def setup_network(self):
config = BertConfig.from_json_file(cfg.CONFIG_FILE)
if cfg.TRAIN.FROM_PRETRAINED:
model = BaseBertPreTraining.from_pretrained(
cfg.TRAIN.FROM_PRETRAINED, config)
else:
model = BaseBertPreTraining(config)
model.to(self.device)
if args.local_rank != -1:
self.model = torch.nn.parallel.DistributedDataParallel(
model,
find_unused_parameters=True,
device_ids=[self.args.local_rank],
output_device=self.args.local_rank,
broadcast_buffers=False)
elif self.n_gpu > 1:
self.model = torch.nn.DataParallel(model)
else:
self.model = model
epoch_steps = len(self.train_dataset_loader)
n_steps = epoch_steps * cfg.SOLVER.NUM_TRAIN_EPOCHS
self.optim = Optimizer(self.model,
epoch_steps=epoch_steps,
n_steps=n_steps)
def test_warm_start(self):
params = Parameter()
params.set("wheel_base", 2.7)
params.set("dt", 0.2)
params.set("function_tolerance", 1e-6)
params.set("max_num_iterations", 1000)
initial_state = np.array([[0., 0., 0., 10.], [2., 0., 0., 10.],
[4., 0., 0., 10.], [6., 0., 0., 10.]])
opt_vec = np.array(
[[0., 0.], [-0.00581466, -0.02969236], [-0.0100573, -0.05063586],
[-0.01290205, -0.06336555], [-0.0145111, -0.06958468],
[-0.01504364, -0.07147759], [-0.01466189, -0.0712203],
[-0.01353391, -0.07066254], [-0.01183398, -0.0711591],
[-0.00974178, -0.0735261], [-0.00744058, -0.07806701],
[-0.00509113, -0.08413195], [-0.0028139, -0.09089476],
[-0.00069383, -0.0976187], [0.00121458, -0.10373539],
[0.00288213, -0.10887319], [0.00430011, -0.11284776],
[0.00547573, -0.11562908], [0.00642782, -0.11729807],
[0.00718276, -0.11800159], [0.0077706, -0.11791354],
[0.00821974, -0.11720583], [0.00855625, -0.11603267],
[0.0088038, -0.11452536], [0.0089836, -0.11278997],
[0.00911419, -0.11090794], [0.00921137, -0.10893774],
[0.00928801, -0.10691821], [0.00935393, -0.10487191],
[0., 0.00059502]])
ref_line = np.array([[0., 4.], [1000., 4.]])
obstacle_outline0 = np.array([[14., 1.7], [22., 1.7], [22., 4.7],
[14., 4.7], [14., 1.7]])
# optimizer
opt = Optimizer(params)
opt.SetOptimizationVector(opt_vec)
# costs
ref_cost = ReferenceLineCost(params, 100.)
ref_cost.SetReferenceLine(ref_line)
jerk_cost = JerkCost(params, 10000.)
outline = ObjectOutline()
obstacle_outline1 = obstacle_outline0 + np.array([[30.0, -2.]])
outline.Add(obstacle_outline0, 0.)
outline.Add(obstacle_outline1, 6.)
object_cost = StaticObjectCost(params, 2.5, 10000.)
object_cost.AddObjectOutline(outline)
input_cost = InputCost(params, 10.)
input_cost.SetLowerBound(np.array([[-0.2, -1.0]]))
input_cost.SetUpperBound(np.array([[0.2, 1.0]]))
speed_cost = SpeedCost(params, 10.)
speed_cost.SetDesiredSpeed(10.)
