def allLocationInitial(self):
self.locationOfBase = self.baseLocationInitial() # 基站位置初始化
self.locationOfUser = self.userLocationInitial(
radius=500,
radius2=100,
) # 用户位置初始化 为了测试注解掉这个初始化
paint = Painter()
paint.paintBasesAndUsers(self.locationOfBase, self.locationOfUser)
def test_MakeRightChessHorseJumpLMove(self):
painter = Painter()
painter.moveOneForward()
painter.turnRight()
painter.moveOneForward()
painter.moveOneForward()
self.assertEqual(2, painter.currentPositionObject.x)
self.assertEqual(1, painter.currentPositionObject.y)
def test_StartPositionPainter(self):
painter = Painter()
self.assertEqual(1, len(painter.map.knownCoordinatesObjects))
self.assertEqual(['0x0y'], painter.map.knownCoordinatesNames)
self.assertEqual(0, painter.currentPositionObject.x)
self.assertEqual(0, painter.currentPositionObject.y)
self.assertEqual(0, painter.currentPositionObject.value)
self.assertEqual(12, painter.currentDirectionAsOnClock)
def __init__(self):
self.painter = Painter()
self.store = Store()
self.selection_store = SelectionStore()
self.object_factory = ObjectFactory(self.store)
self.scene = Scene(self.store, self.painter, self.selection_store)
self.object_state = False
self.move_state = False
self.curr_object = None
self.last_object = False
def test_makeRightChessHorseJumpLMoveMapPrint(self):
painter = Painter()
painter.paintCurrentWhite()
painter.moveOneForward()
painter.paintCurrentWhite()
painter.turnRight()
painter.moveOneForward()
painter.paintCurrentWhite()
painter.moveOneForward()
painter.paintCurrentWhite()
with patch('builtins.print') as p:
painter.map.printMap()
p.assert_called_with([['#', '.', '.'], ['#', '#', '#']])
def test_StartPositionPaintWhiteBlackWhite(self):
painter = Painter()
initialColor = painter.getValueAtCurrentPosition()
self.assertEqual(0, initialColor, 'intial color is black')
painter.paintCurrentWhite()
whiteCoat = painter.getValueAtCurrentPosition()
self.assertEqual(1, whiteCoat, 'white coat')
painter.paintCurrentBlack()
blackCoat = painter.getValueAtCurrentPosition()
self.assertEqual(0, blackCoat, 'black coat')
painter.paintCurrentWhite()
whiteFinish = painter.getValueAtCurrentPosition()
self.assertEqual(1, whiteFinish, 'white finish')
def main():
logger.info('Start')
isShowMatrix = False
isSaveMatrix = False
dim = 1
if dim == 1:
f = lambda x: x*x
q = lambda x: (5-abs(x-5)*1)*2
domains = [[0,10,1]]
elements = [2]
elif dim == 2:
f = lambda x,y: x*x
q = lambda x,y: 0
domains = [[0,1,0.1],[0,1,0.1]]
elements = [1,1]
elif dim == 3:
f = lambda x,y,z: (x*x+y*y)*z
q = lambda x,y,z: ((x*x+y*y)*z)*np.random.rand(1)*0.01 + (100 if abs(x-2.5) < 1 and abs(y-2.5) < 1 else 0)
domains = [[0,5,0.5],[0,5,0.5],[-1,1,0.2]]
elements = [2,2,1]
K = 10
f = PointsFabric(dim, f, q, domains)
clear, noise = f.generate()
s = Spline('input.txt', elements, K)
s.MakeMatrix()
if isSaveMatrix:
np.savetxt('data/before_solveA.txt', s.A, fmt='%1.2e')
np.savetxt('data/before_solveF.txt', s.F, fmt='%1.2f')
if isShowMatrix:
plt.matshow(s.A)
ans = s.Solve()
i = 0
for a in ans:
try:
if len(a) == s.nNodes * (2**dim):
np.savetxt(f'data/answer_{i}.txt', a, fmt='%1.2f')
i += 1
except Exception:
pass
p = Painter('data/answer_0.txt', dim, True, s._Spline__psi, K, s.elements, s.mx, s.kElem, s.h, clearPoints=noise)
testFunc = lambda x,y,z: x+z
p.Paint(True)
def __init__(self, json_dict, images_folder, window_width, window_height):
super(Visualizer, self).__init__()
self.threshold_selector = ThresholdSelector(show_delay=True)
self.image_selector = ImageSelector(
json_dict,
images_folder,
self.threshold_selector.get_current_threshold(),
show_delay=True)
self.viewer = Viewer(window_width, window_height)
self.painter = Painter(json_dict, images_folder,
self.image_selector.get_current_image_idx(),
self.threshold_selector.get_current_threshold())
self.image_selector.imageChanged.connect(self.painter.new_image)
self.threshold_selector.thresholdChanged.connect(
self.image_selector.new_threshold)
self.threshold_selector.thresholdChanged.connect(
self.painter.new_threshold)
self.painter.boxesDrawn.connect(self.viewer.set_scene)
self.init_UI()
self.painter.draw(reset_scale=True)
