Changelog pyGPs v1.3.1

December 15th 2014

  • pyGPs added to pip
  • mathematical definitions of kernel functions available in documentation
  • more error message added

November 25th 2014

structural updates:

  • full inline documentation with input parameter and output specified
  • check for the inputs and provide diagnostic messages for some inputs
  • consistant naming in inline and online documentation
  • string representation for dnlZStruct and postStruct - Now you can do sth like, - nlZ, dnlZ, post = model.getPosterior(x,y) - print post - instead of a python object, we provide now a more informative description.
  • add optimization into unit test routines. Also add checking for cholesky decomposition and checking positive-definite property of kernel matrix.
  • add jitter to the digonal of linear, linARD, and poly covariance for numerical stability.
  • fix several minor problems in unit test framework
  • hierachically rearranged for online documentation
  • add several supplementary instruction in online documentation

October 19th 2014

documentation updates:

  • DOC: model.fit() is now named model.getPosterior
  • DOC: typo fixed: cov.LIN changed to cov.Linear
  • DOC: removed cov.Periodic() in demos because its limited in 1-d data.
  • API file updated accordingly

structural updates:

  • removed unused ScalePrior attribute in most inference method
  • added function jitchol, which added a small jitter(instead of doing Cholesky decomposition directly) to the diagonal when the kernel matrix is ill conditioned.
  • thrown error when using periodic covariance functions for non-1d data. We also removed cov.Periodic() in demos because its limited usage.
  • combined equally spaced positions of inputs as test positions as well in plot methods to get a more accurate plotting.
  • rename model.fit() to model.getPosterior(), while model.optimize() stays the same. (since it is confusing for some users that the name fit() is not doing optimizing.)

August 9th 2014

structural updates:

  • added SM covariance and Gabor covariance
  • bug fixed in dfunc() in Matern covariance
  • change proceed() to evaluate() for inference and likelihood classes
  • added unit test module for SM covariance and Gabor covariance

Changelog pyGPs v1.2

July 14th 2014

documentation updates:

  • online docs updated
  • API file updated

structural updates:

  • made private for methods that users don’t need to call

July 8th 2014

structural updates:

  • add hyperparameter(signal variance s2) for linear covariance
  • add unit testing for inference,likelihood functions as well as models
  • NOT show(print) “maximum number of sweep warning in inference EP” any more
  • documentation updated

bug fixes:

  • typos in lik.Laplace
  • derivative in lik.Laplace

June 30th 2014

structural updates:

  • input target now can either be in 2-d array with size (n,1) or in 1-d array with size (n,)
  • setup.py updated
  • “import pyGPs” instead of “from pyGPs.Core import gp”
  • rename ”.train()” to ”.optimize()”
  • rename “Graph-stuff” to “graphExtension”
  • rename kernelOnGraph to “nodeKernels” and graphKernel to “graphKernels”
  • redundancy removed for model.setData(x,y)
  • rewrite “mean.proceed()” to “getMean()” and “getDerMatrix()”
  • rewrite “cov.proceed()” to “getCovMatrix()” and “getDerMatrix()”
  • rename cov.LIN to cov.Linear (to be consistent with mean.Linear)
  • rename module “valid” to “validation”
  • add graph dataset Mutag in python file. (.npz and .mat)
  • add graphUtil.nomalizeKernel()
  • fix number of iteration problem in graphKernels “PropagationKernel”
  • add unit testing for covariance, mean functions

bug fixes:

  • derivatives for cov.LINard
  • derivative of the scalar for cov.covScale
  • demo_GPR_FITC.py missing pyGPs.mean