Self Organizing Maps in Python
Self-Organizing Maps is a form of machine learning technique which employs unsupervised learning. It means that you don't need to explicitly tell the SOM about what to learn in the input data. It automatically learns the patterns in input data and organizes the data into different groups.
I have developed a Python module for SOM. The SOM which I have written is little different from traditional SOMs because it supports supervised learning also. It means that when you provide it with a sample set of inputs and corresponding outputs, it learns to map future unknown inputs to correct outputs. The code for solving the XOR problem using an SOM is included in the code.
To read more about SOMs, click to:
You are also requested to mail your comments to me.
Click below to download the source code.
Thanks to Kyle Dickerson, I now have two enhanced versions of my code.