Struct ndarray_stats::histogram::strategies::FreedmanDiaconis

pub struct FreedmanDiaconis<T> { /* fields omitted */ }

Robust (resilient to outliers) strategy that takes into account data variability and data size.

Let n be the number of observations.

bin_width = 2 × IQR × n^−1/3

The bin width is proportional to the interquartile range (IQR) and inversely proportional to cube root of n. It can be too conservative for small datasets, but it is quite good for large datasets.

The IQR is very robust to outliers.

Notes

This strategy requires the data

• not being empty
• not being constant
• having positive IQR

Implementations

impl<T> FreedmanDiaconis<T> where
    T: Ord + Clone + FromPrimitive + NumOps + Zero, 

pub fn bin_width(&self) -> T

The bin width (or bin length) according to the fitted strategy.

Trait Implementations

impl<T> BinsBuildingStrategy for FreedmanDiaconis<T> where
    T: Ord + Clone + FromPrimitive + NumOps + Zero, 

type Elem = T

pub fn from_array<S>(a: &ArrayBase<S, Ix1>) -> Result<Self, BinsBuildError> where
    S: Data<Elem = Self::Elem>, 

Returns Err(BinsBuildError::Strategy) if IQR==0. Returns Err(BinsBuildError::EmptyInput) if a.len()==0. Returns Ok(Self) otherwise.

pub fn build(&self) -> Bins<T>

Returns a Bins instance, according to parameters inferred from observations.

pub fn n_bins(&self) -> usize

Returns the optimal number of bins, according to parameters inferred from observations.

impl<T: Debug> Debug for FreedmanDiaconis<T>

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

Formats the value using the given formatter.