def test_life_36(self): l = Life(1,3) l.neighbors = 2 c2 = ConwayCell() c2.evolve(l) assert c2.alive == False
def test_life_35(self): l = Life(1,3) l.neighbors = 3 c1 = ConwayCell() c1.evolve(l) assert c1.alive == True
def docross(self,lives,x, y):
child1 = self.getChild(1,lives, x, y)
child2 = self.getChild(2,lives, x, y)
child1=self.modify_circle(child1)
child2=self.modify_circle(child2)
lives[x] = Life(child1)
lives[y] = Life(child2)
def test_life_34(self): l = Life(2,2) l.neighbors = 2 c1 = ConwayCell(True) c1.evolve(l) assert c1.alive == True
def cross(self, parent1: Life, parent2: Life):
"""
交叉:两染色体相同位置的片段之间的部分交叉
输入：父代染色体(Life类）
输出：交叉后的子代染色体(Life类）
"""
index1 = np.random.randint(len(origincoor))
index2 = np.random.randint(index1 + 1, len(origincoor) + 1)
tempGene1 = [[], []]
tempGene2 = [[], []]
tempGene1[0] = parent1.gene[0][index1 * 3:index2 * 3] # 交叉的基因片段
tempGene1[1] = parent1.gene[1][index1 * 3:index2 * 3]
tempGene2[0] = parent2.gene[0][index1 * 3:index2 * 3] # 交叉的基因片段
tempGene2[1] = parent2.gene[1][index1 * 3:index2 * 3]
parent1.gene[0][index1 * 3:index2 * 3] = tempGene2[0]
parent1.gene[1][index1 * 3:index2 * 3] = tempGene2[1]
parent2.gene[0][index1 * 3:index2 * 3] = tempGene1[0]
parent2.gene[1][index1 * 3:index2 * 3] = tempGene1[1]
if checklegal(parent1.gene[0], len(targetcoor[0])) == False:
parent1.gene = wipe(parent1.gene, index1 * 3, index2 * 3)
parent1.gene = correctchrome(parent1.gene, index1, index2,
TargetNum, ReconNum, UnionNum,
CombatNum)
if checklegal(parent2.gene[0], len(targetcoor[0])) == False:
parent2.gene = wipe(parent2.gene, index1 * 3, index2 * 3)
parent2.gene = correctchrome(parent2.gene, index1, index2,
TargetNum, ReconNum, UnionNum,
CombatNum)
parent1.score = totaldis(parent1.gene, origincoor, targetcoor,
ThreatRadius, TurnRadius, ReconTime, Velocity)
parent2.score = totaldis(parent2.gene, origincoor, targetcoor,
ThreatRadius, TurnRadius, ReconTime, Velocity)
self.crossCount += 1
return parent1, parent2
'''
def test_life_25(self): k = FredkinCell(True) l = Life(1,1) l.addCell(0,0,k) l.countLives() k.evolve(l) assert k.alive == False
def test_life_18(self): k = AbstractCell(True) l = Life(1,1) l.addCell(0,0,k) k.evolve(l) k.evolve(l) k.evolve(l) assert k.alive
def test_foerste(self):
l = Life(0, 20, 0, 20, self.alive)
xmin, xmax, ymin, ymax = l.foerste()
self.assertEqual(xmin, 0)
self.assertEqual(xmax, 20)
self.assertEqual(ymin, 0)
self.assertEqual(xmax, 20)
def make_viewer(n, m, row, col, *strings):
"""Makes a Life and LifeViewer object.
