110 lines
4.0 KiB
Rust
110 lines
4.0 KiB
Rust
//! This crate provides estimators for statistics on a sequence of numbers. The
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//! typical workflow looks like this:
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//!
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//! 1. If necessary, build your custom estimator using [`concatenate`] or
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//! [`define_moments`].
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//! 2. Initialize the estimator of your choice with `new()`.
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//! 3. Add some subset (called "sample") of the sequence of numbers (called
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//! "population") for which you want to estimate the statistic, using `add()`
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//! or `collect()`.
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//! 4. Calculate the statistic with `mean()` or similar.
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//!
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//! You can run several estimators in parallel and merge them into one with
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//! `merge()`.
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//!
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//! Everything is calculated iteratively in a single pass using constant memory,
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//! so the sequence of numbers can be an iterator. The used algorithms try to
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//! avoid numerical instabilities.
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//!
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//! If you want [Serde](https://github.com/serde-rs/serde) support,
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//! include `"serde1"` in your list of features.
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//!
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//! Note that deserializing does not currently check for all invalid inputs.
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//! For example, if you deserialize a corrupted [`Variance`] it may return
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//! a negative value for variance, even though that is mathematically impossible.
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//! In a future minor release some of these checks may be added.
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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::{MeanWithError, Estimate};
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//!
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//! let mut a: MeanWithError = (1..6).map(f64::from).collect();
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//! a.add(42.);
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//! println!("The mean is {} ± {}.", a.mean(), a.error());
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//! ```
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//!
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//!
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//! ## Estimators
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//!
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//! * Mean ([`Mean`]) and its error ([`MeanWithError`]).
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//! * Weighted mean ([`WeightedMean`]) and its error
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//! ([`WeightedMeanWithError`]).
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//! * Variance ([`Variance`]), skewness ([`Skewness`]) and kurtosis
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//! ([`Kurtosis`]).
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//! * Arbitrary higher moments ([`define_moments`]).
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//! * Quantiles ([`Quantile`]).
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//! * Minimum ([`Min`]) and maximum ([`Max`]).
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//!
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//!
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//! ## Estimating several statistics at once
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//!
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//! The estimators are designed to have minimal state. The recommended way to
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//! calculate several of them at once is to create a struct with all the
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//! estimators you need. You can then implement `add` for your struct by
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//! forwarding to the underlying estimators. Everything is inlined, so there
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//! should be no overhead.
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//!
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//! You can avoid the boilerplate code by using the [`concatenate`] macro.
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//!
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//! Note that calculating moments requires calculating the lower moments, so you
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//! only need to include the highest moment in your struct.
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//!
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//!
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//! ## Calculating histograms
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//!
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//! The [`define_histogram`] macro can be used to define a histogram struct that
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//! uses constant memory. See [`Histogram10`] (defined using
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//! `define_histogram!(..., 10)`) and the extension trait [`Histogram`]
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//! for the methods available to the generated struct.
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//!
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//!
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//! [`Mean`]: ./struct.Mean.html
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//! [`MeanWithError`]: ./type.MeanWithError.html
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//! [`WeightedMean`]: ./struct.WeightedMean.html
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//! [`WeightedMeanWithError`]: ./struct.WeightedMeanWithError.html
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//! [`Variance`]: ./struct.Variance.html
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//! [`Skewness`]: ./struct.Skewness.html
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//! [`Kurtosis`]: ./struct.Kurtosis.html
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//! [`Quantile`]: ./struct.Quantile.html
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//! [`Min`]: ./struct.Min.html
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//! [`Max`]: ./struct.Max.html
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//! [`concatenate`]: ./macro.concatenate.html
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//! [`define_moments`]: ./macro.define_moments.html
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//! [`define_histogram`]: ./macro.define_histogram.html
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//! [`Histogram10`]: ./struct.Histogram10.html
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//! [`Histogram`]: ./trait.Histogram.html
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#![cfg_attr(feature = "cargo-clippy", allow(clippy::float_cmp))]
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#![no_std]
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#[macro_use] mod macros;
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#[macro_use] mod moments;
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mod weighted_mean;
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mod minmax;
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mod quantile;
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mod traits;
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#[macro_use] mod histogram;
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pub use crate::moments::{Mean, Variance, Skewness, Kurtosis, MeanWithError};
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pub use crate::weighted_mean::{WeightedMean, WeightedMeanWithError};
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pub use crate::minmax::{Min, Max};
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pub use crate::quantile::Quantile;
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pub use crate::traits::{Estimate, Merge, Histogram};
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define_histogram!(hist, 10);
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pub use crate::hist::Histogram as Histogram10;
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define_moments!(Moments4, 4);
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