IMP.pmi.samplers.MonteCarlo Class Reference

Sample using Monte Carlo. More...

Inherits _SamplerBase.

Detailed Description

Sample using Monte Carlo.

Note

This class is only available in Python.

Definition at line 70 of file samplers.py.

Public Member Functions
def	__init__
	Setup Monte Carlo sampling. More...
def	apply_self_adaptive
	Modify parameters of individual movers to try to keep acceptance rate around 50%. More...
def	get_jax_model
	Get the current JAX Model used by the sampler. More...
def	set_use_jax
	Request that sampling of the scoring function is done using JAX instead of IMP's internal C++ implementation (requires that all PMI restraints used have a JAX implementation). More...

Constructor & Destructor Documentation

def IMP.pmi.samplers.MonteCarlo.__init__	(		self,
			model,
			objects = None,
			temp = 1.0,
			filterbyname = None,
			score_moved = False
	)

Setup Monte Carlo sampling.

Parameters

model	The IMP Model
objects	What to sample (a list of Movers)
temp	The MC temperature
filterbyname	Not used
score_moved	If True, attempt to speed up sampling by caching scoring function terms on particles that didn't move

Definition at line 80 of file samplers.py.

Member Function Documentation

def IMP.pmi.samplers.MonteCarlo.apply_self_adaptive

(

self

)

Modify parameters of individual movers to try to keep acceptance rate around 50%.

Definition at line 175 of file samplers.py.

def IMP.pmi.samplers.MonteCarlo.get_jax_model

(

self

)

Get the current JAX Model used by the sampler.

Definition at line 129 of file samplers.py.

def IMP.pmi.samplers.MonteCarlo.set_use_jax	(		self,
			nstep
	)

Request that sampling of the scoring function is done using JAX instead of IMP's internal C++ implementation (requires that all PMI restraints used have a JAX implementation).

Definition at line 120 of file samplers.py.

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The documentation for this class was generated from the following file:

• samplers.py