def __init__(self, stochastic, inits=None, kernel_probs=[0.4918, 0.4918, 0.0082, 0.0082], walk_theta=1.5, traverse_theta=6.0, n1=4, support=lambda x: True, verbose=None, tally=True):
# Initialize superclass
StepMethod.__init__(self, [stochastic], verbose=verbose, tally=tally)
# Ordered list of proposal kernels
self.kernels = [self.walk, self.traverse, self.blow, self.hop]
# Kernel for current iteration
self.current_kernel = None
self.accepted = zeros(len(kernel_probs))
self.rejected = zeros(len(kernel_probs))
# Cumulative kernel probabilities
self.cum_probs = np.cumsum(kernel_probs)
self.walk_theta = walk_theta
self.traverse_theta = traverse_theta
# Set public attributes
self.stochastic = stochastic
if verbose is not None:
self.verbose = verbose
else:
self.verbose = stochastic.verbose
# Determine size of stochastic
if isinstance(self.stochastic.value, ndarray):
self._len = len(self.stochastic.value.ravel())
else:
self._len = 1
# Create attribute for holding value and secondary value
self.values = [self.stochastic.value]
# Initialize to different value from stochastic or supplied values
if inits is None:
self.values.append(self.stochastic.random())
# Reset original value
self.stochastic.value = self.values[0]
else:
self.values.append(inits)
# Flag for using second point in log-likelihood calculations
self._prime = False
# Proposal adjustment factor for current iteration
self.hastings_factor = 0.0
# Set probability of selecting any parameter
self.p = 1.*min(self._len, n1)/self._len
# Support function
self._support = support
self._state = ['accepted', 'rejected', 'p']
def __init__(self, stochastic, w=1, m=20, n_tune=0, verbose=-1, tally=False):
"""
Slice sampler class initialization
"""
#a Initialize superclass
StepMethod.__init__(self, [stochastic], tally=tally)
# id string
self._id = "Slicer"
# Set public attributes
self.stochastic = stochastic
if verbose > -1:
self.verbose = verbose
else:
self.verbose = stochastic.verbose
self.w_tune = []
self.n_tune = n_tune
self._tuning_info = ["w_tune", "n_tune"]
self.w = w
self.m = m