n, m: rows and columns of the Life array
row, col: upper left coordinate of the cells to be added
strings: list of strings of '0' and '1'
"""
life = Life(n, m) # n行m列的格子
life.add_cells(row, col, *strings) # 左上角坐标
viewer = LifeViewer(life) # 瞅一眼
return viewer
def test_life_26(self): k1 = FredkinCell(True) k2 = FredkinCell(True) l = Life(2,2) l.addCell(0,0,k1) l.addCell(1,0,k2) l.countLives() k1.evolve(l) assert k1.alive == True
def judge(self, f=lambda lf, av: 1):
# 根据传入的方法 f ，计算每个个体的得分
lastAvg = self.bounds / float(self.lifeCount)
self.bounds = 0.0
self.best = Life(self)
self.best.setScore(-1.0)
for lf in self.lives:
lf.score = f(lf, lastAvg)
if lf.score > self.best.score:
self.best = lf
self.bounds += lf.score
def test_life_27(self):
k1 = FredkinCell(True)
k2 = FredkinCell(True)
l = Life(2,2)
l.addCell(0,0,k1)
l.addCell(1,0,k2)
l.countLives()
k1.age = 1
k1.evolve(l)
assert isinstance(k1,ConwayCell)
def test_life_5(self): l = Life(2,2) c1 = FredkinCell(True) c2 = FredkinCell(True) c3 = FredkinCell(True) c4 = FredkinCell(True) l.addCell(0,0,c1) l.addCell(0,1,c2) l.addCell(1,0,c3) l.addCell(1,1,c4) assert str(l) == '00\n00'
def test_life_6(self): l = Life(2,2) c1 = ConwayCell(True) c2 = ConwayCell(False) c3 = FredkinCell(True) c4 = FredkinCell(False) l.addCell(0,0,c1) l.addCell(0,1,c2) l.addCell(1,0,c3) l.addCell(1,1,c4) assert str(l) == '*.\n0-'
def test_life_10(self): l1 = Life(1,1) c1 = FredkinCell(True) l1.addCell(0,0,c1) l1.populationEvolve() l2 = Life(1,1) c2 = ConwayCell(True) l2.addCell(0,0,c2) l2.populationEvolve() assert str(l2) == '.' and str(l1) == '-'
def run_game(board_size, life_cells, refresh):
"""
Play the game with the given parameters.
:param board_size: the grid size of the board
:param life_cells: the number of starter life cells
:param refresh: the refresh rate of the game
"""
print(
f'Total cells: {board_size * board_size}, Life cells: {life_cells}\n')
life_game = Life(size=board_size,
initial_cells=life_cells,
interval=refresh)
life_game.play()
def initPopulation(self):
"""初始化种群"""
self.lives = []
for i in range(self.lifeCount):
if i==0 and self.prim_path:
gene = self.prim_path
life = Life(gene)
self.lives.append(life)
else:
gene = [ x for x in range(self.geneLenght) ]
random.shuffle(gene)
life = Life(gene)
self.lives.append(life)
def test_life_42(self): l = Life(2,2) c1 = ConwayCell(True) c2 = ConwayCell(True) c3 = FredkinCell() c4 = FredkinCell() l.addCell(0,0,c1) l.addCell(0,1,c2) l.addCell(1,0,c3) l.addCell(1,1,c4) l.countLives() l.cellExecute(1,1) assert l.liveNeighbors() == 1
def main():
#initialize
# Assumes Linux setup
cmd = "cd ~/Documents; pwd 2>/dev/null >/tmp/life_directory; cd - > /dev/null"
output = subprocess.check_output(cmd, shell=True).split()
documents_directory_path = subprocess.check_output(
"cat /tmp/life_directory", shell=True).split()[0]
life_directory = documents_directory_path + "/RealLifeGTA/"
life_filename = "gta.data"
life_complete_path = life_directory + life_filename
try:
life = _load_from_file(life_complete_path)
#print "Loading life from datastore"
except:
#traceback.print_exc(file=sys.stdout)
print "Couldn't load life from datastore, creating a new file at path:" + life_directory
subprocess.check_output("mkdir -p " + life_directory, shell=True)
