Struct light_curve_feature::features::Bins [−][src]
pub struct Bins<T, F> where
T: Float,
F: FeatureEvaluator<T>, { /* fields omitted */ }
Expand description
Sampled time series meta-feature
Binning time series to bins with width $\mathrm{window}$ with respect to some $\mathrm{offset}$. $j-th$ bin interval is $[j \cdot \mathrm{window} + \mathrm{offset}; (j + 1) \cdot \mathrm{window} + \mathrm{offset})$. Binned time series is defined by $$ t_j^* = (j + \frac12) \cdot \mathrm{window} + \mathrm{offset}, $$ $$ m_j^* = \frac{\sum{m_i / \delta_i^2}}{\sum{\delta_i^{-2}}}, $$ $$ \delta_j^* = \frac{N_j}{\sum{\delta_i^{-2}}}, $$ where $N_j$ is a number of sampling observations and all sums are over observations inside considering bin. Bins takes any other feature evaluators to extract features from sample time series
- Depends on: time, magnitude, magnitude error
- Minimum number of observations: as required by sub-features, but at least 1
- Number of features: as provided by sub-features
Implementations
Extend a feature to extract from binned time series
Trait Implementations
impl<'de, T, F> Deserialize<'de> for Bins<T, F> where
T: Float,
F: FeatureEvaluator<T>,
T: Float,
F: FeatureEvaluator<T>,
impl<'de, T, F> Deserialize<'de> for Bins<T, F> where
T: Float,
F: FeatureEvaluator<T>,
T: Float,
F: FeatureEvaluator<T>,
fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
__D: Deserializer<'de>,
fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
__D: Deserializer<'de>,
Deserialize this value from the given Serde deserializer. Read more
Size of vectors returned by eval(), get_names() and get_descriptions() Read more
Minimum time series length required to successfully evaluate feature
If time array used by the feature
If magnitude array is used by the feature
If weight array is used by the feature
If feature requires time-sorting on the input TimeSeries
Vector of feature values or EvaluatorError
Returns vector of feature values and fill invalid components with given value
Checks if TimeSeries has enough points to evaluate the feature
impl<T, F> FeatureNamesDescriptionsTrait for Bins<T, F> where
T: Float,
F: FeatureEvaluator<T>,
impl<T, F> FeatureNamesDescriptionsTrait for Bins<T, F> where
T: Float,
F: FeatureEvaluator<T>,
impl<T, F> Serialize for Bins<T, F> where
T: Float,
F: FeatureEvaluator<T>,
T: Float,
F: FeatureEvaluator<T>,
impl<T, F> Serialize for Bins<T, F> where
T: Float,
F: FeatureEvaluator<T>,
T: Float,
F: FeatureEvaluator<T>,
Auto Trait Implementations
impl<T, F> RefUnwindSafe for Bins<T, F> where
F: RefUnwindSafe,
T: RefUnwindSafe,
impl<T, F> UnwindSafe for Bins<T, F> where
F: UnwindSafe,
T: UnwindSafe,
Blanket Implementations
Convert the given value into an approximately equivalent representation.
impl<Dst, Src, Scheme> ApproxInto<Dst, Scheme> for Src where
Dst: ApproxFrom<Src, Scheme>,
Scheme: ApproxScheme,
impl<Dst, Src, Scheme> ApproxInto<Dst, Scheme> for Src where
Dst: ApproxFrom<Src, Scheme>,
Scheme: ApproxScheme,
type Err = <Dst as ApproxFrom<Src, Scheme>>::Err
type Err = <Dst as ApproxFrom<Src, Scheme>>::Err
The error type produced by a failed conversion.
Convert the subject into an approximately equivalent representation.
Mutably borrows from an owned value. Read more
Approximate the subject with the default scheme.
fn approx_by<Scheme>(self) -> Result<Dst, Self::Err> where
Self: ApproxInto<Dst, Scheme>,
Scheme: ApproxScheme,
fn approx_by<Scheme>(self) -> Result<Dst, Self::Err> where
Self: ApproxInto<Dst, Scheme>,
Scheme: ApproxScheme,
Approximate the subject with a specific scheme.
Approximate the subject to a given type with the default scheme.
fn approx_as_by<Dst, Scheme>(self) -> Result<Dst, Self::Err> where
Self: ApproxInto<Dst, Scheme>,
Scheme: ApproxScheme,
fn approx_as_by<Dst, Scheme>(self) -> Result<Dst, Self::Err> where
Self: ApproxInto<Dst, Scheme>,
Scheme: ApproxScheme,
Approximate the subject to a given type with a specific scheme.
Attempt to convert the subject to a given type.