Priority queue Program plan: Define a file “Priority.h” to declare operation and functions. Include the directives for header file. Declare a “PriorityQueue” template class. Define a template class named “Node”. Inside the access specifier “public”, define a constructor of class “Node”. Declare a friend class “PriorityQueue”. Inside the access specifier “private”, create an object for template. Declare an integer variable “priority” and create a pointer variable “*next”. Define a template class named “PriorityQueue”. Inside the access specifier “public”, Declare a constructor of class “PriorityQueue”. Declare the “add()” function to add items in the queue. Declare the “remove()” function to remove items from the queue. Declare the “isEmpty()” Boolean function to return the status of queue. Define a file “main().cpp” to call functions from “Priority.h” and perform all computations. Define a constructor of class “PriorityQueue()” to assign “null” to “head” of the queue. Define “add()” function to add new node onto the front of the queue. Define “remove()” function to remove the smallest priority from the queue. Define “isEmpty()” function to return “true” or “false” if the queue is empty. Define a “main()” function. Create an object for class “PriorityQueue”. Add and remove some items in the queue and print the result.

Question

Want to see the full answer?

Answer 1

Question

Chapter 17, Problem 6PP

Program Plan Intro

Priority queue

Program plan:

Define a file “Priority.h” to declare operation and functions.

Include the directives for header file.

Declare a “PriorityQueue” template class.

Define a template class named “Node”.

Inside the access specifier “public”, define a constructor of class “Node”.

Declare a friend class “PriorityQueue”.

Inside the access specifier “private”, create an object for template.

Declare an integer variable “priority” and create a pointer variable “*next”.

Define a template class named “PriorityQueue”.

Inside the access specifier “public”, Declare a constructor of class “PriorityQueue”.

Declare the “add()” function to add items in the queue.

Declare the “remove()” function to remove items from the queue.

Declare the “isEmpty()” Boolean function to return the status of queue.

Define a file “main().cpp” to call functions from “Priority.h” and perform all computations.

Define a constructor of class “PriorityQueue()” to assign “null” to “head” of the queue.

Define “add()” function to add new node onto the front of the queue.

Define “remove()” function to remove the smallest priority from the queue.

Define “isEmpty()” function to return “true” or “false” if the queue is empty.

Define a “main()” function.

Create an object for class “PriorityQueue”.

Add and remove some items in the queue and print the result.

Expert Solution & Answer

Want to see the full answer?

Check out a sample textbook solution

See solution

Students have asked these similar questions

Why I need ?

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)…