Python Programming: An Introduction to Computer Science, 3rd Ed.
Python Programming: An Introduction to Computer Science, 3rd Ed.
3rd Edition
ISBN: 9781590282755
Author: John Zelle
Publisher: Franklin, Beedle & Associates
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Chapter 9, Problem 10PE
Program Plan Intro

Estimate the Pi value

Program plan:

  • Import the header file.
  • Define the main method.
    • Read the iteration value from the user
    • Call the “simDarts” method
    • Call the “getPi” method
    • Call the “printSummary” method
  • Define the “simDarts” methods
    • Set the window size
    • Set the coords
    • Set the value
    • Iterate “i” until it reaches “n” value
      • Call the “getDarts” method
      • Check the condition
        • Increment the “hits” value
      • Otherwise, set the value
          • Close the window
          • Return the value
  • Define the “getDarts” method
    • Get the “x” and “y” values
    • Check the calculate of value is less than or equal to 1
      • Return true
          • Otherwise, return false
  • Define “getPi” method
    • Calculate the “pi” value
    • Return “pi” value
  • Define “printSummary” method
    • Display the output
  • Call the function “main()”.

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here 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…
details 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…
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