Problem Solving with C++ (10th Edition)
Problem Solving with C++ (10th Edition)
10th Edition
ISBN: 9780134521176
Author: SAVITCH
Publisher: PEARSON
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Chapter 18.2, Problem 9STE
Program Plan Intro

STL Basic sequential containers:

“slist”:

  • The template class for “slist” class is “<slist>”
  • The template class for “slist” is “slist<T>::iterator”.
    • This class uses mutable and forward iterator.
  • The template class for constant iterator is “slist<T>::const_iterator”.
    • This class uses constant and forward iterator.

“list”:

  • The template class for “list” class is “<list>”
  • The template class for “list” is “list<T>::iterator”.
    • This class uses mutable and bidirectional iterator.
  • The template class for constant iterator is “list<T>::const_iterator”.
    • This class uses constant and bidirectional iterator.
  • The template class for reverse iterator is “list<T>::reverse_iterator”.
    • This class uses mutable and bidirectional iterator.
  • The template class for constant reverse iterator is “list<T>::const_reverse_iterator”.
    • This class uses constant and bidirectional iterator.

vector”:

  • The template class for “vector” class is “<vector>”
  • The template class for “vector” is “vector<T>::iterator”.
    • This class uses mutable, random, and access iterator.
  • The template class for constant iterator is “vector<T>::const_iterator”.
    • This class uses constant, random, and access iterator.
  • The template class for reverse iterator is “vector<T>::reverse_iterator”.
    • This class uses mutable, random, and access iterator.
  • The template class for constant reverse iterator is “vector<T>::const_reverse_iterator”.
    • This class uses constant, random, and access iterator.

“deque”:

  • The template class for “deque” class is “<deque>”
  • The template class for “deque” is “deque<T>::iterator”.
    • This class uses mutable, random, and access iterator.
  • The template class for constant iterator is “deque<T>::const_iterator”.
    • This class uses constant, random, and access iterator.
  • The template class for reverse iterator is “deque<T>::reverse_iterator”.
    • This class uses mutable, random, and access iterator.
  • The template class for constant reverse iterator is “deque<T>::const_reverse_iterator”.
    • This class uses constant, random, and access iterator.

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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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