We have learned in class that the weighted majority algorithm $($WMA$)$ and the

randomized weighted majority algorithm $($RWMA$)$ share some key similarities: both of them

place weights on experts that become lower as experts make more inaccurate forecasts. The

key difference arises in how the algorithms aggregate the weights to make a prediction: while

WMA deterministically goes with the majority vote, RWMA randomizes its prediction pro$-$

portional to the weight on each expert. In this problem, you will implement both algorithms

in code and examine the implications of randomization on an $$easy$,$ i$.$e$.,$ predictable se$-$

quence and a $$hard$,$ i$.$e$.,$ unpredictable$/$adversarial sequence