Trait lattice_qcd_rs::simulation::monte_carlo::MonteCarlo
source · pub trait MonteCarlo<State, const D: usize>where
State: LatticeState<D>,{
type Error;
// Required method
fn next_element(&mut self, state: State) -> Result<State, Self::Error>;
}Expand description
Monte-Carlo algorithm, giving the next element in the simulation. It is also a Markov chain.
Example
use lattice_qcd_rs::error::ImplementationError;
use lattice_qcd_rs::simulation::{
LatticeState, LatticeStateDefault, MetropolisHastingsSweep, MonteCarlo,
};
use rand::SeedableRng;
let rng = rand::rngs::StdRng::seed_from_u64(0); // change with your seed
let mut mh = MetropolisHastingsSweep::new(1, 0.1_f64, rng)
.ok_or(ImplementationError::OptionWithUnexpectedNone)?;
// Realistically you want more steps than 10
let mut state = LatticeStateDefault::<3>::new_cold(1_f64, 6_f64, 4)?;
for _ in 0..10 {
state = mh.next_element(state)?;
// or state.monte_carlo_step(&mut hmc)?;
// operation to track the progress or the evolution
}
// operation at the end of the simulation