use crate::evaluator::*;
macro_const! {
const DOC: &str = r#"
Mean magnitude
$$
\langle m \rangle \equiv \frac1{N} \sum_i m_i.
$$
This is non-weighted mean, see [WeightedMean](crate::WeightedMean) for weighted mean.
- Depends on: **magnitude**
- Minimum number of observations: **1**
- Number of features: **1**
"#;
}
#[doc = DOC!()]
#[derive(Clone, Default, Debug, Serialize, Deserialize, JsonSchema)]
pub struct Mean {}
lazy_info!(
MEAN_INFO,
Mean,
size: 1,
min_ts_length: 1,
t_required: false,
m_required: true,
w_required: false,
sorting_required: false,
);
impl Mean {
pub fn new() -> Self {
Self {}
}
pub fn doc() -> &'static str {
DOC
}
}
impl FeatureNamesDescriptionsTrait for Mean {
fn get_names(&self) -> Vec<&str> {
vec!["mean"]
}
fn get_descriptions(&self) -> Vec<&str> {
vec!["mean magnitude"]
}
}
impl<T> FeatureEvaluator<T> for Mean
where
T: Float,
{
fn eval(&self, ts: &mut TimeSeries<T>) -> Result<Vec<T>, EvaluatorError> {
self.check_ts_length(ts)?;
Ok(vec![ts.m.get_mean()])
}
}
#[cfg(test)]
#[allow(clippy::unreadable_literal)]
#[allow(clippy::excessive_precision)]
mod tests {
use super::*;
use crate::tests::*;
check_feature!(Mean);
feature_test!(
mean,
[Mean::new()],
[14.0],
[1.0_f32, 1.0, 1.0, 1.0, 5.0, 6.0, 6.0, 6.0, 99.0],
);
}