What is co-operating process
1 answer:
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Answer:
Explanation:
The following code is written in Java. It creates the Card class and then uses it to create a full house and print out the rank and suit of every card in that hand.
class Card {
int rank;
char suit;
public Card(int rank, char suit) {
this.rank = rank;
this.suit = suit;
}
public int getRank() {
return rank;
}
public char getSuit() {
return suit;
}
}
class CardTester {
public static void main(String[] args) {
Card card1 = new Card(3, '♥');
Card card2 = new Card(3, '♠');
Card card3 = new Card(3, '♦');
Card card4 = new Card(2, '♦');
Card card5 = new Card(2, '♣');
System.out.println("Card 1: " + card1.getRank() + " of " + card1.getSuit());
System.out.println("Card 2: " + card2.getRank() + " of " + card2.getSuit());
System.out.println("Card 3: " + card3.getRank() + " of " + card3.getSuit());
System.out.println("Card 4: " + card4.getRank() + " of " + card4.getSuit());
System.out.println("Card 5: " + card5.getRank() + " of " + card5.getSuit());
}
}
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.