# optimization problem
functor = opt.AddFastSingleTrackFunctor(
initial_state, params,
[jerk_cost, ref_cost, input_cost, object_cost, speed_cost])
opt.FixOptimizationVector(0, 1)
opt.Solve()
opt.Report()
def main_loop(self):
#optimizer = T4l_optimizer(self.optimizer_config)
optimizer = Optimizer(self.optimizer_config)
broker = Broker(self.broker_config, optimizer)
self.print_info()
broker.schedule_tasks(self.tasks)
def simulatorOrdersPerf(plant, orderList, testNum):
machines = plant.machines[:]
orders = orderList.orders[:]
pprint(
"PERF Starting simulator benchmark test " + str(testNum) +
" on orders", BLUE)
orderList.orders = []
plant.machines = machines[:]
times = [0]
for i in range(1, len(orders) + 1):
pprint("PERF Number of orders = " + str(i), BLUE)
orderList.orders = orders[:i]
optimizer = Optimizer(plant, orderList, Simulator(plant),
Evaluator(plant))
schedule = optimizer.initialIndividual()
t = time()
simulator = Simulator(plant)
simulator.simulate(schedule)
t = time() - t
times.append(t)
pprint("PERF Time = " + str(t), GREEN)
try:
from thirdparty.CairoPlot import dot_line_plot
except:
pprint("PERF Will not output to graph.", RED)
return
dot_line_plot(
path.join("benchmarks", "simulator-orders-" + str(testNum)) + FORMAT,
times, 800, 800, (255, 255, 255), 5, True, True, True, None, None,
None, None)
def bench(self):
recipes = []
for o in self.orderList.orders:
recipes.append(o.recipe.recipe[:])
o.recipe.recipe = []
machines = self.plant.machines[:]
self.plant.machines = []
i = self.startValue
while i <= len(machines):
pprint("PERF Number of machines = " + str(i), BLUE)
self.plant.machines = machines[:i]
for j, o in enumerate(self.orderList.orders):
o.recipe.recipe = recipes[j][:i]
optimizer = Optimizer(self.plant, self.orderList,
Simulator(self.plant), Evaluator(self.plant))
optimizer.populationSize = 10
optimizer.iterations = 10
optimizer.indivMutationRate = 0.5
optimizer.selectionRate = 0.5
optimizer.mutationRange = 10
t = time()
optimizer.run()
t = time() - t
t -= optimizer.simulatorTime
self.addCairoPlotTime(t)
self.addGnuPlotTime(i, t)
i += 1
def main_loop(self):
optimizer = Optimizer(self.optimizer_config)
broker = LocalBroker(self.broker_config, optimizer)
self.print_info()
broker.schedule_tasks(self.tasks)
if broker.sys_info is not None:
broker.sys_info.stop_server.set()
def setup_network(self):
model = models.create(cfg.MODEL.TYPE)
self.trainer = model.cuda()
self.checkpointer = CaptionCheckpointer(
self.trainer, os.path.join(cfg.ROOT_DIR, "snapshot"))
if self.args.resume > 0:
self.checkpointer.load(
self.snapshot_path("caption_model", self.args.resume))
self.predictor = models.create(cfg.MODEL.TYPE).cuda()
self.predictor.load_state_dict(self.trainer.state_dict())
self.optim = Optimizer(self.trainer)
def mix(self):
if len(self.loaded_songs) == 0:
self.message.set("No loaded songs...")
