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Vinzent Steinberg 2017-05-24 19:45:58 +02:00
parent 0d1e1c4f12
commit cfab296f58
5 changed files with 22 additions and 32 deletions
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6
README.md
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@ -1,8 +1,8 @@
# average
Calculate the average of a sequence and its error iteratively, using constant
memory and avoiding numerical problems. The calculation can be easily parallelized
by using `Average::merge`.
Calculate the average of a sequence and its error iteratively in a single pass,
using constant memory and avoiding numerical problems. The calculation can be
easily parallelized by using `merge`.
[Documentation](https://docs.rs/average) |
[crates.io](https://crates.io/crates/average)

20
src/average.rs
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@ -5,10 +5,6 @@ use conv::ApproxFrom;
/// Estimate the arithmetic mean of a sequence of numbers ("population").
///
/// Everything is calculated iteratively using constant memory, so the sequence
/// of numbers can be an iterator. The used algorithms try to avoid numerical
/// instabilities.
///
///
/// ## Example
///
@ -33,7 +29,7 @@ impl Average {
Average { avg: 0., n: 0 }
}
/// Add an element sampled from the population.
/// Add an observation sampled from the population.
#[inline]
pub fn add(&mut self, sample: f64) {
// This algorithm introduced by Welford in 1962 trades numerical
@ -45,7 +41,7 @@ impl Average {
self.avg += delta / f64::approx_from(self.n).unwrap();
}
/// Determine whether the samples are empty.
/// Determine whether the sample is empty.
#[inline]
pub fn is_empty(&self) -> bool {
self.n == 0
@ -57,7 +53,7 @@ impl Average {
self.avg
}
/// Return the number of samples.
/// Return the sample size.
#[inline]
pub fn len(&self) -> u64 {
self.n
@ -120,10 +116,6 @@ impl core::iter::FromIterator<f64> for Average {
///
/// This can be used to estimate the standard error of the mean.
///
/// Everything is calculated iteratively using constant memory, so the sequence
/// of numbers can be an iterator. The used algorithms try to avoid numerical
/// instabilities.
///
///
/// ## Example
///
@ -147,7 +139,7 @@ impl AverageWithError {
AverageWithError { avg: Average::new(), v: 0. }
}
/// Add an element sampled from the population.
/// Add an observation sampled from the population.
#[inline]
pub fn add(&mut self, sample: f64) {
// This algorithm introduced by Welford in 1962 trades numerical
@ -159,7 +151,7 @@ impl AverageWithError {
self.v += delta * (sample - self.avg.mean());
}
/// Determine whether the samples are empty.
/// Determine whether the sample is empty.
#[inline]
pub fn is_empty(&self) -> bool {
self.avg.is_empty()
@ -171,7 +163,7 @@ impl AverageWithError {
self.avg.mean()
}
/// Return the number of samples.
/// Return the sample size.
#[inline]
pub fn len(&self) -> u64 {
self.avg.len()

4
src/lib.rs
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@ -13,6 +13,10 @@
//! You can run several estimators in parallel and merge them into one with
//! `merge()`.
//!
//! Everything is calculated iteratively in a single pass using constant memory,
//! so the sequence of numbers can be an iterator. The used algorithms try to
//! avoid numerical instabilities.
//!
//! [`Average`]: ./average/struct.Average.html
//! [`AverageWithError`]: ./average/struct.AverageWithError.html
//! [`WeightedAverage`]: ./weighted_average/struct.WeightedAverage.html

10
src/minmax.rs
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@ -14,9 +14,6 @@ fn max(a: f64, b: f64) -> f64 {
/// Estimate the minimum of a sequence of numbers ("population").
///
/// Everything is calculated iteratively using constant memory, so the sequence
/// of numbers can be an iterator.
///
///
/// ## Example
///
@ -46,7 +43,7 @@ impl Min {
Min::from_value(::core::f64::INFINITY)
}
/// Add an element sampled from the population.
/// Add an observation sampled from the population.
#[inline]
pub fn add(&mut self, x: f64) {
self.r.add(x);
@ -94,9 +91,6 @@ impl core::iter::FromIterator<f64> for Min {
/// Estimate the maximum of a sequence of numbers ("population").
///
/// Everything is calculated iteratively using constant memory, so the sequence
/// of numbers can be an iterator.
///
///
/// ## Example
///
@ -126,7 +120,7 @@ impl Max {
Max::from_value(::core::f64::NEG_INFINITY)
}
/// Add an element sampled from the population.
/// Add an observation sampled from the population.
#[inline]
pub fn add(&mut self, x: f64) {
self.r.add(x);

14
src/weighted_average.rs
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@ -32,7 +32,7 @@ impl WeightedAverage {
}
}
/// Add a weighted element sampled from the population.
/// Add a weighted observation sampled from the population.
#[inline]
pub fn add(&mut self, sample: f64, weight: f64) {
// The algorithm for the unweighted average was suggested by Welford in 1962.
@ -61,7 +61,7 @@ impl WeightedAverage {
self.weight_sum
}
/// Estimate the weighted mean of the sequence.
/// Estimate the weighted mean of the population.
#[inline]
pub fn mean(&self) -> f64 {
self.weighted_avg
@ -149,7 +149,7 @@ impl WeightedAverageWithError {
}
}
/// Add a weighted element sampled from the population.
/// Add a weighted observation sampled from the population.
#[inline]
pub fn add(&mut self, sample: f64, weight: f64) {
// The algorithm for the unweighted average was suggested by Welford in 1962.
@ -182,19 +182,19 @@ impl WeightedAverageWithError {
self.weight_sum_sq
}
/// Estimate the weighted mean of the sequence.
/// Estimate the weighted mean of the population.
#[inline]
pub fn weighted_mean(&self) -> f64 {
self.weighted_avg.mean()
}
/// Estimate the unweighted mean of the sequence.
/// Estimate the unweighted mean of the population.
#[inline]
pub fn unweighted_mean(&self) -> f64 {
self.unweighted_avg.mean()
}
/// Return sample size.
/// Return the sample size.
#[inline]
pub fn len(&self) -> u64 {
self.unweighted_avg.len()
@ -224,7 +224,7 @@ impl WeightedAverageWithError {
self.unweighted_avg.sample_variance()
}
/// Estimate the standard error of the *weighted* mean of the sequence.
/// Estimate the standard error of the *weighted* mean of the population.
///
/// Returns 0 if the sum of weights is 0.
///