def random_walk(npc):
randomX = math.random() * (npc.MaxRoam.X * Constants.TILE_SIZE)
randomY = math.random() * (npc.MaxRoam.Y * Constants.TILE_SIZE)
signx = -1 if math.random() < .5 else 1
signy = -1 if math.random() < .5 else 1
npc.GoTo(Vector(randomX * signX, randomY * signY))
def Maison(ID):
typeShare = int(math.random()*3)+1
cagnotte
tauxConso = int(math.random()*5001) + 7000 # taux à l'année entre 7 000 et 12 000 kWh
initProduct = int(math.random()*5001) + 5000 #production par an entre 5 000 et 10 000 kWh
energie = (initProduct - tauxConso )/365.25
def pointMutate(genome):
step = genome.mutationRates["step"]
for i in range(genome.genes):
gene = genome.genes[i]
if math.random() < PerturbChance:
gene.weight = gene.weight + math.random() * step * 2 - step
else:
gene.weight = math.random() * 4 - 2
def Economics():
ecoPb = False #boucle qui lance un problème éco aléatoirement
while eco == false:
proba = math.random()
if proba > 0.90:
ecoPb = True
typePb = int(math.random()*2)+1 #1 : offre qui augmente, prix qui baisse #2 : offre qui diminue, prix qui augmente
if typePb == 1:
#fonction de market qui augmente le prix
else:
def get_sample(ref, radius, t_diff):
rand_angle = 2 * math.pi * random.random()
samp_rad = radius * random.random()
x = samp_rad * math.cos(rand_angle)
y = samp_rad * math.sin(rand_angle)
t = ref.t + t_diff * (2 * math.random() - 1)
return STPoint(x, y, t)
def crossover(g1, g2):
# Make sure g1 is the highest fitness genome
if g2.fitness > g1.fitness:
tempg = g1
g1 = g2
g2 = tempg
child = newGenome()
innovations2 = {}
# for gene in g2.genes:
for i in range(len(g2.genes)):
gene = g2.genes[i]
innovations2[gene.innovation] = gene
for i in range(len(g1.genes)):
gene1 = g1.genes[i]
gene2 = innovations2[gene1.innovation]
if gene2 != None and math.random(2) == 1 and gene2.enabled:
# table.insert(child.genes, copyGene(gene2))
child.genes.append(copyGene(gene2))
else:
# table.insert(child.genes, copyGene(gene1))
child.genes.append(copyGene(gene1))
child.maxneuron = math.max(g1.maxneuron, g2.maxneuron)
child.mutationRates = g1.mutationRates.copy()
"""
for mutation, rate in pairs(g1.mutationRates) do
child.mutationRates[mutation] = rate
"""
return child
def linkMutate(genome, forceBias):
neuron1 = randomNeuron(genome.genes, False)
neuron2 = randomNeuron(genome.genes, True)
newLink = newGene()
if neuron1 <= inputs_count and neuron2 <= inputs_count:
return
if neuron2 <= inputs_count:
# Swap output and input
temp = neuron1
neuron1 = neuron2
neuron2 = temp
newLink.into = neuron1
newLink.out = neuron2
if forceBias:
newLink.into = inputs_count
if containsLink(genome.genes, newLink):
return
newLink.innovation = newInnovation()
newLink.weight = math.random() * 4 - 2
genome.genes.append(newLink)
def pan_z_instant(self,z):
if self.shake_amount > 0:
z += (math.random() - 0.5) * self.shake_amount
self.shake_amount *= self.shake_dim
if self.shake_amount < 10: self.shake_amount = 0
if GetPlayerId(GetLocalPlayer()) in self.players:
SetCameraField(CAMERA_FIELD_ZOFFSET, z, - 0.01)
SetCameraField(CAMERA_FIELD_ZOFFSET, z, 0.01)
def new_game(cls, user, card):
"""Creates and returns a new game"""
game = Game(user=user,
cards=list(math.random(range(1, 11))),
game_over=False)
game.put()
return game
def generate(self): count, product = 0, 1.0 elambda = math.exp(-self.mean) while product > elambda: product *= math.random() count += 1 return count - 1
def enableDisableMutate(genome, enable):
candidates = []
for gene in genome.genes:
if not gene.enabled == enable:
candidates.append(gene)
if len(candidates) == 0:
return
gene = candidates[math.random(1, len(candidates))]
gene.enabled = not gene.enabled
def on_period(self):
z = self.z