return
first_goal = MixGoal(0.0, 0.0, 0.0, 0.0, 1)
goals = [first_goal]
self.analyze_songs()
print("Chosen Style: {}".format(self.style))
print("Maximum Length: {}".format(self.max_length))
print("First Song: {}".format(self.first_song))
dj = Optimizer(self.loaded_songs, goals, Style_Lib[self.style].value,
self.max_length, self.first_song)
mix = dj.generate_mixtape()
self.log_message(mix)
comp = composer.composer_parser(mix)
comp.compose()
def bench(self):
orders = self.orderList.orders[:]
self.orderList.orders = []
i = self.startValue
while i <= len(orders):
pprint("PERF Number of orders = " + str(i), BLUE)
self.orderList.orders = orders[:i]
optimizer = Optimizer(self.plant, self.orderList,
Simulator(self.plant), Evaluator(self.plant))
schedule = optimizer.initialIndividual()
t = time()
simulator = Simulator(self.plant)
simulator.simulate(schedule)
t = time() - t
self.addCairoPlotTime(t)
self.addGnuPlotTime(i, t)
i += 1
def optimizerMachinesPerf(plant, orderList, testNum):
machines = plant.machines[:]
orders = orderList.orders[:]
pprint(
"PERF Starting optimizer benchmark test " + str(testNum) +
" on machines", BLUE)
orderList.orders = orders[:]
recipes = []
for o in orderList.orders:
recipes.append(o.recipe.recipe[:])
o.recipe.recipe = []
plant.machines = []
times = [0]
for i in range(1, len(machines) + 1):
pprint("PERF Number of machines = " + str(i), BLUE)
plant.machines = machines[:i]
for j, o in enumerate(orderList.orders):
o.recipe.recipe = recipes[j][:i]
optimizer = Optimizer(plant, orderList, Simulator(plant),
Evaluator(plant))
optimizer.populationSize = 10
optimizer.iterations = 10
optimizer.indivMutationRate = 0.5
optimizer.selectionRate = 0.5
optimizer.mutationRange = 10
t = time()
optimizer.run()
t = time() - t
t -= optimizer.simulatorTime
times.append(t)
pprint("PERF Time = " + str(t), GREEN)
try:
from thirdparty.CairoPlot import dot_line_plot
except:
pprint("PERF Will not output to graph.", RED)
return
dot_line_plot(
path.join("benchmarks", "optimizer-machines-" + str(testNum)) + FORMAT,
times, 800, 800, (255, 255, 255), 5, True, True, True, None, None,
None, None)
def bench(self):
i = 10
while i <= 200:
pprint("PERF Mutation Range = " + str(i), BLUE)
optimizer = Optimizer(self.plant, self.orderList,
Simulator(self.plant), Evaluator(self.plant))
optimizer.populationSize = 10
optimizer.iterations = 10
optimizer.indivMutationRate = 0.5
optimizer.selectionRate = 0.5
optimizer.mutationRange = i
t = time()
optimizer.run()
t = time() - t
t -= optimizer.simulatorTime
self.addCairoPlotTime(t)
self.addGnuPlotTime(i, t)
i += 20
def bench(self):
val = 4
i = self.startValue
while i <= 6:
pprint("PERF Large Value = " + str(i * val), BLUE)
for o in self.orderList.orders:
o.deadline *= val
for r in o.recipe.recipe:
r[1] *= val
optimizer = Optimizer(self.plant, self.orderList,
Simulator(self.plant), Evaluator(self.plant))
schedule = optimizer.initialIndividual()
t = time()
simulator = Simulator(self.plant)
simulator.simulate(schedule)
t = time() - t
self.addCairoPlotTime(t)
self.addGnuPlotTime((i + 1) * val, t)
i += 1
def optimizerLargeValuesPerf(plant, orderList, testNum):
machines = plant.machines[:]
orders = orderList.orders[:]
pprint(
"PERF Starting optimizer benchmark test " + str(testNum) +
" with large values", BLUE)
orderList.orders = orders[:6]
plant.machines = machines[:]
times = [0]
val = 2
for i in range(1, 6):
