def calcAverageHollywoodNum(
self, choseAccuracy
): #higher accuracy takes far more time, let user chose
#chose accuracy will be the number of people used in average
if (choseAccuracy > len(self.peoples)):
print("Error: specified accuracy exceeds size of database")
sum = 0
toReset = [] #list of people to set as not checked for future methods
for x in range(choseAccuracy):
#find random person to check
use = self.peoples[random.randInt(0, len(self.peoples) - 1)]
while (use.isChecked()):
use = self.peoples[random.randInt(0, len(self.peoples) - 1)]
toReset.append(use)
if (len(toReset) < 2):
use2 = self.peoples[random.randInt(0, len(self.peoples) - 1)]
while (use2.isChecked()):
use2 = self.peoples[random.randInt(0,
len(self.peoples) - 1)]
toReset.append(use2)
else:
use2 = toReset
sum += self.calculateNumber(use, use2)
avgNum = (sum * 1.0) / choseAccuracy
for node in toReset:
node.flipCheck()
print("Average Bacon Number: " + avgNum + "\n")
def mutateThatJohn(neuralNetToMutate, nodes_per_layer):
#don't mutate the activation functions
activation_funcs=neuralNetToMutate.activation_funcs
#creates new neural network
neuralNetToReturn= nn.NeuralNet(activation_funcs,nodes_per_layer)
weightsOf=neuralNetToMutate.weights
#turn weights to one dimension
weights1d= turnThreeToOne(weightsOf)
#get total amount of weights
totalAmtOfWeights=len(weights1d)
b=0
c=0
#loop until we get two different indices
while c==b:
b= random.randInt(0,totalAmtOfWeights)
c= random.randInt(0,totalAmtOfWeights)
#grab sublist from those indices
if b<c:
subList=weights1d[b:c]
else:
subList=weights1d[c:b]
#reverse the list
subList.reverse()
#replace elements in the list with reversed list
if b<c:
weights1d[b:c]=subList
else:
weights1d[c:b]=subList
#need it back in three dimensions
weightsBack=turnOneToThree(weights1d, nodes_per_layer)
#set mutated weights to new neural net
neuralNetToReturn.weights=weightsBack
#return mutated neural net
return neuralNetToReturn
def evaluation(population, selection, scoresDictionary, nodes_per_layer):
#start list of next population
nextPopulation=[]
#grab the parents we will be using
parents=selection.keys()
#mutate some of the population and append them for the next generation
nextPopulation.append(mutateThoseJohns(population, selection, scoresDictionary, nodes_per_layer))
#append parents to the next population
nextPopulation.append(parents)
#loop until we have a full next population
while len(nextPopulation)<population:
#parent indices
par1=0
par2=0
#until two different parents
while par1==par2:
par1=random.randInt(0,len(parents)-1)
par2=random.randInt(0,len(parents)-1)
#grab the parents
parent1=parents[par1]
parent2=parents[par2]
#create the child
child= crossover(parent1, parent2, nodes_per_layer)
#append the child
nextPopulation.append(child)
return nextPopulation
def generateGameTitle():
title1 = ['Mario', 'Zelda', 'Portal', 'Minecraft', 'Borderlands', 'Roller Coaster']
subtitle = [': Zero Mission', ': Breath of the Wild', ': Sword and Shield', " 2", " Party", " Tycoon"]
title1Loc = random.randInt(0,4)
subtitleLoc = random.randInt(0,5)
title = title1[title1Loc] + subtitle[subtitleLoc]
return title
def generateName():
first = ['Bob', 'Steve', 'Joe', 'Katie', 'Jessica', 'Elizabeth']
last = ['Jones', 'Smith', 'Williams', 'Brown', 'Davis', 'Miller']
firstLoc = random.randInt(0,4)
lastLoc = random.randInt(0,5)
name = first[firstLoc] + " " + last[lastLoc]
return name
def generateConsole():
first = ['Microsoft', 'Sony', 'Nintendo']
last = ['Switch', 'Xbox', 'Playstation']
firstLoc = random.randInt(0,2)
lastLoc = random.randInt(0,2)
console = first[firstLoc] + " " + last[lastLoc]
return console
def get_guess_from_session(intent, session):
session_attributes = {}
reprompt_text = None
if session.get('attributes', {}) and "numberOfDice" in session.get(
'attributes', {}):
number_of_dice = session['attributes']['numberOfDice']
if number_of_dice == 1:
numberRolled = random.randInt(1, 6)
if guess == numberRolled:
speech_output = "You guess correctly. Congratulations."
