Struct bitcoin::key::secp256k1::rand::distributions::WeightedIndex
source · pub struct WeightedIndex<X>where
X: SampleUniform + PartialOrd<X>,{ /* private fields */ }
Expand description
A distribution using weighted sampling of discrete items
Sampling a WeightedIndex
distribution returns the index of a randomly
selected element from the iterator used when the WeightedIndex
was
created. The chance of a given element being picked is proportional to the
value of the element. The weights can use any type X
for which an
implementation of Uniform<X>
exists.
Performance
Time complexity of sampling from WeightedIndex
is O(log N)
where
N
is the number of weights. As an alternative,
rand_distr::weighted_alias
supports O(1)
sampling, but with much higher initialisation cost.
A WeightedIndex<X>
contains a Vec<X>
and a Uniform<X>
and so its
size is the sum of the size of those objects, possibly plus some alignment.
Creating a WeightedIndex<X>
will allocate enough space to hold N - 1
weights of type X
, where N
is the number of weights. However, since
Vec
doesn’t guarantee a particular growth strategy, additional memory
might be allocated but not used. Since the WeightedIndex
object also
contains, this might cause additional allocations, though for primitive
types, Uniform<X>
doesn’t allocate any memory.
Sampling from WeightedIndex
will result in a single call to
Uniform<X>::sample
(method of the Distribution
trait), which typically
will request a single value from the underlying RngCore
, though the
exact number depends on the implementation of Uniform<X>::sample
.
Example
use rand::prelude::*;
use rand::distributions::WeightedIndex;
let choices = ['a', 'b', 'c'];
let weights = [2, 1, 1];
let dist = WeightedIndex::new(&weights).unwrap();
let mut rng = thread_rng();
for _ in 0..100 {
// 50% chance to print 'a', 25% chance to print 'b', 25% chance to print 'c'
println!("{}", choices[dist.sample(&mut rng)]);
}
let items = [('a', 0), ('b', 3), ('c', 7)];
let dist2 = WeightedIndex::new(items.iter().map(|item| item.1)).unwrap();
for _ in 0..100 {
// 0% chance to print 'a', 30% chance to print 'b', 70% chance to print 'c'
println!("{}", items[dist2.sample(&mut rng)].0);
}
Implementations§
source§impl<X> WeightedIndex<X>where
X: SampleUniform + PartialOrd<X>,
impl<X> WeightedIndex<X>where X: SampleUniform + PartialOrd<X>,
sourcepub fn new<I>(weights: I) -> Result<WeightedIndex<X>, WeightedError>where
I: IntoIterator,
<I as IntoIterator>::Item: SampleBorrow<X>,
X: for<'a> AddAssign<&'a X> + Clone + Default,
pub fn new<I>(weights: I) -> Result<WeightedIndex<X>, WeightedError>where I: IntoIterator, <I as IntoIterator>::Item: SampleBorrow<X>, X: for<'a> AddAssign<&'a X> + Clone + Default,
Creates a new a WeightedIndex
Distribution
using the values
in weights
. The weights can use any type X
for which an
implementation of Uniform<X>
exists.
Returns an error if the iterator is empty, if any weight is < 0
, or
if its total value is 0.
sourcepub fn update_weights(
&mut self,
new_weights: &[(usize, &X)]
) -> Result<(), WeightedError>where
X: for<'a> AddAssign<&'a X> + for<'a> SubAssign<&'a X> + Clone + Default,
pub fn update_weights( &mut self, new_weights: &[(usize, &X)] ) -> Result<(), WeightedError>where X: for<'a> AddAssign<&'a X> + for<'a> SubAssign<&'a X> + Clone + Default,
Update a subset of weights, without changing the number of weights.
new_weights
must be sorted by the index.
Using this method instead of new
might be more efficient if only a small number of
weights is modified. No allocations are performed, unless the weight type X
uses
allocation internally.
In case of error, self
is not modified.
Trait Implementations§
source§impl<X> Clone for WeightedIndex<X>where
X: Clone + SampleUniform + PartialOrd<X>,
<X as SampleUniform>::Sampler: Clone,
impl<X> Clone for WeightedIndex<X>where X: Clone + SampleUniform + PartialOrd<X>, <X as SampleUniform>::Sampler: Clone,
source§fn clone(&self) -> WeightedIndex<X>
fn clone(&self) -> WeightedIndex<X>
1.0.0 · source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
source
. Read moresource§impl<X> Debug for WeightedIndex<X>where
X: Debug + SampleUniform + PartialOrd<X>,
<X as SampleUniform>::Sampler: Debug,
impl<X> Debug for WeightedIndex<X>where X: Debug + SampleUniform + PartialOrd<X>, <X as SampleUniform>::Sampler: Debug,
source§impl<X> Distribution<usize> for WeightedIndex<X>where
X: SampleUniform + PartialOrd<X>,
impl<X> Distribution<usize> for WeightedIndex<X>where X: SampleUniform + PartialOrd<X>,
source§fn sample<R>(&self, rng: &mut R) -> usizewhere
R: Rng + ?Sized,
fn sample<R>(&self, rng: &mut R) -> usizewhere R: Rng + ?Sized,
T
, using rng
as the source of randomness.source§impl<X> PartialEq<WeightedIndex<X>> for WeightedIndex<X>where
X: PartialEq<X> + SampleUniform + PartialOrd<X>,
<X as SampleUniform>::Sampler: PartialEq<<X as SampleUniform>::Sampler>,
impl<X> PartialEq<WeightedIndex<X>> for WeightedIndex<X>where X: PartialEq<X> + SampleUniform + PartialOrd<X>, <X as SampleUniform>::Sampler: PartialEq<<X as SampleUniform>::Sampler>,
source§fn eq(&self, other: &WeightedIndex<X>) -> bool
fn eq(&self, other: &WeightedIndex<X>) -> bool
self
and other
values to be equal, and is used
by ==
.impl<X> StructuralPartialEq for WeightedIndex<X>where X: SampleUniform + PartialOrd<X>,
Auto Trait Implementations§
impl<X> RefUnwindSafe for WeightedIndex<X>where X: RefUnwindSafe, <X as SampleUniform>::Sampler: RefUnwindSafe,
impl<X> Send for WeightedIndex<X>where X: Send, <X as SampleUniform>::Sampler: Send,
impl<X> Sync for WeightedIndex<X>where X: Sync, <X as SampleUniform>::Sampler: Sync,
impl<X> Unpin for WeightedIndex<X>where X: Unpin, <X as SampleUniform>::Sampler: Unpin,
impl<X> UnwindSafe for WeightedIndex<X>where X: UnwindSafe, <X as SampleUniform>::Sampler: UnwindSafe,
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