46 #ifndef GRT_MAIN_HEADER 47 #define GRT_MAIN_HEADER 50 #include "Util/GRTVersionInfo.h" 51 #include "Util/GRTCommon.h" 55 #include "Util/LUDecomposition.h" 57 #include "Util/EigenvalueDecomposition.h" 62 #include "Util/CommandLineParser.h" 67 #include "DataStructures/VectorFloat.h" 69 #include "DataStructures/MatrixFloat.h" 86 #include "CoreAlgorithms/GridSearch/GridSearch.h" 132 #include "ClassificationModules/ParticleClassifier/ParticleClassifier.h" 153 #include "ContextModules/Gate.h" 158 #endif //GRT_MAIN_HEADER
The Class Label Timeout Filter is a useful post-processing module which debounces a gesture (i...
The Vector class is a basic class for storing any type of data. The default Vector is an interface fo...
This class implements the Self Oganizing Map clustering algorithm.
This class implements the MeanShift clustering algorithm.
This code is based on the LU Decomposition code from Numerical Recipes (3rd Edition) ...
This class implements a template based ParticleSwarmOptimization algorithm.
The TestResult class provides a data structure for storing the results of a classification or regress...
This class implements a Cluster Tree. This can be used to automatically build a cluster model (where ...
The SOMQuantizer module quantizes the N-dimensional input vector to a 1-dimensional discrete value...
The Matrix class is a basic class for storing any type of data. This class is a template and can ther...
This class implements a Bernoulli Restricted Boltzmann machine.
This class implements a Gaussian Miture Model clustering algorithm. The code is based on the GMM code...
This class implements a threshold crossing detector.
This class implements a template based ParticleFilter. The user is required to implement the predict ...
The UnlabelledData class is the main data container for supporting unsupervised learning.
This class implements the FFTFeatures featue extraction module.
This class implements the MovementTrajectory feature extraction module.
This class implements the KMeans clustering algorithm.
The TimeSeriesClassificationData is the main data structure for recording, labeling, managing, saving, and loading training data for supervised temporal learning problems. Unlike the ClassificationData, in which each sample consists of 1 N dimensional datum, a TimeSeriesClassificationData sample will consist of an N dimensional time series of length M. The length of each time series sample (i.e. M) can be different for each datum in the dataset.
This file contains the Metrics class, it can be used to compute common metrics such as accuracy...
This class implements a basic Linear Least Squares algorithm.
The KMeansQuantizer module quantizes the N-dimensional input vector to a 1-dimensional discrete value...
The ClassificationDataStream is the main data structure for recording, labeling, managing, saving, and loading datasets that can be used to test the continuous classification abilities of the GRT supervised learning algorithms.
The SOMQuantizer module quantizes the N-dimensional input vector to a 1-dimensional discrete value...
The RegressionData is the main data structure for recording, labeling, managing, saving, and loading datasets that can be used to train and test the GRT supervised regression algorithms.
The TrainingDataRecordingTimer is a tool to help record your training data.
This class implements the base class Tree used for the DecisionTree, RegressionTree and ClusterTree...
The RangeTracker can be used to keep track of the expected ranges that might occur in a dataset...
The ClassificationResult class provides a data structure for storing the results of a classification ...
This class implements the MovementIndex feature module. The MovementIndex module computes the amount ...
The Class Label Change Filter signals when the predicted output of a classifier changes. For instance, if the output stream of a classifier was {1,1,1,1,2,2,2,2,3,3}, then the output of the filter would be {1,0,0,0,2,0,0,0,3,0}. This module is useful if you want to debounce a gesture and only care about when the gesture label changes.
This class implements the TimeDomainFeatures feature extraction module.
This class implements the TimeseriesBuffer feature extraction module.
The Class Label Filter is a useful post-processing module which can remove erroneous or sporadic pred...
This class implements a simple movement detection algorithm. This can be used to detect periods of 'l...
This class implements a basic Hierarchial Clustering algorithm.
1.8.11 
