Struct net_ensembles::er_m::ErEnsembleM

pub struct ErEnsembleM<T: Node, R: Rng> { /* fields omitted */ }

Implements Erdős-Rényi graph ensemble

Constant number of edges

Methods

impl<T: Node, R: Rng> ErEnsembleM<T, R>

pub fn new(n: u32, m: usize, rng: R) -> Self

Initialize

create new ErEnsembleM graph with:

• n vertices
• m edges
• rng is consumed and used as random number generator in the following
• internally uses Graph<T>::new(n)
• generates random edges according to ER model

pub fn get_m(&self) -> usize

Return total number of edges

pub fn sort_adj(&mut self)

Sort adjecency lists

If you depend on the order of the adjecency lists, you can sort them

Performance

• internally uses pattern-defeating quicksort as long as that is the standard
• sorts an adjecency list with length d in worst-case: O(d log(d))
• is called for each adjecency list, i.e., self.vertex_count() times

pub fn graph(&self) -> &Graph<T>

returns reference to the underlying topology aka, the Graph<T>

Use this to call functions regarding the topology

pub fn at(&self, index: usize) -> &T

access additional information at vertex

pub fn at_mut(&mut self, index: usize) -> &mut T

mutable access of additional information at index

Trait Implementations

impl<T: Clone + Node, R: Clone + Rng> Clone for ErEnsembleM<T, R>

fn clone(&self) -> ErEnsembleM<T, R>

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<T: Debug + Node, R: Debug + Rng> Debug for ErEnsembleM<T, R>

fn fmt(&self, f: &mut Formatter) -> Result

Formats the value using the given formatter.

impl<T, R> Ensemble<ErStepM, ErStepM> for ErEnsembleM<T, R> where
    T: Node,
    R: Rng, 

fn undo_step(&mut self, step: ErStepM) -> ErStepM

• undo a monte carlo step, return result-state
• if you want to undo more than one step see undo_steps

fn undo_step_quiet(&mut self, step: ErStepM)

• undo a monte carlo step, panic on invalid result state
• for undoing multiple steps see undo_steps_quiet

fn randomize(&mut self)

Randomizes self according to model

• this is intended for creation of initial sample
• used in simple_sample and simple_sample_vec

fn mc_step(&mut self) -> ErStepM

Monte Carlo step

• use this to perform a Monte Carlo step
• for doing multiple mc steps at once, use mc_steps

fn mc_steps(&mut self, count: usize) -> Vec<S>

Monte Carlo steps * use this to perform multiple Monte Carlo steps at once * result Vec<S> can be used to undo the steps with self.undo_steps(result)

fn simple_sample<F>(&mut self, times: usize, f: F) where
    F: FnMut(&Self), 

do the following times times: 1) f(self) 2) self.randomize()

fn simple_sample_vec<F, G>(&mut self, times: usize, f: F) -> Vec<G> where
    F: FnMut(&Self) -> G, 

do the following times times: 1) res = f(self) 2) self.randomize() ## res is collected into Vector

fn undo_steps(&mut self, steps: Vec<S>) -> Vec<Res>

Undo Monte Carlo steps * Note: uses undo_step in correct order and returns result ## Important: * look at specific implementation of undo_step, every thing mentioned there applies to each step

fn undo_steps_quiet(&mut self, steps: Vec<S>)

Undo Monte Carlo steps * Note: uses undo_step_quiet in correct order ## Important: * look at specific implementation of undo_step_quiet, every thing mentioned there applies to each step

impl<T, R> EnsembleRng<ErStepM, ErStepM, R> for ErEnsembleM<T, R> where
    T: Node,
    R: Rng, 

fn rng(&mut self) -> &mut R

Access RNG

If, for some reason, you want access to the internal random number generator: Here you go

fn swap_rng(&mut self, rng: R) -> R

Swap random number generator

• returns old internal rng