'.format(self.number_of_players))
        
        elif self.name == 'BayesJondJond':
            if self.is_spy():
                if spies_win:
                    with open('BayesJondJond.csv','a') as fd:
                        fd.write('{} players,I was spy,Won\n'.format(self.number_of_players))
                else:
                    with open('BayesJondJond.csv','a') as fd:
                        fd.write('{} players,I was spy,Lost\n'.format(self.number_of_players))
            else:
                if spies_win:
                    with open('BayesJondJond.csv','a') as fd:
                        fd.write('{} players,I was not spy,Lost\n'.format(self.number_of_players))
                else:
                    with open('BayesJondJond.csv','a') as fd:
                        fd.write('{} players,I was not spy,Won\n'.format(self.number_of_players))
        
        elif self.name == 'BayesJondCombination':
            if self.is_spy():
                if spies_win:
                    with open('BayesJondCombination.csv','a') as fd:
                        fd.write('{} players,I was spy,Won\n'.format(self.number_of_players))
                else:
                    with open('BayesJondCombination.csv','a') as fd:
                        fd.write('{} players,I was spy,Lost\n'.format(self.number_of_players))
            else:
                if spies_win:
                    with open('BayesJondCombination.csv','a') as fd:
                        fd.write('{} players,I was not spy,Lost\n'.format(self.number_of_players))
                else:
                    with open('BayesJondCombination.csv','a') as fd:
                        fd.write('{} players,I was not spy,Won\n'.format(self.number_of_players))
        """

import itertools
from agent import Agent
import random

# Created by <NAME> (22704832) and <NAME> (22730325)


class BayesJond(Agent):
    """
    All players should start with equal chance of being spy, IF resistance in game of 5 players, each other player has a 50% chance of being a spy, 
    as it is 2/4 chance of being a spy if we are not included in the count. This should be kept in mind and update when playing as spy too, as it can help avoid detection
    
    self.worlds stores a dictionary of all possible worlds, where each key is a tuple of the a combinations players in the world, 
                assuming the players of whose names are in the tuple are both spies, and the value is the likelihood of that world being the real world.
                eg. if (1,2): 0.4, then there is a 40% chance of the real world having players 1 and 2 as spies.
    self.spy_count stores a dict of how many spies are in a game in regards to how many players are in the game, eg a game with 5 players has 2 spies
    """

    def __init__(self, name='BayesJond 007'): # Initialises the agent.
        self.name = name

    def new_game(self, number_of_players, player_number, spy_list): 
        '''
        initialises the game, informing the agent of the 
        number_of_players, the player_number (an id number for the agent in the game),
        and a list of agent indexes which are the spies, if the agent is a spy, or empty otherwise
        '''
        self.spy_count = {5:2, 6:2, 7:3, 8:3, 9:3, 10:4} # Returns the number of spies in a game of a relavent size eg a game with 9 players, self.spy_count[9] returns 3
        self.number_of_worlds = {5:10, 6:15, 7:35, 8:56, 9:84, 10:210}
        self.number_of_players = number_of_players # The number of players in the game
        self.player_number = player_number # The agent's player number
        self.spy_list = spy_list # The list of spy indexes, empty if the agent is not a spy
        self.worlds = {} # Stores the worlds and their probabilities, will be updated as the game progresses
        self.votecount = 0 # Stores the number of failed votes
        self.mission_Number = 0 # The index of the current mission number, for indentifying how many betrayals are required
        self.spy_score = 0 # The number of points that the spies have, 3 == win for spies
        
        # Generates the dictionary all the possible worlds, where each key is a tuple of the a combinations players in the world,
        # and each value is the probability that the world is true. Creates number of worlds based on number of players in the game
        startingChance = 1/self.number_of_worlds[number_of_players]
        if self.spy_count[number_of_players] == 2: # Creates the worlds for a game of 2 spies
            for x in range(number_of_players):
                for y in range(x+1, number_of_players):
                    self.worlds[(x,y)] = startingChance
        elif self.spy_count[number_of_players] == 3: # Creates the worlds for a game of 3 spies
            for x in range(number_of_players):
                for y in range(x+1, number_of_players):
                    for z in range(y+1, number_of_players):
                        self.worlds[(x,y,z)] = startingChance
        elif self.spy_count[number_of_players] == 4: # Creates the worlds for a game of 4 spies
            for x in range(number_of_players):
                for y in range(x+1, number_of_players):
                    for z in range(y+1, number_of_players):
                        for w in range(z+1, number_of_players):
                            self.worlds[(x,y,z,w)] = startingChance