pprint("PERF Large Value = " + str(i * val), BLUE)
for o in orderList.orders:
o.deadline *= val
for r in o.recipe.recipe:
r[1] *= val
optimizer = Optimizer(plant, orderList, Simulator(plant),
Evaluator(plant))
optimizer.populationSize = 5
optimizer.iterations = 5
optimizer.indivMutationRate = 0.5
optimizer.selectionRate = 0.5
optimizer.mutationRange = 500
t = time()
optimizer.run()
t = time() - t
t -= optimizer.simulatorTime
times.append(t)
pprint("PERF Time = " + str(t), GREEN)
try:
from thirdparty.CairoPlot import dot_line_plot
except:
pprint("PERF Will not output to graph.", RED)
return
dot_line_plot(
path.join("benchmarks", "optimizer-largevalues-" + str(testNum)) +
FORMAT, times, 800, 800, (255, 255, 255), 5, True, True, True, None,
None, None, None)
def bench(self):
orders = self.orderList.orders[:]
self.orderList.orders = []
i = self.startValue
while i <= len(orders):
pprint("PERF Number of orders = " + str(i), BLUE)
self.orderList.orders = orders[:i]
optimizer = Optimizer(self.plant, self.orderList,
Simulator(self.plant), Evaluator(self.plant))
optimizer.populationSize = 10
optimizer.iterations = 10
optimizer.indivMutationRate = 0.5
optimizer.selectionRate = 0.5
optimizer.mutationRange = 10
t = time()
optimizer.run()
t = time() - t
t -= optimizer.simulatorTime
self.addCairoPlotTime(t)
self.addGnuPlotTime(i, t)
i += 1
def setup_network(self):
model = models.create(cfg.MODEL.TYPE)
if self.distributed:
# this should be removed if we update BatchNorm stats
self.model = torch.nn.parallel.DistributedDataParallel(
model.to(self.device),
device_ids=[self.args.local_rank],
output_device=self.args.local_rank,
broadcast_buffers=False)
else:
self.model = torch.nn.DataParallel(model).cuda()
if self.args.resume > 0:
self.model.load_state_dict(
torch.load(self.snapshot_path("caption_model",
self.args.resume),
map_location=lambda storage, loc: storage))
self.optim = Optimizer(self.model)
self.xe_criterion = losses.create(cfg.LOSSES.XE_TYPE).cuda()
self.rl_criterion = losses.create(cfg.LOSSES.RL_TYPE).cuda()
def simulatorLargeValuesPerf(plant, orderList, testNum):
machines = plant.machines[:]
orders = orderList.orders[:]
pprint(
"PERF Starting simulator benchmark test " + str(testNum) +
" with large values", BLUE)
orderList.orders = orders[:]
plant.machines = machines[:]
times = [0]
val = 4
for i in range(1, 7):
pprint("PERF Large Value = " + str(i * val), BLUE)
for o in orderList.orders:
o.deadline *= val
for r in o.recipe.recipe:
r[1] *= val
optimizer = Optimizer(plant, orderList, Simulator(plant),
Evaluator(plant))
schedule = optimizer.initialIndividual()
t = time()
simulator = Simulator(plant)
simulator.simulate(schedule)
t = time() - t
times.append(t)
pprint("PERF Time = " + str(t), GREEN)
try:
from thirdparty.CairoPlot import dot_line_plot
except:
pprint("PERF Will not output to graph.", RED)
return
dot_line_plot(
path.join("benchmarks", "simulator-largevalues-" + str(testNum)) +
FORMAT, times, 800, 800, (255, 255, 255), 5, True, True, True, None,
None, None, None)
def bench(self):
val = 2
i = self.startValue
while i < 10:
pprint("PERF Large Value = " + str(i * val), BLUE)
for o in self.orderList.orders:
o.deadline *= val
for r in o.recipe.recipe:
r[1] *= val
optimizer = Optimizer(self.plant, self.orderList,
Simulator(self.plant), Evaluator(self.plant))
optimizer.populationSize = 10
optimizer.iterations = 10
