Struct ndarray_histogram::histogram::strategies::Sturges

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

R’s default strategy, only accounts for data size. Only optimal for gaussian data and underestimates number of bins for large non-gaussian datasets.

Let n be the number of observations. The number of bins is 1 plus the base 2 log of n. This estimator assumes normality of data and is too conservative for larger, non-normal datasets.

This is the default method in R’s hist method.

Notes

This strategy requires the data

• not being empty
• not being constant

Implementations

impl<T> Sturges<T>where T: Ord + Send + Clone + FromPrimitive + ToPrimitive + 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 Sturges<T>where T: Ord + Send + Clone + FromPrimitive + ToPrimitive + NumOps + Zero,

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

Returns Err(BinsBuildError::Strategy) if the array is constant. Returns Err(BinsBuildError::EmptyInput) if a.len()==0. Returns Ok(Self) otherwise.

type Elem = T

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

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

fn n_bins(&self) -> usize

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

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

Returns a strategy that has learnt the required parameter for building Bins for given 1-dimensional array, or an Err if it is not possible to infer the required parameter with the given data and specified strategy.

impl<T: Debug> Debug for Sturges<T>

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

Formats the value using the given formatter.