def adaptOptimizer(problem, seedProblemParams, seedPrimitive):
"""Return the adaptation optimizer used by the primitive library"""
#adapt for 100 iterations
opt = optimize.localOptimizer(problem,
'ddp',
tol=1e-4,
seed=seedPrimitive,
numIters=100)
return opt
def adaptOptimizer(problem, seedProblemParams, seedPrimitive):
"""Return the adaptation optimizer used by the primitive library"""
#100 simulated annealing iterations, 100 perturbation sampling iterations per iteration
opt = optimize.localOptimizer(problem,
'sa',
radius=0.01,
samplecond=100,
temperature=lambda x: 0.2 / (1.0 + x * 0.1),
seed=seedPrimitive,
numIters=100)
return opt
def adaptOptimizer(problem,seedProblemParams,seedPrimitive):
"""Return the adaptation optimizer used by the primitive library"""
opt = optimize.localOptimizer(problem,'gradient',tol=1e-4,x=seedPrimitive,numIters=100)
opt.beginPrint()
return opt