should_end_session = True
else:
speech_output = "You guess incorrectly. Sorry."
else:
numberRolled = random.randInt(2, 12)
if guess == numberRolled:
speech_output = "You guess correctly. Congratulations."
else:
speech_output = "You guess incorrectly. Sorry."
else:
speech_output = "I don't know how many dice to roll. I can roll one or two dice."
should_end_session = False
return build_response(
session_attributes,
build_speechlet_response(card_title, speech_output, reprompt_text,
should_end_session))
def _find_external_v4_ip(config):
v4_id = config.external_v4_id
if v4_id:
return v4_id
else: # fallback to random value
return '0.0.%d.%d' % (random.randInt(0, 255), random.randInt(0, 255))
def addRandom(board):
addCount=0
while addCount<5:
delX=random.randInt(0,boardSize-1)
delY=random.randInt(0,boardSize-1)
if board[delY][delX]==0:
addCount+=1
board[delY][delX]=random.randInt(1,2)
def dropItem(bossAlive, dropResult, dropChance, ownedItems):
if (bossAlive = False): #ezt az ifet kivesszük, ha készen lesz az a függvény, ami a boss halálakor meghívja ezt a függvényt
dropResult = rnd.randInt(0,100)
if (dropResult <= dropChance[2]):
if (dropResult <= dropChance[1]):
if (dropResult <= dropChance[0]):
ownedItems.append(rnd.randInt(20,30)) #tier 3-as itemek sorszámai: 21-30
else:
ownedItems.append(rnd.randInt(10,20)) #tier 2-es itemek sorszámai: 11-20
else:
ownedItems.append(rnd.randInt(0,10)) #tier 1-es itemek sorszámai: 1-10
else:
def crossover(neuralNetwork1, neuralNetwork2, nodes_per_layer):
#get the weights of each neural network
weights1=neuralNetwork1.weights
weights2=neuralNetwork2.weights
#get the activation functions of each neural network
activation1=neuralNetwork1.activation_funcs
activation2=neuralNetwork2.activation_funcs
#get number of activation functions
actives=len(activation1)
#get random int for one-point crossover of activation functions
cross=random.randInt(0,actives)
activation_funcs=[]
for a in range(0,actives):
if a<cross:
activation_funcs.append(activation1[a])
else:
activation_funcs.append(activation2[a])
#create initial neural net to return
net= nn.NeuralNet(activation_funcs,nodes_per_layer)
#get total amount of weights
totalAmtOfWeights=0
for a in range(0,len(weights)):
for b in range(0,len(weights[a])):
for c in range(0,len(weights[a][b])):
totalAmtOfWeights+=1
#grab two random ints for two-point crossover
b=0
c=0
while b==c:
b= random.randInt(0,totalAmtOfWeights)
c= random.randInt(0,totalAmtOfWeights)
#find the lower index
if b<c:
first=b
second=c
else:
first=c
second=b
counter=0
#crossover weights to child
for a in range(0,len(weights)):
for b in range(0,len(weights[a])):
for c in range(0,len(weights[a][b]))
if counter<first:
net.weights[a][b][c]=weights1[a][b][c]
else if counter>=first and counter<second:
net.weights[a][b][c]=weights2[a][b][c]
else:
net.weights[a][b][c]=weights1[a][b][c]
counter+=1
#return child
return net
def createRandomUnweightedGraph(n):
graph = Graph()
numbers = list(range(n))
for number in numbers:
graph.addNode(num)
nodelist = graph.getAllNodes():
for node1 in nodelist:
indexatnode=nodelist.index(node1)
randomNum = random.randInt(0, indexatnode)
randomNum2 =random.randInt(1, n//2)
randomNumUse = randomNum // randomNum2
for node2 in randomNumUse:
graph.addUndirectedEdge(node1, node2)
return graph
def __init__(self, nPlayers, properties, places):
self.players = [Player(i) for i in range(nPlayers)]
self.properties = properties
self.places = places
self.nPlaces = len(places)
possibleOrders = list(itertools.permutations(range(nPlayers)))
turnOrder = possibleOrders[random.randInt(0, len(possibleOrders) - 1)]
