RollingValidNorm

tea_rolling

Trait RollingValidNorm 

Source 
pub trait RollingValidNorm<T: IsNone>: Vec1View<T> {
    // Provided methods
    fn ts_vzscore_to<O: Vec1<U>, U>(
        &self,
        window: usize,
        min_periods: Option<usize>,
        out: Option<O::UninitRefMut<'_>>,
    ) -> Option<O>
       where T::Inner: Number,
             f64: Cast<U> { ... }
    fn ts_vzscore<O: Vec1<U>, U>(
        &self,
        window: usize,
        min_periods: Option<usize>,
    ) -> O
       where T::Inner: Number,
             f64: Cast<U> { ... }
    fn ts_vminmaxnorm_to<O: Vec1<U>, U>(
        &self,
        window: usize,
        min_periods: Option<usize>,
        out: Option<O::UninitRefMut<'_>>,
    ) -> Option<O>
       where T::Inner: Number,
             f64: Cast<U> { ... }
    fn ts_vminmaxnorm<O: Vec1<U>, U>(
        &self,
        window: usize,
        min_periods: Option<usize>,
    ) -> O
       where T::Inner: Number,
             f64: Cast<U> { ... }
}
Expand description

Trait for rolling window normalization operations on valid (non-None) elements.

Provided Methods§

Source

fn ts_vzscore_to<O: Vec1<U>, U>( &self, window: usize, min_periods: Option<usize>, out: Option<O::UninitRefMut<'_>>, ) -> Option<O>
where T::Inner: Number, f64: Cast<U>,

Calculates the rolling z-score (standard score) for valid elements within a window.

§Arguments
  • window - The size of the rolling window.
  • min_periods - The minimum number of observations in window required to have a value.
  • out - Optional output buffer to store the results.
§Returns

A vector containing the rolling z-scores.

§Notes

The z-score is calculated as (x - mean) / standard_deviation. If the standard deviation is zero or if there are fewer than min_periods valid observations, the result will be NaN.

Source

fn ts_vzscore<O: Vec1<U>, U>( &self, window: usize, min_periods: Option<usize>, ) -> O
where T::Inner: Number, f64: Cast<U>,

Calculates the rolling z-score (standard score) for valid elements within a window.

§Arguments
  • window - The size of the rolling window.
  • min_periods - The minimum number of observations in window required to have a value.
  • out - Optional output buffer to store the results.
§Returns

A vector containing the rolling z-scores.

§Notes

The z-score is calculated as (x - mean) / standard_deviation. If the standard deviation is zero or if there are fewer than min_periods valid observations, the result will be NaN.

Source

fn ts_vminmaxnorm_to<O: Vec1<U>, U>( &self, window: usize, min_periods: Option<usize>, out: Option<O::UninitRefMut<'_>>, ) -> Option<O>
where T::Inner: Number, f64: Cast<U>,

Calculates the rolling min-max normalization for valid elements within a window.

§Arguments
  • window - The size of the rolling window.
  • min_periods - The minimum number of observations in window required to have a value.
  • out - Optional output buffer to store the results.
§Returns

A vector containing the rolling min-max normalized values.

§Notes

The min-max normalization is calculated as (x - min) / (max - min). If max equals min or if there are fewer than min_periods valid observations, the result will be NaN.

Source

fn ts_vminmaxnorm<O: Vec1<U>, U>( &self, window: usize, min_periods: Option<usize>, ) -> O
where T::Inner: Number, f64: Cast<U>,

Calculates the rolling min-max normalization for valid elements within a window.

§Arguments
  • window - The size of the rolling window.
  • min_periods - The minimum number of observations in window required to have a value.
  • out - Optional output buffer to store the results.
§Returns

A vector containing the rolling min-max normalized values.

§Notes

The min-max normalization is calculated as (x - min) / (max - min). If max equals min or if there are fewer than min_periods valid observations, the result will be NaN.

Dyn Compatibility§

This trait is not dyn compatible.

In older versions of Rust, dyn compatibility was called "object safety", so this trait is not object safe.

Implementors§

Source§

impl<T: IsNone, I: Vec1View<T>> RollingValidNorm<T> for I