fitness {GADAG} | R Documentation |
Internal function of the genetic algorithm that evaluates the fitness (penalized log-likelihood) of a potential solution, given as a pair of a permutation (P) and a triangular matrix (T).
fitness(P,X,T,lambda)
P |
A permutation from [1,p] in a matrix form. |
X |
Design matrix, with samples (n) in rows and variables (p) in columns. |
T |
A pxp lower-triangular matrix. |
lambda |
Parameter of penalization (>0). |
A numeric value corresponding to the fitness of the potential solution.
Magali Champion, Victor Picheny and Matthieu Vignes
############################################################# # Loading toy data ############################################################# data(toy_data) # toy_data is a list of two matrices corresponding to a "star" # DAG (node 1 activates all other nodes): # - toy_data$X is a 100x10 design matrix # - toy_data$G is the 10x10 adjacency matrix (ground trough) ############################################################ # Creating a candidate solution ############################################################ # define parameters p <- ncol(toy_data$G) # permutation matrix Perm <- sample(p) # permutation in a vector form P <- matrix(0,p,p) P[p*0:(p-1) + Perm] <- 1 # Perm is tranformed into a matrix form # lower-triangular matrix T <- matrix(rnorm(p),p,p) T[upper.tri(T,diag=TRUE)] <- 0 ######################################################## # Computing the fitness of the potential solution ######################################################## Fitness <- fitness(P=P, X=toy_data$X, T=T, lambda=0.1) print(Fitness) # here is the fitness of the candidate solution (P,T)