Package gmisclib :: Module mcmc :: Class rough_acceptor_base

Class rough_acceptor_base

Instance Methods

probe(self, currentpos, stepper)
This computes logP() near the current step position, and sees how much that differs from logP() _at_ the current step position.

source code

float

jitter(self)
In the acoustic distances paper, it is called $\bar{delta}$ . source code

reset(self)
In order to make the overal algorithm asymptotically a Markovian, we need to make sure that (asymptotically) the acceptor function depends only on long-term averages, not on recent history.

source code

__init__(self, probestep, tau, droop)
x.__init__(...) initializes x; see help(type(x)) for signature

source code

float

T(self)
Returns: A decent approximation to the system temperature. (Inherited from gmisclib.mcmc.acceptor_base)

source code

bool

__call__(self, delta)
Accept a step or not? (Inherited from gmisclib.mcmc.acceptor_base)

source code

Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __repr__, __setattr__, __sizeof__, __str__, __subclasshook__

Instance Variables
	last_probe A pair of position_base instances showing the position and logP at each end of the most recent probe for a discontinuity. (Inherited from gmisclib.mcmc.acceptor_base)
	since_last_reset Number of iterations since the last overall MCMC reset.

Properties
Inherited from `object`: `__class__`

Method Details

probe(self, currentpos, stepper)

source code

This computes logP() near the current step position, and sees how much that differs from logP() _at_ the current step position. That difference is used as a measure of the "roughness" or "randomness" of the function. That extra roughness is added onto the temperature to ensure that the MCMC stepper doesn't get trapped in an unimportant local valley. Essentially, we are making the statement that any differences within the region covered by probestep are unimportant.

Overrides: acceptor_base.probe

jitter(self)

source code

In the acoustic distances paper, it is called $\bar{delta}$ .

Returns: float: An estimate of how rough the function is, on a small scale.
Overrides: acceptor_base.jitter: (inherited documentation)

reset(self)

source code

In order to make the overal algorithm asymptotically a Markovian, we need to make sure that (asymptotically) the acceptor function depends only on long-term averages, not on recent history. The problem is the jitter measurement, of course. So, one needs to average the jitter over a time longer than the recurrence time, asymptotically. But that makes it horribly unresponsive in the early stages of the optimization when things are rapidly changing. The solution is to pass along resets, so that it can (temporarily) revert to a short averaging time.

Overrides: acceptor_base.reset: (inherited documentation)

init(self, probestep, tau, droop)
(Constructor)

source code

x.__init__(...) initializes x; see help(type(x)) for signature

Overrides: object.__init__: (inherited documentation)

Class rough_acceptor_base

probe(self, currentpos, stepper)

jitter(self)

reset(self)

__init__(self, probestep, tau, droop) (Constructor)

init(self, probestep, tau, droop)
(Constructor)