Starting Out with Python, Student Value Edition (4th Edition)
4th Edition
ISBN: 9780134444468
Author: Tony Gaddis
Publisher: PEARSON
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Chapter 2.10, Problem 30CP
Program Plan Intro
Turtle:
Turtle graphics is a method that is used to simulate a “turtle” to draw a simple graphics based on the given commands.
- • It provides numerous functions that use decision structures, which determines the state of the turtle and performs actions according to the conditions.
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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 2 Solutions
Starting Out with Python, Student Value Edition (4th Edition)
Ch. 2.1 - Who is a programmers customer?Ch. 2.1 - What is a software requirement?Ch. 2.1 - What is an algorithm?Ch. 2.1 - What is pseudocode?Ch. 2.1 - Prob. 5CPCh. 2.1 - Prob. 6CPCh. 2.3 - Write a statement that displays your name.Ch. 2.3 - Write a statement that displays the following...Ch. 2.3 - Write a statement that displays the following...Ch. 2.5 - Prob. 10CP
Ch. 2.5 - Which of the following are illegal variable names...Ch. 2.5 - Prob. 12CPCh. 2.5 - Is the following assignment statement valid or...Ch. 2.5 - Prob. 14CPCh. 2.5 - Look at the following assignment statements:...Ch. 2.5 - What will be displayed by the following program?...Ch. 2.6 - You need the user of a program to enter a...Ch. 2.6 - Prob. 18CPCh. 2.7 - Prob. 19CPCh. 2.7 - Prob. 20CPCh. 2.7 - Prob. 21CPCh. 2.8 - How do you suppress the print functions ending...Ch. 2.8 - How can you change the character that is...Ch. 2.8 - Prob. 24CPCh. 2.8 - Prob. 25CPCh. 2.8 - What does the statement print (format (65.4321,...Ch. 2.8 - What does the statement print (format (987654.129,...Ch. 2.9 - What are three advantages of using named...Ch. 2.9 - Write a Python statement that defines a named...Ch. 2.10 - Prob. 30CPCh. 2.10 - Prob. 31CPCh. 2.10 - Prob. 32CPCh. 2.10 - Prob. 33CPCh. 2.10 - Prob. 34CPCh. 2.10 - Prob. 35CPCh. 2.10 - Prob. 36CPCh. 2.10 - Prob. 37CPCh. 2.10 - Prob. 38CPCh. 2.10 - Prob. 39CPCh. 2.10 - Prob. 40CPCh. 2.10 - Prob. 41CPCh. 2.10 - Prob. 42CPCh. 2.10 - Prob. 43CPCh. 2.10 - Prob. 44CPCh. 2.10 - Prob. 45CPCh. 2 - A ______ error does not prevent the program from...Ch. 2 - Prob. 2MCCh. 2 - A(n) __________ is a set of well-defined logical...Ch. 2 - An Informal language that has no syntax rules and...Ch. 2 - A _______ is a diagram that graphically depicts...Ch. 2 - A ______ is a sequence of characters. a. char...Ch. 2 - Prob. 7MCCh. 2 - Prob. 8MCCh. 2 - A string literal in Python must be enclosed in...Ch. 2 - Prob. 10MCCh. 2 - A(n) __________ makes a variable reference a value...Ch. 2 - This symbol marks the beginning of a comment in...Ch. 2 - Which of the following statements will cause an...Ch. 2 - In the expression 12 + 7, the values on the right...Ch. 2 - This operator performs integer division. a. // b....Ch. 2 - This is an operator that raises a number to a...Ch. 2 - This operator performs division, but instead of...Ch. 2 - Prob. 18MCCh. 2 - Which built-in function can be used to read input...Ch. 2 - Prob. 20MCCh. 2 - A magic number is _______. a. a number that is...Ch. 2 - A _______ is a name that represents a value that...Ch. 2 - Programmers must be careful not to make syntax...Ch. 2 - In a math expression, multiplication and division...Ch. 2 - Variable names can have spaces in them.Ch. 2 - In Python, the first character of a variable name...Ch. 2 - If you print a variable that has not been assigned...Ch. 2 - What does a professional programmer usually do...Ch. 2 - What is pseudocode?Ch. 2 - Computer programs typically perform what three...Ch. 2 - If a math expression adds a float to an int, what...Ch. 2 - What is the difference between floating-point...Ch. 2 - What is a magic number? Why are magic numbers...Ch. 2 - Assume a program uses the named constant PI to...Ch. 2 - Write Python code that prompts the user to enter...Ch. 2 - Write Python code that prompts the user to enter...Ch. 2 - Write assignment statements that perform the...Ch. 2 - Assume the variables result, w, x, y, and z are...Ch. 2 - Write a Python statement that assigns the sum of...Ch. 2 - Write a Python statement that subtracts the...Ch. 2 - Write a Python statement that multiplies the...Ch. 2 - Prob. 8AWCh. 2 - What would the following