Change trainingmode of Self-Organizing Maps

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Max Bauer
Max Bauer on 17 Jul 2015
Edited: Nick Hobbs on 21 Jul 2015
I want to sort rotordata, which can be shifted, with a SOM. Till now I shifted the data by using some specialpoints in the dataset, but this is not perfect. My solution is to change the SOM-algorithm, so it will not only check all euklidian distances of the outputneurons, but also trys all rotations of the weightvectors.
Where can I change the code? I cant find it in trainbu Thank you!

Answers (1)

Nick Hobbs
Nick Hobbs on 21 Jul 2015
Edited: Nick Hobbs on 21 Jul 2015
In the documentation for trainbu, under the heading 'Network Use', you will see you can assign a learning function to a network using 'NET.inputWeights{i,j}.learnFcn'. If you do this for each weight, you will have effectively changed how the network learns. If we look at the learning function for a standard self-organizing map
net = selforgmap([8 8]);
>> net.inputWeights{1,1}
ans =
Neural Network Weight
delays: 0
initFcn: 'initsompc'
initSettings: .inputSize, .sampleSize, .numElements
learn: true
learnFcn: 'learnsomb'
learnParam: .init_neighborhood, .steps
size: [64 0]
weightFcn: 'negdist'
weightParam: (none)
userdata: (your custom info)
From this output, 'learnFcn' is 'learnsomb'. You cannot edit this file, however you can use it as a template for your own learning function. If you create a new function 'customLearningFunction' that meets the requirements for a learning function and is in your path, then you can set each weight in your network to 'customLearningFunction' and use this instead of the default learning function.

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