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]
from mystic import suppressed
@suppressed(1e-8)
def constrain(x):
return x
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.SetConstraints(constrain)
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
def solve(constraints, guess=None, nvars=None, solver=None, \
lower_bounds=None, upper_bounds=None, termination=None):
"""Use optimization to find a solution to a set of constraints.
Inputs:
constraints -- a constraints solver function or a penalty function
Additional Inputs:
guess -- list of parameter values proposed to solve the constraints.
lower_bounds -- list of lower bounds on solution values.
upper_bounds -- list of upper bounds on solution values.
nvars -- number of parameter values.
solver -- the mystic solver to use in the optimization
termination -- the mystic termination to use in the optimization
NOTE: The resulting constraints will likely be more expensive to evaluate
and less accurate than writing the constraints solver from scratch.
NOTE: The ensemble solvers are available, using the default NestedSolver,
where the keyword 'guess' can be used to set the number of solvers.
NOTE: The default solver is 'diffev', with npop=min(40, ndim*5). The default
termination is ChangeOverGeneration(), and the default guess is randomly
selected points between the upper and lower bounds.
"""
npts = 8
if type(guess) is int: npts, guess = guess, None
ndim = 1 #XXX: better, increase in while loop catching IndexError ?
if nvars is not None: ndim = nvars
elif guess is not None: ndim = len(guess)
elif lower_bounds is not None: ndim = len(lower_bounds)
elif upper_bounds is not None: ndim = len(upper_bounds)
def cost(x):
return 1.
#XXX: don't allow solver string as a short-cut? #FIXME: add ensemble solvers
ensemble = False
if solver is None or solver == 'diffev':
from mystic.solvers import DifferentialEvolutionSolver as TheSolver
solver = TheSolver(ndim, min(40, ndim * 5))
elif solver == 'diffev2':
from mystic.solvers import DifferentialEvolutionSolver2 as TheSolver
solver = TheSolver(ndim, min(40, ndim * 5))
elif solver == 'fmin_powell': #XXX: better as the default? (it's not random)
from mystic.solvers import PowellDirectionalSolver as TheSolver
solver = TheSolver(ndim)
elif solver == 'fmin':
from mystic.solvers import NelderMeadSimplexSolver as TheSolver
solver = TheSolver(ndim)
elif solver == 'buckshot':
from mystic.solvers import BuckshotSolver as TheSolver
solver = TheSolver(ndim, max(8, npts)) #XXX: needs better default?
ensemble = True
elif solver == 'lattice':
from mystic.solvers import LatticeSolver as TheSolver
solver = TheSolver(ndim, max(8, npts)) #XXX: needs better default?
ensemble = True
if termination is None:
from mystic.termination import ChangeOverGeneration as COG
termination = COG()
if not ensemble:
if guess is not None:
solver.SetInitialPoints(guess) #XXX: nice if 'diffev' had methods
else:
solver.SetRandomInitialPoints(lower_bounds, upper_bounds)
if lower_bounds or upper_bounds:
solver.SetStrictRanges(lower_bounds, upper_bounds)
if hasattr(constraints, 'iter') and hasattr(constraints, 'error'):
solver.SetPenalty(constraints) #i.e. is a penalty function
else: # is a constraints solver
solver.SetConstraints(constraints)
from numpy import seterr
settings = seterr(all='ignore')
solver.Solve(cost, termination)
seterr(**settings)
soln = solver.bestSolution
from numpy import ndarray, array
if isinstance(soln, ndarray) and not isinstance(guess, ndarray):
soln = soln.tolist()
elif isinstance(guess, ndarray) and not isinstance(soln, ndarray):
soln = array(soln) #XXX: or always return a list ?
return soln #XXX: check with 'issolution' ?
def solve(constraints, guess=None, nvars=None, solver=None, \
lower_bounds=None, upper_bounds=None, termination=None):
"""Use optimization to find a solution to a set of constraints.
Inputs:
constraints -- a constraints solver function or a penalty function
Additional Inputs:
guess -- list of parameter values proposed to solve the constraints.
lower_bounds -- list of lower bounds on solution values.
upper_bounds -- list of upper bounds on solution values.
nvars -- number of parameter values.
solver -- the mystic solver to use in the optimization
termination -- the mystic termination to use in the optimization
NOTE: The resulting constraints will likely be more expensive to evaluate
and less accurate than writing the constraints solver from scratch.
"""
ndim = 1 #XXX: better, increase in while loop catching IndexError ?
if nvars is not None: ndim = nvars
elif guess is not None: ndim = len(guess)
elif lower_bounds is not None: ndim = len(lower_bounds)
elif upper_bounds is not None: ndim = len(upper_bounds)
def cost(x):
return 1.
#XXX: don't allow solver string as a short-cut?
if solver is None or solver == 'diffev':
from mystic.solvers import DifferentialEvolutionSolver as TheSolver
solver = TheSolver(ndim, min(40, ndim * 5))
elif solver == 'diffev2':
from mystic.solvers import DifferentialEvolutionSolver2 as TheSolver
solver = TheSolver(ndim, min(40, ndim * 5))
elif solver == 'fmin_powell': #XXX: better as the default? (it's not random)
from mystic.solvers import PowellDirectionalSolver as TheSolver
solver = TheSolver(ndim)
elif solver == 'fmin':
from mystic.solvers import NelderMeadSimplexSolver as TheSolver
solver = TheSolver(ndim)
if termination is None:
from mystic.termination import ChangeOverGeneration as COG
termination = COG()
if guess != None:
solver.SetInitialPoints(guess) #XXX: nice if 'diffev' also had methods
else:
solver.SetRandomInitialPoints(lower_bounds, upper_bounds)
if lower_bounds or upper_bounds:
solver.SetStrictRanges(lower_bounds, upper_bounds)
if hasattr(constraints, 'iter') and hasattr(constraints, 'error'):
solver.SetPenalty(constraints) #i.e. is a penalty function
else: # is a constraints solver
solver.SetConstraints(constraints)
solver.Solve(cost, termination)
soln = solver.bestSolution
from numpy import ndarray, array
if isinstance(soln, ndarray) and not isinstance(guess, ndarray):
soln = soln.tolist()
elif isinstance(guess, ndarray) and not isinstance(soln, ndarray):
soln = array(soln) #XXX: or always return a list ?
return soln #XXX: check with 'issolution' ?