Reinforcement learning has been shown to solve complex problems. Recently it has been used with great success by google DeepMind playing Atari games. The success is great but understanding the basic of some of these frameworks/algorithms can be daunting. In this post I hope to eliminate some of the mental entropy caused by trying to understand how and why the algorithms work. I will focus on Logistic Regression as it is a simple and if we can understand this model then more complex models are far easier to comprehend.

In order to be able to access the C++ code from Python (or other languages) I have wrapped some of the functionality using SWIG (Simple Wrapper Interface Generator). There is a short tutorial on this page which covers some of the basics, however this is not enough. I am going to want to wrap far more complex data types and classes.

Simulation for animation is no easy task. There are many steps when constructing an animation and figuring out when particular data should be instantiated can be tricky. I am going to go over one method I have been using to help organize the steps and structure that could be used to construct the simulation flow of control.

I am using a rather large C++ library to run some simulations. I have wrapped a function that calls this library which results in a large amount of processing. Very rarely and somewhat randomly the C++ library will segfault. It is not the point to debug the library because this …

I have spent a far bit of time in the education system and I feel there is a great difference between knowledge and information. The internet is full of information, tons of small independent pieces of data. When you are at school you spend most of your time learning rules …