def e_Greedy(self, mode, network, state, testing_ep, subgoal, lastsubgoal): # handle the learn start print "enter e_greedy: ", mode if mode == 'meta': learn_start = self.meta_learn_start else: learn_start = self.learn_start if testing_ep: self.ep = testing_ep else: self.ep = (self.ep_end + max(0, (self.ep_start - self.ep_end) *\ (self.ep_endt - max(0, self.numSteps - learn_start))/self.ep_endt)) subgoal_id = subgoal[-1] if mode != 'meta' and subgoal_id != 6 and subgoal_id != 8: self.ep = 0.1 n_actions = None if mode == 'meta': n_actions = self.meta_args['n_actions'] else: n_actions = self.transition_args['n_actions'] print "enter e_greedy" # epsilon greedy self.ep = -1 ## To do: delete this after testing network if random.uniform(0,1) < self.ep: if mode == 'meta': chosen_act = random.randint(0, n_actions-1) while chosen_act == lastsubgoal: chosen_act = random.randint(0, n_actions-1) return chosen_act, None else: return random.randInt(0, n_actions-1), None else: return self.greedy(network, n_actions, state, subgoal, lastsubgoal)
def send(self, m):
global cli
global key
cli.sendto(
self.pub.encrypt(key.decrypt(m),
long(random.randInt(1, 999999))).encode(),
self.addr)
def mutateThoseJohns(population, selectionDictionary, scoresDictionary, nodes_per_layer, mutate=0.03):
#get the list of neural networks
listOfPotential=scoresDictionary.keys()
#get the list of the selected neural networks
listOfSelected=selectionDictionary.keys()
#find the length of the list of potential
totalLength=len(listOfPotential)
#set amount selected to 0
amountForMutate=0
#set amount to select to the proportion of the population needed to
#be mutated
amountToMutate=population*mutate
#list of mutated
listOfMutated=[]
#loop until all have been selected for mutation
while(amountForMutate<amountToMutate):
#get a random index to select
randomIndex=random.randInt(0,totalLength-1)
#select it
neuralNetToMutate=listOfPotential[randomIndex]
#if it was not already selected
if(neuralNetToMutate not in listOfSelected):
#mutate it
neuralNetToMutate=mutateThatJohn(neuralNetToMutate, nodes_per_layer)
#add it to the list
listOfMutated.append(neuralNetToMutate)
#increment
amountForMutate+=1
#return list
return listOfMutated
def behaviour3():
strlen = random.randInt(1, 1000)
randomstring = ''.join(
random.choice(string.lowercase) for i in range(strlen))
pyautogui.typewrite(randomstring)
for x in range(1, 7):
pyautogui.hotkey('ctrl', 'v')
def addDLC():
for(int i = 0; i < 180; i++):
if(random.randInt(0,1) == 0):
query = "Insert into DLC(" + i + ", " + generatePrice() + ", " + generateGame() + " Expansion Pass, true, false)"
insertRecord(query)
else:
query = "Insert into DLC(" + i + ", " + generatePrice() + ", " + generateGame() + " Expansion Pass, false, true)"
insertRecord(query)
def spinWheel():
randomVal = random.randInt(0,36)
if randomVal in red:
return ('red', randomVal)
elif randomVal in black:
return ('black', randomVal)
else:
return ('green', randomVal)
def main():
randomlist = []
for i in range(10):
randomlist.append(random.randInt(0, 100))
print(randomlist)
evenindex(randomlist)
evenelement(randomlist)
x = firstandlast(randomlist)
def p2_move():
global board
move = True
# Winning
for row in range(3):
for col in range(3):
copy_board = copy.deepcopy(board)
if copy_board[row][col] == ' ':
copy_board[row][col] = current_player
if move and is_winner(copy_board, current_player):
board[row][col] = current_player
move = False
# Blocking
if move:
for row in range(3):
for col in range(3):
copy_board = copy.deepcopy(board)
if copy_board[row][col] == ' ':
copy_board[row][col] = 'X'
if move and is_winner(copy_board, 'X'):
board[row][col] = current_player
move = False
# Cornering
if move:
corner_list = [(0, 0), (0, 2), (2, 0), (2, 2)]
for pos in corner_list:
if board[pos[0]][pos[1]] != ' ':