optimizer.indivMutationRate = 0.5
optimizer.selectionRate = 0.5
optimizer.mutationRange = 500
t = time()
optimizer.run()
t = time() - t
t -= optimizer.simulatorTime
self.addCairoPlotTime(t)
self.addGnuPlotTime((i + 1) * val, t)
i += 1
def optimizerMutationRangePerf(plant, orderList, testNum):
machines = plant.machines[:]
orders = orderList.orders[:]
pprint(
"PERF Starting optimizer benchmark test " + str(testNum) +
" on mutation range", BLUE)
orderList.orders = orders[:]
plant.machines = machines[:]
times = [0]
for i in range(10, 200, 20):
pprint("PERF Selection rate = " + str(i), BLUE)
optimizer = Optimizer(plant, orderList, Simulator(plant),
Evaluator(plant))
optimizer.populationSize = 5
optimizer.iterations = 5
optimizer.indivMutationRate = 0.5
optimizer.selectionRate = 0.5
optimizer.mutationRange = i
t = time()
optimizer.run()
t = time() - t
t -= optimizer.simulatorTime
times.append(t)
pprint("PERF Time = " + str(t), GREEN)
try:
from thirdparty.CairoPlot import dot_line_plot
except:
pprint("PERF Will not output to graph.", RED)
return
dot_line_plot(
path.join("benchmarks", "optimizer-mutationrange-" + str(testNum)) +
FORMAT, times, 800, 800, (255, 255, 255), 5, True, True, True, None,
None, None, None)
def bench(self):
recipes = []
for o in self.orderList.orders:
recipes.append(o.recipe.recipe[:])
o.recipe.recipe = []
machines = self.plant.machines[:]
self.plant.machines = []
i = self.startValue
while i <= len(machines):
pprint("PERF Number of machines = " + str(i), BLUE)
self.plant.machines = machines[:i]
for j, o in enumerate(self.orderList.orders):
o.recipe.recipe = recipes[j][:i]
optimizer = Optimizer(self.plant, self.orderList,
Simulator(self.plant), Evaluator(self.plant))
schedule = optimizer.initialIndividual()
t = time()
simulator = Simulator(self.plant)
simulator.simulate(schedule)
t = time() - t
self.addCairoPlotTime(t)
self.addGnuPlotTime(i, t)
i += 1
def test_single_track_optimizer(self):
params = Parameter()
params.set("wheel_base", 2.7)
params.set("dt", 0.2)
params.set("function_tolerance", 1e-6)
params.set("max_num_iterations", 1000)
params.set("num_threads", 1)
initial_state = np.array([[0., 0., 0., 10.], [2., 0., 0., 10.],
[4., 0., 0., 10.], [6., 0., 0., 10.]])
opt_vec = np.zeros(shape=(30, 2))
ref_line = np.array([[0., 4.], [1000., 4.]])
obstacle_outline0 = np.array([[14., 1.7], [22., 1.7], [22., 4.7],
[14., 4.7], [14., 1.7]])
# optimizer
opt = Optimizer(params)
opt.SetOptimizationVector(opt_vec)
# costs
ref_cost = ReferenceLineCost(params, 100.)
ref_cost.SetReferenceLine(ref_line)
jerk_cost = JerkCost(params, 10000.)
outline = ObjectOutline()
obstacle_outline1 = obstacle_outline0 + np.array([[30.0, -2.]])
outline.Add(obstacle_outline0, 0.)
outline.Add(obstacle_outline1, 6.)
object_cost = StaticObjectCost(params, 2.5, 10000.)
object_cost.AddObjectOutline(outline)
input_cost = InputCost(params, 10.)
input_cost.SetLowerBound(np.array([[-0.2, -1.0]]))
input_cost.SetUpperBound(np.array([[0.2, 1.0]]))
speed_cost = SpeedCost(params, 10.)
speed_cost.SetDesiredSpeed(10.)
# optimization problem
functor = opt.AddFastSingleTrackFunctor(
initial_state, params,
[jerk_cost, ref_cost, input_cost, object_cost, speed_cost])
opt.FixOptimizationVector(0, 1)
opt.Solve()
opt.Report()
inputs = opt.Result()
trajectory = GenerateTrajectorySingleTrack(initial_state, inputs,
params)
cmap, norm = GetColorMap(0., 6.)
