Data structures and algorithms in C++
Data structures and algorithms in C++
2nd Edition
ISBN: 9780470460443
Author: Goodrich
Publisher: WILEY
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Chapter 3, Problem 19C

Explanation of Solution

Program code:

//include the required header files

#include <iostream>

#include <string>

#include <algorithm>

//use std namespace

using namespace std;

//declare a function revStr()

void revStr(string::iterator begin, string::iterator end);

//define main() function

int main(int argc, char *argv[])

{

//create a string s.

string s;

//iterate a while loop

while (true)

{

//print the symbol

cout << "> ";

//get the values

getline(cin,s);

//call the function revStr() to reverse the value

revStr(s.begin(), s.end());

//print the value of s

cout << s << endl;

}

//return

return 0;

}

//define the functio

void revStr(string::iterator begin, string::iterator end)

{

//if the condition is true,

if (end <= begin)

//return

return;

//swap the values

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Please provide me with the output  image of both of them . below are the diagrams code I have two diagram : first diagram code  graph LR subgraph Teacher Model (Pretrained) Input_Teacher[Input C (Complete Data)] --> Teacher_Encoder[Transformer Encoder T] Teacher_Encoder --> Teacher_Prediction[Teacher Prediction y_T] Teacher_Encoder --> Teacher_Features[Internal Features F_T] end subgraph Student_A_Model[Student Model A (Handles Missing Values)] Input_Student_A[Input M (Data with Missing Values)] --> Student_A_Encoder[Transformer Encoder E_A] Student_A_Encoder --> Student_A_Prediction[Student A Prediction y_A] Student_A_Encoder --> Student_A_Features[Student A Features F_A] end subgraph Knowledge_Distillation_A [Knowledge Distillation (Student A)] Teacher_Prediction -- Logits Distillation Loss (L_logits_A) --> Total_Loss_A Teacher_Features -- Feature Alignment Loss (L_feature_A) --> Total_Loss_A Ground_Truth_A[Ground Truth y_gt] -- Prediction Loss (L_pred_A)…
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