Struct light_curve_feature::LinearTrend

pub struct LinearTrend {}

The slope and noise of the light curve without observation errors in the linear fit

Least squares fit of the linear stochastic model with constant gaussian noise $\Sigma$ assuming observation errors to be zero: $$ m_i = c + \mathrm{slope}\,t_i + \Sigma \varepsilon_i $$ where $c$ is a constant, $\{\varepsilon_i\}$ are standard distributed random values. $\mathrm{slope}$ and $\Sigma$ are returned, if $N = 2$ than no least squares fit is done, a slope between a pair of observations $(m_1 - m_0) / (t_1 - t_0)$ and $\Sigma = 0$ is returned.

• Depends on: time, magnitude
• Minimum number of observations: 2
• Number of features: 2

Implementations

impl LinearTrend

pub fn new() -> Self

Trait Implementations

impl Default for LinearTrend

fn default() -> LinearTrend

Returns the "default value" for a type.

impl<T> FeatureEvaluator<T> for LinearTrend where
    T: Float, 

fn eval(&self, ts: &mut TimeSeries<T>) -> Vec<T>

Should return the non-empty vector of feature values. The length and feature order should correspond to get_names() output

fn get_names(&self) -> Vec<&str>

Should return the non-empty vector of feature names. The length and feature order should correspond to eval() output

fn size_hint(&self) -> usize

Should return the size of vectors returned by eval() and get_names()