def daveAndJane():
cGenome1 = []
nGenome1 = []
#input nodes
nGenome1.append(gene.NodeGene(1, True, False))
nGenome1.append(gene.NodeGene(2, True, False))
nGenome1.append(gene.NodeGene(3, True, False))
#output nodes
nGenome1.append(gene.NodeGene(4, False, True))
#hidden nodes
nGenome1.append(gene.NodeGene(5, False, False))
#connections
cGenome1.append(gene.ConnectGene(1, 4, 1.0, True, 1))
cGenome1.append(gene.ConnectGene(2, 4, 0.5, True, 2))
cGenome1.append(gene.ConnectGene(3, 4, -2, True, 3))
cGenome1.append(gene.ConnectGene(1, 5, 1, True, 4))
cGenome1.append(gene.ConnectGene(2, 5, 1, True, 5))
cGenome1.append(gene.ConnectGene(5, 4, -1, True, 6))
dave = genotype.Genotype(cGenome1, nGenome1)
cGenome2 = []
nGenome2 = []
#input nodes
nGenome2.append(gene.NodeGene(1, True, False))
nGenome2.append(gene.NodeGene(2, True, False))
nGenome2.append(gene.NodeGene(3, True, False))
#output nodes
nGenome2.append(gene.NodeGene(4, False, True))
#hidden nodes
nGenome2.append(gene.NodeGene(5, False, False))
nGenome2.append(gene.NodeGene(6, False, False))
#connections
cGenome2.append(gene.ConnectGene(1, 4, 1.0, True, 1))
cGenome2.append(gene.ConnectGene(2, 4, 0.5, True, 2))
cGenome2.append(gene.ConnectGene(3, 4, 2, True, 3))
cGenome2.append(gene.ConnectGene(1, 5, 1, True, 4))
cGenome2.append(gene.ConnectGene(2, 5, -1, True, 5))
cGenome2.append(gene.ConnectGene(5, 4, 1.6, True, 6))
cGenome2.append(gene.ConnectGene(3, 5, .9, True, 7))
cGenome2.append(gene.ConnectGene(4, 5, -7, True, 8))
cGenome2.append(gene.ConnectGene(4, 5, 2, False, 9))
cGenome2.append(gene.ConnectGene(1, 6, -2, True, 10))
cGenome2.append(gene.ConnectGene(2, 6, 2, True, 11))
cGenome2.append(gene.ConnectGene(6, 4, 2.3, True, 12))
jane = genotype.Genotype(cGenome2, nGenome2)
genetics.innovationNumber = 9
genetics.nodeNumber = 5
return (dave, jane)
def newPop(size):
#created blank pop
pop = []
cGenome = []
nGenome = []
#input nodes
genetics.innovationNumber = 0
genetics.nodeNumber = 0
for j in range(784):
genetics.nodeNumber += 1
nGenome.append(gene.NodeGene(genetics.nodeNumber, True, False))
for k in range(10):
genetics.innovationNumber += 1
cGenome.append(
gene.ConnectGene(genetics.nodeNumber, 785 + k,
random.gauss(0, 1), True,
genetics.innovationNumber))
#output nodes
for k in range(10):
genetics.nodeNumber += 1
nGenome.append(gene.NodeGene(genetics.nodeNumber, False, True))
baseGeno = genotype.Genotype(cGenome, nGenome)
for i in range(size):
print(i)
pop.append(copy.deepcopy(baseGeno))
return pop
def standardMutate(genotype, weightProb, connectionProb, nodeProb):
connections = genotype.connectGenome
nodes = genotype.nodeGenome
#weight mutations
for connection in connections:
if random.random() < weightProb:
connection.weight += random.gauss(0, 0.5)
#new connection
for i in range(int(connectionProb/1.0)):
if random.random() < connectionProb%1.0:
node = random.choice(nodes)
genetics.innovationNumber += 1
newGene = Gene.ConnectGene(node.nodeNum, random.choice(nodes).nodeNum, random.gauss(0, 2), True, genetics.innovationNumber)
connections.append(newGene)
#new node
for i in range(int(nodeProb/1.0)):
if random.random() < nodeProb%1.0:
# print("len of nodes before: " + str(len(nodes)))
# print("ADDED NEW NODE")
connection = random.choice(connections)
connection.enabled = False
genetics.nodeNumber += 1
nodes.append(Gene.NodeGene(genetics.nodeNumber, False, False))
genetics.innovationNumber += 1
connections.append(Gene.ConnectGene(connection.inNode, genetics.nodeNumber, connection.weight, True, genetics.innovationNumber))
genetics.innovationNumber += 1
connections.append(Gene.ConnectGene(genetics.nodeNumber, connection.outNode, 1, True, genetics.innovationNumber))
# print("len of nodes after: " + str(len(nodes)))
return Genotype.Genotype(connections, nodes)
def test_domination(self):
g = genotype.Genotype(data_manager.dm.city_ids)
i1 = individual.Individual(g)
i2 = individual.Individual(g)
