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 18, Problem 4P
Program Plan Intro

Student Enrollment

Program Plan:

  • Include required header file.
  • Include required “std” namespace.
  • Define main function.
    • Declare variable for student ID and course number.
    • Map the student ID to a vector of “string” using “map” and “vector” template class.
    • Create constant iterator in “map” template class.
    • Read the student ID from user.
    • Performs “while” loop. This loop will execute until the student is equal to “-1”.
      • Read course number from user.
      • Check condition. If the student ID in list is equal to student list end, then
        • Declare a “vector” variable to “courseList”.
        • Push the course number into course list using “push_back” function.
        • Insert the pair value into student list.
      • Otherwise
        • Declare a “vector” variable to “courseList”.
        • Find the student ID in student list and then store it in a variable “iterList”.
        • Push the course number into course list.
        • Erase the student ID from student list.
      • Read the student ID from user.
    • Display respective statement.
    • Display student ID and its corresponding course numbers.

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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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