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
Once I managed to get the frame clean and my code working, I checked my results against Dr. Sprint’s to ensure their accuracy. There were a few minor tweaks to make, but the vast majority of my output matched hers. We compared our results for two different subjects to ensure that each case was tested. Now, I’m ready to start calculating the daily statistics for my output. Firstly, I need to change my DataFrame’s indices to DateTime objects so that I can reference each epoch by its time, not an arbitrary index.
My plan for collecting the statistic data is to create a new DataFrame with columns corresponding to each calculated value and rows representing each day during the period the watch was worn. In order to ensure that one “day” will capture an entire day and an entire night, our clock will begin at 7:00 am and end at 6:59 am the following real-day. The day is then further classified into “lights-out” and “lights-on” periods, which I will also be calculating summary statistics for.
My plan for collecting the statistic data is to create a new DataFrame with columns corresponding to each calculated value and rows representing each day during the period the watch was worn. In order to ensure that one “day” will capture an entire day and an entire night, our clock will begin at 7:00 am and end at 6:59 am the following real-day. The day is then further classified into “lights-out” and “lights-on” periods, which I will also be calculating summary statistics for.
Comments
Post a Comment