Struct net_ensembles::er_c::ErEnsembleC

pub struct ErEnsembleC<T, R> where
    T: Node,  { /* 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

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, 

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, R> Contained<T> for ErEnsembleC<T, R> where
    T: Node, 

fn get_contained(&self, index: usize) -> Option<&T>

Returns a reference to the element stored in the specified node or None if out of Bounds

fn get_contained_mut(&mut self, index: usize) -> Option<&mut T>

Returns a mutable reference to the element stored in the specified node or None if out of Bounds

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

For a save alternative see get_contained

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

Returns a mutable reference to the element stored in the specified node

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

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,
    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> Dot for ErEnsembleC<T, R> where
    T: Node, 

fn dot_from_indices<F, W, S1, S2>(
    &self,
    writer: W,
    dot_options: S1,
    f: F
) -> Result<(), Error> where
    S1: AsRef<str>,
    S2: AsRef<str>,
    W: Write,
    F: FnMut(usize) -> S2, 

use function f to create labels depending on the index

for valid dot_options use dot_options! macro and take a look at module dot_constants

fn dot<S, W>(&self, writer: W, dot_options: S) -> Result<(), Error> where
    S: AsRef<str>,
    W: Write, 

create dot file with empty labels

default implementation uses dot_from_indices

fn dot_string<S>(&self, dot_options: S) -> String where
    S: AsRef<str>, 

same as self.dot(), but returns a String instead

fn dot_string_from_indices<F, S1, S2>(&self, dot_options: S1, f: F) -> String where
    S1: AsRef<str>,
    S2: AsRef<str>,
    F: FnMut(usize) -> S2, 

same as self.dot_from_indices but returns String instead

fn dot_string_with_indices<S>(&self, dot_options: S) -> String where
    S: AsRef<str>, 

same as self.dot_with_indices but returns String instead

fn dot_with_indices<S, W>(&self, writer: W, dot_options: S) -> Result<(), Error> where
    S: AsRef<str>,
    W: Write, 

use index as labels for the nodes

default implementation uses dot_from_indices

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>>

iterate over mutable additional information of neighbors of vertex index

iterator returns &mut T

sort_adj will affect the order

panics if index out of bounds

fn contained_iter_neighbors_mut_with_index(
    &mut self,
    index: usize
) -> INContainedIterMut<'_, T, NodeContainer<T>>

iterate over mutable additional information of neighbors of vertex index

iterator returns (index_neighbor: usize, neighbor: &mut T)

sort_adj will affect the order

panics if index out of bounds

fn contained_iter_mut(&mut self) -> ContainedIterMut<'_, T, NodeContainer<T>>

get iterator over mutable additional information stored at each vertex in order of the indices

iterator returns a Node (for example EmptyNode or whatever you used)

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: &mut 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, steps: &mut Vec<S, Global>)

Markov steps

fn m_steps_quiet(&mut self, count: usize)

Markov steps without return

fn m_step_acc<Acc, AccFn>(&mut self, acc: &mut Acc, acc_fn: AccFn) -> S where
    AccFn: FnMut(&Self, &S, &mut Acc), 

Accumulating markov step

fn m_steps_acc<Acc, AccFn>(
    &mut self,
    count: usize,
    steps: &mut Vec<S, Global>,
    acc: &mut Acc,
    acc_fn: AccFn
) where
    AccFn: FnMut(&Self, &S, &mut Acc), 

Accumulating markov steps

fn m_steps_acc_quiet<Acc, AccFn>(
    &mut self,
    count: usize,
    acc: &mut Acc,
    acc_fn: AccFn
) where
    AccFn: FnMut(&Self, &S, &mut Acc), 

Accumulating markov steps

fn undo_steps(&mut self, steps: &[S], res: &mut Vec<Res, Global>)

Undo markov steps

fn undo_steps_quiet(&mut self, steps: &[S])

Undo markov steps

impl<T, R> Serialize for ErEnsembleC<T, R> where
    T: Node,
    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:

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

do the following times times:

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

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

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

access additional information at index

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

mutable access to additional information at index

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

returns reference to the underlying topology aka, the GenericGraph

use this to call functions regarding the topology