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Clean up documentation for latest changes
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Sources/Numerix/Documentation.docc/LinearAlgebra.md

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- ``Vector/sum()``
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- ``Vector/absoluteSum()``
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- ``Vector/cumulativeSum()``
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- ``Numerix/swapValues(_:_:)-(Vector<Scalar>,_)``
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- ``swapValues(_:_:)-8dx3x``

Sources/Numerix/Documentation.docc/MatrixType.md

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# Numeric Structures
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Vector, Matrix, and ShapedArray structures for numerical data.
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## Overview
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Numerix provides structures for working with numerical array data. One-dimensional data is implemented with the Vector structure, two-dimensional with the Matrix structure, and N-dimensional with the ShapedArray structure.
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## Topics
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- ``Vector``
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- ``Matrix``
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- ``ShapedArray``

Sources/Numerix/Documentation.docc/Numerix.md

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Numerix is an open-source Swift package that provides Vector, Matrix, and ShapedArray structures for performing linear algebra and other numerical computations on Apple devices. It uses the Accelerate framework for high-performance and energy-efficient calculations. See the Modules documentation for more information about the Vector, Matrix, and ShapedArray structures.
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## Modules
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Numerix is comprised of modules that provide complex number, vector, matrix, and shaped array structures for working with numerical data. Documentation for each module is available at:
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- [Vector Module](./VectorModule)
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- [Matrix Module](./MatrixModule)
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- [ShapedArray Module](./ShapedArrayModule)
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## Contributing
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See the `CONTRIBUTING.md` document in the [Numerix GitHub repository](https://github.com/wigging/numerix) for contributing guidelines.
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See the `CONTRIBUTING.md` document in the Numerix [GitHub repository](https://github.com/wigging/numerix) for contributing guidelines.
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## Topics
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### Essentials
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- <doc:GetStarted>
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- <doc:NumericStructs>
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- <doc:Trigonometry>
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- <doc:LinearAlgebra>
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- <doc:Random>
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# Random Numbers
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Random number generation.
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## Overview
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Numerix provides random number generators
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## Topics
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- ``WyRand``

Sources/Numerix/Documentation.docc/ShapedArrayType.md

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Sources/Numerix/Documentation.docc/Trigonometry.md

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# Trigonometry
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Trigonometic vector operations.
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Trigonometic vector and matrix operations.
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## Overview
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Numerix provides several element-wise trigonometic functions for vectors. The Accelerate framework is utilized for efficient element-by-element vector operations.
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Numerix provides several element-wise trigonometic functions for vectors and matrices. The Accelerate framework is utilized for efficient element-by-element operations.
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## Topics
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- ``sin(_:)``
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- ``cos(_:)``
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- ``tan(_:)``
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- ``sin(_:)-4rcxg``
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- ``sin(_:)-8nnze``
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- ``cos(_:)-8lr7y``
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- ``cos(_:)-1x2xo``
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- ``tan(_:)-2j3jl``
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- ``tan(_:)-5u0m2``
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- ``asin(_:)``
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- ``acos(_:)``
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- ``atan(_:)``

Sources/Numerix/Inverse.swift

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import Accelerate
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@_documentation(visibility: private)
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public protocol Inverse {
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static func inv(a: Matrix<Self>) -> Matrix<Self>
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}
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@_documentation(visibility: private)
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extension Float: Inverse {
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public static func inv(a: Matrix<Float>) -> Matrix<Float> {
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precondition(a.rows == a.columns, "Input matrix is not square")
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}
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}
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@_documentation(visibility: private)
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extension Double: Inverse {
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public static func inv(a: Matrix<Double>) -> Matrix<Double> {
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precondition(a.rows == a.columns, "Input matrix is not square")

Sources/Numerix/Pad.swift

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import Accelerate
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@_documentation(visibility: private)
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public protocol Pad {
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static func pad(a: Matrix<Self>, value: Self) -> Matrix<Self>
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}
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@_documentation(visibility: private)
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extension Float: Pad {
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public static func pad(a: Matrix<Float>, value: Float) -> Matrix<Float> {
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let m = vDSP_Length(a.columns) // number of columns to copy
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}
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}
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@_documentation(visibility: private)
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extension Double: Pad {
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public static func pad(a: Matrix<Double>, value: Double) -> Matrix<Double> {
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let m = vDSP_Length(a.columns) // number of columns to copy

Sources/Numerix/ShapedArrayModule/NumberStyle.swift

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Number style protocol.
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*/
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@_documentation(visibility: private)
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public protocol NumberStyle {
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var stringDescription: String { get }
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var integerLength: Int { get }

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