Reads the final data set which also includes the cross validation data.

Adding the data.

Uses the third data set to compute the regularization and gaussian parameters that give the best fit with the cross validation set.

Computes the decision boundary of the classification problem using a gaussian kernel.

This file only plots the initial data set and classification decision boundaries using a linear kernel.

Plots the decision boundary using the sklearn SVM classification fit with a gaussian kernel.

Compute the best fit parameters for the regularization and gaussian kernel using the cross validation data set.

Computes a gaussian kernel.

Runs through the first data set and computes the linear kernel svm fit to classify the data sets.

Plots the classification data sets and best fits to the data.

Computes the gaussian kernel for the separation between data sets.

Reads in the exercise data set which are in matlab files.

Uses the scikit-learn software package to classify the test data set using a linear kernel.

Adding in the exercise data.

Reads in the data.