def OnGoClick(self, e):
if self.function.Value:
if self.clear_chk.IsChecked():
self.log.Clear()
g = Genetic(f_xy=self.function.Value.encode('ascii', 'ignore'),
extremum=self.extremum_rbox.GetStringSelection(),
cp=float(self.cross_p.Value),
mp=float(self.mut_p.Value),
size=int(self.size.Value),
search_field=(float(self.minx.Value),
float(self.miny.Value),
float(self.maxx.Value),
float(self.maxy.Value)),
sampling=self.sampling_rbox.GetSelection(),
crossover=self.crossover_rbox.GetSelection(),
elite=int(self.elite.Value),
max_generations=int(self.maxgen.Value),
alpha=float(self.blx.Value))
history = g.start(show_plot=False,
print_stats=self.stats_chk.IsChecked(),
print_rate=self.rate_chk.IsChecked())
elites = g.elites(history)
sol = g.solution(elites)
print u'Найден экстремум\nF(x,y) = {}'.format(sol[1])
print u'x = {}\ny = {}'.format(sol[0][0], sol[0][1])
if self.plot_chk.IsChecked():
genetic.draw_plot(g.fit_population, history, elites)
def test():
population_size = 10
my_registers = read_registries()
initial_population = []
#establishing a population with non-negative fitness
for i in range(0, population_size):
myInd = Individual()
while myInd.fitness <= 0:
myInd.randomize(my_registers)
Genetic.fitness(myInd)
initial_population.append(myInd)
#print_all_gen(initial_population)
#print("----------------")
print(
"\n/////////////////////////////////////////////GENERATION: {}".format(
0))
new_gen = Genetic.breed(initial_population, my_registers)
for i in range(1, 50):
print("\n/////////////////////////////////////////////GENERATION: {}".
format(i))
new_gen = Genetic.breed(new_gen, my_registers)
def test_genetic():
genetic = Genetic(chromosome_gen_prob=0.4,
population_size=100,
generation_limit=500,
crossover_rate=1,
mutation_rate=0.1)
genetic.run()
def test_mutate():
g = Genetic()
for i in range(100):
a = Individual()
o = g.mutate(a)
assert not o.dna.__contains__(None)
def main():
population_size = 10
my_registers = read_registries()
initial_population = []
#establishing a population with non-negative fitness
for i in range(0, population_size):
myInd = Individual()
while myInd.fitness <= 0:
myInd.randomize(my_registers)
Genetic.fitness(myInd)
initial_population.append(myInd)
new_gen = Genetic.breed(initial_population, my_registers)
#evaluating till we get top == 100
top_fitness = 0
try:
while top_fitness < 100:
if top_fitness != 0:
new_gen = Genetic.breed(new_gen, my_registers)
for item in new_gen:
if item.fitness > top_fitness:
top_fitness = item.fitness
#print("{} broke to: {}".format(item, top_fitness))
print_all_gen(new_gen)
except KeyboardInterrupt:
print_all_gen(new_gen)
def __init__(self):
'''A genetic algorithm to Travelling Salesman Problem.
