Trait rstats::Stats

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pub trait Stats {

Show 26 methods    // Required methods
    fn vmag(self) -> f64;
    fn vmagsq(self) -> f64;
    fn vreciprocal(self) -> Result<Vec<f64>, RE>;
    fn vinverse(self) -> Result<Vec<f64>, RE>;
    fn negv(self) -> Result<Vec<f64>, RE>;
    fn vunit(self) -> Result<Vec<f64>, RE>;
    fn hmad(self) -> Result<f64, RE>;
    fn amean(self) -> Result<f64, RE>;
    fn medmad(self) -> Result<Params, RE>;
    fn ameanstd(self) -> Result<Params, RE>;
    fn awmean(self) -> Result<f64, RE>;
    fn awmeanstd(self) -> Result<Params, RE>;
    fn hmean(self) -> Result<f64, RE>;
    fn hmeanstd(self) -> Result<Params, RE>;
    fn hwmean(self) -> Result<f64, RE>;
    fn hwmeanstd(self) -> Result<Params, RE>;
    fn gmean(self) -> Result<f64, RE>;
    fn gmeanstd(self) -> Result<Params, RE>;
    fn gwmean(self) -> Result<f64, RE>;
    fn gwmeanstd(self) -> Result<Params, RE>;
    fn pdf(self) -> Vec<f64>;
    fn entropy(self) -> f64;
    fn autocorr(self) -> Result<f64, RE>;
    fn lintrans(self) -> Result<Vec<f64>, RE>;
    fn dfdt(self, centre: f64) -> Result<f64, RE>;
    fn house_reflector(self) -> Vec<f64>;

}
Expand description

Statistical measures of a single variable (one generic vector of data) and vector algebra applicable to a single (generic) vector. Thus these methods take no arguments.

Required Methods§

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fn vmag(self) -> f64

Vector magnitude

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fn vmagsq(self) -> f64

Vector magnitude squared (sum of squares)

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fn vreciprocal(self) -> Result<Vec<f64>, RE>

vector with reciprocal components

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fn vinverse(self) -> Result<Vec<f64>, RE>

vector with inverse magnitude

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fn negv(self) -> Result<Vec<f64>, RE>

negated vector (all components swap sign)

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fn vunit(self) -> Result<Vec<f64>, RE>

Unit vector

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fn hmad(self) -> Result<f64, RE>

Harmonic spread from median

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fn amean(self) -> Result<f64, RE>

Arithmetic mean

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fn medmad(self) -> Result<Params, RE>

Median and Mad packed into Params struct

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fn ameanstd(self) -> Result<Params, RE>

Arithmetic mean and standard deviation

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fn awmean(self) -> Result<f64, RE>

Weighted arithmetic mean

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fn awmeanstd(self) -> Result<Params, RE>

Weighted arithmetic men and standard deviation

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fn hmean(self) -> Result<f64, RE>

Harmonic mean

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fn hmeanstd(self) -> Result<Params, RE>

Harmonic mean and experimental standard deviation

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fn hwmean(self) -> Result<f64, RE>

Weighted harmonic mean

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fn hwmeanstd(self) -> Result<Params, RE>

Weighted harmonic mean and standard deviation

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fn gmean(self) -> Result<f64, RE>

Geometric mean

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fn gmeanstd(self) -> Result<Params, RE>

Geometric mean and standard deviation ratio

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fn gwmean(self) -> Result<f64, RE>

Weighed geometric mean

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fn gwmeanstd(self) -> Result<Params, RE>

Weighted geometric mean and standard deviation ratio

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fn pdf(self) -> Vec<f64>

Probability density function of a sorted slice

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fn entropy(self) -> f64

Information (entropy) in nats

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fn autocorr(self) -> Result<f64, RE>

(Auto)correlation coefficient of pairs of successive values of (time series) variable.

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fn lintrans(self) -> Result<Vec<f64>, RE>

Linear transform to interval [0,1]

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fn dfdt(self, centre: f64) -> Result<f64, RE>

Linearly weighted approximate time series derivative at the last (present time) point.

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fn house_reflector(self) -> Vec<f64>

Householder reflection

Implementations on Foreign Types§

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impl<T> Stats for &[T]
where T: Clone + PartialOrd + Into<f64>,

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fn vmag(self) -> f64

Vector magnitude

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fn vmagsq(self) -> f64

Vector magnitude squared (sum of squares)

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fn vreciprocal(self) -> Result<Vec<f64>, RE>

Vector with reciprocal components

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fn vinverse(self) -> Result<Vec<f64>, RE>

Vector with inverse magnitude

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fn vunit(self) -> Result<Vec<f64>, RE>

Unit vector

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fn hmad(self) -> Result<f64, RE>

Harmonic spread from median

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fn amean(self) -> Result<f64, RE>

Arithmetic mean

§Example
use rstats::Stats;
let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.];
assert_eq!(v1.amean().unwrap(),7.5_f64);
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fn medmad(self) -> Result<Params, RE>

Median and Mad packed into in Params struct

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fn ameanstd(self) -> Result<Params, RE>

Arithmetic mean and (population) standard deviation

§Example
use rstats::Stats;
let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.];
let res = v1.ameanstd().unwrap();
assert_eq!(res.centre,7.5_f64);
assert_eq!(res.spread,4.031128874149275_f64);
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fn awmean(self) -> Result<f64, RE>

Linearly weighted arithmetic mean of an f64 slice.
Linearly ascending weights from 1 to n.
Time dependent data should be in the order of time increasing. Then the most recent gets the most weight.

