Version 1.4.2 of DynaML, released May 7, 2017. Updates, improvements and new features.

Core API


Package dynaml.models.neuralnets

  • Added GenericAutoEncoder[LayerP, I], the class AutoEncoder is now deprecated
  • Added GenericNeuralStack[P, I, T] as a base class for Neural Stack API
  • Added LazyNeuralStack[P, I] where the layers are lazily spawned.

Package dynaml.kernels

  • Added ScaledKernel[I] representing kernels of scaled Gaussian Processes.

Package dynaml.models.bayes

  • Added * method to GaussianProcessPrior[I, M] which creates a scaled Gaussian Process prior using the newly minted ScaledKernel[I] class
  • Added Kronecker product GP priors with the CoRegGPPrior[I, J, M] class

Package dynaml.models.stp

  • Added multi-output Students' T Regression model of Conti & O' Hagan in class MVStudentsTModel

Package dynaml.probability.distributions

  • Added HasErrorBars[T] generic trait representing distributions which can generate confidence intervals around their mean value.


Package dynaml.probability

  • Fixed issue with creation of MeasurableFunction instances from RandomVariable instances

Package dynaml.probability.distributions

  • Changed error bar calculations and sampling of Students T distributions (vector and matrix) and Matrix Normal distribution.

Package dynaml.models.gp

  • Added Kronecker structure speed up to energy (marginal likelihood) calculation of multi-output GP models

Package dynaml.kernels - Improved implicit paramterization of Matern Covariance classes


  • Updated breeze version to latest.
  • Updated Ammonite version to latest