Package gmisclib :: Module mcmc_big :: Class BootStepper
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Class BootStepper

source code


Instance Methods
 
__init__(self, lop, v, strategy='intermediate', maxArchSize=None, parallelSizeDiv=1)
x.__init__(...) initializes x; see help(type(x)) for signature
source code
 
step(self)
In subclasses, this takes a step and returns 0 or 1, depending on whether the step was accepted or not.
source code
 
step_mixed(self) source code
 
step_parab(self) source code
position_base
current(self)
Returns: the current position. (Inherited from gmisclib.mcmc.stepper)
source code
 
ergodic(self)
A crude measure of how ergodic the MCMC is. (Inherited from gmisclib.mcmc.BootStepper)
source code
 
needs_a_reset(self)
Decides if we we need a reset. (Inherited from gmisclib.mcmc.stepper)
source code
numpy.ndarray
prms(self)
Returns: The current parameters (Inherited from gmisclib.mcmc.stepper)
source code
 
reset(self)
Called internally to mark when the optimization has found a new minimum. (Inherited from gmisclib.mcmc.stepper)
source code
 
reset_adjusters(self) (Inherited from gmisclib.mcmc.BootStepper) source code
int
reset_id(self)
Use this to tell if the stepper has been reset since you last looked at it. (Inherited from gmisclib.mcmc.stepper)
source code
 
set_sort_strategy(self, ss) (Inherited from gmisclib.mcmc.BootStepper) source code
 
set_strategy(self, ss) (Inherited from gmisclib.mcmc.BootStepper) source code
 
status(self)
Provides some printable status information in a=v; format. (Inherited from gmisclib.mcmc.BootStepper)
source code
 
stepV(self) (Inherited from gmisclib.mcmc.BootStepper) source code
 
step_boot(self) (Inherited from gmisclib.mcmc.BootStepper) source code
 
step_probe(self) (Inherited from gmisclib.mcmc.BootStepper) source code

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

Class Variables
  F = 0.234
F is the targeted step acceptance rate. (Inherited from gmisclib.mcmc.BootStepper)
  PBootLim = 0.9
PBootLim Limits the probability of taking a bootstrap step. (Inherited from gmisclib.mcmc.BootStepper)
  SANNEAL = 'intermediate'
Simulated annealing (Inherited from gmisclib.mcmc.BootStepper)
  SSALWAYS = 'hillclimb' (Inherited from gmisclib.mcmc.BootStepper)
  SSAMPLE = 'sample'
Sampling mode (Inherited from gmisclib.mcmc.BootStepper)
  SSAUTO = 'intermediate' (Inherited from gmisclib.mcmc.BootStepper)
  SSLOW = 'intermediate' (Inherited from gmisclib.mcmc.BootStepper)
  SSNEVER = 'sample' (Inherited from gmisclib.mcmc.BootStepper)
  SUPHILL = 'hillclimb'
Go straight uphill (Inherited from gmisclib.mcmc.BootStepper)
  alpha = 0.1
How rapidly should one expand the archive after a reset? (Inherited from gmisclib.mcmc.BootStepper)
Instance Variables
  acceptable
Acceptable is a function that decides whether or not a step is OK. (Inherited from gmisclib.mcmc.stepper)
  last_failed
It should reflect the success or failure of the most recently completed step. (Inherited from gmisclib.mcmc.stepper)
  np
The number of parameters: (Inherited from gmisclib.mcmc.BootStepper)
  np_eff
In a multiprocessor situation, np_eff tells you how much data do you need to store locally, so that the overall group of processors stores enough variety of data. (Inherited from gmisclib.mcmc.BootStepper)
Properties

Inherited from object: __class__

Method Details

__init__(self, lop, v, strategy='intermediate', maxArchSize=None, parallelSizeDiv=1)
(Constructor)

source code 

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

Parameters:
  • maxArchSize - How many position vectors can be stored. This is normally used to (loosely) enforce a memory limitation for large jobs.
  • parallelSizeDiv - For use when there are several cooperating MCMC processes that share data. When >1, this allows each process to have smaller stored lists. Normally, parallelSizeDiv is between 1 and the number of cooperating processes.
  • lop - These are a list of starting postions that should be evaluated first and used as the basis for further exploration.
  • v - A covariance matrix for non-bootstrap steps. Mostly, the system takes bootstrap steps that are adaptively derived from previous steps. However, to generate the list of previous steps, it needs a way to generate steps ab initio. So, v is used to get things going. v is also used occasionally thereafter, just to make sure the stepper doesn't get trapped in some subspace.
Overrides: object.__init__
(inherited documentation)

step(self)

source code 

In subclasses, this takes a step and returns 0 or 1, depending on whether the step was accepted or not.

Overrides: mcmc.stepper.step
(inherited documentation)