Skip to main content

Summer - Week 10

This week, I am working to get the dynamic time warping functionality into my program. The process of doing so includes re-processing the features to include the time series, putting each series back together when we construct sequences, and then performing the DTW to generate a number that will be used to compute the kNN of each sequence which can then be used for predictions with the models. The processing time of these activities has gone up significantly since we have been using five different metrics with each of the F phase datasets. I am returning to school next week, and once I've completed the DTW processing all that will remain before we put together our second paper (The date for the reach journal we would like to submit it to is October 1), I am hoping I will have time to look again into the Agglomerative Hierarchical Clustering concept, which I did not successfully complete when we explored it earlier in the summer and then changed focus to the paper. We heard back
Post a Comment

Week 5

I’ve successfully implemented the sleep-checking script on my test data, but I’m having a bit of trouble transitioning to the real thing. Before I can even run my code, I need to clean up the data and ensure that I get the correct header row while dropping all blank rows and N/A columns. This has been trickier than I expected, as I’m having a bit of trouble discerning the proper indexing method (.loc[], .iloc[], or []) in each situation. I am confident, however, that once I am able to set up the frame properly, my algorithms should work effectively.
    I’m going to spend the next week working on cleaning the DataFrame and picking up any loose ends that remain. My next objective will be to begin calculating summary statistics for the data – minutes of activity in a day, minutes of sleep in a day, etc..

Comments

Post a Comment