def test06(terminate, func=lambda x: x[0], info=False, debug=False):
from mystic.solvers import NelderMeadSimplexSolver as NM
solver = NM(1)
solver.SetRandomInitialPoints()
solver.SetEvaluationLimits(8)
solver.Solve(func, VTR())
if debug: verbosity(solver)
return terminate(solver, info)
def runme():
# instantiate the solver
_solver = NelderMeadSimplexSolver(3)
lb, ub = [0., 0., 0.], [10., 10., 10.]
_solver.SetRandomInitialPoints(lb, ub)
_solver.SetEvaluationLimits(1000)
# add a monitor stream
stepmon = VerboseMonitor(1)
_solver.SetGenerationMonitor(stepmon)
# configure the bounds
_solver.SetStrictRanges(lb, ub)
# configure stop conditions
term = Or(VTR(), ChangeOverGeneration())
_solver.SetTermination(term)
# add a periodic dump to an archive
tmpfile = 'mysolver.pkl'
_solver.SetSaveFrequency(10, tmpfile)
# run the optimizer
_solver.Solve(rosen)
# get results
x = _solver.bestSolution
y = _solver.bestEnergy
# load the saved solver
solver = LoadSolver(tmpfile)
#os.remove(tmpfile)
# obligatory check that state is the same
assert all(x == solver.bestSolution)
assert y == solver.bestEnergy
# modify the termination condition
term = VTR(0.0001)
solver.SetTermination(term)
# run the optimizer
solver.Solve(rosen)
os.remove(tmpfile)
# check the solver serializes
_s = dill.dumps(solver)
return dill.loads(_s)
lb = [-100, -100, -100]
ub = [1000, 1000, 1000]
ndim = len(lb)
maxiter = 10
maxfun = 1e+6
def cost(x):
ax, bx, c = x
return (ax)**2 - bx + c
monitor = Monitor()
solver = NelderMeadSimplexSolver(ndim)
solver.SetRandomInitialPoints(min=lb, max=ub)
solver.SetStrictRanges(min=lb, max=ub)
solver.SetEvaluationLimits(maxiter, maxfun)
solver.SetGenerationMonitor(monitor)
solver.Solve(cost)
solved = solver.bestSolution
monitor.info("solved: %s" % solved)
lmon = len(monitor)
assert solver.bestEnergy == monitor.y[-1]
for xs, x in zip(solved, monitor.x[-1]):
assert xs == x
solver.SetEvaluationLimits(maxiter * 2, maxfun)
solver.SetGenerationMonitor(monitor)