    def is_spy(self): # returns True iff the agent is a spy
        return self.player_number in self.spy_list

    def calculate_probabilities(self):
        '''
        calculates and sorts the suspiciousness of each player using values from self.worlds
        returns a dictionary mapping suspiciousness to player numbers and the average suspiciousness
        '''

        orderedProbs = {} # dictionary to populate
        for x in range(self.number_of_players): # Loop through all players
            temp = [] # list to hold probabilities of worlds containing player x
            for key, value in self.worlds.items(): # Loops through all worlds
                if x in key: 
                    temp.append(value) 
            orderedProbs[x] = (sum(temp) / len(temp)) # Averages the suspicion of each player in the worlds in which they are spies
            
        orderedProbs = {x: y for x, y in sorted(orderedProbs.items(), key=lambda item: item[1], reverse=True)} # Sorts the list in descending order of suspicion
        total = 0 # variable used to calculate average suspiciousness
        for agents, suspicion in orderedProbs.items(): # Loop through each player's suspicion scores
            total += suspicion
        average = total/len(orderedProbs) # Calculates average suspicion of all players in the game
        return orderedProbs, average

    def propose_mission(self, team_size, betrayals_required = 1): # Calculate the probability of each player being a spy, then select the "team_size" lowest people
        '''
        team_size is the required mission size
        betrayals_required is the number of betrayals needed for the mission to fail
        '''
        team = []
        probabilities, average = self.calculate_probabilities() # returns list of agents in descending order of suspicion
        probabilities = list(probabilities) # get only the keys of the dictionary

        # EXPERT RULE: Always include self in missions proposition
        team.append(self.player_number)
        team_size -= 1 # Ensures that we don't add too many players
        
        if self.is_spy(): # If the agent is a spy, choose a team most likely to pass the vote, but still contain a spy
            spiesSelected = 1 # Keeps track of how many spies have been selected, starts as 1 as self is always selected
            for x in range(len(probabilities)-1, 0, -1): # looping backwards over the list, find the first spy (least suspicious), and add them to the team
                # Check that the number of spies selected is less than the number of spies required
                # Check that agent is not adding itself again
                if probabilities[x] in self.spy_list and spiesSelected < betrayals_required and probabilities[x] != self.player_number: 
                    team.append(probabilities[x])
                    spiesSelected += 1 # Increment the number of spies selected, If the number of spies selected is equal to the number of betrayals required, then do not select more spies
            for x in range(team_size - spiesSelected+1): # for the remaining number of players required, add the next least suspicious players to the team, more likely to succeed proposal from other opponents
                if probabilities[x] not in self.spy_list and len(team) < team_size + 1 and probabilities[x] != self.player_number:
                    team.append(probabilities[x])
        else: # If the agent is not a spy, choose a team with the least suspicion, least chance of containing a spy
            probabilities.reverse() # Reverse the list so that the first element is the least likely to be a spy
            for x in range(team_size):
                if probabilities[x] != self.player_number: # To avoid adding self to the team again
                    team.append(probabilities[x]) 
        random.shuffle(team) # Shuffle the team so that the spy is not always last, so that opponents can't model us based off of team proposal order
        return team

    def vote(self, mission, proposer): # If proposer is suspected spy, be careful of voting yes, if members of mission are suspected spies, vote no
        '''
        mission is a list of agents to be sent on a mission. 
        The agents on the mission are distinct and indexed between 0 and number_of_players.
        proposer is an int between 0 and number_of_players and is the index of the player who proposed the mission.
        The function should return True if the vote is for the mission, and False if the vote is against the mission.
        '''
        probabilities, average = self.calculate_probabilities() # returns list of agents in descending order of suspicion

        # EXPERT RULE
        # Always accept the final mission proposal, as voting no is bad for the resistance and will make you suspicious if you are a spy
        if self.votecount == 4:
            return True
        
