Pattern Recogntion Applet Introduction
The pattern recognition applet is perhaps one of the best
examples of a Java applet in our library. It is extremely
useful for learning about the basics of how data is modeled
and classified in a highly interactive environment. The
applet allows the user to observe how various pattern recognition
algorithms can classify different userdefined data sets. The
applet also features an easytouser interface and the classification
algorithms are performed in a stepbystep mode to make them
easier to follow.
This
tutorial will provide a brief overview of the applet by explaining
the menu bar and user interface. Next, follow the appropriate links
to a short tutorial for using each of the classification algorithms.


First, let's take a look at the menu bar and the purpose of each menu item.
 File
 The ability to Save and Load data sets will be implemented in a later
version of the applet.
 Edit
 Settings>Set Ranges: Sets the coordinate ranges of the
input display panel.
 Settings>Set Gaussian: Sets the characteristics of the
Gaussian that can be drawn using the Draw Gaussian feature.
 Settings>Set Class Colors: Sets the color of each data
class.
 Settings>Set Iterations: Sets the number of iterations
for the LBG Clustering and KMeans Algorithms.
 Settings>Set Clusters: Sets the number of
clusters/centroids for the LBG Clustering Algorithm.
 Settings> Set LP Order: Sets the order (number of
coefficients) for a Linear Prediction Algorithm.
 Settings>Set Interpolation: Sets the number of points
used for interpolation within the Linear Prediction Algorithm.
 Settings>Set State Transition Matrix: Sets the
transition model for kalman filter (KF), unscented kalman
filter (UKF), and particle filter (PF), when order is set to
1.
 Settings>Set Observation Matrix: Sets the
observation model for KF, UKF, and PF, when order is set to 1.
 Settings>Set Process Noise Variance: Sets the
process noise covariance matrix as diagonal elements for KF,
UKF, and PF.
 Settings>Set Measurement Noise Variance: Sets the
measurement noise covariance for KF, UKF, and PF.
 Settings>Set Estimate Error Variance: Sets the
estimate error matrix as diagonal elements for KF, UKF, and
PF.
 Settings>Set Number of Particles: Sets the
number of particles for PF.
 Settings>Set Alpha: Sets the default parameter, alpha,
for UKF.
 Settings>Set Beta: Sets the default parameter, beta,
for UKF.
 Settings>Set Kappa: Sets the default parameter, kappa,
for UKF.
 Clear Input: Clears the contents of the input display panel.
 Clear Output: Clears the contents of the output display panel.
 Clear Display: Clears both the input and output display panels.
 Clear Description: Clears the text in the description panel.
 Clear All: Clears the contents of all panels.
 View
 Zoom In: Zooms in and focuses on a user defined area.
To use: Select
Zoom In and click on the desired area of zoom in either the input or
output panel.
 Zoom Out: Zooms out and focuses on a user defined area.
To use: Select
Zoom Out and click on the desired area of zoom in either the input or
output panel
 Classes
 These are the four data classes that can be used to construct a data
set. Each class is represented by a different color.
 Patterns
 Draw Points: Allows single points to be drawn in the input
display panel. Use this to construct a data set one point at a time.
 Draw Gaussian: Allows Gaussian distributions to be drawn or
"sprayed" in the input panel. The Gaussian settings can be set
from the Edit>Settings>Set Gaussian menu.
 Preset Patterns:The following are preset input data sets.
Selecting any of these will clear the input display panel and
display the preset pattern. The following presets are available:
Two Gaussian

Four Gaussian

Overlapping Gaussian

Two Ellipses





Four Ellipses

Rotated Ellipses

Toroidal

Yin and Yang





 Algorithms
 Go
 Initialize: Initializes the the selected algorithm with
the current input data. Prepares the algorithm for execution.
 Previous: Executes the previous step of the current
classification algorithm. If no previous steps exist, the
algorithm will be reinitialized.
 Next: Executes the next step of the current
classification algorithm. If no futher steps exist,
a message will be displayed in the Description Box indicating
that there are no more steps.
