Refactor code into more files
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623c50c936
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196
src/average.rs
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196
src/average.rs
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@ -0,0 +1,196 @@
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use core;
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use conv::ApproxFrom;
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/// Represent the arithmetic mean and the variance of a sequence of numbers.
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///
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/// Everything is calculated iteratively using constant memory, so the sequence
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/// of numbers can be an iterator. The used algorithms try to avoid numerical
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/// instabilities.
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///
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/// ```
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/// use average::Average;
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///
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/// let a: Average = (1..6).map(Into::into).collect();
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/// assert_eq!(a.mean(), 3.0);
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/// assert_eq!(a.sample_variance(), 2.5);
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/// ```
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#[derive(Debug, Clone)]
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pub struct Average {
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/// Average value.
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avg: f64,
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/// Number of samples.
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n: u64,
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/// Intermediate sum of squares for calculating the variance.
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v: f64,
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}
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impl Average {
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/// Create a new average.
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pub fn new() -> Average {
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Average { avg: 0., n: 0, v: 0. }
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}
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/// Add a number to the sequence of which the average is calculated.
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pub fn add(&mut self, x: f64) {
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// This algorithm introduced by Welford in 1962 trades numerical
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// stability for a division inside the loop.
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//
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// See https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance.
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self.n += 1;
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let delta = x - self.avg;
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self.avg += delta / f64::approx_from(self.n).unwrap();
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self.v += delta * (x - self.avg);
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}
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/// Return the mean of the sequence.
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pub fn mean(&self) -> f64 {
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self.avg
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}
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/// Return the number of elements in the sequence.
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pub fn len(&self) -> u64 {
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self.n
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}
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/// Calculate the unbiased sample variance of the sequence.
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///
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/// This assumes that the sequence consists of samples of a larger population.
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pub fn sample_variance(&self) -> f64 {
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if self.n < 2 {
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return 0.;
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}
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self.v / f64::approx_from(self.n - 1).unwrap()
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}
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/// Calculate the population variance of the sequence.
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///
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/// This assumes that the sequence consists of the entire population.
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pub fn population_variance(&self) -> f64 {
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if self.n < 2 {
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return 0.;
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}
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self.v / f64::approx_from(self.n).unwrap()
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}
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/// Calculate the standard error of the mean of the sequence.
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pub fn error(&self) -> f64 {
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if self.n == 0 {
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return 0.;
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}
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(self.sample_variance() / f64::approx_from(self.n).unwrap()).sqrt()
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}
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/// Merge the average of another sequence into this one.
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///
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/// ```
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/// use average::Average;
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///
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/// let sequence: &[f64] = &[1., 2., 3., 4., 5., 6., 7., 8., 9.];
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/// let (left, right) = sequence.split_at(3);
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/// let avg_total: Average = sequence.iter().map(|x| *x).collect();
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/// let mut avg_left: Average = left.iter().map(|x| *x).collect();
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/// let avg_right: Average = right.iter().map(|x| *x).collect();
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/// avg_left.merge(&avg_right);
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/// assert_eq!(avg_total.mean(), avg_left.mean());
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/// assert_eq!(avg_total.sample_variance(), avg_left.sample_variance());
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/// ```
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pub fn merge(&mut self, other: &Average) {
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// This algorithm was proposed by Chan et al. in 1979.
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//
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// See https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance.
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let delta = other.avg - self.avg;
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let len_self = f64::approx_from(self.n).unwrap();
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let len_other = f64::approx_from(other.n).unwrap();
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let len_total = len_self + len_other;
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self.n += other.n;
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self.avg = (len_self * self.avg + len_other * other.avg) / len_total;
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// Chan et al. use
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//
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// self.avg += delta * len_other / len_total;
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//
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// instead but this results in cancelation if the number of samples are similar.
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self.v += other.v + delta*delta * len_self * len_other / len_total;
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}
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}
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impl core::default::Default for Average {
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fn default() -> Average {
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Average::new()
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}
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}
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impl core::iter::FromIterator<f64> for Average {
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fn from_iter<T>(iter: T) -> Average
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where T: IntoIterator<Item=f64>
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{
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let mut a = Average::new();
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for i in iter {
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a.add(i);
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}
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a
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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 super::*;
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use core::iter::Iterator;
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#[test]
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fn average_trivial() {
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let mut a = Average::new();
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assert_eq!(a.len(), 0);
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a.add(1.0);
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assert_eq!(a.mean(), 1.0);
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assert_eq!(a.len(), 1);
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assert_eq!(a.sample_variance(), 0.0);
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assert_eq!(a.error(), 0.0);
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}
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#[test]
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fn average_simple() {
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let a: Average = (1..6).map(f64::from).collect();
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assert_eq!(a.mean(), 3.0);
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assert_eq!(a.len(), 5);
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assert_eq!(a.sample_variance(), 2.5);
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assert_almost_eq!(a.error(), f64::sqrt(0.5), 1e-16);
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}
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#[test]
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fn average_numerically_unstable() {
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// The naive algorithm fails for this example due to cancelation.
