Struct rv::dist::Categorical

pub struct Categorical { /* private fields */ }

Categorical distribution over unordered values in [0, k).

Implementations

impl Categorical

pub fn new(weights: &[f64]) -> Result<Self, CategoricalError>

Construct a new Categorical distribution from weights

Arguments

• weights: A vector describing the proportional likelihood of each outcome. The weights must all be positive, but do not need to sum to 1 because they will be normalized in the constructor.

Examples

let weights: Vec<f64> = vec![4.0, 2.0, 3.0, 1.0];
let cat = Categorical::new(&weights).unwrap();

assert!(cat.supports(&0_u8));
assert!(cat.supports(&3_u8));
assert!(!cat.supports(&4_u8));

assert::close(cat.pmf(&0_u8), 0.4, 1E-12);

pub fn from_ln_weights(ln_weights: Vec<f64>) -> Result<Self, CategoricalError>

Build a Categorical distribution from normalized log weights

Arguments

• ln_weights: A vector describing the proportional likelihood of each outcome in log space. sum(exp(ln_weights)) must be equal to 1.

Example

let ln_weights: Vec<f64> = vec![
    -2.3025850929940455,
    -1.6094379124341003,
    -1.2039728043259361,
    -0.916290731874155
];

let cat = Categorical::from_ln_weights(ln_weights).unwrap();

assert::close(cat.pmf(&0_u8), 0.1, 1E-12);
assert::close(cat.pmf(&1_u8), 0.2, 1E-12);
assert::close(cat.pmf(&2_u8), 0.3, 1E-12);
assert::close(cat.pmf(&3_u8), 0.4, 1E-12);

pub fn new_unchecked(ln_weights: Vec<f64>) -> Self

Creates a new Categorical without checking whether the ln weights are valid.

pub fn uniform(k: usize) -> Self

Creates a Categorical distribution over [0, k) with uniform weights

pub fn weights(&self) -> Vec<f64>

Return the weights (exp(ln_weights))

pub fn k(&self) -> usize

Get the number of possible outcomes

Example

let cat = Categorical::uniform(4);
assert_eq!(cat.k(), 4);

pub fn ln_weights(&self) -> &Vec<f64>

Get a reference to the weights

Trait Implementations

impl<X: CategoricalDatum> Cdf<X> for Categorical

fn cdf(&self, x: &X) -> f64

The value of the Cumulative Density Function at x

fn sf(&self, x: &X) -> f64

Survival function, 1 - CDF(x)

impl Clone for Categorical

fn clone(&self) -> Categorical

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<X: CategoricalDatum> ConjugatePrior<X, Categorical> for Dirichlet

type Posterior = Dirichlet

Type of the posterior distribution

type MCache = (f64, f64)

Type of the cache for the marginal likelihood

type PpCache = (Vec<f64>, f64)

Type of the cache for the posterior predictive

fn posterior(&self, x: &CategoricalData<'_, X>) -> Self::Posterior

fn ln_m_cache(&self) -> Self::MCache

Compute the cache for the log marginal likelihood.

fn ln_m_with_cache( &self, cache: &Self::MCache, x: &CategoricalData<'_, X>, ) -> f64

Log marginal likelihood with supplied cache.

fn ln_pp_cache(&self, x: &CategoricalData<'_, X>) -> Self::PpCache

Compute the cache for the Log posterior predictive of y given x.

fn ln_pp_with_cache(&self, cache: &Self::PpCache, y: &X) -> f64

Log posterior predictive of y given x with supplied ln(norm)

fn posterior_from_suffstat(&self, stat: &Fx::Stat) -> Self::Posterior

Computes the posterior distribution from the data

fn ln_m(&self, x: &DataOrSuffStat<'_, X, Fx>) -> f64

The log marginal likelihood

fn ln_pp(&self, y: &X, x: &DataOrSuffStat<'_, X, Fx>) -> f64

Log posterior predictive of y given x

fn m(&self, x: &DataOrSuffStat<'_, X, Fx>) -> f64

Marginal likelihood of x

fn pp_with_cache(&self, cache: &Self::PpCache, y: &X) -> f64

fn pp(&self, y: &X, x: &DataOrSuffStat<'_, X, Fx>) -> f64

Posterior Predictive distribution

impl<X: CategoricalDatum> ConjugatePrior<X, Categorical> for SymmetricDirichlet

type Posterior = Dirichlet

Type of the posterior distribution

type MCache = f64

Type of the cache for the marginal likelihood

type PpCache = (Vec<f64>, f64)

Type of the cache for the posterior predictive

fn posterior(&self, x: &CategoricalData<'_, X>) -> Self::Posterior

fn ln_m_cache(&self) -> Self::MCache

Compute the cache for the log marginal likelihood.

fn ln_m_with_cache( &self, cache: &Self::MCache, x: &CategoricalData<'_, X>, ) -> f64

Log marginal likelihood with supplied cache.

fn ln_pp_cache(&self, x: &CategoricalData<'_, X>) -> Self::PpCache

Compute the cache for the Log posterior predictive of y given x.

