def __init__(self, dim, **kwds):
"""
Takes one initial input::
dim -- dimensionality of the problem.
Additional inputs::
npop -- size of the trial solution population. [default = 1]
nbins -- tuple of number of bins in each dimension. [default = [1]*dim]
npts -- number of solver instances. [default = 1]
rtol -- size of radial tolerance for sparsity. [default = None]
Important class members::
nDim, nPop = dim, npop
generations - an iteration counter.
evaluations - an evaluation counter.
bestEnergy - current best energy.
bestSolution - current best parameter set. [size = dim]
popEnergy - set of all trial energy solutions. [size = npop]
population - set of all trial parameter solutions. [size = dim*npop]
solution_history - history of bestSolution status. [StepMonitor.x]
energy_history - history of bestEnergy status. [StepMonitor.y]
signal_handler - catches the interrupt signal. [***disabled***]
"""
super(AbstractEnsembleSolver, self).__init__(dim, **kwds)
#self.signal_handler = None
#self._handle_sigint = False
# default settings for nested optimization
#XXX: move nbins and npts to _InitialPoints?
self._dist = None #kwds['dist'] if 'dist' in kwds else None
nbins = kwds['nbins'] if 'nbins' in kwds else [1] * dim
if isinstance(nbins, int):
from mystic.math.grid import randomly_bin
nbins = randomly_bin(nbins, dim, ones=True, exact=True)
self._nbins = nbins
npts = kwds['npts'] if 'npts' in kwds else 1
self._npts = npts
rtol = kwds['rtol'] if 'rtol' in kwds else None
self._rtol = rtol
from mystic.solvers import NelderMeadSimplexSolver
self._solver = NelderMeadSimplexSolver
self._bestSolver = None # 'best' solver (after Solve)
self._total_evals = 0 # total function calls (after Solve)
NP = reduce(lambda x, y: x * y, nbins) if 'nbins' in kwds else npts
self._allSolvers = [None for j in range(NP)]
return
def __init__(self, dim, **kwds):
"""
Takes one initial input::
dim -- dimensionality of the problem.
Additional inputs::
npop -- size of the trial solution population. [default = 1]
nbins -- tuple of number of bins in each dimension. [default = [1]*dim]
npts -- number of solver instances. [default = 1]
rtol -- size of radial tolerance for sparsity. [default = None]
Important class members::
nDim, nPop = dim, npop
generations - an iteration counter.
evaluations - an evaluation counter.
bestEnergy - current best energy.
bestSolution - current best parameter set. [size = dim]
popEnergy - set of all trial energy solutions. [size = npop]
population - set of all trial parameter solutions. [size = dim*npop]
solution_history - history of bestSolution status. [StepMonitor.x]
energy_history - history of bestEnergy status. [StepMonitor.y]
signal_handler - catches the interrupt signal. [***disabled***]
"""
super(AbstractEnsembleSolver, self).__init__(dim, **kwds)
#self.signal_handler = None
#self._handle_sigint = False
# default settings for nested optimization
#XXX: move nbins and npts to _InitialPoints?
self._dist = None #kwds['dist'] if 'dist' in kwds else None
nbins = kwds['nbins'] if 'nbins' in kwds else [1]*dim
if isinstance(nbins, int):
from mystic.math.grid import randomly_bin
nbins = randomly_bin(nbins, dim, ones=True, exact=True)
self._nbins = nbins
npts = kwds['npts'] if 'npts' in kwds else 1
self._npts = npts
rtol = kwds['rtol'] if 'rtol' in kwds else None
self._rtol = rtol
from mystic.solvers import NelderMeadSimplexSolver
self._solver = NelderMeadSimplexSolver
self._bestSolver = None # 'best' solver (after Solve)
NP = reduce(lambda x,y:x*y, nbins) if 'nbins' in kwds else npts
self._allSolvers = [None for j in range(NP)]
return
def __init__(self, dim, **kwds):
"""
Takes one initial input:
dim -- dimensionality of the problem.