display? num = 99 num = 5...Ch. 2 - Assume the variable sales references a float...Ch. 2 - Assume the following statement has been executed:...Ch. 2 - What will the following statement display?...Ch. 2 - Write a turtle graphics statement that draws a...Ch. 2 - Write the turtle graphics statements to draw a...Ch. 2 - Write the turtle graphics statements to draw a...Ch. 2 - Personal Information Write a program that displays...Ch. 2 - Sales Prediction A company has determined that its...Ch. 2 - Land Calculation One acre of land is equivalent to...Ch. 2 - Total Purchase A customer in a store is purchasing...Ch. 2 - Distance Traveled Assuming there are no accidents...Ch. 2 - Sales Tax Write a program that will ask the user...Ch. 2 - Miles-per-Gallon A car's miles-per-gallon (MPG)...Ch. 2 - Tip, Tax, and Total Write a program that...Ch. 2 - Celsius to Fahrenheit Temperature Converter Write...Ch. 2 - Ingredient Adjuster A cookie recipe calls for the...Ch. 2 - Male and Female Percentages Write a program that...Ch. 2 - Stock Transaction Program Last month, Joe...Ch. 2 - Planting Grapevines A vineyard owner is planting...Ch. 2 - Compound Interest When a bank account pays...Ch. 2 - Turtle Graphics Drawings Use the turtle graphics...
Knowledge Booster
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- Here is a clear background and explanation of the full method, including what each part is doing and why. Background & Motivation Missing values: Some input features (sensor channels) are missing for some samples due to sensor failure or corruption. Missing labels: Not all samples have a ground-truth RUL value. For example, data collected during normal operation is often unlabeled. Most traditional deep learning models require complete data and full labels. But in our case, both are incomplete. If we try to train a model directly, it will either fail to learn properly or discard valuable data. What We Are Doing: Overview We solve this using a Teacher–Student knowledge distillation framework: We train a Teacher model on a clean and complete dataset where both inputs and labels are available. We then use that Teacher to teach two separate Student models: Student A learns from incomplete input (some sensor values missing). Student B learns from incomplete labels (RUL labels missing…arrow_forwardhere is a diagram code : graph LR subgraph Inputs [Inputs] A[Input C (Complete Data)] --> TeacherModel B[Input M (Missing Data)] --> StudentA A --> StudentB end subgraph TeacherModel [Teacher Model (Pretrained)] C[Transformer Encoder T] --> D{Teacher Prediction y_t} C --> E[Internal Features f_t] end subgraph StudentA [Student Model A (Trainable - Handles Missing Input)] F[Transformer Encoder S_A] --> G{Student A Prediction y_s^A} B --> F end subgraph StudentB [Student Model B (Trainable - Handles Missing Labels)] H[Transformer Encoder S_B] --> I{Student B Prediction y_s^B} A --> H end subgraph GroundTruth [Ground Truth RUL (Partial Labels)] J[RUL Labels] end subgraph KnowledgeDistillationA [Knowledge Distillation Block for Student A] K[Prediction Distillation Loss (y_s^A vs y_t)] L[Feature Alignment Loss (f_s^A vs f_t)] D -- Prediction Guidance --> K E -- Feature Guidance --> L G --> K F --> L J -- Supervised Guidance (if available) --> G K…arrow_forwarddetails explanation and background We solve this using a Teacher–Student knowledge distillation framework: We train a Teacher model on a clean and complete dataset where both inputs and labels are available. We then use that Teacher to teach two separate Student models: Student A learns from incomplete input (some sensor values missing). Student B learns from incomplete labels (RUL labels missing for some samples). We use knowledge distillation to guide both students, even when labels are missing. Why We Use Two Students Student A handles Missing Input Features: It receives input with some features masked out. Since it cannot see the full input, we help it by transferring internal features (feature distillation) and predictions from the teacher. Student B handles Missing RUL Labels: It receives full input but does not always have a ground-truth RUL label. We guide it using the predictions of the teacher model (prediction distillation). Using two students allows each to specialize in…arrow_forward
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