def paintEvent(self, event=""):
qp = Painter(self)
qp.translate(self.height, 0)
qp.rotate(90)
# Enable antialiasing
qp.setRenderHints(
QPainter.Antialiasing | QPainter.SmoothPixmapTransform, True)
self.drawField(qp)
for enemy in self.enemies:
enemy.draw(qp)
for robot in self.robots:
robot.draw(qp)
self.ball.draw(qp)
self.drawGoal(qp)
qp.end()
def test_StartPositionReadColor(self):
painter = Painter()
retrievedValue = painter.getValueAtCurrentPosition()
self.assertEqual(0, retrievedValue)
from Painter import Painter painter = Painter() painter.paint9()
from initGame import * from Painter import Painter # 定义地图 painter = Painter(global_map, global_mapIndex, global_STEP, global_gateCount) painter.setDataByMap() painter.paintMap() # 启动游戏 painter.startPlay()
def __init__(self, w, h, col, name):
super().__init__()
self.painter = Painter(w, h, col, name)
self.painter.setGeometry(0, 0, 3200, 1600)
self.painter.show()
def ga(self, population, gaFilePath):
population.creatPopulation() # 生成种群
points = []
painter = Painter()
painter.paintNetworkTopology(population.baseRadius,
population.locationOfBase,
population.locationOfUser,
population.basevisitedUE, gaFilePath)
'''存储结果，用于绘图，文件操作'''
f = open(gaFilePath, 'w')
iterations = 1 # 当前迭代次数
while (iterations <= population.iterations):
fitness = population.getAllFitnessIntegral()
maxFitnessInCurrentPopulation = max(fitness)
print("代数：" + str(iterations) + " 最好值：" +
str(maxFitnessInCurrentPopulation))
f.write("iterations: " + str(iterations) + " fitness: " +
str(maxFitnessInCurrentPopulation) + '\n')
if iterations == 1:
maxFitness = maxFitnessInCurrentPopulation
else:
maxFitness = maxFitness if maxFitness > maxFitnessInCurrentPopulation else maxFitnessInCurrentPopulation
points.append([iterations, maxFitnessInCurrentPopulation])
tempIndividulalList = []
while (len(tempIndividulalList) < population.sizeOfPopulation):
individualForCrossover = population.select() # 选择--锦标赛选择法
if len(individualForCrossover) != 2:
print("select error! " + str(len(individualForCrossover)))
exit(1)
individualForMutate = population.crossover(
individualForCrossover) # 交叉随机点位的交叉操作，交叉完毕之后判断是否满足约束
if (len(individualForMutate) == 2):
population.mutate(individualForCrossover)
for i in range(len(individualForCrossover)):
tempIndividulalList.append(individualForCrossover[i])
tempFitness = []
for i in range(len(tempIndividulalList)):
fit = tempIndividulalList[i].getFitnessOfMatching()
tempFitness.append(fit)
for i in range(population.sizeOfPopulation):
if tempIndividulalList == None:
print(str(i) + "个体是空！")
exit(1)
if (tempFitness[i] > fitness[i]):
population.individualList[i] = copy.deepcopy(
tempIndividulalList[i])
iterations += 1
fitness = population.getAllFitnessIntegral()
index = Utils.getImportant(fitness)
print(
"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%9090909090"
)
print(population.individualList[index].getFitnessOfMatching())
print("asm_array: " + str(population.individualList[index].asm_array))
print("ansArray: " + str(population.individualList[index].ansArray))
print("VN： " + str(population.VN))
print("C: " + str(population.individualList[index].C))
print("P: " + str(population.individualList[index].P))
f.write("end" + '\n' + "C: " +
str(population.individualList[index].C) + '\n' + "P: " +
str(population.individualList[index].P))
return [
population.individualList[index].C,
population.individualList[index].P, fitness[index],
population.individualList[index].ansArray
]
def test_goforwardNorth(self):
painter = Painter()
painter.moveOneForward()
self.assertEqual(0, painter.currentPositionObject.x)
self.assertEqual(1, painter.currentPositionObject.y)
def test_turnPlus533FromNorth(self):
#12*44+5 = 533
painter = Painter()
painter.turnSome(533)
curDir = painter.currentDirectionAsOnClock
self.assertEqual(5, curDir)
if __name__ == "__main__":
K.clear_session()
input_shape = (1024,1024, 3)
output_shape = (256, 256, 3)
Dis = Discriminator(input_shape=output_shape)
Dis.input()
Dis.conv_pool_block(n_filter=16, filter_size=(5,5))
Dis.conv_pool_block(n_filter=8, filter_size=(7,7))
Dis.fully_connected(4)
Dis.fully_connected(1) # for W-Loss
Dis.build()