fig, ax = plt.subplots(nrows=1, ncols=2)
for t in np.arange(0, 6, 0.5):
poly = outline.Query(t)
ax[0].plot(poly[:, 0], poly[:, 1], color=cmap(norm(t)))
colorbar_axis = ax[0].plot(trajectory[:, 0], trajectory[:, 1])
for i, pt in enumerate(trajectory[:]):
ax[0].plot(pt[0],
pt[1],
color=cmap(norm(params.set("dt", 0.2) * i)),
marker='o')
ax[0].axis("equal")
ax[1].plot(inputs[:-1, 0], label="Steering angle", marker='o')
ax[1].plot(inputs[:-1, 1],
label="Acceleration",
marker='o',
color="green")
ax[1].legend()
plt.show()
print(repr(trajectory))
print(repr(inputs))
def test_triple_int_optimizer(self):
params = Parameter()
params.set("dt", 0.2)
params.set("function_tolerance", 1e-6)
params.set("max_num_iterations", 1000)
initial_state = np.array([[0., 10., 0., 0., 10., 0., 0., 1., 0.],
[2., 10., 0., 2., 10., 0., 0.2, 1., 0.],
[4., 10., 0., 4., 10., 0., 0.4, 1., 0.],
[6., 10., 0., 6., 10., 0., 0.6, 1., 0.]])
opt_vec = np.zeros(shape=(30, 3))
ref_line = np.array([[10., -100.], [50.1, 100.1]])
# optimizer
opt = Optimizer(params)
opt.SetOptimizationVector(opt_vec)
# costs
ref_cost = ReferenceLineCost(params, 10.)
ref_cost.SetReferenceLine(ref_line)
jerk_cost = JerkCost(params, 100.)
speed_cost = SpeedCost(params, 10.)
speed_cost.SetDesiredSpeed(20.)
# optimization problem
functor = opt.AddTripleIntFunctor(initial_state, params,
[ref_cost, jerk_cost, speed_cost])
opt.FixOptimizationVector(0, 1)
opt.Solve()
opt.Report()
inputs = opt.Result()
trajectory = GenerateTrajectoryTripleInt(initial_state, inputs, params)
fig = plt.figure()
plt.subplot(144)
ax = fig.add_subplot(144, projection='3d')
plt.plot(trajectory[:, 0],
trajectory[:, 3],
trajectory[:, 6],
marker='o')
plt.plot([10, 50.1], [-100, 100], [0., 0.], marker='o')
plt.axis("equal")
plt.subplot(141)
plt.plot(trajectory[:-3, 1], label="vx", marker='o')
plt.plot(trajectory[:-3, 4], label="vy", marker='o', color="green")
plt.plot(trajectory[:-3, 7], label="vz", marker='o', color="red")
plt.xlabel("v [m/s]")
plt.legend()
plt.subplot(142)
plt.plot(trajectory[:, 0], trajectory[:, 3], marker='o')
plt.plot([10, 50.1], [-100, 100], marker='o')
plt.axis("equal")
plt.xlabel("x [m]")
plt.ylabel("y [m]")
plt.subplot(143)
plt.plot(trajectory[:, 3], trajectory[:, 6], marker='o')
plt.axis("equal")
plt.xlabel("y [m]")
plt.ylabel("z [m]")
# fig, ax = plt.subplots(nrows=1, ncols=2)
# colorbar_axis = ax[0].plot(trajectory[:, 0], trajectory[:, 3])
# ax[0].axis("equal")
# ax[1].plot(inputs[:-1, 0], label="ax", marker='o')
# ax[1].plot(inputs[:-1, 1], label="ay", marker='o', color="green")
# ax[1].plot(inputs[:-1, 2], label="az", marker='o', color="red")
# ax[1].legend()
plt.show()
print(repr(trajectory))
print(repr(inputs))
from multiprocessing import Queue, Process, Lock, Manager
from Exchange import MyExchange
from optimizer.optimizer import Optimizer
optimizer = Optimizer(symbol='ETH/USD', recentPerformanceTimeDuration=5)
if __name__ == "__main__":
optimizer.screen_for_best_parameter()
def test_single_track_optimizer(self):
params = Parameter()
params.set("wheel_base", 2.7)
params.set("dt", 0.2)
params.set("function_tolerance", 1e-8)
params.set("max_num_iterations", 1000)
initial_state = np.array([[0., 0., 0., 10.],
[2., 0., 0., 10.],
[4., 0., 0., 10.],
[6., 0., 0., 10.]])
