solver.enable_signal_handler()
solver.SetRandomInitialPoints(min=minrange,max=maxrange)
solver.SetEvaluationLimits(generations=MAX_GENERATIONS)
solver.Solve(cost, termination=ChangeOverGeneration(generations=100))
if __name__ == '__main__':
# x0, y0, R0
#guess = [1,1,1] # bad initial guess
#guess = [5,5,1] # ok guess
guess = [10,15,5] # good initial guess
# plot training set & training set boundary
pylab.plot(xy[:,0],xy[:,1],'k+',markersize=6)
c = circle(x0, y0, R0)
pylab.plot(c[:,0],c[:,1],'r-',linewidth=2)
legend = ['random points','generating circle : %f' % R0]
pylab.axis('equal')
# solve with mystic's differential evolution solver
solution = solver.Solution()
sx, sy, sr = solution
print("DEsol : (%f, %f) @ R = %f" % (sx, sy, sr))
# plot DEsolver solution
c = circle(sx, sy, sr)
pylab.plot(c[:,0],c[:,1],'b-',linewidth=2)
legend.append('DE optimal : %f' % sr)
# solve with scipy.fmin
solver.enable_signal_handler()
solver.SetRandomInitialPoints(min=minrange,max=maxrange)
solver.SetEvaluationLimits(generations=MAX_GENERATIONS)
solver.Solve(cost, termination=ChangeOverGeneration(generations=100))
if __name__ == '__main__':
# x0, y0, R0
#guess = [1,1,1] # bad initial guess
#guess = [5,5,1] # ok guess
guess = [10,15,5] # good initial guess
# plot training set & training set boundary
pylab.plot(xy[:,0],xy[:,1],'k+',markersize=6)
c = circle(x0, y0, R0)
pylab.plot(c[:,0],c[:,1],'r-',linewidth=2)
legend = ['random points','generating circle : %f' % R0]
pylab.axis('equal')
# solve with mystic's differential evolution solver
solution = solver.Solution()
sx, sy, sr = solution
print("DEsol : (%f, %f) @ R = %f" % (sx, sy, sr))
# plot DEsolver solution
c = circle(sx, sy, sr)
pylab.plot(c[:,0],c[:,1],'b-',linewidth=2)
legend.append('DE optimal : %f' % sr)
# solve with scipy.fmin
