Plot xy and the decision boundary make sure that you use a

Question

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.

Part 1

1. Generate 20 points of. Store this dataset in a file.

2. Using Fisher's Linear Discriminant Analysis, find the decision boundary.

3. Plot X,Y and the decision boundary. Make sure that you use a good plotting technique so that it is easy to distinguish which datapoint is X and which is Y .

4. Calculate the accuracy of your linear classifier.

Part 2
In this part we will investigate the effects of mean for jointly Gaussian random variables on accuracy.

1. Generate 20 points of .

2. Using Fisher's Linear Discriminant Analysis, calculate the decision boundary and plot accuracy vs µ₁ ∈ [0, 3] and µ₂ ∈ [0, 3]. Note that this is a 3D plot.

Part 3

1. Generate 20 points of .

2. Using Fisher's Linear Discriminant Analysis, calculate the decision boundary and plot accuracy vs σ₁ ∈ [1, 5] and σ₂ ∈ [1, 5]. Note, as in the previous part this is a 3D plot.