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Statistics, Vector Algebra, Characterising Multidimensional Data, Machine Learning, Data Analysis

Modules

Vector Algerba mutating an f64 vector

Basic statistics on a single generic vector

Vector Algebra on two generic vectors

Stats and Vector Algebra on one or two u8 vectors

Multidimensional operations on sets of vectors

Multidimensional operations on sets of vectors, with additional inputs

Macros

Macro here!() gives &str with the file:line path::function-name of where it was called from. This string will be rendered in bold red on (linux) terminals, so as to easily find the first real error in voluminous confusing traces of avalanching Rust errors.

Structs

Mean and standard deviation (or std ratio for geometric mean)

Median, quartiles, mad (median of absolute diffs)

struct for minimum value, its index, maximum value, its index

Traits

Finding 1D medians, quartiles, and MAD (median of absolute differences)

Mutable vector operations that take one generic argument. A few of the essential Vecg methods are reimplemented here to mutate self in-place (only for f64). This is for efficiency and convenience, for example, in vector iterative methods.

Trait to serialize slices of generic items &[T] (vectors) and slices of Vecs of generic items &Vec (matrices). All are converted into printable strings and optionally coloured.

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. There is just one limitation: data of end type i64 has to be explicitly converted to f64. That is to raise awareness that, in this particular case, some precision may be lost. Function statsg::i64tof64(&s) will convert the whole slice.

Methods applicable to a single argument: a vector of vectors of generic end type. Operations on a set of multidimensional vectors.

Methods applicable to vector of vectors of generic end type and one argument of a similar kind.

Vector Algebra on two vectors (represented here as generic slices). Also included are scalar operations on the self vector.

Methods specialised to, or more efficient for &[u8]

Functions

Necessary downcast of a whole i64 slice to f64

Sum of linear weights 1..n. Also the size of an upper or lower triangle of a square array (including the diagonal)