Problem Solving with C++ plus MyProgrammingLab with Pearson eText-- Access Card Package (9th Edition)
Problem Solving with C++ plus MyProgrammingLab with Pearson eText-- Access Card Package (9th Edition)
9th Edition
ISBN: 9780133862218
Author: Walter Savitch
Publisher: PEARSON
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Chapter 14, Problem 2P

Explanation of Solution

Recursive version of the function “indexOfSmallest”:

The recursive version of the function “indexOfSmallest” is shown below:

/* Recursive function definition for compute index of smallest element */

int indexOfSmallest(const int a[], int startIndex, int numberUsed)

{

    /* Assign minimum to array of starting index */

    int min = a[startIndex];

   /* If starting index is equal to "numberUsed-1", then */

    if(startIndex == numberUsed - 1)

        /* Returns the starting index */

        return startIndex;

   /* Otherwise recursively call the function "indexOfSmallest" */

   int indexOfMin = indexOfSmallest(a, startIndex+1, numberUsed);

   /* If the value of "min" is greater than "a[indexOfMin]" */

    if(min > a[indexOfMin])

        /* Returns index of minimum value */

        return indexOfMin;

    //Otherwise

    else

        /* Returns the starting index */

        return startIndex;

}

Complete executable program code:

The modified complete code is implemented for given function is given below:

//Header file

#include <iostream>

//Function declaration

void fillArray(int a[], int size, int& numberUsed);

//Precondition: size is the declared size of the array a.

//Postcondition: numberUsed is the number of values stored in a.

//a[0] through a[numberUsed - 1] have been filled with

//nonnegative integers read from the keyboard.

void sort(int a[], int numberUsed);

//Precondition: numberUsed <= declared size of the array a.

//The array elements a[0] through a[numberUsed - 1] have values.

//Postcondition: The values of a[0] through a[numberUsed - 1] have

//been rearranged so that a[0] <= a[1] <= ... <= a[numberUsed - 1].

void swapValues(int& v1, int& v2);

//Interchanges the values of v1 and v2.

int indexOfSmallest(const int a[], int startIndex, int numberUsed);

//Precondition: 0 <= startIndex < numberUsed. Referenced array elements have

//values.

//Returns the index i such that a[i] is the smallest of the values

//a[startIndex], a[startIndex + 1], ..., a[numberUsed - 1].

//Main function

int main( )

{

    //For standard input and output

    using namespace std;

    //Prompt statement

   cout << "This program sorts numbers from lowest to highest.\n";

    //Declare variables

    int sampleArray[10], numberUsed;

    //Call fill array function

    fillArray(sampleArray, 10, numberUsed);

    //Prompt statement

    cout << "Index of minimum number in given array: ";

    //Call the function "indexOfSmallest"

   cout << indexOfSmallest(sampleArray, 0, numberUsed) << endl;

    //Call the function "sort"

    sort(sampleArray, numberUsed);

    //Prompt statement

    cout << "In sorted order the numbers are:\n";

    //Display the sorted number using "for" loop

    for (int index = 0; index < numberUsed; index++)

    cout << sampleArray[index] << " ";

    cout << endl;

    return 0;

}

//Uses iostream:

//Function definition for fill array

void fillArray(int a[], int size, int& numberUsed)

{

    //For standard input and output

    using namespace std;

    //Prompt statement

   cout << "Enter up to " << size << " nonnegative whole numbers.\n" << "Mark the end of the list with a negative number...

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Here are two diagrams. Make them very explicit, similar to Example Diagram 3 (the Architecture of MSCTNN). graph LR subgraph Teacher_Model_B [Teacher Model (Pretrained)] Input_Teacher_B[Input C (Complete Data)] --> Teacher_Encoder_B[Transformer Encoder T] Teacher_Encoder_B --> Teacher_Prediction_B[Teacher Prediction y_T] Teacher_Encoder_B --> Teacher_Features_B[Internal Features F_T] end subgraph Student_B_Model [Student Model B (Handles Missing Labels)] Input_Student_B[Input C (Complete Data)] --> Student_B_Encoder[Transformer Encoder E_B] Student_B_Encoder --> Student_B_Prediction[Student B Prediction y_B] end subgraph Knowledge_Distillation_B [Knowledge Distillation (Student B)] Teacher_Prediction_B -- Logits Distillation Loss (L_logits_B) --> Total_Loss_B Teacher_Features_B -- Feature Alignment Loss (L_feature_B) --> Total_Loss_B Partial_Labels_B[Partial Labels y_p] -- Prediction Loss (L_pred_B) --> Total_Loss_B Total_Loss_B -- Backpropagation -->…
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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