Explain the 3 types of control transfer structures in programming using flowchart symbols

Develop a C program that calculates the final score and the average score for a student from his/her (1)class participation, (2)

Ghella [55]

Answer:

#include <iomanip>

#include<iostream>

using namespace std;

int main(){

char name[100];

float classp, test, assgn, exam, prctscore,ave;

cout<<"Student Name: ";

cin.getline(name,100);

cout<<"Class Participation: "; cin>>classp;

while(classp <0 || classp > 100){ cout<<"Class Participation: "; cin>>classp; }

cout<<"Test: "; cin>>test;

while(test <0 || test > 100){ cout<<"Test: "; cin>>test; }

cout<<"Assignment: "; cin>>assgn;

while(assgn <0 || assgn > 100){ cout<<"Assignment: "; cin>>assgn; }

cout<<"Examination: "; cin>>exam;

while(exam <0 || exam > 100){ cout<<"Examination: "; cin>>exam; }

cout<<"Practice Score: "; cin>>prctscore;

while(prctscore <0 || prctscore > 100){ cout<<"Practice Score: "; cin>>prctscore; }

ave = (int)(classp + test + assgn + exam + prctscore)/5;

cout <<setprecision(1)<<fixed<<"The average score is "<<ave;

return 0;}

Explanation:

The required parameters such as cin, cout, etc. implies that the program is to be written in C++ (not C).

So, I answered the program using C++.

Line by line explanation is as follows;

This declares name as character of maximum size of 100 characters

char name[100];

This declares the grading items as float

float classp, test, assgn, exam, prctscore,ave;

This prompts the user for student name

cout<<"Student Name: ";

This gets the student name using getline

cin.getline(name,100);

This prompts the user for class participation. The corresponding while loop ensures that the score s between 0 and 100 (inclusive)

cout<<"Class Participation: "; cin>>classp;

while(classp <0 || classp > 100){ cout<<"Class Participation: "; cin>>classp; }

This prompts the user for test. The corresponding while loop ensures that the score s between 0 and 100 (inclusive)

cout<<"Test: "; cin>>test;

while(test <0 || test > 100){ cout<<"Test: "; cin>>test; }

This prompts the user for assignment. The corresponding while loop ensures that the score s between 0 and 100 (inclusive)

cout<<"Assignment: "; cin>>assgn;

while(assgn <0 || assgn > 100){ cout<<"Assignment: "; cin>>assgn; }

This prompts the user for examination. The corresponding while loop ensures that the score s between 0 and 100 (inclusive)

cout<<"Examination: "; cin>>exam;

while(exam <0 || exam > 100){ cout<<"Examination: "; cin>>exam; }

This prompts the user for practice score. The corresponding while loop ensures that the score s between 0 and 100 (inclusive)

cout<<"Practice Score: "; cin>>prctscore;

while(prctscore <0 || prctscore > 100){ cout<<"Practice Score: "; cin>>prctscore; }

This calculates the average of the grading items

ave = (int)(classp + test + assgn + exam + prctscore)/5;

This prints the calculated average

cout <<setprecision(1)<<fixed<<"The average score is "<<ave;

8 0

3 years ago

True or false?

kenny6666 [7]

Answer:

True

Explanation:

An effective way of protecting programs against classic stack overflow attacks is to instrument the function entry and exit code to setup and then check its sack frame for any evidence of corruption.

5 0

3 years ago

Read 2 more answers

In order to consolidate your theoretical knowledge into technique and skills with practical and applicational value, you will us

Arisa [49]

Answer:

Check the explanation

Explanation:

Lasso: R example

To run Lasso Regression you can re-use the glmnet() function, but with the alpha parameter set to 1.

# Perform 10-fold cross-validation to select lambda --------------------------- lambdas_to_try <- 10^seq(-3, 5, length.out = 100) # Setting alpha = 1 implements lasso regression lasso_cv <- cv.glmnet(X, y, alpha = 1, lambda = lambdas_to_try, standardize = TRUE, nfolds = 10) # Plot cross-validation results plot(lasso_cv)

Best cross-validated lambda lambda_cv <- lasso_cv$lambda.min # Fit final model, get its sum of squared residuals and multiple R-squared model_cv <- glmnet(X, y, alpha = 1, lambda = lambda_cv, standardize = TRUE) y_hat_cv <- predict(model_cv, X) ssr_cv <- t(y - y_hat_cv) %*% (y - y_hat_cv) rsq_lasso_cv <- cor(y, y_hat_cv)^2 # See how increasing lambda shrinks the coefficients -------------------------- # Each line shows coefficients for one variables, for different lambdas. # The higher the lambda, the more the coefficients are shrinked towards zero. res <- glmnet(X, y, alpha = 1, lambda = lambdas_to_try, standardize = FALSE) plot(res, xvar = "lambda") legend("bottomright", lwd = 1, col = 1:6, legend = colnames(X), cex = .7)

Kindly check the Image below.