life = Life()
# start main loop
CommandLineInterface(life)._process_single_command()
#persist data
try:
_save_to_file(life, life_complete_path)
except:
traceback.print_exc(file=sys.stdout)
def next(self):
"""产生下一代"""
self.judge()
newLives = []
"""for lf in self.lives:
if lf.score > 5*self.initScore:
newLives.append(lf)"""
newLives.append(self.best) #把最好的个体加入下一代
basicgene = [0 for x in range(self.geneLenght)] #把最基本的加入下一代
newLives.append(Life(basicgene))
newLives.append(Life(self.init)) #把DFS得到的加入下一代
while len(newLives) < self.lifeCount:
newLives.append(self.newChild())
self.lives = newLives
self.generation += 1
def __init__(self,
xRate=0.7,
mutationRate=0.005,
lifeCount=50,
geneLength=100,
judge=lambda lf, av: 1,
save=lambda: 1,
mkLife=lambda: None,
xFunc=None,
mFunc=None):
self.xRate = xRate
self.mutationRate = mutationRate
self.mutationCount = 0
self.generation = 0
self.lives = []
self.bounds = 0.0 # 得分总数
self.best = None
self.lifeCount = lifeCount
self.geneLength = geneLength
self.__judge = judge
self.save = save
self.mkLife = mkLife # 默认的产生生命的函数
self.xFunc = (xFunc, self.__xFunc)[xFunc == None] # 自定义交叉函数
self.mFunc = (mFunc, self.__mFunc)[mFunc == None] # 自定义变异函数
for i in range(lifeCount):
self.lives.append(Life(self, self.mkLife()))
def __init__(self,
x_rate=0.7,
mutation_rate=0.005,
life_count=50,
gene_length=100,
judge=lambda lf, av: 1,
save=lambda: 1,
mk_life=lambda: None,
x_func=None,
m_func=None):
self.x_rate = x_rate
self.mutation_rate = mutation_rate
self.mutation_count = 0
self.generation = 0
self.lives = []
self.bounds = 0.0 # 得分总数
self.best = None
self.life_count = life_count
self.gene_length = gene_length
self.__judge = judge
self.save = save
self.mk_life = mk_life # 默认的产生生命的函数
self.x_func = (x_func, self.__x_func)[x_func == None] # 自定义交叉函数
self.m_func = (m_func, self.__m_func)[m_func == None] # 自定义变异函数
for i in range(life_count):
self.lives.append(Life(self, self.mk_life()))
def main(script, *args):
"""Constructs the rabbits methusela.
http://www.argentum.freeserve.co.uk/lex_r.htm#rabbits
"""
rabbits = ['1000111', '111001', '01']
n = 400
m = 600
life = Life(n, m)
life.add_cells(n // 2, m // 2, *rabbits)
viewer = LifeViewer(life)
anim = viewer.animate(frames=100, interval=1)
plt.subplots_adjust(left=0.01, right=0.99, bottom=0.01, top=0.99)
plt.show()
def __init__(self, spark = 42, d = -1):
Life.__init__(self)
self.d = d
self.domains = ["Bacteria", "Archaea", "Eukaryota"]
self.domain = self.domains[self.d]
def domainRandomize(self):
self.d = random.randint(0,2)
self.domain = self.domains[self.d]
pass
if (self.d < 0):
domainRandomize(self)
pass
if __name__ == '__main__':
#print "The Answer to Life, the Universe and Everything is: ", self.TheAnswerToLifeTheUniverseAndEverything
pass
def textToLife(self, text):
# figure out game dimensions
width = len(text) * 5 + 1
height = 9
lifeGame = Life(height=height, width=width)
lifeGame.clean(state=LifeState.DEAD)
currI = 1
currJ = 1
for character in text.upper():
for iIdx in range(7):
for jIdx in range(4):
gameIndices = (currI + iIdx, currJ + jIdx)
dictIndices = (iIdx, jIdx)
lifeGame.game[0][gameIndices[0]][gameIndices[1]].state \
= self.strDict[character][dictIndices[0]][dictIndices[1]]
currJ += 5
return lifeGame
def initPopulation(self):
