Module mcmc_helper
source code
This is a helper module to make use of mcmc.py and mcmc_big.py. It
allows you to conveniently run a Monte-Carlo simulation of any kind until
it converges (stepper.run_to_bottom) or until it has explored a large
chunk of parameter space (stepper.run_to_ergodic).
It also helps you with logging the process.
instance of mcmc_mod, typically BootStepper.
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make_stepper_from_lov(problem_def,
vector_generator,
mcmc_mod=<module 'gmisclib.mcmc' from '/scratch/gpk/working_natty/lib/p...,
posn_class=<class 'gmisclib.mcmc.position_repeatable'>,
n=None)
Returns:
A BootStepper, ready to run, from the specified module. |
source code
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instance of mcmc_mod, typically BootStepper.
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make_stepper_from_lop(position_generator,
mcmc_mod=<module 'gmisclib.mcmc' from '/scratch/gpk/working_natty/lib/p...,
n=None)
Returns:
A BootStepper, ready to run, from the specified module. |
source code
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Debug = 0
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__package__ = 'gmisclib' |
Imports:
sys,
math,
random,
Thr,
numpy,
g_implements,
mcmc,
die
make_stepper_from_lov(problem_def,
vector_generator,
mcmc_mod=<module 'gmisclib.mcmc' from '/scratch/gpk/working_natty/lib/p...,
posn_class=<class 'gmisclib.mcmc.position_repeatable'>,
n=None)
| source code
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- Parameters:
vector_generator (A sequence of numpy.ndarray, normally a list of them.) - Initial starting points for the optimization.n (None or int) - The minimum number of samples to use. None means number of
parameters+2. - Returns: instance of
mcmc_mod, typically BootStepper.
- A
BootStepper, ready to run, from the specified
module.
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make_stepper_from_lop(position_generator,
mcmc_mod=<module 'gmisclib.mcmc' from '/scratch/gpk/working_natty/lib/p...,
n=None)
| source code
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- Parameters:
position_generator (A sequence of mcmc.position_base.) - Initial starting points for the optimization.n (None or int) - The minimum number of samples to use. None means number of
parameters+2. - Returns: instance of
mcmc_mod, typically BootStepper.
- A
BootStepper, ready to run, from the specified
module.
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