The Causality Project

This week I tried extending the filter RKHS method to form a filter RFF method. Unfortunately, filter-RFF does not produce the same quality of results as filter-RKHS. Later, I implemented FPROB or Filter-Probability and tested it against JPROB developed by Roger sir and the existing ProbSpace method.  Monday : Tried to extend the same principle behind Filter-RKHS to form a Filter-RFF. Here is a comparison between Filter RKHS vs Filter RFF vs ProbSpace: As shown, it does not work very well. Like expected, it is faster than the RKHS variant (shown in table below). But when we are dealing with a small number of datapoints (<1000) anyway after filtration, the RFF method is ineffective. My own conclusion is that it’d be better to use the RKHS method for low data points and the RFF method when we are dealing with massive datasizes and need to speed up calculations. Conditional Method Time Taken R 2 Avg. Error P(Z|X,Y=0) Filter-RFF 0.0034470558166503906 0.2888416990567383 0.502167548...

 This week I polished the Filter-RKHS technique from last week. We find this method to be faster and more accurate than the existing ProbSpace. I also revisited the RFF method and found a way of making it faster than the RKHS method through matrix calculations.  Monday : Discussed the results of the Filter-RKHS method I’d implemented last week with Roger sir. Found out why there appeared to be an offset in some of the graphs. Started working on comparing it with the Probspace method both in terms of time taken and accuracies in the form of average error and a new metric, r 2 (r squared) which gives us a value from 0 to 1 based on how well the predicted values fit the ideal curve i.e, if  r 2 = 1, it’s a perfect fit and vice versa. Tuesday & Wednesday: Here is a comprehensive report about the Filter RKHS method, in comparison to Probspace.   Model Used: X = logistic(0,1) Y = logistic(1,1) Z = math.sin(X)+math.tanh(Y) Some General Observations: When calcu...