i1.tour_distance = 500
i2.tour_distance = 450
i1.calculate_tour_cost = 40
i2.calculate_tour_cost = 35
self.assertTrue(i2.dominates(i1))
self.assertFalse(i1.dominates(i2))
def loadFromFile(file):
file = open(file, "r")
popJson = file.read()
popList = json.loads(popJson)
pop = []
for genoList in popList:
connectGenome = []
nodeGenome = []
for cGT in genoList[0]:
connectGenome.append(
gene.ConnectGene(cGT[0], cGT[1], cGT[2], cGT[3], cGT[4]))
for nGT in genoList[1]:
nodeGenome.append(gene.NodeGene(nGT[0], nGT[1], nGT[2]))
pop.append(genotype.Genotype(connectGenome, nodeGenome))
return pop
"""matrixList = []
def allocate_agents(self, n_agents, gen):
if gen == None:
gen = genotype.Genotype()
nr = 0
while nr < n_agents:
add = True
ax = np.random.randint(10, self.xmax - 10)
ay = np.random.randint(10, self.ymax - 10)
ao = np.radians(np.random.randint(0, 360))
# simple check to avoid superposition with trees
for t in self.trees:
if np.linalg.norm(np.array([ax, ay]) -
np.array([t.x, t.y])) < t.r + gen.r:
add = False
# create agents
if add:
ag = agent.Agent(ax, ay, ao, gen)
self.agents.append(ag)
nr += 1
# store
self.objects["agents"] = self.agents
def test_calculate_tour_distance_and_cost(self):
g = genotype.Genotype(data_manager.dm.city_ids)
i = individual.Individual(genotype=g)
self.assertEqual(i.tour_distance, 153809)
self.assertEqual(i.tour_cost, 1921)
main_window.title('AIPond v.' + version)
print('Версия бота: ', bot.version)
print('Версия генотипа: ', genotype.version)
print('Версия окружающего мира для ботов:', board.version)
# Создаём среду в которой будет жить AI.
pond = tkinter.Canvas(main_window,
width=settings.WIDTH,
height=settings.HEIGHT)
pond.pack()
# Создаём стартовые генотипы для ботов (10 штук)
# и первых ботов.
for loop in range(0, 10):
bot_genotype = genotype.Genotype('New')
bot_genotype.save()
bot_array.append(bot.Bot(cng=bot_genotype.cng, number=number))
number += 1
# Помещаем начальных ботов в окружающую среду.
for loop in range(0, number - settings.NUMBER_DEFAULT):
birth = True
while birth:
# Положение бота в мире по оси Х.
x = random.randint(1, settings.CELL - 1)
# Положение бота в мире по оси Y.
y = random.randint(1, settings.CELL - 1)
if (main_board.board[main_board.td_in_l(x, y)] == settings.EMPTY):
# Передаём координаты рождения боту.
bot_array[loop].x = x
def simulation(self):
# initial conditions
world_xmax = 250
world_ymax = 250
n_walls = 5
n_trees = 10
self.initial_population()
# for each generation
for n in range(self.n_gen):
print("\ngeneration = {}/{}".format(n + 1, self.n_gen))
simworld = world.World()
max_e = 0
best_group = None
# for each group (same world for all for each generation)
for ng in range(self.n_groups):
print("group: {}/{}".format(ng + 1, self.n_groups))
# new agents
simworld.agents = []
gen = self.genotypes[ng]
simworld.allocate_agents(self.n_pop, gen)
# lifetime simulation
for t in tqdm(range(self.lifetime)):
simworld.update()
# print results
agents_e = [ag.energy for ag in simworld.agents]
total_e = sum(agents_e)
print("agents energy: {}, total = {}".format(
agents_e, total_e))
if total_e > max_e:
max_e = total_e
best_group = simworld.agents
print("new max = {}".format(max_e))
# print best data
print("\nbest agents:")
for ag in best_group:
print("agent energy = {}".format(ag.energy))
print("total energy = {}".format(max_e))
# store best data (all genotypes - clonal)
self.genotype = best_group[0].genotype
self.best_genotypes.append(self.genotype)
# record best case, only if better than previous best
if max_e > self.best_e:
self.best_cases.append([max_e, self.genotype, simworld])
self.best_e = max_e
self.anim_best = True
# animate
if self.animate:
# animate if better than previous best
if self.anim_best:
world_animation.sim_animation(self.lifetime,
[world_xmax, world_ymax],
simworld.walls,
simworld.trees, best_group)
self.anim_best = False
