Version 1.4 of DynaML, released Sept 23, 2016, implements a number of new models (multi-output GP, student T process, random variables, etc) and features (Variance control for CSA, etc).


The following inference models have been added.

Meta Models & Ensembles

  • LSSVM committees.

Stochastic Processes

  • Multi-output, multi-task Gaussian Process models as reviewed in Lawrence et. al.
  • Student T Processes: single and multi output inspired from Shah, Ghahramani et. al
  • Performance improvement to computation of marginal likelihood and posterior predictive distribution in Gaussian Process models.
  • Posterior predictive distribution outputted by the AbstractGPRegression base class is now changed to MultGaussianRV which is added to the dynaml.probability package.


  • Added StationaryKernel and LocallyStationaryKernel classes in the kernel APIs, converted RBFKernel, CauchyKernel, RationalQuadraticKernel & LaplacianKernel to subclasses of StationaryKernel

  • Added MLPKernel which implements the maximum likelihood perceptron kernel as shown here.

  • Added co-regionalization kernels which are used in Lawrence et. al to formulate kernels for vector valued functions. In this category the following co-regionalization kernels were implemented.

  • CoRegRBFKernel
  • CoRegCauchyKernel
  • CoRegLaplaceKernel
  • CoRegDiracKernel

  • Improved performance when calculating kernel matrices for composite kernels.

  • Added :* operator to kernels so that one can create separable kernels used in co-regionalization models.


  • Improved performance of CoupledSimulatedAnnealing, enabled use of 4 variants of Coupled Simulated Annealing, adding the ability to set annealing schedule using so called variance control scheme as outlined in de-Souza, Suykens et. al.


  • Added Scaler and ReversibleScaler traits to represent transformations which input and output into the same domain set, these traits are extensions of DataPipe.

  • Added Discrete Wavelet Transform based on the Haar wavelet.