fn ln_pp_with_cache(&self, cache: &Self::PpCache, y: &X) -> f64

Log posterior predictive of y given x with supplied ln(norm)

fn posterior_from_suffstat(&self, stat: &Fx::Stat) -> Self::Posterior

Computes the posterior distribution from the data

fn ln_m(&self, x: &DataOrSuffStat<'_, X, Fx>) -> f64

The log marginal likelihood

fn ln_pp(&self, y: &X, x: &DataOrSuffStat<'_, X, Fx>) -> f64

Log posterior predictive of y given x

fn m(&self, x: &DataOrSuffStat<'_, X, Fx>) -> f64

Marginal likelihood of x

fn pp_with_cache(&self, cache: &Self::PpCache, y: &X) -> f64

fn pp(&self, y: &X, x: &DataOrSuffStat<'_, X, Fx>) -> f64

Posterior Predictive distribution

impl Debug for Categorical

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for Categorical

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl<X: CategoricalDatum> DiscreteDistr<X> for Categorical

fn pmf(&self, x: &X) -> f64

Probability mass function (PMF) at x

fn ln_pmf(&self, x: &X) -> f64

Natural logarithm of the probability mass function (PMF)

impl Display for Categorical

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Entropy for Categorical

fn entropy(&self) -> f64

The entropy, H(X)

impl From<&Categorical> for String

fn from(cat: &Categorical) -> String

Converts to this type from the input type.

impl HasDensity<Categorical> for Dirichlet

fn ln_f(&self, x: &Categorical) -> f64

Probability function

fn f(&self, x: &X) -> f64

Probability function

impl HasDensity<Categorical> for SymmetricDirichlet

fn ln_f(&self, x: &Categorical) -> f64

Probability function

fn f(&self, x: &X) -> f64

Probability function

impl<X: CategoricalDatum> HasDensity<X> for Categorical

fn ln_f(&self, x: &X) -> f64

Probability function

fn f(&self, x: &X) -> f64

Probability function

impl<X: CategoricalDatum> HasSuffStat<X> for Categorical

type Stat = CategoricalSuffStat

fn empty_suffstat(&self) -> Self::Stat

fn ln_f_stat(&self, stat: &Self::Stat) -> f64

Return the log likelihood for the data represented by the sufficient statistic.

impl KlDivergence for Categorical

fn kl(&self, other: &Self) -> f64

The KL divergence, KL(P|Q) between this distribution, P, and another, Q

fn kl_sym(&self, other: &Self) -> f64

Symmetrized divergence, KL(P|Q) + KL(Q|P)

impl<X: CategoricalDatum> Mode<X> for Categorical

fn mode(&self) -> Option<X>

Returns None if the mode is undefined or is not a single value

impl Parameterized for Categorical

type Parameters = CategoricalParameters

fn emit_params(&self) -> Self::Parameters

fn from_params(params: Self::Parameters) -> Self

impl PartialEq for Categorical

fn eq(&self, other: &Categorical) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl PartialOrd for Categorical

fn partial_cmp(&self, other: &Categorical) -> Option<Ordering>

This method returns an ordering between self and other values if one exists.

fn lt(&self, other: &Rhs) -> bool

This method tests less than (for self and other) and is used by the < operator.

fn le(&self, other: &Rhs) -> bool

This method tests less than or equal to (for self and other) and is used by the <= operator.

fn gt(&self, other: &Rhs) -> bool

This method tests greater than (for self and other) and is used by the > operator.

fn ge(&self, other: &Rhs) -> bool

This method tests greater than or equal to (for self and other) and is used by the >= operator.

impl Sampleable<Categorical> for Dirichlet

fn draw<R: Rng>(&self, rng: &mut R) -> Categorical

Single draw from the Rv

fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>

Multiple draws of the Rv

fn sample_stream<'r, R: Rng>( &'r self, rng: &'r mut R, ) -> Box<dyn Iterator<Item = X> + 'r>

Create a never-ending iterator of samples

impl Sampleable<Categorical> for SymmetricDirichlet

fn draw<R: Rng>(&self, rng: &mut R) -> Categorical

Single draw from the Rv

fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>

Multiple draws of the Rv

fn sample_stream<'r, R: Rng>( &'r self, rng: &'r mut R, ) -> Box<dyn Iterator<Item = X> + 'r>

Create a never-ending iterator of samples

impl<X: CategoricalDatum> Sampleable<X> for Categorical

fn draw<R: Rng>(&self, rng: &mut R) -> X

Single draw from the Rv

fn sample<R: Rng>(&self, n: usize, rng: &mut R) -> Vec<X>

Multiple draws of the Rv

fn sample_stream<'r, R: Rng>( &'r self, rng: &'r mut R, ) -> Box<dyn Iterator<Item = X> + 'r>

Create a never-ending iterator of samples

impl Serialize for Categorical

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl<X: CategoricalDatum> Support<X> for Categorical

fn supports(&self, x: &X) -> bool

Returns true if x is in the support of the Rv

impl StructuralPartialEq for Categorical