Merge pull request #96 from fizyk20/discrete

Add binomial and Poisson distributions

src/distributions/binomial.rs

@@ -0,0 +1,156 @@
+// Copyright 2016-2017 The Rust Project Developers. See the COPYRIGHT
+// file at the top-level directory of this distribution and at
+// https://rust-lang.org/COPYRIGHT.
+//
+// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
+// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! The binomial distribution.
+
+use Rng;
+use distributions::Distribution;
+use distributions::log_gamma::log_gamma;
+use std::f64::consts::PI;
+
+/// The binomial distribution `Binomial(n, p)`.
+///
+/// This distribution has density function: `f(k) = n!/(k! (n-k)!) p^k (1-p)^(n-k)` for `k >= 0`.
+///
+/// # Example
+///
+/// ```rust
+/// use rand::distributions::{Binomial, Distribution};
+///
+/// let bin = Binomial::new(20, 0.3);
+/// let v = bin.sample(&mut rand::thread_rng());
+/// println!("{} is from a binomial distribution", v);
+/// ```
+#[derive(Clone, Copy, Debug)]
+pub struct Binomial {
+    n: u64, // number of trials
+    p: f64, // probability of success
+}
+
+impl Binomial {
+    /// Construct a new `Binomial` with the given shape parameters
+    /// `n`, `p`. Panics if `p <= 0` or `p >= 1`.
+    pub fn new(n: u64, p: f64) -> Binomial {
+        assert!(p > 0.0, "Binomial::new called with p <= 0");
+        assert!(p < 1.0, "Binomial::new called with p >= 1");
+        Binomial { n: n, p: p }
+    }
+}
+
+impl Distribution<u64> for Binomial {
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u64 {
+        // binomial distribution is symmetrical with respect to p -> 1-p, k -> n-k
+        // switch p so that it is less than 0.5 - this allows for lower expected values
+        // we will just invert the result at the end
+        let p = if self.p <= 0.5 {
+            self.p
+        } else {
+            1.0 - self.p
+        };
+
+        // expected value of the sample
+        let expected = self.n as f64 * p;
+
+        let result =
+            // for low expected values we just simulate n drawings
+            if expected < 25.0 {
+                let mut lresult = 0.0;
+                for _ in 0 .. self.n {
+                    if rng.gen::<f64>() < p {
+                        lresult += 1.0;
+                    }
+                }
+                lresult
+            }
+            // high expected value - do the rejection method
+            else {
+                // prepare some cached values
+                let float_n = self.n as f64;
+                let ln_fact_n = log_gamma(float_n + 1.0);
+                let pc = 1.0 - p;
+                let log_p = p.ln();
+                let log_pc = pc.ln();
+                let sq = (expected * (2.0 * pc)).sqrt();
+
+                let mut lresult;
+
+                loop {
+                    let mut comp_dev: f64;
+                    // we use the lorentzian distribution as the comparison distribution
+                    // f(x) ~ 1/(1+x/^2)
+                    loop {
+                        // draw from the lorentzian distribution
+                        comp_dev = (PI*rng.gen::<f64>()).tan();
+                        // shift the peak of the comparison ditribution
+                        lresult = expected + sq * comp_dev;
+                        // repeat the drawing until we are in the range of possible values
+                        if lresult >= 0.0 && lresult < float_n + 1.0 {
+                            break;
+                        }
+                    }
+
+                    // the result should be discrete
+                    lresult = lresult.floor();
+
+                    let log_binomial_dist = ln_fact_n - log_gamma(lresult+1.0) -
+                        log_gamma(float_n - lresult + 1.0) + lresult*log_p + (float_n - lresult)*log_pc;
+                    // this is the binomial probability divided by the comparison probability
+                    // we will generate a uniform random value and if it is larger than this,
+                    // we interpret it as a value falling out of the distribution and repeat
+                    let comparison_coeff = (log_binomial_dist.exp() * sq) * (1.2 * (1.0 + comp_dev*comp_dev));
+
+                    if comparison_coeff >= rng.gen() {
+                        break;
+                    }
+                }
+
+                lresult
+            };
+
+        // invert the result for p < 0.5
+        if p != self.p {
+            self.n - result as u64
+        } else {
+            result as u64
+        }
+    }
+}
+
+#[cfg(test)]
+mod test {
+    use distributions::Distribution;
+    use super::Binomial;
+
+    #[test]
+    fn test_binomial() {
+        let binomial = Binomial::new(150, 0.1);
+        let mut rng = ::test::rng(123);
+        let mut sum = 0;
+        for _ in 0..1000 {
+            sum += binomial.sample(&mut rng);
+        }
+        let avg = (sum as f64) / 1000.0;
+        println!("Binomial average: {}", avg);
+        assert!((avg - 15.0).abs() < 0.5); // not 100% certain, but probable enough
+    }
+
+    #[test]
+    #[should_panic]
+    #[cfg_attr(target_env = "msvc", ignore)]
+    fn test_binomial_invalid_lambda_zero() {
+        Binomial::new(20, 0.0);
+    }
+    #[test]
+    #[should_panic]
+    #[cfg_attr(target_env = "msvc", ignore)]
+    fn test_binomial_invalid_lambda_neg() {
+        Binomial::new(20, -10.0);
+    }
+}