        if not self.is_spy(): # If Resistance, deny missions with suspicious proposers or mission members
            # EXPERT RULE
            # If you are not in a mission when the number of players on the mission is equal to the number of resistance members in the game
            # then the mission is guaranteed to have a spy, so deny mission
            if len(mission) == (self.number_of_players - self.spy_count[self.number_of_players]) and self.player_number not in mission:
                return False
            # EXPERT RULE
            # Trust players that cannot possibly be spies, assume they will make informed decisions
            if probabilities.get(proposer) == 0: 
                return True
            if probabilities.get(proposer) > average: # If the proposer is more suspicious than average, deny mission
                return False
            else: # If the proposer is less suspicious than average, check the mission members
                for x in mission:
                    if probabilities.get(x) > average: # If any agents in mission are more suspicious than average, deny mission
                        return False
                return True # If mission and proposer pass the suspicion test, vote yes
        else: # If the agent is a spy, pass missions with spies on them, but not too suspicious, as it may incriminate yourself
            if probabilities.get(proposer) > average: # If the proposer is more suspicious than average, then vote no
                return False
            else:
                for x in mission:
                    if probabilities.get(x) > average: # if a player on the mission is more suspicious than average, deny mission
                        return False
                if sorted(mission) == sorted(self.spy_list):
                    # If mission of only spies, would be incriminating, deny mission
                    return False
                return True

    def vote_outcome(self, mission, proposer, votes): # use to keep track of how many votes have failed
        '''
        mission is a list of agents to be sent on a mission. 
        The agents on the mission are distinct and indexed between 0 and number_of_players.
        proposer is an int between 0 and number_of_players and is the index of the player who proposed the mission.
        votes is a list of players that voted in favour of the mission
        No return value is required or expected.
        '''
        if len(votes) >= self.number_of_players/2: # increment if the vote fails
            self.votecount += 1
        else: # reset if the vote succeeds
            self.votecount = 0

    def betray(self, mission, proposer): 
        '''
        mission is a list of agents to be sent on a mission. 
        The agents on the mission are distinct and indexed between 0 and number_of_players, and include this agent.
        proposer is an int between 0 and number_of_players and is the index of the player who proposed the mission.
        The method should return True if this agent chooses to betray the mission, and False otherwise. 
        By default, spies will betray 30% of the time. 
        '''
        
        if self.is_spy():
            Spies_in_mission = 0
            # Check the number of spies in the mission
            for x in mission: 
                if x in self.spy_list:
                    Spies_in_mission += 1
            # If 2 missions have already failed, always betray the mission if there are enough spies, as it will guarantee a win
            if self.spy_score == 2 and self.fails_required[self.number_of_players][self.mission_Number-1] <= Spies_in_mission:
                return True
            # If there are enough spies to betray the mission, choose to betray
            if self.fails_required[self.number_of_players][self.mission_Number-1] <= Spies_in_mission:
                return True
            else: # If there are not enough spies to betray the mission, choose not to betray
                return False
        else:
            return False

    def mission_outcome(self, mission, proposer, betrayals, mission_success):
        # Update internal perception of worlds
        '''
        mission is a list of agents to be sent on a mission. 
        The agents on the mission are distinct and indexed between 0 and number_of_players.
        proposer is an int between 0 and number_of_players and is the index of the player who proposed the mission.
        betrayals is the number of people on the mission who betrayed the mission, 
        and mission_success is True if there were not enough betrayals to cause the mission to fail, False otherwise.
        It iss not expected or required for this function to return anything.
        '''
        # if there was at least 1 betrayal, the worlds probabilities can be updated
        if betrayals != 0:
            fail_chance = self.worlds.copy() # this dictionary will store the P(F|W) values
            total_fail = 0
            # iterate through all the worlds
            for combination in fail_chance:
                # overlap is the agents in both the mission and current combination
                overlap = set(combination)&set(mission)

                # comb is the combinations of which agents could have chosen to betray
                # e.g. if we know that there were 2 betrayals, and overlap was [0, 1, 2],
                #      then the possible betrayal outcomes are:
                #      (0, 1), (0, 2) and (1, 2)
                comb = list(itertools.combinations(overlap, betrayals))

                # total is the total number of combinations that a mission could end in
                # each agent either passes the mission (True), or fails the mission (False)
                # e.g. for a mission with 3 agents, the possible outcomes are:
                # (True, True, True), (True, True, False), (True, False, True), (True, False, False), (False, True, True), (False, True, False), (False, False, True), (False, False, False)
                total = list(itertools.product([True, False], repeat = len(mission)))
                
                # the chance of failing a mission is the number of combinations divided by the total
                fail_chance[combination] = len(comb)/len(total)
                
