Self organizing maps

2
votes

I have question regarding the self organizing maps algorithm

I know that we have an input vector and weight vectors. The calculation of the min distance between the weight and input is the best match unit which make the weight column that relates to the min value update and then update its neighbors.After that we update the rate (assuming you have an experience in SOM).

example 

input 
i1: (1, 1, 0, 0)

weight =
[.8 .4 .7 .3
.2 .6 .5 .9]

learning rate .6

steps (simply and dropping Gaussian function)

first iteration.

1- find the min distance 

 d2 = (.2-1)2 + (.6-1)2 + (.5-0)2 + (.9-0)2 = 1.86
 d2 = (.8-1)2 + (.4-1)2 + (.7-0)2 + (.3-0)2 = .98   this is the BMU

2- update weight vector 

new−unit2−weights = [.8 .4 .7 .3] + 0.6([1 1 0 0]-[.8 .4 .7 .3]) 
                  = [.92 .76 .28 .12]

the result of the weight is 

.8   .4  .7  .3
.92 .76 .28 .12

my questions

1- at the end, I'll be getting new weight vector values and the same input vectors.

what should be plotted? Weight or input or what?

If am using matlab do you have any idea what function to use to get good illustration

2
mapsselfsom
if your question is only regarding plotting I redirect you to this. stackoverflow.com/questions/25258191/… - ASantosRibeiro

2 Answers

0
votes

Following you very simple example, the initial weights are:

Initial weight = [.8 .4 .7 .3 .2 .6 .5 .9]

and final weights should be (assuming all your calculations are correct):

Final weights = [.92 .76 .28 .12 .2 .6 .5 .9]

Note that the winning unit - called best matching unit - is the only one that should be updated/changed here since you have disregarded the neighborhood learning aspect of SOM. This becomes your results and is the one that will be plotted.

-1
votes

I am learning SOM algorithm these days, and I am going to use Python to implement the algorithm, if you are familiar with Python I think you can click this link, som_test.

Your weight is

weight =
[.8 .4 .7 .3
.2 .6 .5 .9]

and you input value is

vector = [1, 1, 0, 0]

And I think the output layer is 2 because the initial weight is 2 by 4 matrix. And you can plot both the input data and the weight.

The input value is 

[[0.1961, 0.9806],
[-0.1961, 0.9806],
[0.9806, 0.1961],
[0.9806, -0.1961],
[-0.5812, -0.8137],
[-0.8137, -0.5812],]

And the plot is, the weight is 3 by 2 matrix, as you can see in the image, there are 3 Xs, that are weights.

plot