corner_list.remove(pos)
if len(corner_list) > 0:
rand_side = random.choice(corner_list)
board[rand_side[0]][rand_side[1]] = current_player
move = False
# Center
if move:
if board[1][1] == ' ':
board[1][1] = current_player
move = False
# Sides
else:
while move == True:
rand_int_row = random.randInt(0, 2)
rand_int_col = random.randInt(0, 2)
if board[rand_int_row][rand_int_col] == ' ':
board[rand_int_row][rand_int_col] = current_player
move = False
def move(my_history, their_history, my_score, their_score):
chance = random.randInt(1, 8)
if 'b' in their_history:
if chance == int(1):
return 'c'
else:
return 'b'
else:
return 'c'
def genRandom(population, nodes_per_layer):
neuralNets=[]
layers=len(nodes_per_layer)
for p in range(0,population):
activation_funcs=[]
for q in range(0,layers):
activation_funcs.append(random.randInt(0,9))
net= nn.NeuralNet(activation_funcs,nodes_per_layer)
neuralNets.append(net)
return neuralNets
def arcPath():
loopNum = 0
sideLength = 0
import random
i = 0
sideLength = random.randInt(10)
angle = 360 / loopNum
for _ in range(loopNum):
controller.move("forward", sideLength)
controller.turn("left", angle)
def __init__(self, dataHandler): # Player 1 = 0 # Player 2 = 1 playerTurn = 0 # Access player 1: # faceUpFigures[0] # Access player 2: # faceUpFigures[1] indices = range(0,dataHandler.numFigures) faceUpFigures = indices + indices # Access player 1: # playerFigures[0] # Access player 2: # playerFigures[1] playerFigures = [0,0] while playerFigures[0] == playerFigures[1]: playerFigures = [rand.randInt(0,dataHandler.numFigures-1), rand.randInt(0,dataHandler.numFigures-1)]
def admit(self, patient):
if len(self.patients) >= capacity:
print(
"{} is currently at full capacity. We cannot admit {} at this time."
.format(self.name, patient.name))
return self
else:
self.patients.append(patient)
patient.bed_number = random.randInt(1, self.capacity)
print("Admission successful!")
return self
def dropSingle(u):
r = random.randint(0, 10000)
if r >= 9900:
return rand_three_star(u)
if r >= 9000:
return rand_two_star(u)
elif r >= 4000:
return rand_one_star(u)
else:
r = random.randInt(100, 1000)
return item.Item("Mora", r)
def splitData( self, k, seed, data=None, M=8 ):
self.testdata = {}
self.traindata = {}
data = data or self.data
random.seed( seed )
for user, item, record in data:
if random.randInt(0, M) == k:
self.testdata.setdefault( user, {} )
self.testdata[user][item] = record
else:
self.traindata.setdefault( user, {} )
self.traindata[user][item] = record
def main():
win = gr.GraphWin("Press a key", 400, 400, False)
shapes = []
c = gr.Circle(gr.Point(250, 250, 10))
c.draw(win)
shapes.append(c)
while True
time.sleep(0.5)
for thing in shapes:
dx=random.randInt(-10,10)
dy = random.randInt(-10,10)
thing.move(dx,dy)
win.update()
if win.checkMouse()
break
win.close()
def splitData(self, k, seed, data=None, M=8):
self.testdata = {}
self.traindata = {}
data = data or self.data
random.seed(seed)
for user, item, record in data:
if random.randInt(0, M) == k:
self.testdata.setdefault(user, {})
self.testdata[user][item] = record
else:
self.traindata.setdefault(user, {})
self.traindata[user][item] = record
def coinPath():
coinFlip = random.randInt(1,2)
if coinFlip == 1:
path = "left"
coinResult = "heads"
print("Heads. You begin taking the longer path to the left")
time(6)
if coinFlip == 2:
path = right
coinResult = "tails"
print("Tails. You rush into the forest")
time(3)
def __init__(self): # random number for inital direction of ball num = random.randInt(0,9) # keep score self.tally = 0 # initalize positions of our paddle self.paddle1YPos = WINDOW_HEIGHT / 2 - PADDLE_HEIGHT / 2 self.paddle2YPos = WINDOW_HEIGHT /2 - PADDLE_HEIGHT / 2 # ball direction definition self.ballXDirection = 1 self.ballYDirection = 1 # starting point self.ballXPos = WINDOW_HEIGHT / 2 - BALL_WIDTH / 2
