[−][src]Struct rv::dist::Bernoulli
Bernoulli distribution with success probability p
Example
let b = Bernoulli::new(0.75).unwrap(); assert::close(b.pmf(&true), 0.75, 1E-12);
The following example panics because 2 is out of outside the Bernoulli support
let b = Bernoulli::new(0.75).unwrap(); assert!(!b.supports(&2_u8)); b.pmf(&2_u8); // panics
Implementations
impl Bernoulli
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pub fn new(p: f64) -> Result<Self, BernoulliError>
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Create a new Bernoulli distribution.
Examples
let b = Bernoulli::new(0.5).unwrap(); let coin_flips: Vec<bool> = b.sample(5, &mut rng); assert_eq!(coin_flips.len(), 5);
Bernoulli::new
will return an Error
type if given an invalid
paramter.
assert!(Bernoulli::new(-1.0).is_err()); assert!(Bernoulli::new(1.1).is_err());
pub fn new_unchecked(p: f64) -> Self
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Creates a new Bernoulli without checking whether parameter value is valid.
pub fn uniform() -> Self
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A Bernoulli distribution with a 50% chance of success
Example
let b = Bernoulli::uniform(); assert_eq!(b.p(), 0.5); assert_eq!(b.q(), 0.5);
pub fn p(&self) -> f64
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Get p, the probability of success.
Example
let b = Bernoulli::new(0.2).unwrap(); assert_eq!(b.p(), 0.2);
pub fn set_p(&mut self, p: f64) -> Result<(), BernoulliError>
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Set p, the probability of success.
Example
let mut b = Bernoulli::new(0.2).unwrap(); b.set_p(0.5).unwrap(); assert_eq!(b.p(), 0.5);
Will error for invalid values
assert!(b.set_p(0.0).is_ok()); assert!(b.set_p(1.0).is_ok()); assert!(b.set_p(-1.0).is_err()); assert!(b.set_p(1.1).is_err()); assert!(b.set_p(std::f64::INFINITY).is_err()); assert!(b.set_p(std::f64::NEG_INFINITY).is_err()); assert!(b.set_p(std::f64::NAN).is_err());
pub fn set_p_unchecked(&mut self, p: f64)
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Set p without input validation
pub fn q(&self) -> f64
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The complement of p
, i.e. (1 - p)
.
Example
let b = Bernoulli::new(0.2).unwrap(); assert_eq!(b.q(), 0.8);
Trait Implementations
impl<X: Booleable> Cdf<X> for Bernoulli
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impl Cdf<bool> for Bernoulli
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impl Clone for Bernoulli
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impl<X: Booleable> ConjugatePrior<X, Bernoulli> for Beta
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type Posterior = Self
fn posterior(&self, x: &DataOrSuffStat<X, Bernoulli>) -> Self
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fn ln_m(&self, x: &DataOrSuffStat<X, Bernoulli>) -> f64
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fn ln_pp(&self, y: &X, x: &DataOrSuffStat<X, Bernoulli>) -> f64
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fn m(&self, x: &DataOrSuffStat<X, Fx>) -> f64
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fn pp(&self, y: &X, x: &DataOrSuffStat<X, Fx>) -> f64
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impl ConjugatePrior<bool, Bernoulli> for Beta
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type Posterior = Self
fn posterior(&self, x: &DataOrSuffStat<bool, Bernoulli>) -> Self
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fn ln_m(&self, x: &DataOrSuffStat<bool, Bernoulli>) -> f64
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fn ln_pp(&self, y: &bool, x: &DataOrSuffStat<bool, Bernoulli>) -> f64
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fn m(&self, x: &DataOrSuffStat<X, Fx>) -> f64
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fn pp(&self, y: &X, x: &DataOrSuffStat<X, Fx>) -> f64
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impl ContinuousDistr<Bernoulli> for Beta
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impl Debug for Bernoulli
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impl Default for Bernoulli
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impl<X: Booleable> DiscreteDistr<X> for Bernoulli
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impl DiscreteDistr<bool> for Bernoulli
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impl Display for Bernoulli
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impl Entropy for Bernoulli
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impl<'_> From<&'_ Bernoulli> for String
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impl<X: Booleable> HasSuffStat<X> for Bernoulli
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type Stat = BernoulliSuffStat
fn empty_suffstat(&self) -> Self::Stat
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impl HasSuffStat<bool> for Bernoulli
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type Stat = BernoulliSuffStat
fn empty_suffstat(&self) -> Self::Stat
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impl KlDivergence for Bernoulli
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impl Kurtosis for Bernoulli
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impl Mean<f64> for Bernoulli
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impl Median<f64> for Bernoulli
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impl<X: Booleable> Mode<X> for Bernoulli
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impl Mode<bool> for Bernoulli
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impl PartialEq<Bernoulli> for Bernoulli
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impl PartialOrd<Bernoulli> for Bernoulli
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fn partial_cmp(&self, other: &Bernoulli) -> Option<Ordering>
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fn lt(&self, other: &Bernoulli) -> bool
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fn le(&self, other: &Bernoulli) -> bool
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fn gt(&self, other: &Bernoulli) -> bool
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fn ge(&self, other: &Bernoulli) -> bool
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impl Rv<Bernoulli> for Beta
