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.
- 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
AbstractGPRegressionbase class is now changed to
MultGaussianRVwhich is added to the
LocallyStationaryKernelclasses in the kernel APIs, converted
LaplacianKernelto subclasses of
MLPKernelwhich 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.
Improved performance when calculating kernel matrices for composite kernels.
:*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.
ReversibleScalertraits to represent transformations which input and output into the same domain set, these traits are extensions of
Added Discrete Wavelet Transform based on the Haar wavelet.