Struct light_curve_feature::features::LinearTrend

pub struct LinearTrend {}

The slope, its error and noise level of the light curve 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 $c$ and $\Sigma$ are constants, ${\varepsilon_i}$ are standard distributed random variables. $\mathrm{slope}$, $\sigma_\mathrm{slope}$ and $\Sigma$ are returned.

• Depends on: time, magnitude
• Minimum number of observations: 3
• Number of features: 3

Implementations

impl LinearTrend

pub fn new() -> Self

pub fn doc() -> &'static str

Trait Implementations

impl Clone for LinearTrend

fn clone(&self) -> LinearTrend

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for LinearTrend

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for LinearTrend

fn default() -> LinearTrend

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for LinearTrend

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
    __D: Deserializer<'de>, 

Deserialize this value from the given Serde deserializer.

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

fn eval(&self, ts: &mut TimeSeries<'_, T>) -> Result<Vec<T>, EvaluatorError>

Should return the vector of feature values or EvaluatorError

fn get_info(&self) -> &EvaluatorInfo

Get feature evaluator meta-information

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

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

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

Should return the vector of feature descriptions. The length and feature order should correspond to eval() output

fn eval_or_fill(&self, ts: &mut TimeSeries<'_, T>, fill_value: T) -> Vec<T>

Should return the vector of feature values and fill invalid components with given value

fn size_hint(&self) -> usize

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

fn min_ts_length(&self) -> usize

Should return minimum time series length to successfully find feature value

fn is_t_required(&self) -> bool

fn is_m_required(&self) -> bool

fn is_w_required(&self) -> bool

fn is_sorting_required(&self) -> bool

fn check_ts_length(
    &self,
    ts: &TimeSeries<'_, T>
) -> Result<usize, EvaluatorError>

impl<T> From<LinearTrend> for Feature<T> where
    T: Float, 

fn from(v: LinearTrend) -> Feature<T>

Performs the conversion.

impl JsonSchema for LinearTrend

fn schema_name() -> String

The name of the generated JSON Schema.

fn json_schema(gen: &mut SchemaGenerator) -> Schema

Generates a JSON Schema for this type.

fn is_referenceable() -> bool

Whether JSON Schemas generated for this type should be re-used where possible using the $ref keyword.

impl Serialize for LinearTrend

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error> where
    __S: Serializer, 

Serialize this value into the given Serde serializer.

impl<T> TryInto<LinearTrend> for Feature<T> where
    T: Float, 

type Error = &'static str

The type returned in the event of a conversion error.

fn try_into(self) -> Result<LinearTrend, <Self as TryInto<LinearTrend>>::Error>

Performs the conversion.