Data structures and algorithms in C++
Data structures and algorithms in C++
2nd Edition
ISBN: 9780470460443
Author: Goodrich
Publisher: WILEY
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Chapter 1, Problem 2C

Explanation of Solution

Program code:

//include the required header files

#include<iostream>

//define the main() method

int main()

{

//declare an integer variable

const int size=3;

//declare an integer array

int arr[size],count=0;

//prompt the array elements

std::cout<<"\nEnter the contents of the array below\n";

//iterate a for loop

for(int i=0;i<size;i++)

    //scan for the array values

    std::cin>>arr[i];

//print the statement

std::cout<<"\nNOTE:It would not count pairs like (1,1) or (2,2)";

//print the pairs using for loop

std::cout<<"\nThese are the Pairs\n";

for(int i=0;i<size;i++)

{

    //int temp=arr[i];

    for(int j=0;j<size;j++)

    {

        //if i and j are equal

        if(i==j)

            //print

            std::cout<<"";

        //if i and j are not equal

        else

        {

            //if the condition is true

            if(arr[i]*arr[j]%2==0)

            {

                //print the pairs

      ;&#...

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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…
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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Data structures and algorithms in C++

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