1.5

Additions

Package dynaml.algebra

• Added support for dual numbers.

  //Zero Dual
  val zero = DualNumber.zero[Double]  
  
  val dnum = DualNumber(1.5, -1.0) 
  val dnum1 = DualNumber(-1.5, 1.0) 
  
  //Algebraic operations: multiplication and addition/subtraction
  dnum1*dnum2
  dnum1 - dnum
  dnum*zero 

Package dynaml.probability

• Added support for mixture distributions and mixture random variables. MixtureRV, ContinuousDistrMixture for random variables and MixtureDistribution for constructing mixtures of breeze distributions.

Package dynaml.optimization

• Added ModelTuner[T, T1] trait as a super trait to GlobalOptimizer[T]
• GridSearch and CoupledSimulatedAnnealing now extend AbstractGridSearch and AbstractCSA respectively.
• Added ProbGPMixtureMachine: constructs a mixture model after a CSA or grid search routine by calculating the mixture probabilities of members of the final hyper-parameter ensemble.

Stochastic Mixture Models

Package dynaml.models

• Added StochasticProcessMixtureModelas top level class for stochastic mixture models.
• Added GaussianProcessMixture: implementation of gaussian process
  mixture models.
• Added MVTMixture: implementation of mixture model over
  multioutput matrix T processes.

Kulback-Leibler Divergence

Package dynaml.probability

• Added method KL() to probability package object, to calculate
  the Kulback Leibler divergence between two continuous random
  variables backed by breeze distributions.

Adaptive Metropolis Algorithms.

• AdaptiveHyperParameterMCMC which
  adapts the exploration covariance with each sample.

• HyperParameterSCAM adapts
  the exploration covariance for each hyper-parameter independently.

Splines and B-Spline Generators

Package dynaml.analysis

• B-Spline generators
• Bernstein and Cardinal b-spline generators.
• Arbitrary spline functions can be created using the SplineGenerator class.

Cubic Spline Interpolation Kernels

Package dynaml.kernels

• Added cubic spline interpolation kernel CubicSplineKernel and its ARD analogue CubicSplineARDKernel

Gaussian Process Models for Linear Partial Differential Equations

Based on a legacy ICML 2003 paper by Graepel. DynaML now ships with capability of performing PDE forward and inverse inference using the Gaussian Process API.

Package dynaml.models.gp

• GPOperatorModel: models a quantity of interest which is governed by a linear PDE in space and time.

Package dynaml.kernels

• LinearPDEKernel: The core kernel primitive accepted by the GPOperatorModel class.

• GenExpSpaceTimeKernel: a kernel of the exponential family which can serve as a handy base kernel for LinearPDEKernel class.

Basis Function Gaussian Processes

DynaML now supports GP models with explicitly incorporated basis
functions as linear mean/trend functions.

Package dynaml.models.gp

• GPBasisFuncRegressionModel can
  be used to create GP models with trends incorporated as a linear
  combination of basis functions.

Log Gaussian Processes

• LogGaussianProcessModel represents
  a stochastic process whose natural logarithm follows a gaussian process.

Improvements

Package dynaml.probability

• Changes to RandomVarWithDistr: made type parameter Dist covariant.
• Reform to IIDRandomVar hierarchy.

Package dynaml.probability.mcmc

• Bug-fixes to the HyperParameterMCMC class.

General

• DynaML now ships with Ammonite v1.0.0.

Fixes

Package dynaml.optimization

• Corrected energy calculation in CoupledSimulatedAnnealing; added
  log likelihood due to hyper-prior.

Package dynaml.optimization

• Corrected energy calculation in CoupledSimulatedAnnealing; added
  log likelihood due to hyper-prior.