"""初始化种群"""
self.lives = []
for i in range(self.lifeCount):
gene = [x for x in range(self.geneLenght)]
random.shuffle(gene)
life = Life(gene)
self.lives.append(life)
def AngleMutation(self, parent: Life):
flag = 0
'''
角度突变:某一个任务的角度变化
输入：要突变的染色体(Life类）
输出：突变后的染色体(Life类）
'''
###############################待修改突变函数##################################
while flag == 0:
index = np.random.randint(len(parent.gene[0]) + 1)
if parent.gene[0][index] != 0:
flag = 1
parent.gene[1][index] = np.random.randint(1, 361)
parent.score = totaldis(parent.gene, origincoor, targetcoor,
ThreatRadius, TurnRadius, ReconTime, Velocity)
self.mutationCount += 1
return parent
'''
def initPopulation(self):
"""初始化种群"""
self.lives = []
for i in range(self.lifeCount):
gene = [x for x in range(self.geneLenght)] # 初始化染色体
# shuffle() 方法将序列的所有元素随机排序。
random.shuffle(gene)
life = Life(gene) # 生成个体
self.lives.append(life)
def initPopulation(self):
"""初始化种群"""
self.lives = []
for i in range(self.lifeCount):
gene = self.initOrder
#print(gene)
#random.shuffle(gene) # 随机洗牌 #
life = Life(gene)
self.lives.append(life)
def newChild(self):
"""产生新后的"""
parent1 = self.getOne()
parent2 = self.getOne()
rate = random.random()
# 按概率交叉
if rate < self.croessRate:
# 交叉
gene1, gene2 = self.cross(parent1, parent2)
else:
gene1, gene2 = parent1.gene, parent2.gene
# 按概率突变
rate = random.random()
if rate < self.mutationRate:
gene1, gene2 = self.mutation(gene1), self.mutation_bk(gene2)
return Life(gene1), Life(gene2)
def newChild(self):
"""产生新后的"""
mutationLives = []
# 按概率突变
for i in self.notBests:
gene = self.mutation(i)
mutationLives.append(Life(gene))
return mutationLives
def initPopulation(self):
"""initial the population"""
self.population = []
for i in range(self.population_size):
#gene = [0,1,…… ,self.chromosome_length-1]
gene = self.init_chromosome[:]
random.shuffle(gene)
pop = Life(gene)
# put individual in the population
self.population.append(pop)
def initPopulation(self):
"""初始化种群"""
self.lives = []
count = 0
while count < self.lifeCount:
gene = np.random.randint(0, 2, self.geneLenght)
life = Life(gene)
random.shuffle(gene) # 随机洗牌 #
self.lives.append(life)
count += 1
def test_life_45(self): l = Life(2,2) c1 = ConwayCell(True) c2 = ConwayCell(True) c3 = ConwayCell(True) c4 = ConwayCell(False) l.addCell(0,0,c1) l.addCell(0,1,c2) l.addCell(1,0,c3) l.addCell(1,1,c4) l.countLives() l.cellExecute(2,2) assert c4.getAlive()
def test_life_47(self): l = Life(2,2) c1 = FredkinCell(True) c2 = FredkinCell(True) c3 = FredkinCell(True) c4 = FredkinCell(False) l.addCell(0,0,c1) l.addCell(0,1,c2) l.addCell(1,0,c3) l.addCell(1,1,c4) l.countLives() l.cellExecute(1,1) assert not c1.getAlive()
def initPopulation(self):
"""初始化种群"""
self.lives = []
for i in range(self.lifeCount):
gene = [ x for x in range(self.geneLenght) ]
random.shuffle(gene) #将序列的所有元素随机排序
self.modify_circle(gene)
life = Life(gene)
self.lives.append(life)
self.best=self.lives[0]
self.judge()
def test_life_8(self): l = Life(2,2) c1 = FredkinCell(True) c2 = FredkinCell(True) c3 = FredkinCell(True) c4 = FredkinCell(True) l.addCell(0,0,c1) l.addCell(0,1,c2) l.addCell(1,0,c3) l.addCell(1,1,c4) l.countLives() l.livesCount = [[2,2],[2,2]]
def main(script, *args):
"""Constructs the rabbits methusela.
