Although manuals and books are nice, nothing is more enlightening than an example session in Matlab to give an impression of PRTools. Let us define a task. The task is to generate some artificial data, train a few classifiers on it, estimate the error of the classifiers and make a plot of their decision boundaries.

First generate some artificial 2D data, and split it in a training and test set. In this example we use 20% for training, and the remaining 80% will be used for testing.

`>> A = gendatb([50 50]); >> [C,D] = gendat(A,0.2);`

Next, we train two classifiers on the training set, a quadratic classifier and a k-nearest neighbor classifier (with k=3).

`>> w1 = qdc(C); >> w2 = knnc(C,3);`

Objects from the test set can be evaluated by the classifier by mapping the dataset using the classifier:

`>> mappedD = D*w1`

To obtain the classification labels, use

`>> labels = mappedD*labeld`

Estimate the error of both classifiers on the test set

`>> err1 = D*w1*testc >> err2 = D*w2*testc`

We are dealing with a 2D dataset so we can make a scatterplot of this data and plot the decision boundary in it.

`>> figure(1); clf; >> scatterd(D); >> plotc(w1,'k'); >> plotc(w2,'r');`

The result might look something like:

More examples can be found here

There is also a set of advanced examples.