Trait rstats::Vecf64[−][src]

pub trait Vecf64 {

Show methods    fn smult(self, s: f64) -> Vec<f64>;

    fn sadd(self, s: f64) -> Vec<f64>;

    fn dotp(self, v: &[f64]) -> f64;

    fn vinverse(self) -> Vec<f64>;

    fn cosine(self, _v: &[f64]) -> f64;

    fn vsub(self, v: &[f64]) -> Vec<f64>;

    fn negv(self) -> Vec<f64>;

    fn vadd(self, v: &[f64]) -> Vec<f64>;

    fn vmag(self) -> f64;

    fn vmagsq(self) -> f64;

    fn vdist(self, v: &[f64]) -> f64;

    fn vdistsq(self, v: &[f64]) -> f64;

    fn cityblockd(self, v: &[f64]) -> f64;

    fn vunit(self) -> Vec<f64>;

    fn vsubunit(self, v: &[f64]) -> Vec<f64>;

    fn varea(self, v: &[f64]) -> f64;

    fn varc(self, v: &[f64]) -> f64;

    fn vsim(self, v: &[f64]) -> f64;

    fn vdisim(self, v: &[f64]) -> f64;

    fn correlation(self, _v: &[f64]) -> f64;

    fn kendalcorr(self, _v: &[f64]) -> f64;

    fn spearmancorr(self, _v: &[f64]) -> f64;

    fn autocorr(self) -> f64;

    fn covone(self, m: &[f64]) -> Vec<f64>;

    fn symmatrix(self) -> Vec<Vec<f64>>;

    fn minmax(self) -> (f64, usize, f64, usize);

    fn lintrans(self) -> Vec<f64>;

    fn sortf(self) -> Vec<f64>;

}
Expand description

Vector algebra on one or two vectors.

Required methods

Scalar multiplication of a vector

Scalar addition to vector

Scalar product of two vectors

Inverse vecor of magnitude 1/|v|

Cosine = a.dotp(b)/(a.vmag*b.vmag)

Vector subtraction

Vector negtion

Vector addition

Vector magnitude

Vector magnitude squared

Euclidian distance between two points

vdist between two points squared

cityblock distance

Unit vector

Unit vector of difference (done together for efficiency)

Area of parallelogram between two vectors (magnitude of cross product)

Area proportional to the swept arc

Vector similarity in the interval [0,1]: (1+cos(theta))/2

Vector dissimilarity in the interval [0,1]: (1-cos(theta))/2

Correlation

Kendall’s tau-b (rank order) correlation

Spearman’s rho (rank differences) correlation

Autocorrelation

Lower triangular part of a covariance matrix for a single f64 vector.

Reconstructs the full symmetric matrix from its lower diagonal compact form

Minimum, minimum’s index, maximum, maximum’s index.

Linear transformation to [0,1]

Sort vector in a standard way

Implementations on Foreign Types

Scalar multiplication of a vector, creates new vec

Scalar addition to a vector, creates new vec

Scalar product of two f64 slices.
Must be of the same length - no error checking (for speed)

Cosine of an angle between two vectors.

Example

use rstats::Vecf64;
let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.];
let v2 = vec![14_f64,1.,13.,2.,12.,3.,11.,4.,10.,5.,9.,6.,8.,7.];
assert_eq!(v1.cosine(&v2),0.7517241379310344);

Vector subtraction, creates a new Vec result

Vector addition, creates a new Vec result

Euclidian distance between two n dimensional points (vectors).
Slightly faster than vsub followed by vmag, as both are done in one loop

Euclidian distance squared between two n dimensional points (vectors).
Slightly faster than vsub followed by vmasq, as both are done in one loop Same as vdist without taking the square root

cityblock distance

Vector magnitude

Vector magnitude squared

Unit vector

Unit vectors difference (done together for efficiency)

Area of a parallelogram between two vectors. Same as the magnitude of their cross product |a ^ b| = |a||b|sin(theta). Attains maximum |a|.|b| when the vectors are othogonal.

We define vector similarity S in the interval [0,1] as S = (1+cos(theta))/2

We define vector dissimilarity D in the interval [0,1] as D = 1-S = (1-cos(theta))/2

Area proportional to the swept arc up to angle theta. Attains maximum of 2|a||b| when the vectors have opposite orientations. This is really |a||b|(1-cos(theta)) = 2|a||b|D

Pearson’s correlation coefficient of a sample of two f64 variables.

Example

use rstats::Vecf64;
let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.];
let v2 = vec![14_f64,1.,13.,2.,12.,3.,11.,4.,10.,5.,9.,6.,8.,7.];
assert_eq!(v1.correlation(&v2),-0.1076923076923077);

Kendall Tau-B correlation coefficient of a sample of two f64 variables. Defined by: tau = (conc - disc) / sqrt((conc + disc + tiesx) * (conc + disc + tiesy)) This is the simplest implementation with no sorting.

Example

use rstats::Vecf64;
let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.];
let v2 = vec![14_f64,1.,13.,2.,12.,3.,11.,4.,10.,5.,9.,6.,8.,7.];
assert_eq!(v1.kendalcorr(&v2),-0.07692307692307693);

Spearman rho correlation coefficient of two f64 variables. This is the simplest implementation with no sorting.

Example

use rstats::Vecf64;
let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.];
let v2 = vec![14_f64,1.,13.,2.,12.,3.,11.,4.,10.,5.,9.,6.,8.,7.];
assert_eq!(v1.spearmancorr(&v2),-0.1076923076923077);

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

Example

use rstats::Vecf64;
let v1 = vec![1_f64,2.,3.,4.,5.,6.,7.,8.,9.,10.,11.,12.,13.,14.];
assert_eq!(v1.autocorr(),0.9984603532054123_f64);

Finds minimum, minimum’s first index, maximum, maximum’s first index of &f64

Linear transform to interval [0,1]

New sorted vector. Immutable sort. Copies self and then sorts it in place, leaving self unchanged. Calls mutsortf and that calls the standard self.sort_unstable_by. Consider using our sortm instead.

Flattened lower triangular part of a covariance matrix for a single f64 vector. Since covariance matrix is symmetric (positive semi definite), the upper triangular part can be trivially added for all j>i by: c(j,i) = c(i,j). N.b. the indexing is always assumed to be in this order: row,column. The items of the resulting lower triangular array c[i][j] are pushed flat into a single vector in this double loop order: left to right, top to bottom

Reconstructs the full symmetric square matrix from its lower diagonal compact form, as produced by covar, covone, wcovar

Implementors