Struct ndarray_stats::histogram::strategies::FreedmanDiaconis

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

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.

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,

fn from_array<S>(a: &ArrayBase<S, Ix1>) -> Selfwhere
    S: Data<Elem = Self::Elem>,

Panics if IQR==0 or if a.len()==0.

type Elem = T

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

Returns a Bins instance, built accordingly to the parameters inferred from observations in from_array.

fn n_bins(&self) -> usize

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