Exemple #1
0
 def optimize(self):
     p = DotMap()
     p.verbosity = 1
     p.acq_func = EI(model=None, logs=None)  # EI(model = None, logs = logs)
     p.model = regression.GP
     self.opt = opto.BO(parameters=p,
                        task=self.task,
                        stopCriteria=self.Stop)
     self.opt.optimize()
     print("Highest number of iterations: ", max(its))
 def optimize(self):
     p = DotMap()
     p.verbosity = 1
     p.acq_func = EI(model=None, logs=None)
     # p.acq_func = UCB(model=None, logs=None)
     p.model = regression.GP
     self.opt = opto.BO(parameters=p,
                        task=self.task,
                        stopCriteria=self.Stop)
     self.opt.optimize()
from opto.opto.classes import StopCriteria
from opto.utils import bounds
from opto.opto.acq_func import EI
from opto import regression

from objective_functions import *

import numpy as np
import matplotlib.pyplot as plt
from dotmap import DotMap

init_vrep()
load_scene('scenes/normal.ttt')
obj_f = generate_f(parameter_mode='normal', objective_mode='single', steps=400)
task = OptTask(f=obj_f, n_parameters=4, n_objectives=1, \
    bounds=bounds(min=[1, -np.pi, 0, 0], max=[60, np.pi, 1, 1]), \
    vectorized=False)
stopCriteria = StopCriteria(maxEvals=50)

p = DotMap()
p.verbosity = 1
p.acq_func = EI(model=None, logs=None)
p.optimizer = opto.CMAES
p.model = regression.GP
opt = opto.BO(parameters=p, task=task, stopCriteria=stopCriteria)
opt.optimize()
logs = opt.get_logs()
print("Parameters: " + str(logs.get_parameters()))
print("Objectives: " + str(logs.get_objectives()))
exit_vrep()
Exemple #4
0

# task = Rolling.Rolling(visualize=True)
task = Rolling.Rolling(visualize=False)
stopCriteria = opto.opto.classes.StopCriteria(maxEvals=1)

objList = []
paramList = []
optNameList = ["BO", "Random"]

for epoch in range(0, 30):
    for optName in optNameList:
        print(optName, epoch)
        p = DotMap()
        p.verbosity = 1
        p.acq_func = UCB(model=[], logs=[], parameters={"alpha": 0.1})
        # p.acq_func = EI(model=None, logs=None)
        # p.optimizer = opto.CMAES
        p.visualize = True
        p.model = rregression.GP

        if optName == "BO":
            opt = opto.BO(parameters=p, task=task, stopCriteria=stopCriteria)
        elif optName == "Random":
            opt = opto.RandomSearch(parameters=p, task=task, stopCriteria=stopCriteria)
        try:
            opt.optimize()
        except:
            continue
        logs = opt.get_logs()