Answer:
The program to this question as follows:
Program:
//header file iostream
#include<iostream> //including file for use basic function
//using name space
using namespace std;
//main method
int main() //defining main method
{
int a[3][3]; //defining two dimension array
int x,y,sum=0; //defining variables
cout<<"Enter array elements: "<<endl; //message
for(x=0;x<3;x++) // for row
{
for(y=0;y<3;y++) //for column
{
cin>>a[x][y]; //input values from user.
}
}
//loop for calculting sum.
for(x=0;x<3;x++)
{
for(y=0;y<3;y++)
{
sum=sum+a[x][y];//add all elements
}
}
cout<<"Sum: "<<sum; //print sum.
return 0;
}
Output:
Enter array elements:
1
2
3
4
5
6
7
8
9
Sum: 45
Explanation:
In the above C++ programming language code first, a header file is included then the main method is declared, inside a main method 2D array that is "a[][]", and an integer variable is defined that are "i, j, and sum". In the next line for loop is used, this loop is used two times that can be described as follows:
- The first time it is used for inserting elements from user ends.
- The second time, it uses the sum variable to add all array elements. and in the last print function that is "cout" is used for print sum variable value.
I think it's having a trusted adult with you to drive
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.
Explanation:
when a form is created in Microsoft Access using the form tool it displays a single record at a time.To display multiple records and the form should be more customizable then in this case we use Multiple Items tool.
Creating a form using Multiple Items Tool:-
In navigation pane click query or table which contains the data that we want to see on the form.
On create tab,in the group Forms,click more Forms,then click Multiple Items.