                # sum the product of P(C) and P(F|C) to get P(F)
                total_fail += fail_chance[combination] * self.worlds[combination]
            
            for combination in fail_chance: # set the world probabilities to the newly calculated values
                self.worlds[combination] = fail_chance[combination] * self.worlds[combination] / total_fail

    def round_outcome(self, rounds_complete, missions_failed):
        '''
        basic informative function, where the parameters indicate:
        rounds_complete, the number of rounds (0-5) that have been completed
        missions_failed, the number of missions (0-3) that have failed.
        '''
        #nothing to do here
        pass
    
    def game_outcome(self, spies_win, spies): 
        '''
        basic informative function, where the parameters indicate:
        spies_win, True iff the spies caused 3+ missions to fail
        spies, a list of the player indexes for the spies.
        '''
        # Outputs result of each game to csv, used for testing performance of the agent.
        # Commented out from Code for final submission and tournament play
        pass
        """
        if self.name == 'BayesJondRandom':
            if self.is_spy():
                if spies_win:
                    with open('BayesJondRandom.csv','a') as fd:
                        fd.write('{} players,I was spy,Won\n'.format(self.number_of_players))
                else:
                    with open('BayesJondRandom.csv','a') as fd:
                        fd.write('{} players,I was spy,Lost\n'.format(self.number_of_players))
            else:
                if spies_win:
                    with open('BayesJondRandom.csv','a') as fd:
                        fd.write('{} players,I was not spy,Lost\n'.format(self.number_of_players))
                else:
                    with open('BayesJondRandom.csv','a') as fd:
                        fd.write('{} players,I was not spy,Won\n'.format(self.number_of_players))
                        
        elif self.name == 'BayesJondBasic':
            if self.is_spy():
                if spies_win:
                    with open('BayesJondBasic.csv','a') as fd:
                        fd.write('{} players,I was spy,Won\n'.format(self.number_of_players))
                else:
                    with open('BayesJondBasic.csv','a') as fd:
                        fd.write('{} players,I was spy,Lost\n'.format(self.number_of_players))
            else:
                if spies_win:
                    with open('BayesJondBasic.csv','a') as fd:
                        fd.write('{} players,I was not spy,Lost\n'.format(self.number_of_players))
                else:
                    with open('BayesJondBasic.csv','a') as fd:
                        fd.write('{} players,I was not spy,Won\n'.format(self.number_of_players))
        
        elif self.name == 'BayesJondJond':
            if self.is_spy():
                if spies_win:
                    with open('BayesJondJond.csv','a') as fd:
                        fd.write('{} players,I was spy,Won\n'.format(self.number_of_players))
                else:
                    with open('BayesJondJond.csv','a') as fd:
                        fd.write('{} players,I was spy,Lost\n'.format(self.number_of_players))
            else:
                if spies_win:
                    with open('BayesJondJond.csv','a') as fd:
                        fd.write('{} players,I was not spy,Lost\n'.format(self.number_of_players))
                else:
                    with open('BayesJondJond.csv','a') as fd:
                        fd.write('{} players,I was not spy,Won\n'.format(self.number_of_players))
        
        elif self.name == 'BayesJondCombination':
            if self.is_spy():
                if spies_win:
                    with open('BayesJondCombination.csv','a') as fd:
                        fd.write('{} players,I was spy,Won\n'.format(self.number_of_players))
                else:
                    with open('BayesJondCombination.csv','a') as fd:
                        fd.write('{} players,I was spy,Lost\n'.format(self.number_of_players))
            else:
                if spies_win:
                    with open('BayesJondCombination.csv','a') as fd:
                        fd.write('{} players,I was not spy,Lost\n'.format(self.number_of_players))
                else:
                    with open('BayesJondCombination.csv','a') as fd:
                        fd.write('{} players,I was not spy,Won\n'.format(self.number_of_players))
        """

