def test_dequeue():
q = MyQueue()
q.enqueue(1)
q.enqueue(2)
assert 2 == q.size()
assert 1 == q.dequeue()
assert 1 == q.size()
assert 2 == q.dequeue()
assert 0 == q.size()
assert 1 == q.isEmpty()
def __init__(self, coordinatorId, txNodeId, rxNodeId, cost, playerNumber, gameFreq, gameType=0):
"""
Create a player and initialize internal parameters.
Keyword arguments:
coordinatorId -- Numerical cluster id.
txNodeId -- Numerical id for transmission node.
rxNodeId -- Numerical id for receiver node.
cost -- Energy cost.
playerNumber -- Numerical number used to identify players.
gateType -- Type of game played.
"""
self.coordinatorId = coordinatorId
self.cost = cost
self.playerNumber = playerNumber
self.gameFreq = gameFreq
self.gameType = gameType
self.txNodeId = txNodeId
self.rxNodeId = rxNodeId
self.sourceOfEvent = False
self.queueList = MyQueue(5)
self.playerIterations = 0
# initialize physical layer
self.physicalLayer = PlayerPhysicalLayer(self, txNodeId, self.rxNodeId, self.coordinatorId, self.gameFreq, self.cost, self.gameType)
class SearchTree:
def __init__(self, instance, verbose=True):
self.instance = instance
self.queue = MyQueue() # 初始化列表，默认深度优先（depth-first）
self.incumbent = Solution() # 初始化最优解
self.verbose = verbose # 是否打印相关参数
self.n_nodes = 0 # 求解的总结点数目
def solve(self):
start_time = time.time()
m = MasterModel(
self.instance) # 初始化限制主问题(restrict master problem, RMP)
node = Node(m) # 初始化根节点
if self.verbose:
print("creating RMP in root node: done")
self.queue.push(node) # 节点入队列
if self.verbose:
print(f"\nSearch strategy: {self.queue.strategy}-first")
while not self.queue.empty():
node = self.queue.pop() # 弹出节点
self.n_nodes += 1
if self.verbose:
print(
f"\nThe {self.n_nodes}th iteration, level = {node.level}")
# 列生成求解该节点对应的RMP
# print(f"{node.rmp.data.n=}")
cg = CG(node)
node.solution = cg.solve() # 返回列生成求解的结果
# node.rmp.model.write(f"rmp{self.n_nodes}.lp")
# fathomed节点的两种情形
# 1.该节点最小值大于当前最佳可行解目标值
if self.incumbent.value is not None and \
self.incumbent.value <= node.solution.value:
if self.verbose:
print(
f"The node is not promising with value being {node.solution.value}"
)
continue
# 2.该节点是可行解
if node.solution.is_integer_solution(): # 如果是整数解，比较更新结果
self.incumbent.update(node.solution)
if self.verbose:
print(
f"\nFind a new feasible solution, value={node.solution.value}"
)
continue
if self.verbose:
print(
f"The node should be branched, and value={node.solution.value}"
)
branches = BinaryBranch().branching(node)
for branch in branches: # 结点分支定添加进入队列
self.queue.push(branch.apply(node))
# assert self.queue.data[0].rmp.data.items is not self.queue.data[1].rmp.data.items
end_time = time.time()
if self.verbose:
print(
f"\nSolved {self.n_nodes} node(s) in {end_time - start_time}s\n"
f"objective value = {self.incumbent.value}")
def getMostRecentGainMeasurments(coordinator_id, tx_node_id, rx_node_id, number = 6):
#number represents the number of measurements to be returned. How many measurements you want to get. Example: if number=3, return last 3 measurements made
#use this method when you want to get a list with a specified number of last measurements made
if number == 0:
#that means nothing
return None
#define a queue
queue = MyQueue(number)
#read from file (if it exists)
#first, check if the file exists
path = "./gain measurements/coor_%d/gain_between_tx_%d_and_rx_%d.dat" %(coordinator_id ,tx_node_id, rx_node_id)
if not os.path.isfile(path) or not os.path.exists(path):
return None
f = open(path, "r")
#read first line, it's a header
f.readline()
#read file line by line
while True:
line = f.readline()
if not line:
break
#define a line list: [gain - linear , received_power[w] , noise_power[w], transmitted_power[w], date ] - this is how results are saved in the file
line_list = line.split()
try:
queue.append(float(line_list[0]))
except:
pass
f.close()
return queue.getListReverse()
def gameType3(self):
#number of iterations until player reached the Nash equilibrium
iterations = 0
#USe this because I want to see how long it takes to reach the Nash equilibrium
process_start_time = time.time()
print "Player %d Game type 3 started" %self.player.player_number
#want to save the experiment results. results_list_ [[new power calculated truncated, new_power_calculated, received power, anticipated useful received power], [same thing],[same thing].. ]
self.results_list = [[self.getTheNearestPower(self.current_transmitting_power), self.current_transmitting_power, "None", "None", self.player.cost]]
#a queue_list which will help us to know if the equilibrium has been reached
self.queue_list = MyQueue(5)
while True:
#infinite loop
#see if there is any power change alert from the neighbor
if self.neighborPowerEvent :
#that means neighbor changed its own power, so I have to update my power
#reset the event because is being treated
self.neighborPowerEvent = False
print "Player %d detected a change of power and it's recalculating its own transmission power. Iteration %d" %(self.player.player_number, iterations)
#check if I am transmitting something right now
if( self.playerApp.isSendingAPacket()):
#I can't measure in this case, I want to measure only interference and noise
while self.playerApp.isSendingAPacket():
print "Player %d : bad news, I'm transmitting packets right now, wait to stop!" %self.player.player_number
time.sleep(1)
#Ask the neighbor to recalculate power and regenerate a signal
self.alertNeighborAboutAChangeOfPower()
continue
#measure received power. received_power has the following structure: [[frequency], [power_dbm]]
received_power = self.player.rx_node.senseStartQuick()
print "Player %d received noise and interference power: %.3f dBm" %(self.player.player_number, received_power[1][0])
#now we have anything we need to calculate new power
self.previous_transmitted_power = self.current_transmitting_power
#the best response returned is in dBm
self.current_transmitting_power = self.getBestResponsePracticallyForm2(received_power[1][0])
#sometimes formula returns a negative power in linear, that can't be possible in practice, so if that happens, continue this iteration
if self.current_transmitting_power is None:
print "Player %d negative power" %self.player.player_number
self.current_transmitting_power = self.previous_transmitted_power
self.neighborPowerEvent = True
continue
#the best response give us a real power, in practice we can't generate a power at any resolution ( The formula doesn't take into account this thing)
#On VESNA platform there are a few power values that can be generated
print "Player %d best response: %.6f. Choosing the right value for VESNA..." %(self.player.player_number, self.current_transmitting_power)
#I want to keep the best response given by the formula