src/distributions/log_gamma.rs

@@ -0,0 +1,51 @@
+// Copyright 2016-2017 The Rust Project Developers. See the COPYRIGHT
+// file at the top-level directory of this distribution and at
+// https://rust-lang.org/COPYRIGHT.
+//
+// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
+// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+/// Calculates ln(gamma(x)) (natural logarithm of the gamma
+/// function) using the Lanczos approximation.
+///
+/// The approximation expresses the gamma function as:
+/// `gamma(z+1) = sqrt(2*pi)*(z+g+0.5)^(z+0.5)*exp(-z-g-0.5)*Ag(z)`
+/// `g` is an arbitrary constant; we use the approximation with `g=5`.
+///
+/// Noting that `gamma(z+1) = z*gamma(z)` and applying `ln` to both sides:
+/// `ln(gamma(z)) = (z+0.5)*ln(z+g+0.5)-(z+g+0.5) + ln(sqrt(2*pi)*Ag(z)/z)`
+///
+/// `Ag(z)` is an infinite series with coefficients that can be calculated
+/// ahead of time - we use just the first 6 terms, which is good enough
+/// for most purposes.
+pub fn log_gamma(x: f64) -> f64 {
+    // precalculated 6 coefficients for the first 6 terms of the series
+    let coefficients: [f64; 6] = [
+        76.18009172947146,
+        -86.50532032941677,
+        24.01409824083091,
+        -1.231739572450155,
+        0.1208650973866179e-2,
+        -0.5395239384953e-5,
+    ];
+
+    // (x+0.5)*ln(x+g+0.5)-(x+g+0.5)
+    let tmp = x + 5.5;
+    let log = (x + 0.5) * tmp.ln() - tmp;
+
+    // the first few terms of the series for Ag(x)
+    let mut a = 1.000000000190015;
+    let mut denom = x;
+    for j in 0..6 {
+        denom += 1.0;
+        a += coefficients[j] / denom;
+    }
+
+    // get everything together
+    // a is Ag(x)
+    // 2.5066... is sqrt(2pi)
+    return log + (2.5066282746310005 * a / x).ln();
+}

src/distributions/mod.rs

@@ -25,6 +25,10 @@ pub use self::gamma::{Gamma, ChiSquared, FisherF, StudentT};
 pub use self::normal::{Normal, LogNormal, StandardNormal};
 #[cfg(feature="std")]
 pub use self::exponential::{Exp, Exp1};
+#[cfg(feature = "std")]
+pub use self::poisson::Poisson;
+#[cfg(feature = "std")]
+pub use self::binomial::Binomial;
 
 pub mod range;
 #[cfg(feature="std")]
@@ -33,9 +37,14 @@ pub mod gamma;
 pub mod normal;
 #[cfg(feature="std")]
 pub mod exponential;
+#[cfg(feature = "std")]
+pub mod poisson;
+#[cfg(feature = "std")]
+pub mod binomial;
 
 mod float;
 mod integer;
+mod log_gamma;
 mod other;
 #[cfg(feature="std")]
 mod ziggurat_tables;