# Created by <NAME> (22704832) and <NAME> (22730325) All players should start with equal chance of being spy, IF resistance in game of 5 players, each other player has a 50% chance of being a spy,      as it is 2/4 chance of being a spy if we are not included in the count. This should be kept in mind and update when playing as spy too, as it can help avoid detection          self.worlds stores a dictionary of all possible worlds, where each key is a tuple of the a combinations players in the world,                  assuming the players of whose names are in the tuple are both spies, and the value is the likelihood of that world being the real world.                 eg. if (1,2): 0.4, then there is a 40% chance of the real world having players 1 and 2 as spies.     self.spy_count stores a dict of how many spies are in a game in regards to how many players are in the game, eg a game with 5 players has 2 spies # Initialises the agent. initialises the game, informing the agent of the          number_of_players, the player_number (an id number for the agent in the game),         and a list of agent indexes which are the spies, if the agent is a spy, or empty otherwise # Returns the number of spies in a game of a relavent size eg a game with 9 players, self.spy_count[9] returns 3 # The number of players in the game # The agent's player number # The list of spy indexes, empty if the agent is not a spy # Stores the worlds and their probabilities, will be updated as the game progresses # Stores the number of failed votes # The index of the current mission number, for indentifying how many betrayals are required # The number of points that the spies have, 3 == win for spies # Generates the dictionary all the possible worlds, where each key is a tuple of the a combinations players in the world, # and each value is the probability that the world is true. Creates number of worlds based on number of players in the game # Creates the worlds for a game of 2 spies # Creates the worlds for a game of 3 spies # Creates the worlds for a game of 4 spies # returns True iff the agent is a spy calculates and sorts the suspiciousness of each player using values from self.worlds         returns a dictionary mapping suspiciousness to player numbers and the average suspiciousness # dictionary to populate # Loop through all players # list to hold probabilities of worlds containing player x # Loops through all worlds # Averages the suspicion of each player in the worlds in which they are spies # Sorts the list in descending order of suspicion # variable used to calculate average suspiciousness # Loop through each player's suspicion scores # Calculates average suspicion of all players in the game # Calculate the probability of each player being a spy, then select the "team_size" lowest people team_size is the required mission size         betrayals_required is the number of betrayals needed for the mission to fail # returns list of agents in descending order of suspicion # get only the keys of the dictionary # EXPERT RULE: Always include self in missions proposition # Ensures that we don't add too many players # If the agent is a spy, choose a team most likely to pass the vote, but still contain a spy # Keeps track of how many spies have been selected, starts as 1 as self is always selected # looping backwards over the list, find the first spy (least suspicious), and add them to the team # Check that the number of spies selected is less than the number of spies required # Check that agent is not adding itself again # Increment the number of spies selected, If the number of spies selected is equal to the number of betrayals required, then do not select more spies # for the remaining number of players required, add the next least suspicious players to the team, more likely to succeed proposal from other opponents # If the agent is not a spy, choose a team with the least suspicion, least chance of containing a spy # Reverse the list so that the first element is the least likely to be a spy # To avoid adding self to the team again # Shuffle the team so that the spy is not always last, so that opponents can't model us based off of team proposal order # If proposer is suspected spy, be careful of voting yes, if members of mission are suspected spies, vote no mission is a list of agents to be sent on a mission.          The agents on the mission are distinct and indexed between 0 and number_of_players.         proposer is an int between 0 and number_of_players and is the index of the player who proposed the mission.         The function should return True if the vote is for the mission, and False if the vote is against the mission. # returns list of agents in descending order of suspicion # EXPERT RULE # Always accept the final mission proposal, as voting no is bad for the resistance and will make you suspicious if you are a spy # If Resistance, deny missions with suspicious proposers or mission members # EXPERT RULE # If you are not in a mission when the number of players on the mission is equal to the number of resistance members in the game # then the mission is guaranteed to have a spy, so deny mission # EXPERT RULE # Trust players that cannot possibly be spies, assume they will make informed decisions # If the proposer is more suspicious than average, deny mission # If the proposer is less suspicious than average, check the mission members # If any agents in mission are more suspicious than average, deny mission # If mission and proposer pass the suspicion test, vote yes # If the agent is a spy, pass missions with spies on them, but not too suspicious, as it may incriminate yourself # If the proposer is more suspicious than average, then vote no # if a player on the mission is more suspicious than average, deny mission # If mission of only spies, would be incriminating, deny mission # use to keep track of how many votes have failed mission is a list of agents to be sent on a mission.          The agents on the mission are distinct and indexed between 0 and number_of_players.         proposer is an int between 0 and number_of_players and is the index of the player who proposed the mission.         votes is a list of players that voted in favour of the mission         No return value is required or expected. # increment if the vote fails # reset if the vote succeeds mission is a list of agents to be sent on a mission.          