Homework 4CS XXXXXXXXXX), Fall 2022, Introduction to Data ScienceDue Date: Dec 9 (Friday), 11:59 PM...

Question

Homework 4CS XXXXXXXXXX), Fall 2022, Introduction to Data ScienceDue Date: Dec 9 (Friday), 11:59 PM (EST)This homework will focus on understanding and implementation of algorithms relevant to online learning.The dataset we will use is hw4.txt. It consists of T = 103 lines, and each line t ∈ [T ] .= {1, 2, . . . , T} has ainary string of length m = 16, which is followed by a binary value. Please try to interpret the binary stringon line t as the predictions from all the m = 16 experts at day t. In other words, the binary string is thevector et = {e1,t, e2,t, . . . , em,t}, where ei,t refers to the prediction of expert i at time t for all i ∈ [m]. Thelast binary value on line t is rt, i.e., the true value at the time t.1 Please implement and run the following heuristic policy π to the dataset hw4.txt, where π will make adecision during time t as follows. Let St be the set of experts who have made the fewest mistakes so faduring the first t − 1 days with S1 = {1, 2, . . . ,m}. The policy π will follow the majority vote among allexperts in St and eak the tie aitrarily if any.1 Please state clearly (1) your pseudo codes and (2) thenumber of mistakes your algorithm makes at the end of T . (30 points)2 Please implement and run the Weighted Majority algorithm with η = 1/3, denoted by WM, to the datasethw4.txt. Please state clearly (1) your pseudo codes, and (2) the number of mistakes your algorithm makesat the end of T , and the exact value of the regret bound shown in the class. Note that to compute theegret bound explicitly, you need to compute Ri∗(T ), which denotes the number of mistakes made by theest expert i∗. (40 points)3 Please implement and run the Randomized Weighted Majority algorithm with parameter η = 0.1, denotedy RWM(η), to the dataset hw4.txt. Please state clearly (1) your pseudo codes, and (2) the expected numbeof mistakes your algorithm makes at the end of T , and (3) the exact value of the regret bound presented inthe class. For part (2), please run the algorithm to the input instance independently for k times and takethe average as the final output, where you can choose a proper integer k from {10, 11, . . . , 100}2. For part(3), you need to compute Ri∗(T ), which denotes the number of mistakes made by the best expert i∗. (30points)1Note that St may change as time t ∈ [T ]. Specifically, it is even possible that St ∩ St′ = ∅ for some t 6= t′.2Please choose an integer value k as large as possible if the running time is acceptable to you.1  XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX XXXXXXXXXX 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Pratyush · Accepted Answer

1. The pseudo code is stated below:
π* Algorithm:
1.  ExpertMistakeCount  NumPredZero:
i. HeuresticResults.append(1)
Else:
HeuresticResult.append(0)
	
Code:
import queue
def readData():
    with open('hw4-l3ogowjw.txt') as file:
        data  = file.readlines()
    return data
def printError(Arr):
    print(f'

#######   Error List   #######
')
    for ind,Act,Pred in Arr:
        print(f'Error at pos {ind} with Actual prediction as {Act} and Predicted value as {Pred}')
    print(f'
			##############################

')
if __name__ == "__main__":
    
    Data = readData()
    ExpertMistakeCount = queue.PriorityQueue()
    WrongPredictions = []    
    for i in range(1,17):
        #contains the number of mistakes and the Expertid
        ExpertMistakeCount.put((0,i))
    
    HeuresticResults = []
    NumErrors = 0
    ActualPredictions = []
    ptr = 0
    for entry in Data:
        
        ptr += 1
        # print(f'round{ptr}:')
        ActualPred = entry[16]
        ActualPredictions.append(ActualPred)
        topExperts = []
        tempExpert = []
        topEx = ExpertMistakeCount.get()
        topExperts.append(topEx)
        tempExpert.append(topEx)
        # print(f'{topEx}', end= ",")
        while (ExpertMistakeCount.empty() == False) :
            nextEx = ExpertMistakeCount.get()
            # print(f'{nextEx}', end= ",")
            if nextEx[0] == topEx[0]:
                topExperts.append(nextEx)
            tempExpert.append(nextEx)
        
        # print()
        NumPredOne = 0
        NumPredZero = 0
        for Expert in topExperts:
            ExpertInd = Expert[1]-1
            # print(f'Prediction of Person {ExpertInd}:{entry[ExpertInd]} and actual it is {ActualPred}')  
            if entry[ExpertInd] == '0':
                NumPredZero += 1
            else:
                NumPredOne += 1
        
        for Expert in tempExpert:
            ExpertInd = Expert[1] - 1
            if entry[ExpertInd] == ActualPred:
                ExpertMistakeCount.put(Expert)
            else:
                ExpertMistakeCount.put((Expert[0]+1,Expert[1]))
        if NumPredOne > NumPredZero:
            HeuresticResults.

Homework 4 CS XXXXXXXXXX), Fall 2022, Introduction to Data Science Due Date: Dec 9 (Friday), 11:59 PM (EST) This homework will focus on understanding and implementation of algorithms relevant to...

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