def __init__(self,
stochastic,
cov=None,
delay=1000,
scales=None,
interval=100,
greedy=True,
verbose=None):
self.verbose = verbose
if getattr(stochastic, '__class__') is pymc.PyMCObjects.Stochastic:
stochastic = [stochastic]
StepMethod.__init__(self, stochastic, verbose)
self._id = 'AdaptiveMetropolis_' + '_'.join(
[p.__name__ for p in self.stochastics])
# State variables used to restore the state in a latter session.
self._state += [
'_trace_count', '_current_iter', 'C', '_sig', '_proposal_deviate',
'_trace'
]
self.delay = delay
self.isdiscrete = {}
self.interval = interval
self.greedy = greedy
self.check_type()
self.dimension()
self.C_0 = self.initialize_cov(cov, scales)
#self._sig = msqrt(self.C_0)
self._sig = np.linalg.cholesky(self.C_0)
# Keep track of the internal trace length
# It may be different from the iteration count since greedy
# sampling can be done during warm-up period.
self._trace_count = 0
self._current_iter = 0
self._proposal_deviate = np.zeros(self.dim)
self.C = self.C_0.copy()
self.chain_mean = np.asmatrix(np.zeros(self.dim))
self._trace = []
if self.verbose >= 1:
print "Initialization..."
print 'Dimension: ', self.dim
print "C_0: ", self.C_0
print "Sigma: ", self._sig
def __init__(self, stochastic, cov, phi, verbose=-1, tally=False):
# assign cooling step
self.phi = phi
# Verbosity flag
self.verbose = verbose
self.accepted = 0
self.rejected = 0
if not np.iterable(stochastic) or isinstance(stochastic, Variable):
stochastic = [stochastic]
# Initialize superclass
StepMethod.__init__(self, stochastic, verbose, tally)
self._id = 'SMC_Metropolis_' + '_'.join(
[p.__name__ for p in self.stochastics])
# State variables used to restore the state in a latter session.
self._state += [
'accepted', 'rejected', '_trace_count', '_current_iter', 'C',
'proposal_sd', '_proposal_deviate', '_trace', 'shrink_if_necessary'
]
self._tuning_info = ['C']
self.proposal_sd = None
self.shrink_if_necessary = False
# Initialization methods
self.check_type()
self.dimension()
# Set the initial covariance using cov
self.C = cov
self.updateproposal_sd()
# Keep track of the internal trace length
self._trace_count = 0
self._current_iter = 0
self._proposal_deviate = np.zeros(self.dim)
self.chain_mean = np.asmatrix(np.zeros(self.dim))
self._trace = []
if self.verbose >= 2:
print("Initialization...")
print('Dimension: ', self.dim)
print("C_0: ", self.C)
print("Sigma: ", self.proposal_sd)
def __init__(self, stochs, indicator, p, rp, g, q, rq, inv_q, Jacobian):
StepMethod.__init__(self, nodes = stochs)
self.g = g
self.q = q
self.rq = rq
self.p = p
self.rp = rp
self.inv_q = inv_q
self.Jacobian = Jacobian
self.stoch_dict = {}
for stoch in stochs:
self.stoch_dict[stoch.__name__] = stoch
self.indicator = indicator
def __init__(self, stochs, indicator, p, rp, g, q, rq, inv_q, Jacobian):
StepMethod.__init__(self, nodes=stochs)
self.g = g
self.q = q
self.rq = rq
self.p = p
self.rp = rp
self.inv_q = inv_q
self.Jacobian = Jacobian
self.stoch_dict = {}
for stoch in stochs:
self.stoch_dict[stoch.__name__] = stoch
self.indicator = indicator
def __init__(self, stochastic, cov=None, delay=1000, scales=None, interval=100, greedy=True, verbose=None):
self.verbose = verbose
if getattr(stochastic, "__class__") is pymc.PyMCObjects.Stochastic:
stochastic = [stochastic]
StepMethod.__init__(self, stochastic, verbose)
self._id = "AdaptiveMetropolis_" + "_".join([p.__name__ for p in self.stochastics])
# State variables used to restore the state in a latter session.
self._state += ["_trace_count", "_current_iter", "C", "_sig", "_proposal_deviate", "_trace"]
self.delay = delay
self.isdiscrete = {}
self.interval = interval
self.greedy = greedy
self.check_type()
self.dimension()
self.C_0 = self.initialize_cov(cov, scales)
# self._sig = msqrt(self.C_0)
self._sig = np.linalg.cholesky(self.C_0)
# Keep track of the internal trace length
# It may be different from the iteration count since greedy
# sampling can be done during warm-up period.
self._trace_count = 0
self._current_iter = 0
self._proposal_deviate = np.zeros(self.dim)
self.C = self.C_0.copy()
self.chain_mean = np.asmatrix(np.zeros(self.dim))
self._trace = []
if self.verbose >= 1:
print "Initialization..."
print "Dimension: ", self.dim
print "C_0: ", self.C_0
print "Sigma: ", self._sig
def __init__(self,
stochastic,
cov=None,
delay=1000,
interval=200,
greedy=True,
drscale=0.1,
shrink_if_necessary=False,
scales=None,
verbose=-1,
tally=False):
# Verbosity flag
self.verbose = verbose
self.accepted = 0
self.rejected_then_accepted = 0
self.rejected_twice = 0
if not np.iterable(stochastic) or isinstance(stochastic, Variable):
stochastic = [stochastic]
# Initialize superclass
StepMethod.__init__(self, stochastic, verbose, tally)
self._id = 'DelayedRejectionAdaptiveMetropolis_' + '_'.join(
[p.__name__ for p in self.stochastics])
# State variables used to restore the state in a latter session.
self._state += [
'accepted', 'rejected_then_accepted', 'rejected_twice',
'_trace_count', '_current_iter', 'C', 'proposal_sd',
'_proposal_deviate', '_trace', 'shrink_if_necessary'
]
self._tuning_info = ['C']
self.proposal_sd = None
self.shrink_if_necessary = shrink_if_necessary
# Number of successful steps before the empirical covariance is
# computed
self.delay = delay
# Interval between covariance updates
self.interval = interval
# Flag for tallying only accepted jumps until delay reached
self.greedy = greedy
# Scale for second attempt
self.drscale = drscale
# Initialization methods
self.check_type()
self.dimension()
# Set the initial covariance using cov, or the following fallback
# mechanisms:
# 1. If scales is provided, use it.
# 2. If a trace is present, compute the covariance matrix empirically
# from it.
# 3. Use the stochastics value as a guess of the variance.
if cov is not None:
self.C = cov
elif scales:
self.C = self.cov_from_scales(scales)
else:
try:
self.C = self.cov_from_trace()
except AttributeError:
self.C = self.cov_from_value(100.)
self.updateproposal_sd()
# Keep track of the internal trace length
# It may be different from the iteration count since greedy
# sampling can be done during warm-up period.
self._trace_count = 0
self._current_iter = 0
self._proposal_deviate = np.zeros(self.dim)
self.chain_mean = np.asmatrix(np.zeros(self.dim))
self._trace = []
if self.verbose >= 2:
print("Initialization...")
print('Dimension: ', self.dim)
print("C_0: ", self.C)
print("Sigma: ", self.proposal_sd)