Programming and Problem Solving With C++
6th Edition
ISBN: 9781449694265
Author: Nell Dale
Publisher: Jones & Bartlett Learning
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Chapter 3, Problem 3PP
Program Plan Intro
Following functions are used in the program:
Cout: To display the explanatory text.
Exp(): It is used to return the exponential value raised to the given argument.
Pow(): It finds a raised to the power of b i.e. ab.
Sqrt(): It will find the square root of the number.
Following variables are used in the program:
sFactorial: is used to store the factorial of number calculated using Stirling's method.
number, n: is used to define the number for which we need to compute the factorial.
facto: is used to store the factorial of number calculated using the normal method.
Summary Introduction:
Program will use the Main () method, which will first calculate the factorial of a number using the normal method and then use Stirling's formula to calculate the factorial of the same number. Finally, print the output. Stirling's formula used to calculate factorial is given as
Program Description:
The purpose of the program is to calculate the factorial of the given number using two different methods.
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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)…
Chapter 3 Solutions
Programming and Problem Solving With C++
Ch. 3 - Prob. 1PSCSCh. 3 - Prob. 1EPECh. 3 - Prob. 2EPECh. 3 - Prob. 3EPECh. 3 - Prob. 4EPECh. 3 - Prob. 5EPECh. 3 - Prob. 6EPECh. 3 - Prob. 7EPECh. 3 - Prob. 8EPECh. 3 - Prob. 9EPE
Ch. 3 - Prob. 10EPECh. 3 - Prob. 11EPECh. 3 - Prob. 12EPECh. 3 - Prob. 13EPECh. 3 - Prob. 14EPECh. 3 - Prob. 15EPECh. 3 - Prob. 16EPECh. 3 - Prob. 17EPECh. 3 - Prob. 18EPECh. 3 - Prob. 19EPECh. 3 - Prob. 20EPECh. 3 - Prob. 1PWECh. 3 - Prob. 2PWECh. 3 - Prob. 3PWECh. 3 - Prob. 4PWECh. 3 - Prob. 5PWECh. 3 - Prob. 6PWECh. 3 - Prob. 7PWECh. 3 - Prob. 8PWECh. 3 - Prob. 9PWECh. 3 - Prob. 10PWECh. 3 - Prob. 11PWECh. 3 - Prob. 12PWECh. 3 - Prob. 13PWECh. 3 - Prob. 14PWECh. 3 - Prob. 15PWECh. 3 - Prob. 1PPCh. 3 - Prob. 2PPCh. 3 - Prob. 3PPCh. 3 - Prob. 4PPCh. 3 - Prob. 5PPCh. 3 - Prob. 6PPCh. 3 - Prob. 7PPCh. 3 - Prob. 8PPCh. 3 - Prob. 1CSFCh. 3 - Prob. 2CSFCh. 3 - Prob. 3CSFCh. 3 - Prob. 4CSF
Knowledge Booster
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