HGF Toolbox - Release ID: c021283  (HEAD -> condhalluc, tag: condhalluc1.4)

************************************************************************
Copyright (C) 2012-2015 Christoph Mathys <chmathys@ethz.ch>
Translational Neuromodeling Unit (TNU)
University of Zurich and ETH Zurich
------------------------------------------------------------------------

The HGF toolbox is free software: you can redistribute it and/or
modify it under the terms of the GNU General Public License as
published by the Free Software Foundation, either version 3 of the
License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program (see the file COPYING).  If not, see
<http://www.gnu.org/licenses/>.
************************************************************************

INSTALLATION

Move this folder to a location of your choice and add it to your Matlab
path.


DOCUMENTATION AND CONFIGURATION

Documentation can be found in the manual contained in the Manual.pdf
file. This will point you to the relevant configuration files. Further
documentation is available throughout the source code.


TUTORIAL DEMO

There is a tutorial demo that can be launched with the command
tapas_hgf_demo().

RELEASE NOTES

v4.10
~~~~~
- Added hgf_categorical_norm
- Added Boltzmann distribution (i.e., softmax normalization) as tapas_boltzmann()


v4.9
~~~~
- Set implied learning rate at first level to 0 if update is zero

v4.8
~~~~
- Give choice of using predictions or posteriors with softmax_binary

v4.7
~~~~
- Added cdfgaussian_obs model
- Added hgf_binary_pu (perceptual uncertainty) model
- Improvements for beta_obs with hgf_whichworld

v4.6
~~~~
- Adapted beta_obs to deal with ph_binary
- Added Pearce-Hall in ph_binary
- Clarified the role of default settings in comments of fitModel
- Brought softmax_binary_sim up to date

v4.5
~~~~
- Improved comments in softmax_binary_sim
- Improved comments in tapas_beta_obs.m
- Added tapas_beta_obs_{sim,namep}.m

v4.4
~~~~
- Added tapas_hgf_ar1_binary_namep.m
- Improved rw_binary

v4.3
~~~~
- Added bayes_optimal_categorical
- Improved hgf_categorical_plotTraj

v4.2
~~~~
- Adapted softmax_sim to hgf_categorical
- Added hgf_categorical
- Added datagen_categorical and categorical data example

v4.1
~~~~
- Improved hgf_jget

v4.0
~~~~
- Added PDF manual
- Added interactive demo in hgf_demo
- Added file of raw commands from hgf_demo in hgf_demo_commands
- Adapted fitModel to calculate AIC and BIC
- Renamed F (negative variational free energy) to LME (log-model evidence, to
  which it is an approximation)
- Calculate accuracy and complexity in fitModel
- Save everything relating to model quality under r.optim
- Improved output of fitModel
- Added hierarchical hidden Markov model (hhmm)
- Added hidden Markov model (hmm)
- Added WhatWorld (hgf_whatworld) model
- Added linear log-reaction time (logrt_linear_whatworld) model for WhatWorld
- Added WhichWorld (hgf_whichworld) model
- Added AR(1) model for binary outcomes (hgf_ar1_binary)
- Added Jumping Gaussian Estimation (hgf_jget) model
- Added unitsq_sgm_mu3 decision model
- Added binary multi-armed bandit model hgf_binary_mab
- Added beta_obs observation model for decision noise on the unit interval
- Added softmax decision model with different inverse temperature for each
  kind of binary decision (softmax_2beta)
- Added logrt_linear_binary decision model
- Added Rescorla-Wagner model with different learning rate for each kind of
  binary outcome (rw_binary_dual)
- Included additional trajectories in output of hgf, hgf_ar1, hgf_ar1_mab,
  hgf_binary, hgf_ar1_binary, hgf_binary_mab, hgf_whichworld, and
  hgf_whatworld
- Made infStates more consistent across models
- Removed deprecated hgf_binary3l
- Made fitModel explicitly return negative log-joint probability and negative
  log-likelihood
- Modified simModel to read configuration files of perceptual and observation
  models
- Abolished theta in hgf, hgf_binary, hgf_ar1, hgf_ar1_mab, hgf_ar1_binary,
  hgf_binary_mab, and hgf_jget
- Moved kappa estimation from logit-space to log-space for hgf, hgf_binary,
  hgf_ar1, hgf_ar1_mab, hgf_ar1_binary, hgf_binary_mab, and hgf_jget
- Introduced checking for implausible jumps in trajectories for hgf,
  hgf_binary, hgf_ar1, hgf_ar1_mab, hgf_ar1_binary, hgf_binary_mab, and
  hgf_jget
- Adapted fitModel to deal with cases the <prc_model>_transp() function
  performs operations important to the <model>() function
- Introduced multinomial softmax decision model
- Improved documentation for hgf_ar1_mab model
- Added error IDs for all errors

v3.0
~~~~
- Improved error handling in tapas_fitModel()
- Prefixed all function names with “tapas_”
- Added rs_precision
- Added rs_belief
- Added rs_surprise
- Added sutton_k1
- Added hgf_ar1_mab
- Added softmax for continuous responses
- Improved checking of trajectory validity in HGF models
- Debugged input handling in softmax_binary

v2.1
~~~~
- Introduced Bayesian parameter averaging
- Amended calculation of log-priors in fitModel.m
- Debugged construction of time axis in hgf_plotTraj
- Debugged removal of placeholder field in estimate structure

v2.0
~~~~
- Estimation of Bayes optimal parameters added
- infStates the same 3-dim array in hgf_binary as in hgf
- Changes to softmax_binary: trial-by-trial rewards read from input matrix
- hgf_binary generalized to n levels
- Old hgf_binary lives on as hgf_binary3l
- Input at irregular intervals enabled in hgf and hgf_binary
- Support for constant drift in hgf and hgf_binary
- Introduced use of placeholders in config files
- quasinewton_optim: increased default maximum number of regularizations to 16
- Automatic detection of upper bound on theta for hgf
- Improved input checks
- Support for AR(1) processes in new hgf_ar1
- quasinewton_optim: improved resetting after exhaustion of regularizations

v1.0
~~~~
- Original release
