DataStructures Directory Reference

Files
file	ClassificationData.h [code]
file	ClassificationDataStream.h [code]
	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.
file	ClassificationSample.h [code]
file	Matrix.h [code]
	The Matrix class is a basic class for storing any type of data. This class is a template and can therefore be used with any generic data type.
file	RegressionData.h [code]
	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.
file	RegressionSample.h [code]
	This class stores the input vector and target vector for a single labelled regression instance.
file	TimeSeriesClassificationData.h [code]
	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.
file	TimeSeriesClassificationSample.h [code]
	This class stores the timeseries data for a single labelled timeseries classification sample.
file	TimeSeriesPositionTracker.h [code]
	This class can be used to track the class label, start and end indexs for labelled data.
file	UnlabelledData.h [code]
	The UnlabelledData class is the main data container for supporting unsupervised learning.
file	Vector.h [code]
	The Vector class is a basic class for storing any type of data. The default Vector is an interface for std::vector, but the idea is this can easily be changed when needed (e.g., when running the GRT on an embedded device with limited STL support). This class is a template and can therefore be used with any generic data type.