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#Gesture Recognition Toolkit Change Log

This file contains some notes about significant changes to the GRT.

Version 0.2.5

  • Decision Tree Cleanup: cleaned up decision tree base class and fixed bug caused when branching left at node with no left child on complex datasets
  • Backend cleanup: added GRT_LOG to most backend classes that prints filename, function, and line number for warnings and error messages
  • batch learning: added batch option to LogisticRegression, this helps improve the learning algorithm by updating the weights based on a mini-batch dataset, as opposed to updating the weights on every single example in the training dataset
  • neural net initialization change: changed default initialization of MLP neural nets to scale with size of network
  • new unit tests: added unit tests for Linear and Logistic Regression
  • improved LinearLeastSquares: improved the linear least squares algorithm, adding it to the main GRT header

Version 0.2.4

4th December 2016

  • Better unit tests: improved unit tests for MLP neural nets and several GRT classifiers
  • Added Metrics class: the Metrics class will act as the main backend for computing the various ML metrics needed for all GRT classification and regression modules
  • Added Training and Validation metrics: all GRT classifiers now automatically compute training accuracy and validation accuracy (if a validation set is used) when training the classification model. These metrics can be accessed via: classifier.getTrainingSetAccuracy(); classifier.getValidationSetAccuracy();
  • Improved GMM and GaussianMixtureModels algorithm: improved both the GMM and GaussianMixtureModels algorithms by adding a restart option, this allows the learning algorithm to reattempt to train a model if the previous attempt fails to converge
  • Added classifier unit test helper function: added a base class for computing common unit tests for GRT classification modules
  • Updated GRT GUI: updated GRT GUI to support latest neuron API

Version 0.2.3

24th October 2016

  • C++11: moved to using C++11 as default

22nd October 2016

  • backend updates: set all classification, regression, cluster, preprocessing, and postprocessing modules to use new ID and base class functionality
  • unit tests: working to improve and extend all GRT unit tests
  • removed LDA: removing old algorithm as it has never been fully implemented, will add back again when it is complete

29th September 2016

  • added RMSFilter class: new filter class for root mean squared low pass filtering of data, in PreProcessing
  • moved various PreProcessing init to PreProcessing base class: moved several common init work to PreProcessing base class, such as module naming and init of logs

18th September 2016

  • improved MLP learning algorithm: improved back propagation algorithm and added TANH function
  • added Dict class: added new dictionary class that can be used to dynamically store pairs of key-values, where keys are strings and values can be any type
  • added unit tests: added unit tests for MLP and Dict classes
  • added XOR dataset: added XOR dataset for regression

Version 0.2.2

21th August 2016

  • fixed minor bug in TimeSeriesClassificationData: added if statement to catch K==0
  • updated wiki: migrating wiki from nickgillian.com/wiki to GRT github wiki page

Version 0.2.1

11th August 2016

  • fixed HMM continuous bug: caused if downsampleFactor > timeseriesLength

Version 0.2.0

7th August 2016

  • depreciated saveModelToFile/loadModelFromFile functions, replaced with save/load functions
  • depreciated dataset partition function, replaced split function

Version 0.1.0

21th February 2016

  • added new << operator to GestureRecognitionPipeline: modules can now be added via: pipeline << ANBC();

20th February 2016

  • updated version to 0.1.0, merged dev to master
  • added unit tests: unit tests can be run via the: make test command

Version 0.0.1

20th February 2016

  • moved to semantic versioning, set version to 0.0.1

19th Novemeber 2015

  • moved swig files from GRT directory to jni directory: The .i files were causing some issues for non-swig users, so moving them to the JNI directory to clean things up, updated .i file paths accordingly.
  • updated revision number: 19-Nov-2015

2nd Novemeber 2015

  • merged dev into master: Added new tools directory with some command line tools for training/testing various pipelines. Updated DecisionTree logging functions.

