Train a single perceptron ANN with ADALINE so that it is capable of classifying 5 datasets (X, t) as it is shown in the above figure. Employ LMS learning rules with initial weight W11 = 3.0, W12 = 1.0, b = 1.0 respectively with learning rate ɳ = 0.05 to compute the next bias and weight values. Since we need you to find the optimal bias and weight values that might require many iterations, implement your training process in python notebook and show the result of the process by drawing the MSE (mean-square error) against the number of iteration. You might need to stop the training by the time when you find the bias and weight values relatively remain the same from current iteration to the next. Based on the obtained optimal bias and weight values,

"Determine the corresponding boundary decision function and check if the function optimally separate the two classes. Give your comment on it. 

  

X2 -1 -3 -2 -1 O O class -1 class 1 3121 11 2 3 -1 -2 1 -3