'''
print('''*******************Genetic algorithm*********************
* Authors: Oraldo Jacinto Simon
* Marco Emanuel Casavantes Moreno
* ***********************************************************
''')
obj_graph = Graph()
fichero = open("cities/five_d.txt", 'r')
matrix_adjacency = np.loadtxt(fichero)
for i, row in enumerate(matrix_adjacency):
for j, item in enumerate(row):
graph = obj_graph.addEdge(i + 1, j + 1, item, directed=False)
fichero.close()
pop_size = 100
epochs = 80
# Can be cities or nodes
elite = 80
obj_genetic = Genetic(pop_size,
epochs,
graph,
elite,
rate_mutation=0.5,
selection_type='tourney_probabilistic')
path = obj_genetic.run()
#Draw graph
G = nx.DiGraph()
for vertex in graph.__iter__():
for v, w in vertex.adjacency.items():
G.add_edge(str(vertex.id), str(v), weight=w)
val_map = {'A': 1.0, 'D': 0.5714285714285714, 'H': 0.0}
values = [val_map.get(node, 0.45) for node in G.nodes()]
node_labels = {n: str(n) for n in G.nodes()}
edge_labels = dict([((
u,
v,
), d['weight']) for u, v, d in G.edges(data=True)])
red_edges = [(str(e), str(path[index + 1]))
for index, e in enumerate(path[:-1])]
edge_colors = [
'gray' if not edge in red_edges else 'red' for edge in G.edges()
]
pos = nx.spring_layout(G)
nx.draw_networkx_labels(G, pos, labels=node_labels)
nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels)
nx.draw(G,
pos,
node_color=values,
node_size=1500,
edge_color=edge_colors,
edge_cmap=plt.cm.Reds)
pylab.show()
def test_mate():
g = Genetic()
for i in range(100):
a = Individual()
b = Individual()
o = g.mate(a, b)
assert not o[0].dna.__contains__(None)
assert not o[1].dna.__contains__(None)
def _load(self, filename):
""" Loads the layer from file. For use by ModelHandler.
Note: The node ID in the file is ignored
"""
with open(filename, 'r') as f:
data = f.read()
loadme = next(iter(eval(data).values()))
ID = self._nodeID
node = Genetic(ID, 2, 0, 0, 0, 0, 0, CONSOLE_OUT, False) # skeleton
node._load(loadme, not_file=True)
self._node = node
def main(show):
level = logging.getLevelName(LOG_LEVEL.upper())
print(f"Set log level to {level}")
logging.basicConfig(level=level)
target_image = get_image(TARGET_IMAGE, SIZE)
g = Genetic(target_image)
g.run(show)
def genetic(coords, generations=500, population_size=100, elite_size=10, mutation_rate=0.01):
genetic = Genetic(coords, population_size=population_size, elitist_factor=elite_size, mutation_rate=mutation_rate)
population = genetic.initial_population()
steps = []
for i in range(generations):
population = genetic.next_generation(population)
best_solution = genetic.best_solution(population)
steps.append(best_solution)
best_fitness = fitness(coords, best_solution)
return best_fitness, best_solution, steps
def generate(generations, population, nn_param_chices, dataset):
genetic = Genetic(nn_param_chices)
gans = genetic.create_population(population)
for i in range(generations):
train_gan(gans, dataset)
average_accuracy = get_average_accuracy(gans)
if i != generations - 1:
gans = genetic.evolve(gans)
gans = sorted(gans, key=lambda x: x.accuracy, reverse=True)
def __init__(self, p, n, log='log', vis=0, al=0): # al - plot analyze
# pu.db
s = """ Keyboard:
Space - show
Up - log now
Down - stop"""
print s
self.WD = FIELD_SIZE[0] * TILE_WIDTH + INFO[0] * TILE_WIDTH * 2