self.best_response_untouched = self.current_transmitting_power
#truncate the power
self.current_transmitting_power = self.getTheNearestPower(self.current_transmitting_power)
#make some list with iterations and calculated powers
self.results_list.append([self.current_transmitting_power, self.best_response_untouched, received_power[1][0], "None", self.player.cost])
self.queue_list.append(self.best_response_untouched)
#we want to count how many iterations the algorithm used to reach the Nash equilibrium
iterations+= 1
#see if we have reached the equilibrium
if not self.checkEquilibrium(equal_condition=False):
#the equilibrium has not been reached yet
print "Player %d has not reached the Nash Equilibrium yet" %(self.player.player_number)
#wait for the neighbor to finish his transmission
while self.neighborPowerAllocation.playerApp.isSendingAPacket():
print "Player %d : waiting for the neighbor to finish his transmission" %self.player.player_number
time.sleep(1)
#generate at new power. This is needed because this way the neighbor player can see the change I made
#send a packet for a few seconds
self.playerApp.sendPacket(6)
#to be sure the neighbor will catch my generated signal, wait for 1 second (this way I consider others delay that can appear within the testbed)
time.sleep(1)
#announce the other player about my change
self.alertNeighborAboutAChangeOfPower()
else:
#that means equilibrium
print "Player %d : Equilibrium has been reached" %(self.player.player_number)
#check if neighbor has reached equilibrium too
if(not self.neighborPowerAllocation.checkEquilibrium(equal_condition=False)):
print "Player %d : My neighbor has not reached the Nash equilibrium yet, send a packet again" %self.player.player_number
#wait for the neighbor to finish his transmission
while self.neighborPowerAllocation.playerApp.isSendingAPacket():
print "Player %d : waiting for the neighbor to finish his transmission" %self.player.player_number
time.sleep(1)
#send packet
self.playerApp.sendPacket(6)
time.sleep(1)
self.alertNeighborAboutAChangeOfPower()
continue
#finish this game
self.neighborPowerAllocation.equilibriumDectected()
self.equilibriumDectected()
else:
if self.equilibriumDetected:
print "\x1b[1;31m"
print "Player %d Equilibrium. No events had been detected. Number of iterations:%d. Time passed: %.6f \n" %(self.player.player_number, iterations, (time.time() - process_start_time) - 45)
print "Player %d power allocated: %.3f" %(self.player.player_number, self.current_transmitting_power)
print "\x1b[0m"
#write experiment results in a file
self.printIterationsToFile(self.results_list, "Using threshold with a 5 size queue, predicted gains")
if self.player.player_number == 1:
self.printStrategyTable(self.best_response_untouched, self.neighborPowerAllocation.best_response_untouched, iterations)
while True:
time.sleep(1)
time.sleep(0.05)
def gameType2(self):
#number of iterations until player reach the Nash equilibrium
iterations = 0
#USe this because I want to see how long it takes to reach the Nash equilibrium
process_start_time = time.time()
print "Player %d Game type 2 started" %self.player.player_number
#want to save the experiment results. results_list_ [new power calculated, received power, anticipated useful received power]
self.results_list = [[self.getTheNearestPower(self.current_transmitting_power), self.current_transmitting_power, "None", "None", self.player.cost]]
self.queue_list = MyQueue(5)
while True:
#see if there is any power change alert from the neighbor
if self.neighborPowerEvent :
#that means neighbor changed its own power
#reset the event because is being treated
self.neighborPowerEvent = False
print "Player %d detected a change of power and it's recalculating its own transmission power. Iteration %d" %(self.player.player_number, iterations)
#anticipate transmitted_power. Initialize transmited_power with -100000 dBm, equivalent with almost 0 in linear scale
transmited_power_dbm_before_measurement = -100000
if self.playerApp.isSendingAPacket():
#yes, I am sending a packet right now, I have to consider that because I want to know what is interference and noise and what is not
transmited_power_dbm_before_measurement = self.playerApp.getTransmittingPower()
#there hass been a change of power on the neighbor player, so I have to recalculate my own power
#measure received power. received_power has the following structure: [[frequency], [power_dbm]]
received_power = self.player.rx_node.senseStartQuick()
#see if there is any power transmitting right now again. First initialize transmited_power_dbm with a very low power: -10000 dBm it's very small
transmited_power_dbm_after_measurement = -100000
if self.playerApp.isSendingAPacket():
#yes, I am sending a packet right now, I have to consider that because want to know when is interference and noise and when is not
transmited_power_dbm_after_measurement = self.playerApp.getTransmittingPower()
#see if the useful transmitted power before and after measurements are equal, if not I can't be sure that when I did the measure, I was generating signal or no, that way I won't be able to determine the interference level
if transmited_power_dbm_after_measurement != transmited_power_dbm_before_measurement:
print "Player %d bad anticipation of useful received power, continue this iteration and measure again" %(self.player.player_number)
#set event to true because it wasn't complete treated
self.neighborPowerEvent = True
#measure again
continue
if (transmited_power_dbm_after_measurement + 10.00 * math.log10(self.player.direct_gain)) > received_power[1][0]:
#the anticipated useful received power can't be higher than the received power
print "Anticipated received power > received power : %.6f > %.6f" %(transmited_power_dbm_after_measurement + 10.00 * math.log10(self.player.direct_gain), received_power[1][0])
self.neighborPowerEvent = True
continue
#just print some info
if float(transmited_power_dbm_after_measurement) < -1000:
print "Player %d received power: [%.3f dBm] and he is not transmitting anything at this moment" %(self.player.player_number, received_power[1][0])
else :
print "Player %d received power: [%.3f dBm] and he is transmitting with %.3f dBm. Anticipated useful received power: %.3f" %(self.player.player_number, received_power[1][0], transmited_power_dbm_after_measurement, transmited_power_dbm_after_measurement + 10.00 * math.log10(self.player.direct_gain))
#now we have anything we need to calculate new power
self.previous_transmitted_power = self.current_transmitting_power
self.current_transmitting_power = self.getBestResponsePracticallyForm1(transmited_power_dbm_after_measurement, float(received_power[1][0]))
#Sometimes formula returns a negative power (in linear!), which can't be possible.
