A perceptron with a unipolar step function has two inputs with weights W = 0.2 and = -0.5, and a threshold = -0.2 (e can therefore be considered as a weight for an extra input which is always set to -1). The perceptron is trained using the learning rule Aw=n(d-y)x where x is the input vector, n is the learning rate, w is the weight vector, d is the desired output, and y is the actual output. What are the new values of the weights and threshold after one step of training with the input vector x=(11) and desired output 1, using a learning rate n = 0.2?