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_poles2(problem, population):
def midpoint(population):
def median(lst):
import numpy
return numpy.median(numpy.array(lst))
mdpnt = []
for dec in xrange(len(population[0].decisionValues)):
mdpnt.append(
median([pop.decisionValues[dec] for pop in population]))
assert (len(mdpnt) == len(
population[0].decisionValues)), "Something's wrong"
# print mdpnt
return jmoo_individual(problem, mdpnt, None)
def generate_directions(problem):
def gen(point):
r = sum([pp**2 for pp in point])**0.5
return [round(p / r, 3) for p in point]
coordinates = [
gen([random.uniform(0, 1) for _ in xrange(len(problem.decisions))])
for _ in xrange(jmoo_properties.ANYWHERE_POLES * 2)
]
for co in coordinates:
assert (int(
round(
euclidean_distance(
co, [0 for _ in xrange(len(problem.decisions))
]))) == 1), "Something's wrong"
return coordinates
# find midpoint
mid_point = midpoint(population)
# find directions
directions = generate_directions(problem)
# draw a star
poles = []
for direction in directions:
mine = -1e32
temp_pole = None
for pop in population:
transformed_dec = [
(p - m)
for p, m in zip(pop.decisionValues, mid_point.decisionValues)
]
y = perpendicular_distance(direction, transformed_dec)
c = euclidean_distance(transformed_dec,
[0 for _ in xrange(len(problem.decisions))])
# print c, y
if mine < (c - y):
mine = c - y
temp_pole = pop
poles.append(temp_pole)
stars = rearrange(problem, mid_point, poles)
return stars
def find_poles2(problem, population):
def midpoint(population):
def median(lst):
import numpy
return numpy.median(numpy.array(lst))
mdpnt = []
for dec in xrange(len(population[0].decisionValues)):
mdpnt.append(median([pop.decisionValues[dec] for pop in population]))
assert len(mdpnt) == len(population[0].decisionValues), "Something's wrong"
# print mdpnt
return jmoo_individual(problem, mdpnt, None)
def generate_directions(problem):
def gen(point):
r = sum([pp ** 2 for pp in point]) ** 0.5
return [round(p / r, 3) for p in point]
coordinates = [
gen([random.uniform(0, 1) for _ in xrange(len(problem.decisions))])
for _ in xrange(jmoo_properties.ANYWHERE_POLES * 2)
]
for co in coordinates:
assert (
int(round(euclidean_distance(co, [0 for _ in xrange(len(problem.decisions))]))) == 1
), "Something's wrong"
return coordinates
# find midpoint
mid_point = midpoint(population)
# find directions
directions = generate_directions(problem)
# draw a star
poles = []
for direction in directions:
mine = -1e32
temp_pole = None
for pop in population:
transformed_dec = [(p - m) for p, m in zip(pop.decisionValues, mid_point.decisionValues)]
y = perpendicular_distance(direction, transformed_dec)
c = euclidean_distance(transformed_dec, [0 for _ in xrange(len(problem.decisions))])
# print c, y
if mine < (c - y):
mine = c - y
temp_pole = pop
poles.append(temp_pole)
stars = rearrange(problem, mid_point, poles)
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