if self.current_transmitting_power is None:
self.neighborPowerEvent = True
#anyway, I want to keep previous transmitted power
self.current_transmitting_power = self.previous_transmitted_power
continue
#copy best response
self.best_response_untouched = self.current_transmitting_power
self.current_transmitting_power = self.getTheNearestPower(self.current_transmitting_power)
#make some list with iterations and calculated powers
self.results_list.append([self.current_transmitting_power, self.best_response_untouched, received_power[1][0], transmited_power_dbm_after_measurement + 10.00 * math.log10(self.player.direct_gain), self.player.cost])
self.queue_list.append(self.best_response_untouched)
#we want to count how many iterations the algorithm used to reach the Nash equilibrium
iterations+= 1
#see if the difference between new power and old power exceeds the threshold_power
if not self.checkEquilibrium(equal_condition=False):
#Nash equilibrium hasn't reached yet
print "Player %d has not reached the Nash Equilibrium yet" %(self.player.player_number)
#generate at new power. This is needed because this way the neighbor player can see the change I made
#send a packet for 12 seconds
self.playerApp.sendPacket(8)
#to be sure the neighbor will catch my generated signal, a wait for 1 second (this way I consider the programming time)
time.sleep(1)
#announce the other player about my change
self.alertNeighborAboutAChangeOfPower()
else:
#that means equilibrium
print "Player %d : Equilibrium has been reached" %(self.player.player_number)
#finish the game by stopping the threads
self.equilibriumDectected()
self.neighborPowerAllocation.equilibriumDectected()
else:
if self.equilibriumDetected:
print "Player %d Equilibrium. No events had been detected. Number of iterations:%d. Time passed: %.6f" %(self.player.player_number, iterations, (time.time() - process_start_time) - 45)
print "Player %d power allocated: %.3f" %(self.player.player_number, self.current_transmitting_power)
#write experiment results in a file
self.printIterationsToFile(self.results_list)
#if this is player 1, then print the strategy table( is enough that only one player to do that)
if self.player.player_number == 1:
self.printStrategyTable(self.current_transmitting_power, self.neighborPowerAllocation.current_transmitting_power, iterations=iterations)
break
time.sleep(0.03)
class PowerAllocation(threading.Thread):
#This class is responsible with the allocation of powers which players application can use
#Needs a player object and playerApp object in order to work
#VESNA power generating list. This must be sorted. Powers are in dBm
available_generating_powers = [0, -2, -4, -6, -8, -10, -12, -14, -16, -18, -20, -22, -24, -26, -28, -30, -55]
#current transmitting power [dBm]
current_transmitting_power = random.choice(available_generating_powers)
#previous transmitted_power [dBm]
previous_transmitted_power = current_transmitting_power
#neighbor current_transmitting power [dBm]
neighbor_current_transmitting_power = random.choice(available_generating_powers)
#neighbor previous transmitted_power [dBm]
neighbor_previous_transmitted_power = neighbor_current_transmitting_power
#temporary - this best response is not truncated according to VESNA power list
best_response_untouched = current_transmitting_power
#game type - can be: 1, 2, 3 . Depends on the implementation you want to use
game_type = 3
#neighborPowerEvent. When this is True, It means that neighbor player changed its power
neighborPowerEvent = False
#threshold_power. This can be set any time. We need this when we determine if Nash equilibrium was reached
threshold_power = 0.8
#A list in which we will save experiment results
results_list = []
#a boolean variable which tells the program if the cost is fixed or adaptive [not implemented yet]
adaptive_cost = False
#When this is triggered( =True ) then it means that equilibrium has been reached and the threads stops here.
equilibriumDetected = False
def __init__(self, playerObject ,playerAppObject, gameType = 3):
print "Player power Allocation"
threading.Thread.__init__(self)
#save player object, that way, I can have any data I want from this player. Player object contains info about nodes (tx and rx), gains, cost..
self.player = playerObject
#Save playerAppObject this is how I can communicate with the PlayerApp object.
self.playerApp = playerAppObject
#configure sense node here, it's enough to be done only once
self.player.rx_node.setSenseConfiguration(self.playerApp.frequency, self.playerApp.frequency, 400e3)
#Let playerApp have this powerAllocationObject
self.playerApp.setPowerAllocationOjbect(self)
#save gameType
self.game_type = gameType
def printPowerAllocationInfo(self):
print "PowerAllocation associated to player %d" %self.player.player_number
def setGameType(self, game_type):
if game_type<1 or game_type>3:
print "Game type can be 1, 2 or 3"
self.game_type = 1
return
self.game_type = game_type
def setNeighborPowerAllocationObject(self, powerAllocationObject):
#This is how I can communicate with the neighbor player
#save powerAllocationObject
self.neighborPowerAllocation = powerAllocationObject
def setThresholdPower(self, threshold_power):
self.threshold_power = threshold_power
print "Player %d : threshold power was set to: %.3f" %(self.player.player_number, self.threshold_power)
def getTransmittingPower(self):
#if PlayerApp needs to know the power which he can use, it's calling this method and he will get the allowed transmitting power
#return self.current_transmitting_power
return self.getTheNearestPower(self.current_transmitting_power)
#there are several bestReponse methods
def getBestResponseTheoreticallyForm(self):
#based on the cost, cross gain, direct gain , neighbor power transmitting and noise we can determine new power
#for best understanding see the formula in the article
#We want to return a power in dBm. This power is the best power in interference condition
#this formula anticipates the received power basing on constant gains and noise
#convert power from dBm to W
#I need to know what power my neighbor is using. I know this from self.neighbor_current_transmitting_power, but is always worth checking.