def __init__(self):
#definindo número aleatório para direção inicial da bola
num = random.randInt(0, 9)
#recupera o ponto
self.tally = 0
#Inicializando a posição inicial da raquete
self.paddle1YPos = WINDOW_HEIGHT / 2 - PADDLE_HEIGHT / 2
self.paddle2YPos = WINDOW_HEIGHT / 2 - PADDLE_HEIGHT / 2
#definindo a direção da bola
self.ballXDirection = 1
self.ballYDirection = 1
#ponto inicial
self.ballXpos = WINDOW_HEIGHT / 2 - BALL_WIDTH / 2
def tell(self,character,obj):
if random.rand()>0.8:
self.refuse()
return false
myobj = remember(obj)
if myobj is None:
printutils.formatdialog(self.pronouns,"I'm afraid I don't know anything about that","said")
printutils.new_paragraph()
return false
if type(myobj) is Location:
#TODO: say its catchphrase and attributes and up to five times either: a character who is there, a route that goes there, an item that's there
printutils.print_single("Ah, yes, I've been there. "+myobj.catchphrase+" "+myobj.attributes)
for i in range(random.randInt(1,5)):
if i>0:
else:
#TODO: learn the asker the relevant info, add knowledge item to character's knowledge
if type(myobj) is Item:
#TODO: say its catchphrase and attributes and up to five times either: where it is/who has it, what it does, or what it can accomplish (one state it can be used to get to)
for i in range(random.randInt(1,5)):
pass
#TODO: learn the asker the relevant info
re
if type(myobj) is Character:
#TODO: say its catchphrase and attributes and up to five times either: their current location, their current goal, an item they have, a bit of history
for i in range(random.randInt(1,5)):
pass
#TODO: learn the asker the relevant info
if type(myobj) is State:
if myobj.character.name != character.name:
printutils.formatdialog(self.pronouns,"I'm afraid you won't be able to do that at all!","explained")
else:
pass
#TODO: learn the asker all inputs to this state (yes i realize a person won't always know everything about how to achieve something, but we're going to assume they do to simiplify algorithms
return true
def encourage(e, q):
x = randInt(1,6)
if q >= .75:
if x <= 3:
output("You are doing fantastic! Do you even need me?")
else:
output("Absolute perfection! Why am I here?")
if .75 > q >= .5:
if x <= 2:
output("Almost there, try concentrating on your form!")
if 4 >= x > 2:
output("I knew you could do it, now make it perfect!")
else:
output("Keep going, today is the day you set a new PR")
if .5 > q >= .25:
if x <= 3:
output("Try slowing down and really working on your technique.")
else:
output("Nobody was perfect on their first try, keep working at it.")
if q < .25:
output("Is everything okay? Try watching the help videos.")
def explode(self, maxObjects=3):
for geom in self.objExplode:
x, y, z = geom.getPosition()
if geom.getBody():
self.space[0].remove(geom)
self.removeGeom(geom)
try:
for _ in range(randInt(0, maxObjects)):
g = self.create_geom(maxSize=0.3)
for _ in range(self.maxInsertTrials):
g.setPosition(self.randomPos(x, y, z, 0.3))
if not self.insertCollision(g):
self.addGeom(g)
self.GetProperty(g).SetOpacity(0.25)
break
except ValueError:
print ("not my fault")
self.objExplode = []
def boss_room():
global maxhealth
global health
global attack_min
global attack_max
global bosshealth
global bossdamage
print "You open the door and walk inside to see a room that is warm and cozy. Not dark like the other but well lit with torches and candles"
print "You also see the one that has kept you here an old slim man"
print "So you think you can escape my castle good luck"
print "What do you do?"
print "1) Kill the boss."