self.HG = FIELD_SIZE[1] * TILE_WIDTH + INFO[0] * TILE_WIDTH * 2
# self.WD = 1024
# self.HG = 768
self.al = al
self.vis = vis
size = FIELD_SIZE
self.timestamp = reduce(lambda x, y: str(x) + '_' + str(y), [i for i in dt.now().timetuple()][0:6])
self.foldername = 'Logs'
self.full_folder = self.foldername + os.sep + self.timestamp + os.sep
self.logfile = str(p) + '_' + str(n) + '_' + str(DEFAULT_TYPE) + log
self.mousex, self.mousey = 0, 0
self.Field = Field(size)
self.MainWindow = None
self.mousexy = pygame.mouse.get_pos()
self.fpsClock = pygame.time.Clock()
if self.vis:
pygame.display.set_mode((self.WD, self.HG), pygame.NOFRAME)
self.MainWindow = Window(self.Field, self.WD, self.HG)
self.g = Genetic(p, n, field_size=size, delta=-0.5)
self.pos = [2, 2]
self.a = None
self.b = None
self.his = None
self.show = 0
self.run = 1
self.ep_num = 0
self.log_now = 0
def main(argv):
if len(sys.argv) < 3:
print(
'Please provide 3 command line arguments (e.g. input_filename, output_filename, n_results'
)
exit(-1)
in_filename = ''
out_filename = ''
niteration = 0
in_filename = sys.argv[1]
out_filename = sys.argv[2]
niteration = int(sys.argv[3])
c = Constraint(in_filename)
v = c.get_example()
print(v)
n = c.get_ndim() #this is the chromosome size for each population
print(n)
# print(c.apply([0.5, 1.0, 0.0, 0.5]))
s = 300 #population size
ga = Genetic(
s, n, in_filename
) #initializes ga type object with populaton size, chromosome size, input file name
p = ga.get_population()
print(p)
print(ga.cal_pop_fitness())
temp_solution = ga.eval_ga(niteration)
# print(temp_solution)
# write result to the output
with open(out_filename, 'w') as filehandle:
for item in temp_solution:
s = " ".join(str(e) for e in item)
# print(s)
l = s
# l = item
print(l)
filehandle.write('%s\n' % l)
filehandle.close()
def main():
pop = init.initPop(300)
itera = 100 #迭代次数
each = 30 #每轮迭代的遗传计算次数
tplt = "{:8}\t{:8}\t{:8}"
gen = Genetic()
bests = [] #用以存储每代最优值
print(tplt.format("Gen", "Avg", "Best"))
for i in range(1, itera):
pop = gen.evaluate(pop)
bests.append(gen.printGen(pop, i))
for j in range(1, each):
rand = random.random()
if rand < 0.7: #变异率设为0.3
ind = gen.select(pop)
pop.remove(ind)
ind.mark = 2
ind.gene = gen.mutate(ind.gene, 4)
pop.append(ind)
else:
pop = gen.mate(pop)
res = numpy.array(bests)
x = numpy.arange(1, 99)
y = res[x]
pyplot.title('GP result')
pyplot.xlabel('Gen')
pyplot.ylabel('Best')
pyplot.plot(x, y)
pyplot.show()
def test(repeats,
generations,
populations,
cross_probs,
mutat_probs,
choice=1,
path="/matrixes/large"):
files = [
f for f in listdir(path) if isfile(join(get_script_path() + path, f))
]
print(files)
for f in files:
graph = Graph(f, choice)
print("Wczytano graf " + graph.file_name + " z " +
str(graph.number_of_cities) + " wierzchołkami")
for generation in generations:
for population in populations:
for cross_prob in cross_probs:
for mutat_prob in mutat_probs:
for a in range(repeats):
ga = Genetic(graph)
start = timer()
ga.start(generation, population, cross_prob,
mutat_prob)
end = timer()
time = format((end - start), '.8f')
text = (str(generation) + "\t" + str(population) +
"\t" + str(cross_prob) + "\t" +
str(mutat_prob) + "\t" + time + "\t" +
str(ga.best_cycle_cost) + "\n").replace(
'.', ',')
print(text)
with open(
"./measurements/ga/" +
f.rsplit(".", 1)[0] + ".txt",