if self.shake_amount > 0:
z += (math.random() - 0.5) * self.shake_amount
self.shake_amount *= self.shake_dim
if self.shake_amount < 10: self.shake_amount = 0
if GetPlayerId(GetLocalPlayer()) in self.players and self.lock_z:
if 'z' not in self.transitions or not self.transitions['z'].active:
z += GetCameraField(CAMERA_FIELD_ZOFFSET) - GetCameraTargetPositionZ()
SetCameraField(CAMERA_FIELD_ZOFFSET, z, - 0.01)
SetCameraField(CAMERA_FIELD_ZOFFSET, z, 0.01)
def translation_circuit(signal, regions, lights = 0, board = False):
# r = num_lights if num_lights < r
vals = [len(signals)][r]
# TODO: This is where the signal gets filtered into interpretable data
# filtered_signal = []
# TODO: This is where the filtered signal gets converted into RGB values
if board:
for i in range(1, r+1, type=float):
return [math.random(255) for val in vals]# placeholder for random values
def translation_rgb(signal, regions, lights = 0, grad_type = 0, board = False):
rgb_vals = [r][3]
# TODO: This is where the signal gets filtered into interpretable data
# filtered_signal = []
# TODO: This is where the filtered signal gets converted into RGB values
if
return [[math.random(255) for val in vals] for vals in rgb_vals]# placeholder for random values
def output_lights(light_vals):
return # TODO?
def calculateSpawn(self):
if self.TIME > 25:
spawnLevel = int((self.roadPop / 10) * math.random())
elif self.TIME == 25:
spawnLevel = self.roadPop / 10
elif self.TIME < 25:
spawnLevel = self.roadPop / 10
if self.TIME > 15 and self.TIME < 25 or self.TIME > 60 and self.TIME < 75:
#use rush hour multiplier
return spawnLevel * self.RH_multiplier
else:
return spawnLevel
def atencionCliente(self):
catidad = 0
self.reloj = self.reloj + 1
self.eventos = self.eventos + 1
self.cajeros = False
self.agregarEvento()
self.cantidad = int(math.random() * 3000) + 100
if self.dinero < self.cantidad:
self.abastecimientoCajero()
else:
self.dinero = self.cantidad
self.satisfechos = self.satisfechos + 1
self.salidaCliente()
if (self.fila > 0):
self.fila = self.fila - 1
def submit(self):
m = hashlib.sha256()
m.update(math.random(10000))
self.digest = self.hash.hexdigest()
with self.client.post(
"/api/v1/submit",
{
"jsonrpc": "2.0",
"id": 0,
"method": "submit",
"params": {"checksum": self.digest},
},
catch_response=True
) as response:
if response.status_code != 200:
response.failure("The stamp response was an eror")
def estrai(lProb):
pTotVincenti = sum(lProb)
numRnd = m.random()
# numRnd = 1e-9
# print "numRnd ", numRnd
if numRnd < pTotVincenti:
cumulata = lProb[0]
i = 1
while i < len(lProb):
# print "cumul ", cumulata, " premio ", i
if numRnd < cumulata:
return i - 1
else:
cumulata += lProb[i]
i += 1
return i - 1
else:
return len(lProb)
def mutate(genome):
for mutation, rate in genome.mutationRates.items():
if math.random(1, 2) == 1:
genome.mutationRates[mutation] = 0.95 * rate
else:
genome.mutationRates[mutation] = 1.05263 * rate
if math.random() < genome.mutationRates["connections"]:
pointMutate(genome)
p = genome.mutationRates["link"]
while p > 0:
if math.random() < p :
linkMutate(genome, False)
p = p - 1
p = genome.mutationRates["bias"]
while p > 0:
if math.random() < p:
linkMutate(genome, True)
p = p - 1
p = genome.mutationRates["node"]
while p > 0 :
if math.random() < p:
nodeMutate(genome)
p = p - 1
p = genome.mutationRates["enable"]
while p > 0:
if math.random() < p:
enableDisableMutate(genome, True)
p = p - 1
p = genome.mutationRates["disable"]
while p > 0:
if math.random() < p:
enableDisableMutate(genome, False)
p = p - 1
def nodeMutate(genome):
if len(genome.genes) == 0:
return
genome.maxneuron = genome.maxneuron + 1
gene = genome.genes[math.random(1, len(genome.genes) )]
