use crate::evaluator::*;
use itertools::Itertools;
macro_const! {
const DOC: &str = r#"
Von Neummann $\eta$
$$
\eta \equiv \frac1{(N - 1)\\,\sigma_m^2} \sum_{i=0}^{N-2}(m_{i+1} - m_i)^2,
$$
where $N$ is the number of observations,
$\sigma_m = \sqrt{\sum_i (m_i - \langle m \rangle)^2 / (N-1)}$ is the magnitude standard deviation.
- Depends on: **magnitude**
- Minimum number of observations: **2**
- Number of features: **1**
Kim et al. 2014, [DOI:10.1051/0004-6361/201323252](https://doi.org/10.1051/0004-6361/201323252)
"#;
}
#[doc = DOC!()]
#[derive(Clone, Default, Debug, Serialize, Deserialize, JsonSchema)]
pub struct Eta {}
impl Eta {
pub fn new() -> Self {
Self {}
}
pub fn doc() -> &'static str {
DOC
}
}
lazy_info!(
ETA_INFO,
Eta,
size: 1,
min_ts_length: 2,
t_required: false,
m_required: true,
w_required: false,
sorting_required: true,
);
impl FeatureNamesDescriptionsTrait for Eta {
fn get_names(&self) -> Vec<&str> {
vec!["eta"]
}
fn get_descriptions(&self) -> Vec<&str> {
vec!["Von Neummann eta-coefficient for magnitude sample"]
}
}
impl<T> FeatureEvaluator<T> for Eta
where
T: Float,
{
fn eval(&self, ts: &mut TimeSeries<T>) -> Result<Vec<T>, EvaluatorError> {
self.check_ts_length(ts)?;
let m_std2 = get_nonzero_m_std2(ts)?;
let value =
ts.m.as_slice()
.iter()
.tuple_windows()
.map(|(&a, &b)| (b - a).powi(2))
.sum::<T>()
/ (ts.lenf() - T::one())
/ m_std2;
Ok(vec![value])
}
}
#[cfg(test)]
#[allow(clippy::unreadable_literal)]
#[allow(clippy::excessive_precision)]
mod tests {
use super::*;
use crate::tests::*;
check_feature!(Eta);
feature_test!(
eta,
[Eta::new()],
[1.11338],
[1.0_f32, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 109.0],
);
}