[−][src]Struct rv::dist::Gamma
Gamma distribution G(α, β) over x in (0, ∞).
NOTE: The gamma distribution is parameterized in terms of shape, α, and rate, β.
β^α
f(x|α, β) = ---- x^(α-1) e^(-βx)
Γ(α)
Implementations
impl Gamma
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pub fn new(shape: f64, rate: f64) -> Result<Self, GammaError>
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Create a new Gamma
distribution with shape (α) and rate (β).
pub fn new_unchecked(shape: f64, rate: f64) -> Self
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Creates a new Gamma without checking whether the parameters are valid.
pub fn shape(&self) -> f64
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Get the shape parameter
Example
let gam = Gamma::new(2.0, 1.0).unwrap(); assert_eq!(gam.shape(), 2.0);
pub fn set_shape(&mut self, shape: f64) -> Result<(), GammaError>
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Set the shape parameter
Example
let mut gam = Gamma::new(2.0, 1.0).unwrap(); assert_eq!(gam.shape(), 2.0); gam.set_shape(1.1).unwrap(); assert_eq!(gam.shape(), 1.1);
Will error for invalid values
assert!(gam.set_shape(1.1).is_ok()); assert!(gam.set_shape(0.0).is_err()); assert!(gam.set_shape(-1.0).is_err()); assert!(gam.set_shape(std::f64::INFINITY).is_err()); assert!(gam.set_shape(std::f64::NEG_INFINITY).is_err()); assert!(gam.set_shape(std::f64::NAN).is_err());
pub fn set_shape_unchecked(&mut self, shape: f64)
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Set the shape parameter without input validation
pub fn rate(&self) -> f64
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Get the rate parameter
Example
let gam = Gamma::new(2.0, 1.0).unwrap(); assert_eq!(gam.rate(), 1.0);
pub fn set_rate(&mut self, rate: f64) -> Result<(), GammaError>
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Set the rate parameter
Example
let mut gam = Gamma::new(2.0, 1.0).unwrap(); assert_eq!(gam.rate(), 1.0); gam.set_rate(1.1).unwrap(); assert_eq!(gam.rate(), 1.1);
Will error for invalid values
assert!(gam.set_rate(1.1).is_ok()); assert!(gam.set_rate(0.0).is_err()); assert!(gam.set_rate(-1.0).is_err()); assert!(gam.set_rate(std::f64::INFINITY).is_err()); assert!(gam.set_rate(std::f64::NEG_INFINITY).is_err()); assert!(gam.set_rate(std::f64::NAN).is_err());
pub fn set_rate_unchecked(&mut self, rate: f64)
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Set the rate parameter without input validation
Trait Implementations
impl Cdf<f32> for Gamma
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impl Cdf<f64> for Gamma
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impl Clone for Gamma
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fn clone(&self) -> Self
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fn clone_from(&mut self, source: &Self)
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impl ConjugatePrior<u16, Poisson> for Gamma
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type Posterior = Self
fn posterior(&self, x: &DataOrSuffStat<u16, Poisson>) -> Self
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fn ln_m(&self, x: &DataOrSuffStat<u16, Poisson>) -> f64
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fn ln_pp(&self, y: &u16, x: &DataOrSuffStat<u16, Poisson>) -> 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<u32, Poisson> for Gamma
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type Posterior = Self
fn posterior(&self, x: &DataOrSuffStat<u32, Poisson>) -> Self
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fn ln_m(&self, x: &DataOrSuffStat<u32, Poisson>) -> f64
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fn ln_pp(&self, y: &u32, x: &DataOrSuffStat<u32, Poisson>) -> 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<u8, Poisson> for Gamma
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type Posterior = Self
fn posterior(&self, x: &DataOrSuffStat<u8, Poisson>) -> Self
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fn ln_m(&self, x: &DataOrSuffStat<u8, Poisson>) -> f64
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fn ln_pp(&self, y: &u8, x: &DataOrSuffStat<u8, Poisson>) -> 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<Poisson> for Gamma
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impl ContinuousDistr<f32> for Gamma
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impl ContinuousDistr<f64> for Gamma
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impl Debug for Gamma
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impl Default for Gamma
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impl Display for Gamma
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impl Entropy for Gamma
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impl<'_> From<&'_ Gamma> for String
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impl Kurtosis for Gamma
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impl Mean<f32> for Gamma
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impl Mean<f64> for Gamma
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impl Mode<f32> for Gamma
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impl Mode<f64> for Gamma
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impl PartialEq<Gamma> for Gamma
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impl Rv<Poisson> for Gamma
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fn ln_f(&self, x: &Poisson) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> Poisson
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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 Rv<f32> for Gamma
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fn ln_f(&self, x: &f32) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> f32
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<f32>
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fn f(&self, x: &X) -> f64
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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<f64> for Gamma
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fn ln_f(&self, x: &f64) -> f64
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fn draw<R: Rng>(&self, rng: &mut R) -> f64
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fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<f64>
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fn f(&self, x: &X) -> f64
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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 Gamma
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impl Support<Poisson> for Gamma
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impl Support<f32> for Gamma
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impl Support<f64> for Gamma
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impl Variance<f64> for Gamma
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Auto Trait Implementations
impl RefUnwindSafe for Gamma
impl Send for Gamma
impl Sync for Gamma
impl Unpin for Gamma
impl UnwindSafe for Gamma
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> ContinuousDistr<X> for Fx where
Fx: Deref,
<Fx as Deref>::Target: ContinuousDistr<X>,
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Fx: Deref,
<Fx as Deref>::Target: ContinuousDistr<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<T, U> Into<U> for T where
U: From<T>,
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U: From<T>,
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> 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>,