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 14, Problem 8PP
Program Plan Intro

Finding all permutations for a set

Program Plan:

  • Include required file.
  • Define the structure for node.
    • Declare elements in “vector” type.
    • Declare variable for next value in “NodeValue” type.
  • Declare function for display permutations.
  • Declare function for compute permutations with recursively.
  • Declare function for display vector elements of set.
  • Define main function.
    • Call the function “displayPermutations” with one parameter.
  • Define function “displayPermutations”.
    • Create a pointer for node.
    • Declare the set in “vector” type.
    • Fill the set with first “n” whole elements.
    • Call the function “displayVectorElements” to print the vectors.
    • Then compute the permutation for given set by calling the function “recursivePermutations”.
    • Performs “while” loop. This loop executes until the pointer is equal to “NULL”.
      • Display the values in set by calling the function “displayVectorElements”.
      • Then delete and move to the next value.
  • Define function “recursivePermutations”.
    • This function is used to returns a list holding all of the permutations of the given list of elements.
    • In this function, first assign the pointer list to “NULL”.
    • Then performs base case if the size of the vector element is “1”. Otherwise performs recursive case.
    • Compute the permutations for smaller set of elements by recursively call the function “recursivePermutations”.
  • Define function “displayVectorElements”.
    • This function is used to display the elements of set.

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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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