multilinear-math

Swift library for multidimensional data, tensor operations and machine learning on OS X. For additional comments and documentation, please take a look at the Wiki.

Already implemented:

• Swift wrappers of many important functions from the Accelerate framework and LAPACK (vector summation, addition, substraction, matrix and elementwise multiplication, division, matrix inverse, pseudo inverse, eigendecomposition, singular value decomposition...)
• MultidimensionData protocol for elegant handling of multidimensional data of any kind
• Clear, compact and powerful syntax for mathematical operations on tensors
• Principal component analysis
• Multilinear subspace learning algorithms for dimensionality reduction
• Linear and logistic regression
• Stochastic gradient descent
• Feedforward neural networks
• Sigmoid, ReLU, Softplus activation functions
• Easy regularizations

Tensor reading, writing, slicing

Create data tensor:

var a = Tensor<Float>(modeSizes: [3, 3, 3], repeatedValue: 0)

Read and write single values:

let b: Float = a[1, 2, 0]
a[2, 0, 1] = 3.14

Read and write a tensor slice

let c: Tensor<Float> = a[1..<3, all, [0]]
a[1...1, [0, 2], all] = Tensor<Float>(modeSizes: [2, 3], values: [1, 2, 3, 4, 5, 6])

modes of size 1 will be trimmed

Einstein notation

Modes with same symbolic index will be summed over. Simple matrix multiplication:

var m = Tensor<Float>(modeSizes: [4, 6], repeatedValue: 1)
var n = Tensor<Float>(modeSizes: [6, 5], repeatedValue: 2)
let matrixProduct = m[.i, .j] * n[.j, .k]

Geodesic deviation:

var tangentVector = Tensor<Float>(modeSizes: [4], values: [0.3, 1.7, 0.2, 0.5])
tangentVector.isCartesian = false

var deviationVector = Tensor<Float>(modeSizes: [4], values: [0.1, 0.9, 0.4, 1.2])
deviationVector.isCartesian = false

var riemannianTensor = Tensor<Float>(diagonalWithModeSizes: [4, 4, 4, 4])
riemannianTensor.isCartesian = false
riemannianTensor.variances = [.contravariant, .covariant, .covariant, .covariant]

let relativeAcceleration = riemannianTensor[.μ, .ν, .ρ, .σ] * tangentVector[.ν] * tangentVector[.ρ] * deviationTensor[.σ]

Neural networks

Setting up and training a simple feedforward neural net:

var estimator =  NeuralNet(layerSizes: [28*28, 40, 10])
estimator.layers[0].activationFunction = ReLU(secondarySlope: 0.01) 
estimator.layers[1].activationFunction = ReLU(secondarySlope: 0.01)

var neuralNetCost = SquaredErrorCost(forEstimator: estimator)

stochasticGradientDescent(neuralNetCost, inputs: trainingData[.a, .b], targets: trainingLabels[.a, .c], updateRate: 0.1, minibatchSize: 50, validationCallback: ({ (epoch, estimator) -> (Bool) in
    if(epoch >= 30) {return true} 
    else {return false}
}))

documentation

Multilinear subspace learning

Extended PCA algorithms to work with tensors with arbitrary mode count

• multilinear principal component analysis (MPCA)
• uncorrelated multilinear principal component analysis (UMPCA)
  (Lu, Plataniotis, Venetsanopoulos)

Installation

To use this framework in an OSX XCode project:

• clone this repository
• open your project in XCode
• drag and drop MultilinearMath.xcodeproj into the project navigator of your project
• select the .xcodeproj file of your project in the navigator
• go to the "General" tab and add MultilinearMath.framework to the Linked Frameworks and Libraries
• go to the "Build Settings" tab and set "Embedded Content Contains Swift Code" to YES
• import MultilinearMath in your Swift files

If possible, whole module optimization should be used for very significant performance gains.