def __init__(self, dim): """ Takes one initial input: dim -- dimensionality of the problem """ AbstractSolver.__init__(self,dim) self._direc = None # this is the easy way to return 'direc'... x1 = self.population[0] fx = self.popEnergy[0] # [x1, fx, bigind, delta] self.__internals = [x1, fx, 0, 0.0] ftol, gtol = 1e-4, 2 from mystic.termination import NormalizedChangeOverGeneration as NCOG self._termination = NCOG(ftol,gtol)
def __init__(self, dim): """ Takes one initial input: dim -- dimensionality of the problem """ AbstractSolver.__init__(self, dim) self._direc = None # this is the easy way to return 'direc'... x1 = self.population[0] fx = self.popEnergy[0] # [x1, fx, bigind, delta] self.__internals = [x1, fx, 0, 0.0] ftol, gtol = 1e-4, 2 from mystic.termination import NormalizedChangeOverGeneration as NCOG self._termination = NCOG(ftol, gtol)
def __init__(self, dim): """ Takes one initial input: dim -- dimensionality of the problem The size of the simplex is dim+1. """ simplex = dim+1 #XXX: cleaner to set npop=simplex, and use 'population' as simplex AbstractSolver.__init__(self,dim) #,npop=simplex) self.popEnergy.append(self._init_popEnergy) self.population.append([0.0 for i in range(dim)]) xtol, ftol = 1e-4, 1e-4 from mystic.termination import CandidateRelativeTolerance as CRT self._termination = CRT(xtol,ftol)
def __init__(self, dim): """ Takes one initial input: dim -- dimensionality of the problem The size of the simplex is dim+1. """ simplex = dim + 1 #XXX: cleaner to set npop=simplex, and use 'population' as simplex AbstractSolver.__init__(self, dim) #,npop=simplex) self.popEnergy.append(self._init_popEnergy) self.population.append([0.0 for i in range(dim)]) xtol, ftol = 1e-4, 1e-4 from mystic.termination import CandidateRelativeTolerance as CRT self._termination = CRT(xtol, ftol)
def __init__(self, dim, NP=4): """ Takes two initial inputs: dim -- dimensionality of the problem NP -- size of the trial solution population. [requires: NP >= 4] All important class members are inherited from AbstractSolver. """ NP = max(NP, dim, 4) #XXX: raise Error if npop <= 4? AbstractSolver.__init__(self,dim,npop=NP) self.genealogy = [ [] for j in range(NP)] self.scale = 0.8 self.probability = 0.9 ftol = 5e-3 from mystic.termination import VTRChangeOverGeneration self._termination = VTRChangeOverGeneration(ftol)
def __init__(self, dim): """ Takes one initial input: dim -- dimensionality of the problem The size of the simplex is dim+1. """ simplex = dim+1 #XXX: cleaner to set npop=simplex, and use 'population' as simplex AbstractSolver.__init__(self,dim) #,npop=simplex) self.popEnergy.append(self._init_popEnergy) self.population.append([0.0 for i in range(dim)]) self.radius = 0.05 #percentage change for initial simplex values self.adaptive = False #use adaptive algorithm parameters xtol, ftol = 1e-4, 1e-4 from mystic.termination import CandidateRelativeTolerance as CRT self._termination = CRT(xtol,ftol)
def __init__(self, dim): """ Takes one initial input: dim -- dimensionality of the problem The size of the simplex is dim+1. """ simplex = dim + 1 #XXX: cleaner to set npop=simplex, and use 'population' as simplex AbstractSolver.__init__(self, dim) #,npop=simplex) self.popEnergy.append(self._init_popEnergy) self.population.append([0.0 for i in range(dim)]) self.radius = 0.05 #percentage change for initial simplex values self.adaptive = False #use adaptive algorithm parameters xtol, ftol = 1e-4, 1e-4 from mystic.termination import CandidateRelativeTolerance as CRT self._termination = CRT(xtol, ftol)