def Torrent(itorrent, dir1, dir2, idelta, dirD, files):
torrent = opentorrent(itorrent)
sfiles = []
d = 0
files = sortByFiles(files)
for i in files:
S = True
df1 = os.path.join(dir1, i[0])
df2 = os.path.join(dir2, i[1])
if not os.path.isfile(df1):
print("File not found: {}".format(i[0]))
S = False
if not os.path.isfile(df2):
print("File not found: {}".format(i[1]))
S = False
if not fileintorr(torrent, i[0]):
print("File not found in torrent: {}".format(i[0]))
S = False
if S:
execute('xdelta3 -e -s "{}" "{}" "{}.delta"'.format(df1, df2, d))
sfiles.append({
'file1':
i[0],
'file2':
i[1],
'delta':
Delta("d.delta".format(d), idelta, i[0], i[1])
})
d += 1
CDelta(sfiles, dirD, dir2)
def __init__(self, robot: str):
# Thread-safe event flags
self.program_stopped = Event()
self.ignore_controller = Event()
self._current_mode = 'off'
self.controller = None
self.already_connected = False
self.controller_poll_rate = 5
self.mode_poll_rate = 0.1
self.mode_lock = RLock()
self.lcd = LCD()
# Preprocess string
robot_str = robot.strip().lower()
if robot_str == '6rus':
self.robot = SixRUS(stepper_mode=1 / 32, step_delay=0.002)
elif robot_str == 'quattro':
self.robot = Quattro(stepper_mode=1 / 32, step_delay=0.002)
elif robot_str == 'delta':
self.robot = Delta(stepper_mode=1 / 32, step_delay=0.002)
else:
raise ValueError(f"Unknown robot type: {robot}")
self.lcd.print_status(f'Started {robot}')
import random
from Delta import Delta
from Active_Function import Sigmoid
count_Training_Data = 4
count_Node = 1
X = [[0, 0, 1], [0, 1, 1], [1, 0, 1], [1, 1, 1]]
D = [0, 0, 1, 1]
Weight = list()
for i in range(0, 3):
Weight.append(random.uniform(-1, 1))
bias = [0]
Learning_rate = 0.9
for epoch in range(0, 10000):
Delta("SGD", Learning_rate, Sigmoid, bias, Weight, X, D,
count_Training_Data, count_Node)
for k in range(0, count_Training_Data):
v = 0
for W_count in range(0, len(Weight)):
x = X[k][W_count]
v = v + (Weight[W_count] * x + 0)
print(Sigmoid(v, False))
import random
from Delta import Delta, Sigmoid
#N = int(input("학습데이터의 수: "))
count_Training = 4 # 학습데이터의 수
count_Node = 1
# 1. 데이터 입력
X = [[0, 0, 1], [0, 1, 1], [1, 0, 1], [1, 1, 1]]
D = [0, 0, 1, 1]
Weight = list()
for i in range(0, 3): # 노드의 입력값들의 가중치 count
Weight.append(random.uniform(-1, 1)) # 가중치 랜덤값으로 초기화
bias = [0]
Learning_rate = 0.9 #학습률(0<alpha<=1), 크면 수렴하지 못함, 작으면 정답에 근접속도가 느림
for epoch in range(0, 10000): # SGD 방식
Delta(Learning_rate, Sigmoid, bias, Weight, X, D, count_Training,
count_Node) #활성함수(시그모이드)를 델타 규칙으로 학습
for k in range(0, count_Training):
v = 0
for W_count in range(0, len(Weight)):
x = X[k][W_count] #학습데이터의 입력데이터
#v = W*x+0 #노드의 가중합(가중행렬*입력데이터+bias)
#의도가 담긴 방향성
v = v + (Weight[W_count] * x + 0)
# 최종v = v + 0(행렬계산 + bias)
print(Sigmoid(v, False))
