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Pattern Recognition in MATLAB

The Pattern Recognition Toolbox for MATLAB® provides an easy to use and robust interface to dozens of pattern classification tools making cross-validation, data exploration, and classifier development rapid and simple. The PRT gives you the power to apply sophisticated data analysis techniques to your problem. If you have data and need to make predictions based on your data, the PRT can help you do more in less time.

Visualize Your Data

The PRT’s prtDataSet objects make using and visualizing your data a breeze. The multiple built in techniques for data visualization will help you interactively understand your data and develop the insights to help you make breakthroughs.

Streamline Your Processing

The PRT provides a wide array of inter-connectable pattern recognition approaches. Every PRT action can be connected to any other PRT actions to enable you to build the powerful processing pipelines to solve the problems you need to solve with a single tool.

Get Answers

Built in cross-validation techniques ensure that your performance estimates are robust, and are indicative of expected operating performance, and built in support for decision making takes the guesswork out of setting optimal thresholds to make binary or M-ary decisions based on your data.

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Rt()

In the PRT we have a a few hidden features that we don’t really advertise but at times they make life easier. This quick post is to reveal one of those quick features, prtAction.rt()

rt() stands run, train. You can use it to perform a “run on train” operation and you only the output dataset. In other words, it’s a quick and dirty method for when you would otherwise need to call run(train(action, dataset), dataset). To see how one might use rt(), let’s calculated the first two principal components of Fisher’s Iris dataset.

ds = prtDataGenIris;
pca = train(prtPreProcPca('nComponents',2), ds);
dsPca = run(pca, ds);

If we didn’t really care about keeping the (trained) PCA object around we could have done this all in one line.

dsPca = run(train(prtPreProcPca('nComponents',2), ds),ds);

That string at the end ds),ds) is odd looking and this is when rt() comes to the rescue.

dsPca = rt(prtPreProcPca('nComponents',2), ds);

That’s how you can use rt() in a nutshell. It’s a nice method to keep in your back pocket when you are just beginning to explore a dataset and cross-validation isn’t yet on your mind. Remember rt() is a (hidden) method of prtAction and therefore can be used with all classifiers, pre-processors etc. Let us know if you find any use for it. We do.