use crate::evaluator::*;
use crate::straight_line_fit::fit_straight_line;
macro_const! {
const DOC: &str = r#"
Slope, its error and reduced $\chi^2$ of the light curve in the linear fit
Least squares fit of the linear stochastic model with Gaussian noise described by observation
errors $\{\delta_i\}$:
$$
m_i = c + \mathrm{slope} t_i + \delta_i \varepsilon_i
$$
where $c$ is a constant,
$\{\varepsilon_i\}$ are standard distributed random variables.
Feature values are $\mathrm{slope}$, $\sigma_\mathrm{slope}$ and
$\frac{\sum{((m_i - c - \mathrm{slope} t_i) / \delta_i)^2}}{N - 2}$.
- Depends on: **time**, **magnitude**, **magnitude error**
- Minimum number of observations: **3**
- Number of features: **3**
"#;
}
#[doc = DOC!()]
#[derive(Clone, Default, Debug, Serialize, Deserialize, JsonSchema)]
pub struct LinearFit {}
impl LinearFit {
pub fn new() -> Self {
Self {}
}
pub fn doc() -> &'static str {
DOC
}
}
lazy_info!(
LINEAR_FIT_INFO,
LinearFit,
size: 3,
min_ts_length: 3,
t_required: true,
m_required: true,
w_required: true,
sorting_required: true,
);
impl FeatureNamesDescriptionsTrait for LinearFit {
fn get_names(&self) -> Vec<&str> {
vec![
"linear_fit_slope",
"linear_fit_slope_sigma",
"linear_fit_reduced_chi2",
]
}
fn get_descriptions(&self) -> Vec<&str> {
vec![
"slope of linear fit",
"error of slope of linear fit",
"linear fit quality (reduced chi2)",
]
}
}
impl<T> FeatureEvaluator<T> for LinearFit
where
T: Float,
{
fn eval(&self, ts: &mut TimeSeries<T>) -> Result<Vec<T>, EvaluatorError> {
self.check_ts_length(ts)?;
let result = fit_straight_line(ts, true);
Ok(vec![
result.slope,
T::sqrt(result.slope_sigma2),
result.reduced_chi2,
])
}
}
#[cfg(test)]
#[allow(clippy::unreadable_literal)]
#[allow(clippy::excessive_precision)]
mod tests {
use super::*;
use crate::tests::*;
check_feature!(LinearFit);
feature_test!(
linear_fit,
[LinearFit::default()],
[1.0544186045473263, 0.7963978113902943, 0.013781209302325587],
[0.0_f32, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
[0.0_f32, 0.01, 0.04, 0.09, 0.16, 0.25, 0.36, 0.49, 0.64, 0.81, 1.0],
[1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0],
);
}