7th July 2015

  • merged pull request from terziman: Fixed ParticleFilter cast issue by resolving type

2nd July 2015

  • merged pull request from terziman: Added option in CMakeLists to avoid creating install targets

23rd June 2015

  • updated GUI version: 0.1.18
  • new GUI features: added timeseries mode and new timeseries data graphs.

18th April 2015

  • removed boost dependancy for the GRT GUI. You no longer need to install boost to build the GRT GUI. Instead of boost, the GUI now uses C++11 for threading and shared pointers.
  • improved GRT GUI plots. There are now additional options to control the data plots and you have more control over how fast the plots should be refreshed.

3rd April 2015

  • updated version: 03-04-15

  • moved to C++11. The toolkit has now moved to using C++11. This is to take advantage of the numerous updates in C++11, such as threading, smart pointers and lamda functions.

  • added new ThreadPool class. The ThreadPool class can be used as a base class for managing the number of threads used by other GRT classes. It also features a useful thread pool option for batch processing large analysis tasks.

  • added instance enable/disable logging option. This lets you disable the logging for a specific GRT class, instead of having to disable the logging across all instances. For example, to disable training messages for a KMeans instance called kmeans, use:

    kmeans.setTrainingLoggingEnabled( false );

19th February 2015

  • merged cyberluke pull request. This improves building the GRT on Windows, using MSVC compiler instead of mingw.
  • allowNullGestureClass defaults to true. The default value for the allowNullGestureClass parameter for all classification data structures has been changed to true. This enables you to use the null class label value of 0 in your datasets if needed.

4th January 2015

  • add new Continuous HMM algorithm. The HMM algorithm has been improved to support both Discrete and Continuous HMMs. The toolkit used to only support Discrete HMMs (the input to the HMM had to be quantized to a discrete integer value using a quantization algorithm), but this update enables the user to now use a Continuous HMM (the input to the HMM can now consist of an N-dimensional vector like all the other GRT classifiers). See the new HMM Discrete and HMM Continuous examples for more information on each algorithm.

4th December 2014

  • updated Decision Tree, user can now dynamically select the DecisionTreeNode. The GRT Decision Tree algorithm has been improved to enable the user to dynamically select the type of DecisionTreeNode used for the main train and prediction functions. There are currently two types of DecisionTreeNodes, DecisionTreeThresholdNode and DecisionTreeClusterNode. Each node will give result in a different decision boundary and one node may therefore out perform another for a given classification task. This new update also enables the user to create their own custom DecisionTreeNode (by inheriting from the GRT::DecistionTreeNode base class), and use this in the GRT DecisionTree.

2nd December 2014

  • updated GUI to version 0.1.16. Added new graphs and fixed PCA plot bug.

23rd October 2014

  • updated GUI to version 0.1.15.

4th October 2014

  • added null rejection support to Decision Tree. The GRT Decision Tree algorithm now has full support for null rejection, like all the GRT Classifiers, you can enable this functionality in a Decision Tree instance via:
    DecisionTree dtree;
    dtree.enableNullRejection( true );

the Decision Tree algorithm will then reject the predicted class labels of unlikely predictions, setting the predicted class label to the default null rejection label of zero.

23rd September 2014

  • added getSupportsNullRejection() to classifiers. You can now query each classifier to see if it fully supports null rejection, if so then the classifier will return true when queried.

18th September 2014

  • build support moved from 'make' to 'cmake'. You can now build the toolkit using a CMakeLists file, which will autogenerate a makefile for your system. See the readme file in the build directory for more information.
  • added abstract 'save' and 'load' functions. All classes that inherit from the MLBase class can now save and load their models using the more generic 'save' and 'load' functions, these can be used instead of the more complex 'saveModelToFile' and 'loadModelFromFile' functions respectively.
  • changed default fileformat extension from '.txt' to '.grt'. The files remain an ASCII formated files (so you can still open and edit them with any text editor), but the extension is being changed to reflect the custom GRT header information in each file.