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