http://www.argentum.freeserve.co.uk/lex_r.htm#rabbits
"""
rabbits = [
'1000111',
'111001',
'01'
]
n = 400
m = 600
life = Life(n, m)
life.add_cells(n//2, m//2, *rabbits)
viewer = LifeViewer(life)
anim = viewer.animate(frames=100, interval=1)
plt.subplots_adjust(left=0.01, right=0.99, bottom=0.01, top=0.99)
plt.show()
def test_life_40(self): l = Life(2,2) c1 = ConwayCell(True) c2 = ConwayCell(False) c3 = ConwayCell(False) c4 = ConwayCell(True) l.addCell(0,0,c1) l.addCell(0,1,c2) l.addCell(1,0,c3) l.addCell(1,1,c4) l.cellExecute(1,1) assert l.liveNeighbors() == 0
def judge(self, f=lambda lf, av: 1):
# 根据传入的方法 f ，计算每个个体的得分
lastAvg = self.bounds / float(self.lifeCount)
self.bounds = 0.0
self.best = Life(self)
self.best.setScore(-1.0)
for lf in self.lives:
lf.score = f(lf, lastAvg)
if lf.score > self.best.score:
self.best = lf
self.bounds += lf.score
def newChild(self):
"""产生新后的"""
parent1 = self.getOne()
gene = parent1.gene
# 按概率突变
rate = random.random()
if rate < self.mutationRate:
gene = self.mutation(gene)
return Life(gene)
def add_life(self, life_img, count):
if count < 3:
count = 3
self.life = count
for num in range(0, count):
life = Life(life_img, [20 + num * life_img.get_rect().width, 20])
self.lifeGroup.add(life)
self.lifeC.append(life)
def next(self):
"""产生下一代"""
self.generation += 1
self.judge()
do_best = self.domutation(self.best1.gene)
push_best = self.pushmutation(self.best1.gene)
newLives = []
newLives.append(Life(self.best.gene)) # 把最好的个体加入下一代
newLives.append(Life(do_best))
newLives.append(Life(push_best))
newLives.append(Life(self.best1.gene))
self.setRoulette()
while len(newLives) < self.lifeCount:
newLives.append(self.getOne())
newLives = self.crossover(newLives)
newLives = self.mutation(newLives)
self.lives = newLives
def newChild(self):
parent1 = self.getOne()
rate = random.random()
if rate < self.crossRate:
parent2 = self.getOne()
gene = self.cross(parent1, parent2)
else:
gene = parent1.gene
rate = random.random()
if rate < self.mutationRate:
gene = self.mutation(gene)
return Life(gene)
def initPopulation(self):
"""初始化种群"""
start1 = time.clock()
self.lives = []
for i in range(self.lifeCount):
gene = self.initLife()
life = Life(gene)
self.lives.append(life)
end1 = time.clock()
runtime = end1 - start1
print("----------------初始化种群,用时" + str(("%.2f") % (runtime)) +
"秒------------------")
def __init__(self, sense):
self.sense = sense
self.FACE = 1, RobotFace(sense)
self.RADIO = 2, Radio(sense)
self.LIFE = 3, Life(sense, 8, 8)
self.MUSICPLAYER = 4, MusicPlayer(sense)
self.stateList = (self.FACE, self.RADIO, self.LIFE, self.MUSICPLAYER)
# Start with some state
self.current = self.MUSICPLAYER
self.current[1].select()
def test_fut_curr_3(self): x = Life(5, 3, [['-','-','-'], ['0','1','0'], ['0','-','-'], ['-','-','0'], ['*','0','-']]) x.future = '-' x.moat_grid() x.fut_curr() x.grid[2][3].count = 0 x.grid[2][3].state = '-'
def test_fut_curr_2(self): x = Life(5, 3, [['.','.','.'], ['*','.','.'], ['*','*','*'], ['.','.','*'], ['.','.','*']]) x.future = '*' x.moat_grid() x.fut_curr() x.grid[2][2].count = 0 x.grid[2][2].state = '*'\
def initPopulation(self):
"""初始化种群"""
self.lives = []
for _ in range(self.lifeCount):
# gene = [0,1,…… ,self.geneLength-1]
gene = list(range(self.geneLength))
# 将0到33序列的所有元素随机排序得到一个新的序列
random.shuffle(gene)
# Life这个类就是一个基因序列，初始化life的时候,两个参数，一个是序列gene，一个是这个序列的初始适应度值
# 因为适应度值越大，越可能被选择，所以一开始种群里的所有基因都被初始化为-1
life = Life(gene)
#把生成的这个基因序列life填进种群集合里
self.lives.append(life)
def __init__(self, width, height):
super(SubWindow, self).__init__()
self.width = width
self.height = height
self.setAttribute(QtCore.Qt.WA_DeleteOnClose)
self.isUntitled = True
widget = QtGui.QScrollArea()
self.qp = QtGui.QPainter()
if width and height:
