Filler Journal - 10/18/19 - 11/15/19
On Thursday, November 14th, I ran into a multitude of issues. All of which was because of pent up anxiety and downright disappointment. It all began in my debate class. I had a tournament coming up soon and my case was barely finished. I know I could finish it late in the day but I felt the need to do it during class. I was compelled to do so since I wanted to do well but I ended up not because I had a math test one period away. For the rest of the class, I did a last-minute cram session but I guess that was bad luck. Right before the test, my teacher, Mrs. Linton, said that if we had 6 minutes left, we "should get on the calculator section ASAP." Judging from her statement, I wrongfully judged that I would need around 10 minutes since I thought she implied that the calc section would take 6. Instead, I really needed much more time. I ended up doing extremely poor and got disappointed. During my "alone" time of thinking about what could I have done to do better, my friends have tried calling me. Due to my ignorance and current state in mind, I was in no mood to talk and hung up the calls. It turns out that those calls were about my biweekly trip to Caltech for this class. I ended up missing the bus and spent my time at the office to explain my troubles.
In other news, I recently went to CodeDay LA to help out with the event. CodeDay is a 24-hour hackathon where teams of up to 4 create a project during that time window. Because I was volunteering for the event, I spent most of my time helping teams with their projects. During there, Matthew and I got to host a small seminar to introduce people to machine learning and a separate one on how to make a website. We used the Iris dataset for the ML class which did plenty but I wish we could've made the seminar go more smooth. A video below is an explanation of what CodeDay LA is. The image below is a table showing the data given from the dataset.
Finally, something related to this class, I did a technical journal about the Girvan-Newman Algorithm. It took me some time but I'm proud of my explanation and review. The algorithm, in short, is a hierarchical method used to detect communities in a graph depending on the iterative elimination of edges with the highest number of shortest paths that go through them. In pseudo-code:
In other news, I recently went to CodeDay LA to help out with the event. CodeDay is a 24-hour hackathon where teams of up to 4 create a project during that time window. Because I was volunteering for the event, I spent most of my time helping teams with their projects. During there, Matthew and I got to host a small seminar to introduce people to machine learning and a separate one on how to make a website. We used the Iris dataset for the ML class which did plenty but I wish we could've made the seminar go more smooth. A video below is an explanation of what CodeDay LA is. The image below is a table showing the data given from the dataset.
Previously mentioned, I had a debate tournament this week at Arroyo High School. My partner, Eric Jiang, and I did Public Forum and we believe that we did okay in our rounds. While the records have not yet shown up, I can rightfully assume that our record is 3-1 which, in my opinion, is not terrible. Our weak point was most definitely our affirmative case because it was a last-minute trick. Unfortunately out of the four rounds, we did, three of them we were on the affirmative-side but luckily we were able to somehow outdo all but one team (partly in due to our bad judges). A photo below is a picture of the high school we debated at:
Finally, something related to this class, I did a technical journal about the Girvan-Newman Algorithm. It took me some time but I'm proud of my explanation and review. The algorithm, in short, is a hierarchical method used to detect communities in a graph depending on the iterative elimination of edges with the highest number of shortest paths that go through them. In pseudo-code:
Repeat until no edges are left:
- Calculate edge betweenness for every node in the graph
- Remove the edge with the highest edge betweenness
- Recalculate edge betweenness for all remaining nodes
- Connected nodes are considered communities
You can find more information on the algorithm in the following video:
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