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)