A swift library package with matrix algebraic objects and functions necessary for landscape and population genetic analyses on the iOS and MacOS platforms.

Current Version:

This package is the foundation layer for all matrix algebra routines needed in software developed for the iOS and OSX platforms from the DyerLab. The motivation herenotion here is to provide an abstraction layer relying as much upon the Swift `Accelerate`

framework to allow population genetic and spatial analytic routines to be easily added to any deliverable product.

**Swift Package Manager** (XCode 13)

- Select
**File**>**Swift Packages**>**Add Package Dependency…**from the**File**menu. - Paste
`https://github.com/dyerlab/DLabMatrix.git`

in the dialog box. - Follow the Xcode’s instruction to complete the installation.

Why not CocoaPods, or Carthage, or blank?

Supporting multiple dependency managers makes maintaining a library exponentially more complicated and time consuming. Since, the **Swift Package Manager** is integrated with Xcode 11 (and greater), it’s the easiest choice to support going further.

This package defines the following two objects:

- Vector A
`Double`

vector object representing a single numerical vector. - Matrix A 2-dimensional matrix object that holds
`Double`

types. - Random Range Enumerator A quick enumerator to define the range of random numbers to estimate.

This packages defines the following general functions:

- GeneralizedInverse() A generalized matrix inverse.
- PCRotation() A principal component analysis function.
- SingularValueDecomposition() An implementation of a Singular Value Decomposition.

A Vector object is simple `[Double]`

useable for normal algebraic operations. The

**Instance Variables**:

`sum: Double`

`magnitude:Vector`

The vector length.`x:Double`

&`y:Double`

(for length >1 vectors)`normal:Vector`

Normalize vector for length = 1.0`asCGPoint:CGPoint`

Quick conversion to`CGPoint`

`asSCNVector3:SCNVector3`

Quick conversion to`SCNVector3`

`asCovariance: Matrix`

Converts (presumably) instance of distance to covariance.`asDistance: Matrix`

Converts (presumably) instance of covariance matrix to distance.

**Static Constructors**

`zeros(_ length: Int) -> Vector`

Make a new vector with zeros.`random( length: Int, type: RangeEnum = .uniform_0_1) -> Vector`

Make a

**Instance Functions**

`.distance(other: Vector) -> Double`

Distance separating two vectors.`.smallest(other: Vector) -> Vector`

Returns a`Vector`

with minimal values from each.`.largest(other: Vector) -> Vector`

Returns a`Vector`

with maximal values from each.`.constrain(minimum: Double, maximum: Double) -> Vector`

Returns vector with values bound on the range`[minimum ... maximum]`

`.limitAnnealingMagnitude( temp: Double) -> Vector`

Limits movement vector distance for simulated annealing functions.

**Static Functions** - `.designMatrix( strata: [String] ) -> Matrix`

Returns (N x K) design matrix. - `.idempotentHatMatrix( strata: [String] ) -> Matrix`

Returns N x N idempotent Hat matrix, **H**.

The following operators are defined for the `Vector`

object `v`

:

`v + scalar`

`v - scalar`

`v * scalar`

`v / scalar`

Scaling of a vector`v + v`

Vector elementwise addition`v - v`

Vector elementwise subtraction`v * v`

Vector elementwise multiplication`v .* v`

Vector Multiplication (scalar result)`v == v`

Equality

The `Vector`

object defines the protocol `VectorConvertable`

which defined the required function `asVector() -> Vector`

to advertise that they can yield a `Vector`

object.

A matrix object is a class that represents a 2-dimensional representation of type `Double`

’s. A `Matrix`

has the following instance variables:

`.rows: Int`

The number of rows in the matrix.`.cols: Int`

The number of columns in the matrix.`rowNames:[String]`

Labels for rows in the matrix.`colNames:[String]`

Labels for columns in the matrix.`diagonal:Vector`

The diagonal of the matrix (get, set)`trace:Double`

The trace of the matrix.`sum:Double`

The sum of the values in the Matrix.`transpose:Matrix`

Return the transposed version of this matrix.`description:String`

Conforms to`CustomStringConvertible`

Constructors - `Matrix(r,c,Vector)`

Creates a new `Matrix`

with `r`

rows and `c`

columns with values from `Vector`

. - `Matrix(r,c,ClosedRange)`

Creates a new `Matrix`

with `r`

rows and `c`

whose falues are equally spaced along a `ClosedRange<Double>`

. - `Matrix(r,c,rowNames,colNames)`

Creates a new `Matrix`

with `r`

rows and `c`

columns with values set to `0.0`

but with the row and column names set by the vectors `rowNames`

and `colNames`

of length `r`

and `c`

(respectively).

The following operators are overloaded for an object of type `Matrix`

:

`[row,col]: Double`

Overload of the subscript operator to access elements withing the`Matrix`

. Asking for values outside the size of the`Matrix`

return`Double.nan`

and setting those outside the size do nothing.`==`

Conforms to`Equatable`

`M + scalar`

Shift values of a matrix`M - scalar`

Shift values of a matrix`M * scalar`

Scaling of a matrix`M / scalar`

Scaling of a matrix`M + M`

Matrix elementwise addition`M - M`

Matrix elementwise addition`M * M`

Matrix elementwise multiplication`M .* M`

Matrix Multiplication`M / M`

Matrix elementwise division

The following functions are available for `Matrix`

objects: - `.center()`

Centers the values of the matrix to `0.0`

- `.submatrix([rows],[cols]) -> Matrix`

Returns submatrix defined by the integer arrays `[rows]`

and `[cols`

]. -

The `Matrix`

object defines the protocol `MatrixConvertable`

that defines a required function `asMatrix() -> Matrix`

is to advertise that they can yield a `Matrix`

object.

A simple enum defining the following values: - `uniform_0_1`

A uniform distribution bound to `[0.0 ... 1.0]`

. - `uniform_neg1_1`

A uniform distribution bound on `[-1.0 ... 1.0]`

. - `normal_0_1`

A value from the normal probability density function bound on `[0 ... 1]`

.

This enum conforms to the following protocols: - `Int`

- `CaseIterable`

- `Comoparable`

Defines `<`

operator.

`GeneralizedInverse( X: Matrix ) -> Matrix`

This returns a generalized inverse of the original matrix.

`PCRotation(X: Matrix) -> (d: Vector, V: Matrix, X: Matrix)?`

Performs a principal component rotation on the original data matrix `X`

returning the eigenvalues in `d`

, the loadings in `V`

and the predicted projections of the original data in `X`

. If the original matrix was not factorable, no values are returned.

`SingularValueDecomposition(A: Matrix) -> (U: Matrix, d: Vector, V: Matrix)?`

Performs a singular value spectral decomposition on the matrix `A`

resulting in left and right eigenvalues (`U`

and `V`

) as well as eigenvalues in `d`

. If the original matrix was not factorable, no values are returned.

1.0.3 - Refactoring some stuff.

1.0.2 - added designMatrix, idepotentHatMatrix, asCovariance, asDistance, rowMatrix

1.0.1 - Added Unit tests.

1.0.0 - Intial Import

For attribution, please cite this work as

Dyer (2021, Dec. 24). The Dyer Laboratory: DLabMatrix Swift Package. Retrieved from https://dyerlab.github.io/DLabWebsite/software/DLabMatrix/

BibTeX citation

@misc{dyer2021dlabmatrix, author = {Dyer, Rodney}, title = {The Dyer Laboratory: DLabMatrix Swift Package}, url = {https://dyerlab.github.io/DLabWebsite/software/DLabMatrix/}, year = {2021} }