Gen = Painter(Dis, input_shape, output_shape)
Gen.input()
Gen.conv_block(16, filter_size=(5,5), padding="same")
Gen.conv_block(16, filter_size=(7,7), padding="same")
Gen.pooling_block((2,2))
Gen.conv_block(32, filter_size=(9,9), padding="same")
Gen.conv_block(32, filter_size=(11,11), padding="same")
Gen.pooling_block((2,2))
Gen.top_block()
#Gen.build_monitored([2,5],[0.5,0.5])
Gen.build()
painter_optimizer = Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07)
disc_optimizer = Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07)
def test_turnRightFromNorth(self):
painter = Painter()
painter.turnRight()
curDir = painter.currentDirectionAsOnClock
self.assertEqual(3, curDir)
import sys from Painter import Painter painter = Painter(sys.argv[1:-1], sys.argv[-1]) painter.print_figure()
def test_turnMinus332FromNorth(self):
#12*27+8 = 332
painter = Painter()
painter.turnSome(-332)
curDir = painter.currentDirectionAsOnClock
self.assertEqual(8, curDir)
from Engine import Engine from Screen import Screen from Painter import Painter from DJ import DJ screen = Screen() dj = DJ() painter = Painter(dj) e = Engine(screen, painter, dj) e.run()
fon = pygame.sprite.Group()
background = pygame.sprite.Sprite()
background.image = load_image("background.jpg")
background.rect = background.image.get_rect()
background.rect.x = 0
background.rect.y = 0
fon.add(background)
x = (gameParams.getWidth() * 0.45)
y = (gameParams.getHeight() * 0.8)
player = SpaceShip(x, y, pygame, gameDisplay, gameParams)
running = True
firstStage = FirstStage(pygame, gameDisplay, gameParams)
contentManager = ContentManager()
contentManager.setContents(firstStage.loadStageContent())
painter = Painter(time.time(), contentManager.getContent())
start_screen()
def getShootContent(player):
strike = EnergyStrike(pygame, gameDisplay, gameParams)
shootContent = Content(
strike,
firstStage.getShootConduct((player.get_x() + 20, player.get_y() - 5)),
1)
return [shootContent]
while running:
for event in pygame.event.get():
gameDisplay.fill(colour_white)
def ga(population, fileName, maxFitnessFile, typeOfVQD):
population.initializationOfVQD(typeOfVQD)
population.creatPopulation() # 生成种群
print(str(fileName) + " VQD: " + str(population.VQD))
print("typeOfVQD: " + str(typeOfVQD))
# 存储结果，用于绘图
points = []
maxFitness = -100
minFitness = -100
f = open(fileName, 'w')
f.write(fileName + '\n')
m = open(maxFitnessFile, 'w')
m.write(maxFitnessFile + '\n')
painter = Painter()
painter.paintNetworkTopology(population.baseRadius,
population.locationOfBase,
population.locationOfUser,
population.basevisitedUE, maxFitnessFile)
'''存储结果，用于绘图，文件操作'''
iterations = 1 # 当前迭代次数
while (iterations <= population.iterations):
fitness = population.getAllFitnessIntegral()
maxFitness = max(maxFitness, max(fitness))
minFitness = min(minFitness, min(fitness))
print("代数：" + str(iterations) + " 最好值：" +
str(maxFitness / population.VNTimes))
m.write("iterations " + str(iterations) + ": " +
str(maxFitness / population.VNTimes) + '\n')
f.write("iterations " + str(iterations + 1) + '\n')
f.write(str(fitness) + '\n')
points.append([iterations, maxFitness])
tempIndividulalList = []
while (len(tempIndividulalList) < population.sizeOfPopulation):
individualForCrossover = population.select() # 选择--锦标赛选择法
if len(individualForCrossover) != 2:
print("select error! " + str(len(individualForCrossover)))
exit(1)
individualForMutate = population.crossover(
individualForCrossover) # 交叉随机点位的交叉操作，交叉完毕之后判断是否满足约束
if (len(individualForMutate) == 2):
population.mutate(individualForCrossover)
for i in range(len(individualForCrossover)):
tempIndividulalList.append(individualForCrossover[i])
tempFitness = []
for i in range(len(tempIndividulalList)):
fit = tempIndividulalList[i].getFitnessWithIntegral()
tempFitness.append(fit)
for i in range(population.sizeOfPopulation):
if tempIndividulalList == None:
print(str(i) + "个体是空！")
exit(1)
if (tempFitness[i] > fitness[i]):
population.individualList[i] = copy.deepcopy(
tempIndividulalList[i])
iterations += 1
m.write(str(points) + '\n')
f.close()
m.close()
# 返回画图所需的数据
result = Result(fileName, minFitness, points, maxFitness)
return result
