In the lectures and Prac 2 we have considered parametric probabilistic classification for a binary (2-class) problem with one-dimensional input data. This can be extended to the case where we have more classes. Write a function (e.g. in Matlab or python) that takes 4 inputs: A n 2 set of (training) input data. Where the first column is a one-dimensional set of data, and the second column is the class, ranging from 0 to k-1, k, the number of classes, x, an test input to classify, and p, the k-dimensional class prior probability vector satisfying P k1 i=0 p i = 1. Your function should return the posterior probabilities for each class for the given x-value. Using your function, plot a labelled posterior graph similar to that produced in Prac 2, where the horizontal axis represents the value of x and the vertical axis represents the posterior probabilities for each of the k classes. Use the data provided in iris.csv, taking the first column as x and the last column as the class label. Assume an equal class prior probability. For this question, submit your plot and a listing of your code. You can also include command line output demonstrating usage of the code. Marking is primarily about the output of the code rather than the design.