The Causality Project

 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...

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 thu...