[−][src]Struct net_ensembles::er_c::ErEnsembleC
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,
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T: Node + SerdeStateConform,
R: Rng,
pub fn new(n: usize, c_target: f64, rng: R) -> Self
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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)
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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
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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)
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- 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)
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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,
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T: Node,
R: Rng,
impl<T, R> Borrow<GenericGraph<T, NodeContainer<T>>> for ErEnsembleC<T, R> where
T: Node,
R: Rng,
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T: Node,
R: Rng,
impl<T: Clone, R: Clone> Clone for ErEnsembleC<T, R> where
T: Node,
R: Rng,
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T: Node,
R: Rng,
fn clone(&self) -> ErEnsembleC<T, R>
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fn clone_from(&mut self, source: &Self)
1.0.0[src]
impl<T: Debug, R: Debug> Debug for ErEnsembleC<T, R> where
T: Node,
R: Rng,
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T: Node,
R: Rng,
impl<'de, T, R> Deserialize<'de> for ErEnsembleC<T, R> where
T: Node,
R: Rng,
T: Deserialize<'de>,
R: Deserialize<'de>,
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T: Node,
R: Rng,
T: Deserialize<'de>,
R: Deserialize<'de>,
fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
__D: Deserializer<'de>,
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__D: Deserializer<'de>,
impl<T, R> GraphIteratorsMut<T, GenericGraph<T, NodeContainer<T>>, NodeContainer<T>> for ErEnsembleC<T, R> where
T: Node + SerdeStateConform,
R: Rng,
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T: Node + SerdeStateConform,
R: Rng,
fn contained_iter_neighbors_mut(
&mut self,
index: usize
) -> NContainedIterMut<T, NodeContainer<T>>
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&mut self,
index: usize
) -> NContainedIterMut<T, NodeContainer<T>>
fn contained_iter_neighbors_mut_with_index(
&mut self,
index: usize
) -> INContainedIterMut<T, NodeContainer<T>>
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&mut self,
index: usize
) -> INContainedIterMut<T, NodeContainer<T>>
fn contained_iter_mut(&mut self) -> ContainedIterMut<T, NodeContainer<T>>
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impl<T, R> HasRng<R> for ErEnsembleC<T, R> where
T: Node,
R: Rng,
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T: Node,
R: Rng,
fn rng(&mut self) -> &mut R
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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
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Swap random number generator
- returns old internal rng
impl<T, R> MarkovChain<ErStepC, ErStepC> for ErEnsembleC<T, R> where
T: Node + SerdeStateConform,
R: Rng,
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T: Node + SerdeStateConform,
R: Rng,
fn m_step(&mut self) -> ErStepC
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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 withself.undo_step(result)
fn undo_step(&mut self, step: ErStepC) -> ErStepC
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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)
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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>
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fn undo_steps(&mut self, steps: Vec<S>) -> Vec<Res>
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fn undo_steps_quiet(&mut self, steps: Vec<S>)
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impl<T, R> Serialize for ErEnsembleC<T, R> where
T: Node,
R: Rng,
T: Serialize,
R: Serialize,
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T: Node,
R: Rng,
T: Serialize,
R: Serialize,
fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error> where
__S: Serializer,
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__S: Serializer,
impl<T, R> SimpleSample for ErEnsembleC<T, R> where
T: Node + SerdeStateConform,
R: Rng,
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T: Node + SerdeStateConform,
R: Rng,
fn randomize(&mut self)
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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),
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F: FnMut(&Self),
fn simple_sample_vec<F, G>(&mut self, times: usize, f: F) -> Vec<G> where
F: FnMut(&Self) -> G,
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F: FnMut(&Self) -> G,
impl<T, R> WithGraph<T, GenericGraph<T, NodeContainer<T>>> for ErEnsembleC<T, R> where
T: Node + SerdeStateConform,
R: Rng,
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T: Node + SerdeStateConform,
R: Rng,
