ths

This is a framework for classifying datastreams.

It includes a few interfaces, a function to run an experiment with specified modules, and a few implementations of different modules.

Interfaces

StreamingData

An object that can provide a stream of observations and corresponding labels at arbitrary points in time.

Properties:

• tMax: the maximum time (ending time) of the experiment
• y: A column vector specifying the possible class labels
• d: A scalar specifying the number of dimensions in the dataset

Methods:

• [x, y] = obj.sample(t, y): Sample a new observation at time t from class y. y should be optional and drawn from a uniform distribution containing all possible classes by default. t is required.

Implementations:

RotatingGaussians- a 2d environment that generates observations from Gaussians that are rotating around the origin

obj = RotatingGaussians(n);

• n is the number of Gaussian classes to use. They will be divided equally around the origin, and will all rotate in the same direction around a circle of radius 5 at 1 full rotation per second.

TrailingGaussians- an experiment where Gaussians will move uniformly along a line.

obj = TrailingGaussians(opts)

• opts is a struct with the following fields:

Field	Description
C	The number of classes (Gaussians) to sample from (defualt: 3)
dmu	The rate at which the distributions move. Either a scalar or a vector in units / second to move in each dimension. (defualt: 1)
spread	How far (Euclidean distance) each Gaussian will be from the one in front / behind it (default: 5)
sig	Either a scalar specifying the diagonal covariances of each Gaussian or a covariance matrix (default: 1)
d	The number of dimensions for the experiment (must agree with sig) (default: 2)
tMax	The maximum time of the experiment (default: 1.1 times the number of seconds required for the last distribution to reach the original position of the first)

StreamPlotter

This object is used to plot datasets that come from a stream. Objects that implement this interface should hide older observations as the data changes.

Properties:

• axh: The handle to the axes the plotter is using
• n: The number of most recent points to retain on the graph
• colors: A * by 3 matrix specifying the RGB values of the plotter's colormap. This can be any length, and the plotter should restart from the beginning of the matrix if there are more classes than colors

Methods:

• obj.plot(X, c) plot observations in X as color specified by index c. c must have the same number of rows as X or be scalar.

Implementations:

Plotter2d- a plotter in 2 dimensions that will discard old observations in a FIFO manner. Note: observations that were provided to the plotter in batches will be deleted in batches. So if plot was called with X being matrix with 50 rows, those observations will remain on the axes until plot is called opts.N more times, at which point they will all be deleted at once.

obj = Plotter2d(opts);

• opts is a struct with the following fields:

Field	Description
axh	An existing axis handle that Plotter2d should attach to
N	The number of points to retain on the graph at once (FIFO)
colors	A Cx3 matrix where C is the number of colors in the colormap and columns are RGB values

ClassifierModel

The actual classifier for data streams

Methods:

• obj.train(X, y, t): Train the classifier with observations specified in X that were drawn at time t from class y. The number of rows in X, y, and t must match. y can be a vector or matrix containing posterior probabilities. If y is a matrix, the sum of its columns must be a vector of 1s.

• h = obj.classify(X, t): Predict the labels of the observations in X drawn at time t. The number of rows in X and t must be the same. h is a vector containing the same number of rows as X and t, with integers specifying the class label that the observations belong to, according to the classifier.

Implementations:

ForgettingKnnClassifier

obj = ForgettingKnnClassifier(opts);

• opts is a struct with the following fields:

Field	Description
rowPadding	The number of rows to grow the resizable array X when it gets full (default: 500)
k	The number of nearest neighbors to consider (default: 25)
beta	The forgetting rate over time (default: .1)