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AffineInvariantDES.h
Go to the documentation of this file.
1//* This file is part of the MOOSE framework
2//* https://mooseframework.inl.gov
3//*
4//* All rights reserved, see COPYRIGHT for full restrictions
5//* https://github.com/idaholab/moose/blob/master/COPYRIGHT
6//*
7//* Licensed under LGPL 2.1, please see LICENSE for details
8//* https://www.gnu.org/licenses/lgpl-2.1.html
9
10#pragma once
11
12#include "PMCMCBase.h"
13
18{
19public:
21
23
24 virtual int decisionStep() const override { return 2; }
25
26protected:
27 virtual void proposeSamples() override;
28
38 const Real & state1, const Real & state2, const Real & rnd, const Real & scale, Real & diff);
39
46 void tuneParams(Real & gamma, Real & b, const Real & scale);
47
49 const std::vector<std::vector<Real>> & _previous_state;
50
52 const std::vector<Real> & _previous_state_var;
53
56
58 std::vector<Real> _scales;
59};
A class for performing Affine Invariant Ensemble MCMC with differential sampler.
void tuneParams(Real &gamma, Real &b, const Real &scale)
Tune the internal parameters.
const std::vector< Real > & _previous_state_var
Reporter value with the previous state of all the walkers for variance.
std::vector< Real > _scales
Scales for the parameters.
virtual void proposeSamples() override
Fill in the _new_samples vector of vectors (happens within sampleSetUp)
static InputParameters validParams()
const std::vector< std::vector< Real > > & _previous_state
Reporter value with the previous state of all the walkers.
void computeDifferential(const Real &state1, const Real &state2, const Real &rnd, const Real &scale, Real &diff)
Compute the differential evolution from the current state.
const MooseEnum & _tuning_option
Tuning options for the internal params.
virtual int decisionStep() const override
Return the step after which decision making can begin.
const InputParameters & parameters() const
A base class used to perform Parallel Markov Chain Monte Carlo (MCMC) sampling.
Definition PMCMCBase.h:20