New to Python and Fuzziness

[Brandon O'Briant]

Hello, 

Currently, I am a BS Mathematics major who is moving into the data science realm (that is where I want to land within the career field-just fascinating).. I have become quite interested in fuzzy logic and during this excursion I have found that combining fuzzy logic with neural networks can be a very powerful tool for predictive models.  I have been working on the theoretical mathematics behind fuzzy logic, fuzzy quantities, alpha-cuts, and neural networks;  It was not too difficult to grasp those concepts since it is very similar to classical logic/abstract algebra and the theory behind neural networks is pretty straight forward. My advisers now (with a couple of weeks to go) want to see this applied even though this is supposed to be a pure math capstone. They pointed me in the direction of MatLab, but I am not familiar with that programming language and frankly I do not have enough funds to purchase its packages.

Having worked in R for my econometrics and abstract algebra class (building crypto systems) I though I could try to do it right there. I did run into some trouble and landed on using python since it seems like a more reasonable fit to accomplish this task. 

I am working through some Udemy and Machinelearningmastery tutorials/books to get familiar with python (I am more familiar with R) so that I can use more than just packages to build a neural network. 

During my research in combining these powerful tools I have found that the Fuzzy Cmeans clustering algorithm works well to clusters data (i.e. stock indicators for the S&P500) to as inputs for the neural network. This is the approach I will be taking, but understanding the theory and math behind this is much different than applying it in a programming language (obvious). 

So, my question is using scikit-fuzzy to implement the Fuzzy Cmeans clustering algorithm  would anyone be able to point me in the right direction on how to take the resulting clusters and inputs those into a neural network. 

Any tips, links, or such is welcomed. 

Thank you, 

Brandon