Struct net_ensembles::er_c::ErEnsembleC

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

Implements Erdős-Rényi graph ensemble

• variable number of edges
• targets a connectivity

Sampling

• for simple sampling look at SimpleSample trait
• for markov steps look at MarkovChain trait

Other

• for topology functions look at GenericGraph
• to access underlying topology or manipulate additional data look at WithGraph trait
• to use or swap the random number generator, look at HasRng trait

Implementations

impl<T, R> ErEnsembleC<T, R> where
    T: Node + SerdeStateConform,
    R: Rng, 

pub fn new(n: usize, c_target: f64, rng: R) -> Self

Initialize

create new ErEnsembleC with:

• n vertices
• target connectivity c_target
• 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 make_connected(&mut self)

Experimental! Connect the connected components

• adds edges, to connect the connected components
• panics if no vertices are in the graph
• intended as starting point for a markov chain, if you require connected graphs
• do not use this to independently (simple-) sample connected networks, as this will skew the statistics
• This is still experimental, this member might change the internal functionallity resulting in different connected networks, without prior notice
• This member might be removed in braking releases

pub fn target_connectivity(&self) -> f64

returns target connectivity

Explanation

The target connectivity c_target is used to calculate the probability p, that any two vertices i and j (where i != j) are connected.

p = c_target / (N - 1) where N is the number of vertices in the graph

pub fn set_target_connectivity(&mut self, c_target: f64)

• set new value for target connectivity

Note

• will only set the value (and probability), which will be used from now on
• if you also want to create a new sample, call randomize afterwards

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

Trait Implementations

impl<T, R> AsRef<GenericGraph<T, NodeContainer<T>>> for ErEnsembleC<T, R> where
    T: Node,
    R: Rng, 

fn as_ref(&self) -> &Graph<T>

Performs the conversion.

impl<T, R> Borrow<GenericGraph<T, NodeContainer<T>>> for ErEnsembleC<T, R> where
    T: Node,
    R: Rng, 

fn borrow(&self) -> &Graph<T>

Immutably borrows from an owned value.

impl<T: Clone, R: Clone> Clone for ErEnsembleC<T, R> where
    T: Node,
    R: Rng, 

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

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<T: Debug, R: Debug> Debug for ErEnsembleC<T, R> where
    T: Node,
    R: Rng, 

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

Formats the value using the given formatter.

impl<'de, T, R> Deserialize<'de> for ErEnsembleC<T, R> where
    T: Node,
    R: Rng,
    T: Deserialize<'de>,
    R: Deserialize<'de>, 

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
    __D: Deserializer<'de>, 

Deserialize this value from the given Serde deserializer.

impl<T, R> GraphIteratorsMut<T, GenericGraph<T, NodeContainer<T>>, NodeContainer<T>> for ErEnsembleC<T, R> where
    T: Node + SerdeStateConform,
    R: Rng, 

fn contained_iter_neighbors_mut(
    &mut self,
    index: usize
) -> NContainedIterMut<T, NodeContainer<T>>

index``&mut T``sort_adj

fn contained_iter_neighbors_mut_with_index(
    &mut self,
    index: usize
) -> INContainedIterMut<T, NodeContainer<T>>

index``(index_neighbor: usize, neighbor: &mut T)``sort_adj

fn contained_iter_mut(&mut self) -> ContainedIterMut<T, NodeContainer<T>>

Node``EmptyNode

impl<T, R> HasRng<R> for ErEnsembleC<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

impl<T, R> MarkovChain<ErStepC, ErStepC> for ErEnsembleC<T, R> where
    T: Node + SerdeStateConform,
    R: Rng, 

fn m_step(&mut self) -> ErStepC

Markov step

• use this to perform a markov step, e.g., to create a markov chain
• result ErStepC can be used to undo the step with self.undo_step(result)

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

Undo a markcov step

• adds removed edge, or removes added edge, or does nothing
• if it returns an Err value, you probably used the function wrong

Important:

Restored graph is the same as before the random step except the order of nodes in the adjacency list might be shuffled!

fn undo_step_quiet(&mut self, step: ErStepC)

Undo a markov step

• adds removed edge, or removes added edge, or does nothing
• if it returns an Err value, you probably used the function wrong

Important:

Restored graph is the same as before the random step except the order of nodes in the adjacency list might be shuffled!

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

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

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

Undo markov 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 markov 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> Serialize for ErEnsembleC<T, R> where
    T: Node,
    R: Rng,
    T: Serialize,
    R: Serialize, 

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error> where
    __S: Serializer, 

Serialize this value into the given Serde serializer.

impl<T, R> SimpleSample for ErEnsembleC<T, R> where
    T: Node + SerdeStateConform,
    R: Rng, 

fn randomize(&mut self)

Randomizes the edges according to Er probabilities

• this is used by ErEnsembleC::new to create the initial topology
• you can use this for sampling the ensemble
• runs in O(vertices * vertices)

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

impl<T, R> WithGraph<T, GenericGraph<T, NodeContainer<T>>> for ErEnsembleC<T, R> where
    T: Node + SerdeStateConform,
    R: Rng, 

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

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

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

GenericGraph