'a+') as the_file:
the_file.write(text)
print_to_continue()
def main():
in_data = input()
vector = list(map(int, in_data.split()))
time = vector[0]
vector.pop(0)
multiset = []
values = {}
for i in range(vector[0]):
in_data = input()
x = list(map(str, in_data.split()))
multiset.append(x[0])
values[x[0]] = int(x[1])
start_sol = []
for i in range(vector[1]):
in_data = input().replace("\r", "")
start_sol.append(in_data)
Searcher = Genetic('dict.txt', values, multiset, 100)
Searcher.find_maxima(start_sol, time)
print(Searcher.best_value())
print(Searcher.best_argument(), file=sys.stderr)
def __init__(self, ID, size, mem_depth):
""" Accepts:
ID (str) : This layers unique ID
size (int) : Num input terminals
mem_depth (int) : Num prev inputs to keep in memory
"""
self.ID = ID
self._size = size
self._mem_depth = mem_depth
self._node = None # The gentic programming (GP) obj
self._nodeID = ID + '_node' # GP node's unique ID
# Init the layer's node - a genetically evolving tree of expressions
self._node = Genetic(ID=self._nodeID,
kernel=L2_KERNEL_MODE,
max_pop=L2_MAX_POP,
max_depth=L2_MAX_DEPTH,
max_inputs=self._size,
mem_depth=self._mem_depth,
tourn_sz=L2_POOLSZ,
console_out=CONSOLE_OUT,
persist=False)
# Set default node mutation ratios (overwritten if loading from file)
self._node.set_mratio(repro=L2_MUT_REPRO,
point=L2_MUT_POINT,
branch=L2_MUT_BRANCH,
cross=L2_MUT_CROSS)
# Init the model handler
f_save = "self._save('MODEL_FILE')"
f_load = "self._load('MODEL_FILE')"
self.model = ModelHandler(self,
CONSOLE_OUT,
PERSIST,
model_ext=L2_EXT,
save_func=f_save,
load_func=f_load)
def main():
C = Configuration('conf.json')
debug = Debug(debug=C.debug, info=C.showInfo)
saver = TestSaver('out.txt')
executorObject = Executor(srcPath=C.srcPath, whatToConsider = C.whatToConsider, testSaver=saver, debugger = debug);
geneticObject = Genetic(populationSize=C.populationSize, # Population pop_size
chromosomeSize=C.chromosomeSize, # size of chromosome (can be bytes, size of a number, size of a string...)
parentsNumber=C.parentsNumber, # How many chromosomes are entering crossover
mutationRate=C.mutationRate, # selfexplanatory
generationsCount=C.generationsCount, # Number of generations (iterations)
geneTypeList=C.geneTypeList, #list of
executor=executorObject,
debugger=debug)
geneticObject.start_evolution();
saver.export_to_file()
return 0;
def test_stohastic(self):
g = Genetic(TestGenetic.f, size=11, cp=1)
population = [(0.2, 0.5), (-0.2, 1), (0.2, 0.2), (-1, 0.3), (0.4, 0.8)]
fit = g.fit_population(population)
g.start()
print population
print_with_score(population, g.fit_population(population))
print 'avg fitness = ' + str(sum(fit) / float(len(fit)))
print Sampling.stochastic_sampling(g.fit_population, g.extremum, population)
def main():
in_dir = args.in_dir
seed = args.seed
method = args.method
random.seed(seed)
np.random.seed(seed)
Position = load_data(in_dir)
method = 'annealing'
if method=='local' or method=='annealing':
ans, Route = LocalSearch(Position, method=method)
elif method=='genetic':
ans, Route = Genetic(Position)
print(ans)
def test_select_best_too_much(self):
pop = [-1, 2, 0]
res = Genetic.select_best(lambda pop: [i for i in pop], 'min', pop, 4)
self.assertEqual(pop, res)
def test_select_best_zero(self):
res = Genetic.select_best(lambda pop: [i for i in pop], 'min', [-1, 2, 0], 0)