The agents on the mission are distinct and indexed between 0 and number_of_players, and include this agent.         proposer is an int between 0 and number_of_players and is the index of the player who proposed the mission.         The method should return True if this agent chooses to betray the mission, and False otherwise.          By default, spies will betray 30% of the time. # Check the number of spies in the mission # If 2 missions have already failed, always betray the mission if there are enough spies, as it will guarantee a win # If there are enough spies to betray the mission, choose to betray # If there are not enough spies to betray the mission, choose not to betray # Update internal perception of worlds mission is a list of agents to be sent on a mission.          The agents on the mission are distinct and indexed between 0 and number_of_players.         proposer is an int between 0 and number_of_players and is the index of the player who proposed the mission.         betrayals is the number of people on the mission who betrayed the mission,          and mission_success is True if there were not enough betrayals to cause the mission to fail, False otherwise.         It iss not expected or required for this function to return anything. # if there was at least 1 betrayal, the worlds probabilities can be updated # this dictionary will store the P(F|W) values # iterate through all the worlds # overlap is the agents in both the mission and current combination # comb is the combinations of which agents could have chosen to betray # e.g. if we know that there were 2 betrayals, and overlap was [0, 1, 2], #      then the possible betrayal outcomes are: #      (0, 1), (0, 2) and (1, 2) # total is the total number of combinations that a mission could end in # each agent either passes the mission (True), or fails the mission (False) # e.g. for a mission with 3 agents, the possible outcomes are: # (True, True, True), (True, True, False), (True, False, True), (True, False, False), (False, True, True), (False, True, False), (False, False, True), (False, False, False) # the chance of failing a mission is the number of combinations divided by the total # sum the product of P(C) and P(F|C) to get P(F) # set the world probabilities to the newly calculated values basic informative function, where the parameters indicate:         rounds_complete, the number of rounds (0-5) that have been completed         missions_failed, the number of missions (0-3) that have failed. #nothing to do here basic informative function, where the parameters indicate:         spies_win, True iff the spies caused 3+ missions to fail         spies, a list of the player indexes for the spies. # Outputs result of each game to csv, used for testing performance of the agent. # Commented out from Code for final submission and tournament play if self.name == 'BayesJondRandom':             if self.is_spy():                 if spies_win:                     with open('BayesJondRandom.csv','a') as fd:                         fd.write('{} players,I was spy,Won\n'.format(self.number_of_players))                 else:                     with open('BayesJondRandom.csv','a') as fd:                         fd.write('{} players,I was spy,Lost\n'.format(self.number_of_players))             else:                 if spies_win:                     with open('BayesJondRandom.csv','a') as fd:                         fd.write('{} players,I was not spy,Lost\n'.format(self.number_of_players))                 else:                     with open('BayesJondRandom.csv','a') as fd:                         fd.write('{} players,I was not spy,Won\n'.format(self.number_of_players))                                  elif self.name == 'BayesJondBasic':             if self.is_spy():                 if spies_win:                     with open('BayesJondBasic.csv','a') as fd:                         fd.write('{} players,I was spy,Won\n'.format(self.number_of_players))                 else:                     with open('BayesJondBasic.csv','a') as fd:                         fd.write('{} players,I was spy,Lost\n'.format(self.number_of_players))             else:                 if spies_win:                     with open('BayesJondBasic.csv','a') as fd:                         fd.write('{} players,I was not spy,Lost\n'.format(self.number_of_players))                 else:                     with open('BayesJondBasic.csv','a') as fd:                         fd.write('{} players,I was not spy,Won\n'.format(self.number_of_players))                  elif self.name == 'BayesJondJond':             if self.is_spy():                 if spies_win:                     with open('BayesJondJond.csv','a') as fd:                         fd.write('{} players,I was spy,Won\n'.format(self.number_of_players))                 else:                     with open('BayesJondJond.csv','a') as fd:                         fd.write('{} players,I was spy,Lost\n'.format(self.number_of_players))             else:                 if spies_win:                     with open('BayesJondJond.csv','a') as fd:                         fd.write('{} players,I was not spy,Lost\n'.format(self.number_of_players))                 else:                     with open('BayesJondJond.csv','a') as fd:                         fd.write('{} players,I was not spy,Won\n'.format(self.number_of_players))                  elif self.name == 'BayesJondCombination':             if self.is_spy():                 if spies_win:                     with open('BayesJondCombination.csv','a') as fd:                         fd.write('{} players,I was spy,Won\n'.format(self.number_of_players))                 else:                     with open('BayesJondCombination.csv','a') as fd:                         fd.write('{} players,I was spy,Lost\n'.format(self.number_of_players))             else:                 if spies_win:                     with open('BayesJondCombination.csv','a') as fd:                         fd.write('{} players,I was not spy,Lost\n'.format(self.number_of_players))                 else:                     with open('BayesJondCombination.csv','a') as fd:                         fd.write('{} players,I was not spy,Won\n'.format(self.number_of_players))