Auto Trait Implementations
impl<T, R> RefUnwindSafe for ErEnsembleC<T, R> where
R: RefUnwindSafe,
T: RefUnwindSafe,
R: RefUnwindSafe,
T: RefUnwindSafe,
impl<T, R> Send for ErEnsembleC<T, R> where
R: Send,
T: Send,
R: Send,
T: Send,
impl<T, R> Sync for ErEnsembleC<T, R> where
R: Sync,
T: Sync,
R: Sync,
T: Sync,
impl<T, R> Unpin for ErEnsembleC<T, R> where
R: Unpin,
T: Unpin,
R: Unpin,
T: Unpin,
impl<T, R> UnwindSafe for ErEnsembleC<T, R> where
R: UnwindSafe,
T: UnwindSafe,
R: UnwindSafe,
T: UnwindSafe,
Blanket Implementations
impl<T> Any for T where
T: 'static + ?Sized,
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T: 'static + ?Sized,
impl<T> Borrow<T> for T where
T: ?Sized,
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T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
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T: ?Sized,
fn borrow_mut(&mut self) -> &mut T
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impl<T> DeserializeOwned for T where
T: for<'de> Deserialize<'de>,
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T: for<'de> Deserialize<'de>,
impl<T> From<T> for T
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impl<T, U> Into<U> for T where
U: From<T>,
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U: From<T>,
impl<S, Res, A> Metropolis<S, Res> for A where
A: MarkovChain<S, Res>,
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A: MarkovChain<S, Res>,
fn metropolis<Rng, F, G, H>(
&mut self,
rng: Rng,
temperature: f64,
stepsize: usize,
steps: usize,
valid_self: F,
energy: G,
measure: H
) -> MetropolisState<Rng> where
F: FnMut(&mut Self) -> bool,
G: FnMut(&mut Self) -> f64,
H: FnMut(&mut Self, usize, f64, bool),
Rng: Rng,
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&mut self,
rng: Rng,
temperature: f64,
stepsize: usize,
steps: usize,
valid_self: F,
energy: G,
measure: H
) -> MetropolisState<Rng> where
F: FnMut(&mut Self) -> bool,
G: FnMut(&mut Self) -> f64,
H: FnMut(&mut Self, usize, f64, bool),
Rng: Rng,
fn metropolis_while<Rng, F, G, H, B>(
&mut self,
rng: Rng,
temperature: f64,
stepsize: usize,
steps: usize,
valid_self: F,
energy: G,
measure: H,
brake_if: B
) -> MetropolisState<Rng> where
F: FnMut(&mut Self) -> bool,
G: FnMut(&mut Self) -> f64,
H: FnMut(&mut Self, usize, f64, bool),
B: FnMut(&Self, usize) -> bool,
Rng: Rng,
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&mut self,
rng: Rng,
temperature: f64,
stepsize: usize,
steps: usize,
valid_self: F,
energy: G,
measure: H,
brake_if: B
) -> MetropolisState<Rng> where
F: FnMut(&mut Self) -> bool,
G: FnMut(&mut Self) -> f64,
H: FnMut(&mut Self, usize, f64, bool),
B: FnMut(&Self, usize) -> bool,
Rng: Rng,
fn continue_metropolis_while<R, F, G, H, B>(
&mut self,
state: MetropolisState<R>,
ignore_energy_missmatch: bool,
valid_self: F,
energy: G,
measure: H,
brake_if: B
) -> MetropolisState<R> where
F: FnMut(&mut Self) -> bool,
G: FnMut(&mut Self) -> f64,
H: FnMut(&mut Self, usize, f64, bool),
B: FnMut(&Self, usize) -> bool,
R: Rng,
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&mut self,
state: MetropolisState<R>,
ignore_energy_missmatch: bool,
valid_self: F,
energy: G,
measure: H,
brake_if: B
) -> MetropolisState<R> where
F: FnMut(&mut Self) -> bool,
G: FnMut(&mut Self) -> f64,
H: FnMut(&mut Self, usize, f64, bool),
B: FnMut(&Self, usize) -> bool,
R: Rng,
fn continue_metropolis<Rng, F, G, H>(
&mut self,
state: MetropolisState<Rng>,
ignore_energy_missmatch: bool,
valid_self: F,
energy: G,
measure: H
) -> MetropolisState<Rng> where
F: FnMut(&mut Self) -> bool,
G: FnMut(&mut Self) -> f64,
H: FnMut(&mut Self, usize, f64, bool),
Rng: Rng,
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&mut self,
state: MetropolisState<Rng>,
ignore_energy_missmatch: bool,
valid_self: F,
energy: G,
measure: H
) -> MetropolisState<Rng> where
F: FnMut(&mut Self) -> bool,
G: FnMut(&mut Self) -> f64,
H: FnMut(&mut Self, usize, f64, bool),
Rng: Rng,
impl<T> SerdeStateConform for T where
T: Serialize,
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T: Serialize,
impl<T> ToOwned for T where
T: Clone,
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T: Clone,
type Owned = T
The resulting type after obtaining ownership.
fn to_owned(&self) -> T
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fn clone_into(&self, target: &mut T)
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impl<T, U> TryFrom<U> for T where
U: Into<T>,
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U: Into<T>,
type Error = Infallible
The type returned in the event of a conversion error.
fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>
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impl<T, U> TryInto<U> for T where
U: TryFrom<T>,
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U: TryFrom<T>,
type Error = <U as TryFrom<T>>::Error
The type returned in the event of a conversion error.
fn try_into(self) -> Result<U, <U as TryFrom<T>>::Error>
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impl<V, T> VZip<V> for T where
V: MultiLane<T>,
V: MultiLane<T>,