DynaML v1.5 release notes (2017-08-15)

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DynaML v1.5 Release Notes

Release Date: 2017-08-15 // over 6 years ago

➕ Additions

📦 Package dynaml.algebra
➕ Added support for dual numbers.
```
//Zero Dualval zero = DualNumber.zero[Double] val dnum = DualNumber(1.5, -1.0) val dnum1 = DualNumber(-1.5, 1.0) //Algebraic operations: multiplication and addition/subtractiondnum1\*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.