Kohonen self-organizing maps Write a computer program to implement a Kohonen self-organizing neural network. Use 2 input units, 50 cluster units, and a linear topology (one-dimensional lattice) for the cluster units. Allow the winner and its topological neighbors to learn such that: the radius must be chosen as 4 initially, 3 between 10005000 epochs, 2 between 5000 10000 epochs, 1 between 1000025000 epochs and 0 after 25000 epochs (In other words, if unit J is the winner, then nearest 2r units located on the both sides of J also learn, unless Jr50.) Use an initial learning rate of 0.5, and gradually reduce it to 0.01 (over 20000 epochs). The learning rate should be kept constant at 0.01 after 20000 epochs. The initial weights on all cluster units are to be random numbers between 1 and +1 (for each component of the weight vector for each unit). Generate training data as follows: Choose two random numbers between 0.5 and 0.5, and call them x1 and x2. Put the point (x1,x2) in the training set if x12+x22