opt_vec = np.array([[ 0. , 0. ],
[-0.00581466, -0.02969236],
[-0.0100573 , -0.05063586],
[-0.01290205, -0.06336555],
[-0.0145111 , -0.06958468],
[-0.01504364, -0.07147759],
[-0.01466189, -0.0712203 ],
[-0.01353391, -0.07066254],
[-0.01183398, -0.0711591 ],
[-0.00974178, -0.0735261 ],
[-0.00744058, -0.07806701],
[-0.00509113, -0.08413195],
[-0.0028139 , -0.09089476],
[-0.00069383, -0.0976187 ],
[ 0.00121458, -0.10373539],
[ 0.00288213, -0.10887319],
[ 0.00430011, -0.11284776],
[ 0.00547573, -0.11562908],
[ 0.00642782, -0.11729807],
[ 0.00718276, -0.11800159],
[ 0.0077706 , -0.11791354],
[ 0.00821974, -0.11720583],
[ 0.00855625, -0.11603267],
[ 0.0088038 , -0.11452536],
[ 0.0089836 , -0.11278997],
[ 0.00911419, -0.11090794],
[ 0.00921137, -0.10893774],
[ 0.00928801, -0.10691821],
[ 0.00935393, -0.10487191],
[ 0. , 0.00059502]])
ref_line = np.array([[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
1.00000000e+01],
[ 2.00000000e+00, 0.00000000e+00, 0.00000000e+00,
1.00000000e+01],
[ 4.00000000e+00, 0.00000000e+00, 0.00000000e+00,
1.00000000e+01],
[ 6.00000000e+00, 0.00000000e+00, 0.00000000e+00,
1.00000000e+01],
[ 8.00000000e+00, 0.00000000e+00, 0.00000000e+00,
1.00000000e+01],
[ 9.99939997e+00, -4.30463875e-03, -4.30592393e-03,
9.99406153e+00],
[ 1.19971306e+01, -2.03405218e-02, -1.17478308e-02,
9.98393436e+00],
[ 1.39923704e+01, -5.32967584e-02, -2.12840277e-02,
9.97126125e+00],
[ 1.59845144e+01, -1.06379431e-01, -3.19953707e-02,
9.95734431e+00],
[ 1.79731414e+01, -1.81067097e-01, -4.30841450e-02,
9.94304879e+00],
[ 1.99579824e+01, -2.77367780e-01, -5.38759931e-02,
9.92880473e+00],
[ 2.19388867e+01, -3.94078028e-01, -6.38232626e-02,
9.91467222e+00],
[ 2.39157899e+01, -5.29044532e-01, -7.25085470e-02,
9.90044040e+00],
[ 2.58886800e+01, -6.79428615e-01, -7.96477572e-02,
9.88573518e+00],
[ 2.78575651e+01, -8.41973335e-01, -8.50921200e-02,
9.87012178e+00],
[ 2.98224543e+01, -1.01325561e+00, -8.88112060e-02,
9.85329539e+00],
[ 3.17833555e+01, -1.18989285e+00, -9.08631109e-02,
9.83511644e+00],
[ 3.37402750e+01, -1.36869381e+00, -9.13680809e-02,
9.81559270e+00],
[ 3.56932177e+01, -1.54676003e+00, -9.04859136e-02,
9.79484562e+00],
[ 3.76421873e+01, -1.72154515e+00, -8.83971179e-02,
9.77307098e+00],
[ 3.95871864e+01, -1.89087947e+00, -8.52877092e-02,
9.75050143e+00],
[ 4.15282170e+01, -2.05296693e+00, -8.13374631e-02,
9.72737561e+00],
[ 4.34652803e+01, -2.20636175e+00, -7.67114427e-02,
9.70391600e+00],
[ 4.53983772e+01, -2.34993117e+00, -7.15546057e-02,
9.68031568e+00],
[ 4.73275086e+01, -2.48281085e+00, -6.59892879e-02,
9.65673297e+00],
[ 4.92526750e+01, -2.60435938e+00, -6.01165980e-02,
9.63329181e+00],
[ 5.11738771e+01, -2.71411662e+00, -5.40182610e-02,
9.61008527e+00],
[ 5.30911154e+01, -2.81176608e+00, -4.77585100e-02,
9.58718020e+00],
