use crate::evaluator::*;
macro_const! {
const DOC: &str = r"
Skewness of magnitude $G_1$
$$
G_1 \equiv \frac{N}{(N - 1)(N - 2)} \frac{\sum_i(m_i - \langle m \rangle)^3}{\sigma_m^3},
$$
where $N$ is the number of observations,
$\langle m \rangle$ is the mean magnitude,
$\sigma_m = \sqrt{\sum_i (m_i - \langle m \rangle)^2 / (N-1)}$ is the magnitude standard deviation.
- Depends on: **magnitude**
- Minimum number of observations: **3**
- Number of features: **1**
[Wikipedia](https://en.wikipedia.org/wiki/Skewness#Sample_skewness)
";
}
#[doc = DOC!()]
#[derive(Clone, Default, Debug, Serialize, Deserialize, JsonSchema)]
pub struct Skew {}
lazy_info!(
SKEW_INFO,
Skew,
size: 1,
min_ts_length: 3,
t_required: false,
m_required: true,
w_required: false,
sorting_required: false,
);
impl Skew {
pub fn new() -> Self {
Self {}
}
pub const fn doc() -> &'static str {
DOC
}
}
impl FeatureNamesDescriptionsTrait for Skew {
fn get_names(&self) -> Vec<&str> {
vec!["skew"]
}
fn get_descriptions(&self) -> Vec<&str> {
vec!["skew of magnitude sample"]
}
}
impl<T> FeatureEvaluator<T> for Skew
where
T: Float,
{
fn eval(&self, ts: &mut TimeSeries<T>) -> Result<Vec<T>, EvaluatorError> {
self.check_ts_length(ts)?;
let m_std = get_nonzero_m_std(ts)?;
let m_mean = ts.m.get_mean();
let n = ts.lenf();
let n_1 = n - T::one();
let n_2 = n_1 - T::one();
let third_moment =
ts.m.sample
.fold(T::zero(), |sum, &m| sum + (m - m_mean).powi(3));
Ok(vec![third_moment / m_std.powi(3) * n / (n_1 * n_2)])
}
}
#[cfg(test)]
#[allow(clippy::unreadable_literal)]
#[allow(clippy::excessive_precision)]
mod tests {
use super::*;
use crate::tests::*;
check_feature!(Skew);
feature_test!(
skew,
[Skew::new()],
[0.4626804756753222],
[2.0_f32, 3.0, 5.0, 7.0, 11.0, 13.0],
);
}