Week 8 Post 2

Today was a productive day. As I was struggling to understand the KNN algorithm, I found out that I had another problem, which was in my coding for interpolation. I was glad to see that there was an issue because I wanted to switch my attention from classifier algorithms and do something else because it felt like I had stopped producing any results since I started with classification. The problem with interpolation for my up, down, left, and right hand movements were that I did not implement the code correctly, and it turned out to be extrapolated instead of interpolated. A colleague of mine pointed that out when he looked over my progress. I realized that he was right because, on the plotting image, I could see the dots of interpolation all over the plotted x and y values that were plotted. After that, I played around with some values in the NumPy array that I made for interpolation, and I was able to interpolate the image to 20 frames. That action also smoothed out the plotted points, and there are 20 points plotted over the x and y values. Now it seems right, and it was a very helpful notice from my colleague because I was able to put my mind successfully into problem-solving direction again. I believe that this mindset will stay with me and will become my flow as I go through the other tasks on the project. As for the classifiers, I also tried to switch my attention from the KNN algorithm to the SVM classifier. It does not seem any easier to me; however, it feels like I have started getting all the ideas about clustering. Personally, I believe that it would have helped me if I took Linear Algebra and Probability classes back at Berea College to be able to learn these algorithms faster. It really does require a math background to work on such a project, but I am grateful for being able to learn at one of the best engineering schools in the country.

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