if not gene.enabled:
return
gene.enabled = False
gene1 = copyGene(gene)
gene1.out = genome.maxneuron
gene1.weight = 1.0
gene1.innovation = newInnovation()
gene1.enabled = True
genome.genes.append(gene1)
gene2 = copyGene(gene)
gene2.into = genome.maxneuron
gene2.innovation = newInnovation()
gene2.enabled = True
genome.genes.append(gene2)
def randomNeuron(genes, nonInput):
neurons = [False] * len(inputs_count)
if not nonInput:
for i in range(inputs_count):
neurons[i] = True
for i in range(inputs_count):
neurons[MaxNodes + i] = True
for i in range(len(genes)):
if not nonInput or genes[i].into > inputs_count:
neurons[genes[i].into] = True
if not nonInput or genes[i].out > inputs_count:
neurons[genes[i].out] = True
count = inputs_count
"""
count = 0
for _, _ in pairs(neurons) do
count = count + 1
"""
n = math.random(1, count)
for k, v in neurons:
n = n - 1
if n == 0:
return k
"""
for k, v in pairs(neurons) do
n = n - 1
if n == 0 then
return k
"""
return 0
def randomElement(l):
s = len(l)
return l[s * math.random()]
def random(self, a):
return math.random() * (a or 1)
def stimuli():
stimuli_list = []
for i in range(math.random(100,300)):
beta = math.random(0.05, 0.15)
n = math.random(int(10e6), int(10e8))
areas_list.append(Stimulus(beta, n))
def areas():
areas_list = []
for i in range(math.random(100,300)):
beta = math.random(0.05, 0.15)
n = math.random(int(10e6), int(10e8))
areas_list.append(Area(beta, n, math.sqrt(n)))
return connectome(areas, stimuli)
@pytest.fixture
def areas():
areas_list = []
for i in range(math.random(100,300)):
beta = math.random(0.05, 0.15)
n = math.random(int(10e6), int(10e8))
areas_list.append(Area(beta, n, math.sqrt(n)))
return areas_list
@pytest.fixture
def area(request):
beta, n, k = request.param
if beta is None:
beta = math.random(0.05, 0.15)
if n is None:
n = math.random(int(10e6), int(10e8))
if k is None:
k = math.sqrt(n)
return Area(beta, n, k)
@pytest.fixture
def stimuli():
stimuli_list = []
for i in range(math.random(100,300)):
beta = math.random(0.05, 0.15)
n = math.random(int(10e6), int(10e8))
areas_list.append(Stimulus(beta, n))
return stimuli_list
import math
if (math.random() < 0.5 ):
print("Heads")
else:
print("Tails")
def random(self, a):
return math.random() * (a or 1)
def setSeed(self,val=None): self.z = self.seed = (math.random()*self.m if val == None else val) >> 0
def breedChild(species):
child = []
if math.random() < CrossoverChance:
def getApiCid(): return math.floor(1e9 * math.random())
Python 3.7.1rc1 (v3.7.1rc1:2064bcf6ce, Sep 26 2018, 14:21:39) [MSC v.1914 32 bit (Intel)] on win32
Type "help", "copyright", "credits" or "license()" for more information.
>>> import math
>>> math.fmod(37,5)
2.0
>>>
math.random(5.0, 20.0)
Traceback (most recent call last):
File "<pyshell#2>", line 2, in <module>
math.random(5.0, 20.0)
AttributeError: module 'math' has no attribute 'random'
>>> import random
>>> random.random(5.0, 20.0)
Traceback (most recent call last):
File "<pyshell#4>", line 1, in <module>
random.random(5.0, 20.0)
TypeError: random() takes no arguments (2 given)
>>> random.random(5.0,20.0)
Traceback (most recent call last):
File "<pyshell#5>", line 1, in <module>
random.random(5.0,20.0)
TypeError: random() takes no arguments (2 given)
>>> random.randint(5,20)
18
>>>
def random_vec(self):
vector = Quat(0, 0, 0, 0)
while len(vector) >= 1:
vector = 2 * Quat(0, random(), random(), random()) - Quat(0, 1, 1, 1)
return vector
def getRandomColor():
letters = '0123456789ABCDEF'
color = '#'
for i in range(6):
color += letters[math.floor(math.random() * 16)]
return color
def api(request):
return HttpResponse('<h1>You can find your API key here: '+ math.random(10,100)+'</h1>')