[ 5.50043903e+01, -2.89710162e+00, -4.13860399e-02,
9.56462221e+00],
[ 5.69137022e+01, -2.96999832e+00, -3.49360318e-02,
9.54244062e+00],
[ 5.88190514e+01, -3.03038789e+00, -2.84322483e-02,
9.52065307e+00],
[ 6.07204383e+01, -3.07823825e+00, -2.18892006e-02,
9.49926943e+00],
[ 6.26178630e+01, -3.11353783e+00, -1.53143860e-02,
9.47829505e+00],
[ 6.45133116e+01, -3.14256773e+00, -1.53143860e-02,
9.47841405e+00]])
obstacle_outline0 = np.array([[14., 1.7],
[22., 1.7],
[22., 4.7],
[14., 4.7],
[14., 1.7]])
# optimizer
opt = Optimizer(params)
opt.SetOptimizationVector(opt_vec)
# costs
ref_cost = ReferenceCost(params, 100.)
ref_cost.SetReference(ref_line)
jerk_cost = JerkCost(params, 10000.)
outline = ObjectOutline()
obstacle_outline1 = obstacle_outline0 + np.array([[30.0, -3.]])
outline.Add(obstacle_outline0, 0.)
outline.Add(obstacle_outline1, 6.)
object_cost = StaticObjectCost(params, 2.5, 10000.)
object_cost.AddObjectOutline(outline)
input_cost = InputCost(params, 10.)
input_cost.SetLowerBound(np.array([[-0.2, -1.0]]))
input_cost.SetUpperBound(np.array([[0.2, 1.0]]))
# optimization problem
functor = opt.AddFastSingleTrackFunctor(initial_state,
params,
[jerk_cost,
object_cost,
input_cost,
ref_cost])
opt.FixOptimizationVector(0, 1)
opt.Solve()
opt.Report()
inputs = opt.Result()
trajectory = GenerateTrajectorySingleTrack(initial_state, inputs, params)
cmap, norm = GetColorMap(0., 6.)
fig, ax = plt.subplots(nrows=1, ncols=2)
for t in np.arange(0, 6, 0.5):
poly = outline.Query(t)
ax[0].plot(poly[:, 0], poly[:, 1], color=cmap(norm(t)))
colorbar_axis = ax[0].plot(trajectory[:, 0], trajectory[:, 1])
for i, pt in enumerate(trajectory[:]):
ax[0].plot(pt[0], pt[1], color=cmap(norm(params.set("dt", 0.2)*i)), marker='o')
ax[0].axis("equal")
ax[1].plot(inputs[:-1, 0], label="Steering angle", marker='o')
ax[1].plot(inputs[:-1, 1], label="Acceleration", marker='o', color="green")
ax[1].legend()
plt.show()
print(repr(inputs))
print(repr(trajectory))
logging.info('Using default depth of 1')
labels_true = change_label_depth(b.labels, depth=1)
else:
labels_true = change_label_depth(b.labels, depth=args.ldepth)
# Choose appropriate clusterer and initialize.
if args.algorithm == 'km':
c = KMClust(v, labels_true)
elif args.algorithm == 'lda':
c = LDAClust(v, labels_true)
else:
logging.error('Clusterer name not recognized. Exiting...')
sys.exit()
logging.info('Clustering...')
o = Optimizer()
o.optimize(c, args.n_clusters, one_run=(not args.optimize))
o.latest_clusterer.export_csv_doc_topic()
file_name = o.latest_clusterer.export_csv_topic_word()
# Generate the heatmap for the last clusterer
generate_heatmap(file_name)
# Plot the Purities and Topic categories so far
if args.optimize:
o.plot_current()
if args.search:
# Compute wordclouds for the latest clusterer
og = OfficialGoogler()