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How to get input values from SOM sample hits plot in Matlab?

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How to get input values from SOM sample hits plot in Matlab?

The self-organizing map (SOM) sample hits plot in the Matlab [1] shows the number of inputs in the neurons but one would be interested in knowing what these input values are. In this article, we are going to get the input values from the neurons.

Let’s say we have a sample hits plot (Figure 1) and we want to know the input values of the 3rd neuron (highlighted with yellow).  The numbering of neurons starts from bottom left to right. Here, the 3rd neuron shows 2 input values.

Figure 1 Sample hits plot from Matlab [1].

Now, after you train your data, you have to add one command to convert vectors to indices.

For example, the following code performs SOM analysis on your data.

>> x = data;
>> dimension1 = 10;
>> dimension2 = 10;
>> net = selforgmap([dimension1 dimension2]);
>> [net,tr] = train(net,x);
>> y = net(x);
% ---------Convert vector to indices----------------------
>> input_neuron_mapping = vec2ind(net(x))';

vec2ind function will convert vectors to indices. ‘input_neuron_mapping’ is a vector now.
Now, if you want to see the input values of neuron 3, then first see the inputs by which neuron 3 was classified.

>> neuron_3_input_indices = find(input_neuron_mapping == 3)

Now, see the original input values.

>> neuron_3_input_values = x(neuron_3_input_indices)

It will display 3 input values.


References

  1. MATLAB. (2022). version 9.12.0 (R2022a). Natick, Massachusetts: The MathWorks Inc.

Dr. Muniba is a Bioinformatician based in New Delhi, India. She has completed her PhD in Bioinformatics from South China University of Technology, Guangzhou, China. She has cutting edge knowledge of bioinformatics tools, algorithms, and drug designing. When she is not reading she is found enjoying with the family. Know more about Muniba

Bioinformatics Programming

How to preprocess data for clustering in MATLAB?

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How to preprocess data for clustering in MATLAB?

Data preprocessing is a foremost and essential step in clustering based on machine learning methods. It removes noise and provides better results. In this article, we are going to discuss the steps involved in data preprocessing using MATLAB [1]. (more…)

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Matlab

Video Tutorial: Self-Organizing Maps in MATLAB

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Video Tutorial: Self-Organizing Maps in MATLAB

Here is a video tutorial for performing self-organizing maps in MATLAB [1].

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Clustering

How to define features/predictors of user dataset for SOM analysis in MATLAB?

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How to define features/predictors of user dataset for SOM analysis in MATLAB?

Most of the examples that demonstrate the SOM analysis use the default Iris dataset. It is also mentioned in the SOM example of MATLAB that for the user dataset, the predictors need to be defined [1]. But it is not quite clear how to define the features or predictors of our dataset. In this article, we will learn how to define predictors for user datasets. (more…)

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