#def annotate(label, x, y):
# return plt.annotate(
# label,
# xy = (x, y), xytext = (-20, 20),
# textcoords = 'offset points',
# ha = 'right', va = 'bottom',
# bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.5),
# arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'))
#annotates = [annotate(label, x, y) for label, x, y in zip(labels, xs, ys)]
#for particle in particles:
# print(str(particle) + ' fitness {}'.format(utility_function(particle.position)))
global_solution = None
for i in arange(1, 1000):
#for annotate, particle_lines in zip(annotates, particles_lines):
for particle in particles:
factor = -1 if random() > 0.5 else 1
delta = Point(factor * random() / 100, factor * random() / 100)
particle.calculate_fitness(delta)
global_best = max([(particle.p_best, utility_function.f(particle.p_best))
for particle in particles],
key=lambda x: x[1])
global_solution = global_best[0]
for particle in particles:
particle.move(global_best[0])
print('Global solution {}'.format(global_solution))
#def annotate(label, x, y):
# return plt.annotate(
# label,
# xy = (x, y), xytext = (-20, 20),
# textcoords = 'offset points',
# ha = 'right', va = 'bottom',
# bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.5),
# arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'))
#annotates = [annotate(label, x, y) for label, x, y in zip(labels, xs, ys)]
#for particle in particles:
# print(str(particle) + ' fitness {}'.format(utility_function(particle.position)))
global_solution = None
for i in arange(1, 1000):
#for annotate, particle_lines in zip(annotates, particles_lines):
for particle in particles:
factor = -1 if random() > 0.5 else 1
delta = Point(factor * random()/100, factor * random()/100)
particle.calculate_fitness(delta)
global_best = max([(particle.p_best, utility_function.f(particle.p_best)) for particle in particles],
key=lambda x: x[1])
global_solution = global_best[0]
for particle in particles:
particle.move(global_best[0])
print('Global solution {}'.format(global_solution))
def calculate_fitness(self, delta):
candidate = self.position + delta
if utility_function.f(candidate) > utility_function.f(self.__position):
self.__particle_best = candidate
