rust-average/src/macros.rs
2019-12-02 12:53:18 +01:00

166 lines
4.4 KiB
Rust

/// 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, $b));
}
);
}
/// Concatenate several iterative estimators into one.
///
/// `$name` is the name of the new struct. `$statistic` is the name of a
/// statistic and must exist as a method of the corresponding type `$estimator`.
/// `$estimator` must have an `add` method for adding new observations to the
/// sample (taking an `f64` as an argument). It must also implement `Default`.
///
/// If the short syntax is used, the fields will be named `$statistic`. Use the
/// long syntax and `$field` to give them explicit names. The long syntax also
/// supports calculating several statistics from one estimator.
///
/// For moments, only an estimator for the highest moment should be used and
/// reused for the lower moments (see the example below).
///
/// The following methods will be implemented: `new`, `add`, `$statistic`.
///
/// The following traits will be implemented: `Default`, `FromIterator<f64>`.
///
///
/// # Examples
///
/// ```
/// use average::{Min, Max, Estimate, concatenate};
///
/// concatenate!(MinMax, [Min, min], [Max, max]);
///
/// let s: MinMax = (1..6).map(f64::from).collect();
///
/// assert_eq!(s.min(), 1.0);
/// assert_eq!(s.max(), 5.0);
/// ```
///
/// The generated code looks roughly like this:
///
/// ```
/// # use average::{Min, Max, Estimate};
/// #
/// struct MinMax {
/// min: Min,
/// max: Max,
/// }
///
/// impl MinMax {
/// pub fn new() -> MinMax {
/// MinMax { min: Min::default(), max: Max::default() }
/// }
///
/// pub fn add(&mut self, x: f64) {
/// self.min.add(x);
/// self.max.add(x);
/// }
///
/// pub fn min(&self) -> f64 {
/// self.min.min()
/// }
///
/// pub fn max(&self) -> f64 {
/// self.max.max()
/// }
/// }
/// ```
///
/// If you want to calculate the mean, variance and the median in one pass, you
/// can do the following:
///
/// ```
/// use average::{Variance1, Quantile, Estimate, concatenate};
///
/// concatenate!(Estimator,
/// [Variance1, variance, mean, sample_variance],
/// [Quantile, quantile, quantile]);
/// ```
#[macro_export]
macro_rules! concatenate {
( $name:ident, $([$estimator:ident, $statistic:ident]),+ ) => {
concatenate!( $name, $([$estimator, $statistic, $statistic]),* );
};
( $name:ident, $( [$estimator:ident, $field:ident, $($statistic:ident),+] ),+ ) => {
struct $name {
$(
$field: $estimator,
)*
}
impl $name {
#[inline]
pub fn new() -> $name {
$name {
$(
$field: ::core::default::Default::default(),
)*
}
}
#[inline]
pub fn add(&mut self, x: f64) {
$(
self.$field.add(x);
)*
}
$( $(
#[inline]
pub fn $statistic(&self) -> f64 {
self.$field.$statistic()
}
)* )*
}
impl Default for $name {
fn default() -> $name {
$name::new()
}
}
$crate::impl_from_iterator!($name);
};
}
/// Implement `FromIterator<f64>` for an iterative estimator.
#[macro_export]
macro_rules! impl_from_iterator {
( $name:ident ) => {
impl ::core::iter::FromIterator<f64> for $name {
fn from_iter<T>(iter: T) -> $name
where T: IntoIterator<Item=f64>
{
let mut e = $name::new();
for i in iter {
e.add(i);
}
e
}
}
impl<'a> ::core::iter::FromIterator<&'a f64> for $name {
fn from_iter<T>(iter: T) -> $name
where T: IntoIterator<Item=&'a f64>
{
let mut e = $name::new();
for &i in iter {
e.add(i);
}
e
}
}
};
}