if (self.neighborPowerAllocation.current_transmitting_power != self.neighbor_current_transmitting_power):
#Update neighbor_current_transmiting_power
print "Neighbor current transmitting power uncertainty.Updating neighbor_current_transmitting_power"
self.neighbor_current_transmitting_power = self.neighborPowerAllocation.current_transmitting_power
#convert power to W
neighbor_transmitting_power = math.pow(10.00, float(self.neighbor_current_transmitting_power) / 10.00) * 0.001
best_response = (1.00/float(self.player.cost)) - (self.player.cross_gain * neighbor_transmitting_power + self.player.noise_power) / float(self.player.direct_gain)
#return a dBm power
try:
return 10.00 * math.log10(best_response/0.001)
except:
print "Player %d: negative best response" %(self.player.player_number)
return None
def getBestResponsePracticallyForm1(self, transmitted_power_dbm, received_power_dbm):
#For best understanding see the formula in the article
#This method returns a power in dBm. This power is the best power in interference condition
#This method use the practically formula which will be used in empirical games
#this formula will take into account the useful transmitted power
#parameters are powers in dBm, we must convert them to W
transmitted_power_watts = math.pow(10.00, float(transmitted_power_dbm)/10.00) * 0.001
useful_received_power_watts = transmitted_power_watts * self.player.direct_gain
received_power_watts = math.pow(10.00, float(received_power_dbm)/10.00) * 0.001
#now we have everything we need to calculate the new power based on formula: 1/c - (I+N)/gain, where I+N = received_power - useful_received_power
if (transmitted_power_dbm < -1000):
#In this case we consider that current player is not sending something useful, so he measured only noise and interference
best_response = (1.00/float(self.player.cost)) - (received_power_watts)/float(self.player.direct_gain)
else :
best_response = (1.00/float(self.player.cost)) - (received_power_watts - useful_received_power_watts)/float(self.player.direct_gain)
#I want to return a dBm power, because that is what we are sending to the nodes
try:
return 10.00 * math.log10(best_response/0.001)
except:
print "Player %d: negative best response" %(self.player.player_number)
return None
def getBestResponsePracticallyForm2(self, received_power_dbm):
#For best understanding see the formula in the article
#This method return a power in dBm. This power is the best power in interference condition
#This method use the practically formula which will be used in empirical games
#This formula is based on the received power dBm, which must be only noise and interference
#convert received_power in W
received_power_watts = math.pow(10.00, float(received_power_dbm)/10.00) * 0.001
best_response = (1.00/float(self.player.cost)) - (received_power_watts)/float(self.player.direct_gain)
#I want to return a dBm power
try:
return 10.00 * math.log10(best_response/0.001)
except:
print "Player %d: negative best response" %(self.player.player_number)
return None
'''
#not implemented
def costAdaptiveChange_Fang(self, received_power_dbm, useful_received_power_dbm):
#must measure sinr first
received_power = math.pow(10.00, float(received_power_dbm)/10.00) * 0.001
useful_received_power = math.pow(10.00, float(useful_received_power_dbm)/10.000) * 0.001
sinr = (useful_received_power)/(received_power-useful_received_power)
print "Player %d new cost adaptive changed: %.6f" %(self.player.player_number, 10/sinr)
def costAdaptiveChange_Huang(self, received_power_dbm):
received_power = math.pow(10.00, float(received_power_dbm)/10.00) * 0.001
new_cost = (self.player.priority / (received_power))
print "Player %d new cost adaptive changed: %.6f" %(self.player.player_number, new_cost)
self.player.setCost(new_cost)
def enableAdaptiveCost(self):
self.adaptive_cost = True
def disableAdaptiveCost(self):
self.adaptive_cost = False
'''
def sharePowerToTheNeighbor(self, power_dBm):
#Player i will call this method to share his new power to the neighbor
#This is used in theoretically game
self.neighborPowerAllocation.changeNeighborPowerAtribute(power_dBm)
def changeNeighborPowerAtribute(self, new_power_dBm):
#this method only changes self.neighbor_current_transmitting_power
self.neighbor_previous_transmitted_power = self.neighbor_current_transmitting_power
self.neighbor_current_transmitting_power = new_power_dBm
def alertNeighborAboutAChangeOfPower(self):
#Player i will call this method to alert the neighbor player that he made a change of power.
self.neighborPowerAllocation.neighborPowerEvent = True
def changeCurrentPower(self, new_power_dbm):
#change power on current object. Use this method to unbalance the system by generating an event.
#save new power
self.previous_transmitted_power = self.current_transmitting_power
self.current_transmitting_power = new_power_dbm
print "Player %d intentionally changed his power: previous power = %.6f current power = %.6f" %(self.player.player_number ,self.previous_transmitted_power, self.current_transmitting_power)
#before we generate, let's make sure the other players are not transmitting now
#wait for the neighbor to finish his transmission
while self.neighborPowerAllocation.playerApp.isSendingAPacket():
print "Player %d : waiting for the neighbor to finish his transmission" %self.player.player_number
time.sleep(1)
#transmit for a few seconds.
if self.game_type != 1:
#if it's a theoretical game (simulation) it's no needed to transmit anything
self.playerApp.sendPacket(8)
#announce the other player that I made a change
#but first, wait 1-2 seconds to be sure that the neighbor player catch the interference (there might be other delays in the testbed)
time.sleep(1)
#announce neighbor about a change
self.sharePowerToTheNeighbor(self.current_transmitting_power)
self.alertNeighborAboutAChangeOfPower()
#add an iteration to the results_list (We want to save all experiment results in a file). results_list has the following structure: [new power calculated_truncated, new_power_calculated_non_truncated, received power, anticipated useful received power, player cost]
try:
self.results_list[0] = ([self.current_transmitting_power, self.current_transmitting_power, "None", "None", self.player.cost])
except:
pass
return
def checkEquilibrium(self,equal_condition = False):
#check difference between last best responses
#Depends on the implementation, if you want to compare equilibrium considering truncated values, than equal_condition= True, otherwise set it to False
#list :[most recent, less recent, less recent, ..]