self.assertEqual([], res)
def test_mutate2(self):
point, mutations = Genetic.mutate((1, 0), 0, 0.5, 0.5)
self.assertEqual((1, 0), point)
self.assertEqual(0, mutations)
def test_mutate(self):
point, mutations = Genetic.mutate((1, 0), 1, 0.5, 0.5)
self.assertNotEqual((1, 0), point)
self.assertEqual(2, mutations)
def __handle_button_pressed(self):
print("clicked")
algorithm_config = self.__get_variable_configurations()
self.__genetic = Genetic(algorithm_config)
self.__print_result(self.__genetic)
def test_normalize_fitness_equals_all_best(self):
res = Genetic.normalize_fitness([1, 1, 1], 'max')
self.assertEqual([1, 1, 1], res)
def test_normalize_fitness_min(self):
res = Genetic.normalize_fitness([1, 2, 4, 1], 'min')
self.assertEqual([0.375, 0.25, 0.0, 0.375], res)
def test_normalize_fitness_sum_is_1(self):
res = Genetic.normalize_fitness([1, 1, 2, 5, 1, 3], 'max')
self.assertEqual(1, fsum(res))
class Optymalizacja(QtGui.QMainWindow):
LIMIT_X = [-5, 5]
LIMIT_Y = [-5, 5]
def __init__(self, parent=None):
QtGui.QWidget.__init__(self, parent)
self.ui = MainForm()
self.ui.setupUi(self)
self.genetic = None
self.epochNumber = 0
self.steps = []
self.function = None
self.range = []
self.chance = 10
self.m = 2
QtCore.QObject.connect(self.ui.calculateButton, QtCore.SIGNAL("clicked()"), self.calculate)
QtCore.QObject.connect(self.ui.stepButton, QtCore.SIGNAL("clicked()"), self.epoch)
QtCore.QObject.connect(self.ui.drawButton, QtCore.SIGNAL("clicked()"), self.draw)
QtCore.QObject.connect(self.ui.complate, QtCore.SIGNAL("clicked()"), self.complate)
QtCore.QObject.connect(self.ui.complate_without_draw, QtCore.SIGNAL("clicked()"), self.complate_without_draw)
QtCore.QObject.connect(self.ui.points, QtCore.SIGNAL("clicked()"), self.points)
def calculate(self, draw=True):
# Cross
if self.ui.cross_simple_2.isChecked():
crossing = mix_simple
elif self.ui.cross_artmetic_2.isChecked():
crossing = aritmic_selection
else:
raise TypeError('Not selected crossing')
# Mutation
if self.ui.mutation_unity_2.isChecked():
fmutation = mutation
elif self.ui.mutation_nonunity_2.isChecked():
fmutation = mutation_nonlinear
elif self.ui.mutacion_gradient_2.isChecked():
fmutation = None
else:
raise TypeError('Not selected mutattion')
# Function
if self.ui.function_rosenbrock_2.isChecked():
self.function = rosenbrock
elif self.ui.function_akley_2.isChecked():
self.function = ackley
elif self.ui.function_gem_2.isChecked():
self.function = geem
elif self.ui.function_golstein_2.isChecked():
self.function = goldstein
elif self.ui.function_rastring_2.isChecked():
self.function = restring
else:
tmp_function = self.ui.function_recipce.toPlainText()
self.function = lambda *x: eval(tmp_function)
m = re.findall("x\[\d\]", tmp_function)
chop = lambda x: int(x[2:-1])
self.m = max(list(map(chop, m))) + 1
if self.ui.strategy_genetic.isChecked():
strategy = 'genetic'
elif self.ui.strategy_two.isChecked():
strategy = 'two'
elif self.ui.strategy_milambda.isChecked():
strategy = 'mi_lambda'
elif self.ui.strategy_mipluslambda.isChecked():
strategy = 'mi_plus_lambda'
#limit
try:
tmp = self.ui.range.toPlainText()
tmp = tmp.split(";")
self.range = [list(map(float, t.split())) for t in tmp]
except:
self.range = [self.LIMIT_X, self.LIMIT_Y]
try:
