Tag Archives: Python

Access Elements in a Django JSONField List

I set up my Django project to use MySQL, so that I could use a list in a JSONField. Little did I know that accessing elements of that list by their index would be another problem entirely. There are two confounding things. First, in Jinja2, you can access a for-loop index with the loop.index or loop.index0 keywords, but in a Django project, you need to use forloop.counter or forloop.counter0`. The trailing zero on those keywords specify a zero-indexed counter rather than a one-indexed counter. The second thing is that you need to provide a custom template tag in order to cleanly access elements in a JSONField list by their index.

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Using the State Machine Compiler with Python

Today I learned about a state machine compiler and code generator. It provides a small DSL that you can use to describe a state machine and the transitions between the states, which will be compiled to create a number of classes, and then all you have to do is provide the code for the actions. Here, actions, states, and transitions are defined terms that are described in the documentation for the smc tool.

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Very Very Verbose Cosine Similarity

This material was a teaching aid for a crash course I gave at work about cosine similarity. Cosine similarity is a blunt instrument used to compare two sets of text. If two the two texts have high numbers of common words, then the texts are assumed to be similar. The ultimate goal is to plug two texts into a function and get an easy to understand number out that describes how similar the texts are, and cosine similarity is one way to skin that cat.

Please note, there are plenty of other very fast implementations for cosine similarity, but this one was written for educational purposes.

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How Many Trials Before the Nth Success

TLDR: the negative binomial counts the number of trials needed before the Nth success.

I had this problem where we were considering running some very expensive tests that had a known success rate, and we wanted to know, given the success rate and the cost, whether we should run them at all. To make things more interesting, we were only interested in a set number n of successes, and we could stop all testing after the first n successes. My initial thought was to use the binomial distribution, but the binomial doesn’t “cut off” after a set number of successes. It turns out that we needed to use a version of the negative binomial distribution.

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Setting Up Sphinx Documentation

It took me a while to get Sphinx documentation set up correctly. Since it is highly configurable, it is highly easy to not configure correctly. In this guide I’ll assume that you’re using a Python virtual environment, and that you’ve placed the source code that you want to document in a directory called src/. I’ll walk through installing and configuring what you need to create documentation from inline comments using the Google or NumPy style, and create API documentation for a Flask server. I’ll be extra-explicit about what directory I’m in when I make calls that make assumptions about the working directory.

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Reinforcement Learning with Monte Carlo and Tabulation Methods

I was reading another blog post about reinforcement learning using Monte Carlo and tabulation methods that provided an example of the technique using Blackjack. I decided to implement my own method using Tic-Tac-Toe as an example. The basic idea is that you generate a random state of a game, and you generate a random action based on that state. Then you play the rest of the game through until the end and record the outcome. Then you should be able to store the state, action, and outcome as a key in a dictionary that refers to a count. Each time that state-action-outcome occurs again, you update the count by one. Over time, your dictionary will encode information about the relative strengths of different actions for a given state.

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