print "Player %d check equilibrium :" %(self.player.player_number)
list = self.queue_list.getList()
length = self.queue_list.len
print list
if len(list) != length :return False
if equal_condition == False:
for i in range(0, len(list)):
if math.fabs(math.fabs(float(list[0])) - math.fabs(float(list[i])) ) > float(self.threshold_power):
return False
#if I've got here it means that the Nash equilibrium condition is reached
return True
else:
#in this case, all best responses must be equal
for i in range(0, len(list)-1):
if float(list[i]) != float(list[i+1]):
return False
return True
def limitCurrentTransmittingPower(self):
if self.current_transmitting_power > 0 :
print "Player %d limited his own power to 0 dBm from %.3f" %(self.player.player_number, self.current_transmitting_power)
self.current_transmitting_power = 0
if self.current_transmitting_power < -55:
print "Player %d limited his own power to -55 dBm from %.3f" %(self.player.player_number, self.current_transmitting_power)
self.current_transmitting_power = -55
def printIterationsToFile(self, results_list, other_observations = None):
#first make folders
try:
os.mkdir("./iterations")
except:
pass
try:
if self.player.player_number == 1:
os.mkdir("./iterations/c1_%d-c2_%d" %(self.player.cost, self.neighborPowerAllocation.player.cost))
elif self.player.player_number == 2:
os.mkdir("./iterations/c1_%d-c2_%d" %(self.neighborPowerAllocation.player.cost, self.player.cost))
except:
pass
if self.player.player_number == 1:
path = "./iterations/c1_%d-c2_%d/player_%d_with_tx_%d_and_rx_%d.dat" %(self.player.cost, self.neighborPowerAllocation.player.cost,self.player.player_number, self.player.tx_node.node_id, self.player.rx_node.node_id)
else:
path = "./iterations/c1_%d-c2_%d/player_%d_with_tx_%d_and_rx_%d.dat" %(self.neighborPowerAllocation.player.cost,self.player.cost,self.player.player_number, self.player.tx_node.node_id, self.player.rx_node.node_id)
#see if the path exits and if not create if for the first time
if (not os.path.exists(path) or not os.path.isfile(path)):
#create the file
f = open(path, "w")
f.write("In this file you will find experiments results about the convergence of the player %d with tx_%d and rx_%d\n" %(self.player.player_number, self.player.tx_node.node_id, self.player.rx_node.node_id))
f.close()
f = open(path, "a")
if other_observations == None:
other_observations = "None"
if self.adaptive_cost:
f.write("Experiment made at: %s. Configuration: cost = %s threshold = %.3f . Type of the game: %d . Direct gain: %.3f dB Cross gain: %.3f dB Observations:%s\n" %(str(datetime.datetime.now()),str(self.player.cost)+ " adaptive", self.threshold_power, self.game_type, 10.00*math.log10(self.player.direct_gain), 10.00 * math.log10(self.player.cross_gain), other_observations))
else:
f.write("Experiment made at: %s. Configuration: cost = %s threshold = %.3f . Type of the game: %d . Direct gain: %.3f dB Cross gain: %.3f dB Observations:%s\n" %(str(datetime.datetime.now()), str(self.player.cost) + " fixed", self.threshold_power, self.game_type, 10.00*math.log10(self.player.direct_gain), 10.00 * math.log10(self.player.cross_gain), other_observations))
for i in range(0, len(results_list)):
f.write("Iteration: %d New_Power: %.12f ( %.6f ) Measured_Power: %s Anticipated_useful_received_power: %s Instant cost: %.3f\n" %(i, results_list[i][0], results_list[i][1],str(results_list[i][2]), str(results_list[i][3]), results_list[i][4] ) )
f.write("\n")
f.close()
def printStrategyTable(self, nash_value_player1, nash_value_player2, iterations=-1):
#prints to a file, only the Nash equilibrium value
path = "./iterations/strategyTable.dat"
#see if the path exits and if not create it for the first time
if (not os.path.exists(path) or not os.path.isfile(path)):
#create the file
f = open(path, "w")
f.write("In this file you will find Nash Equilibrium values\n")
f.close()
f = open(path, "a")
if self.player.player_number == 1:
f.write("c1= %d c2= %d Nash value player 1= %.9f dBm Nash value player 2= %.9f dBm Iterations= %d h11= %.3f dB h21= %.3f dB h22= %.3f dB h12= %.3f dB Player1Noise=%.3fE-13 Player2Noise=%.3fE-13 Date= %s\n" % (self.player.cost, self.neighborPowerAllocation.player.cost, nash_value_player1, nash_value_player2, iterations, 10.00 * math.log10(self.player.direct_gain), 10.00*math.log10(self.player.cross_gain), 10.00*math.log10(self.neighborPowerAllocation.player.direct_gain), 10.00*math.log10(self.neighborPowerAllocation.player.cross_gain), self.player.noise_power*1e13, self.neighborPowerAllocation.player.noise_power*1e13,datetime.datetime.now() ) )
f.close()
def getTheNearestPower(self, power):
#from the available power list choose the nearest from the power
min_diferrence = float("inf")
nearest_power = None
#Small discussion: self.available_generating_powers it's ascending sorted
#The update takes only when you find an available power more appropriate to given power. If the there are 2 available power which gives the same distance from the given power, the greater value will be chosen. Example: available power list [0 , -2, -4, ..] and we have a given power= -1, the result will be 0
for i in range(0, len(self.available_generating_powers)):
if (math.fabs(power-self.available_generating_powers[i]) < min_diferrence):
min_diferrence = math.fabs(power-self.available_generating_powers[i])
nearest_power = self.available_generating_powers[i]
return nearest_power
def equilibriumDectected(self):
#when other players detect the equilibrium
self.equilibriumDetected = True
def gameType1(self):
#number of iterations until player reach the Nash equilibrium
iterations = 0
#Use this because I want to see how long it takes to reach the Nash equilibrium
process_start_time = time.time()
#initialize results_list because I want to save all experiment results
self.results_list = [[self.getTheNearestPower(self.current_transmitting_power), self.current_transmitting_power, "None", "None", self.player.cost]]
print "Player %d: game type 1 started" %self.player.player_number
while True:
#infinite loop
#see if neighbor changed it's power
if (self.neighbor_previous_transmitted_power != self.neighbor_current_transmitting_power):
#neighbor power change event triggered
#reset the event by equaling the old power with the current power
self.neighbor_previous_transmitted_power = self.neighbor_current_transmitting_power
print "Player %d detected a change of power and it's recalculating its own transmission power" %self.player.player_number
#there has been a change of power on the neighbor player, so I have to recalculate my own power
#in this game type we use the theoretically formula
self.previous_transmitted_power = self.current_transmitting_power
self.current_transmitting_power = self.getBestResponseTheoreticallyForm()
#save best response
self.best_response_untouched = self.current_transmitting_power
#truncate the value
#self.current_transmitting_power = self.getTheNearestPower(self.current_transmitting_power)
#append data in results list: [new power calculated, new_power_untouched, received power, anticipated useful received power]
self.results_list.append([self.current_transmitting_power, self.best_response_untouched, "None", "None", self.player.cost])
iterations+= 1
#now see if the new calculated power is different from the old one. If no, that means Nash equilibrium
if self.previous_transmitted_power != self.current_transmitting_power :
