EBK DATA STRUCTURES AND ALGORITHMS IN C
EBK DATA STRUCTURES AND ALGORITHMS IN C
4th Edition
ISBN: 9781285415017
Author: DROZDEK
Publisher: YUZU
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Chapter 3, Problem 1PA
Program Plan Intro

Linked list of Farey fractions at level “n”:

Program plan:

  • Define a function named “printList()” that displays the elements of linked list.
  • Define a function named “insert()” that takes two integer values as arguments and inserts the values into the linked list.
  • Declare a function named “gcd()” that takes two integer values as arguments and evaluates greatest common divisor of the numbers.
  • Define a function named “cmp()” that takes two fractional terms as input and compares their values.
  • Define the function “reverse()” to reverse elements of a linked list.
  • Define the function “farey()” that takes an integer value as argument and calculates the farey sequence corresponding to the value.

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Need help with coding in this in python!
In the diagram, there is a green arrow pointing from Input C (complete data) to Transformer Encoder S_B, which I don’t understand. The teacher model is trained on full data, but S_B should instead receive missing data—this arrow should not point there. Please verify and recreate the diagram to fix this issue. Additionally, the newly created diagram should meet the same clarity standards as the second diagram (Proposed MSCATN). Finally provide the output image of the diagram in image format .
Please provide me with the output  image of both of them . below are the diagrams code  make sure to update the code and mentionned clearly each section also the digram should be clearly describe like in the attached image. please do not provide the same answer like in other question . I repost this question because it does not satisfy the requirment I need in terms of clarifty the output of both code are not very well details  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…
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