One of my colleagues brought the following puzzle to work:
The puzzle is called Skyline and it’s a packing puzzle. The objective is to place the metal rod in one of the holes in the base and place the nine wooden pieces around it. It was designed by Jean Claude Constantin. When solved, the puzzle looks something like this:
Sometimes with these kinds of puzzles it’s quicker to write a program that finds a solution than trying to solve it by hand. Check out this github repository for a Prolog program that finds solutions for a given rod location.
To use this program open the file skyline.pl in your favorite Prolog interpreter (e.g. SWI-Prolog) and execute the following:
You can press ; to find alternative solutions. The pos(X,Y) part refers to the location of the metal rod.
In this post I present my Christmas gift to the world: A multilayer perceptron written in C++11.
I mainly wrote this to get some practice with some of the new C++11 features such as variadic templates and lambda functions. It uses template metaprogramming to construct (but not train) the neural network at compile time. You can download the code from its github repository. It’s lacking proper documentation, but I’ve included two examples that should get you started: the xor problem and Fisher’s Iris data set.
I’ve made it my life’s goal to replace all statisticians with cute little robot bunnies. Watch the following video for a demo of my first prototype.
I developed a server in Prolog for the Nabaztag:tag bunny and hooked it up with a dialogue system I created during my masters. It uses an unofficial Google API for speech recognition and generation. It’s quite slow sometimes because of the poor Wi-Fi connection, the inefficient polling of the Nabaztag and the speech recognition. I have some ideas though for improving its speed. Read on for a transcript of the dialogue with comments.