print "Player %d changed his own power: previous power = %.6f dBm current power = %.6f dBm" %(self.player.player_number, self.previous_transmitted_power, self.current_transmitting_power)
#announce the other player about my change
self.sharePowerToTheNeighbor(self.current_transmitting_power)
else:
#best response is the same from the previous iteration
print "Player %d : new power is not different from the old one, that means Equilibrium. Number of iterations:%d previous power=%.3f current power=%.3f\n" %(self.player.player_number,iterations, self.previous_transmitted_power, self.current_transmitting_power)
#finish the game by stopping the threads
self.equilibriumDectected()
self.neighborPowerAllocation.equilibriumDectected()
else:
if self.equilibriumDetected:
print "Player %d Equilibrium. No events had been detected. Number of iterations:%d. Time passed: %.6f" %(self.player.player_number, iterations, (time.time() - process_start_time))
print "Player %d power allocated: %.3f" %(self.player.player_number, self.current_transmitting_power)
#write experiment results in a file
self.printIterationsToFile(self.results_list)
#if this is player 1, then print the strategy table( is enough that only one player to do that)
if self.player.player_number == 1:
self.printStrategyTable(self.current_transmitting_power, self.neighborPowerAllocation.current_transmitting_power, iterations=iterations)
break
time.sleep(0.01)
def gameType2(self):
#number of iterations until player reach the Nash equilibrium
iterations = 0
#USe this because I want to see how long it takes to reach the Nash equilibrium
process_start_time = time.time()
print "Player %d Game type 2 started" %self.player.player_number
#want to save the experiment results. results_list_ [new power calculated, received power, anticipated useful received power]
self.results_list = [[self.getTheNearestPower(self.current_transmitting_power), self.current_transmitting_power, "None", "None", self.player.cost]]
self.queue_list = MyQueue(5)
while True:
#see if there is any power change alert from the neighbor
if self.neighborPowerEvent :
#that means neighbor changed its own power
#reset the event because is being treated
self.neighborPowerEvent = False
print "Player %d detected a change of power and it's recalculating its own transmission power. Iteration %d" %(self.player.player_number, iterations)
#anticipate transmitted_power. Initialize transmited_power with -100000 dBm, equivalent with almost 0 in linear scale
transmited_power_dbm_before_measurement = -100000
if self.playerApp.isSendingAPacket():
#yes, I am sending a packet right now, I have to consider that because I want to know what is interference and noise and what is not
transmited_power_dbm_before_measurement = self.playerApp.getTransmittingPower()
#there hass been a change of power on the neighbor player, so I have to recalculate my own power
#measure received power. received_power has the following structure: [[frequency], [power_dbm]]
received_power = self.player.rx_node.senseStartQuick()
#see if there is any power transmitting right now again. First initialize transmited_power_dbm with a very low power: -10000 dBm it's very small
transmited_power_dbm_after_measurement = -100000
if self.playerApp.isSendingAPacket():
#yes, I am sending a packet right now, I have to consider that because want to know when is interference and noise and when is not
transmited_power_dbm_after_measurement = self.playerApp.getTransmittingPower()
#see if the useful transmitted power before and after measurements are equal, if not I can't be sure that when I did the measure, I was generating signal or no, that way I won't be able to determine the interference level
if transmited_power_dbm_after_measurement != transmited_power_dbm_before_measurement:
print "Player %d bad anticipation of useful received power, continue this iteration and measure again" %(self.player.player_number)
#set event to true because it wasn't complete treated
self.neighborPowerEvent = True
#measure again
continue
if (transmited_power_dbm_after_measurement + 10.00 * math.log10(self.player.direct_gain)) > received_power[1][0]:
#the anticipated useful received power can't be higher than the received power
print "Anticipated received power > received power : %.6f > %.6f" %(transmited_power_dbm_after_measurement + 10.00 * math.log10(self.player.direct_gain), received_power[1][0])
self.neighborPowerEvent = True
continue
#just print some info
if float(transmited_power_dbm_after_measurement) < -1000:
print "Player %d received power: [%.3f dBm] and he is not transmitting anything at this moment" %(self.player.player_number, received_power[1][0])
else :
print "Player %d received power: [%.3f dBm] and he is transmitting with %.3f dBm. Anticipated useful received power: %.3f" %(self.player.player_number, received_power[1][0], transmited_power_dbm_after_measurement, transmited_power_dbm_after_measurement + 10.00 * math.log10(self.player.direct_gain))
#now we have anything we need to calculate new power
self.previous_transmitted_power = self.current_transmitting_power
self.current_transmitting_power = self.getBestResponsePracticallyForm1(transmited_power_dbm_after_measurement, float(received_power[1][0]))
#Sometimes formula returns a negative power (in linear!), which can't be possible.
if self.current_transmitting_power is None:
self.neighborPowerEvent = True
#anyway, I want to keep previous transmitted power
self.current_transmitting_power = self.previous_transmitted_power
continue
#copy best response
self.best_response_untouched = self.current_transmitting_power
self.current_transmitting_power = self.getTheNearestPower(self.current_transmitting_power)
#make some list with iterations and calculated powers
self.results_list.append([self.current_transmitting_power, self.best_response_untouched, received_power[1][0], transmited_power_dbm_after_measurement + 10.00 * math.log10(self.player.direct_gain), self.player.cost])
self.queue_list.append(self.best_response_untouched)
#we want to count how many iterations the algorithm used to reach the Nash equilibrium
iterations+= 1
#see if the difference between new power and old power exceeds the threshold_power
if not self.checkEquilibrium(equal_condition=False):
#Nash equilibrium hasn't reached yet
print "Player %d has not reached the Nash Equilibrium yet" %(self.player.player_number)
#generate at new power. This is needed because this way the neighbor player can see the change I made
#send a packet for 12 seconds
self.playerApp.sendPacket(8)
#to be sure the neighbor will catch my generated signal, a wait for 1 second (this way I consider the programming time)
time.sleep(1)
#announce the other player about my change
self.alertNeighborAboutAChangeOfPower()
else:
#that means equilibrium
print "Player %d : Equilibrium has been reached" %(self.player.player_number)
#finish the game by stopping the threads
self.equilibriumDectected()
self.neighborPowerAllocation.equilibriumDectected()
else:
if self.equilibriumDetected:
print "Player %d Equilibrium. No events had been detected. Number of iterations:%d. Time passed: %.6f" %(self.player.player_number, iterations, (time.time() - process_start_time) - 45)
print "Player %d power allocated: %.3f" %(self.player.player_number, self.current_transmitting_power)
#write experiment results in a file
self.printIterationsToFile(self.results_list)
#if this is player 1, then print the strategy table( is enough that only one player to do that)
if self.player.player_number == 1:
