In this post I’ll demonstrate an iterative closest point (ICP) algorithm that works reasonably well. An ICP algorithm seeks to find a transformation between two sets of points that minimizes the error between them, i.e., you are trying to find a transformation that will lay one set of points exactly on top of another.
In this post I’ll present a solution to a puzzle using Python. I think the primary value of this post is that it provides an example of how to translate an objective and a set of constraints into data structures and functions that can be interpreted by a computer. This problem breaks down into two interrelated parts:
- Translate the problem into data structures and functions
- Choose a strategy for finding the solution
If you create a bootable USB flash drive for installing linux on a machines, you may have trouble reformatting it later in Windows. It is possible to create a bootable USB in such a way that the Windows reformatting utility, obtained by right-clicking on the drive and selecting Format…, does not see the partition containing the bootloader. In such a situation, you may have a 8GB drive, but the reformatting utility only sees 6GB, and reformatting will not recover the original 8GB of space.
In this post I’ll consider performing a local hypothesis test for a difference in means with spatial data. I do not know if this is the optimal way to go about this sort of thing, but I have not yet found another solution. I think the best way to describe the problem is to consider the artificial data, and then wade through the code.
I didn’t finish the two posts I was editing this week, but I did draw a shoddy Arduino in GIMP with a Wacom tablet that you can use if you’d like. Below are PNG and XCF files with white and transparent backgrounds.
This is one of the fundamental tasks in science. You do a study, and then you have to determine if there is a statistically meaningful difference between the test and control data. It is important to be able to understand the hypothesis testing, because a lot of interesting functions in R are hypothesis tests. I’ll consider the simple z-test for testing whether the mean of the simple is the same as the hypothesized mean of the population. We’ll see how statistical power, which is the probability of detecting a difference in means, changes with sample size and effect size, which is the size of the difference between the observed sample mean, and the hypothesized population mean. We’ll also see that the significance level is comparable to the Type-II (false negative) error rate.
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.
In this post I’ll demonstrate how to build a object oriented Tkinter GUI application for associating labels to filenames in order to quickly and easily build a set of training data. The Submit button will associate the label with the file, and the Save and Quit button will dump the file and its associated label into a Python dict, and then a cPickle file for later use. This is still a little rough around the edges; it assumes that you’re looking for PNG data in the current directory, and the output overwrites previous output, but it’s a start.