Additional inputs:
npop -- size of the trial solution population. [default = 1]
nbins -- tuple of number of bins in each dimension. [default = [1]*dim]
npts -- number of solver instances. [default = 1]
Important class members:
nDim, nPop = dim, npop
generations - an iteration counter.
evaluations - an evaluation counter.
bestEnergy - current best energy.
bestSolution - current best parameter set. [size = dim]
popEnergy - set of all trial energy solutions. [size = npop]
population - set of all trial parameter solutions. [size = dim*npop]
solution_history - history of bestSolution status. [StepMonitor.x]
energy_history - history of bestEnergy status. [StepMonitor.y]
signal_handler - catches the interrupt signal. [***disabled***]
"""
super(AbstractEnsembleSolver, self).__init__(dim, **kwds)
#self.signal_handler = None
#self._handle_sigint = False
# default settings for nested optimization
nbins = [1]*dim
if kwds.has_key('nbins'): nbins = kwds['nbins']
if isinstance(nbins, int):
from mystic.math.grid import randomly_bin
nbins = randomly_bin(nbins, dim)
self._nbins = nbins
npts = 1
if kwds.has_key('npts'): npts = kwds['npts']
self._npts = npts
from mystic.solvers import NelderMeadSimplexSolver
self._solver = NelderMeadSimplexSolver
self._bestSolver = None # 'best' solver (after Solve)
self._total_evals = 0 # total function calls (after Solve)
return
def __init__(self, dim, **kwds):
"""
Takes one initial input:
dim -- dimensionality of the problem.
Additional inputs:
npop -- size of the trial solution population. [default = 1]
nbins -- tuple of number of bins in each dimension. [default = [1]*dim]
npts -- number of solver instances. [default = 1]
Important class members:
nDim, nPop = dim, npop
generations - an iteration counter.
evaluations - an evaluation counter.
bestEnergy - current best energy.
bestSolution - current best parameter set. [size = dim]
popEnergy - set of all trial energy solutions. [size = npop]
population - set of all trial parameter solutions. [size = dim*npop]
solution_history - history of bestSolution status. [StepMonitor.x]
energy_history - history of bestEnergy status. [StepMonitor.y]
signal_handler - catches the interrupt signal. [***disabled***]
"""
super(AbstractNestedSolver, self).__init__(dim, **kwds)
#self.signal_handler = None
#self._handle_sigint = False
# default settings for nested optimization
nbins = [1] * dim
if kwds.has_key('nbins'): nbins = kwds['nbins']
if isinstance(nbins, int):
from mystic.math.grid import randomly_bin
nbins = randomly_bin(nbins, dim)
self._nbins = nbins
npts = 1
if kwds.has_key('npts'): npts = kwds['npts']
self._npts = npts
from mystic.solvers import NelderMeadSimplexSolver
self._solver = NelderMeadSimplexSolver
self._bestSolver = None # 'best' solver (after Solve)
self._total_evals = 0 # total function calls (after Solve)
return
def _InitialPoints(self):
"""Generate a grid of starting points for the ensemble of optimizers"""
nbins = self._nbins
if isinstance(nbins, int):
from mystic.math.grid import randomly_bin
nbins = randomly_bin(nbins, self.nDim, ones=True, exact=True)
if len(self._strictMax): upper = list(self._strictMax)
else:
upper = list(self._defaultMax)
if len(self._strictMin): lower = list(self._strictMin)
else:
lower = list(self._defaultMin)
# generate arrays of points defining a grid in parameter space
grid_dimensions = self.nDim
bins = []
for i in range(grid_dimensions):
step = abs(upper[i] - lower[i]) / nbins[i]
bins.append([lower[i] + (j + 0.5) * step for j in range(nbins[i])])
# build a grid of starting points
from mystic.math import gridpts
return gridpts(bins, self._dist)