self.printStrategyTable(self.current_transmitting_power, self.neighborPowerAllocation.current_transmitting_power, iterations=iterations)
break
time.sleep(0.03)
def gameType3(self):
#number of iterations until player reached the Nash equilibrium
iterations = 0
#USe this because I want to see how long it takes to reach the Nash equilibrium
process_start_time = time.time()
print "Player %d Game type 3 started" %self.player.player_number
#want to save the experiment results. results_list_ [[new power calculated truncated, new_power_calculated, received power, anticipated useful received power], [same thing],[same thing].. ]
self.results_list = [[self.getTheNearestPower(self.current_transmitting_power), self.current_transmitting_power, "None", "None", self.player.cost]]
#a queue_list which will help us to know if the equilibrium has been reached
self.queue_list = MyQueue(5)
while True:
#infinite loop
#see if there is any power change alert from the neighbor
if self.neighborPowerEvent :
#that means neighbor changed its own power, so I have to update my power
#reset the event because is being treated
self.neighborPowerEvent = False
print "Player %d detected a change of power and it's recalculating its own transmission power. Iteration %d" %(self.player.player_number, iterations)
#check if I am transmitting something right now
if( self.playerApp.isSendingAPacket()):
#I can't measure in this case, I want to measure only interference and noise
while self.playerApp.isSendingAPacket():
print "Player %d : bad news, I'm transmitting packets right now, wait to stop!" %self.player.player_number
time.sleep(1)
#Ask the neighbor to recalculate power and regenerate a signal
self.alertNeighborAboutAChangeOfPower()
continue
#measure received power. received_power has the following structure: [[frequency], [power_dbm]]
received_power = self.player.rx_node.senseStartQuick()
print "Player %d received noise and interference power: %.3f dBm" %(self.player.player_number, received_power[1][0])
#now we have anything we need to calculate new power
self.previous_transmitted_power = self.current_transmitting_power
#the best response returned is in dBm
self.current_transmitting_power = self.getBestResponsePracticallyForm2(received_power[1][0])
#sometimes formula returns a negative power in linear, that can't be possible in practice, so if that happens, continue this iteration
if self.current_transmitting_power is None:
print "Player %d negative power" %self.player.player_number
self.current_transmitting_power = self.previous_transmitted_power
self.neighborPowerEvent = True
continue
#the best response give us a real power, in practice we can't generate a power at any resolution ( The formula doesn't take into account this thing)
#On VESNA platform there are a few power values that can be generated
print "Player %d best response: %.6f. Choosing the right value for VESNA..." %(self.player.player_number, self.current_transmitting_power)
#I want to keep the best response given by the formula
self.best_response_untouched = self.current_transmitting_power
#truncate the power
self.current_transmitting_power = self.getTheNearestPower(self.current_transmitting_power)
#make some list with iterations and calculated powers
self.results_list.append([self.current_transmitting_power, self.best_response_untouched, received_power[1][0], "None", self.player.cost])
self.queue_list.append(self.best_response_untouched)
#we want to count how many iterations the algorithm used to reach the Nash equilibrium
iterations+= 1
#see if we have reached the equilibrium
if not self.checkEquilibrium(equal_condition=False):
#the equilibrium has not been reached yet
print "Player %d has not reached the Nash Equilibrium yet" %(self.player.player_number)
#wait for the neighbor to finish his transmission
while self.neighborPowerAllocation.playerApp.isSendingAPacket():
print "Player %d : waiting for the neighbor to finish his transmission" %self.player.player_number
time.sleep(1)
#generate at new power. This is needed because this way the neighbor player can see the change I made
#send a packet for a few seconds
self.playerApp.sendPacket(6)
#to be sure the neighbor will catch my generated signal, wait for 1 second (this way I consider others delay that can appear within the testbed)
time.sleep(1)
#announce the other player about my change
self.alertNeighborAboutAChangeOfPower()
else:
#that means equilibrium
print "Player %d : Equilibrium has been reached" %(self.player.player_number)
#check if neighbor has reached equilibrium too
if(not self.neighborPowerAllocation.checkEquilibrium(equal_condition=False)):
print "Player %d : My neighbor has not reached the Nash equilibrium yet, send a packet again" %self.player.player_number
#wait for the neighbor to finish his transmission
while self.neighborPowerAllocation.playerApp.isSendingAPacket():
print "Player %d : waiting for the neighbor to finish his transmission" %self.player.player_number
time.sleep(1)
#send packet
self.playerApp.sendPacket(6)
time.sleep(1)
self.alertNeighborAboutAChangeOfPower()
continue
#finish this game
self.neighborPowerAllocation.equilibriumDectected()
self.equilibriumDectected()
else:
if self.equilibriumDetected:
print "\x1b[1;31m"
print "Player %d Equilibrium. No events had been detected. Number of iterations:%d. Time passed: %.6f \n" %(self.player.player_number, iterations, (time.time() - process_start_time) - 45)
print "Player %d power allocated: %.3f" %(self.player.player_number, self.current_transmitting_power)
print "\x1b[0m"
#write experiment results in a file
self.printIterationsToFile(self.results_list, "Using threshold with a 5 size queue, predicted gains")
if self.player.player_number == 1:
self.printStrategyTable(self.best_response_untouched, self.neighborPowerAllocation.best_response_untouched, iterations)
while True:
time.sleep(1)
time.sleep(0.05)
def run(self):
if self.game_type == 1:
self.gameType1()
elif self.game_type == 2:
self.gameType2()
elif self.game_type == 3:
self.gameType3()
def test_empty():
q = MyQueue()
assert 1 == q.isEmpty()
q.enqueue(1)
assert 0 == q.isEmpty()
def test_enqueue():
q = MyQueue()
assert 2 == q.enqueue(2)
assert 1 == q.size()
assert 5 == q.enqueue(5)
assert 2 == q.size()
def test_size():
q = MyQueue()
q.enqueue(1)
q.enqueue(2)
q.enqueue(3)
assert 3 == q.size()
q.enqueue(4)
assert 4 == q.size()
q.dequeue()
q.dequeue()
q.dequeue()
assert 1 == q.size()
class GamePlayer:
"""
Defines a game player (tx node, rx node, tx characteristics..)
Has attached a playerApp and a powerAllocation object.
"""
# player power cost [1/W], must be in [0,1]
cost = 0
scaledCost = 0
# channel gains, dimensionless
directGain = 0.0
crossGain = 0.0
# noise power, must be measured
noisePower = 6.43e-13
# player number used for identification
playerNumber = None
# current transmission power [dBm]. updated based in game iterations
currentTxPower = 0.0
# used for transmitting data and sensing the spectrum
physicalLayer = None
# compute best response only if player is not the source of the event
sourceOfEvent = False
# count number of iterations
playerIterations = 0
# save result in this list
queueList = None
# threshold_power. This can be set any time. We need this when we determine if Nash equilibrium was reached
thresholdPower = 0.8
def __init__(self, coordinatorId, txNodeId, rxNodeId, cost, playerNumber, gameFreq, gameType=0):
"""
Create a player and initialize internal parameters.