src/distributions/poisson.rs

@@ -0,0 +1,144 @@
+// Copyright 2016-2017 The Rust Project Developers. See the COPYRIGHT
+// file at the top-level directory of this distribution and at
+// https://rust-lang.org/COPYRIGHT.
+//
+// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
+// https://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+// <LICENSE-MIT or https://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! The Poisson distribution.
+
+use Rng;
+use distributions::Distribution;
+use distributions::log_gamma::log_gamma;
+use std::f64::consts::PI;
+
+/// The Poisson distribution `Poisson(lambda)`.
+///
+/// This distribution has a density function:
+/// `f(k) = lambda^k * exp(-lambda) / k!` for `k >= 0`.
+///
+/// # Example
+///
+/// ```rust
+/// use rand::distributions::{Poisson, Distribution};
+///
+/// let poi = Poisson::new(2.0);
+/// let v = poi.sample(&mut rand::thread_rng());
+/// println!("{} is from a Poisson(2) distribution", v);
+/// ```
+#[derive(Clone, Copy, Debug)]
+pub struct Poisson {
+    lambda: f64,
+    // precalculated values
+    exp_lambda: f64,
+    log_lambda: f64,
+    sqrt_2lambda: f64,
+    magic_val: f64,
+}
+
+impl Poisson {
+    /// Construct a new `Poisson` with the given shape parameter
+    /// `lambda`. Panics if `lambda <= 0`.
+    pub fn new(lambda: f64) -> Poisson {
+        assert!(lambda > 0.0, "Poisson::new called with lambda <= 0");
+        let log_lambda = lambda.ln();
+        Poisson {
+            lambda: lambda,
+            exp_lambda: (-lambda).exp(),
+            log_lambda: log_lambda,
+            sqrt_2lambda: (2.0 * lambda).sqrt(),
+            magic_val: lambda * log_lambda - log_gamma(1.0 + lambda),
+        }
+    }
+}
+
+impl Distribution<u64> for Poisson {
+    fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> u64 {
+        // using the algorithm from Numerical Recipes in C
+
+        // for low expected values use the Knuth method
+        if self.lambda < 12.0 {
+            let mut result = 0;
+            let mut p = 1.0;
+            while p > self.exp_lambda {
+                p *= rng.gen::<f64>();
+                result += 1;
+            }
+            result - 1
+        }
+        // high expected values - rejection method
+        else {
+            let mut int_result: u64;
+
+            loop {
+                let mut result;
+                let mut comp_dev;
+
+                // we use the lorentzian distribution as the comparison distribution
+                // f(x) ~ 1/(1+x/^2)
+                loop {
+                    // draw from the lorentzian distribution
+                    comp_dev = (PI * rng.gen::<f64>()).tan();
+                    // shift the peak of the comparison ditribution
+                    result = self.sqrt_2lambda * comp_dev + self.lambda;
+                    // repeat the drawing until we are in the range of possible values
+                    if result >= 0.0 {
+                        break;
+                    }
+                }
+                // now the result is a random variable greater than 0 with Lorentzian distribution
+                // the result should be an integer value
+                result = result.floor();
+                int_result = result as u64;
+
+                // this is the ratio of the Poisson distribution to the comparison distribution
+                // the magic value scales the distribution function to a range of approximately 0-1
+                // since it is not exact, we multiply the ratio by 0.9 to avoid ratios greater than 1
+                // this doesn't change the resulting distribution, only increases the rate of failed drawings
+                let check = 0.9 * (1.0 + comp_dev * comp_dev)
+                    * (result * self.log_lambda - log_gamma(1.0 + result) - self.magic_val).exp();
+
+                // check with uniform random value - if below the threshold, we are within the target distribution
+                if rng.gen::<f64>() <= check {
+                    break;
+                }
+            }
+            int_result
+        }
+    }
+}
+
+#[cfg(test)]
+mod test {
+    use distributions::Distribution;
+    use super::Poisson;
+
+    #[test]
+    fn test_poisson() {
+        let poisson = Poisson::new(10.0);
+        let mut rng = ::test::rng(123);
+        let mut sum = 0;
+        for _ in 0..1000 {
+            sum += poisson.sample(&mut rng);
+        }
+        let avg = (sum as f64) / 1000.0;
+        println!("Poisson average: {}", avg);
+        assert!((avg - 10.0).abs() < 0.5); // not 100% certain, but probable enough
+    }
+
+    #[test]
+    #[should_panic]
+    #[cfg_attr(target_env = "msvc", ignore)]
+    fn test_poisson_invalid_lambda_zero() {
+        Poisson::new(0.0);
+    }
+    #[test]
+    #[should_panic]
+    #[cfg_attr(target_env = "msvc", ignore)]
+    fn test_poisson_invalid_lambda_neg() {
+        Poisson::new(-10.0);
+    }
+}