self.chance = float(self.ui.proc_mutation.toPlainText().split(';')[0])
except:
self.chance = 10
if draw:
f = paint_function(self.range, self.function)
scene = QtGui.QGraphicsScene()
scene.addPixmap(QtGui.QPixmap(f.name))
self.ui.graphicsView_2.setScene(scene)
f.close()
os.unlink(f.name)
mi = int(self.ui.mi.toPlainText())
lambd = int(self.ui.lambd.toPlainText())
self.genetic = Genetic(self.function, crossing, fmutation, strategy, mi, lambd, self.range, self.m, self.chance)
self.steps = []
self.ui.graphicsView_2.setViewport(QtOpenGL.QGLWidget())
self.epochNumber = 0
def epoch(self):
self.genetic.sort()
self.genetic.newEpoch(self.epochNumber)
self.epochNumber += 1
self.ui.epochNumber.display(self.epochNumber)
self.genetic.sort()
self.ui.targetLabel.setText(str(round(self.genetic.population[0].cost, 7)))
self.ui.coordsLabel.setText("Rozwiązanie: x: {0} y: {1}".format(round(self.genetic.population[0].getValue(self.range[0][0], self.range[0][1], 0), 7),
round(self.genetic.population[0].getValue(self.range[1][0], self.range[1][1], 1), 7)))
self.steps.append((round(self.genetic.population[0].getValue(self.range[0][0], self.range[0][1], 0), 7),
round(self.genetic.population[0].getValue(self.range[1][0], self.range[1][1], 1), 7)))
def complate(self):
n = int(self.ui.n.toPlainText())
if not n:
raise TypeError('Nie podano warunku zakończenia')
for i in range(n):
self.genetic.sort()
self.genetic.newEpoch(self.epochNumber)
self.epochNumber += 1
self.ui.epochNumber.display(self.epochNumber)
self.genetic.sort()
self.steps.append((round(self.genetic.population[0].getValue(self.range[0][0], self.range[0][1], 0), 7),
round(self.genetic.population[0].getValue(self.range[1][0], self.range[1][1], 1), 7)))
self.ui.targetLabel.setText(str(round(self.genetic.population[0].cost, 7)))
self.ui.coordsLabel.setText("Rozwiązanie: x: {0} y: {1}".format(round(self.genetic.population[0].getValue(self.range[0][0], self.range[0][1], 0), 7),
round(self.genetic.population[0].getValue(self.range[1][0], self.range[1][1], 1), 7)))
def complate_without_draw(self):
self.calculate(False)
n = int(self.ui.n.toPlainText())
if not n:
raise TypeError('Nie podano warunku zakończenia')
for i in range(n):
self.genetic.sort()
self.genetic.newEpoch(self.epochNumber)
self.epochNumber += 1
self.ui.epochNumber.display(self.epochNumber)
self.genetic.sort()
self.steps.append(self.genetic.population[0].getValues(self.range))
self.ui.targetLabel.setText(str(round(self.genetic.population[0].cost, 7)))
result = self.genetic.population[0].getValues(self.range)
tmp = "Rozwiazanie: "
i = 1
for r in result:
tmp += "x{0} = {1} ".format(i, round(r, 6))
if i % 4 == 0:
tmp += '\n'
i += 1
self.ui.coordsLabel.setText(tmp)
def draw(self):
f = paint_function(self.range, self.function, self.steps)
scene = QtGui.QGraphicsScene()
scene.addPixmap(QtGui.QPixmap(f.name))
self.ui.graphicsView_2.setScene(scene)
f.close()
os.unlink(f.name)
def points(self):
with open('points.txt', 'w') as f:
for point in self.steps:
f.write("{0} : {1}\n".format(str(point), self.function(*point)))
pass
def calculate(self, draw=True):
# Cross
if self.ui.cross_simple_2.isChecked():
crossing = mix_simple
elif self.ui.cross_artmetic_2.isChecked():
crossing = aritmic_selection
else:
raise TypeError('Not selected crossing')
# Mutation
if self.ui.mutation_unity_2.isChecked():
