Struct Empirical 

pub struct Empirical { /* private fields */ }

An empirical distribution derived from samples.

WARNING: The ln_f and f methods are poor approximations. They both are likely be have unbound errors.

use rv::dist::{Gaussian, Empirical};
use rv::prelude::*;
use rv::misc::linspace;
use rand_xoshiro::Xoshiro256Plus;
use rand::SeedableRng;

let mut rng = Xoshiro256Plus::seed_from_u64(0xABCD);
let dist = Gaussian::standard();

let sample: Vec<f64> = dist.sample(1000, &mut rng);
let emp_dist = Empirical::new(sample);

let ln_f_err: Vec<f64> = linspace(emp_dist.range().0, emp_dist.range().1, 1000)
    .iter()
    .map(|x| {
        dist.ln_f(x) - emp_dist.ln_f(x)
    }).collect();

Implementations

impl Empirical

pub fn new(xs: Vec<f64>) -> Self

Create a new Empirical distribution with the given observed values

pub fn empcdfs(&self, values: &[f64]) -> Vec<f64>

Compute the CDF of a number of values

pub fn pp(&self, other: &Self) -> (Vec<f64>, Vec<f64>)

A utility for computing a P-P plot.

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

Area between CDF-CDF (1-1) line

pub fn range(&self) -> &(f64, f64)

Return the range of non-zero support for this distribution.

Trait Implementations

impl Cdf<f64> for Empirical

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

The value of the Cumulative Density Function at x

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

Survival function, 1 - CDF(x)

impl Clone for Empirical

fn clone(&self) -> Empirical

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for Empirical

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

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for Empirical

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

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl HasDensity<f64> for Empirical

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

Probability function

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

Probability function

impl Mean<f64> for Empirical

fn mean(&self) -> Option<f64>

Returns None if the mean is undefined

impl Parameterized for Empirical

type Parameters = EmpiricalParameters

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

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

fn map_params(&self, f: impl Fn(Self::Parameters) -> Self::Parameters) -> Self

impl PartialEq for Empirical

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

Tests for self and other values to be equal, and is used by ==.

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

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

impl PartialOrd for Empirical

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

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

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

Tests less than (for self and other) and is used by the < operator.

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

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

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

Tests greater than (for self and other) and is used by the > operator.

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

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

impl Sampleable<f64> for Empirical

fn draw<R: Rng>(&self, rng: &mut R) -> f64

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 Empirical

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

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl Variance<f64> for Empirical

fn variance(&self) -> Option<f64>

Returns None if the variance is undefined

impl StructuralPartialEq for Empirical