The Self Organizing Map: Unsupervised Competitive Learning

Exercise 1: Select each of the input patterns in turn and click on cycle to see which of the outputs represents it. Complete the Before Learning column of the following table. Now click on the learn button (to execute 80 epochs of training) and complete the After Learning column.

Gang Member	Before Learning Map Node (1-5)	After Learning Map Node (1-5)
Robin
Bill
Mike
Joan
Catherine
John
Joshua
Bert
Margaret
Janet
Alfred
Gerry
Brett
Sandra
Beth
Maria

Exercise 2: Table one shows the features for all of the gang members. Use the table to explain the pattern of results you collected in the previous exercise.

In the table below, write on a separate line the attributes of each of the individual in each of the 5 classes. I.e enter (30,JH,M,BK) for an individual who is in their 30s, went to Junior High, is married, and is a Bookie.

What is the pattern that emerges?

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Exercise 3: Toggle the 20s, Col, Single and Pusher units and click on cycle. Which map unit becomes active?

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Record the netinput values of each of the map units. These values are the distances of each map units weight vector from the current input.

Map Unit	1	2	3	4	5
Net Input

Which unit has the largest distance?

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Exercise 4: Now toggle the 40s, JH, Married and Bookie units and click cycle. Which map unit becomes active?

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What is the special significance of these patterns and why do they map to the units that they do (hint – refer to your answers to Q2)?

Exercise 5: Select map unit 1 and click on the Examine Weights button. The input units will now show the weight vector associated with unit 1. Record the pattern of weights below (i.e. Fill the appropriate square with the appropriate level of activation).

20	30	40	JH	HS	COL	SIng	Mar	Div	Bk	Psh	Bg

Perform the same exercise for unit 5.

20	30	40	JH	HS	COL	SIng	Mar	Div	Bk	Psh	Bg

Compare these patterns to the patterns of activation you activated in the previous question.

Exercise 6: Complete the same exercise for the other map units.

COL

SIng

Mar

Div

Psh

# 2

Describe how the patterns to which each unit responds changes as you move from map unit 1 to map unit 5.

Exercise 7: We will begin by considering the patterns in Input Set 1. Randomize the weights and plot each of the weight vectors (w1, w2) of the map units. You should now have a graph with four points on it. Now, connect those units that are adjacent within the map layer.

Then train the network for five cycles and plot the weight vectors on a separate graph.

Finally, train for another 50 cycles and plot the resulting weight vectors on a third graph.

How does the plot change as training progresses?

Exercise 8: Complete the same exercise for Input Set 2. Randomize the weights and plot each of the weight vectors (w1, w2) of the map units. You should now have a graph with four points on it. Now, connect those units that are adjacent within the map layer.

Then train the network for five cycles and plot the weight vectors on a separate graph.

Finally, train for another 50 cycles and plot the resulting weight vectors on a third graph.

How does the pattern change as compared to the first input set?

Examine the patterns in Input Set 1 and Input Set 2. Why do the patterns unfold as they do in each set?

Exercise 9. Test the effect of the neighbourhood size by changing the "Neighbourhood Size" parameter to 1, and cycling the first input pattern. Remember to click on the SetNeighbourSize button to update the map weights. Also test for size=8. Change it back to 4 for the next section. What changes occur when the "Neighbourhood Size" parameter is decreased, or increased, compared to the default value of 4?

Exercise 10. Test the effect of the proximity between output units, by moving the top left hand unit away from the other units. Activate the Set Neighbourhood Size button again and cycle the first input pattern. Describe what happens to the activation of the neighbours of the winning unit. ________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________