def read(name, keys=None, keymap=None, type=None):
"""read klepto db with name 'name'
Args:
name (string): filename of the klepto db
keys (iterable): keys to load; or boolean to load all/no keys
keymap (klepto.keymap): used for key encoding
type (klepto.archive): type of klepto archive
Returns:
klepto db object (has dictionary-like interface)
Notes:
If keys is None, create a direct handle to the db.
If a key in keys is not found, it will be ignored.
"""
if type is None:
from klepto.archives import dir_archive as _archive
else:
_archive = type
if keys is None:
return _archive(name, cached=False)
if keymap is None:
from klepto.keymaps import keymap as _kmap
keymap = _kmap()
archive = _archive(name, cached=True)
if keys is False:
return archive
if keys is True:
archive.load()
else: # apply keymap to keys
archive.load(*(keymap(*(k if hasattr(k, '__len__') else (k, )))
for k in keys))
return archive # archive with keymapped keys
def __init__(self,
npts=4,
retry=1,
tol=8,
memtol=1,
memsize=None,
map=None,
archive=None,
cache=None,
sprayer=None,
seeker=None,
traj=False,
disp=False,
repeat=0):
"""searcher, which searches for all minima of a response surface
Input:
npts - number of solvers in the ensemble
retry - max consectutive retries w/o a cache 'miss'
tol - rounding precision for the minima comparator
memtol - rounding precision for memoization
memsize - maximum size of cache to hold in memory
map - map used for spawning solvers in the ensemble
archive - the sampled point archive(s)
cache - the trajectory cache(s)
sprayer - the mystic.ensemble instance
seeker - the mystic.solvers instance
traj - if True, save the parameter trajectories
disp - if True, be verbose
repeat - number of times to repeat the search
"""
#XXX: better not to use klepto as default? just dict and false cache?
from klepto.archives import dict_archive as _archive
from mystic.solvers import BuckshotSolver #XXX: or SparsitySolver?
from mystic.solvers import PowellDirectionalSolver
from mystic.pools import SerialPool as Pool
import sys
if (sys.hexversion >= 0x30000f0):
from builtins import map as _map
else:
from __builtin__ import map as _map
del sys
self.archive = _archive(cached=False) if archive is None else archive
self.cache = _archive(cached=False) if cache is None else cache
self.sprayer = BuckshotSolver if sprayer is None else sprayer
self.seeker = PowellDirectionalSolver if seeker is None else seeker
self.map = Pool().map if map in (None, _map) else map
self.traj = traj
self.disp = disp
self.npts = npts # number of solvers
self.retry = retry # max consectutive retries w/o a cache 'miss'
self.repeat = repeat # number of times to repeat the search
self.tol = tol # rounding precision
self.memtol = memtol # memoization rounding precision
self.memsize = memsize # max in-memory cache size
self._allSolvers = []
self._inv = False # self-awareness: am I inverted (maximizing)?
return
def __init__(self, npts=4, retry=1, tol=8, memtol=1, memsize=None,
map=None, archive=None, cache=None, sprayer=None,
seeker=None, traj=False, disp=False, repeat=0):
"""searcher, which searches for all minima of a response surface
Input:
npts - number of solvers in the ensemble
retry - max consectutive retries w/o a cache 'miss'
tol - rounding precision for the minima comparator
memtol - rounding precision for memoization
memsize - maximum size of cache to hold in memory
map - map used for spawning solvers in the ensemble
archive - the sampled point archive(s)
cache - the trajectory cache(s)
sprayer - the mystic.ensemble instance
seeker - the mystic.solvers instance
traj - if True, save the parameter trajectories
disp - if True, be verbose
repeat - number of times to repeat the search
"""
#XXX: better not to use klepto as default? just dict and false cache?
from klepto.archives import dict_archive as _archive
from mystic.solvers import BuckshotSolver #XXX: or SparsitySolver?
from mystic.solvers import PowellDirectionalSolver
from mystic.pools import SerialPool as Pool
import sys
if (sys.hexversion >= 0x30000f0):
from builtins import map as _map
else:
from __builtin__ import map as _map
del sys
self.archive = _archive(cached=False) if archive is None else archive
self.cache = _archive(cached=False) if cache is None else cache
self.sprayer = BuckshotSolver if sprayer is None else sprayer
self.seeker = PowellDirectionalSolver if seeker is None else seeker
self.map = Pool().map if map in (None, _map) else map
self.traj = traj
self.disp = disp
self.npts = npts # number of solvers
self.retry = retry # max consectutive retries w/o a cache 'miss'
self.repeat = repeat # number of times to repeat the search
self.tol = tol # rounding precision
self.memtol = memtol # memoization rounding precision
self.memsize = memsize # max in-memory cache size
self._allSolvers = []
self._inv = False # self-awareness: am I inverted (maximizing)?
return
def __init__(self,
npts=4,
retry=1,
tol=8,
memtol=1,
map=None,
archive=None,
sprayer=None,
seeker=None,
traj=False,
disp=False):
#XXX: better not to use klepto as default? just dict and false cache?
from klepto.archives import dict_archive as _archive
from mystic.solvers import BuckshotSolver
from mystic.solvers import PowellDirectionalSolver
from mystic.pools import SerialPool as Pool
from __builtin__ import map as _map
self.archive = _archive(cached=False) if archive is None else archive
self.sprayer = BuckshotSolver if sprayer is None else sprayer
self.seeker = PowellDirectionalSolver if seeker is None else seeker
self.map = Pool().map if map in (None, _map) else map
self.traj = traj
self.disp = disp
self.npts = npts # number of solvers
self.retry = retry # max consectutive retries w/o a cache 'miss'
self.tol = tol # rounding precision
self.memtol = memtol # memoization rounding precision
self._allSolvers = []
self._inv = False # self-awareness: am I inverted (maximizing)?
return
def __init__(self, npts=4, retry=1, tol=8, memtol=1, map=None,
archive=None, sprayer=None, seeker=None, traj=False, disp=False):
#XXX: better not to use klepto as default? just dict and false cache?
from klepto.archives import dict_archive as _archive
from mystic.solvers import BuckshotSolver
from mystic.solvers import PowellDirectionalSolver
from mystic.pools import SerialPool as Pool
from __builtin__ import map as _map
self.archive = _archive(cached=False) if archive is None else archive
self.sprayer = BuckshotSolver if sprayer is None else sprayer
self.seeker = PowellDirectionalSolver if seeker is None else seeker
self.map = Pool().map if map in (None, _map) else map
self.traj = traj
self.disp = disp
self.npts = npts # number of solvers
self.retry = retry # max consectutive retries w/o a cache 'miss'
self.tol = tol # rounding precision
self.memtol = memtol # memoization rounding precision
self._allSolvers = []
self._inv = False # self-awareness: am I inverted (maximizing)?
return