In this post I’ll look at creating a presentation using the R ecosystem. I’ve used beamer before, and I love it, but I haven’t used the
knitr R package yet. Incidentally, the creator of knitr, Yihui Xie, does not like beamer. This is fine, I have been wrong about technology before–I recall thinking in college that facebook was for losers and that it would never catch on. Anyway, Yihui’s work is really impressive and I strongly suggest checking it out.
Continue reading Creating a Presentation with R
If you are running a Debian Linux system, like Ubuntu or CrunchBang, you’ll occasionally need to install a
.deb file, which is a binary file used to install packages. Usually you can simply run,
sudo dpkg -i ./path/to/file.deb
Continue reading Installing Debian Packages
In this post I’ll discuss building a Windows executable from a Python script for 32-bit and 64-bit Windows. Producing a 64-bit executable on a 64-bit machine in Windows is easy using PyInstaller, but producing a 32-bit executable on a 64-bit machine takes some tinkering. I ended up setting up a chroot environment on Ubuntu for this task.
Continue reading Develop Windows Executables from Python Scripts for 32-bit and 64-bit Architecures
I noticed that when I photocopy and email documents, the resulting attachment has relatively low resolution, and the digits get melded to one another. I decided to try to build a classifier to begin to sort this out. To this end, I needed to build a data set. First, I used svgfig to produce SVG sans-serif digit pairs, with kerning adjusted at four intervals. Then I used inkscape to create PNG images from the SVG files. Finally, I read the PNG images and wrote them to a NumPy array. I created a set of clean images, and images polluted with Gaussian noise, with a mean of zero, and a variance of 0.1. (The pixels were then rescaled back to the range of 0 to 1.) I also shifted each pair in eight directions. This produced a data set with 7200, 16×16 pixel images, half of which were noisy. I used a random forests classifier from sklearn, and performed 10-fold cross validation.
Continue reading Compound Digit Recognition with Random Forests