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fn ln_f(&self, x: &Bernoulli) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> Bernoulli
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fn f(&self, x: &X) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
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fn sample_stream<'r, R: Rng>(
&'r self,
rng: &'r mut R
) -> Box<dyn Iterator<Item = X> + 'r>
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&'r self,
rng: &'r mut R
) -> Box<dyn Iterator<Item = X> + 'r>
impl<X: Booleable> Rv<X> for Bernoulli
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fn f(&self, x: &X) -> f64
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fn ln_f(&self, x: &X) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> X
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>
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fn sample_stream<'r, R: Rng>(
&'r self,
rng: &'r mut R
) -> Box<dyn Iterator<Item = X> + 'r>
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&'r self,
rng: &'r mut R
) -> Box<dyn Iterator<Item = X> + 'r>
impl Rv<bool> for Bernoulli
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fn f(&self, x: &bool) -> f64
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fn ln_f(&self, x: &bool) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> bool
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<bool>
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fn sample_stream<'r, R: Rng>(
&'r self,
rng: &'r mut R
) -> Box<dyn Iterator<Item = X> + 'r>
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&'r self,
rng: &'r mut R
) -> Box<dyn Iterator<Item = X> + 'r>
impl Skewness for Bernoulli
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impl StructuralPartialEq for Bernoulli
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impl Support<Bernoulli> for Beta
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impl<X: Booleable> Support<X> for Bernoulli
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impl Support<bool> for Bernoulli
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impl Variance<f64> for Bernoulli
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Auto Trait Implementations
impl RefUnwindSafe for Bernoulli
impl Send for Bernoulli
impl Sync for Bernoulli
impl Unpin for Bernoulli
impl UnwindSafe for Bernoulli
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<Fx, X> Cdf<X> for Fx where
Fx: Deref,
<Fx as Deref>::Target: Cdf<X>,
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Fx: Deref,
<Fx as Deref>::Target: Cdf<X>,
impl<Fx, X> DiscreteDistr<X> for Fx where
Fx: Deref,
<Fx as Deref>::Target: DiscreteDistr<X>,
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Fx: Deref,
<Fx as Deref>::Target: DiscreteDistr<X>,
impl<Fx> Entropy for Fx where
Fx: Deref,
<Fx as Deref>::Target: Entropy,
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Fx: Deref,
<Fx as Deref>::Target: Entropy,
impl<T> From<T> for T
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impl<Fx, X> HasSuffStat<X> for Fx where
Fx: Deref,
<Fx as Deref>::Target: HasSuffStat<X>,
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Fx: Deref,
<Fx as Deref>::Target: HasSuffStat<X>,
type Stat = <<Fx as Deref>::Target as HasSuffStat<X>>::Stat
fn empty_suffstat(&Self) -> <Fx as HasSuffStat<X>>::Stat
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impl<T, U> Into<U> for T where
U: From<T>,
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U: From<T>,
impl<Fx> KlDivergence for Fx where
Fx: Deref,
<Fx as Deref>::Target: KlDivergence,
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Fx: Deref,
<Fx as Deref>::Target: KlDivergence,
impl<Fx> Kurtosis for Fx where
Fx: Deref,
<Fx as Deref>::Target: Kurtosis,
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Fx: Deref,
<Fx as Deref>::Target: Kurtosis,
impl<Fx, X> Mean<X> for Fx where
Fx: Deref,
<Fx as Deref>::Target: Mean<X>,
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Fx: Deref,
<Fx as Deref>::Target: Mean<X>,
impl<Fx, X> Median<X> for Fx where
Fx: Deref,
<Fx as Deref>::Target: Median<X>,
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Fx: Deref,
<Fx as Deref>::Target: Median<X>,
impl<Fx, X> Mode<X> for Fx where
Fx: Deref,
<Fx as Deref>::Target: Mode<X>,
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Fx: Deref,
<Fx as Deref>::Target: Mode<X>,
impl<Fx, X> Rv<X> for Fx where
Fx: Deref,
<Fx as Deref>::Target: Rv<X>,
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Fx: Deref,
<Fx as Deref>::Target: Rv<X>,
fn ln_f(&Self, &X) -> f64
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fn f(&Self, &X) -> f64
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fn draw<R>(&Self, &mut R) -> X where
R: Rng,
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R: Rng,
fn sample<R>(&Self, usize, &mut R) -> Vec<X> where
R: Rng,
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R: Rng,
fn sample_stream<'r, R: Rng>(
&'r self,
rng: &'r mut R
) -> Box<dyn Iterator<Item = X> + 'r>
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&'r self,
rng: &'r mut R
) -> Box<dyn Iterator<Item = X> + 'r>
impl<T> Same<T> for T
type Output = T
Should always be Self
impl<Fx> Skewness for Fx where
Fx: Deref,
<Fx as Deref>::Target: Skewness,
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Fx: Deref,
<Fx as Deref>::Target: Skewness,
impl<SS, SP> SupersetOf<SS> for SP where
SS: SubsetOf<SP>,
SS: SubsetOf<SP>,
fn to_subset(&self) -> Option<SS>
fn is_in_subset(&self) -> bool
unsafe fn to_subset_unchecked(&self) -> SS
fn from_subset(element: &SS) -> SP
impl<Fx, X> Support<X> for Fx where
Fx: Deref,
<Fx as Deref>::Target: Support<X>,
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Fx: Deref,
<Fx as Deref>::Target: Support<X>,
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> ToString for T where
T: Display + ?Sized,
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T: Display + ?Sized,
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>,
fn vzip(self) -> V
impl<Fx, X> Variance<X> for Fx where
Fx: Deref,
<Fx as Deref>::Target: Variance<X>,
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Fx: Deref,
<Fx as Deref>::Target: Variance<X>,