# R/L2Lnormalized.R In mdpeer: Graph-Constrained Regression with Enhanced Regularization Parameters Selection

#### Documented in L2L.normalized

```#' Compute normalized version of graph Laplacian matrix
#'
#' @param L graph Laplcian matrix
#' @return normalized graph Laplacian matrix
#'
#' @examples
#' # Define exemplary adjacency matrix
#' p1 <- 10
#' p2 <- 40
#' p <- p1 + p2
#' A <- matrix(rep(0, p * p), p, p)
#' A[1:p1, 1:p1] <- 1
#' A[(p1 + 1):p, (p1 + 1):p] <- 1
#' vizu.mat(A, "adjacency matrix")
#'
#' # Compute corresponding Laplacian matrix
#' L <- Adj2Lap(A)
#' vizu.mat(L, "Laplacian matrix")
#'
#' # Compute corresponding Laplacian matrix - normalized
#' L.norm <- L2L.normalized(L)
#' vizu.mat(L.norm, "L Laplacian matrix (normalized)")
#'
#' @export
#'
L2L.normalized <- function(L){
L.d <- diag(L)
L.normalized <- L
p <- dim(L)[2]
for (u in 1:p){
for (v in 1:p){
if (L.normalized[u, v] != 0){
L.normalized[u, v] <- L.normalized[u,v] / sqrt(L.d[u] * L.d[v])
}
}
}
L.normalized.d <- rep(1, p)
L.normalized.d[which(L.d == 0)] <- 0
diag(L.normalized) <- L.normalized.d
return(L.normalized)
}
```

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mdpeer documentation built on May 31, 2017, 5:21 a.m.