nextfinal = raw_input(">")
if nextfinal == "1":
while bosshealth <= 0:
bossdamage = bossdamage + random.randInt(0,5)
health = bossdamage - health
bosshealth = bosshealth - random.randint(0+ attack_min,20+attack_max)
if health <= 0:
print "Now you are here forever"
exit()
print "it cant be"
def create_geom(self, minSize=0.1, maxSize=0.5,
minDensity=100, maxDensity=100, minmaxForce=100):
"""Create a box body and its corresponding geom."""
element = randInt(1, 3)
if element == 1:
body, geom = self.create_sphere(rand(minDensity, maxDensity),
rand(minSize, maxSize))
elif element == 2:
body, geom = self.create_capsule(rand(minDensity, maxDensity),
rand(minSize, maxSize),
rand(minSize, maxSize))
elif element == 3:
body, geom = self.create_box(rand(minDensity, maxDensity),
rand(minSize, maxSize),
rand(minSize, maxSize),
rand(minSize, maxSize))
body.addForce((rand(-minmaxForce, minmaxForce),
rand(-minmaxForce, minmaxForce),
rand(-minmaxForce, minmaxForce)))
return geom
def parsecodes(allStates):
url = 'https://web3.ncaa.org/hsportal/exec/hsAction'
codehtmlfilename = 'schoollist.html'
codetextfile = 'codes.txt'
user_agent = {
'User-agent':
'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.116 Safari/537.36'}
session = requests.Session()
try:
os.remove(codetextfile)
except OSError:
pass
try:
for state in allStates:
print state
response = session.post(url, headers=user_agent, data={'hsActionSubmit': 'Search', 'state': state})
time.sleep(randInt(2, 4))
with open(codehtmlfilename, 'w') as codefile:
print >> codefile, response.text.encode('utf-8')
with open(codehtmlfilename) as f:
pagedata = f.read()
soup = BeautifulSoup(pagedata, "html.parser")
allvalues = soup.find_all('input', {'name': 'hsCode'})
for s in allvalues:
codevalue = s.attrs['value'].strip()
print codevalue
if codevalue:
with open(codetextfile, 'a') as myfile:
myfile.write(state + '|' + codevalue + '\n')
finally:
session.close()
print 'Finished saving the codes'
" 정재훈 ", " 정지윤 ", " 정현지 ", " 정희영 ", " 조대연 ", " 조승완 ", " 진유림 ", " 차강혁 ", " 천상원 ", " 최가람 ", " 최병훈 ", " 최봉재 ", " 최정연 ", " 최현묵 ", " 추은선 ", " 하동현 ", " 하조은 ", " 하태지 ", " 한웅제 ", " 홍은진 ", " Hanur Lee ", " Hyesu Bae ", " Jinho Jung ", " xodla " ] for i in range(0, 20): member_count = len(9xd_members) idx = random.randInt(member_count) print(9xd_members.pop(idx))
import random
class AI(BaseAI):
"""The class implementing gameplay logic."""
@staticmethod
def username():
return "Shell AI"
@staticmethod
def password():
return "password"
CLAW, ARCHER, REPAIRER, HACKER, TURRET, WALL, TERMINATOR, HANGAR = range(8)
def spawn(self)
self.players[self.playerID].orbitalDrop(0,0,random.randInt(0,7))
return
##This function is called once, before your first turn
def init(self):
pass
##This function is called once, after your last turn
def end(self):
pass
##This function is called each time it is your turn
##Return true to end your turn, return false to ask the server for updated information
def run(self):
import RPi.GPIO as GPIO from time import sleep import random GPIO.setmode(GPIO.BCM) GPIO.setup(24, GPIO.IN, pull_up_down = GPIO.PUD_UP) GPIO.setup(25, GPIO.OUT) coins = 6 GPIO.output(25,GPIO.LOW) result = random.randInt(1,9) jackpot = 10 halfWin = 5 moneyBack = 1 while True: sleep(2) if result == jackpot or result == halfWin or result == moneyBack: GPIO.wait_for_edge(24, GPIO.RISING) coins += 1 if result == jackpot: if coins == 10: GPIO.output(25,GPIO.LOW) print "jackpot" else: GPIO.output(25,GPIO.HIGH)
def getRandomKey(): while True: keyA = random.randInt(2,len(SYMBOLS)) keyB = random.randInt(2,len(SYMBOLS)) if(gcd(keyA,len(SYMBOLS)) == 1): return keyA * len(SYMBOLS) + keyB