fmutation = mutation
elif self.ui.mutation_nonunity_2.isChecked():
fmutation = mutation_nonlinear
elif self.ui.mutacion_gradient_2.isChecked():
fmutation = None
else:
raise TypeError('Not selected mutattion')
# Function
if self.ui.function_rosenbrock_2.isChecked():
self.function = rosenbrock
elif self.ui.function_akley_2.isChecked():
self.function = ackley
elif self.ui.function_gem_2.isChecked():
self.function = geem
elif self.ui.function_golstein_2.isChecked():
self.function = goldstein
elif self.ui.function_rastring_2.isChecked():
self.function = restring
else:
tmp_function = self.ui.function_recipce.toPlainText()
self.function = lambda *x: eval(tmp_function)
m = re.findall("x\[\d\]", tmp_function)
chop = lambda x: int(x[2:-1])
self.m = max(list(map(chop, m))) + 1
if self.ui.strategy_genetic.isChecked():
strategy = 'genetic'
elif self.ui.strategy_two.isChecked():
strategy = 'two'
elif self.ui.strategy_milambda.isChecked():
strategy = 'mi_lambda'
elif self.ui.strategy_mipluslambda.isChecked():
strategy = 'mi_plus_lambda'
#limit
try:
tmp = self.ui.range.toPlainText()
tmp = tmp.split(";")
self.range = [list(map(float, t.split())) for t in tmp]
except:
self.range = [self.LIMIT_X, self.LIMIT_Y]
try:
self.chance = float(self.ui.proc_mutation.toPlainText().split(';')[0])
except:
self.chance = 10
if draw:
f = paint_function(self.range, self.function)
scene = QtGui.QGraphicsScene()
scene.addPixmap(QtGui.QPixmap(f.name))
self.ui.graphicsView_2.setScene(scene)
f.close()
os.unlink(f.name)
mi = int(self.ui.mi.toPlainText())
lambd = int(self.ui.lambd.toPlainText())
self.genetic = Genetic(self.function, crossing, fmutation, strategy, mi, lambd, self.range, self.m, self.chance)
self.steps = []
self.ui.graphicsView_2.setViewport(QtOpenGL.QGLWidget())
self.epochNumber = 0
for model_file in glob.glob(os.path.join(models_dir,'*.poly')):
model_tmp = np.loadtxt(model_file, dtype=[('x',float),('y',float)])
model = interp1d(model_tmp['x'], model_tmp['y'])
chi2.append(np.sum((model(self._x)-self._y)**2))
name = model_file.split('/')[-1].replace('.poly','')
model_name.append(name)
fig = mpl.figure()
ax = fig.add_subplot(111)
ax.scatter(self._x, self._y)
ax.plot(model_tmp['x'], model_tmp['y'])
fig.savefig(plots_dir + name + '.png')
t = atpy.Table()
t.add_column('model_name', model_name, dtype='|S30')
t.add_column('chi2', chi2)
t.write(output_file)
poly_model = os.path.abspath('run_poly_model.py')
model_fitter = PolyFitter('data_example_serial_file')
g = Genetic(100, 'models_example_serial_file', 'template.par', 'example_serial_file.conf', existing=False, mode='serial_file')
for generation in range(1,50):
g.initialize(generation)
g.make_par_table(generation)
g.make_par_indiv(generation, parser)
g.compute_models(generation, poly_model)
g.compute_fits(generation, model_fitter)
#!/usr/bin/env python3
#from prepa import prepa
from genetic import Genetic
import json
source_file_name = 'settings.json'
#source_file_name = 'settings_2.json'
with open(source_file_name) as json_data:
settings = json.load(json_data)
print(settings)
engine = Genetic(settings)
engine.generate_first_population()
engine.show_population(True)
engine.cross()
engine.show_population()
#engine.mutate()
#engine.selection()
#engine.show_population(True)
#engine.loop(15)
#engine.solve()