self.field = Field(width, height)
self.setWidget(self.field)
self.life = Life(width, height, False)
self.reset()
self.field.cells = self.life.cells
def next(self):
start = time.clock()
"""产生下一代"""
self.judge()
newLives = []
newLives.extend(self.newChild())
while len(newLives) < self.lifeCount:
newLives.append(Life(self.initLife()))
self.lives = newLives
self.generation += 1
end = time.clock()
runtime = end - start
print("----------------迭代一轮需要用时" + str(("%.2f") % (runtime)) +
"秒------------------")
def test_life_11(self): l = Life(2,2) c1 = FredkinCell(True) c2 = FredkinCell(False) c3 = FredkinCell(False) c4 = FredkinCell(False) l.addCell(0,0,c1) l.addCell(0,1,c2) l.addCell(1,0,c3) l.addCell(1,1,c4) l.populationEvolve() assert str(l) == '-0\n0-'
def test_life_7(self): l = Life(2,2) c1 = ConwayCell(True) c2 = ConwayCell(True) c3 = ConwayCell(True) c4 = ConwayCell(True) l.addCell(0,0,c1) l.addCell(0,1,c2) l.addCell(1,0,c3) l.addCell(1,1,c4) l.countLives() l.livesCount == [[3,3],[3,3]]
def test_life_12(self): l = Life(2,2) c1 = ConwayCell(True) c2 = ConwayCell(True) c3 = ConwayCell(False) c4 = ConwayCell(True) l.addCell(0,0,c1) l.addCell(0,1,c2) l.addCell(1,0,c3) l.addCell(1,1,c4) l.populationEvolve() assert str(l) == '**\n**'
def next(self, n=1):
# 演化至下n代
while n > 0:
# self.__getBounds()
self.judge(self.__judge)
new_lives = [Life(self, self.best.gene)]
# self.bestHistory.append(self.best)
while len(new_lives) < self.life_count:
new_lives.append(self.__new_child())
self.lives = new_lives
self.generation += 1
# print("gen: %d, mutation: %d, best: %f" % (self.generation, self.mutationCount, self.best.score))
self.save(self.best, self.generation)
n -= 1
def test_life_50(self): l = Life(2,2) c1 = FredkinCell(False) c2 = FredkinCell(False) c3 = FredkinCell(False) c4 = FredkinCell(False) l.addCell(0,0,c1) l.addCell(0,1,c2) l.addCell(1,0,c3) l.addCell(1,1,c4) assert l.getPopulation() == 0
def loadFile(self, fileName):
file = QtCore.QFile(fileName)
if not file.open( QtCore.QFile.ReadOnly | QtCore.QFile.Text):
QtGui.QMessageBox.warning(self, "Reading error",
"Cannot read file %s:\n%s." % (fileName, file.errorString()))
return False
instr = QtCore.QTextStream(file)
start = False
width = 0
lines = []
while not instr.atEnd():
line = instr.readLine()
if width < len(line):
width = len(line)
if line[0] != '#':
start = True
lines.append(line)
if line[0] == '#' and start:
break
height = len(lines)
if not width or not height:
return False
self.width = width
self.height = height
self.field = Field(width, height)
self.setWidget(self.field)
self.life = Life(width, height, False)
self.field.cells = self.life.cells
y=0
for line in lines:
x = 0
for char in line:
if char == '*':
self.life.cells[x,y] = True
x += 1
y += 1
self.field.update()
self.setCurrentFile(fileName)
return True
# -------
import sys
from Life import Life, AbstractCell, ConwayCell, FredkinCell, life_read
# ---------------------
# Life ConwayCell 21x13
# ---------------------
print("*** Life ConwayCell 21x13 ***")
"""
Simulate 12 evolutions.
Print every grid (i.e. 0, 1, 2, 3, ... 12)
"""
row, col, grid = life_read(sys.stdin)
x = Life(row, col, grid)
x.moat_grid()
x.print_grid(12, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
# ---------------------
# Life ConwayCell 20x29
# ---------------------
print("*** Life ConwayCell 20x29 ***")
"""
Simulate 28 evolutions.
Print every 4th grid (i.e. 0, 4, 8, ... 28)
"""
row, col, grid = life_read(sys.stdin)
x = Life(row, col, grid)
x.moat_grid()
def test_find_edges(self):
l = Life(0, 20, 0, 20, self.alive)
l.foerste()
self.assertEqual(l.find_edges(0, 0), [(0, 1), (1, 1), (1, 0)])
def test_levende(self):
l = Life(0, 20, 0, 20, self.alive)
l.foerste()
self.assertEqual(l.levende(10, 9), True)