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let big = 1e9;
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let sample = &[big + 4., big + 7., big + 13., big + 16.];
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let a: Average = sample.iter().map(|x| *x).collect();
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assert_eq!(a.sample_variance(), 30.);
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}
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#[test]
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fn average_normal_distribution() {
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use rand::distributions::{Normal, IndependentSample};
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let normal = Normal::new(2.0, 3.0);
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let mut a = Average::new();
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for _ in 0..1_000_000 {
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a.add(normal.ind_sample(&mut ::rand::thread_rng()));
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}
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assert_almost_eq!(a.mean(), 2.0, 1e-2);
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assert_almost_eq!(a.sample_variance().sqrt(), 3.0, 1e-2);
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}
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#[test]
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fn merge() {
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let sequence: &[f64] = &[1., 2., 3., 4., 5., 6., 7., 8., 9.];
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for mid in 0..sequence.len() {
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let (left, right) = sequence.split_at(mid);
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let avg_total: Average = sequence.iter().map(|x| *x).collect();
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let mut avg_left: Average = left.iter().map(|x| *x).collect();
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let avg_right: Average = right.iter().map(|x| *x).collect();
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avg_left.merge(&avg_right);
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assert_eq!(avg_total.n, avg_left.n);
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assert_eq!(avg_total.avg, avg_left.avg);
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assert_eq!(avg_total.v, avg_left.v);
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}
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}
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}
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213
src/lib.rs
213
src/lib.rs
@ -4,214 +4,7 @@ extern crate conv;
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#[cfg(test)] extern crate rand;
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#[cfg(test)] extern crate rand;
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#[cfg(test)] #[macro_use] extern crate std;
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#[cfg(test)] #[macro_use] extern crate std;
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use conv::ApproxFrom;
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#[macro_use] mod macros;
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mod average;
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/// Represent the arithmetic mean and the variance of a sequence of numbers.
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pub use average::Average;
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///
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/// Everything is calculated iteratively using constant memory, so the sequence
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/// of numbers can be an iterator. The used algorithms try to avoid numerical
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/// instabilities.
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///
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/// ```
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/// use average::Average;
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///
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/// let a: Average = (1..6).map(Into::into).collect();
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/// assert_eq!(a.mean(), 3.0);
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/// assert_eq!(a.sample_variance(), 2.5);
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/// ```
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#[derive(Debug, Clone)]
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pub struct Average {
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/// Average value.
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avg: f64,
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/// Number of samples.
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n: u64,
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/// Intermediate sum of squares for calculating the variance.
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v: f64,
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}
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impl Average {
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/// Create a new average.
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pub fn new() -> Average {
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Average { avg: 0., n: 0, v: 0. }
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}
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/// Add a number to the sequence of which the average is calculated.
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pub fn add(&mut self, x: f64) {
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// This algorithm introduced by Welford in 1962 trades numerical
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// stability for a division inside the loop.
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//
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// See https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance.
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self.n += 1;
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let delta = x - self.avg;
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self.avg += delta / f64::approx_from(self.n).unwrap();
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self.v += delta * (x - self.avg);
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}
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/// Return the mean of the sequence.
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pub fn mean(&self) -> f64 {
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self.avg
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}
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/// Return the number of elements in the sequence.
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pub fn len(&self) -> u64 {
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self.n
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}
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/// Calculate the unbiased sample variance of the sequence.
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///
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/// This assumes that the sequence consists of samples of a larger population.
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pub fn sample_variance(&self) -> f64 {
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if self.n < 2 {
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return 0.;
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}
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self.v / f64::approx_from(self.n - 1).unwrap()
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}
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/// Calculate the population variance of the sequence.
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///
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/// This assumes that the sequence consists of the entire population.
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pub fn population_variance(&self) -> f64 {
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if self.n < 2 {
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return 0.;
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}
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self.v / f64::approx_from(self.n).unwrap()
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}
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/// Calculate the standard error of the mean of the sequence.
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pub fn error(&self) -> f64 {
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if self.n == 0 {
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return 0.;
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}
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(self.sample_variance() / f64::approx_from(self.n).unwrap()).sqrt()
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}
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/// Merge the average of another sequence into this one.
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///
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/// ```
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/// use average::Average;
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///
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/// let sequence: &[f64] = &[1., 2., 3., 4., 5., 6., 7., 8., 9.];
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/// let (left, right) = sequence.split_at(3);
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/// let avg_total: Average = sequence.iter().map(|x| *x).collect();
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/// let mut avg_left: Average = left.iter().map(|x| *x).collect();
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/// let avg_right: Average = right.iter().map(|x| *x).collect();
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/// avg_left.merge(&avg_right);
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/// assert_eq!(avg_total.mean(), avg_left.mean());
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/// assert_eq!(avg_total.sample_variance(), avg_left.sample_variance());
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/// ```
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pub fn merge(&mut self, other: &Average) {
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// This algorithm was proposed by Chan et al. in 1979.
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//
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// See https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance.
