pattern recognition

EENG 4350/5340: Project 1Assigned: September 21,2015Due: October 9, 2015Rules 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 ahefty penalty. Everyone should participate…no excuses, no exceptions.Part 11. Generate 20 points of X N (3 4, I) and Y N (3 2, 2I). 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 iseasy to distinguish which datapoint is X and which is Y .4. Calculate the accuracy of your linear classifier.Part 2In this part we will investigate the effects of mean for jointly Gaussian random variables on accuracy.1. Generate 20 points of X N (3 + 14 + 1, I) and Y N (3 + 22 + 2, 2I).2. Using Fisher’s Linear Discriminant Analysis, calculate the decision boundary and plot accuracy vs1 [0, 3] and 2 [0, 3]. Note that this is a 3D plot.Part 31. Generate 20 points of X N (34, 1) and Y N (32, 2), where 1 =1 00 1and 2 =2 00 2.2. Using Fisher’s Linear Discriminant Analysis, calculate the decision boundary and plot accuracy vs1 [1, 5] and 2 [1, 5]. Note, as in the previous part this is a 3D plot.1