std::rand: full exponential & normal distributions
Complete the implementation of Exp and Normal started by Exp1 and StandardNormal by creating types implementing Sample & IndependentSample with the appropriate parameters.
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@ -94,17 +94,6 @@ fn ziggurat<R:Rng>(rng: &mut R,
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///
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/// Note that this has to be unwrapped before use as an `f64` (using either
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/// `*` or `cast::transmute` is safe).
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///
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/// # Example
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///
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/// ```
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/// use std::rand::distributions::StandardNormal;
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///
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/// fn main() {
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/// let normal = 2.0 + (*rand::random::<StandardNormal>()) * 3.0;
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/// println!("{} is from a N(2, 9) distribution", normal)
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/// }
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/// ```
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pub struct StandardNormal(f64);
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impl Rand for StandardNormal {
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@ -142,23 +131,52 @@ impl Rand for StandardNormal {
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}
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}
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/// The `N(mean, std_dev**2)` distribution, i.e. samples from a normal
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/// distribution with mean `mean` and standard deviation `std_dev`.
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///
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/// # Example
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///
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/// ```
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/// use std::rand;
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/// use std::rand::distributions::{Normal, IndependentSample};
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///
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/// fn main() {
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/// let normal = Normal::new(2.0, 3.0);
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/// let v = normal.ind_sample(rand::task_rng());
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/// println!("{} is from a N(2, 9) distribution", v)
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/// }
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/// ```
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pub struct Normal {
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priv mean: f64,
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priv std_dev: f64
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}
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impl Normal {
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/// Construct a new `Normal` distribution with the given mean and
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/// standard deviation. Fails if `std_dev < 0`.
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pub fn new(mean: f64, std_dev: f64) -> Normal {
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assert!(std_dev >= 0.0, "Normal::new called with `std_dev` < 0");
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Normal {
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mean: mean,
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std_dev: std_dev
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}
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}
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}
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impl Sample<f64> for Normal {
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fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 { self.ind_sample(rng) }
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}
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impl IndependentSample<f64> for Normal {
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fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 {
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self.mean + self.std_dev * (*rng.gen::<StandardNormal>())
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}
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}
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/// A wrapper around an `f64` to generate Exp(1) random numbers. Dividing by
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/// the desired rate `lambda` will give Exp(lambda) distributed random
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/// numbers.
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///
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/// Note that this has to be unwrapped before use as an `f64` (using either
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/// `*` or `cast::transmute` is safe).
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///
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/// # Example
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///
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/// ```
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/// use std::rand::distributions::Exp1;
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///
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/// fn main() {
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/// let exp2 = (*rand::random::<Exp1>()) * 0.5;
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/// println!("{} is from a Exp(2) distribution", exp2);
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/// }
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/// ```
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pub struct Exp1(f64);
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// This could be done via `-rng.gen::<f64>().ln()` but that is slower.
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@ -181,10 +199,53 @@ impl Rand for Exp1 {
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}
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}
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/// The `Exp(lambda)` distribution; i.e. samples from the exponential
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/// distribution with rate parameter `lambda`.
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///
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/// This distribution has density function: `f(x) = lambda *
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/// exp(-lambda * x)` for `x > 0`.
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///
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/// # Example
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///
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/// ```
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/// use std::rand;
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/// use std::rand::distributions::{Exp, IndependentSample};
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///
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/// fn main() {
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/// let exp = Exp::new(2.0);
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/// let v = exp.ind_sample(rand::task_rng());
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/// println!("{} is from a Exp(2) distribution", v);
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/// }
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/// ```
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pub struct Exp {
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/// `lambda` stored as `1/lambda`, since this is what we scale by.
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priv lambda_inverse: f64
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}
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impl Exp {
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/// Construct a new `Exp` with the given shape parameter
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/// `lambda`. Fails if `lambda <= 0`.
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pub fn new(lambda: f64) -> Exp {
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assert!(lambda > 0.0, "Exp::new called with `lambda` <= 0");
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Exp { lambda_inverse: 1.0 / lambda }
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}
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}
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impl Sample<f64> for Exp {
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fn sample<R: Rng>(&mut self, rng: &mut R) -> f64 { self.ind_sample(rng) }
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}
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impl IndependentSample<f64> for Exp {
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fn ind_sample<R: Rng>(&self, rng: &mut R) -> f64 {
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(*rng.gen::<Exp1>()) * self.lambda_inverse
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}
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}
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#[cfg(test)]
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mod tests {
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use rand::*;
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use super::*;
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use iter::range;
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use option::{Some, None};
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struct ConstRand(uint);
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impl Rand for ConstRand {
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@ -200,4 +261,39 @@ mod tests {
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assert_eq!(*rand_sample.sample(task_rng()), 0);
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assert_eq!(*rand_sample.ind_sample(task_rng()), 0);
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}
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#[test]
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fn test_normal() {
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let mut norm = Normal::new(10.0, 10.0);
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let rng = task_rng();
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for _ in range(0, 1000) {
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norm.sample(rng);
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norm.ind_sample(rng);
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}
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}
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#[test]
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#[should_fail]
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fn test_normal_invalid_sd() {
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Normal::new(10.0, -1.0);
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}
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#[test]
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fn test_exp() {
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let mut exp = Exp::new(10.0);
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let rng = task_rng();
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for _ in range(0, 1000) {
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assert!(exp.sample(rng) >= 0.0);
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assert!(exp.ind_sample(rng) >= 0.0);
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}
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}
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#[test]
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#[should_fail]
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fn test_exp_invalid_lambda_zero() {
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Exp::new(0.0);
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}
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#[test]
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#[should_fail]
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fn test_exp_invalid_lambda_neg() {
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Exp::new(-10.0);
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}
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}
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