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let delta = other.avg - self.avg;
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let len_self = f64::approx_from(self.n).unwrap();
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let len_other = f64::approx_from(other.n).unwrap();
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let len_total = len_self + len_other;
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self.n += other.n;
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self.avg = (len_self * self.avg + len_other * other.avg) / len_total;
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// Chan et al. use
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//
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// self.avg += delta * len_other / len_total;
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//
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// instead but this results in cancelation if the number of samples are similar.
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self.v += other.v + delta*delta * len_self * len_other / len_total;
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}
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}
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impl core::default::Default for Average {
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fn default() -> Average {
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Average::new()
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}
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}
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impl core::iter::FromIterator<f64> for Average {
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fn from_iter<T>(iter: T) -> Average
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where T: IntoIterator<Item=f64>
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{
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let mut a = Average::new();
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for i in iter {
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a.add(i);
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}
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a
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}
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}
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/// Assert that two numbers are almost equal to each other.
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///
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/// On panic, this macro will print the values of the expressions with their
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/// debug representations.
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#[macro_export]
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macro_rules! assert_almost_eq {
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($a:expr, $b:expr, $prec:expr) => (
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let diff = ($a - $b).abs();
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if diff > $prec {
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panic!(format!(
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"assertion failed: `abs(left - right) = {:.1e} < {:e}`, \
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(left: `{}`, right: `{}`)",
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diff, $prec, $a, $b));
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}
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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 super::*;
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use core::iter::Iterator;
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#[test]
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fn average_trivial() {
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let mut a = Average::new();
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assert_eq!(a.len(), 0);
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a.add(1.0);
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assert_eq!(a.mean(), 1.0);
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assert_eq!(a.len(), 1);
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assert_eq!(a.sample_variance(), 0.0);
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assert_eq!(a.error(), 0.0);
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}
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#[test]
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fn average_simple() {
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let a: Average = (1..6).map(f64::from).collect();
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assert_eq!(a.mean(), 3.0);
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assert_eq!(a.len(), 5);
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assert_eq!(a.sample_variance(), 2.5);
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assert_almost_eq!(a.error(), f64::sqrt(0.5), 1e-16);
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}
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#[test]
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fn average_numerically_unstable() {
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// The naive algorithm fails for this example due to cancelation.
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let big = 1e9;
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let sample = &[big + 4., big + 7., big + 13., big + 16.];
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let a: Average = sample.iter().map(|x| *x).collect();
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|
||||||
assert_eq!(a.sample_variance(), 30.);
|
|
||||||
}
|
|
||||||
|
|
||||||
#[test]
|
|
||||||
fn average_normal_distribution() {
|
|
||||||
use rand::distributions::{Normal, IndependentSample};
|
|
||||||
let normal = Normal::new(2.0, 3.0);
|
|
||||||
let mut a = Average::new();
|
|
||||||
for _ in 0..1_000_000 {
|
|
||||||
a.add(normal.ind_sample(&mut ::rand::thread_rng()));
|
|
||||||
}
|
|
||||||
assert_almost_eq!(a.mean(), 2.0, 1e-2);
|
|
||||||
assert_almost_eq!(a.sample_variance().sqrt(), 3.0, 1e-2);
|
|
||||||
}
|
|
||||||
|
|
||||||
#[test]
|
|
||||||
fn merge() {
|
|
||||||
let sequence: &[f64] = &[1., 2., 3., 4., 5., 6., 7., 8., 9.];
|
|
||||||
for mid in 0..sequence.len() {
|
|
||||||
let (left, right) = sequence.split_at(mid);
|
|
||||||
let avg_total: Average = sequence.iter().map(|x| *x).collect();
|
|
||||||
let mut avg_left: Average = left.iter().map(|x| *x).collect();
|
|
||||||
let avg_right: Average = right.iter().map(|x| *x).collect();
|
|
||||||
avg_left.merge(&avg_right);
|
|
||||||
assert_eq!(avg_total.n, avg_left.n);
|
|
||||||
assert_eq!(avg_total.avg, avg_left.avg);
|
|
||||||
assert_eq!(avg_total.v, avg_left.v);
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
16
src/macros.rs
Normal file
16
src/macros.rs
Normal file
@ -0,0 +1,16 @@
|
|||||||
|
/// Assert that two numbers are almost equal to each other.
|
||||||
|
///
|
||||||
|
/// On panic, this macro will print the values of the expressions with their
|
||||||
|
/// debug representations.
|
||||||
|
#[macro_export]
|
||||||
|
macro_rules! assert_almost_eq {
|
||||||
|
($a:expr, $b:expr, $prec:expr) => (
|
||||||
|
let diff = ($a - $b).abs();
|
||||||
|
if diff > $prec {
|
||||||
|
panic!(format!(
|
||||||
|
"assertion failed: `abs(left - right) = {:.1e} < {:e}`, \
|
||||||
|
(left: `{}`, right: `{}`)",
|
||||||
|
diff, $prec, $a, $b));
|
||||||
|
}
|
||||||
|
);
|
||||||
|
}
|
Loading…
Reference in New Issue
Block a user