These last few days I have realized a dream of a few years – to have a personal cloud-based bookmark management service.

## Iterated Prisoner’s Dilemma

This weekend I watched Dr Hannah Fry‘s documentary on game theory, and was inspired by her discussion of Robert Axelrod‘s tournament to simulate the iterated prisoner’s dilemma (IPD) for myself.

## Prime Number Digit Frequencies

Today I became curious about the relative frequencies of the digits that appear in prime numbers (in base 10). Naturally, I wrote a little program to show the answer (digit-freq2):

## Halstead Software Complexity of Perl Code

The other day I stumbled across the Wikipedia entry for Halstead software complexity and became curious about how the language Perl would measure up. Now I knew the age old adage that “only perl can parse perl”, but I also knew about the PPI set of modules on the CPAN. So I got to reading/experimenting…

## Personal Developer Productivity Tools

It seems that everyone has a “Here’s my toolbox” write-up. So here’s mine (in abbreviated laundry-list form)!

## Bach Choral Harmony Network Diagrams

Today I decided to revisit the Bach Choral Harmony data set and look at chord progression transitions. In order to do this I wrote a small program that tallies the movement from one chord to another, and then outputs a Graphviz dot file that can be turned into an image.

## Zen Markov Machine

Today I used the requests and BeautifulSoup Python libraries to harvest 101 Zen koans. Next I used the NLTK and markovify libraries to generate random statements:

## How Many Primes Are In A Slice Of Pi?

Questions: What is the number of primes per n-slice of the decimal part of pi? What does the graph look like? Are there many overlapping primes (i.e. start at the same position in the slice)?

## Predicting Beatles Song Authorship with scikit-Learn

Yesterday I read about a 10 year effort to predict the author of Beatles songs by lyrics, tonal contour and chord analysis. Hmm very curious. Can I replicate a part of this I wonder? tl;dr: beatles.py

## Predicting Star Trek TOS Spoken Lines with scikit-Learn

In a previous post, I collected the transcripts of all the original Star Trek seasons and analyzed linguistic features of Kirk and Spock. In this post, I train a scikit-Learn model on the Kirk, Spock and McCoy lines spoken in hopes that one of those three speakers can be guessed when given unknown lines.