Rules • Use basic Python, numpy and matplotlib modules. Any other modules need my approval.
• Produce a LATEX-generated PDF of your report.
• Ask plenty of questions to ensure you have a good understanding of the project. • The code (and reports) should look vastly different for different groups. Very similar code will incur a hefty penalty.
• Everyone should participate…no excuses, no exceptions.
In this project we use pattern recognition to determine whether a subject is relaxed or planning. In this study EEG data was collected from patients in each of these two states. We build a classifier to help make automated decisions.
1. Study the dataset located here: https://archive.ics.uci.edu/ml/datasets/Planning+Relax. You will note that there are 182 training patterns of 2 classes with the last column being the class label.
2. Write code to produce produce the posterior probability P (C1|x)
3. Calculate the training error of your classifier, that is over the whole training set.
To ensure that our classifiers generalize, in practice we split the data into a training and a test set.
1. Split the data into 40%, 50%, 60%, 70%, 80%, and 90% training with the remainder being testing data.
2. Produce a table and a plot showing training error and testing error vs. percentage of training