# То, что мы называем особью, ещё называют хромосомой.
def test_sorted_normed_fitness(self):
res = Genetic.sorted_normed_fitness(lambda pop: [sum(i) for i in pop], 'max', [(1, 0), (3, 4)])
self.assertEqual([(1, 1.0), (0, 0.0)], res)
from genetic import Genetic
g = Genetic('data.xml')
g.__main__()
def test_sorted_normed_fitness2(self):
res = Genetic.sorted_normed_fitness(lambda pop: [i for i in pop], 'min', [-1, 2, 0])
self.assertEqual([(0, 0.6), (2, 0.4), (1, 0.0)], res)
#-------------------------------------------------------#
# main #
#-------------------------------------------------------#
print('Setting Generation Class')
initial = Generation(numOfInd, 0)
print('Generating random initial chromosomes')
initial.randomGenerateChromosomes(
chromosome_length) # initial generate chromosome
print('Setting Clustering Class')
clustering = Clustering(initial, data, kmax) # eval fit of chromosomes
# ------------------calc fitness------------------#
print('Calculating initial fitness')
generation = clustering.calcChromosomesFit()
# ------------------------GA----------------------#
print('Looping through each generation')
while generationCount <= budget:
print('Generation ' + str(generationCount) + ':')
print('\tSetting up Genetic class')
GA = Genetic(numOfInd, Ps, Pm, Pc, budget, data, generationCount, kmax)
print('\tExecuting genetic process')
generation, generationCount = GA.geneticProcess(generation)
iBest = generation.chromosomes[0]
clustering.printIBest(iBest)
# ------------------output result-------------------#
clustering.output_result(iBest, data)
class Game(object):
def __init__(self, p, n, log='log', vis=0, al=0): # al - plot analyze
# pu.db
s = """ Keyboard:
Space - show
Up - log now
Down - stop"""
print s
self.WD = FIELD_SIZE[0] * TILE_WIDTH + INFO[0] * TILE_WIDTH * 2
self.HG = FIELD_SIZE[1] * TILE_WIDTH + INFO[0] * TILE_WIDTH * 2
# self.WD = 1024
# self.HG = 768
self.al = al
self.vis = vis
size = FIELD_SIZE
self.timestamp = reduce(lambda x, y: str(x) + '_' + str(y), [i for i in dt.now().timetuple()][0:6])
self.foldername = 'Logs'
self.full_folder = self.foldername + os.sep + self.timestamp + os.sep
self.logfile = str(p) + '_' + str(n) + '_' + str(DEFAULT_TYPE) + log
self.mousex, self.mousey = 0, 0
self.Field = Field(size)
self.MainWindow = None
self.mousexy = pygame.mouse.get_pos()
self.fpsClock = pygame.time.Clock()
if self.vis:
pygame.display.set_mode((self.WD, self.HG), pygame.NOFRAME)
self.MainWindow = Window(self.Field, self.WD, self.HG)
self.g = Genetic(p, n, field_size=size, delta=-0.5)
self.pos = [2, 2]
self.a = None
self.b = None
self.his = None
self.show = 0
self.run = 1
self.ep_num = 0
self.log_now = 0
def mouse_button_up(self, GameMap):
pass
def do_update(self, i, be):
if not i % 100:
self.log_now = 1
self.MainWindow.loading(i, WD=self.WD, HG=self.HG, EN=self.g.epoch_num, BE=be)
pygame.display.update()
self.user_action()
self.fpsClock.tick(1)
if self.log_now:
l = Logger(None, self.g.score_history, self.g.time, self.logfile + str(i), folder=self.full_folder)
l2 = Logger(None, self.g.best_ever[0], self.g.time, self.logfile + 'BE' + str(i), folder=self.full_folder)
l.log()
l2.log()
self.log_now = 0
return self.run
def do_genetic(self):
try:
self.ep_num = self.g.start(f=self.do_update)
except Exception, e:
print 'Error in genetic ' + str(e)
self.a = self.g.get_score()
self.b = self.g.best_ever[1]
# print self.a
# self.Field = self.a[2]
# pers = self.a[-2]
# self.his = pers.history
l = Logger(self.a, self.g.score_history, self.g.time, self.logfile, folder=self.full_folder)
l2 = Logger(self.b, self.g.best_ever[0], self.g.time, self.logfile + 'BE', folder=self.full_folder)
l.log()
l2.log()
