from mystic.monitors import VerboseMonitor mon = VerboseMonitor(10) # solve the dual for alpha from mystic.solvers import DifferentialEvolutionSolver as DESolver from mystic.termination import Or, ChangeOverGeneration, CollapseAt ndim = len(lb) npop = nx * 3 stop = Or(ChangeOverGeneration(1e-8, 200), CollapseAt(0.0)) solver = DESolver(ndim, npop) solver.SetRandomInitialPoints(min=lb, max=_b) solver.SetStrictRanges(min=lb, max=ub) solver.SetGenerationMonitor(mon) solver.SetConstraints(conserve) solver.SetTermination(stop) solver.Solve(objective, ExtraArgs=(Q, b), disp=1) alpha = solver.bestSolution print 'solved x: ', alpha print "constraint A*x == 0: ", inner(Aeq, alpha) print "minimum 0.5*x'Qx + b'*x: ", solver.bestEnergy # calculate weight vectors, support vectors, and bias wv = WeightVector(alpha, X, y) sv1, sv2 = SupportVectors(alpha, y, eps=1e-6) bias = Bias(alpha, X, y) ym = (y.flatten() < 0).nonzero()[0] yp = (y.flatten() > 0).nonzero()[0] ii = inner(wv, X)
target = 0.0 n = 10 #term = Or((COG(generations=300), CollapseAt(None, generations=100), CollapseAs(generations=100))) term = Or((COG(generations=500), CollapseAt(target, generations=100))) #term = COG(generations=500) from mystic.solvers import DifferentialEvolutionSolver as TheSolver #from mystic.solvers import PowellDirectionalSolver as TheSolver from mystic.solvers import BuckshotSolver #solver = BuckshotSolver(n, 10) solver = TheSolver(n) solver.SetRandomInitialPoints() solver.SetStrictRanges(min=[0] * n, max=[5] * n) solver.SetEvaluationLimits(evaluations=320000, generations=1000) solver.SetTermination(term) #from mystic.termination import state #print state(solver._termination).keys() solver.Solve(model, disp=verbose) # while collapse and solver.Collapse(verbose): # solver.Solve(model) # we are done; get result print solver.Terminated(info=True) print solver.bestEnergy, "@" print solver.bestSolution # EOF
