a7dde93df8
* Follow the suggestions from the Serde docs. * Rename the feature from `serde` to `serde1`. * Fix a doctest. * Mention the feature in the README.
128 lines
3.3 KiB
Rust
128 lines
3.3 KiB
Rust
/// Estimate the arithmetic mean of a sequence of numbers ("population").
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///
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///
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/// ## Example
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///
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/// ```
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/// use average::Mean;
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///
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/// let a: Mean = (1..6).map(f64::from).collect();
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/// println!("The mean is {}.", a.mean());
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/// ```
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#[derive(Debug, Clone)]
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#[cfg_attr(feature = "serde1", derive(Serialize, Deserialize))]
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pub struct Mean {
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/// Mean value.
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avg: f64,
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/// Sample size.
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n: u64,
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}
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impl Mean {
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/// Create a new mean estimator.
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#[inline]
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pub fn new() -> Mean {
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Mean { avg: 0., n: 0 }
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}
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/// Increment the sample size.
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///
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/// This does not update anything else.
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#[inline]
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fn increment(&mut self) {
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self.n += 1;
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}
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/// Add an observation given an already calculated difference from the mean
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/// divided by the number of samples, assuming the inner count of the sample
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/// size was already updated.
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///
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/// This is useful for avoiding unnecessary divisions in the inner loop.
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#[inline]
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fn add_inner(&mut self, delta_n: 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.avg += delta_n;
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}
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/// Determine whether the sample is empty.
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#[inline]
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pub fn is_empty(&self) -> bool {
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self.n == 0
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}
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/// Estimate the mean of the population.
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///
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/// Returns 0 for an empty sample.
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#[inline]
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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 sample size.
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#[inline]
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pub fn len(&self) -> u64 {
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self.n
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}
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}
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impl core::default::Default for Mean {
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fn default() -> Mean {
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Mean::new()
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}
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}
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impl Estimate for Mean {
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#[inline]
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fn add(&mut self, sample: f64) {
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self.increment();
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let delta_n = (sample - self.avg)
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/ f64::approx_from(self.n).unwrap();
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self.add_inner(delta_n);
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}
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fn estimate(&self) -> f64 {
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self.mean()
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}
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}
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impl Merge for Mean {
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/// Merge another sample into this one.
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///
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///
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/// ## Example
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///
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/// ```
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/// use average::{Mean, Merge};
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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: Mean = sequence.iter().collect();
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/// let mut avg_left: Mean = left.iter().collect();
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/// let avg_right: Mean = right.iter().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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/// ```
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#[inline]
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fn merge(&mut self, other: &Mean) {
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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 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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}
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}
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impl_from_iterator!(Mean);
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