# animate after some number of generations
else:
if (n + 1) % self.anim_step == 0:
world_animation.sim_animation(self.lifetime,
[world_xmax, world_ymax],
simworld.walls,
simworld.trees,
best_group)
# reproduce (create new groups, keep best)
self.genotypes = [self.genotype]
for ng in range(self.n_groups - 1):
# starting energy and sensors (keep for now)
energy = self.genotype.energy
r = self.genotype.r
max_speed = self.genotype.max_speed
feed_range = self.genotype.feed_range
feed_rate = self.genotype.feed_rate
olf_angle = self.genotype.olf_angle
olf_range = self.genotype.olf_range
ir_angle = self.genotype.ir_angle
ray_length = self.genotype.ray_length
beam_spread = self.genotype.beam_spread
aud_angle = self.genotype.aud_angle
aud_range = self.genotype.aud_range
# inputs and outputs (keep for now)
n_in = self.genotype.n_in
n_hidden = self.genotype.n_hidden
n_out = self.genotype.n_out
# thresholds
ut = self.genotype.ut + np.random.randint(-1,
2) * self.mut_rate
lt = self.genotype.lt + np.random.randint(-1,
2) * self.mut_rate
vt = self.genotype.vt + np.random.randint(-1,
2) * self.mut_rate
# weights
W = self.mut_weights(self.genotype.W)
V = self.mut_weights(self.genotype.V)
# plasticity #TODO
plasticity = self.genotype.plasticity
# create and save new genotype
new_genotype = genotype.Genotype(
energy, r, max_speed, feed_range, feed_rate, olf_angle,
olf_range, ir_angle, ray_length, beam_spread, aud_angle,
aud_range, n_in, n_hidden, n_out, ut, lt, vt, W, V,
plasticity)
self.genotypes.append(new_genotype)
def initial_population(self):
# for each group (clones)
for ng in range(self.n_groups):
initial_genotype = genotype.Genotype()
self.genotypes.append(initial_genotype)
import genotype as g
import main
import data
N = 100
df = data.data(main.path_to_datafile)
evaluator = main.fit
subset_size = 6
rep = [i for i in range(0, 6)]
objs = []
for i in range(N):
obj = g.Genotype(evaluator)
obj.make_permutation(rep)
def test_genotype_init():
# Init
# Creating objects
for obj in objs:
for element in obj.r:
try:
rep.index(element)
except ValueError:
raise ValueError("test_genotype failed!")
def test_genotype_score():
# Calculating scores
def crossover ( genoFit1, genoFit2):
geno1 = genoFit1[0]
geno2 = genoFit2[0]
fit1 = genoFit1[1]
fit2 = genoFit2[1]
newNodeGenome = []
newConnectGenome = []
#node genomes
i = 0
j = 0
while i < len(geno1.nodeGenome) and j < len(geno2.nodeGenome):
if geno1.nodeGenome[i].nodeNum == geno2.nodeGenome[j].nodeNum:
newNodeGenome.append(geno1.nodeGenome[i])
i += 1
j += 1
else:
if geno1.nodeGenome[i].nodeNum < geno2.nodeGenome[j].nodeNum:
newNodeGenome.append(geno1.nodeGenome[i])
i += 1
else:
newNodeGenome.append(geno2.nodeGenome[j])
j += 1
#excess from 2
if i == len(geno1.nodeGenome) and j != len(geno2.nodeGenome):
newNodeGenome += geno2.nodeGenome[j:]
#Excess from 1
elif j == len(geno2.nodeGenome) and i != len(geno1.nodeGenome):
newNodeGenome += geno1.nodeGenome[i:]
#connect Genomes
i = 0
j = 0
while i < len(geno1.connectGenome) and j < len(geno2.connectGenome):
#if same gene
if geno1.connectGenome[i].innovationNum == geno2.connectGenome[j].innovationNum:
if random.choice((0,1)) == 0:
newConnectGenome.append(geno1.connectGenome[i])
else:
newConnectGenome.append(geno2.connectGenome[j])
i += 1
j += 1
else:
if geno1.connectGenome[i].innovationNum < geno2.connectGenome[j].innovationNum:
i += 1
if fit1 >= fit2 and i < len(geno1.connectGenome):
newConnectGenome.append(geno1.connectGenome[i])
else:
j += 1
if fit2 >= fit1 and j < len(geno2.connectGenome):
newConnectGenome.append(geno2.connectGenome[j])
#Excess from 2
if i == len(geno1.connectGenome) and j <len(geno2.connectGenome) and fit2 >= fit1:
newConnectGenome += geno2.connectGenome[j:]
#Excess from 1
elif j == len(geno2.connectGenome) and i < len(geno1.connectGenome) and fit1 >= fit2:
newConnectGenome += geno1.connectGenome[i:]
return genotype.Genotype(newConnectGenome, newNodeGenome)
