def test_sumt1():
def costfunc(x):
x1 = x[0]
x2 = x[1]
return x1**4 - 2.*x1**2*x2 + x1**2 + x1*x2**2 - 2.*x1 + 4.
constraints_string = """
x1**2 + x2**2 - 2. = 0.
0.25*x1**2 + 0.75*x2**2 - 1. <= 0.
"""
ndim = 2
x0 = [3., 2.]
npop = 25
# print("constraints equations:%s" % (constraints_string.rstrip(),))
from mystic.solvers import DifferentialEvolutionSolver
from mystic.solvers import NelderMeadSimplexSolver
from mystic.termination import VTR
#solver = DifferentialEvolutionSolver(ndim, npop)
solver = NelderMeadSimplexSolver(ndim)
solver.SetInitialPoints(x0)
term = VTR()
#FIXME: sumt, issolution no longer take constraints strings
end_solver = sumt(constraints_string, ndim, costfunc, solver,\
term, disp=True)
soln = end_solver.Solution()
assert issolution(constraints_string, soln)
def run(self):
print('Start local optimization...')
maxf = self.request_data['optimization']['parameters']['maxf']
xtol = self.request_data['optimization']['parameters']['xtol']
ftol = self.request_data['optimization']['parameters']['ftol']
solver = NelderMeadSimplexSolver(len(self.initial_values))
solver.SetInitialPoints(self.initial_values)
solver.SetStrictRanges([i[0] for i in self.bounds], [i[1] for i in self.bounds])
solver.SetEvaluationLimits(evaluations=maxf)
solver.SetTermination(CRT(xtol=ftol, ftol=ftol))
# Inverting weights (*-1) to convert problem to minimizing
solver.Solve(
self.evaluate_single_solution,
ExtraArgs=([weight * -1 for weight in self.weights]),
callback=self.callback
)
solver.enable_signal_handler()
#Finally
self.callback(
individual=solver.Solution(),
final=True
)
return
def test_sumt2():
def costfunc(x):
return (x[0] - 1.)**2 + (x[1] - 2.)**2 + (x[2] - 3.)**4
x0 = [0., 0., 0.]
ndim = 3
constraints_string = """
x2 > 5.
4.*x1-5.*x3 < -1.
(x1-10.)**2 + (x2+1.)**2 < 50.
"""
print "constraints equations:%s" % (constraints_string.rstrip(), )
from mystic.solvers import DifferentialEvolutionSolver
from mystic.solvers import NelderMeadSimplexSolver
from mystic.termination import VTR
#solver = DifferentialEvolutionSolver(ndim, npop)
solver = NelderMeadSimplexSolver(ndim)
solver.SetInitialPoints(x0)
term = VTR()
#FIXME: sumt, issolution no longer take constraints strings
end_solver = sumt(constraints_string, ndim, costfunc, solver,\
term, disp=True)
soln = end_solver.Solution()
print "final answer:", soln
print "constraints satisfied:", issolution(constraints_string, soln)
print "expected: [ 6.25827968 4.999961 5.20662288]", "\n"
def optimize_linear(self, initial_values: List[float],
function) -> List[float]:
""" Function to optimize one solution linear by using the mystic library
Args:
initial_values: the initial solution that the solver starts with
function: the callback function that sends out the task to the database, awaits the result and takes it
back in
Returns:
solution: a linear optimized solution
"""
solver = NelderMeadSimplexSolver(dim=len(initial_values))
solver.SetInitialPoints(x0=initial_values)
solver.SetStrictRanges(self.low, self.up)
solver.SetEvaluationLimits(generations=self.maxf)
solver.SetTermination(CRT(self.xtol, self.ftol))
solver.Solve(function)
return list(solver.Solution())
def run(self):
self.logger.info('Start local optimization...')
maxf = self.request_data['optimization']['parameters']['maxf']
xtol = self.request_data['optimization']['parameters']['xtol']
ftol = self.request_data['optimization']['parameters']['ftol']
solver = NelderMeadSimplexSolver(len(self.initial_values))
solver.SetInitialPoints(self.initial_values)
solver.SetStrictRanges([i[0] for i in self.bounds], [i[1] for i in self.bounds])
solver.SetEvaluationLimits(evaluations=maxf)
solver.SetTermination(CRT(xtol=ftol, ftol=ftol))
solver.Solve(
self.evaluate_single_solution,
callback=self.callback
)
solver.enable_signal_handler()
self.callback(
individual=solver.Solution(),
final=True
)
return
# draw frame and exact coefficients
plot_exact()
# select parameter bounds constraints
from numpy import inf
min_bounds = [0, -1, -300, -1, 0, -1, -100, -inf, -inf]
max_bounds = [200, 1, 0, 1, 200, 1, 0, inf, inf]
# configure monitors
stepmon = VerboseMonitor(100)
evalmon = Monitor()
# use Nelder-Mead to solve 8th-order Chebyshev coefficients
solver = NelderMeadSimplexSolver(ndim)
solver.SetInitialPoints(x0)
solver.SetEvaluationLimits(generations=999)
solver.SetEvaluationMonitor(evalmon)
solver.SetGenerationMonitor(stepmon)
solver.SetStrictRanges(min_bounds, max_bounds)
solver.enable_signal_handler()
solver.Solve(chebyshev8cost, termination=CRT(1e-4,1e-4), \
sigint_callback=plot_solution)
solution = solver.bestSolution
# get solved coefficients and Chi-Squared (from solver members)
iterations = solver.generations
cost = solver.bestEnergy
print "Generation %d has best Chi-Squared: %f" % (iterations, cost)
print "Solved Coefficients:\n %s\n" % poly1d(solver.bestSolution)