Keyword arguments:
coordinatorId -- Numerical cluster id.
txNodeId -- Numerical id for transmission node.
rxNodeId -- Numerical id for receiver node.
cost -- Energy cost.
playerNumber -- Numerical number used to identify players.
gateType -- Type of game played.
"""
self.coordinatorId = coordinatorId
self.cost = cost
self.playerNumber = playerNumber
self.gameFreq = gameFreq
self.gameType = gameType
self.txNodeId = txNodeId
self.rxNodeId = rxNodeId
self.sourceOfEvent = False
self.queueList = MyQueue(5)
self.playerIterations = 0
# initialize physical layer
self.physicalLayer = PlayerPhysicalLayer(self, txNodeId, self.rxNodeId, self.coordinatorId, self.gameFreq, self.cost, self.gameType)
def resetPlayerObject(self):
"""Use this to reset player variables, used for multiple runs"""
self.queueList.emptyList()
self.playerIterations = 0
self.physicalLayer.changeTxPowerRandomly()
def getPlayerNumber(self):
return self.playerNumber
def getPlayerCost(self):
return self.cost
def setPlayerCost(self, newCost):
self.cost = newCost
def getScaledCost(self):
return self.scaledCost
def setScaledCost(self, newScaledCost):
self.scaledCost = newScaledCost
self.physicalLayer.setCost(self.scaledCost)
def getNrPlayerIterations(self):
return self.playerIterations
def generatePowerEvent(self):
"""Generate new tx power"""
self.sourceOfEvent = True
self.physicalLayer.changeTxPowerRandomly()
return self.physicalLayer.getCrtTxPower()
def updateTxPowerAsAverage(self):
"""Update current transmission power as average of last 5 tx powers"""
bestRespList = self.queueList.getList()
nrBestResp = len(bestRespList)
mySum = 0
for i in range(0, nrBestResp):
mySum += float(bestRespList[i])
avgTxPw = mySum / float(nrBestResp)
self.physicalLayer.changeTxPower(avgTxPw)
self.queueList.append(self.physicalLayer.getCrtTxPower())
def updateTxPowerWithMask(self, pTxj, hjiDict, mask):
self.playerIterations += 1
opponentPowers = list()
crossG = list()
updateResult = False
for key in pTxj:
if key != self.playerNumber and mask[key]:
opponentPowers.append(pTxj[key])
crossG.append(hjiDict[key])
bestResp = self.physicalLayer.computeBestResponseForm1(opponentPowers, crossG)
if bestResp is not None:
updateResult = True
self.physicalLayer.changeTxPower(bestResp)
self.queueList.append(self.physicalLayer.getCrtTxPower())
return updateResult
def updateTxPower(self, pTxj, hjiDict):
"""
Update current transmission power based on best response relation.
Must extract from pj a list of tx power for adversaries, delete from list tx power of current player
Keyword arguments:
pTxj -- dictionary with players tc powers in [dBm]
hjiList -- dictionary with player cross gains for current player, e.g. {2:h_21,3:h_31}
"""
# create list with opponent tx powers and cross gains
self.playerIterations += 1
opponentPowers = list()
crossG = list()
updateResult = False
for key in pTxj:
if key != self.playerNumber:
opponentPowers.append(pTxj[key])
crossG.append(hjiDict[key])
bestResp = self.physicalLayer.computeBestResponseForm1(opponentPowers, crossG)
if bestResp is not None:
# sometimes the difference in best response in negative
# in that case do nothing, player will continue to transmit
# with current power
updateResult = True
self.physicalLayer.changeTxPower(bestResp)
# self.result.append([iteration, self.physicalLayer.getCrtTxPower(), self.physicalLayer.bestResponseUntouched, self.cost])
# TODO: in jocul facut de studenti cond de echilibru e verificata pe val reala, mai corect e pt cea discretizata
self.queueList.append(self.physicalLayer.getCrtTxPower())
return updateResult
def updateTxPower1(self, measuredRxPower):
"""
Update current transmission power based on best response relation, and measured power.
Keyword arguments:
measuredRxPower -- Measured receiving power, i.e. sum(h_ji * p_j) + n_0
"""
self.playerIterations += 1
bestResp = self.physicalLayer.computeBestResponseForm2(measuredRxPower)
if bestResp is not None:
# sometimes the difference in best response in negative
# in that case do nothing, player will continue to transmit
# with current power
updateResult = True
self.physicalLayer.changeTxPower(bestResp)
self.queueList.append(self.physicalLayer.getCrtTxPower())
return updateResult
def updateTxPower2(self, measuredRxpower):
"""
Update current transmission power based on best response relation, and measured power.
Keyword arguments:
measuredRxPower -- Measured receiving power, i.e. sum(h_ji * p_j) + n_0
"""
self.playerIterations += 1
bestResp = self.physicalLayer.computeBestResponseForm3(measuredRxpower)
if bestResp is not None:
updateResult = True
self.physicalLayer.changeTxPower(bestResp)
else:
updateResult = False
self.queueList.append(self.physicalLayer.getCrtTxPower())
return updateResult
def setThresholdPower(self, newThresholdPower):
"""Set threshold power used to determine if player has reached equilibrium"""
self.thresholdPower = newThresholdPower
print "Player %d : threshold power was set to: %.3f" % (self.playerObject.playerNumber, self.thresholdPower)
def isInEquilibrium(self, equalCondition=True):
"""
Check if player has reached a stable state
Keyword arguments:
equalCondition -- true->all elements from history must be equal.
"""
bestrespList = self.queueList.getList()
length = self.queueList.len
if len(bestrespList) != length:
return False
if equalCondition:
# all best responses must be equal
for i in range(1, len(bestrespList)):
if float(bestrespList[0]) != float(bestrespList[i]):
return False
# print "Player %d has reached a stable state, i.e. equilibrium." % (self.playerNumber+1)
return True
else:
for i in range(1, len(bestrespList)):
if math.fabs(math.fabs(float(list[0])) - math.fabs(float(bestrespList[i]))) > float(self.thresholdPower):
return False
# print "Player %d has reached a stable state, i.e. equilibrium." % (self.playerNumber+1)
return True
def __init__(self, instance, verbose=True):
self.instance = instance
self.queue = MyQueue() # 初始化列表，默认深度优先（depth-first）
self.incumbent = Solution() # 初始化最优解
self.verbose = verbose # 是否打印相关参数
self.n_nodes = 0 # 求解的总结点数目