Building and Breaking

I've been tracking my training and ultimate frisbee games for some time now, and I recently decided to turn the tracked data into metrics to quantify my running performance. The initial motivation stems from watching a lot (a lot!) more Premier League soccer and starting to learn more about how high level soccer teams track their players to quantify statistics. Some metrics are out of my league, but some are much more attainable. For example, I can implement metrics such as: Total Distance covered, Sprint Distance, ...

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Design Driven Design builds feedback loops that allow for continuous improvements to open source projects during each pull request. The loops are: Design Writing informs Feature Testing which in turn informs the Design, Feature Testing informs Unit Testing which in turn informs the Feature Testing, Unit Testing informs Design Writing which in turn informs the Unit Testing. These loops form seams for collaboration across partners or for self-re-evaluation at mindful points in the process to maximize success.

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A new experiment for FormaK has landed: Superoptimization. Instead of individual peephole optimization, the experiment prototypes using search and a model of the CPU to find the optimal ordering of all operations.

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A new feature for FormaK has landed: Hyperparameter selection. Automatically select optimal parameters based on data from the modeled system.

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Over multiple iterations of improving FormaK (reference IMU model rocket model, the original Python code generation), I've wanted to leverage the power of Sympy to provide efficient implementations of symbolic concepts before converting to Python or C++. The tool for this job is simplify. With one call, it can simplify polynomials, simplify trigonometry and other approaches. Combine this with Common Subexpression Elimination and we have a powerful pair of tools to write efficient code regardless of the model. There's just one problem: Sympy can be incredibly sluggish for some functions. Each call can take 10s of seconds. These 10s of seconds can stack up to minutes of time spent waiting and hoping for a result. For this experiment, I take some time to dive into what's going on and try to understand why it can be so darn slow sometimes.

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A new feature for FormaK has landed: the Strapdown IMU Reference model. The model is now available for inclusion into new models and use as a reference for implementing future models. This post covers some of the aspects of the design and development that didn't make it into the final design and feature.

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A new feature for FormaK has landed: the Strapdown IMU Reference model. The model is now available for inclusion into new models and use as a reference for implementing future models.

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A new feature for FormaK has landed: Innovation filtering. Automatically make Kalman Filters more robust.

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A new feature for FormaK has landed: The FormaK runtime. The first tool in the runtime is a Managed Filter that handles coordinating process and sensor updates.

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A new feature for FormaK has landed: improved model generation by leveraging a subset of the C++ Abstract Syntax Tree

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Launch day is here! Presenting Mast, an open source, privacy first recommender and discovery tool for Mastodon

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Launch day is here! Presenting Mast, an open source, privacy first recommender and discovery tool for Mastodon

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A new feature for FormaK has landed: generating models with calibrated sensors

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The first day of the project started with a focus on getting TF-IDF set up and connecting the data to the format that scikit-learn expects. Also, a brief diversion to start with small data before going to the big data approach.

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I'll admit it. I'm a document hoarder. For years I've been collecting and tagging notes in Evernote as I'll read through them to save for later. Can I use that hoarding to learn better?

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It's been a little while since I last wrote about Formak ( FormaK Under The Hood: Optimization for scikit-learn integration , Sat 08 October 2022). Since then, I've been busy adding functionality to FormaK and I've also been improving the tooling for the project, inspired by Boring Python: code quality. The latest piece of functionality is the C++ code generation. The PR is in progress, but the initial experiments have proved out that the generation pipeline is feasible and able to be integrated into bazel.

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For the new scikit-learn feature, let's dive into how the model gets fit to data.

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There's a new feature coming to Formak: integration with sckit-learn. This enables quickly integrating new features such as model selection, pipelines and other data tooling.

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The second feature for FormaK landed: generating Python models

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Let's automatically construct programs for testing

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Taking notes in Markdown (LaTeX?) and Python

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