We want to train a single-neuron ADALINE network without a bias, using the following training set, which categorizes vectors into two classes. Each pattern occurs with equal probability. P1 P2 = ,t2 =-1}{P3 = 0 i. Draw the network diagram. ii. Take one step of the LMS algorithm (present p, only) starting from the initial weight W(0) = [o o]. Use a learning rate of 0.1. iii. What are the optimal weights? Show all calculations. iv. Sketch the optimal decision boundary. v. How do you think the boundary would change if the network were allowed to have a bias? Indicate the approximate new position on your sketch of part iv. vi. What is the maximum stable learning rate for the LMS algorithm? vii. Sketch the contour plot of the mean square error performance sur- face. a