def local_optimize(cost, x0, lb, ub):
from mystic.solvers import PowellDirectionalSolver
from mystic.termination import NormalizedChangeOverGeneration as NCOG
from mystic.monitors import VerboseMonitor, Monitor
maxiter = 1000
maxfun = 1e+6
convergence_tol = 1e-4
#stepmon = VerboseMonitor(100)
stepmon = Monitor()
evalmon = Monitor()
ndim = len(lb)
solver = PowellDirectionalSolver(ndim)
solver.SetInitialPoints(x0)
solver.SetStrictRanges(min=lb, max=ub)
solver.SetEvaluationLimits(maxiter, maxfun)
solver.SetEvaluationMonitor(evalmon)
solver.SetGenerationMonitor(stepmon)
tol = convergence_tol
solver.Solve(cost, termination=NCOG(tol))
solved_params = solver.bestSolution
solved_energy = solver.bestEnergy
func_evals = solver.evaluations
return solved_params, solved_energy, func_evals
def local_optimize(cost,x0,lb,ub): from mystic.solvers import PowellDirectionalSolver from mystic.termination import NormalizedChangeOverGeneration as NCOG from mystic.monitors import VerboseMonitor, Monitor maxiter = 1000 maxfun = 1e+6 convergence_tol = 1e-4 #def func_unpickle(filename): # """ standard pickle.load of function from a File """ # import dill as pickle # return pickle.load(open(filename,'r')) #stepmon = VerboseMonitor(100) stepmon = Monitor() evalmon = Monitor() ndim = len(lb) solver = PowellDirectionalSolver(ndim) solver.SetInitialPoints(x0) solver.SetStrictRanges(min=lb,max=ub) solver.SetEvaluationLimits(maxiter,maxfun) solver.SetEvaluationMonitor(evalmon) solver.SetGenerationMonitor(stepmon) tol = convergence_tol #cost = func_unpickle(cost) #XXX: regenerate cost function from file solver.Solve(cost, termination=NCOG(tol)) solved_params = solver.bestSolution solved_energy = solver.bestEnergy func_evals = solver.evaluations return solved_params, solved_energy, func_evals
