To build krr model
build_krr_model(K_train, y_train, gamma, set_b_0 = FALSE)
K_train | |
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y_train | |
gamma | |
set_b_0 |
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (K_train, y_train, gamma, set_b_0 = FALSE) { ntrain <- length(y_train) if (set_b_0) { if (rcond(K_train) < 1e-15) return(NULL) a <- solve(K_train + gamma * diag(ntrain), y_train) b <- 0 } else { K1 <- K_train + gamma * diag(ntrain) K2 <- cbind(K1, matrix(1, nrow = ntrain)) K3 <- rbind(K2, matrix(1, ncol = ntrain + 1)) K3[ntrain + 1, ntrain + 1] <- 0 if (rcond(K3) < 1e-15) return(NULL) y0 <- c(y_train, 0) ab <- solve(K3, y0) a <- ab[1:ntrain] b <- ab[ntrain + 1] } list(a = a, b = b) }#> function (K_train, y_train, gamma, set_b_0 = FALSE) #> { #> ntrain <- length(y_train) #> if (set_b_0) { #> if (rcond(K_train) < 1e-15) #> return(NULL) #> a <- solve(K_train + gamma * diag(ntrain), y_train) #> b <- 0 #> } #> else { #> K1 <- K_train + gamma * diag(ntrain) #> K2 <- cbind(K1, matrix(1, nrow = ntrain)) #> K3 <- rbind(K2, matrix(1, ncol = ntrain + 1)) #> K3[ntrain + 1, ntrain + 1] <- 0 #> if (rcond(K3) < 1e-15) #> return(NULL) #> y0 <- c(y_train, 0) #> ab <- solve(K3, y0) #> a <- ab[1:ntrain] #> b <- ab[ntrain + 1] #> } #> list(a = a, b = b) #> } #> <environment: 0x10e9602d0>