def find_poles(problem, population):
poles = []
# remove duplicates
temp_poles = []
for _ in xrange(jmoo_properties.ANYWHERE_POLES):
while True:
one = random.choice(population)
east = find_extreme(one, population)
west = find_extreme(east, population)
if (
east != west
and east != one
and west != one
and east not in list(temp_poles)
and west not in list(temp_poles)
):
break
poles.append(east)
poles.append(west)
if look_for_duplicates(east, temp_poles) is False:
temp_poles.append(east)
else:
assert True, "Something'S wrong"
if look_for_duplicates(west, temp_poles, lambda x: x.decisionValues) is False:
temp_poles.append(west)
else:
assert True, "Something'S wrong"
min_point, max_point = find_extreme_point([pop.decisionValues for pop in poles])
mid_point = find_midpoint(min_point, max_point)
mid_point = jmoo_individual(problem, mid_point, None)
stars = rearrange(problem, mid_point, poles)
return stars
def find_poles(problem, population):
poles = []
#remove duplicates
temp_poles = []
for _ in xrange(jmoo_properties.ANYWHERE_POLES):
while True:
one = random.choice(population)
east = find_extreme(one, population)
west = find_extreme(east, population)
if east != west and east != one and west != one and east not in list(
temp_poles) and west not in list(temp_poles):
break
poles.append(east)
poles.append(west)
if look_for_duplicates(east, temp_poles) is False:
temp_poles.append(east)
else:
assert (True), "Something'S wrong"
if look_for_duplicates(west, temp_poles,
lambda x: x.decisionValues) is False:
temp_poles.append(west)
else:
assert (True), "Something'S wrong"
min_point, max_point = find_extreme_point(
[pop.decisionValues for pop in poles])
mid_point = find_midpoint(min_point, max_point)
mid_point = jmoo_individual(problem, mid_point, None)
stars = rearrange(problem, mid_point, poles)
return stars
def find_poles3(problem, population):
poles = []
min_point, max_point = find_extreme_point([pop.decisionValues for pop in population])
mid_point = find_midpoint(min_point, max_point)
directions = [population[i] for i in sorted(random.sample(xrange(len(population)), jmoo_properties.ANYWHERE_POLES * 2)) ]
mid_point = jmoo_individual(problem, mid_point, None)
stars = rearrange(problem, mid_point, directions)
return stars
def find_poles3(problem, population, configurations):
"""This version of find_poles, randomly selects individuals from the population and considers
assumes them to be the directions
Intuition: Random directions are better than any
"""
# min_point = minimum in all dimensions, max_point = maximum in all dimensions
min_point, max_point = find_extreme_point([pop.decisionValues for pop in population])
# find mid point between min_point and max_point (mean)
temp_mid_point = find_midpoint(min_point, max_point)
# Randomly Sample the population to generate directions. This is a simpler approach to find_poles2()
directions = [population[i] for i in sorted(random.sample(xrange(len(population)),
configurations["STORM"]["STORM_POLES"] * 2))]
# Encapsulate the mid_point into an jmoo_individual structure
mid_point = jmoo_individual(problem, temp_mid_point, None)
assert(problem.validate(temp_mid_point) is True), "Mid point is not a valid solution and this shouldn't happen"
# stars now is a list of poles in the Poles format
stars = rearrange(problem, mid_point, directions)
return stars
def find_poles3(problem, population, configurations):
"""This version of find_poles, randomly selects individuals from the population and considers
assumes them to be the directions
Intuition: Random directions are better than any
"""
# min_point = minimum in all dimensions, max_point = maximum in all dimensions
min_point, max_point = find_extreme_point(
[pop.decisionValues for pop in population])
# find mid point between min_point and max_point (mean)
temp_mid_point = find_midpoint(min_point, max_point)
# Randomly Sample the population to generate directions. This is a simpler approach to find_poles2()
directions = [
population[i] for i in sorted(
random.sample(xrange(len(population)),
configurations["STORM"]["STORM_POLES"] * 2))
]
# Encapsulate the mid_point into an jmoo_individual structure
mid_point = jmoo_individual(problem, temp_mid_point, None)
assert (problem.validate(temp_mid_point) is True
), "Mid point is not a valid solution and this shouldn't happen"
# stars now is a list of poles in the Poles format
stars = rearrange(problem, mid_point, directions)
return stars