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Posts about itp (old posts, page 7)

Multi-Layer Perceptron Study

Our next assignment is to use a Multi-Layer Perceptron to study a dataset.

The dataset I selected is the commonly studied Poker Hand data. Each record contains data for 5 playing cards and a poker hand classification, such as full house or straight.

This dataset proved to be difficult to work with. It is an example of an imbalanced dataset in that the more common poker hands like two-of-a-kind are heavily represented and the less common hands like straight and flush are not.

I found that the Perceptron was able to correctly classify some poker hands very well while performing terribly for others. I suspect a very different training methodology is required to properly train a Perceptron with this dataset.

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Final User Testing

Our final project continues to progress towards our final presentation next week. Our project is in good shape and we were even able to do some user testing yesterday with some people in the lounge. I'm looking forward to tomorrow's user testing and additional feedback.


We made progress on several fronts. First, I collected much more raw pulse data and studied the data in Python. I was able to identify the shortcomings in the provided pulse sensor code and make some improvements. This is documented in my previous post. Our project uses this modified version that has noticeable improvements over the original version. It still isn't perfect but for people for whom the sensor gets a good reading, it works very well. This modified version has been shared with two other groups who are also using the pulse sensor.

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Modified Pulse Sensing Algorithm

Our Physical Computing final project depends on a Pulse Sensor to detect a user's heartbeat. The people at World Famous Electronics created an Arduino library for their customers to use with their sensor. The library adds a lot of value because it provides users with a well researched algorithm for using the sensor to properly detect a heartbeat. Pulse Sensor users don't have to re-invent the wheel and code their own algorithms. Writing your own algorithm to do this is difficult, and the one provided by the company is better than the one that I came up with for our midterm.

Still, the provided algorithm isn't perfect. For some people it seems to miss some heartbeats and add extra heartbeats. A fellow ITP student, Ellen, showed me that it would have odd spikes in the beats-per-minute (BPM) value. It wasn't clear why this was happening. Since I previously had been analyzing the sensor's data in Python, I came up with a plan to figure out why the Arduino code was doing this and to figure out if there was anything I could do about it. After studying the data and making some plots, I was able to make some improvements the algorithm. It still isn't perfect but my changes address many of the weaknesses of the algorithm.

The original Pulse Sensor Arduino code is available online on GitHub. I am sharing this code with my fellow students who are also using the same sensor. After our projects are complete I will submit my modified code to GitHub as a pull request to share with the rest of the community.

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Basic Perceptron

This week's assignment is to code a Perceptron in Python and train it to learn the basic AND, OR, and XOR logic operations.

I created a Perceptron function with parameters that will let me study the operation of this algorithm.

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Clustering and NumPy

K-means clustering

Our second assignment in our Learning Machines class is to implement k-means clustering in Python. I've implemented this in other programming languages but not in Python. Normally I'd use scikit-learn for this but it is a worthwhile exercise to think through how to do this in Python.

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More After Effects and Understanding Comics

Understanding Comics

Scott McCloud’s book Understanding Comics is ostensibly a book about comic books as an art form, including its history, evolution, and modern structure.

The author has much love for comics and fell in love with them as a child. I can partially relate in that when I was younger I looked forward to the Sunday newspaper and reading the comics. However, I never had an affinity for comic books and found them to be a waste of time. These days I only read Dilbert on a regular basis.

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Playtesting Results

Last week Camilla and I did Playtesting with our fellow students. In general the results were positive, with some testers expressing an interest in using the final project. Some of our ideas about how the interaction would work were not as well received as I expected.

Results Summary

One tester told us he had previously discussed our project idea with his wife, who works as a therapist. She thought it would be helpful to have some way to help patients practice breathing exercises before the session begins. I find it encouraging that somebody found our project to be sufficiently intriguing to talk about it with someone else. I know she'll be at the Winter Show and I'm looking forward to showing her the final result.

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Run Length Encoding

Our first assignment in our Learning Machines class is to implement a run length encoder and decoder. This is a simple data compression algorithm that benefits from repeated patterns.

It happens that I previously had an idea for an Arduino project that requires a light-weight data decompression algorithm to decode audio data. I was going to use run length encoding because it is simple to implement and the code itself won't take up much of the Arduino's precious memory. I'll also need to encode the audio files in Python, and I'll use the below code to do it.

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