§Example
use rstats::Stats;
let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.];
assert_eq!(v1.awmean().unwrap(),9.666666666666666_f64);
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fn awmeanstd(self) -> Result<Params, RE>

Linearly weighted arithmetic mean and standard deviation of an f64 slice.
Linearly ascending weights from 1 to n.
Time dependent data should be in the order of time increasing.

§Example
use rstats::Stats;
let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.];
let res = v1.awmeanstd().unwrap();
assert_eq!(res.centre,9.666666666666666_f64);
assert_eq!(res.spread,3.399346342395192_f64);
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fn hmean(self) -> Result<f64, RE>

Harmonic mean of an f64 slice.

§Example
use rstats::Stats;
let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.];
assert_eq!(v1.hmean().unwrap(),4.305622526633627_f64);
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fn hmeanstd(self) -> Result<Params, RE>

Harmonic mean and standard deviation std is based on reciprocal moments

§Example
use rstats::Stats;
let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.];
let res = v1.hmeanstd().unwrap();
assert_eq!(res.centre,4.305622526633627_f64);
assert_eq!(res.spread,1.1996764516690959_f64);
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fn hwmean(self) -> Result<f64, RE>

Linearly weighted harmonic mean of an f64 slice.
Linearly ascending weights from 1 to n.
Time dependent data should be ordered by increasing time.

§Example
use rstats::Stats;
let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.];
assert_eq!(v1.hwmean().unwrap(),7.5_f64);
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fn hwmeanstd(self) -> Result<Params, RE>

Weighted harmonic mean and standard deviation std is based on reciprocal moments

§Example
use rstats::Stats;
let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.];
let res = v1.hmeanstd().unwrap();
assert_eq!(res.centre,4.305622526633627_f64);
assert_eq!(res.spread,1.1996764516690959_f64);
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fn gmean(self) -> Result<f64, RE>

Geometric mean of an i64 slice.
The geometric mean is just an exponential of an arithmetic mean of log data (natural logarithms of the data items).
The geometric mean is less sensitive to outliers near maximal value.
Zero valued data is not allowed!

§Example
use rstats::Stats;
let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.];
assert_eq!(v1.gmean().unwrap(),6.045855171418503_f64);
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fn gmeanstd(self) -> Result<Params, RE>

Geometric mean and std ratio of an f64 slice.
Zero valued data is not allowed.
Std of ln data becomes a ratio after conversion back.

§Example
use rstats::Stats;
let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.];
let res = v1.gmeanstd().unwrap();
assert_eq!(res.centre,6.045855171418503_f64);
assert_eq!(res.spread,2.1084348239406303_f64);
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fn gwmean(self) -> Result<f64, RE>

Linearly weighted geometric mean of an i64 slice.
Ascending weights from 1 down to n.
Time dependent data should be in time increasing order.
The geometric mean is an exponential of an arithmetic mean of log data (natural logarithms of the data items).
The geometric mean is less sensitive to outliers near maximal value.
Zero valued data is not allowed!

§Example
use rstats::Stats;
let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.];
assert_eq!(v1.gwmean().unwrap(),8.8185222496341_f64);
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fn gwmeanstd(self) -> Result<Params, RE>

Linearly weighted version of gmeanstd.

§Example
use rstats::Stats;
let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.];
let res = v1.gwmeanstd().unwrap();
assert_eq!(res.centre,8.8185222496341_f64);
assert_eq!(res.spread,1.626825493266009_f64);
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fn pdf(self) -> Vec<f64>

Probability density function of a sorted slice with repeats. Repeats are counted and removed

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fn entropy(self) -> f64

Information (entropy) (in nats)

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fn autocorr(self) -> Result<f64, RE>

(Auto)correlation coefficient of pairs of successive values of (time series) f64 variable.

§Example
use rstats::Stats;
let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.];
assert_eq!(v1.autocorr().unwrap(),0.9984603532054123_f64);
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fn lintrans(self) -> Result<Vec<f64>, RE>

Linear transform to interval [0,1]

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fn dfdt(self, centre: f64) -> Result<f64, RE>

Linearly weighted approximate time series derivative at the last point (present time). Weighted sum (backwards half filter), minus the median. Rising values return positive result and vice versa.

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fn house_reflector(self) -> Vec<f64>

Householder reflector

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fn negv(self) -> Result<Vec<f64>, RE>

Implementors§