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Week 12: Final Week

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  These past 12 weeks have flown by so fast! It still feels like yesterday I started working on this project. There were highs and lows but at the end of the day we overcame our obstacles to create something to be proud of. It was a great learning experience and I had fun working with the team.  Monday: Implemented the prototype of the K decrement algorithm to reduce the number of filter variables in case of shortage of data points upon filtration. Here are some results: K decrement- condP() K = 100 rf = 0.8, minmin = 20 dims, datSize, tries, K =  3 1000 1 100 Average R2: JP, UP, PS =  0.67025996194      0.726423475099       0.7434199290171855 K=100 , rf = 0.8, minmin = 5 dims, datSize, tries, K =  3 1000 5 100 Average R2: JP, UP, PS =  0.70784384556      0.87296540935         0.764876980843549 K=100 , rf = 0.8, minmin = 4 dims, datSize, tries, K =  4 1000 1 100 Average R2: JP, UP, PS =  0.70162883565      0.80123074862         0.6089856810665888  K=100 in condP() function means that w
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Week 11: Improving UPROB

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 This week I improved the UPROB module, implementing a UPROB(K-100) functionality, internalizing the filtration operation to automatically choose optimal minimum and maximum points. I also overcame last weeks challenge of caching and managed to implement a working cache functionality for CondP(). Monday : Discussed functionalities of UPROB with Roger sir and got some suggestions and feedback. Some of the things I will be implementing to improve UPROB are: Calculate minPoints and maxPoints for filtering operations internally and abstract it from the user.  Implement UPROB(K=100) Caching the RKHS depending on the filter values rather than filtered variables to avoid re-initializing it. This solves the issue I had last week with caching. Tuesday and Wednesday: Implemented the caching functionality. This will especially be helpful in condP() calculation to perform operations where we are only altering the value of expected variable with constant conditionals. For ex: The following operatio
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Week 10: Implementing UPROB

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Week 10 Report This week, I developed the UPROB module (you can check out the code here ). Since this was the main focus throughout the week and I spent all my time developing, bug-fixing , testing and documenting results, I feel that a day-wise summary would not be the most effective method to show results. Instead I have opted to explain the functionalities, working and usage of this module. This will also serve as documentation for future reference. I have listed the results and conclusion towards the end of this report. UPROB Init: Initializing a UPROB Object is very similar to that of rkhsMV except for the final argument k , which denotes the % of variables to be filtered before applying JPROB methods on the remaining ones.  R1 and R2 are two separate rkhsMV instances, one for each of the UPROB methods, condE() and condP(). The idea is to store R1 and R2 such that if we are repeatedly performing operations on the same dataset, we need not initialize a new kernel each time thus sa
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