For each feature and combination of numbers apply the Fisher's Discriminant Ratio a vs b, a vs c, a vs d. The final result should be a 6 x 10 matrix. Can you explain how you got the a vs b values though? Can you implement this in python? This is the process. 1. Compute d-dimensional mean vectors for each class 2. Compute within-class scatter matric 3. compute between-class scatter matrix 4. Compute orthonormal eigen vectors 5. Compute corresponding eigen values