def __init__(self, dim):
self.__Pop = [] # population set
self.__PosPop = [] # positive sample set
self.__Optimal = [] # the best sample so far
self.__NextPop = [] # the next population set
self.__region = [] # the region of model
self.__label = [] # the random label, if true random in this dimension
self.__SampleSize = 0 # the instance number of sampling in an iteration
self.__MaxIteration = 0 # the number of iterations
self.__Budget = 0 # budget of evaluation
self.__PositiveNum = 0 # the set size of PosPop
self.__RandProbability = 0 # the probability of sample in model
self.__UncertainBits = 0 # the dimension size that is sampled randomly
self.__OnlineSwitch = False
self.__dimension = dim
for i in range(dim.getSize()):
reg = []
reg.append(0)
reg.append(0)
self.__region.append(reg)
self.__label.append(True)
self.__ro = RandomOperator()
return
def __init__(self, dimension):
self.__pop = [] # population set
self.__pos_pop = [] # positive sample set
self.__optimal = None # the best sample so far
self.__region = [] # the region of model
self.__label = [] # the random label, if true random in this dimension
self.__sample_size = 0 # the instance size of sampling in an iteration
self.__budget = 0 # budget of evaluation
self.__positive_num = 0 # positive sample set size
self.__rand_probability = 0.0 # the probability of sampling in model
self.__uncertain_bit = 0 # the dimension size of sampling randomly
self.__dimension = dimension
# sampling saving
self.__model_ins = [] # positive sample used to modeling
self.__negative_set = [] # negative set used to modeling
self.__new_ins = [] # new sample
self.__sample_label = [] # the label of each sample
for i in range(self.__dimension.get_size()):
region = [0.0, 0.0]
self.__region.append(region)
self.__label.append(0)
self.__ro = RandomOperator()
return
def __init__(self, dim=None, bias_region=[-0.2, 0.2]):
ro = RandomOperator()
self.__dimension = dim
# generate bias randomly
self.__bias = []
for i in range(self.__dimension.get_size()):
# self.__bias.append(ro.getUniformDouble(self.__dimension.getRegion(i)[0], self.__dimension.getRegion(i)[1]))
self.__bias.append(
ro.get_uniform_double(bias_region[0], bias_region[1]))
# print 'bias:', self.__bias
return
from ObjectiveFunction import DistributedFunction
from Components import Dimension
from Tools import RandomOperator
import numpy as np
dimension_size = 10
dimension = Dimension()
dimension.set_dimension_size(dimension_size)
dimension.set_regions([[-0.5, 0.5] for _ in range(dimension_size)],
[0 for _ in range(dimension_size)])
func = DistributedFunction(dimension, bias_region=[-0.5, 0.5])
target_bias = [0.25 for _ in range(dimension_size)]
func.setBias(target_bias)
ro = RandomOperator()
prob_fct = func.DisRosenbrock
x0 = [ro.get_uniform_double(-0.5, 0.5) for _ in range(dimension_size)]
ans = []
for i in range(10):
x, cost, _ = fmin_smac(func=prob_fct,
x0=x0,
bounds=[[-0.5, 0.5] for _ in range(dimension_size)],
maxfun=50,
rng=3)
ans.append(x)
# print("Optimum at {} with cost of {}".format(x, cost))
print(np.mean(ans))
print(np.std(ans))
