Starting Out with C++: Early Objects
Starting Out with C++: Early Objects
8th Edition
ISBN: 9780133360929
Author: Tony Gaddis, Judy Walters, Godfrey Muganda
Publisher: Addison-Wesley
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Chapter 15, Problem 18RQE
Program Plan Intro

Algorithm:

  • Start a program.
  • Declare a class named “Sorter”.
    • Declare a required member variables and pure virtual function.
    • In public, define the member function “set_Array()”.
      • Inside the function set the array and size of an array.
    • Define the “sort” function.
      • Inside the function, call the “sort()” function with an argument size.
  • Define the “sort” function.
    • If the size is less than 1, the condition will ended.
    • Find the position of largest value in array and put it at the end of the array.
      • Use the “compare ()” function for sorting the given array.
      • Swap a pair of array elements.
      • Decrement the size by 1.
  • Define the derived class “Incr_Sorter” from the class “Sorter”.
    • In private, define the pure virtual function “compare()”.
      • If the “x” value is greater than “y” value return true.
  • Define the derived class “Decr_Sorter” from the class “Sorter”.
    • In private, define the pure virtual function “compare()”.
      • If the “x” value is less than “y” value return true.
  • Inside the “main” function,
    • Create the objects for the classes.
    • Declare and initialize an array of 5 values.
    • Call the “Incr_Sorter” function.
    • Call the “print_Array()” function for displaying the output.
    • Call the “Decr_Sorter” function.
    • Call the “print_Array()” function for displaying the output.
  • Define the “print_Array()” function.
    • Display the array.

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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)…

Chapter 15 Solutions

Starting Out with C++: Early Objects

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